oc-p10-content-recommendation/notebook/p10_notebook.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyNgYgViOnz2/RUT/4eqpi7X"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# 📚 Projet 10 : Application de recommandation de contenu\n","\n","## 🎯 Contexte\n","\n","My Content est une start-up qui souhaite encourager la lecture en recommandant des contenus pertinents à ses utilisateurs. En tant que CTO et cofondateur avec Samia (CEO), nous développons un premier MVP sous forme d'application de recommandation d'articles et de livres.\n","\n","## 🚀 Objectifs du projet\n","\n","### 🎪 Fonctionnalité critique identifiée\n","**User Story principale :** \"En tant qu'utilisateur de l'application, je vais recevoir une sélection de cinq articles.\"\n","\n","### ⚡ Défis techniques\n","- Absence de données utilisateurs propres → utilisation de données disponibles en ligne\n","- Prise en compte future de l'ajout de nouveaux utilisateurs et nouveaux articles dans l'architecture cible\n","\n","## 🏗️ Architecture technique recommandée\n","\n","D'après les conseils de Julien (développeur web freelance), deux architectures serverless sont envisageables avec Azure Functions :\n","\n","1. **🔌 Architecture avec API** : Création d'une API pour développer et exposer le système de recommandation, avec Azure Functions comme interface\n","2. **⚡ Architecture directe** : Intégration directe des prédictions dans Azure Functions avec Azure Blob storage input binding pour récupérer fichiers et modèles\n","\n","## 📊 Données disponibles\n","\n","- **📋 articles_metadata.csv** : Métadonnées des articles (ID, catégorie, nombre de mots, etc.)\n","- **👆 clicks.csv / clicks_sample.csv** : Interactions utilisateurs avec les articles. Comme nous sommes dans un projet minimal viable (MVP), nous nous contenterons de **clicks_sample.csv**\n","- **🧠 articles_embeddings.pickle** : Représentations vectorielles des articles (réduction dimensionnelle possible via ACP si trop volumineux)\n","\n","## 📦 Livrables attendus\n","\n","1. **🤖 Système de recommandation** sous forme d'Azure Functions\n","2. **💻 Application simple** avec interface de gestion :\n","   - Liste des ID utilisateurs\n","   - Appel des Azure Functions pour l'utilisateur sélectionné  \n","   - Affichage des 5 articles recommandés\n","3. **🐙 Stockage GitHub** des scripts développés\n","4. **📋 Synthèse technique** comprenant :\n","   - Architecture technique actuelle et description fonctionnelle\n","   - Architecture cible pour la prise en compte de nouveaux utilisateurs/articles\n","   - Présentation des recommandations à Samia\n","\n","## 🛠️ Approche de développement\n","\n","Ce projet suit une logique MVP avec focus sur la fonctionnalité critique tout en préparant l'évolutivité de la solution pour les besoins futurs de la start-up."],"metadata":{"id":"Lk0yeR9G13zj"}},{"cell_type":"code","source":["!pip install \"numpy<2.0\" --force-reinstall"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"n5jcLoxdccKq","executionInfo":{"status":"ok","timestamp":1757320208955,"user_tz":-120,"elapsed":24005,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"a21e6b26-75e6-4831-a1ee-3639ce2847bb"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting numpy<2.0\n","  Downloading numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.0 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.0/18.0 MB\u001b[0m \u001b[31m22.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: numpy\n","  Attempting uninstall: numpy\n","    Found existing installation: numpy 2.0.2\n","    Uninstalling numpy-2.0.2:\n","      Successfully uninstalled numpy-2.0.2\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n","opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n","opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n","thinc 8.3.6 requires numpy<3.0.0,>=2.0.0, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed numpy-1.26.4\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["numpy"]},"id":"0b081f751c584453a0a4e3fd7c992d36"}},"metadata":{}}]},{"cell_type":"code","source":["!pip install scikit-surprise --quiet"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lIl9PoLbXBiv","executionInfo":{"status":"ok","timestamp":1757320304022,"user_tz":-120,"elapsed":80968,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"1ea21e0f-f29b-4958-b552-0545d011d67c"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import pickle\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.metrics.pairwise import cosine_similarity\n","from sklearn.decomposition import PCA\n","from sklearn.model_selection import train_test_split\n","import warnings\n","from surprise import Dataset, Reader, SVD, accuracy, dump\n","from surprise.model_selection import train_test_split as surprise_train_test_split\n","warnings.filterwarnings('ignore')\n","\n","print(\"🚀 Projet 10 - Application de recommandation de contenu\")\n","print(\"=\" * 60)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"87QBdfsj6x-D","executionInfo":{"status":"ok","timestamp":1757332427876,"user_tz":-120,"elapsed":4,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"4bc0c9d9-18b3-4a1d-b650-2de351037158"},"execution_count":60,"outputs":[{"output_type":"stream","name":"stdout","text":["🚀 Projet 10 - Application de recommandation de contenu\n","============================================================\n"]}]},{"cell_type":"markdown","source":["# I. Setup et chargement des données"],"metadata":{"id":"4DbpZuPcGAcy"}},{"cell_type":"code","source":["# 1. Montage Google Drive et navigation\n","from google.colab import drive\n","drive.mount('/content/drive')\n","import os\n","import zipfile\n","\n","# Accès au dossier du projet 10\n","drive_folder = \"/content/drive/MyDrive/Colab_Notebooks/Project_10/\"\n","os.chdir(drive_folder)\n","print(f\"📁 Contenu du dossier: {os.listdir()}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fCByIP9pWKe_","executionInfo":{"status":"ok","timestamp":1757320338864,"user_tz":-120,"elapsed":17577,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"12b9b3b1-b428-4457-cb45-09748c94b97c"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","📁 Contenu du dossier: ['news-portal-user-interactions-by-globocom.zip', 'p10_notebook.ipynb']\n"]}]},{"cell_type":"code","source":["# 2. Décompression des données\n","zip_path = \"news-portal-user-interactions-by-globocom.zip\"\n","extract_path = \"/content/data\"\n","\n","print(\"📦 Décompression des données principales...\")\n","with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","    zip_ref.extractall(extract_path)\n","\n","print(f\"✅ Données extraites dans: {extract_path}\")\n","print(f\"📂 Fichiers extraits: {os.listdir(extract_path)}\")\n","\n","# Décompression du clicks.zip si présent\n","clicks_zip_path = os.path.join(extract_path, \"clicks.zip\")\n","if os.path.exists(clicks_zip_path):\n","    print(\"📦 Décompression de clicks.zip...\")\n","    with zipfile.ZipFile(clicks_zip_path, 'r') as zip_ref:\n","        zip_ref.extractall(extract_path)\n","    print(\"✅ clicks.csv décompressé\")\n","    print(f\"📂 Fichiers finaux: {os.listdir(extract_path)}\")\n","else:\n","    print(\"⚠️ clicks.zip non trouvé\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kF7dTwGSWMuI","executionInfo":{"status":"ok","timestamp":1757320350403,"user_tz":-120,"elapsed":11532,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"8fd62d8c-ec36-4055-bedd-2fba77197fca"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["📦 Décompression des données principales...\n","✅ Données extraites dans: /content/data\n","📂 Fichiers extraits: ['clicks.zip', 'articles_embeddings.pickle', 'clicks_sample.csv', 'articles_metadata.csv']\n","📦 Décompression de clicks.zip...\n","✅ clicks.csv décompressé\n","📂 Fichiers finaux: ['clicks.zip', 'articles_embeddings.pickle', 'clicks_sample.csv', 'articles_metadata.csv', 'clicks']\n"]}]},{"cell_type":"code","source":["# 3. Définition du chemin des données\n","data_path = \"/content/data\"\n","\n","# 4. Chargement des fichiers\n","print(\"📂 Chargement des fichiers...\")\n","\n","# Chargement des clics (interactions utilisateur-article)\n","try:\n","    clicks_df = pd.read_csv(f\"{data_path}/clicks_sample.csv\")\n","    print(f\"✅ Clicks sample chargés: {clicks_df.shape}\")\n","    print(\"💡 Utilisation de l'échantillon pour le développement du MVP\")\n","except Exception as e:\n","    print(f\"❌ Erreur clicks_sample.csv: {e}\")\n","\n","# Chargement des métadonnées articles\n","try:\n","    articles_df = pd.read_csv(f\"{data_path}/articles_metadata.csv\")\n","    print(f\"✅ Articles metadata chargés: {articles_df.shape}\")\n","except Exception as e:\n","    print(f\"❌ Erreur articles_metadata.csv: {e}\")\n","\n","# Chargement des embeddings\n","try:\n","    with open(f\"{data_path}/articles_embeddings.pickle\", 'rb') as f:\n","        embeddings = pickle.load(f)\n","    print(f\"✅ Embeddings chargés: {embeddings.shape}\")\n","except Exception as e:\n","    print(f\"❌ Erreur articles_embeddings.pickle: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9iUHTYWpWQLn","executionInfo":{"status":"ok","timestamp":1757320350855,"user_tz":-120,"elapsed":451,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"0287b7df-13a5-459f-baa9-61760140e2bc"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","📂 Chargement des fichiers...\n","✅ Clicks sample chargés: (1883, 12)\n","💡 Utilisation de l'échantillon pour le développement du MVP\n","✅ Articles metadata chargés: (364047, 5)\n","✅ Embeddings chargés: (364047, 250)\n"]}]},{"cell_type":"code","source":["# 5. Exploration générale des données avec visualisations\n","print(\"🔍 Exploration générale:\")\n","\n","if 'clicks_df' in locals():\n","    print(f\"\\n📊 CLICKS DATA:\")\n","    print(f\"Shape: {clicks_df.shape}\")\n","    print(f\"Colonnes: {list(clicks_df.columns)}\")\n","    print(f\"Période: {pd.to_datetime(clicks_df['click_timestamp'], unit='ms').min()} - {pd.to_datetime(clicks_df['click_timestamp'], unit='ms').max()}\")\n","\n","    # Statistiques de base\n","    n_users = clicks_df['user_id'].nunique()\n","    n_articles_clicked = clicks_df['click_article_id'].nunique()\n","    n_interactions = len(clicks_df)\n","\n","    print(f\"\\n📈 Statistiques générales:\")\n","    print(f\"- Utilisateurs uniques: {n_users}\")\n","    print(f\"- Articles uniques cliqués: {n_articles_clicked}\")\n","    print(f\"- Interactions totales: {n_interactions}\")\n","    print(f\"- Interactions par utilisateur (moyenne): {n_interactions/n_users:.2f}\")\n","\n","    # Distribution des interactions par utilisateur\n","    user_interactions = clicks_df['user_id'].value_counts()\n","    print(f\"\\n📊 Distribution interactions par utilisateur:\")\n","    print(f\"- Min: {user_interactions.min()}\")\n","    print(f\"- Médiane: {user_interactions.median()}\")\n","    print(f\"- Moyenne: {user_interactions.mean():.2f}\")\n","    print(f\"- Max: {user_interactions.max()}\")\n","\n","    # VISUALISATION 1: Distribution des interactions par utilisateur\n","    print(\"\\n📈 Visualisation 1: Distribution des interactions par utilisateur\")\n","    print(f\"📂 Source: clicks_sample.csv ({clicks_df.shape[0]} interactions)\")\n","    print(f\"🔍 Périmètre: Échantillon de {n_users} utilisateurs sur 47h35min\")\n","\n","    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n","\n","    # Histogramme\n","    ax1.hist(user_interactions.values, bins=20, alpha=0.7, color='skyblue', edgecolor='black')\n","    ax1.axvline(user_interactions.median(), color='red', linestyle='--', linewidth=2, label=f'Médiane: {user_interactions.median()}')\n","    ax1.axvline(user_interactions.mean(), color='orange', linestyle='--', linewidth=2, label=f'Moyenne: {user_interactions.mean():.2f}')\n","    ax1.set_xlabel('Nombre d\\'interactions par utilisateur')\n","    ax1.set_ylabel('Nombre d\\'utilisateurs')\n","    ax1.set_title('Distribution des interactions par utilisateur')\n","    ax1.legend()\n","    ax1.grid(True, alpha=0.3)\n","\n","    # Boxplot\n","    ax2.boxplot(user_interactions.values, vert=True)\n","    ax2.set_ylabel('Nombre d\\'interactions')\n","    ax2.set_title('Boxplot des interactions par utilisateur')\n","    ax2.grid(True, alpha=0.3)\n","\n","    plt.tight_layout()\n","    plt.show()\n","\n","    # VISUALISATION 2: Top articles les plus cliqués\n","    print(\"\\n📈 Visualisation 2: Articles les plus populaires\")\n","    print(f\"📂 Source: clicks_sample.csv ({clicks_df.shape[0]} interactions)\")\n","    print(f\"🔍 Périmètre: Popularité basée sur l'échantillon de 47h35min\")\n","\n","    article_popularity = clicks_df['click_article_id'].value_counts().head(15)\n","\n","    plt.figure(figsize=(12, 6))\n","    bars = plt.bar(range(len(article_popularity)), article_popularity.values, color='lightgreen', alpha=0.7)\n","    plt.xlabel('Articles (par rang de popularité)')\n","    plt.ylabel('Nombre de clics')\n","    plt.title('Top 15 des articles les plus cliqués')\n","    plt.xticks(range(len(article_popularity)), [f'Art. {id}' for id in article_popularity.index], rotation=45)\n","\n","    # Ajouter les valeurs sur les barres\n","    for i, bar in enumerate(bars):\n","        height = bar.get_height()\n","        plt.text(bar.get_x() + bar.get_width()/2., height + 0.05,\n","                f'{int(height)}', ha='center', va='bottom')\n","\n","    plt.grid(True, alpha=0.3)\n","    plt.tight_layout()\n","    plt.show()\n","\n","    print(f\"📊 Top 5 des articles les plus cliqués:\")\n","    for i, (article_id, count) in enumerate(article_popularity.head().items(), 1):\n","        print(f\"  {i}. Article {article_id}: {count} clics\")\n","\n","    # Note sur la période temporelle\n","    print(f\"\\n⏱️ Note temporelle:\")\n","    print(f\"Dataset couvre seulement 47h35min (1-3 oct 2017)\")\n","    print(f\"Période trop courte pour analyser des patterns temporels significatifs\")\n","\n","if 'articles_df' in locals():\n","    print(f\"\\n📰 ARTICLES METADATA:\")\n","    print(f\"Shape: {articles_df.shape}\")\n","    print(f\"Colonnes: {list(articles_df.columns)}\")\n","\n","    # VISUALISATION 3: Métadonnées des articles\n","    print(\"\\n📈 Visualisation 3: Analyse des métadonnées\")\n","    print(f\"📂 Source: articles_metadata.csv ({articles_df.shape[0]} articles)\")\n","    print(f\"🔍 Périmètre: Exhaustif - TOUS les articles du catalogue (2006-2018)\")\n","\n","    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))\n","\n","    # Distribution des catégories\n","    category_counts = articles_df['category_id'].value_counts().head(10)\n","    ax1.bar(range(len(category_counts)), category_counts.values, color='purple', alpha=0.7)\n","    ax1.set_xlabel('Category ID')\n","    ax1.set_ylabel('Nombre d\\'articles')\n","    ax1.set_title('Top 10 des catégories d\\'articles')\n","    ax1.set_xticks(range(len(category_counts)))\n","    ax1.set_xticklabels(category_counts.index, rotation=45)\n","    ax1.grid(True, alpha=0.3)\n","\n","    # Distribution du nombre de mots - version avec continuité\n","    words_count = articles_df['words_count']\n","\n","    # PARAMÈTRES CONFIGURABLES\n","    percentile_threshold = 99  # Seuil pour la dernière catégorie\n","    bin_size = 50  # Taille des tranches\n","\n","    # Calculs basés sur les paramètres\n","    threshold_value = np.percentile(words_count, percentile_threshold)\n","\n","    # Créer les bins avec continuité jusqu'au seuil\n","    max_regular_bin = int(threshold_value // bin_size) * bin_size\n","    bins = list(range(0, max_regular_bin + bin_size, bin_size))\n","    bins.append(words_count.max())  # Borne supérieure finale\n","\n","    # Créer les labels avec continuité\n","    labels = []\n","    for i in range(len(bins) - 1):\n","        start = bins[i]\n","        if i == len(bins) - 2:  # Dernière catégorie\n","            labels.append(f'{start}+')\n","        else:\n","            end = bins[i+1] - 1\n","            labels.append(f'{start}-{end}')\n","\n","    # Catégoriser les données\n","    words_categorized = pd.cut(words_count, bins=bins, labels=labels, include_lowest=True)\n","    category_counts_words = words_categorized.value_counts().sort_index()\n","\n","    # Créer le graphique\n","    bars = ax2.bar(range(len(category_counts_words)), category_counts_words.values, alpha=0.7, color='orange', edgecolor='black')\n","\n","    # Position du trait pointillé dans la dernière barre\n","    last_bar_index = len(category_counts_words) - 1\n","    last_bar_start = bins[-2]  # Début de la dernière catégorie\n","    last_bar_width = threshold_value - last_bar_start\n","\n","    # Position relative du percentile dans la dernière barre (0 = début, 1 = fin)\n","    relative_position = last_bar_width / bin_size if last_bar_width <= bin_size else 1\n","    line_position = last_bar_index + relative_position - 0.5\n","\n","    # Ajouter le trait pointillé\n","    ax2.axvline(line_position, color='red', linestyle='--',\n","               label=f'{percentile_threshold}e percentile: {threshold_value:.0f} mots')\n","\n","    ax2.set_xlabel(f'Nombre de mots (tranches de {bin_size})')\n","    ax2.set_ylabel('Nombre d\\'articles')\n","    ax2.set_title('Distribution du nombre de mots par article')\n","    ax2.set_xticks(range(len(category_counts_words)))\n","    ax2.set_xticklabels(labels, rotation=45)\n","    ax2.legend()\n","    ax2.grid(True, alpha=0.3)\n","\n","    # Distribution des publishers\n","    publisher_counts = articles_df['publisher_id'].value_counts().head(10)\n","    ax3.bar(range(len(publisher_counts)), publisher_counts.values, color='teal', alpha=0.7)\n","    ax3.set_xlabel('Publisher ID')\n","    ax3.set_ylabel('Nombre d\\'articles')\n","    ax3.set_title('Top 10 des éditeurs')\n","    ax3.set_xticks(range(len(publisher_counts)))\n","    ax3.set_xticklabels(publisher_counts.index, rotation=45)\n","    ax3.grid(True, alpha=0.3)\n","\n","    # Articles par année (basé sur created_at_ts) - PERTINENT car historique de publication\n","    articles_df['year'] = pd.to_datetime(articles_df['created_at_ts'], unit='ms').dt.year\n","    year_counts = articles_df['year'].value_counts().sort_index()\n","    ax4.plot(year_counts.index, year_counts.values, marker='o', linewidth=2, markersize=6, color='red')\n","    ax4.set_xlabel('Année de publication')\n","    ax4.set_ylabel('Nombre d\\'articles publiés')\n","    ax4.set_title('Historique de publication des articles')\n","    ax4.grid(True, alpha=0.3)\n","\n","    plt.tight_layout()\n","    plt.show()\n","\n","    print(f\"📊 Statistiques des métadonnées:\")\n","    print(f\"- Catégories uniques: {articles_df['category_id'].nunique()}\")\n","    print(f\"- Éditeurs uniques: {articles_df['publisher_id'].nunique()}\")\n","    print(f\"- Nombre de mots médian: {articles_df['words_count'].median():.0f}\")\n","    print(f\"- Période de publication: {articles_df['year'].min()} - {articles_df['year'].max()}\")\n","\n","    # VISUALISATION 4: Sparsité de la matrice user-item\n","    print(\"\\n📈 Visualisation 4: Sparsité des données\")\n","    print(f\"📂 Source: clicks_sample.csv + articles_metadata.csv\")\n","    print(f\"🔍 Périmètre: Matrice {n_users} utilisateurs × {n_articles_clicked} articles cliqués (échantillon)\")\n","    print(f\"⚠️ Note: Heatmap limitée à un échantillon 50×50 pour lisibilité\")\n","\n","    # Créer une matrice user-article échantillonnée pour visualisation\n","    user_article_sample = clicks_df.groupby(['user_id', 'click_article_id']).size().unstack(fill_value=0)\n","\n","    # Prendre un échantillon pour la visualisation (premiers 50 users et 50 articles les plus populaires)\n","    top_articles = clicks_df['click_article_id'].value_counts().head(50).index\n","    sample_users = user_article_sample.index[:50]\n","    sample_matrix = user_article_sample.loc[sample_users, user_article_sample.columns.intersection(top_articles)]\n","\n","    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n","\n","    # Heatmap de sparsité\n","    im1 = ax1.imshow(sample_matrix.values, cmap='YlOrRd', aspect='auto')\n","    ax1.set_xlabel('Articles (échantillon)')\n","    ax1.set_ylabel('Utilisateurs (échantillon)')\n","    ax1.set_title('Heatmap user-article (échantillon 50x50)')\n","    plt.colorbar(im1, ax=ax1, label='Nombre de clics')\n","\n","    # Statistiques de sparsité\n","    total_cells = user_article_sample.shape[0] * user_article_sample.shape[1]\n","    non_zero_cells = np.count_nonzero(user_article_sample.values)\n","    sparsity = (1 - non_zero_cells / total_cells) * 100\n","\n","    # Graphique de sparsité\n","    labels = ['Cellules non-nulles', 'Cellules nulles (sparsité)']\n","    sizes = [non_zero_cells, total_cells - non_zero_cells]\n","    colors = ['lightblue', 'lightcoral']\n","\n","    ax2.pie(sizes, labels=labels, colors=colors, autopct='%1.2f%%', startangle=90)\n","    ax2.set_title(f'Sparsité de la matrice complète\\n({sparsity:.2f}% de cellules vides)')\n","\n","    plt.tight_layout()\n","    plt.show()\n","\n","    print(f\"📊 Métriques de sparsité:\")\n","    print(f\"- Matrice user-article: {user_article_sample.shape}\")\n","    print(f\"- Cellules totales: {total_cells:,}\")\n","    print(f\"- Cellules non-nulles: {non_zero_cells:,}\")\n","    print(f\"- Sparsité: {sparsity:.2f}%\")\n","\n","    print(f\"\\n🎯 EXPLICATION DE LA SPARSITÉ:\")\n","    print(f\"La sparsité mesure le pourcentage de cellules vides dans une matrice.\")\n","    print(f\"Dans le contexte des systèmes de recommandation:\")\n","    print(f\"- Matrice user-item: lignes = utilisateurs, colonnes = articles\")\n","    print(f\"- Cellule (i,j) = nombre d'interactions entre utilisateur i et article j\")\n","    print(f\"- Sparsité = % de cellules à 0 (pas d'interaction)\")\n","    print(f\"\")\n","    print(f\"Ici, {sparsity:.2f}% de sparsité signifie que:\")\n","    print(f\"- La plupart des utilisateurs n'ont pas interagi avec la plupart des articles\")\n","    print(f\"- C'est typique des systèmes de recommandation réels\")\n","    print(f\"- Rend le collaborative filtering difficile (peu de données communes)\")\n","    print(f\"- Justifie l'approche content-based (utilise les caractéristiques des articles)\")\n","\n","    # Vérification alignement avec embeddings\n","    if 'embeddings' in locals():\n","        print(f\"\\n🔗 Vérification alignement données:\")\n","        print(f\"- Articles metadata: {len(articles_df)} articles\")\n","        print(f\"- Embeddings: {embeddings.shape[0]} articles\")\n","        print(f\"- Articles avec clics: {n_articles_clicked}\")\n","\n","        # Test alignement article_id <-> index embeddings\n","        max_article_id = articles_df['article_id'].max()\n","        min_article_id = articles_df['article_id'].min()\n","        print(f\"- Range article_id: {min_article_id} - {max_article_id}\")\n","\n","        if max_article_id == embeddings.shape[0] - 1 and min_article_id == 0:\n","            print(\"✅ Alignement article_id <-> embeddings confirmé\")\n","        else:\n","            print(\"⚠️ Vérifier l'alignement article_id <-> embeddings\")\n","\n","print(\"\\n\" + \"=\"*60)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"QSunuKtH76ZN","executionInfo":{"status":"ok","timestamp":1757328039418,"user_tz":-120,"elapsed":2175,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"cf0b9969-eac4-4b77-8fbe-64406744bd1a"},"execution_count":50,"outputs":[{"output_type":"stream","name":"stdout","text":["🔍 Exploration générale:\n","\n","📊 CLICKS DATA:\n","Shape: (1883, 15)\n","Colonnes: ['user_id', 'session_id', 'session_start', 'session_size', 'click_article_id', 'click_timestamp', 'click_environment', 'click_deviceGroup', 'click_os', 'click_country', 'click_region', 'click_referrer_type', 'datetime', 'hour', 'date']\n","Période: 2017-10-01 03:00:00.026000 - 2017-10-03 02:35:54.157000\n","\n","📈 Statistiques générales:\n","- Utilisateurs uniques: 707\n","- Articles uniques cliqués: 323\n","- Interactions totales: 1883\n","- Interactions par utilisateur (moyenne): 2.66\n","\n","📊 Distribution interactions par utilisateur:\n","- Min: 2\n","- Médiane: 2.0\n","- Moyenne: 2.66\n","- Max: 24\n","\n","📈 Visualisation 1: Distribution des interactions par utilisateur\n","📂 Source: clicks_sample.csv (1883 interactions)\n","🔍 Périmètre: Échantillon de 707 utilisateurs sur 47h35min\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x500 with 2 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Visualisation 2: Articles les plus populaires\n","📂 Source: clicks_sample.csv (1883 interactions)\n","🔍 Périmètre: Popularité basée sur l'échantillon de 47h35min\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["📊 Top 5 des articles les plus cliqués:\n","  1. Article 119592: 202 clics\n","  2. Article 96663: 153 clics\n","  3. Article 108854: 126 clics\n","  4. Article 284847: 126 clics\n","  5. Article 235840: 106 clics\n","\n","⏱️ Note temporelle:\n","Dataset couvre seulement 47h35min (1-3 oct 2017)\n","Période trop courte pour analyser des patterns temporels significatifs\n","\n","📰 ARTICLES METADATA:\n","Shape: (364047, 6)\n","Colonnes: ['article_id', 'category_id', 'created_at_ts', 'publisher_id', 'words_count', 'year']\n","\n","📈 Visualisation 3: Analyse des métadonnées\n","📂 Source: articles_metadata.csv (364047 articles)\n","🔍 Périmètre: Exhaustif - TOUS les articles du catalogue (2006-2018)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x1000 with 4 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Statistiques des métadonnées:\n","- Catégories uniques: 461\n","- Éditeurs uniques: 1\n","- Nombre de mots médian: 186\n","- Période de publication: 2006 - 2018\n","\n","📈 Visualisation 4: Sparsité des données\n","📂 Source: clicks_sample.csv + articles_metadata.csv\n","🔍 Périmètre: Matrice 707 utilisateurs × 323 articles cliqués (échantillon)\n","⚠️ Note: Heatmap limitée à un échantillon 50×50 pour lisibilité\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x600 with 3 Axes>"],"image/png":"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Métriques de sparsité:\n","- Matrice user-article: (707, 323)\n","- Cellules totales: 228,361\n","- Cellules non-nulles: 1,883\n","- Sparsité: 99.18%\n","\n","🎯 EXPLICATION DE LA SPARSITÉ:\n","La sparsité mesure le pourcentage de cellules vides dans une matrice.\n","Dans le contexte des systèmes de recommandation:\n","- Matrice user-item: lignes = utilisateurs, colonnes = articles\n","- Cellule (i,j) = nombre d'interactions entre utilisateur i et article j\n","- Sparsité = % de cellules à 0 (pas d'interaction)\n","\n","Ici, 99.18% de sparsité signifie que:\n","- La plupart des utilisateurs n'ont pas interagi avec la plupart des articles\n","- C'est typique des systèmes de recommandation réels\n","- Rend le collaborative filtering difficile (peu de données communes)\n","- Justifie l'approche content-based (utilise les caractéristiques des articles)\n","\n","🔗 Vérification alignement données:\n","- Articles metadata: 364047 articles\n","- Embeddings: 364047 articles\n","- Articles avec clics: 323\n","- Range article_id: 0 - 364046\n","✅ Alignement article_id <-> embeddings confirmé\n","\n","============================================================\n"]}]},{"cell_type":"markdown","source":["# II. Content-based Filtering"],"metadata":{"id":"MaenQmkBWVyx"}},{"cell_type":"markdown","source":["**Principe :** Recommande des articles similaires à ceux que l'utilisateur a déjà consultés, en se basant sur les caractéristiques du contenu des articles.\n","\n","**Fonctionnement général :**\n","1. **Représentation du contenu** : Chaque article est représenté par un vecteur numérique (embedding) qui encode ses caractéristiques sémantiques\n","2. **Profil utilisateur** : Basé sur les embeddings des articles déjà consultés par l'utilisateur\n","3. **Calcul de similarité** : Mesure la proximité (similarité cosinus) entre le profil utilisateur et tous les autres articles\n","4. **Recommandation** : Sélectionne les articles les plus similaires non encore consultés\n","\n","**Implémentation technique :**\n","- Utilise des embeddings pré-calculés (250 dimensions par article)\n","- Calculs de distance vectorielle pure (pas d'apprentissage en temps réel)\n","- Stratégies : dernier article vu, moyenne des articles vus, ou sélection aléatoire\n","\n","**Avantages :**\n","- Fonctionne dès le premier clic de l'utilisateur\n","- Pas de problème de démarrage à froid pour les nouveaux articles\n","- Recommandations explicables et transparentes\n","\n","**Inconvénients :**\n","- Risque de \"bulle de filtre\" (recommandations trop similaires)\n","- Dépend de la qualité de la représentation du contenu\n","- Difficulté à découvrir des contenus différents"],"metadata":{"id":"9pA6mD0ip6u_"}},{"cell_type":"code","source":["# 1. Analyse spécifique Content-Based\n","print(\"\\n🔍 Analyse spécifique pour Content-Based:\")\n","\n","if 'embeddings' in locals():\n","    print(f\"✅ Embeddings disponibles: {embeddings.shape}\")\n","    print(f\"- Dimensions: {embeddings.shape[1]} features par article\")\n","    print(f\"- Type: {embeddings.dtype}\")\n","    print(f\"- Taille mémoire: ~{embeddings.nbytes / (1024**2):.1f} MB\")\n","\n","    # Vérification de la qualité des embeddings\n","    print(f\"\\n📊 Qualité des embeddings:\")\n","    print(f\"- Valeurs nulles: {np.isnan(embeddings).sum()}\")\n","    print(f\"- Valeurs infinies: {np.isinf(embeddings).sum()}\")\n","    print(f\"- Range des valeurs: [{embeddings.min():.3f}, {embeddings.max():.3f}]\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MKAjtTWs58rb","executionInfo":{"status":"ok","timestamp":1757329821892,"user_tz":-120,"elapsed":739,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"dc2b3e1b-18a1-4cdd-c156-fee7bf90edc9"},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","🔍 Analyse spécifique pour Content-Based:\n","✅ Embeddings disponibles: (364047, 250)\n","- Dimensions: 250 features par article\n","- Type: float32\n","- Taille mémoire: ~347.2 MB\n","\n","📊 Qualité des embeddings:\n","- Valeurs nulles: 0\n","- Valeurs infinies: 0\n","- Range des valeurs: [-0.999, 0.998]\n"]}]},{"cell_type":"code","source":["# 2. Préparation des données pour Content-Based\n","def prepare_content_based_data(clicks_df, embeddings):\n","    \"\"\"\n","    Prépare les données pour le content-based filtering\n","    \"\"\"\n","    print(\"🔧 Création de la structure de données...\")\n","\n","    # Créer un dictionnaire: user_id -> liste des articles cliqués\n","    user_articles = {}\n","    for _, row in clicks_df.iterrows():\n","        user_id = row['user_id']\n","        article_id = row['click_article_id']\n","\n","        if user_id not in user_articles:\n","            user_articles[user_id] = []\n","        user_articles[user_id].append(article_id)\n","\n","    print(f\"✅ Structure créée pour {len(user_articles)} utilisateurs\")\n","\n","    # Statistiques sur les articles par utilisateur\n","    articles_per_user = [len(articles) for articles in user_articles.values()]\n","    print(f\"📊 Articles par utilisateur:\")\n","    print(f\"- Min: {min(articles_per_user)}\")\n","    print(f\"- Médiane: {np.median(articles_per_user)}\")\n","    print(f\"- Moyenne: {np.mean(articles_per_user):.2f}\")\n","    print(f\"- Max: {max(articles_per_user)}\")\n","\n","    return user_articles\n","\n","if 'clicks_df' in locals() and 'embeddings' in locals():\n","    user_articles_dict = prepare_content_based_data(clicks_df, embeddings)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bDqihqSiWaRQ","executionInfo":{"status":"ok","timestamp":1757329822004,"user_tz":-120,"elapsed":111,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"cda75f8c-b12d-4e9b-9b25-6f0f04ef9167"},"execution_count":52,"outputs":[{"output_type":"stream","name":"stdout","text":["🔧 Création de la structure de données...\n","✅ Structure créée pour 707 utilisateurs\n","📊 Articles par utilisateur:\n","- Min: 2\n","- Médiane: 2.0\n","- Moyenne: 2.66\n","- Max: 24\n"]}]},{"cell_type":"code","source":["# 3. Développement du modèle Content-Based\n","def content_based_recommender(user_id, user_articles_dict, embeddings, n_recommendations=5, strategy='last'):\n","    \"\"\"\n","    Système de recommandation basé sur le contenu\n","\n","    Args:\n","        user_id: ID de l'utilisateur\n","        user_articles_dict: Dict {user_id: [article_ids]}\n","        embeddings: Matrice des embeddings articles\n","        n_recommendations: Nombre de recommandations\n","        strategy: 'last', 'random', 'mean'\n","\n","    Returns:\n","        Liste des article_ids recommandés avec scores\n","    \"\"\"\n","\n","    if user_id not in user_articles_dict:\n","        print(f\"❌ Utilisateur {user_id} non trouvé\")\n","        return []\n","\n","    clicked_articles = user_articles_dict[user_id]\n","    print(f\"👤 Utilisateur {user_id} - {len(clicked_articles)} articles cliqués\")\n","\n","    # Vérifier que les articles existent dans les embeddings\n","    valid_articles = [art for art in clicked_articles if art < len(embeddings)]\n","    if not valid_articles:\n","        print(\"❌ Aucun article valide trouvé dans les embeddings\")\n","        return []\n","\n","    print(f\"✅ {len(valid_articles)} articles valides pour le calcul\")\n","\n","    # Stratégies pour choisir l'embedding de référence\n","    if strategy == 'last':\n","        # Prendre le dernier article cliqué\n","        reference_article = valid_articles[-1]\n","        reference_embedding = embeddings[reference_article].reshape(1, -1)\n","        print(f\"📰 Stratégie 'last': article {reference_article}\")\n","\n","    elif strategy == 'random':\n","        # Prendre un article au hasard\n","        reference_article = np.random.choice(valid_articles)\n","        reference_embedding = embeddings[reference_article].reshape(1, -1)\n","        print(f\"🎲 Stratégie 'random': article {reference_article}\")\n","\n","    elif strategy == 'mean':\n","        # Moyenne des embeddings des articles cliqués\n","        articles_embeddings = embeddings[valid_articles]\n","        reference_embedding = np.mean(articles_embeddings, axis=0).reshape(1, -1)\n","        print(f\"📊 Stratégie 'mean': moyenne de {len(valid_articles)} articles\")\n","\n","    # Calculer similarité cosinus (angle entre deux vecteurs de dimension N) avec tous les articles\n","    # Plus la similarité cosinus a une valeur élevée, plus la similarité entre 1 article et le \"profil\" de l'utilisateur\n","    #    (caractérisé par un ou plusieurs articles) est importante\n","    similarities = cosine_similarity(reference_embedding, embeddings)[0]\n","\n","    # Créer liste des candidats (exclure articles déjà cliqués)\n","    candidates = []\n","    for article_id, similarity in enumerate(similarities):\n","        if article_id not in clicked_articles:  # Exclure articles déjà vus\n","            candidates.append((article_id, similarity))\n","\n","    # Trier par similarité décroissante\n","    candidates.sort(key=lambda x: x[1], reverse=True)\n","\n","    # Retourner top N recommandations\n","    recommendations = candidates[:n_recommendations]\n","\n","    print(f\"✅ {len(recommendations)} recommandations générées\")\n","    if recommendations:\n","        scores = [sim for _, sim in recommendations]\n","        print(f\"📊 Scores de similarité: [{scores[0]:.3f} - {scores[-1]:.3f}]\")\n","\n","    return recommendations"],"metadata":{"id":"0yN7YvysWfBo","executionInfo":{"status":"ok","timestamp":1757330942773,"user_tz":-120,"elapsed":13,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":54,"outputs":[]},{"cell_type":"code","source":["# 4. Tests du modèle Content-Based\n","print(\"🧪 Tests du modèle Content-Based:\")\n","\n","def test_content_based_strategies():\n","    \"\"\"\n","    Teste les différentes stratégies du content-based filtering\n","    \"\"\"\n","    if 'user_articles_dict' not in globals() or 'embeddings' not in globals():\n","        print(\"❌ Données non préparées\")\n","        return\n","\n","    # Choisir un utilisateur test\n","    test_users = list(user_articles_dict.keys())[:3]  # 3 premiers utilisateurs\n","\n","    strategies = ['last', 'mean', 'random']\n","\n","    for user_id in test_users:\n","        print(f\"\\n👤 TEST UTILISATEUR {user_id}:\")\n","        print(f\"Articles cliqués: {user_articles_dict[user_id]}\")\n","\n","        for strategy in strategies:\n","            print(f\"\\n  🔄 Stratégie: {strategy}\")\n","            recommendations = content_based_recommender(\n","                user_id, user_articles_dict, embeddings,\n","                n_recommendations=5, strategy=strategy\n","            )\n","\n","            if recommendations:\n","                print(f\"  📋 Recommandations:\")\n","                for i, (article_id, score) in enumerate(recommendations, 1):\n","                    print(f\"    {i}. Article {article_id} (score: {score:.3f})\")\n","            print(\"  \" + \"-\"*30)\n","\n","if 'user_articles_dict' in globals() and 'embeddings' in globals():\n","    test_content_based_strategies()\n","\n","print(\"\\n\" + \"=\"*60)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wI6YsMt-Wn7Q","executionInfo":{"status":"ok","timestamp":1757330957373,"user_tz":-120,"elapsed":10167,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"b04d840f-209b-4c98-d5c7-ecf36b515e0c"},"execution_count":55,"outputs":[{"output_type":"stream","name":"stdout","text":["🧪 Tests du modèle Content-Based:\n","\n","👤 TEST UTILISATEUR 0:\n","Articles cliqués: [157541, 68866]\n","\n","  🔄 Stratégie: last\n","👤 Utilisateur 0 - 2 articles cliqués\n","✅ 2 articles valides pour le calcul\n","📰 Stratégie 'last': article 68866\n","✅ 5 recommandations générées\n","📊 Scores de similarité: [0.827 - 0.804]\n","  📋 Recommandations:\n","    1. Article 358037 (score: 0.827)\n","    2. Article 159170 (score: 0.816)\n","    3. Article 65360 (score: 0.812)\n","    4. Article 152054 (score: 0.806)\n","    5. Article 153876 (score: 0.804)\n","  ------------------------------\n","\n","  🔄 Stratégie: mean\n","👤 Utilisateur 0 - 2 articles cliqués\n","✅ 2 articles valides pour le calcul\n","📊 Stratégie 'mean': moyenne de 2 articles\n","✅ 5 recommandations générées\n","📊 Scores de similarité: [0.904 - 0.893]\n","  📋 Recommandations:\n","    1. Article 157519 (score: 0.904)\n","    2. Article 162856 (score: 0.900)\n","    3. Article 159495 (score: 0.899)\n","    4. Article 157944 (score: 0.898)\n","    5. Article 156690 (score: 0.893)\n","  ------------------------------\n","\n","  🔄 Stratégie: random\n","👤 Utilisateur 0 - 2 articles cliqués\n","✅ 2 articles valides pour le calcul\n","🎲 Stratégie 'random': article 68866\n","✅ 5 recommandations générées\n","📊 Scores de similarité: [0.827 - 0.804]\n","  📋 Recommandations:\n","    1. Article 358037 (score: 0.827)\n","    2. Article 159170 (score: 0.816)\n","    3. Article 65360 (score: 0.812)\n","    4. Article 152054 (score: 0.806)\n","    5. Article 153876 (score: 0.804)\n","  ------------------------------\n","\n","👤 TEST UTILISATEUR 1:\n","Articles cliqués: [235840, 96663]\n","\n","  🔄 Stratégie: last\n","👤 Utilisateur 1 - 2 articles cliqués\n","✅ 2 articles valides pour le calcul\n","📰 Stratégie 'last': article 96663\n","✅ 5 recommandations générées\n","📊 Scores de similarité: [0.865 - 0.846]\n","  📋 Recommandations:\n","    1. Article 90118 (score: 0.865)\n","    2. Article 93471 (score: 0.854)\n","    3. Article 96664 (score: 0.854)\n","    4. Article 118683 (score: 0.849)\n","    5. Article 90299 (score: 0.846)\n","  ------------------------------\n","\n","  🔄 Stratégie: mean\n","👤 Utilisateur 1 - 2 articles cliqués\n","✅ 2 articles valides pour le calcul\n","📊 Stratégie 'mean': moyenne de 2 articles\n","✅ 5 recommandations générées\n","📊 Scores de similarité: [0.786 - 0.758]\n","  📋 Recommandations:\n","    1. Article 95797 (score: 0.786)\n","    2. Article 90583 (score: 0.777)\n","    3. Article 246739 (score: 0.761)\n","    4. Article 231234 (score: 0.758)\n","    5. Article 90167 (score: 0.758)\n","  ------------------------------\n","\n","  🔄 Stratégie: random\n","👤 Utilisateur 1 - 2 articles cliqués\n","✅ 2 articles valides pour le calcul\n","🎲 Stratégie 'random': article 235840\n","✅ 5 recommandations générées\n","📊 Scores de similarité: [0.888 - 0.885]\n","  📋 Recommandations:\n","    1. Article 60509 (score: 0.888)\n","    2. Article 235312 (score: 0.887)\n","    3. Article 235410 (score: 0.887)\n","    4. Article 108443 (score: 0.886)\n","    5. Article 234819 (score: 0.885)\n","  ------------------------------\n","\n","👤 TEST UTILISATEUR 2:\n","Articles cliqués: [119592, 30970]\n","\n","  🔄 Stratégie: last\n","👤 Utilisateur 2 - 2 articles cliqués\n","✅ 2 articles valides pour le calcul\n","📰 Stratégie 'last': article 30970\n","✅ 5 recommandations générées\n","📊 Scores de similarité: [0.929 - 0.914]\n","  📋 Recommandations:\n","    1. Article 31116 (score: 0.929)\n","    2. Article 22450 (score: 0.918)\n","    3. Article 22019 (score: 0.914)\n","    4. Article 22974 (score: 0.914)\n","    5. Article 31714 (score: 0.914)\n","  ------------------------------\n","\n","  🔄 Stratégie: mean\n","👤 Utilisateur 2 - 2 articles cliqués\n","✅ 2 articles valides pour le calcul\n","📊 Stratégie 'mean': moyenne de 2 articles\n","✅ 5 recommandations générées\n","📊 Scores de similarité: [0.839 - 0.805]\n","  📋 Recommandations:\n","    1. Article 25856 (score: 0.839)\n","    2. Article 30693 (score: 0.818)\n","    3. Article 30847 (score: 0.810)\n","    4. Article 115968 (score: 0.806)\n","    5. Article 23608 (score: 0.805)\n","  ------------------------------\n","\n","  🔄 Stratégie: random\n","👤 Utilisateur 2 - 2 articles cliqués\n","✅ 2 articles valides pour le calcul\n","🎲 Stratégie 'random': article 119592\n","✅ 5 recommandations générées\n","📊 Scores de similarité: [0.918 - 0.903]\n","  📋 Recommandations:\n","    1. Article 118495 (score: 0.918)\n","    2. Article 118614 (score: 0.912)\n","    3. Article 118498 (score: 0.907)\n","    4. Article 118494 (score: 0.906)\n","    5. Article 119448 (score: 0.903)\n","  ------------------------------\n","\n","============================================================\n"]}]},{"cell_type":"markdown","source":["# III. Collaborative Filtering"],"metadata":{"id":"BdhNEIpZWt9E"}},{"cell_type":"markdown","source":["**Principe :** Recommande des articles en se basant sur les préférences d'utilisateurs ayant des goûts similaires (\"Les utilisateurs qui aiment A aiment aussi B\").\n","\n","**Fonctionnement général :**\n","1. **Matrice user-item** : Construction d'une matrice interactions utilisateurs × articles\n","2. **Recherche d'utilisateurs similaires** : Identification de profils d'usage comparables\n","3. **Factorisation matricielle** : Décomposition SVD pour extraire des facteurs latents\n","4. **Prédiction** : Estimation des préférences basée sur les utilisateurs similaires\n","\n","**Implémentation technique :**\n","- Ratings implicites basés sur le nombre de clics\n","- Algorithme SVD (librairie Surprise) pour la factorisation. SVD = machine learning léger (comme k-means, par exemple)\n","- Apprentissage sur matrice user-item pour découvrir des patterns cachés\n","\n","**Avantages :**\n","- Peut découvrir des contenus inattendus et diversifiés\n","- Capture des patterns complexes sans analyser le contenu\n","- Améliore avec l'augmentation du nombre d'utilisateurs\n","\n","**Inconvénients :**\n","- Problème de démarrage à froid (nouveaux utilisateurs/articles)\n","- Nécessite beaucoup d'interactions pour être efficace\n","- Sensible à la sparsité des données"],"metadata":{"id":"sY2jgIyjp_YE"}},{"cell_type":"code","source":["# 1. Analyse spécifique Collaborative Filtering\n","print(\"🔍 Analyse spécifique pour Collaborative Filtering:\")\n","\n","def analyze_for_collaborative(clicks_df):\n","    \"\"\"\n","    Analyse les données pour le collaborative filtering\n","    \"\"\"\n","    print(\"📊 Analyse de la matrice user-item:\")\n","\n","    # Créer matrice user-article avec comptage des clics\n","    user_item_matrix = clicks_df.groupby(['user_id', 'click_article_id']).size().unstack(fill_value=0)\n","\n","    print(f\"✅ Matrice user-item créée: {user_item_matrix.shape}\")\n","\n","    # Calcul de sparsité\n","    total_cells = user_item_matrix.shape[0] * user_item_matrix.shape[1]\n","    non_zero_cells = np.count_nonzero(user_item_matrix.values)\n","    sparsity = (1 - non_zero_cells / total_cells) * 100\n","\n","    print(f\"📈 Métriques de sparsité:\")\n","    print(f\"- Cellules totales: {total_cells:,}\")\n","    print(f\"- Cellules non-nulles: {non_zero_cells:,}\")\n","    print(f\"- Sparsité: {sparsity:.2f}%\")\n","\n","    # Distribution des ratings (nombre de clics)\n","    ratings = user_item_matrix.values[user_item_matrix.values > 0]\n","    print(f\"\\n📊 Distribution des 'ratings' (nb clics):\")\n","    print(f\"- Min: {ratings.min()}\")\n","    print(f\"- Médiane: {np.median(ratings)}\")\n","    print(f\"- Moyenne: {ratings.mean():.2f}\")\n","    print(f\"- Max: {ratings.max()}\")\n","\n","    return user_item_matrix\n","\n","if 'clicks_df' in locals():\n","    user_item_matrix = analyze_for_collaborative(clicks_df)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gNUJaXELWrco","executionInfo":{"status":"ok","timestamp":1757321248724,"user_tz":-120,"elapsed":57,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"96b30760-9ba0-4b56-ef69-50418237daf9"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["🔍 Analyse spécifique pour Collaborative Filtering:\n","📊 Analyse de la matrice user-item:\n","✅ Matrice user-item créée: (707, 323)\n","📈 Métriques de sparsité:\n","- Cellules totales: 228,361\n","- Cellules non-nulles: 1,883\n","- Sparsité: 99.18%\n","\n","📊 Distribution des 'ratings' (nb clics):\n","- Min: 1\n","- Médiane: 1.0\n","- Moyenne: 1.00\n","- Max: 1\n"]}]},{"cell_type":"code","source":["# 2. Développement du modèle Collaborative Filtering\n","\n","def collaborative_filtering_surprise(clicks_df):\n","    \"\"\"\n","    Collaborative filtering avec la librairie Surprise\n","    \"\"\"\n","    print(\"🔧 Préparation des données pour Surprise...\")\n","\n","    # Préparer les données au format Surprise\n","    # Créer un rating implicite basé sur le nombre de clics\n","    ratings_data = clicks_df.groupby(['user_id', 'click_article_id']).size().reset_index(name='rating')\n","\n","    print(f\"📊 Données préparées: {len(ratings_data)} interactions\")\n","\n","    # Définir l'échelle des ratings\n","    reader = Reader(rating_scale=(1, ratings_data['rating'].max()))\n","\n","    # Créer le dataset Surprise\n","    data = Dataset.load_from_df(ratings_data[['user_id', 'click_article_id', 'rating']], reader)\n","\n","    # Split train/test\n","    trainset, testset = surprise_train_test_split(data, test_size=0.2, random_state=42)\n","\n","    # Correction: convertir le générateur en liste pour len()\n","    trainset_size = len(list(trainset.all_ratings()))\n","    print(f\"✅ Dataset divisé: {trainset_size} train, {len(testset)} test\")\n","\n","    # Entraîner le modèle SVD\n","    print(\"🏋️ Entraînement du modèle SVD...\")\n","    model = SVD(random_state=42)\n","    model.fit(trainset)\n","\n","    # Évaluation\n","    predictions = model.test(testset)\n","    rmse = accuracy.rmse(predictions, verbose=False)\n","\n","    print(f\"📊 RMSE sur le test set: {rmse:.4f}\")\n","\n","    return model, trainset\n","\n","def get_collaborative_recommendations(model, trainset, user_id, n_recommendations=5):\n","    \"\"\"\n","    Génère des recommandations avec le modèle collaboratif\n","    \"\"\"\n","    if model is None:\n","        return []\n","\n","    # Récupérer tous les articles\n","    all_articles = trainset.all_items()\n","\n","    # Articles déjà vus par l'utilisateur\n","    try:\n","        user_inner_id = trainset.to_inner_uid(user_id)\n","        user_items = set([trainset.to_raw_iid(item_inner_id)\n","                         for item_inner_id, rating in trainset.ur[user_inner_id]])\n","    except ValueError:\n","        print(f\"❌ Utilisateur {user_id} non trouvé dans trainset\")\n","        return []\n","\n","    # Prédire pour tous les articles non vus\n","    predictions = []\n","    for item_inner_id in all_articles:\n","        item_id = trainset.to_raw_iid(item_inner_id)\n","        if item_id not in user_items:\n","            pred = model.predict(user_id, item_id)\n","            predictions.append((item_id, pred.est))\n","\n","    # Trier par score prédit décroissant\n","    predictions.sort(key=lambda x: x[1], reverse=True)\n","\n","    print(f\"✅ {len(predictions)} articles candidats évalués\")\n","\n","    return predictions[:n_recommendations]"],"metadata":{"id":"sc1nx0rqW3F5","executionInfo":{"status":"ok","timestamp":1757332226539,"user_tz":-120,"elapsed":2,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":56,"outputs":[]},{"cell_type":"code","source":["print(\"🧪 Tests du modèle Collaborative:\")\n","\n","if 'clicks_df' in locals():\n","    try:\n","        collaborative_model, trainset = collaborative_filtering_surprise(clicks_df)\n","\n","        if collaborative_model is not None:\n","            # Test avec quelques utilisateurs\n","            test_users = clicks_df['user_id'].unique()[:3]\n","\n","            for user_id in test_users:\n","                print(f\"\\n👤 TEST UTILISATEUR {user_id} (Collaborative):\")\n","\n","                collab_recs = get_collaborative_recommendations(\n","                    collaborative_model, trainset, user_id, n_recommendations=5\n","                )\n","\n","                if collab_recs:\n","                    print(f\"📋 Recommandations collaboratives:\")\n","                    for i, (article_id, score) in enumerate(collab_recs, 1):\n","                        print(f\"  {i}. Article {article_id} (score: {score:.3f})\")\n","                else:\n","                    print(\"❌ Aucune recommandation générée\")\n","\n","    except Exception as e:\n","        print(f\"❌ Erreur lors du test collaborative: {e}\")\n","        print(f\"Type d'erreur: {type(e).__name__}\")\n","        import traceback\n","        traceback.print_exc()\n","\n","print(\"\\n\" + \"=\"*60)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fbaguv6xW8E5","executionInfo":{"status":"ok","timestamp":1757332227928,"user_tz":-120,"elapsed":52,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"9029fa3c-9c09-46d7-d33f-b12c17f8260c"},"execution_count":57,"outputs":[{"output_type":"stream","name":"stdout","text":["🧪 Tests du modèle Collaborative:\n","🔧 Préparation des données pour Surprise...\n","📊 Données préparées: 1883 interactions\n","✅ Dataset divisé: 1506 train, 377 test\n","🏋️ Entraînement du modèle SVD...\n","📊 RMSE sur le test set: 0.0000\n","\n","👤 TEST UTILISATEUR 0 (Collaborative):\n","✅ 280 articles candidats évalués\n","📋 Recommandations collaboratives:\n","  1. Article 235840 (score: 1.000)\n","  2. Article 234995 (score: 1.000)\n","  3. Article 207122 (score: 1.000)\n","  4. Article 119592 (score: 1.000)\n","  5. Article 251735 (score: 1.000)\n","\n","👤 TEST UTILISATEUR 1 (Collaborative):\n","✅ 279 articles candidats évalués\n","📋 Recommandations collaboratives:\n","  1. Article 234995 (score: 1.000)\n","  2. Article 207122 (score: 1.000)\n","  3. Article 119592 (score: 1.000)\n","  4. Article 251735 (score: 1.000)\n","  5. Article 283819 (score: 1.000)\n","\n","👤 TEST UTILISATEUR 2 (Collaborative):\n","✅ 279 articles candidats évalués\n","📋 Recommandations collaboratives:\n","  1. Article 235840 (score: 1.000)\n","  2. Article 234995 (score: 1.000)\n","  3. Article 207122 (score: 1.000)\n","  4. Article 251735 (score: 1.000)\n","  5. Article 283819 (score: 1.000)\n","\n","============================================================\n"]}]},{"cell_type":"code","source":["dump.dump(drive_folder+'svd_model.pkl', algo=collaborative_model)"],"metadata":{"id":"FDHE6T-_LJKP","executionInfo":{"status":"ok","timestamp":1757332464381,"user_tz":-120,"elapsed":27,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}}},"execution_count":63,"outputs":[]},{"cell_type":"markdown","source":["# IV. Comparaison et Choix du Modèle"],"metadata":{"id":"KC1FilOzXOhc"}},{"cell_type":"code","source":["def compare_approaches():\n","    \"\"\"\n","    Compare les deux approches et recommande pour Azure Functions\n","    \"\"\"\n","    print(\"📊 COMPARAISON DES APPROCHES:\")\n","    print(\"\\n🔍 CONTENT-BASED FILTERING:\")\n","    print(\"✅ Avantages:\")\n","    print(\"  - Fonctionne avec peu d'interactions (1 clic suffit)\")\n","    print(\"  - Pas de cold start problem pour nouveaux articles\")\n","    print(\"  - Recommandations explicables (similarité de contenu)\")\n","    print(\"  - Embeddings pré-calculés = performance optimale\")\n","    print(\"❌ Inconvénients:\")\n","    print(\"  - Risque de bulle de filtres\")\n","    print(\"  - Dépend de la qualité des embeddings\")\n","\n","    print(\"\\n👥 COLLABORATIVE FILTERING:\")\n","    print(\"✅ Avantages:\")\n","    print(\"  - Découvre des patterns cachés entre utilisateurs\")\n","    print(\"  - Peut recommander du contenu diversifié\")\n","    print(\"  - Apprentissage des préférences globales\")\n","    print(\"❌ Inconvénients:\")\n","    print(\"  - Sparsité très élevée (>99%)\")\n","    print(\"  - Cold start problem (nouveaux utilisateurs)\")\n","    print(\"  - Nécessite plus d'interactions pour être efficace\")\n","\n","    print(\"\\n🎯 RECOMMANDATION POUR AZURE FUNCTIONS:\")\n","    print(\"🏆 CONTENT-BASED FILTERING choisi car:\")\n","    print(\"  1. Adapté aux données éparses (2.66 interactions/user)\")\n","    print(\"  2. Robuste pour nouveaux utilisateurs\")\n","    print(\"  3. Embeddings pré-calculés = rapidité d'exécution\")\n","    print(\"  4. Facilite l'implémentation serverless\")\n","    print(\"  5. Moins de ressources computationnelles requises\")\n","\n","    print(\"\\n📋 SPÉCIFICATIONS POUR AZURE FUNCTIONS:\")\n","    print(\"- Algorithme: Content-Based avec embeddings\")\n","    print(\"- Stratégie: 'mean' (moyenne des embeddings des articles vus)\")\n","    print(\"- Fallback: 'last' si un seul article vu\")\n","    print(\"- Optimisation: Réduction PCA si embeddings trop volumineux\")\n","    print(\"- Format entrée: user_id\")\n","    print(\"- Format sortie: [article_id1, article_id2, ..., article_id5]\")\n","\n","compare_approaches()\n","\n","print(\"\\n\" + \"=\"*60)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xg1L4MB9XN_S","executionInfo":{"status":"ok","timestamp":1757336952950,"user_tz":-120,"elapsed":23,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"6386b1ce-1359-4caf-d44b-ee2930c72c32"},"execution_count":79,"outputs":[{"output_type":"stream","name":"stdout","text":["📊 COMPARAISON DES APPROCHES:\n","\n","🔍 CONTENT-BASED FILTERING:\n","✅ Avantages:\n","  - Fonctionne avec peu d'interactions (1 clic suffit)\n","  - Pas de cold start problem pour nouveaux articles\n","  - Recommandations explicables (similarité de contenu)\n","  - Embeddings pré-calculés = performance optimale\n","❌ Inconvénients:\n","  - Risque de bulle de filtres\n","  - Dépend de la qualité des embeddings\n","\n","👥 COLLABORATIVE FILTERING:\n","✅ Avantages:\n","  - Découvre des patterns cachés entre utilisateurs\n","  - Peut recommander du contenu diversifié\n","  - Apprentissage des préférences globales\n","❌ Inconvénients:\n","  - Sparsité très élevée (>99%)\n","  - Cold start problem (nouveaux utilisateurs)\n","  - Nécessite plus d'interactions pour être efficace\n","\n","🎯 RECOMMANDATION POUR AZURE FUNCTIONS:\n","🏆 CONTENT-BASED FILTERING choisi car:\n","  1. Adapté aux données éparses (2.66 interactions/user)\n","  2. Robuste pour nouveaux utilisateurs\n","  3. Embeddings pré-calculés = rapidité d'exécution\n","  4. Facilite l'implémentation serverless\n","  5. Moins de ressources computationnelles requises\n","\n","📋 SPÉCIFICATIONS POUR AZURE FUNCTIONS:\n","- Algorithme: Content-Based avec embeddings\n","- Stratégie: 'mean' (moyenne des embeddings des articles vus)\n","- Fallback: 'last' si un seul article vu\n","- Optimisation: Réduction PCA si embeddings trop volumineux\n","- Format entrée: user_id\n","- Format sortie: [article_id1, article_id2, ..., article_id5]\n","\n","============================================================\n"]}]},{"cell_type":"markdown","source":["# V. Réduction dimensionnelle des embeddings pour Azure Functions"],"metadata":{"id":"VzKu8j6YVKl1"}},{"cell_type":"code","source":["print(\"🔬 V. RÉDUCTION DIMENSIONNELLE DES EMBEDDINGS\")\n","print(\"-\" * 50)\n","\n","print(\"\"\"\n","## OBJECTIF DE CETTE SECTION\n","\n","Pour déployer le content-based filtering sur Azure Functions (plan gratuit), nous devons\n","réduire la taille du fichier embeddings (347 MB) qui dépasse les limites du plan gratuit.\n","\n","**Stratégie :** Utiliser l'Analyse en Composantes Principales (ACP/PCA) pour réduire\n","les 250 dimensions à un nombre plus restreint tout en conservant l'information essentielle.\n","\n","**Analyses prévues :**\n","- Variance expliquée par composante\n","- Choix du nombre optimal de composantes\n","- Comparaison des tailles de fichiers\n","- Impact sur les performances de recommandation\n","\"\"\")\n","\n","# 1. ANALYSE DE LA VARIANCE EXPLIQUÉE\n","print(\"\\n📊 1. Analyse de la variance expliquée\")\n","\n","if 'embeddings' in locals():\n","    print(f\"Embeddings originaux: {embeddings.shape}\")\n","    print(f\"Taille mémoire: {embeddings.nbytes / (1024**2):.1f} MB\")\n","\n","    # Fit PCA sur toutes les composantes pour analyser la variance\n","    print(\"\\n🔄 Calcul de la variance expliquée par composante...\")\n","    pca_full = PCA()\n","    pca_full.fit(embeddings)\n","\n","    # Variance expliquée par composante\n","    explained_variance_ratio = pca_full.explained_variance_ratio_\n","    cumulative_variance = np.cumsum(explained_variance_ratio)\n","\n","    # Visualisation de la variance expliquée\n","    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n","\n","    # Variance par composante (premières 50 composantes)\n","    ax1.plot(range(1, 51), explained_variance_ratio[:50], 'b-', marker='o', markersize=3)\n","    ax1.set_xlabel('Numéro de composante')\n","    ax1.set_ylabel('Variance expliquée')\n","    ax1.set_title('Variance expliquée par composante (50 premières)')\n","    ax1.grid(True, alpha=0.3)\n","\n","    # Variance cumulée\n","    ax2.plot(range(1, len(cumulative_variance) + 1), cumulative_variance, 'r-', linewidth=2)\n","    ax2.axhline(y=0.95, color='g', linestyle='--', label='95% variance')\n","    ax2.axhline(y=0.90, color='orange', linestyle='--', label='90% variance')\n","    ax2.axhline(y=0.85, color='purple', linestyle='--', label='85% variance')\n","    ax2.set_xlabel('Nombre de composantes')\n","    ax2.set_ylabel('Variance cumulée expliquée')\n","    ax2.set_title('Variance cumulée expliquée')\n","    ax2.legend()\n","    ax2.grid(True, alpha=0.3)\n","\n","    plt.tight_layout()\n","    plt.show()\n","\n","    # Trouver le nombre de composantes pour différents seuils\n","    thresholds = [0.85, 0.90, 0.95, 0.99]\n","    print(f\"\\n📈 Nombre de composantes nécessaires pour conserver:\")\n","    for threshold in thresholds:\n","        n_components = np.argmax(cumulative_variance >= threshold) + 1\n","        print(f\"  {threshold*100:.0f}% de variance: {n_components} composantes\")\n","\n","    print(f\"\\n📊 Variance des premières composantes:\")\n","    for i in range(10):\n","        print(f\"  Composante {i+1}: {explained_variance_ratio[i]:.4f} ({explained_variance_ratio[i]*100:.2f}%)\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"iAVq3VguWZlo","executionInfo":{"status":"ok","timestamp":1757335524061,"user_tz":-120,"elapsed":1504,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"5d9d4984-8c44-4ed2-f949-ac6ea66ebd67"},"execution_count":70,"outputs":[{"output_type":"stream","name":"stdout","text":["🔬 V. RÉDUCTION DIMENSIONNELLE DES EMBEDDINGS\n","--------------------------------------------------\n","\n","## OBJECTIF DE CETTE SECTION\n","\n","Pour déployer le content-based filtering sur Azure Functions (plan gratuit), nous devons\n","réduire la taille du fichier embeddings (347 MB) qui dépasse les limites du plan gratuit.\n","\n","**Stratégie :** Utiliser l'Analyse en Composantes Principales (ACP/PCA) pour réduire\n","les 250 dimensions à un nombre plus restreint tout en conservant l'information essentielle.\n","\n","**Analyses prévues :**\n","- Variance expliquée par composante\n","- Choix du nombre optimal de composantes\n","- Comparaison des tailles de fichiers\n","- Impact sur les performances de recommandation\n","\n","\n","📊 1. Analyse de la variance expliquée\n","Embeddings originaux: (364047, 250)\n","Taille mémoire: 347.2 MB\n","\n","🔄 Calcul de la variance expliquée par composante...\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x600 with 2 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CQtJ9XHx8vOLj453r58+flyTZbDbZHAfH2chms8kwjBx5LpiHfvYd9LVvoJ9zmGGkFHgdf4mJGa+nVxzObAE5KUlGUpJCz52TERwsI1W7VwrUnsRfI8ZiGGb3Tr5gkRQsyahQIce/154+H0X0XCI6OqWA7pCcLO3dSxEdAAAA+df06dM1efLkNO2xsbG6cuVKtj+/zWbTuXPnZBiGrFYuGZVf0c++g772Dbmin5OTpcREWVIVjR3Llv+KrC73OeJTFWctqYq0lv8K0Wkem859Sk6WxVG0dtzveIzj8YmJKfe5iXfJ9b/49HK1JCWZ8x7/J8zUZ0dOSk5O1qmTJ3P0OS9cuOBRHEX0XKJqVfsULqkL6X5+UpUq5uUEAAAAeKpkyZI6ceKES9uJEycUFhbmdhS6JI0bN05jxoxxrp8/f17lypVTeHi4wsKy/7TZZrPJYrEoPDycgls+Rj/7Dvo6lzMM+4jlq/8SEtJvd/NnJCQo9MwZFQoOdhZ8HduxZGI7ztHTWciHEcj5h+HvL/n72wtx13vrhW0Y/v72IqFje45lq9X1L737rm7L6n3XirdYvNoHNptNp0+fVvGICEVERHh129cSHBzsURxF9FyibFn7HOiDB9v/TbFY7HOkMwodAAAAeUHjxo31zTffuLStXr1ajRs3zvBxQUFBCgoKStNutVpzrABmsVhy9PlgDvrZd/hcX9tsKYXfnPyLj/c81lF89uJcykW8tiUf5Cga+/tLAQFZX3d3n6MgfD3F5P+WbVarzl28qMLFi8t69fa9cWu1yrvl4OuX2/LJETabjMBAWSMicnzf7enzUUTPRQYNkv74Q5ozR+rdm4uKAgAAwDwXL17U3r17nesxMTHavn27ihUrphtuuEHjxo3TkSNH9O6770qSHnroIb322mt64oknNHDgQP3www/69NNPtWLFCrNeAgBkD8OwF5Dj46UrV1KWr7WemdhrPTZ1gdrkqTbyFavVXgi++i8wMP321H/XW3jOqXU/P6+PIs5WNpviT56UIiLs/QOYhCJ6LlO3rv02h6f/AQAAAFxs3rxZrVu3dq47plyJiorSokWLdOzYMR06dMh5f8WKFbVixQo9+uijmjNnjsqWLau33npL7dq1y/HcAfiQ5GTp8mXp0iX7bVyc/A8flkJC7MXm1Pc5/jJadyy7K2BfuWIfUY20HMXmq/88LUJnomht8/fXhcuXVahYMVmDgrK8nTR/FGkBuEERPZepVMl+u3+/uXkAAADAt7Vq1UpGBvO9Llq0KN3HbNu2LRuzApAnJSVJFy/a/+Li0i6n15beclxc2mL3VQVtq6QS5rzK7OPnJwUFScHB9tvAwJTbzPxl5TGe/gUE5OzoZptNl0+eVCFGJwPIIRTRcxlHEf3gQft/qPv5mZsPAAAAAMCHGYZ07px06pT077/2Zcff+fPpL1+9fumS2a8i8/z9U4rWqQvY6a1ndN/1PjYoyJ4LAMBU7IlzmdKl7f+Jm5AgHT4slS9vdkYAAAAAgHzBMOyF8FOnpNOnM751LJ8+7dWLQWaZ1SqFhtqnaQkJcV1OtW4EB+uypJDixWXxIN7tOiPaAACpUETPZfz8pAoVpH/+sU/pQhEdAAAAAJAhm006flw6elQ6dsz+d/x4ynLqtpyaz7tQIalwYftfWJj9r2BBqUAB+21ml4ODPZouxLDZdP7kSQVHRMjCNB8AAC+hiJ4LVaqUUkRPdS0nAAAAAIAvMgwpNlbat0+KiZEOHHD9O3jQ/nNmbypQQCpeXCpRIuW2WLGUorijQJ7eeqFCzFMNAMhXKKLnQhUr2m+5uCgAAAAA+JBLl6ToaOnvv+0jq/75J2X57Nmsb9dikcLDpZIl7X/h4a7F8fRug4O99rIAAMjrKKLnQo6Li1JEBwAAAIB86uJFads2aetWacsW++2ePfapWTKjYEH7nKAVKkhlykilSqX9i4iQAgKy41UAAOATKKLnQo4iekyMuXkAAAAAALzAMOxTrmzcmPK3Y4dnBXOLRbrhBunGG6WqVe0/Xa5QIeW2WDGP5goHAABZRxE9F2IkOgAAAADkYYYh/fGH9P33KUXzY8cyfkxAgFSrllSnjnTTTfai+Y03SlWqSCEhOZM3AABIF0X0XMgxJ3psrHThgv2aLAAAAACAXOzECWn1aum77+x/J064j7VY7MXyRo2kBg2kW26xF9CDgnIuXwAA4DGK6LlQ4cL2X+SdOWOf0qVOHbMzAgAAAACksX+/9Pnn0rJl0qZN7uPCwqTGjaUmTex/DRva2wAAQJ5AET2XqlTJXkTfv58iOgAAAADkGgcPSu+/Ly1dKm3fnn5MgQJS69ZS27ZSq1ZSjRqSn19OZgkAALyIInouVamStHkz86IDAAAAgOkuX7aPOF+4UFqzxj7n+dVq1ZI6d5batbOPOg8MzPk8AQBAtqCInks5Li4aE2NuHgAAAADgs/76S3r1VemDD6Rz59Lef+ut0r332v9uvDHn8wMAADmCInou5SiiMxIdAAAAAHKQYUhr10ovvyytWJH2/sqVpf79pb59pfLlczw9AACQ8yii51IU0QEAAAAgByUlSR99JM2cKe3c6XpfaKjUvbs0cKDUvLlksZiTIwAAMAVF9FyqYkX7bUyMZLNJVqu5+QAAAABAvpScbC+eP/ecFB3tel+5ctKoUdKDD0qFC5uTHwAAMB1F9FyqXDn7xdvj46Vjx6QyZczOCAAAAADykeRkackSafJk+9znqTVsKI0ZI3XrJvlz2gwAgK/jaCCXCgiQbrjBPhI9JoYiOgAAAHxLXEKc/BL80rT7Wf0U7B/sEueO1WJVSEBIhrE2m02XEi/pcuJlFQgq4Gy/lHhJhmGku12LxaLQgNAsxV5OvCybYXObc4HAAlmKvZJ0Rcm2ZK/EhgaEyvLfdCXxSfFKsiV5JTYkIERWi/0ntgnJCUpMTvRKbLB/sPysfhnGOvo52ZYsq9UqrVmjxDGjlbBnlz0g4L/A5s2lZ56WmjRVUECw/K32U+bE5EQlJCe4zSHIP8gZm2RLUnxSvNvYQL9ABfgFZDo22ZasK0lX3MYG+AUo0C8w07E2w6bLiZe9Eutv9VeQf5AkyTAMXUq85JXYzHzvLXKdaud69xHuYtlHmLuPCPILci57Yx+RXmxmvvfsI7J/HxGXEGfff6cjM/sIbx9HuItlH5GFfURyfIb9nF3HERm9ltQooudilSrZC+j790vNmpmdDQAAAJBzSs8qLQWnbe9YtaNW9E652GPEzAi3J+Ety7fUuv7rnOsV5lTQqUun0o1tUKqBfh/yu3O9xus1dPDcwXRja4TX0O5HdjvXb11wq/6M/TPd2PKFy+vA6APO9RaLWmjz0c3pxpYILaHYsbHO9Q4fdNCPB39MNzY0IFRx41NO5rt92k3fRH+TbqwkGZNSTs77ft5XS/9c6jb24riLzpPloV8P1eIdi93Gnnz8pMILhEuSxqwao7mb57qNjRkVowpFKkiSnl7ztGZumuk2dtfDu1QzoqYkadr6aZr842S3sb89+JtuLXOrJGnOL3P0xPdPuI1dc+cHun3Gp9KXX2r+rdLwp6+OWC/91F76Sfq619fqdGMnSdIHf3ygAV8OcLvdT+/7VN1rdpckfb7nc/VY2sNt7MK7F6p/vf6SpFV7V+muj+5yG/tah9c0rOEwe2aH1qv14tZuY2e0maGxTcdKkrYe26qGbzV0Gzup5SQ92+pZSdKe2D2q9UYtt7GPN35cL7V9SZJ06NwhVZxT0W3sIw0e0eudXpcknbp0ShEzI9zGRtWN0qKuiyTZC0gFpxd0G3tfjfu0pPsS53pGsR2qdNA7d7zjXPfaPqJ0A/0+mH2ElDv2ETuH7lS47Nv15j5ibdRatarQSpI0f8t8Df92uNtY9hF2ObGPuJx0WaVfLO02NjP7iGw7jmAf4ZTVfcSzG5/Voj8XuY3NruOIH3r+4Pa+1JhpOxfj4qIAAAAA4D2WgQOlL780Ow0AAJDHWAx3vxfwcefPn1fhwoV17tw5hYWFZXk7NptNJ0+eVEREhNufI7gzfbo0frzUt6/07rtZTgE56Hr6G3kTfe5b6G/fQ5/7FjP721vHnvmB4704Gns03fciO6ZziY2NVWREJNO5/Cc3TNXg1elcdv0hY9gwWbZsVXCS5GdIKlVKidOfV0LP7pKb7ztTNWQ+NrdM53L+zHnnvpypGuzy2z4iyC9Ip2JPKSIiQklGEtO5KP/uI2w2m06cOKGCRQsynYvy7z7CZrPpf8f+p2LFi+X4dC4JlxJUrGixax6HM51LLsZIdAAAAPiqAoEFXE7YMorLzDavZrPZFBcQ53LiK8nlhPVaMhN79fN4KzZ1QcCbsUH+QQpS0LUDMxkb6BfoLLpkW2x8vPT889ILL0hJ9hN4w2KRhj0iTZumgLAw51To1xLgF+AsVF2Lv9Vf/oGenWpnJtbP6ufx5z0zsVaLNVtiLRZLtsRKGX/vbTabzuu8R7GZ2e7V2EfYmbWPsNlsHsdmZrupZeZ7zz4i87FZ2Ud4Osghu7737CMyH5upfYRfkMf97M3vffIV5kTP8yiiAwAAAEAW/P231KuXtG2bsympalVZ335blubNTUwMAADkRfwuORdzFNGPHZMuu/81DAAAAABAkgxDeucd6ZZbUgroAQEyJkzQqdWrpaZNzc0PAADkSRTRc7FixSTHVDwHDpiaCgAAAADkbmfPSvffLw0aJF36b57datWk336T8eyzUpBnPycHAAC4GkX0XMxikSpWtC8zpQsAAAAAuLFtm1SvnvTppyltgwdLmzfb2wEAAK4DRfRcjnnRAQAAACADn30mNWsmHTxoXy9SRFqyRJo/Xyrg+UXgAAAA3KGInstRRAcAAACAdBiGNG2a1K1byvQtt90m7dgh3XefubkBAIB8xd/sBJAxRxE9JsbcPAAAAAAg17hyRXrwQemDD1LaHnhAWrBACg42Ly8AAJAvUUTP5RiJDgAAAACpxMZKXbpIv/yS0jZtmvTUU/YLSwEAAHgZRfRcLnUR3TA4JgQAAADgw2JipHbtpOho+3poqPTee9K995qbFwAAyNeYEz2XK1/eXjiPi7MPuAAAAAAAn7R9u9SkSUoBvXRp6eefKaADAIBsRxE9lwsKksqUsS8zpQsAAAAAn/TDD1KLFtLx4/b1atWkjRulm282Ny8AAOATKKLnAVxcFAAAAIDP+uQTqX176cIF+3rjxvYR6OXLm5sXAADwGRTR8wAuLgoAAADAJ73zjtSrl5SYaF+/6y7p+++l4sXNzQsAAPgUiuh5AEV0AAAAAD5nwQJp0CDJMOzrAwdKn39uv5goAABADqKIngdUrGi/pYgOAAAAwCfMmycNGZKyPmqU9NZbkr+/eTkBAACfRRE9D2AkOgAAAACf8dpr0sMPp6w/9pj0yiuSxWJeTgAAwKdRRM8DHEX0//1PSkgwNxcAAAAAyDZz5kgjRqSsP/mk9NJLFNABAICpKKLnAZGRUkiIfSrAQ4fMzgYAAAAAssGHH0qjR6esP/20NH06BXQAAGA6iuh5gMXClC4AAAAA8rENG6QBA1LWJ06UpkyhgA4AAHIFiuh5BEV0AAAAAPnSvn1S164pc1cOHiw9+ywFdAAAkGtwafM8omJF+y1FdAAAAPiEpDgpyS9tu8VP8gt2jXPLKvmHZBxrs8mSfElKuiwFFkgVe0mS4Wa7Fsk/NIuxlyXZ3KfsnzqHTMQmX5GMZO/E+oWmFLCT4yUjyUuxIZLlv3FcyQmSkShdvCh17yJdOCUFSWrdWpozQ/b30+Ia6441WLL6ZRzr6GdbsmT9LwdbomTL4KJT1iDJ6p+F2CTJFp9BbKBkDchCbLJku+I+1hIg+QVmPtawScmXvRTrL/kF/RdrSMmXvBSbie+9cdV/vlzvPsJtLPsIe2w27yPcsQSlytcL+4j0YtlH2Jdzyz4iKS5l/50mNhP7CG8fR7iNZR9hj83EPsIWn3E/Z2YfkZlYWwavJRWK6HkEI9EBAADgUz4rLYWm0166o9RqRcr6sgj3J+ERLaU261LWv6wgxZ9yCbFKipRkFGsgtf895Y4VNaS4g+lvt3ANqdPulPVVt0rn/kw/tkB56e4DKevft5DObE4/NqiE1C02ZX1dB+nkj+nH+oVKPVOdzK/vJh39Jv1YSeqd6uR8Y1/pf0vdx/a4mHKy/NtQKWax+9h7T0rB4fblrWOk6LnuY7vESAUr2Jd3Pi3tmWlffjx10Frp86JSx11SkZr2pt3TpF2T3W+33W9S8Vvty3/PkbY/kSbE0c+21mukUrfbG/fOlzYPd7/dll9LZTrZlw98IP0ywH1ss0+lG7rblw9/Lv3cw33sbQulSv3ty8dWST/e5T62wWvSjcPsy7HrpTWt3cfWmyHVGGtf/nertKqh+9hak6Q6z9qXz+2RvqnlPrb649LNL9mX4w5Jyyu6j636iHTr6/bl+FPSZxHuYytGSY0X2ZeTL0mfFnQfW+4+qfmSlPUMYi2lOkjV30lpuM59hBP7iBQ5vY9IT4edkv7brhf2EU53rJUiW9mX2UfYl3PBPsJiuyzr0tLuYzOxj/D2cYQT+4gUWdxHFIp+VtZ1i9zHZmYfkZnjiCY/uL8vFaZzySMcRfSYGHPzAAAAAAAAAABfYjEMw93vBXza+fPnVbhwYZ07d05hYWFZ3o7NZtPJkycVEREhq7ufI3hg926pVi2pSBHp33+zvBlkM2/1N/IO+ty30N++hz73LWb2t7eOPfMD53tx+mj674WXf4Zts9kUGxur8PBIWZnOxS4npmpYtUK6p7Nk++99+/RTqWPH9GO9MFWDs58jy8nq75j6gKkaJOWeqRq8NJ2LzbDo5OnzKftypmqwy2v7iGt8722WIJ2MPWXvZyOJ6VykfLuPsNlsOnnihCKKF3R/fMZ0Liny6D7CZrPp5PH/KaJEMff9nE3TuZy/mKDCRYtd8zic6VzyCMec6GfP2ovoRYuamg4AAACQvfwLuJ6wZRSXmW1ezWaT4RfneuIruZ6wXnO7mYkNuXZMVmJTFwS8Ghsk+2TlXozdv1/q1Ve6/F/B4NlnpS7dM9huoKRAD3NwE+voZ2uqefatASnFp2vJVKx/SrHMq7F+ktXDz3tmYi1Wz79HmYq1ZE+slHGszSbpvGexmdlumlj2EfbYbNhHSNf+3ttSFQa9sY9ID/sIu9y0j/B0kEO2fe/ZR2Q+NhPfe2uQ5/3sze+91bM50RlSlUeEhkqRkfZl5kUHAAAAkCfFxUn33JPy89rOnaUJE8zNCQAA4BooouchXFwUAAAAQJ42YoS0c6d9+cYbpffe83xkIQAAgEk4WslDKKIDAAAAyLM+/FBauNC+XKCA9MUXUuHCpqYEAADgCYroeYijiB4TY24eAAAAAJAp+/ZJDz2Usv7GG1L16ublAwAAkAkU0fMQRqIDAAAAyHMSE6VevaQLF+zrffva/wAAAPIIiuh5SMWK9luK6AAAAADyjOeek37/3b5cpYr0+uvm5gMAAJBJuaKI/vrrr6tChQoKDg5Wo0aN9Ntvv2UYv2TJElWrVk3BwcGqXbu2vvnmG5f7L168qOHDh6ts2bIKCQlRjRo1NG/evOx8CTnCMRL94EEpKcncXAAAAADgmjZulKZNsy/7+9vnRS9UyNycAAAAMsn0Ivonn3yiMWPGaNKkSdq6davq1q2rdu3a6eTJk+nGb9y4Ub169dKgQYO0bds2de3aVV27dtWuXbucMWPGjNHKlSv1/vvva8+ePRo9erSGDx+u5cuX59TLyhalS0uBgfYC+uHDZmcDAAAAABm4cEF64AHJZrOvT5ok3XqruTkBAABkgelF9JdfflmDBw/WgAEDnCPGQ0ND9c4776QbP2fOHLVv315jx45V9erVNWXKFN1yyy167bXXnDEbN25UVFSUWrVqpQoVKmjIkCGqW7fuNUe453Z+flKFCvZlLi4KAAAAIFcbNSrlxKVJE+mpp8zNBwAAIItMLaInJCRoy5YtatOmjbPNarWqTZs22rRpU7qP2bRpk0u8JLVr184lvkmTJlq+fLmOHDkiwzC0du1a/fPPP2rbtm32vJAcxMVFAQAAAOR6y5ZJCxfalwsWlN57zz6dCwAAQB5k6lHMqVOnlJycrMjISJf2yMhI/fXXX+k+5vjx4+nGHz9+3Ln+6quvasiQISpbtqz8/f1ltVq1YMECtWjRwm0u8fHxio+Pd66fP39ekmSz2WRz/PwwC2w2mwzDuK5tpFaxokWSRfv2GbLZDK9sE97j7f5G7kef+xb62/fQ577FzP7mM4Z85ehRaciQlPVXX00ZDQQAAJAH5cuhAK+++qp++eUXLV++XOXLl9dPP/2kYcOGqXTp0mlGsTtMnz5dkydPTtMeGxurK1euZDkXm82mc+fOyTAMWa3XP/A/PDxUUpj27LmikyfPXff24F3e7m/kfvS5b6G/fQ997lvM7O8LFy7k6PMB2cYwpIEDpTNn7Ov33itFRZmbEwAAwHUytYheokQJ+fn56cSJEy7tJ06cUMmSJdN9TMmSJTOMv3z5ssaPH6/PP/9cnTp1kiTVqVNH27dv18yZM90W0ceNG6cxY8Y418+fP69y5copPDxcYWFhWX6NNptNFotF4eHhXjkZq13bfnv0aLAiIoKue3vwLm/3N3I/+ty30N++hz73LWb2d3BwcI4+H5Bt3n5bWrXKvlyqlPTmm5LFYm5OAAAA18nUInpgYKDq16+vNWvWqGvXrpLsJy9r1qzR8OHD031M48aNtWbNGo0ePdrZtnr1ajVu3FiSlJiYqMTExDQnPn5+fhn+TDYoKEhBQWmL0lar9bpPoiwWi1e2I0lVqthv9++3yGrlYDQ38mZ/I2+gz30L/e176HPfYlZ/8/lCvnDokJRqYJLeeksqUcK8fAAAALzE9OlcxowZo6ioKDVo0EANGzbU7NmzFRcXpwEDBkiS+vXrpzJlymj69OmSpFGjRqlly5aaNWuWOnXqpI8//libN2/W/PnzJUlhYWFq2bKlxo4dq5CQEJUvX14//vij3n33Xb388sumvU5vqVjRfnvqlHThglSokLn5AAAAAIAMQ3rwQftJiiT17y917GhqSgAAAN5iehG9Z8+eio2N1cSJE3X8+HHVq1dPK1eudF489NChQy4jc5o0aaIPP/xQzzzzjMaPH6+qVavqiy++UK1atZwxH3/8scaNG6c+ffrozJkzKl++vKZOnaqHHnoox1+ftxUuLBUvLp0+LcXESHXqmJ0RAAAAAJ+3YIG0erV9uUwZ6ZVXzM0HAADAi0wvokvS8OHD3U7fsm7dujRt3bt3V/fu3d1ur2TJklq4cKG30st1Kla0F9H376eIDgAAAMBkBw9Kjz2Wsr5ggVSkiGnpAAAAeBuTL+ZBlSrZb/fvNzcPAAAAAD7OMY3LxYv29YEDpQ4dzM0JAADAyyii50EU0QEAAADkCgsWSN9/b18uW1bKB9ehAgAAuBpF9DzIUUSPiTE3DwAAAAA+7PhxaezYlPW33rJfxAkAACCfyRVzoiNzGIkOAACA/C4hLkEJfglp2q1+VvkH+7vEuWOxWhQQEpBhrM1mU+KlRCVeTlRQgSBne+KlRBmGkf52LRYFhAZkLfZyogxb+rGSFFggMEuxSVeSZEu2eSU2IDRAFovFHhufJFuSm9hRjyvg/HlZJKl/fyW1ukO2DPojICRAFqt9u8kJyUpOTPZKrH+wv6x+1gxjHf1sS7bJav0vNjFZyQkZbDfIX1b/zMfakmxKik9yG+sX6Ce/AL/MxybblHQlg9gAP/kFZj7WsBlKvJzolVirv1X+Qfbvp2EYSrzkpdhMfO/tH8gU17uPcBfLPsKDfURmYzPxvfcL8nMue2MfkW4s+whJuWcfkRCX4Nx/p4nNxD7C28cR7mLZR2R+H5Ecn5xhP2fXcURGryU1iuh5UOqR6Dab5OazBQAAAORZs0rPUrCC07RX7VhVvVf0dq7PjJjp9iS8fMvy6r+uv3N9ToU5unTqUrqxpRqU0pDfhzjXX6/xus4dPJdubHiNcD2y+xHn+oJbFyj2z9h0YwuXL6zRB0Y71xe1WKSjm4+mGxtaIlRjY1NGdn/Q4QMd/PFgurEBoQEaHzfeuf5pt08V/U10urGSNMmY5Fz+vO/n+nPpn25jx10c5zxZ/nro19qxeIebyKp6XKEqUCxYeuklrRqzSpvnbna73VExo1SkQhFJ0pqn12jTzE1uYx/e9bAiakZIktZPW68fJ//oNvbB3x5UmVvLSJJ+mfOLvn/ie7exfdf0VaXb7SdUW+Zv0bfDv3Ub2+vrXrqx042SpD8++ENfDvjSbex9n96nmt1rSpL2fL5HS3ssdRt798K7Va9/PUnS3lV79dFdH7mN7fBaBzUc1lCSdGj9IS1uvdhtbJsZbdR0bFNJ0rGtx/RWw7fcxrac1FKtnm0lSYrdE6s3ar3hNrbx443V9qW2kqRzh85pTsU5bmMbPNJAnV7vJEm6dOqSZkbMdBtbN6quui7qKsleQJpecLrb2Br31VD3Jd2d6xnFVulQRXe8c4dz3Vv7iNINSmvw74Od6+wjPNlHSI+ffFwFwgtIklf3EUN3DpXC7cve3EdErY1ShVYVJLGPyE37iKTLSXqx9ItuYzOzj8iu4wj2ESmyuo/Y+OxG/bnI/Xaz6zii5w893d6XGuXXPKhcOcnPT4qPl44dMzsbAAAAAD5txgypRAmzswAAAMg2FsPd7wV83Pnz51W4cGGdO3dOYWFhWd6OzWbTyZMnFRER4fbnCFlRqZJ9JPpPP0nNm3tts7hO2dXfyL3oc99Cf/se+ty3mNnf3jr2zA8c70Xs0dh034vsmM4lNjZWEZERTOfyn2v+DPuVV6QJE+yxjW+V5ef1ktXq1akasmM6l9jYWJUqV0r+AfbPD1M15K6pGrw5ncuZ82ec+3KmarDLDVM1eHs6l9hTsYqIiJCRZDCdi/LvPsJms+nEiRMqWrAo07ko/+4jbDabjv3vmIoXK57j07lcSrikosWKXvM4nOlc8ihHEX3/foroAAAAyH8CCwS6nLBlFJeZbV7NZrMpIC7A5cRXkssJ67VkKjYke2JTFwS8GhvkLwWlajh+XHrxeUmJ9nkl35jrnF8yTWwG/AJTii45EevoZ0dxTPqv8BPg4XYzEWv1tyrQ37PPZaZi/awef94zE2uxWrIn1pI9sVLG33ubzSad9yw2M9u9GvuI/2Iz8b335j7CZrN5HJuZ7brEso+QlLv2EZ4Ocsiu7z37iCzEZuZ7H+TncT9783t/JfmKR9thSFUelXpedAAAAADIEePHSxcv2peHDJHq1jU3HwAAgBxAET2PchTR9+83Nw8AAAAAPmLzZmnhQvtykSLSc8+Zmg4AAEBOoYieR1WsaL+liA4AAAAg2xmG9NhjKeuTJknh4eblAwAAkIMooudRjEQHAAAAkGO+/Vb66Sf78o03SsOGmZsPAABADqKInkc5iujHjkmXL5ubCwAAAIB8zGaTxo1LWZ86VQrw/GJlAAAAeR1F9DyqWDEpLMy+fOCAqakAAAAAyM8+/FDaudO+fOutUrdu5uYDAACQwyii51EWC1O6AAAAAMhmCQnShAkp6y+8YD8ZAQAA8CEU0fMwLi4KAAAAIFu9+27KT1/vvFO6/XZT0wEAADADRfQ8jJHoAAAAALJNYqI0bVrK+pQp5uUCAABgIoroeRhFdAAAAADZ5oMPpJgY+3K7dlKjRubmAwAAYBKK6HkYRXQAAAAA2SIpSZo6NWV94kTzcgEAADAZRfQ8zFFEj4mRDMPcXAAAAADkIx9/LO3da1++/XapSRNz8wEAADARRfQ8rHx5yWKR4uKk2FizswEAAACQL9hsrnOhT5hgXi4AAAC5AEX0PCwoSIqMtC//8ou5uQAAAADIJz7/XNqzx77crJnUqpWp6QAAAJiNInoe9vbb0vHj9uWuXe3rAAAAAJBlhuE6F/rTT5uXCwAAQC5BET2POnxYGjIkZd0wpKFD7e0AAAAAkCUrV0rbttmX69eX2rUzNx8AAIBcgCJ6HhUdbZ+qMLXk5JRr/wAAAABAplw9Cn38ePtFmAAAAHwcRfQ8qmpVyXpV7/n5SVWqmJMPAAAAgDzup5+kDRvsyzVq2OeMBAAAAEX0vKpsWWn+fHvh3KFPH3s7AAAA4C2vv/66KlSooODgYDVq1Ei//fZbhvGzZ8/WTTfdpJCQEJUrV06PPvqorly5kkPZ4rqkHoU+blzaUTsAAAA+iqOiPGzQIOnAAenBB+3rJ0+amg4AAADymU8++URjxozRpEmTtHXrVtWtW1ft2rXTSTcHnh9++KGeeuopTZo0SXv27NHbb7+tTz75ROPHj8/hzJFpv/8urV5tX65USbr/fnPzAQAAyEUooudxZctKjz9uX16zRjp71tR0AAAAkI+8/PLLGjx4sAYMGKAaNWpo3rx5Cg0N1TvvvJNu/MaNG9W0aVP17t1bFSpUUNu2bdWrV69rjl5HLpB6FPqTT0r+/ublAgAAkMtQRM8HbrrJPmVhYqK0YoXZ2QAAACA/SEhI0JYtW9SmTRtnm9VqVZs2bbRp06Z0H9OkSRNt2bLFWTTfv3+/vvnmG3Xs2DFHckYW7dolffmlfblMGSkqytx8AAAAchmGF+QT994r/fmntGyZfW50AAAA4HqcOnVKycnJioyMdGmPjIzUX3/9le5jevfurVOnTqlZs2YyDENJSUl66KGHMpzOJT4+XvHx8c718+fPS5JsNptsNpsXXknGbDabDMPIkefKrSwzZ8ry37LtscekgAApn70f9LPvoK99A/3sO+hr32BmP3v6nBTR84lu3aTnn5dWrpTi4qQCBczOCAAAAL5m3bp1mjZtmubOnatGjRpp7969GjVqlKZMmaIJEyak+5jp06dr8uTJadpjY2Nz5IKkNptN586dk2EYsvrghTStsbEK/+gjSZKtcGHFdukiIx9ebMnX+9mX0Ne+gX72HfS1bzCzny9cuOBRHEX0fKJuXaliRSkmRlq1yj4yHQAAAMiqEiVKyM/PTydOnHBpP3HihEqWLJnuYyZMmKC+ffvqwf+ufF+7dm3FxcVpyJAhevrpp9M9KRo3bpzGjBnjXD9//rzKlSun8PBwhYWFefEVpc9ms8lisSg8PNw3T87nzZMlIUGSZBkyROEVK5qcUPbw+X72IfS1b6CffQd97RvM7Ofg4GCP4iii5xMWi71wPmuW9NlnFNEBAABwfQIDA1W/fn2tWbNGXbt2lWQ/wVmzZo2GDx+e7mMuXbqU5sTHz89PkmQYRrqPCQoKUlBQUJp2q9WaYydRFoslR58v14iPl+bNsy/7+ckyfLgs+fg98Nl+9kH0tW+gn30Hfe0bzOpnT5+PT18+4iicf/WV9N9gEgAAACDLxowZowULFmjx4sXas2ePHn74YcXFxWnAgAGSpH79+mncuHHO+M6dO+uNN97Qxx9/rJiYGK1evVoTJkxQ586dncV05CKffCI5fmlw773SDTeYmw8AAEAuxUj0fOS226SSJaXjx6UffpDatzc7IwAAAORlPXv2VGxsrCZOnKjjx4+rXr16WrlypfNio4cOHXIZvfPMM8/IYrHomWee0ZEjRxQeHq7OnTtr6tSpZr0EuGMY0pw5KeujR5uWCgAAQG5HET0fsVqle+6R3njDPqULRXQAAABcr+HDh7udvmXdunUu6/7+/po0aZImTZqUA5nhumzZIm3dal9u0EBq3NjcfAAAAHIxpnPJZxxTunzxhZScbGoqAAAAAHKrBQtSlh96yH6RJQAAAKSLIno+07KlVLSoFBsrbdhgdjYAAAAAcp2LF6UPP7QvFywo9expbj4AAAC5HEX0fCYgQOrSxb68bJm5uQAAAADIhT7+2F5Il6Teve2FdAAAALhFET0f6tbNfvvZZ/brBQEAAACAU+qpXAYPNi8PAACAPIIiej50551SgQLS4cPS5s1mZwMAAAAg19i5U/rtN/tyvXpS/fqmpgMAAJAXUETPh4KDpU6d7MuffWZuLgAAAABykatHoXNBUQAAgGuiiJ5P3Xuv/XbZMqZ0AQAAACDp8mXp/fftyyEhUp8+5uYDAACQR1BEz6c6dpQCA6XoaOnPP83OBgAAAIDpli6Vzp61L/foIRUubGo6AAAAeQVF9HyqUCGpbVv7MlO6AAAAAND8+SnLXFAUAADAYxTR8zHHlC4U0QEAAAAft2eP9PPP9uXq1aUmTczNBwAAIA+hiJ6Pde4s+flJ27dL+/ebnQ0AAAAA07z1VsoyFxQFAADIFIro+ViJElLLlvZlRqMDAAAAPioxUXrvPftyYKDUr5+5+QAAAOQxFNHzuW7d7LcU0QEAAAAf9e23UmysfblrV6l4cVPTAQAAyGsooudzXbvabzdtko4eNTUVAAAAAGZYvDhlOSrKvDwAAADyKIro+Vzp0lLjxvblL74wNRUAAAAAOe30aemrr+zLJUtKbduamw8AAEAeRBHdB9x7r/2WKV0AAAAAH/PRR/Y50SWpTx/J39/cfAAAAPIgiug+4J577Lfr1tkHogAAAADwEUzlAgAAcN0oovuAypWlunWl5OSUX3ICAAAAyOf+/FPavNm+fMstUu3a5uYDAACQR1FE9xFM6QIAAAD4mA8/TFnu18+8PAAAAPI4iug+wlFE/+476cIFc3MBAAAAkM0MQ/r4Y/uy1Sr17GluPgAAAHkYRXQfUbOmVLWqFB8vffON2dkAAAAAyFabN0v79tmXW7eWSpY0Nx8AAIA8jCK6j7BYmNIFAAAA8BkffZSy3KuXeXkAAADkAxTRfUi3bvbbFSukK1fMzQUAAABANklOlj75xL4cEJAymgYAAABZQhHdhzRoIJUtK8XFSbNmSYcPm50RAAAAAK9bv146etS+3L69VLSoufkAAADkcRTRfYjFIt10k335mWek8uWlt982NycAAAAAXua4oKjEVC4AAABeQBHdhxw+LK1dm7Jus0lDhzIiHQAAID9JSkrS999/rzfffFMXLlyQJB09elQXL140OTPkiOTklIsghYRIXbqYmw8AAEA+4G92Asg50dH2wnlqycnS3r32aV4AAACQtx08eFDt27fXoUOHFB8frzvvvFOFChXSiy++qPj4eM2bN8/sFJHdNm6UYmPty+3bSwUKmJsPAABAPsBIdB9StapkvarH/fykKlXMyQcAAADeNWrUKDVo0ED//vuvQkJCnO333HOP1qxZY2JmyDGff56yzAVFAQAAvIIiug8pW1aaPz9l3WqV3nyTUegAAAD5xfr16/XMM88oMDDQpb1ChQo6cuSISVkhxxhGylQu/v5Sp07m5gMAAJBPUET3MYMGSb1725eHD7evAwAAIH+w2WxKTk5O03748GEVKlTIhIyQo7Zvlw4etC+3bi0VLWpqOgAAAPkFRXQfVLeu/dYxVSIAAADyh7Zt22r27NnOdYvFoosXL2rSpEnq2LGjeYkhZ6SeyuWee8zLAwAAIJ/hwqI+qHJl++2+febmAQAAAO+aNWuW2rVrpxo1aujKlSvq3bu3oqOjVaJECX300Udmp4fs5pjKxWKRunY1NRUAAID8hCK6D3JcSJQiOgAAQP5StmxZ7dixQx9//LF27typixcvatCgQerTp4/LhUaRD0VHS7t325dvu00qVcrcfAAAAPIRiug+qFIl++3p09LZs1KRImZmAwAAAG/y9/fXAw88YHYayGlM5QIAAJBtmBPdBxUqJEVE2JcZjQ4AAJC/vPfee2rWrJlKly6tg/9dZPKVV17Rl19+aXJmyFaOqVwkiugAAABeRhHdRzEvOgAAQN63atUqnTt3zrn+xhtvaMyYMerQoYP+/fdfJScnS5KKFi3qcsFR5DNHjki//mpfrl07Zf5GAAAAeAVFdB9FER0AACDvO378uJo2barDhw9Lkl599VUtWLBATz/9tPz9U2ZubNCggf744w+z0kR2S/0rA0ahAwAAeB1zovsoiugAAAB5X1RUlAoWLKh27dpp9+7diomJ0c0335wmLigoSHFxcSZkiBzBfOgAAADZipHoPooiOgAAQP7QrVs3LV++XJJUsWJFbd++PU3MypUrVb169RzODDnizBlp7Vr7csWKUt265uYDAACQDzES3UdRRAcAAMg/Kv93cDdmzBgNGzZMV65ckWEY+u233/TRRx9p+vTpeuutt0zOEtni66+l/+a+1z33SBaLufkAAADkQxTRfZTjWkOHD0tXrkjBwebmAwAAgOv34IMPKiQkRM8884wuXbqk3r17q3Tp0pozZ47uv/9+s9NDdmA+dAAAgGxHEd1HhYdLBQtKFy9KMTESv+4FAADIH/r06aM+ffro0qVLunjxoiIiIsxOCdklPl767jv7cokSUuPG5uYDAACQTzEnuo+yWJjSBQAAID8LDQ2lgJ7f/fSTfVSMJHXsKPn5mZsPAABAPsVIdB9WubK0YwdFdAAAgPyiYsWKsmQwJ/b+/ftzMBtku6+/Tlm+6y7z8gAAAMjnKKL7MEaiAwAA5C+jR492WU9MTNS2bdu0cuVKjR071pykkD0MI6WI7u8vtW1rbj4AAAD5GEV0H0YRHQAAIH8ZNWpUuu2vv/66Nm/enMPZIFv9/bfk+GVBixZS4cLm5gMAAJCPMSe6D6OIDgAA4Bs6dOigZcuWmZ0GvImpXAAAAHIMRXQf5iiix8RIycnm5gIAAIDss3TpUhUrVszsNOBNFNEBAAByDNO5+LBy5aSAACkhQTpyRLrhBrMzAgAAwPW4+eabXS4sahiGjh8/rtjYWM2dO9fEzOBVZ89KP/9sX65a1f4HAACAbEMR3Yf5+0sVKkjR0fYpXSiiAwAA5G1du3Z1WbdarQoPD1erVq1UrVo1c5KC961Zk/JT0o4dzc0FAADAB1BE93GVK9uL6Hv3Sq1bm50NAAAArsekSZPMTgE5YdWqlOX27c3LAwAAwEdQRPdxXFwUAAAg/zh//rzHsWFhYdmYCbKNYaQU0YOCpBYtzM0HAADAB1BE93EU0QEAAPKPIkWKuMyJnh7DMGSxWJTMleXzpr/+kg4dsi+3aCGFhpqbDwAAgA+giO7jKKIDAADkHwsXLtRTTz2l/v37q3HjxpKkTZs2afHixZo+fboqVKhgboK4fqmncmnXzrw8AAAAfAhFdB+XuohuGNI1Bi4BAAAgF3v33Xf18ssvq1evXs62Ll26qHbt2po/f77WrVtnXnLwDoroAAAAOc5qdgKS9Prrr6tChQoKDg5Wo0aN9Ntvv2UYv2TJElWrVk3BwcGqXbu2vvnmmzQxe/bsUZcuXVS4cGEVKFBAt956qw45fvYIp0qV7Lfnz0unT5ubCwAAAK7Ppk2b1KBBgzTtDRo0uOYxNvKAK1ekH3+0L5cpI9WsaW4+AAAAPsL0Ivonn3yiMWPGaNKkSdq6davq1q2rdu3a6eTJk+nGb9y4Ub169dKgQYO0bds2de3aVV27dtWuXbucMfv27VOzZs1UrVo1rVu3Tjt37tSECRMUHBycUy8rzwgJsR9/S0zpAgAAkNeVK1dOCxYsSNP+1ltvqVy5ciZkBK9av166fNm+3LYtPyMFAADIIVmezuXs2bNaunSp9u3bp7Fjx6pYsWLaunWrIiMjVcZRlfXAyy+/rMGDB2vAgAGSpHnz5mnFihV655139NRTT6WJnzNnjtq3b6+xY8dKkqZMmaLVq1frtdde07x58yRJTz/9tDp27KgZM2Y4H1fZMW8J0qhcWTpyxF5Eb9TI7GwAAACQVa+88oq6deumb7/9Vo3+O7D77bffFB0drWXLlpmcHa7bd9+lLLdvb14eAAAAPiZLRfSdO3eqTZs2Kly4sA4cOKDBgwerWLFi+uyzz3To0CG9++67Hm0nISFBW7Zs0bhx45xtVqtVbdq00aZNm9J9zKZNmzRmzBiXtnbt2umLL76QJNlsNq1YsUJPPPGE2rVrp23btqlixYoaN26cunbt6jaX+Ph4xcfHO9fPnz/v3J7NZvPo9aTHZrPJMIzr2kZ2q1TJop9+smjvXptycZp5Ql7ob3gXfe5b6G/fQ5/7FjP721vP2bFjR/3zzz9644039Ndff0mSOnfurIceeoiR6PnBDz+kLN9+u3l5AAAA+JgsFdHHjBmj/v37a8aMGSpUqJCzvWPHjurdu7fH2zl16pSSk5MVGRnp0h4ZGek86L/a8ePH040/fvy4JOnkyZO6ePGiXnjhBT3//PN68cUXtXLlSt17771au3atWrZsme52p0+frsmTJ6dpj42N1ZUrVzx+TVez2Ww6d+6cDMOQ1Wr67DnpiowsIKmQdu2K18mT58xOJ0/LC/0N76LPfQv97Xvoc99iZn9fuHDBa9sqV66cpk2b5rXtIZc4c0bats2+XK+eVKKEqekAAAD4kiwV0X///Xe9+eabadrLlCnjLGabxTGK5+6779ajjz4qSapXr542btyoefPmuS2ijxs3zmWE+/nz51WuXDmFh4crLCzsuvKxWCwKDw/PtSffderYb48eDVZERJC5yeRxeaG/4V30uW+hv30Pfe5bzOzv67l2z86dO1WrVi1ZrVbt3Lkzw9g6jgM/5D0//igZhn25dWtzcwEAAPAxWSqiBwUFOac7Se2ff/5ReHi4x9spUaKE/Pz8dOLECZf2EydOqGTJkuk+pmTJkhnGlyhRQv7+/qpRo4ZLTPXq1fXzzz+7zSUoKEhBQWkLyFar9bpPoiwWi1e2k12qVrXf7ttnkdXKxYmuV27vb3gffe5b6G/fQ5/7FrP6+3qer169ejp+/LgiIiJUr149WSwWGY5iayoWi0XJycnXkybMxFQuAAAApsnS0XqXLl303HPPKTExUZL9gPzQoUN68skn1a1bN4+3ExgYqPr162vNmjXONpvNpjVr1qhx48bpPqZx48Yu8ZK0evVqZ3xgYKBuvfVW/f333y4x//zzj8qXL+9xbr7Ecc3V48eluDhzcwEAAEDmxMTEOAeyxMTEaP/+/YqJiUnzt3//fpMzxXVZu9Z+6+cntWhhbi4AAAA+Jksj0WfNmqX77rtPERERunz5slq2bKnjx4+rcePGmjp1aqa2NWbMGEVFRalBgwZq2LChZs+erbi4OA0YMECS1K9fP5UpU0bTp0+XJI0aNUotW7bUrFmz1KlTJ3388cfavHmz5s+f79zm2LFj1bNnT7Vo0UKtW7fWypUr9dVXX2ndunVZebn5XrFiUtGi0r//Svv3S7Vrm50RAAAAPJV6oAiDRvKpEyek3bvtyw0aSNcx3SQAAAAyL0tF9MKFC2v16tX6+eeftXPnTl28eFG33HKL2rRpk+lt9ezZU7GxsZo4caKOHz+uevXqaeXKlc6Lhx46dMjl561NmjTRhx9+qGeeeUbjx49X1apV9cUXX6hWrVrOmHvuuUfz5s3T9OnTNXLkSN10001atmyZmjVrlpWX6xMqV5Y2b5b27aOIDgAAkJcsX77c49guXbpkYybINo5R6BJTuQAAAJggS0V0h2bNmqlBgwYKCgqSxZL1ubSHDx+u4cOHp3tfeqPHu3fvru7du2e4zYEDB2rgwIFZzsnXpC6iAwAAIO/o2rWrR3HMiZ6HMR86AACAqbI0J7rNZtOUKVNUpkwZFSxYUDExMZKkCRMm6O233/ZqgsgZjnnRKaIDAADkLTabzaM/Cuh5mKOIHhgoNWlibi4AAAA+yKMi+ieffKJDhw45159//nktWrRIM2bMUGBgoLO9Vq1aeuutt7yfJbKdo4i+d6+5eQAAAABI5eDBlJEujRtLoaHm5gMAAOCDPCqiBwcHq0WLFtqxY4ckafHixZo/f7769OkjPz8/Z1zdunX1119/ZU+myFaMRAcAAMgf1qxZo7vuukuVK1dW5cqVddddd+n77783Oy1kVer50Fu3Ni8PAAAAH+ZREf3uu+/Wxx9/rAceeECSdPToUVWpUiVNnM1mU2JionczRI5wFNEPHpToQgAAgLxp7ty5at++vQoVKqRRo0Zp1KhRCgsLU8eOHfX666+bnR6ygouKAgAAmM7jC4vedttt+vHHHyVJNWrU0Pr161W+fHmXmKVLl+rmm2/2bobIEaVLS8HB0pUr0qFDKUV1AAAA5B3Tpk3TK6+8ouHDhzvbRo4cqaZNm2ratGkaNmyYidkh0wwjZT70kBCpUSNz8wEAAPBRHhfRJalYsWKSpIkTJyoqKkpHjhyRzWbTZ599pr///lvvvvuuvv7662xJFNnLapUqVZL+/NM+pQtFdAAAgLzn7Nmzat++fZr2tm3b6sknnzQhI1yXvXulw4fty82b2y8sCgAAgBzn0XQuV7v77rv11Vdf6fvvv1eBAgU0ceJE7dmzR1999ZXuvPNOb+eIHMK86AAAAHlbly5d9Pnnn6dp//LLL3XXXXeZkBGui2MUusRULgAAACbK1Ej01Jo3b67Vq1d7MxeYjCI6AABA3lajRg1NnTpV69atU+PGjSVJv/zyizZs2KDHHntM//d//+eMHTlypFlpwlMU0QEAAHKFLBfRkf9QRAcAAMjb3n77bRUtWlR//vmn/vzzT2d7kSJF9PbbbzvXLRYLRfTczmZLuaho4cIS154CAAAwTZaK6FarVRaLxe39ycnJWU4I5qGIDgAAkLfFxMSYnQK8ZfduKTbWvtyiheTP+CcAAACzZOlI7Op5FhMTE7Vt2zYtXrxYkydP9kpiyHmpi+iGIWXw/yQAAADIha5cuaLg4OB07zt27JhKlSqVwxkhyxyj0CWmcgEAADBZlorod999d5q2++67TzVr1tQnn3yiQYMGXXdiyHkVKkhWq3TpknT8uMQ5FgAAQN5yyy236MMPP1S9evVc2pctW6aHHnpIsY6Rzcj9fvopZblVK9PSAAAAgGT15sZuu+02rVmzxpubRA4KDJRuuMG+zJQuAAAAeU+rVq1022236cUXX5QkxcXFqX///urbt6/Gjx9vcnbwmGFIP/9sXw4Lk2rXNjcfAAAAH+e1ifUuX76s//u//1OZMmW8tUmYoHJl6cABexG9WTOzswEAAEBmzJ07V506ddKDDz6or7/+WseOHVPBggX122+/qVatWmanB0/t3SudOGFfbtpU8vMzNx8AAAAfl6UietGiRV0uLGoYhi5cuKDQ0FC9//77XksOOa9yZWnNGkaiAwAA5FUdOnTQvffeqzfeeEP+/v766quvKKDnNevXpyw3b25eHgAAAJCUxSL6K6+84lJEt1qtCg8PV6NGjVS0aFGvJYecl/riogAAAMhb9u3bp969e+v48eNatWqVfvzxR3Xp0kWjRo3S1KlTFRAQYHaK8ARFdAAAgFwlS0X0/v37ezkN5BYU0QEAAPKuevXqqVOnTlq1apWKFCmiO++8Ux07dlS/fv20evVqbdu2zewU4QlHET0oSLr1VnNzAQAAQNaK6Dt37vQ4tk6dOll5CpiEIjoAAEDeNXfuXPXt29elrUmTJtq2bZtGjx5tTlLInGPHUg7GGza0F9IBAABgqiwV0evVq+cynUt6DMOQxWJRcnJylhKDORxF9FOnpHPnpMKFzc0HAAAAnnMU0BMSEhQTE6PKlSvL399fhQoV0ttvv21ydvAIU7kAAADkOtasPOizzz5TxYoVNXfuXG3btk3btm3T3LlzVblyZS1btkz79+9XTEyM9u/f7+18kc0KFZLCw+3LjEYHAADIWy5fvqxBgwYpNDRUNWvW1KFDhyRJI0aM0IsvvmhydvAIRXQAAIBcJ0sj0adNm6b/+7//U8eOHZ1tderUUbly5TRhwgRt2bLFawki51WpIsXG2ovot9xidjYAAADw1FNPPaUdO3Zo3bp1at++vbO9TZs2evbZZ/Xkk0+amB084iiiWyxS48bm5gIAAABJWRyJ/scff6hixYpp2itWrKg///zzupOCuZgXHQAAIG/64osv9Nprr6lZs2Yu0y/WrFlT+zi4y/3OnpUc15+qW5e5FQEAAHKJLBXRq1evrunTpyshIcHZlpCQoOnTp6t69epeSw7moIgOAACQN8XGxioiIiJNe1xc3DWvaYRcYONGyTDsy0zlAgAAkGtkaTqXefPmqXPnzipbtqzq1KkjSdq5c6csFou++uorryaInEcRHQAAIG9q0KCBVqxYoREjRkiSs3D+1ltvqTFTg+R+zIcOAACQK2VpJHrDhg21f/9+Pf/886pTp47q1KmjqVOnav/+/WrYsKG3c0QOo4gOAACQN02bNk3jx4/Xww8/rKSkJM2ZM0dt27bVwoULNXXq1Cxt8/XXX1eFChUUHBysRo0a6bfffssw/uzZsxo2bJhKlSqloKAg3Xjjjfrmm2+y9Nw+hyI6AABArpSlkeiSVKBAAQ0ZMsSbuSCXcBTR//c/KT5eCgoyNx8AAAB4plmzZtq+fbteeOEF1a5dW999951uueUWbdq0SbVr18709j755BONGTNG8+bNU6NGjTR79my1a9dOf//9d7rTxiQkJOjOO+9URESEli5dqjJlyujgwYMqUqSIF15dPnflivT77/blKlWkkiXNzQcAAABOHhfRly9frg4dOiggIEDLly/PMLZLly7XnRjMExEhFSggxcVJBw5IN91kdkYAAADwVOXKlbVgwQKvbOvll1/W4MGDNWDAAEn2aR1XrFihd955R0899VSa+HfeeUdnzpzRxo0bFRAQIEmqUKGCV3LJ9377TXJcc4pR6AAAALmKx0X0rl276vjx44qIiFDXrl3dxlksFiUnJ3sjN5jEYrEPftmxQ9q7lyI6AACAL0pISNCWLVs0btw4Z5vValWbNm20adOmdB+zfPlyNW7cWMOGDdOXX36p8PBw9e7dW08++aT8/PzSfUx8fLzi4+Od6+fPn5ck2Ww22Ww2L76i9NlsNhmGkSPPlaGffnLOtWlr2lQyO598Jtf0M7Idfe0b6GffQV/7BjP72dPn9LiInnqDfHDzv8qV7UV05kUHAADwTadOnVJycrIiIyNd2iMjI/XXX3+l+5j9+/frhx9+UJ8+ffTNN99o7969euSRR5SYmKhJkyal+5jp06dr8uTJadpjY2N15cqV638h12Cz2XTu3DkZhiGrNUuXjPKKomvWyDGL4ukaNZR88qRpueRHuaWfkf3oa99AP/sO+to3mNnPFy5c8Cguy3OiI3/j4qIAAADILJvNpoiICM2fP19+fn6qX7++jhw5opdeesltEX3cuHEaM2aMc/38+fMqV66cwsPDFRYWliM5WywWhYeHm3dynpwsy5YtkiSjZEkVb9jQ/vNQeE2u6GfkCPraN9DPvoO+9g1m9nNwcLBHcR4X0f/v//7P4ycfOXKkx7HInSiiAwAA+LYSJUrIz89PJ06ccGk/ceKESrq56GWpUqUUEBDgMnVL9erVdfz4cSUkJCgwMDDNY4KCghSUzpXsrVZrjp1EWSyWHH2+NLZvl/4bBWVp3lwWN1Pf4PqY3s/IMfS1b6CffQd97RvM6mdPn8/jIvorr7ziUZzFYqGIng9QRAcAAMi79u7dq3379qlFixYKCQmRYRiyZHJkc2BgoOrXr681a9Y4r4lks9m0Zs0aDR8+PN3HNG3aVB9++KFsNpvzhOSff/5RqVKl0i2g4z8bN6YsN2tmXh4AAABIl8dF9JiYmOzMA7mMo4geE2O/phH/2QcAAJD7nT59Wj179tQPP/wgi8Wi6OhoVapUSYMGDVLRokU1a9asTG1vzJgxioqKUoMGDdSwYUPNnj1bcXFxGjBggCSpX79+KlOmjKZPny5Jevjhh/Xaa69p1KhRGjFihKKjozVt2jQG2VzLL7+kLDdpYl4eAAAASNd1l0YNw5BhGN7IBblIuXKSv78UHy8dOWJ2NgAAAPDEo48+Kn9/fx06dEihoaHO9p49e2rlypWZ3l7Pnj01c+ZMTZw4UfXq1dP27du1cuVK58VGDx06pGPHjjnjy5Urp1WrVun3339XnTp1NHLkSI0aNUpPPfXU9b+4/MxRRA8OlurUMTcXAAAApJHlC4u+/fbbeuWVVxQdHS1Jqlq1qkaPHq0HH3zQa8nBPP7+UoUK0t699ildypUzOyMAAABcy3fffadVq1apbNmyLu1Vq1bVwYMHs7TN4cOHu52+Zd26dWnaGjdurF9Sj6xGxmJjU+ZQrF9fYtobAACAXCdLRfSJEyfq5Zdf1ogRI9S4cWNJ0qZNm/Too4/q0KFDeu6557yaJMxRpYq9iL53r9SqldnZAAAA4Fri4uJcRqA7nDlzJt2LdyIX+PXXlOXbbjMvDwAAALiVpSL6G2+8oQULFqhXr17Oti5duqhOnToaMWIERfR8gouLAgAA5C3NmzfXu+++qylTpkiSLBaLbDabZsyYodatW5ucHdKVetQ+RXQAAIBcKUtF9MTERDVo0CBNe/369ZWUlHTdSSF3oIgOAACQt8yYMUN33HGHNm/erISEBD3xxBPavXu3zpw5ow0bNpidHtJDER0AACDXy9KFRfv27as33ngjTfv8+fPVp0+f604KuQNFdAAAgLylVq1a+ueff9S0aVPdfffdiouL07333qtt27apsuPgDrlHcrL022/25TJlpKvmsgcAAEDucF0XFv3uu+9023+jJX799VcdOnRI/fr105gxY5xxL7/88vVnCVOkLqIbhmSxmJsPAAAArq1w4cJ65plnzE4DntizR7pwwb7MKHQAAIBcK0tF9F27dumWW26RJO37b5hyiRIlVKJECe3atcsZZ6HqmqdVqmS/PXdOOnNGKl7c3HwAAABwbevXr9ebb76p/fv3a8mSJSpTpozee+89VaxYUc2aNTM7PaTGVC4AAAB5QpaK6GvXrvV2HsiFQkKk0qWlo0fto9EpogMAAOQuv/76q2655RYFBARIkpYtW6a+ffuqT58+2rp1q+Lj4yVJ586d07Rp0/TNN9+YmS6uRhEdAAAgT8jSnOixsbFu7/vjjz+ynAxynypV7LfMiw4AAJD7/Prrr2rbtq0u/DclyPPPP6958+ZpwYIFzsK6JDVt2lRbt241K0248+uv9lt/f+m/X/oCAAAg98lSEb127dpasWJFmvaZM2eqYcOG150Ucg8uLgoAAJB7jRw5UnfddZdatmwpSfr777/VokWLNHGFCxfW2bNnczg7ZOjSJenPP+3LtWpJoaHm5gMAAAC3slREHzNmjLp166aHH35Yly9f1pEjR3THHXdoxowZ+vDDD72dI0zkKKLv3WtuHgAAAEjfY489ptdee02SVLJkSe1N58Dt559/ViXHBW+QO+zcKdls9mVGoQMAAORqWSqiP/HEE9q0aZPWr1+vOnXqqE6dOgoKCtLOnTt1zz33eDtHmIiR6AAAALlfkyZNJEmDBw/WqFGj9Ouvv8pisejo0aP64IMP9Pjjj+vhhx82OUu42LIlZbl+ffPyAAAAwDVl6cKiklSlShXVqlVLy5YtkyT17NlTJUuW9FpiyB0oogMAAOQdTz31lGw2m+644w5dunRJLVq0UFBQkB5//HGNGDHC7PSQWuo56hmJDgAAkKtlaST6hg0bVKdOHUVHR2vnzp164403NGLECPXs2VP//vuvt3OEiRxF9GPH7NM2AgAAIPeyWCx6+umndebMGe3atUu//PKLYmNjNWXKFLNTw9UcI9GtVqlOHXNzAQAAQIayVES//fbb1bNnT/3yyy+qXr26HnzwQW3btk2HDh1S7dq1vZ0jTFSsmBQWZl/euNHcXAAAAOCZwMBA1ahRQw0bNlTBggXNTgdXu3JF2r3bvlyjBhcVBQAAyOWyNJ3Ld999p5YtW7q0Va5cWRs2bNDUqVO9khhyh7ffls6fty+3bSstWCANGmRuTgAAAEhx7733ehz72WefZWMm8Ngff0hJSfZlpnIBAADI9bJURHcU0Pfu3at9+/apRYsWCgkJkcVi0YQJE7yaIMxz+LA0ZEjKumFIQ4dK7dpJZcualxcAAABSFC5c2OwUkFmp50PnoqIAAAC5XpaK6KdPn1aPHj20du1aWSwWRUdHq1KlSho0aJCKFSummTNnejtPmCA6WrLZXNuSk6W9eymiAwAA5BYLFy40OwVkFhcVBQAAyFOyNCf6o48+qoCAAB06dEihqebv69mzp7799luvJQdzVa1qv85RalarVKWKOfkAAAAA+YLjoqIWi1SvnqmpAAAA4NqyPCf6qlWrVPaq4chVq1bVwYMHvZIYzFe2rDR/vn0Kl+Rke1uzZoxCBwAAyK0qVqwoi8Xi9v79+/fnYDZIV0KCfU50SbrpJokLvwIAAOR6WSqix8XFuYxAdzhz5oyCgoKuOynkHoMG2edAX7JEGjNG2rFDunxZCgkxOzMAAABcbfTo0S7riYmJ2rZtm1auXKmxY8eakxRc7d5tL6RLzIcOAACQR2SpiN68eXO9++67mjJliiTJYrHIZrNpxowZat26tVcThPnKlpVGjZLmzJEOHpS+/FK6/36zswIAAMDVRo0alW7766+/rs2bN+dwNkgX86EDAADkOVmaE33GjBmaP3++OnTooISEBD3xxBOqVauWfvrpJ7344ovezhG5gNUq9e1rX1682NxcAAAAkDkdOnTQsmXLzE4DUsp86BIj0QEAAPKILBXRa9WqpX/++UfNmjXT3Xffrbi4ON17773atm2bKleu7O0ckUv062e//e476ehRc3MBAACA55YuXapixYqZnQYk15HoXFQUAAAgT8jSdC6SVLhwYT399NPezAW5XNWqUtOm0oYN0gcfSEyrCQAAkLvcfPPNLhcWNQxDx48fV2xsrObOnWtiZpAkJSXZLzIk2Q+uCxc2Nx8AAAB4JMtFdPimqCh7EX3xYunxx6VU52gAAAAwWdeuXV3WrVarwsPD1apVK1WrVs2cpJBizx7pyhX7MvOhAwAA5BkU0ZEpPXpII0dKu3fbp3Ns0MDsjAAAAOAwadIks1NARpgPHQAAIE+iiI5MKVxY6tpV+vhj+2h0iugAAAC5z8mTJ3Xy5EnZbDaX9jp16piUESS5zofOSHQAAIA8gyI6Mq1/f3sR/aOPpFmzpMBAszMCAACAJG3ZskVRUVHas2ePDMNwuc9isSg5OdmkzCDJdST6zTeblwcAAAAyJctF9KSkJK1bt0779u1T7969VahQIR09elRhYWEqWLCgN3NELtOmjVS6tHT0qLRihXTPPWZnBAAAAEkaOHCgbrzxRr399tuKjIx0ucgoTJacLG3fbl+uWFEqVszUdAAAAOC5LBXRDx48qPbt2+vQoUOKj4/XnXfeqUKFCunFF19UfHy85s2b5+08kYv4+UkPPCDNmCEtWkQRHQAAILfYv3+/li1bpipVqpidCq7299/SpUv2ZaZyAQAAyFOsWXnQqFGj1KBBA/37778KCQlxtt9zzz1as2aN15JD7hUVZb/95hspNtbcXAAAAGB3xx13aMeOHWangfSkng+di4oCAADkKVkaib5+/Xpt3LhRgVdNhl2hQgUdOXLEK4khd6tRw35R0c2bpQ8/lEaNMjsjAAAAvPXWW4qKitKuXbtUq1YtBQQEuNzfpUsXkzKDy3zojEQHAADIU7JURLfZbOlelOjw4cMqVKjQdSeFvKF/f3sRffFiiugAAAC5waZNm7RhwwZ9++23ae7jwqImc8yHLlFEBwAAyGOyNJ1L27ZtNXv2bOe6xWLRxYsXNWnSJHXs2NFbuSGXu/9+KSBA2rZN2rnT7GwAAAAwYsQIPfDAAzp27JhsNpvLHwV0ExlGygFzqVJSeLi5+QAAACBTslREnzVrljZs2KAaNWroypUr6t27t3MqlxdffNHbOSKXKl5c6tzZvrx4sbm5AAAAQDp9+rQeffRRRUZGmp0KUjt2TDpzxr5cu7a5uQAAACDTslREL1u2rHbs2KGnn35ajz76qG6++Wa98MIL2rZtmyIiIrydI3IxxwVGP/hASkoyNxcAAABfd++992rt2rVmp4Grpf7ZZp065uUBAACALMnSnOiS5O/vrz59+qhPnz7ezAd5TIcO9l+jnjghrVoldepkdkYAAAC+68Ybb9S4ceP0888/q3bt2mkuLDpy5EiTMvNxf/yRssxIdAAAgDwnS0X06dOnKzIyUgMHDnRpf+eddxQbG6snn3zSK8kh9wsIkPr0kWbPtk/pQhEdAADAPG+99ZYKFiyoH3/8UT/++KPLfRaLhSK6WSiiAwAA5GlZms7lzTffVLVq1dK016xZU/PmzbvupJC3OKZ0+fLLlKkeAQAAkPNiYmLc/u3fv9/s9HyXYzoXPz+penVzcwEAAECmZamIfvz4cZUqVSpNe3h4uI4dO3bdSSFvqVfPPrVjQoL0ySdmZwMAAADkIomJ0p499uUbb5SCg83NBwAAAJmWpelcypUrpw0bNqhixYou7Rs2bFDp0qW9khjylv79pTFj7FO6PPyw2dkAAAD4pqunW7zaO++8k0OZwCk62j7aRGIqFwAAgDwqS0X0wYMHa/To0UpMTNTtt98uSVqzZo2eeOIJPfbYY15NEHlD797S2LHSr79Kf/8t3XST2RkBAAD4nn///ddlPTExUbt27dLZs2edx+3IYY6pXCT7zzcBAACQ52SpiD527FidPn1ajzzyiBL+G1URHBysJ598UuPGjfNqgsgbIiOlDh2kr7+2j0afNs3sjAAAAHzP559/nqbNZrPp4YcfVuXKlU3ICFxUFAAAIO/L0pzoFotFL774omJjY/XLL79ox44dOnPmjCZOnOjt/JCHOC4w+u67UnKyubkAAADAzmq1asyYMXrllVfMTsU3UUQHAADI87I0Et2hYMGCuvXWW72VC/K4zp2lokWlI0ekH36Q7rzT7IwAAAAgSfv27VNSUpLZafgmx3QuhQpJ5cubmwsAAACyJEtF9Li4OL3wwgtas2aNTp48KZvN5nL//v37vZIc8pagIKlXL2nuXPuULhTRAQAActaYMWNc1g3D0LFjx7RixQpFOX42iJxz/rx08KB9uVYtyZqlHwIDAADAZFkqoj/44IP68ccf1bdvX5UqVUoWi8XbeSGPioqyF9GXLZPuv1+qV08qW9bsrAAAAHzDtm3bXNatVqvCw8M1a9YsDRw40KSsfNju3SnLTOUCAACQZ2WpiP7tt99qxYoVatq0qbfzQR53661SyZLS8eP26V2sVmn+fGnQILMzAwAAyP/Wrl1rdgpILXURvWZN8/IAAADAdcnS7wmLFi2qYsWKeTsX5ANHjkgnTqSs22zS0KHS4cPm5QQAAOArYmJiFB0dnaY9OjpaBw4cyPmEfN2uXSnLFNEBAADyrCwV0adMmaKJEyfq0qVL3s4HeVx0tGQYrm3JydLevebkAwAA4Ev69++vjRs3pmn/9ddf1b9//5xPyNelHoleq5Z5eQAAAOC6ZGk6l1mzZmnfvn2KjIxUhQoVFBAQ4HL/1q1bvZIc8p6qVe1TuFx1rVmVKWNOPgAAAL5k27Zt6U65eNttt2n48OEmZOTjHEX04sWliAhzcwEAAECWZamI3rVrVy+ngfyibFn7HOhDh9pHoDsMGyZ9+aUUEmJebgAAAPmdxWLRhQsX0rSfO3dOyakPzpD9/v1XOnbMvlyzpmSxmJsPAAAAsixLRfRJkyZ5Ow/kI4MGSe3a2adwOX1aioqSVq+WunSxF9JDQ83OEAAAIH9q0aKFpk+fro8++kh+fn6SpOTkZE2fPl3NmjUzOTsfw0VFAQAA8o0sFdGBaylb1v4nSZGRUocO0vffS507S199RSEdAAAgO7z44otq0aKFbrrpJjVv3lyStH79ep0/f14//PCDydn5GIroAAAA+UaWLiyanJysmTNnqmHDhipZsqSKFSvm8gek1qyZtHKlVLCg9MMP9kI616QFAADwvho1amjnzp3q0aOHTp48qQsXLqhfv37666+/VIsLW+asXbtSlnnvAQAA8rQsjUSfPHmy3nrrLT322GN65pln9PTTT+vAgQP64osvNHHiRG/niHygaVN7Ib19e3sh/a677CPSCxQwOzMAAID8pXTp0po2bZrZaYCR6AAAAPlGlkaif/DBB1qwYIEee+wx+fv7q1evXnrrrbc0ceJE/fLLL5ne3uuvv64KFSooODhYjRo10m+//ZZh/JIlS1StWjUFBwerdu3a+uabb9zGPvTQQ7JYLJo9e3am84J3NW0qrVolFSokrV1rL6THxZmdFQAAAJANHEX0iAipRAlzcwEAAMB1yVIR/fjx46pdu7YkqWDBgjp37pwk6a677tKKFSsyta1PPvlEY8aM0aRJk7R161bVrVtX7dq108mTJ9ON37hxo3r16qVBgwZp27Zt6tq1q7p27apdqX8u+Z/PP/9cv/zyi0qXLp3JV4js0qRJSiF93ToK6QAAAMiHTp2SHOczjEIHAADI87JURC9btqyOHTsmSapcubK+++47SdLvv/+uoKCgTG3r5Zdf1uDBgzVgwADVqFFD8+bNU2hoqN5555104+fMmaP27dtr7Nixql69uqZMmaJbbrlFr732mkvckSNHNGLECH3wwQcKCAjIwqtEdmncWPruOykszF5I79RJ+ucf++j0w4fNzg4AAAC4TkzlAgAAkK9kqYh+zz33aM2aNZKkESNGaMKECapatar69eungQMHerydhIQEbdmyRW3atElJyGpVmzZttGnTpnQfs2nTJpd4SWrXrp1LvM1mU9++fTV27FjV5KA1V7rttpRC+o8/SjfdJN1+u1S+vPT222ZnBwAAAFyH1EV0LioKAACQ52XpwqIvvPCCc7lnz5664YYbtGnTJlWtWlWdO/9/e3ceH9PZ/nH8M4ksYguNbIJYQktjLRqq9aBC/YqqUlVUbVWKalW11qpa+tRWnqpd+1C0SvvQUk3FEql9qVJLaiex1BI0Qeb8/jjNyEhCQmQi832/XvOamXPuc+aa3MKZyzXX/WyGz3P27FmSkpLw8/Oz2+7n58cff/yR5jGxsbFpjo+NjbU9HzNmDHny5KF3794ZjiUxMZHExETb80uXLgFmQt5qtWb4PLeyWq0YhnFP58itatSAL76AFi0sgAUAqxW6dzd4+mmDoCDHxnc3NN/OR3PuXDTfzkdz7lwcOd9Z+Zo3btwgMjKSmJgYXnrpJQoUKMDJkycpWLAg+fPnz7LXkdtQJbqIiIhIrnJXSfRbhYWFERYWlhWnumdbt25l4sSJbNu2DYvFkuHjRo0axfDhw1NtP3PmDAkJCXcdj9Vq5eLFixiGgYvLXRX+52o3brgDRey2JSVZ2LLlPO7u1xwT1D3QfDsfzblz0Xw7H825c3HkfMfHx2fJeY4cOULjxo05evQoiYmJPP300xQoUIAxY8aQmJjI1KlTs+R15A727bv5+OGHHReHiIiIiGSJDCfRv//+e5o0aYKbmxvff//9bcc2a9YsQ+f08fHB1dWVuLg4u+1xcXH4+/uneYy/v/9tx69bt47Tp09TokQJ2/6kpCTeeustJkyYwOHDh9M878CBA+nXr5/t+aVLlyhevDhFixalYMGCGXo/abFarVgsFooWLaoP32moUQNcXAys1pv/4WGxGFSv7o2vrwMDu0uab+ejOXcumm/nozl3Lo6cb09Pzyw5T58+fXjsscfYuXMnDz30kG37c889R9euXbPkNSQD9u8374sUAR8fx8YiIiIiIvcsw0n0Fi1aEBsbi6+vLy1atEh3nMViISkpKUPndHd3p3r16kRERNjOabVaiYiIoFevXmkeExYWRkREBH379rVtW7Vqla0Svn379mn2TG/fvj2dOnVKNxYPD480F0V1cXG55w9RFoslS86TG5UoAdOmQffukPzHxjAszJplIY0vBjwQNN/OR3PuXDTfzkdz7lwcNd9Z9Xrr1q1jw4YNuLu7220PDg7mxIkTWfIacgdXr8KxY+bjcuUcG4uIiIiIZIkMJ9FT9mnMyp6N/fr1o2PHjjz22GPUrFmTCRMmcOXKFVvCu0OHDhQrVoxRo0YBZnXNU089xSeffELTpk1ZsGABW7ZsYdq0aQA89NBDdlU3AG5ubvj7+1O+fPksi1uyTufOEB4OBw/Cxo3w7rvwwQdQrBh06+bo6EREREQeHFarNc2CluPHj1OgQAEHROSEDh68+VifP0RERERyhUyXvFy/fp0GDRpw4MCBLAmgTZs2/Pvf/2bIkCFUqVKFHTt2sGLFCtvioUePHuXUqVO28bVr12b+/PlMmzaNypUr880337B06VIe1ar3D7SgIKhXDwYMgMGDzW09esCyZQ4NS0REROSB0qhRIyZMmGB7brFYuHz5MkOHDuWZZ55xXGDOJGU/dFWii4iIiOQKmV5Y1M3NjV27dmVpEL169Uq3fUtkZGSqbS+88AIvvPBChs+fXh90yZmGD4fjx2H2bGjdGlavhlq1HB2ViIiISM73ySefEB4eToUKFUhISOCll17iwIED+Pj48NVXXzk6POeQ3A8dlEQXERERySXuqvniyy+/zMyZM7M6FhEALBb4/HNo0gT+/hv+7//sP4uIiIiISNqCgoLYuXMn77//Pm+++SZVq1Zl9OjRbN++Hd8HcdX2B5GS6CIiIiK5TqYr0QFu3LjBrFmz+Pnnn6levTr58uWz2z9u3LgsCU6cl5sbLFoE//oXbNkCjRtDdDT80+VHRERERNKRJ08e2rVrR7t27RwdinNKmUQvW9ZxcYiIiIhIlrmrJPru3bupVq0aAPtvKRG2WCz3HpUIkD8/LF8OYWHw55/wzDMQGQlaE0tEREQkbaNGjcLPz49XX33VbvusWbM4c+YMAwYMcFBkTsIwbvZEL1ECvLwcG4+IiIiIZIm7SqKvXr06q+MQSZOvL6xYAbVrw7Zt8MIL8L//mZXqIiIiImLv888/Z/78+am2V6xYkRdffFFJ9Pvt3Dk4f958rFYuIiIiIrnGXfVEF8lOISFmRbqXF6xcCd26mUU+IiIiImIvNjaWgICAVNuLFi3KqVOnHBCRk1E/dBEREZFc6a4q0QG2bNnCokWLOHr0KNeuXbPb9+23395zYCIp1axp9khv3hzmzIGgIOjeHQ4cMJPsQUGOjlBERETE8YoXL05UVBSlSpWy2x4VFUVgYKCDonIiSqKLiIiI5Ep3VYm+YMECateuzd69e1myZAnXr1/n999/55dffqFQoUJZHaMIAE2bwtSp5uMPPzTbTNavDyVLwsyZjo1NREREJCfo2rUrffv2Zfbs2Rw5coQjR44wa9Ys3nzzTbp27ero8HK/lEn08uUdF4eIiIiIZKm7qkT/6KOPGD9+PD179qRAgQJMnDiRUqVK0b179zS/PiqSVbp0gT17YPz4my1drFazKj08XBXpIiIi4tz69+/PuXPneP31123fFvX09GTAgAEMHDjQwdE5geRFRUGV6CIiIiK5yF1VosfExNC0aVMA3N3duXLlChaLhTfffJNp06ZlaYAit/q//0u9LSkJDh7M/lhEREREchKLxcKYMWM4c+YMv/76Kzt37uSvv/5iyJAhjg7NOSRXoru5mV+XFBEREZFc4a4q0QsXLkx8fDwAxYoVY/fu3YSGhnLhwgWuXr2apQGK3KpcOXBxMSvQk7m6QtmyjotJREREJCfJnz8/NWrUcHQYzsVqNRfsAfPC1NXVsfGIiIiISJa5qyT6k08+yapVqwgNDeWFF16gT58+/PLLL6xatYoGDRpkdYwidoKCYNo06NbtZiJ9xAi1chERERG5cuUKo0ePJiIigtOnT2NNWXUA/Pnnnw6KzAkcOwaJieZjtXIRERERyVUylUTfvXs3jz76KJMnTyYhIQGA999/Hzc3NzZs2MDzzz/PoEGD7kugIil17gyNGkHLlrBlC2zb5uiIRERERByvS5curFmzhvbt2xMQEIDFYnF0SM4jZT90LSoqIiIikqtkKoleqVIlatSoQZcuXXjxxRcBcHFx4d13370vwYncTvHiMGsWVKkC33wD69ZB3bqOjkpERETEcX788UeWL19OnTp1HB2K80nuhw6qRBcRERHJZTK1sOiaNWuoWLEib731FgEBAXTs2JF169bdr9hE7ig0FLp0MR/362ffJ11ERETE2RQuXJgiRYo4OgznpCS6iIiISK6VqSR63bp1mTVrFqdOneLTTz/l8OHDPPXUU5QrV44xY8YQGxt7v+IUSdcHH0CBAmZbl/nzHR2NiIiIiOOMGDGCIUOGcPXqVUeH4nyURBcRERHJtTKVRE+WL18+OnXqxJo1a9i/fz8vvPACU6ZMoUSJEjRr1iyrYxS5LT8/eO898/HAgaDPjCIiIuKsPvnkE1auXImfnx+hoaFUq1bN7ib3UXISvWBB8PV1bCwiIiIikqUy1RM9LWXLluW9996jZMmSDBw4kOXLl2dFXCKZ0rcvTJ0KR47AJ5/A4MGOjkhEREQk+7Vo0cLRITinhAQ4fNh8XL48aEFXERERkVzlnpLoa9euZdasWSxevBgXFxdat25N586dsyo2kQzz9ITRo6FtW/O+c2cIDHR0VCIiIiLZa+jQoY4OwTnFxIBhmI/VykVEREQk18l0O5eTJ0/y0UcfUa5cOerVq8fBgweZNGkSJ0+eZPr06Tz++OP3I06RO2rTBh5/3Gznokp0EREREck26ocuIiIikqtlqhK9SZMm/Pzzz/j4+NChQwdeffVVypcvf79iE8kUiwXGj4ewMJg9G954A6pUcXRUIiIiItknKSmJ8ePHs2jRIo4ePcq1a9fs9v/1118OiiyXUxJdREREJFfLVCW6m5sb33zzDcePH2fMmDFKoEuO8/jj8OKL5rdp+/W7+a1aEREREWcwfPhwxo0bR5s2bbh48SL9+vWjZcuWuLi4MGzYMEeHl3vt23fzsT4jiYiIiOQ6mUqif//99zRv3hxXV9f7FY/IPRs1Cjw8YPVq+N//HB2NiIiISPaZN28e06dP56233iJPnjy0bduWGTNmMGTIEH799VdHh5d7paxEDwlxXBwiIiIicl9kuie6SE4XHAxvvmk+fvttuOVbzCIiIiK5VmxsLKGhoQDkz5+fixcvAvB///d/LF++3JGh5W7JSfTAQMif37GxiIiIiEiWUxJdcqWBA8HXFw4cgKlTHR2NiIiISPYICgri1KlTAJQpU4affvoJgM2bN+Ph4XFX55wyZQrBwcF4enpSq1YtNm3alKHjFixYgMVioUWLFnf1ug+M8+fhzBnzsfqhi4iIiORKSqJLrlSwIIwYYT4eNgy0hpaIiIg4g+eee46IiAgA3njjDQYPHkxISAgdOnTg1VdfzfT5Fi5cSL9+/Rg6dCjbtm2jcuXKhIeHc/r06dsed/jwYd5++23q1q17V+/jgXLgwM3HSqKLiIiI5EpKokuu9eqr8OijZnFQckJdREREJDcbPXo07733HgBt2rRh7dq19OjRg2+++YbRo0dn+nzjxo2ja9eudOrUiQoVKjB16lS8vLyYNWtWusckJSXRrl07hg8fTunSpe/6vTwwtKioiIiISK6Xx9EBiNwvefLAuHHQqBFMngw9eqg4SERERJxLWFgYYWFhd3XstWvX2Lp1KwMHDrRtc3FxoWHDhkRHR6d73AcffICvry+dO3dm3bp1d3ydxMREEhMTbc8vXboEgNVqxWq13lXsmWG1WjEM465fy7J/P5bkc5UpA9kQs2Tevc6zPDg0185B8+w8NNfOwZHznNHXVBJdcrWnn4ZnnoEffoABA2DJEkdHJCIiIpK1vv/+e5o0aYKbmxvff//9bcc2a9Ysw+c9e/YsSUlJ+Pn52W338/Pjjz/+SPOY9evXM3PmTHbs2JHh1xk1ahTDhw9Ptf3MmTMkJCRk+Dx3y2q1cvHiRQzDwMUl81/ULbRnD3n/eXyucGGS7tDqRhzjXudZHhyaa+egeXYemmvn4Mh5jo+Pz9A4JdEl1/v4Y1i5EpYuhfHj4YUXICjI0VGJiIiIZI0WLVoQGxuLr6/vbRfxtFgsJCUl3bc44uPjad++PdOnT8fHxyfDxw0cOJB+/frZnl+6dInixYtTtGhRChYseD9CtWO1WrFYLBQtWvSuPrRZ/lnIFeCh6tUhb97bjBZHudd5lgeH5to5aJ6dh+baOThynj09PTM0Tkl0yfUqVIC6dSEyEvr1g7ffhqlToWtXR0cmIiIicu9SfgU1K78C6+Pjg6urK3FxcXbb4+Li8Pf3TzU+JiaGw4cP8+yzz6aKJ0+ePOzbt48yZcqkOs7DwwMPD49U211cXLLtQ5TFYrn71zt0yLwPCMAlX76sDUyy1D3NszxQNNfOQfPsPDTXzsFR85zR19OfPsn1jh+HtWtvPrdaoVs3M7E+bBj89BP803ozzWNXrzbvRURERHKy69ev06BBAw4cOJAl53N3d6d69epERETYtlmtViIiItLss/7www/z22+/sWPHDtutWbNm/Otf/2LHjh0UL148S+LKUa5ehdhY83GpUo6NRURERETuG1WiS6534EDa6zutX2/eAFxcIDQUateGOnXM288/Q/fu5rEuLjBtGnTunL2xi4iIiGSUm5sbu3btytJz9uvXj44dO/LYY49Rs2ZNJkyYwJUrV+jUqRMAHTp0oFixYowaNQpPT08effRRu+O9vb0BUm3PNQ4fvvm4dGmHhSEiIiIi95eS6JLrhYSYSfCUiXQXF/jwQ9izB6KizG/h7txp3j77LPU5rFYzoR4ern7qIiIiknO9/PLLzJw5k9GjR2fJ+dq0acOZM2cYMmQIsbGxVKlShRUrVtgWGz169Khzf7X6zz9vPlYluoiIiEiupSS65HpBQWYVeffukJQErq7w+ef2VeWnTsGGDWZCPSoKtmxJXb2elAQHDyqJLiIiIjnXjRs3mDVrFj///DPVq1cn3y09useNG5fpc/bq1YtevXqluS8yMvK2x86ZMyfTr/dASe6HDqpEFxEREcnFlEQXp9C5s1lFfvAglC2bOhEeEADPP2/ewGwB8/DD9ol0V1fzWBEREZGcavfu3VSrVg2A/fv32+2zWCyOCCl3UyW6iIiIiFNQEl2cRlBQxqvIQ0LM6vVu3W4m0l96SVXoIiIikrOtXr3a0SE4F1Wii4iIiDgFJ25gKHJ7nTvDkSPQo4f5fPFiuKWgS0REREScWXIlupsbBAY6NhYRERERuW9UiS5yG0FBMHky7NsHv/wCL79s9kx3c3N0ZCIiIiJp27JlC4sWLeLo0aNcu3bNbt+3337roKhyIcO4WYkeHGz2/hMRERGRXEmV6CJ34OICc+eCtzds3gwjRjg6IhEREZG0LViwgNq1a7N3716WLFnC9evX+f333/nll18oVKiQo8PLXc6ehcuXzcfqhy4iIiKSqymJLpIBQUEwdar5eORI2LDBsfGIiIiIpOWjjz5i/Pjx/O9//8Pd3Z2JEyfyxx9/0Lp1a0qUKOHo8HIX9UMXERERcRpKootkUJs2ZjsXqxXat4f4eEdHJCIiImIvJiaGpk2bAuDu7s6VK1ewWCy8+eabTJs2zcHR5TLJ/dBBlegiIiIiuZyS6CKZMHkylChhfmbq29fR0YiIiIjYK1y4MPH//E9/sWLF2L17NwAXLlzg6tWrjgwt91EluoiIiIjTUBJdJBMKFYIvvgCLBWbNAq3NJSIiIjnJk08+yapVqwB44YUX6NOnD127dqVt27Y0aNDAwdHlMqpEFxEREXEaSqKLZNJTT8E775iPu3WDU6ccG4+IiIhIcsX55MmTefHFFwF4//336devH3FxcTz//PPMnDnTkSHmPqpEFxEREXEaeRwdgMiD6IMPYOVK2LEDOnWCH380q9NFREREHKFSpUrUqFGDLl262JLoLi4uvPvuuw6OLBdLrkQvVAgKF3ZsLCIiIiJyX6kSXeQuuLvDvHng6Wkm06dMcXREIiIi4szWrFlDxYoVeeuttwgICKBjx46sW7fO0WHlXjduwLFj5mO1chERERHJ9ZREF7lLFSrA2LHm4/79Ye9ex8YjIiIizqtu3brMmjWLU6dO8emnn3L48GGeeuopypUrx5gxY4iNjXV0iLnLyZNmIh0gONihoYiIiIjI/ackusg96NkTwsMhIQHatYNr1xwdkYiIiDizfPny0alTJ9asWcP+/ft54YUXmDJlCiVKlKBZs2aODi/3OHLk5uOSJR0Xh4iIiIhkCyXRRe6BiwvMmgVFisD27dCvn4WoKHeOH3d0ZCIiIuLsypYty3vvvcegQYMoUKAAy5cvd3RIuYeS6CIiIiJORUl0kXsUGAjTp5uPP/sMWrUqQqlSFmbOdGxcIiIi4rzWrl3LK6+8gr+/P/3796dly5ZERUU5OqzcI2USXe1cRERERHK9PI4OQCQ3qFkTLBYwDAsAVquF7t3NVi9BQQ4OTkRERJzCyZMnmTNnDnPmzOHgwYPUrl2bSZMm0bp1a/Lly+fo8HKXw4dvPlYluoiIiEiupyS6SBY4cAAMw35bUhKsXw8vvuiYmERERMR5NGnShJ9//hkfHx86dOjAq6++Svny5R0dVu6ldi4iIiIiTkVJdJEsEBJi9ke3Wu23v/IK/P47vPsuqABMRERE7hc3Nze++eYb/u///g9XV1dHh5P7JSfR8+UzF8cRERERkVxNPdFFskBQEEybBq6uZjm6i4tB+fKQmAgffgjlysF//5s6yS4iIiKSFb7//nuaN2+uBHp2MAw4etR8XLKk2dNPRERERHI1JdFFskjnzvDnnwaLF//FoUMGe/fC4sVQqhScPAnt20Pt2rBxo6MjFREREZG7dvo0JCSYj7WoqIiIiIhTUBJdJAsFBUHt2tcICjKLklq2hD174KOPIH9+M4H++ONmQv3ECfOY48dh9WrzXkRERERyOC0qKiIiIuJ0lEQXuc88PWHgQNi/3+yRDmZrl3LloEUL87NX/frm/cyZjoxURERERO5Ii4qKiIiIOB0l0UWySUAAzJ4NmzdDnTpw9Sp8993NPulWK3Tvrop0ERERkRxNSXQRERERp6Mkukg2e+wxWLcOBg1KvS8pCQ4ezP6YRERERCSDlEQXERERcTpKoos4gMViVp273PIb6OICZcs6JiYRERERyYCUSXQtLCoiIiLiFJREF3GQoCCYNg1cXW9uMwxYs8ZxMYmIiIjIHSQvLOruDn5+Dg1FRERERLKHkugiDtS5s/k57Oef4YUXzCR6+/ZaYFREREQkRzKMm5XoJUqk/lqhiIiIiORKeRwdgIizCwoyb//6F/j4wGefQZcu8Pff0KuXo6MTEREREZsLFyA+3nysfugiIiIiTkOlEyI5hIsLTJkC/fqZz994Az7+2LExiYiIiEgKWlRURERExCkpiS6Sg1gs8O9/w+DB5vN33oHhw81vDouIiIiIgyX3QwctKioiIiLiRJREF8lhLBb44AP46CPz+bBhMHCgEukiIiIiDnf06M3HJUo4Lg4RERERyVZKoovkUAMHwoQJ5uMxY6BPH7BaHRqSiIiIiHNTEl1ERETEKSmJLpKD9ekDn39uVqd/+il072624ly9Go4fd3R0IiIiIk7m2LGbj5VEFxEREXEaSqKL5HDdusGcOebCozNmmO0369c317KaOdPR0YmIiIg4kZSV6EFBjotDRERERLKVkugiD4AOHWDKFPttVqtZma6KdBEREZFsklyJ7ucHHh6OjUVEREREso2S6CIPiPLlU29LSoI33oANG7TwqIiIiMh9df06nDplPi5e3LGxiIiIiEi2UhJd5AEREmK2dLnV0qVQpw6UKgXvvgs7dyqhLiIiIpLlTp68ucq7+qGLiIiIOBUl0UUeEEFBMG0auLqaz11doW9fs9VLgQLmgqNjxkCVKlCxIowYAQcPmmOPH9dipCIiIiL3JOWioqpEFxEREXEqSqKLPEA6d4bDh82E+OHDMH48zJ0LcXHwzTfQsqXZnnPvXhgyxKxeDw42i6W0GKmIiIjIPUi5qKiS6CIiIiJORUl0kQdMUBDUq2feJ8ubF55/HhYvNhPqc+ZAeLjZ/uXIkZvtXbQYqYiIiMhdSlmJrnYuIiIiIk5FSXSRXKZQIejYEVasMKvTb5WUdLPNi4iIiIhkkCrRRURERJyWkugiuViNGmkvRrpzZ/bHIiIiIvJAUyW6iIiIiNNSEl0kF7t1MVKLxbzv2xfee89s7yIiIiIiGZCcRM+TB/z8HBuLiIiIiGQrJdFFcrlbFyN97z1z+6hR0KoVXLniyOhEREREHhDJ7VyCgm5WKIiIiIiIU1ASXcQJJC9GWqIEjBwJX34J7u6wZAk88YT9t5NFRERE5BZXrsBff5mP1Q9dRERExOkoiS7ihF5+GSIjwdcXduyAmjVh40ZHRyUiIiKSQ6WsOFASXURERMTpKIku4qTCwmDTJggNhdhYeOop+OorR0clIiIikgNpUVERERERp6YkuogTK1kSoqLg2WchMRFeegmGDjVbfq5eDcePOzpCERERkRxAlegiIiIiTk1JdBEnV6CA2Rv9nXfM5x98YCbX69c372fOdGx8IiIiIg6XvKgoqBJdRERExAkpiS4iuLrCmDHwySf2261W6N5dFekiIiLi5FSJLiIiIuLUlEQXEZuqVVNvS0qCzZuzPxYRERGRHCNlJbqS6CIiIiJOR0l0EbEJCQGXNP5WeOUVmDLFTKiLiIiIOJ3kSnQvLyhc2LGxiIiIiEi2UxJdRGyCgmDaNLO9C5gJ9RIl4NIl6NULHnsMNmxwbIwiIiIi2e7kSfM+KAgsFsfGIiIiIiLZTkl0EbHTuTMcPgyrV8ORI/Dnn/Cf/5hFVzt2QJ06ZmV6XJyDAxURERHJDpcvQ3y8+Tgw0LGxiIiIiIhDKIkuIqkEBUG9eua9qyv06AH79pkJdoC5c6F8efj0U7hxw6GhioiIiNxfp07dfBwQ4Lg4RERERMRhlEQXkQwpWhRmzIDoaKhWDS5ehN69zRYvS5aYlevHjzs6ShEREZEsltzKBVSJLiIiIuKklEQXkUx5/HHYtAk++8xs8bJzJ7RsCfXrQ8mSZk91ERERkVxDlegiIiIiTi9HJNGnTJlCcHAwnp6e1KpVi02bNt12/Ndff83DDz+Mp6cnoaGh/PDDD7Z9169fZ8CAAYSGhpIvXz4CAwPp0KEDJ1NWkIjIPXF1hddeg8hI+7W1rFbo3h1atIB58+DcOUdFKCIiIpJFVIkuIiIi4vQcnkRfuHAh/fr1Y+jQoWzbto3KlSsTHh7O6dOn0xy/YcMG2rZtS+fOndm+fTstWrSgRYsW7N69G4CrV6+ybds2Bg8ezLZt2/j222/Zt28fzZo1y863JeIUzp0Dw0i9/bvv4OWXwdfXXIj0o4/MivWUY48fVwsYEREReQCoEl1ERETE6Tk8iT5u3Di6du1Kp06dqFChAlOnTsXLy4tZs2alOX7ixIk0btyY/v3788gjjzBixAiqVavG5MmTAShUqBCrVq2idevWlC9fnscff5zJkyezdetWjh49mp1vTSTXCwkBl1v+FnFxgddfh0qVzMr0DRvg/fehShUoUcKsVO/Vy2z9ktwCZuZMh4QvIiIicmeqRBcRERFxenkc+eLXrl1j69atDBw40LbNxcWFhg0bEh0dneYx0dHR9OvXz25beHg4S5cuTfd1Ll68iMViwdvbO90xiYmJJCYm2p5funQJAKvVitVqzcC7SZvVasUwjHs6hzw4nG2+AwNh6lTo0cNCUpIFV1eDzz4z6NzZ3H/sGPzwA/zwg4VffoHjxy2peqabLWAMnn7aICgo+9/DvXK2OXd2mm/nozl3Lo6cb/0Zy8FUiS4iIiLi9ByaRD979ixJSUn4+fnZbffz8+OPP/5I85jY2Ng0x8fGxqY5PiEhgQEDBtC2bVsKFiyYbiyjRo1i+PDhqbafOXOGhISEO72VdFmtVi5evIhhGLjcWrIruY4zzvezz0L16i4cPpyH4OAbBAZaSe7G5OEBzz1n3hISYMMGd/7737z8+GNeu3MkJVl4//2/GTDgMj4+D1YSwRnn3Jlpvp2P5ty5OHK+4+Pjs/X1JBOSK9Hz54cCBRwbi4iIiIg4hEOT6Pfb9evXad26NYZh8Nlnn9127MCBA+0q3C9dukTx4sUpWrTobZPvd2K1WrFYLBQtWlQfvp2As863r6/ZruVOSpSAJ56AUqUMrFaL3b7//teLhQvz0rIlvPaaQd269ouW5lTOOufOSvPtfDTnzsWR8+3p6ZmtryeZkFyJrip0EREREafl0CS6j48Prq6uxMXF2W2Pi4vD398/zWP8/f0zND45gX7kyBF++eWXOybCPTw88PDwSLXdxcXlnj9EWSyWLDmPPBg037dXogRMm2b2Rk9KAldXaN8e9uyBTZssLFwICxdaqFABXnvN3OftbS5AeuCA2Yc9p7V90Zw7F82389GcOxdHzbf+fOVQV67AP20e1Q9dRERExHk59Grd3d2d6tWrExERYdtmtVqJiIggLCwszWPCwsLsxgOsWrXKbnxyAv3AgQP8/PPPPPTQQ/fnDYjIXencGQ4fhtWrzfvZs2HjRti6Fbp2BS8vM6neuzcUKwZ162ohUhEREXEA9UMXEREREXJAO5d+/frRsWNHHnvsMWrWrMmECRO4cuUKnTp1AqBDhw4UK1aMUaNGAdCnTx+eeuopPvnkE5o2bcqCBQvYsmUL0/5ZrfD69eu0atWKbdu2sWzZMpKSkmz90osUKYK7u7tj3qiI2AkKSl1RXq2aWaX+8cfw3//CZ5/B77/D+vU3x1itZqL9r7/M8aVKQfHi4OaW+jVycvW6iIiIPABSJtFViS4iIiLitByeRG/Tpg1nzpxhyJAhxMbGUqVKFVasWGFbPPTo0aN2X2+tXbs28+fPZ9CgQbz33nuEhISwdOlSHn30UQBOnDjB999/D0CVW5o0r169mnr16mXL+xKRu1eoEPTsCa+/Dp9+Cn362O83DHjnnZvPXVzMJHlwsJlUL1UKjhyBuXPNpLuLi5mc79w5W9+GiIiIPOiSFxUFVaKLiIiIODGHJ9EBevXqRa9evdLcFxkZmWrbCy+8wAsvvJDm+ODgYAzDyMrwRMRBLBZo2RLefNNMhqfc/tRTEBtrtoNJSICjR83b2rWpz2O1mj3Yw8NVkS4iIiKZoEp0EREREcHBPdFFRO4kKMisInd1NZ+7usL06WY/9b174epV8/Pthg0wfz6MHAnPPJP6PElJMG4cXLuWvfGLiIjIA0yV6CIiIiJCDqlEFxG5nc6dzSrygwehbFn7anKLBfz9zVvy+sLHj5sLkKasXgcYPx6WLoVhw6Bdu5uJeREREZE0qRJdRERERFAluog8IIKCoF69jLVjSat6vW1b8PODQ4egY0d49FH4+uvUiXYRERERG1Wii4iIiAhKootILtW5s9kvffVq837+fPjzTxgzBooUgT/+gNatoXp1WLbMXKz0+HFz/PHjjo5eREREcoTkSvR8+aBAAcfGIiIiIiIOoyS6iORat1ave3nBO++YyfRhw8zPwjt2wLPPmm1iSpaE+vXN+5kzHRi4iIiI5AzJlegBAWYPORERERFxSkqii4jTKVQIhg41W7u88w54epqJ9eTWLlYrdO+uinQRERGndvUqXLxoPlY/dBERERGnpiS6iDithx4y27v897+p9yUlwcSJkJCQ/XGJiIhIDpByUVH1QxcRERFxakqii4jTq1ULXNL42/Df/4YSJczWL6dPZ3tYIiIi4kgpk+iqRBcRERFxakqii4jTCwqCadPA1dV87upqLjpaogScOQPDh5uPu3aF3393bKwiIiKSTWJjbz7293dcHCIiIiLicEqii4gAnTvD4cOwerV5v3AhxMSY9zVrQmIizJgBjz4KTZrAqlVgGGbf9Kgod/VPFxERyW3Onbv52MfHcXGIiIiIiMMpiS4i8o+gIKhXz7wHyJPHrEj/9VeIioLnnzfbvqxYAY0ameOCgy20alWEUqUszJzp0PBFREQkK6VMoj/0kOPiEBERERGHUxJdROQOLBaoXRu++QYOHIA+fcDLC06eBMOwAGC1WujWDY4dc3CwIiIikjVSJtGLFHFcHCIiIiLicEqii4hkQunSMGECLFiQep/VCk88AWPHmgl2EREReYD99dfNx6pEFxEREXFqSqKLiNyFqlXN1i63OnoUBgyA4sXhmWdg0SJISMj++EREROQeqZ2LiIiIiPxDSXQRkbsQFATTpoGrqwGY959+ai4++sQTZlX6jz9CmzYQEACvvw6bN5vtXlavRguRioiI5HQpk+iFCzsuDhERERFxuDyODkBE5EHVuTM8/bTBli3neewxb0qUsNi2HzgAc+eat+PH4bPPzFsyiwWGDjWr1j09b/86x4+b5wsJubnoqYiIiNxnye1cChQAd3fHxiIiIiIiDqVKdBGRexAUBLVrX0uV3A4JgQ8/hMOHYdUqeO45+/2GAcOGQd68UKoUNG4MvXvDlCnw889mxbrVCjNnQsmSUL++eT9zZna9MxERESeXXImuVi4iIiIiTk+V6CIi95GrKzRsaN4vWZL2mMOHzdvKlfbbPT3t+6lbrdC9O4SHqyJdRETkvjKMm5XoSqKLiIiIOD0l0UVEskFIiLkQqdV6c5urK2zZAvHxsG+fedu/37yPiUl7QdKkJNizR0l0ERGR++riRfMfXYAiRRwbi4iIiIg4nJLoIiLZIHkh0u7dzc/krq7w+edQpYq5v25d+/HXr0N0NNSrZxbDpfTyy9CvH7z2Gnh7Z0PwIiIizia5Ch1UiS4iIiIi6okuIpJdOnc227asXm3ed+6c/lg3N3jySZg+3Uy4g7kYaeHCcOYMDBwIJUpA//5w4kR2RC8iIuJEkvuhg5LoIiIiIqIkuohIdgoKMqvLM9qOJWXi/ehRiIuDuXOhYkWzDcy//20uTPrqq7B3r3nM8ePm+OPH79e7EBERZzJlyhSCg4Px9PSkVq1abNq0Kd2x06dPp27duhQuXJjChQvTsGHD247PsVIm0dXORURERMTpKYkuIpLDpUy8u7lBhw6waxcsW2a2gbl+HWbPhgoVoGpVKFkS6tc372fOdHT0IiLyIFu4cCH9+vVj6NChbNu2jcqVKxMeHs7p06fTHB8ZGUnbtm1ZvXo10dHRFC9enEaNGnHiQfvalNq5iIiIiEgKSqKLiDyAXFygaVNYuxY2bIDmzc3tO3bcXLzUajV7sKsiXURE7ta4cePo2rUrnTp1okKFCkydOhUvLy9mzZqV5vh58+bx+uuvU6VKFR5++GFmzJiB1WolIiIimyO/R2rnIiIiIiIpaGFREZEHXFgYLF1qtnl55RX7fUlJ0KULvPeeWbVusTgiQhEReRBdu3aNrVu3MnDgQNs2FxcXGjZsSHR0dIbOcfXqVa5fv06R27RESUxMJDEx0fb80qVLAFitVqzJ/zN8H1mtVgzDsHsty9mzJP+TaS1c+Ob/UMsDK615ltxJc+0cNM/OQ3PtHBw5zxl9TSXRRURyiQYNzAr1W//+X7nSvJUtaybZO3bMeE92ERFxXmfPniUpKQk/Pz+77X5+fvzxxx8ZOseAAQMIDAykYcOG6Y4ZNWoUw4cPT7X9zJkzJCQkZC7ou2C1Wrl48SKGYeDiYn5Rt8CJE+T7Z/95i4Xr6bSvkQdHWvMsuZPm2jlonp2H5to5OHKe4+PjMzROSXQRkVwiKAimTTNbuCQlgasr9O8Pp0/DokVw8CAMGgSDB0OjRuZipM2agaen2fLlwAEICVGCXUREssbo0aNZsGABkZGReHp6pjtu4MCB9OvXz/b80qVLFC9enKJFi1KwYMH7HqfVasVisVC0aFHbhzbL1au2/YXLlgVf3/seh9xfac2z5E6aa+egeXYemmvn4Mh5vt11akpKoouI5CKdO0N4uJkwL1v2ZkJ84kRYvBhmzTL7qCdXpxcuDFWqwJo1ZgW7i4uZiO/c2aFvQ0REcgAfHx9cXV2Ji4uz2x4XF4e/v/9tj/33v//N6NGj+fnnn6lUqdJtx3p4eODh4ZFqu4uLS7Z9iLJYLPavl2JhUZeiRc1/IOWBl2qeJdfSXDsHzbPzSDnXSUlJXL9+3dEhSRazWq3cuHGDa9euZfnvtJubG66urunuz+jrKYkuIpLLBAWlribPn99s49Kxo5lgnzPH7KF+/DisXn1znNUK3bpBzZoQGpqtYYuISA7j7u5O9erViYiIoEWLFgC2RUJ79eqV7nFjx45l5MiRrFy5ksceeyybos1iyQuLurhAoUKOjUVEREQwDINTp05x4cIFR4ci90FyP/T4+Hgs92ExN29vb/z9/e/p3Eqii4g4mbJl4cMPYfhw+OQTGDDAfr/VCpUqmYn0Ro3M2+OPg5vbzTFq/yIi4hz69etHx44deeyxx6hZsyYTJkzgypUrdOrUCYAOHTpQrFgxRo0aBcCYMWMYMmQI8+fPJzg4mNjYWADy589P/vz5HfY+Mi25Er1wYVWhi4iI5ABxcXFcvHgRX19fvLy87kuiVRzHMAxu3LhBnjx5snRuDcPg6tWrnP5nfZuAgIC7PpeS6CIiTsrVFV56CQYOTL0YKcCmTebtww+hQAGoX99MqF+8aPZWz0z7FyXdRUQeTG3atOHMmTMMGTKE2NhYqlSpwooVK2yLjR49etTuK7CfffYZ165do1WrVnbnGTp0KMOGDcvO0O9NciX6Qw85Ng4RERHBarVy4cIF/Pz8eEj/NudK9yuJDpA3b14ATp8+ja+v721bu9yOkugiIk4srcVIP/8cGjeGVavgp5/M+7Nn4bvvzFtKVit07QpnzkD58ua6a8m3ggXBYoGZM80WMeq5LiLyYOrVq1e67VsiIyPtnh8+fPj+B3S/3bhh/o8xQJEijo1FRERESEpKAsDLy8vBkciDKvnPzvXr15VEFxGRu5PeYqSvvGLerFbYvt1MqC9cCDt32h9vGGY1+63c3c3cwz/f5AfMc3Xvbr6eKtJFRCRHOn/+5mNVu4mIiOQYauEidysr/uyowZ+IiBAUBPXqpZ3YdnGB6tXNRPmyZalbw1os8PTTZt/00qXN1i8A167ZJ9CTJSXB5MmQmJjlb0NEROTeJbdyASXRRURERARQEl1ERDIhuf1L8refXF1h+nSzSj06GmJi4NIluHoVjhwxk+5p/YfvmDFQsiSMGGG2ghEREckxlEQXERERyTCLxcLSpUsdHcZ9pyS6iIhkSufOcPgwrF5t3qfV3zxvXihRApo2NZPsKZPuzz8PxYpBXBwMGWKO69YN9uzJznchIiKSjpRJdPVEFxERkbsUHx9P3759KVmyJHnz5qV27dps3rzZbswrr7yCxWKxuzVu3Ni2PzExkfbt21OwYEHKlSvHzz//bHf8xx9/zBtvvJEt7yc9p06dokmTJg6NITuoJ7qIiGRaUFDGe5qn1XP9+nX4+msYNw62bjUT7dOnmwuavvmm2R7mxAk4cABCQtQ/XUREstFff918rEp0ERERuUtdunRh9+7dfPnllwQGBvLf//6Xhg0bsmfPHooVK2Yb17hxY2bPnm177uHhYXs8bdo0tm7dSnR0ND/++CMvvfQScXFxWCwWDh06xPTp09myZUu2vq9k165dw93dHX9/f4e8fnZTJbqIiNx3t/Zcd3ODl16CzZth7Vp47jmz7cuKFTcXHS1RAurXN9u+zJzp0PBFRMSZqJ2LiIiI3KO///6bxYsXM3bsWJ588knKli3LsGHDKFu2LJ999pndWA8PD/z9/W23woUL2/bt3buXZs2aUbFiRXr27MmZM2c4e/YsAD169GDMmDEULFjwtrH89NNPeHp6cuHCBbvtffr0oX79+gCcO3eOtm3bUqxYMby8vAgNDeWrr76yG1+vXj169epF37598fHxITw8HEjdzmXAgAGUK1cOLy8vSpcuzeDBg7l+/bpt/7Bhw6hSpQpffvklwcHBFCpUiLZt2xIfH28bY7VaGTt2LGXLlsXDw4MSJUowcuRI2/5jx47RunVrvL29KVKkCM2bN+fw4cO3/TncKyXRRUTEYSwWqFsXvv3WrDrv3Ru8vODkSTAMc4zVarZ7OXbMsbGKiIiTUDsXERGRB8KVa1fSvSXcSMjw2L+v/52hsZlx48YNkpKS8PT0tNueN29e1q9fb7ctMjISX19fypcvT48ePTiX4lqkcuXKrF+/nr///puVK1cSEBCAj48P8+bNw9PTk+eee+6OsTRo0ABvb28WL15s25aUlMTChQtp164dAAkJCVSvXp3ly5eze/duunXrRvv27dm0aZPduebOnYu7uztRUVFMnTo1zdcrUKAAc+bMYc+ePUycOJHp06czfvx4uzExMTEsXbqUZcuWsWzZMtasWcPYsWNt+wcOHMjo0aMZPHgwe/bsYf78+fj5+QFw/fp1wsPDKVCgAOvWrSMqKor8+fPTuHFjrl27dsefx91SOxcREckRypSBiROhYUNo1sx+n9UKjz9uJtPbt4fSpR0To4iIOAG1cxEREXkg5B+VP919z4Q8w/KXltue+/7bl6vXr6Y59qmSTxH5SqTtefDEYM5ePZtqnDHUyHBsBQoUICwsjBEjRvDII4/g5+fHV199RXR0NGXLlrWNa9y4MS1btqRUqVLExMTw3nvv0aRJE6Kjo3F1deXVV19l165dVKhQAR8fHxYtWsT58+cZMmQIkZGRDBo0iAULFlCmTBlmzZpl1yYmmaurKy+++CLz58+n8z+LmkVERHDhwgWef/55AIoVK8bbb79tO+aNN95g5cqVLFq0iJo1a9q2h4SE2CW70zJo0CDb4+DgYN5++20WLFjAO++8Y9tutVqZM2cOBQoUAODll19m9erVgNlLfuLEiUyePJmOHTsCUKZMGZ544gkAFi5ciNVqZcaMGVgsFgBmz56Nt7c3kZGRNGrU6E7Tc1eURBcRkRylalVwcTET5ymdPAnDhpm3J56ADh3ghRfA29vcf/y4eqiLiEgWUDsXERERyQJffvklr776KsWKFcPV1ZVq1arRtm1btm7dahvz4osv2h6HhoZSqVIlypQpQ2RkJA0aNMDNzY0pU6bYnbdTp0707t2b7du3s3TpUnbu3MnYsWPp3bu3XbV5Su3atePxxx/n5MmTBAYGMm/ePJo2bYr3Px+ok5KS+Oijj1i0aBEnTpzg2rVrJCYm4uXlZXee6tWr3/F9L1y4kEmTJhETE8Ply5e5ceNGqpYzwcHBtgQ6QEBAAGfOnAHMFjaJiYk0aNAgzfPv3LmTgwcP2h0PZjV9TEzMHeO7W0qii4hIjhIUBNOmQffukJQErq4waRIULAhffgk//wzr15u3N94wq9b9/WHKFDPx7uJiHv/Pf7CLiIhkjtq5iIiIPBAuD7yc7j5XF1e756ffPp3uWBeLfbfrw30O31NcycqUKcOaNWu4cuUKly5dIiAggDZt2lD6Nl+tLl26ND4+Phw8eDDNJPLq1av5/fffmTFjBv379+eZZ54hX758tG7dmsmTJ6d73ho1alCmTBkWLFhAjx49WLJkCXPmzLHt//jjj5k4cSITJkwgNDSUfPny0bdv31TtUfLly3fb9xwdHU27du0YPnw44eHhFCpUiAULFvDJJ5/YjXNzc7N7brFYsP5TSZc3b97bvsbly5epXr068+bNS7WvaNGitz32XiiJLiIiOU7nzuYCowcPQtmyNyvLX34ZTpyA+fPhiy9g9274+mv7Y5N7qHt7m1XtAQGQ3r/Bma1eP34cNm92p0YNc+FTERHJhc6fN+/d3OAOHxRFRETEcfK5Z/zf6fs1NkPny5ePfPnycf78eVauXHnbdijHjx/n3LlzBAQEpNqXkJBAz549mTdvHq6uriQlJWH8s5jY9evXSUpKum0c7dq1Y968eQQFBeHi4kLTpk1t+6KiomjevDkvv/wyYLZb2b9/PxUqVMjUe92wYQMlS5bk/ffft207cuRIps4REhJC3rx5iYiIoEuXLqn2V6tWjYULF+Lr63vHRVWzkhYWFRGRHCkoCOrVS53cLlYM+veHXbtg+3Zo1Sr1sVarub1MGXOh0iJFIDTUTMy/+ioMGmQm5EuUgPr1oWRJ+PBDc/HSv/6CxMSbC5smmzkTSpWy0KpVEUqVsjBz5n176yIi4kjJSfTChc0VsEVERETuwsqVK1mxYgWHDh1i1apV/Otf/+Lhhx+mU6dOgFlR3b9/f3799VcOHz5MREQEzZs3p2zZsoSHh6c634gRI3jmmWeoWrUqAHXq1OHbb79l165dTJ48mTp16tw2nnbt2rFt2zZGjhxJq1at8PDwsO0LCQlh1apVbNiwgb1799K9e3fi4uIy/Z5DQkI4evQoCxYsICYmhkmTJrFkyZJMncPT05MBAwbwzjvv8MUXXxATE8Ovv/7KzH8+hLdr1w4fHx+aN2/OunXrOHToEJGRkfTu3Zvjx49nOuaMUiW6iIg8kCwWqFIFxo+Hb79N3UO9ZEmIi4OEBDMfcv68WbmeFqsVBg82b8lcXc0CxPz5wd0dDh8GsPwz3kL37mZSXv3XRURymZRJdBEREZG7dPHiRQYOHMjx48cpUqQIzz//PCNHjrS1MnF1dWXXrl3MnTuXCxcuEBgYSKNGjRgxYoRdghtg9+7dLFq0iB07dti2tWrVisjISOrWrUv58uWZP3/+beMpW7YsNWvWZNOmTUyYMMFu36BBg/jzzz8JDw/Hy8uLbt260aJFCy5evJip99ysWTPefPNNevXqRWJiIk2bNmXw4MEMGzYsU+cZPHgwefLkYciQIZw8eZKAgABee+01ALy8vFi7di0DBgygZcuWxMfHU6xYMRo0aHBfK9MthnFrrZ0AXLp0iUKFCnHx4sV7mgCr1crp06fx9fXFxUWF/7md5tv5aM5zhpkz7Xuof/652RLGMODCBXNR0hMnbt5v3gzffZf6PK6u5jkyqlo16NoVnnsO/Pyy7O1IDqLfcefiyPnOqmvP3CC7fxZ2824YkOefOqPHH4fo6Pv++pI99Pe589BcOwfNs/OwWq2cOHGC+Ph4Spcujaenp6NDkvvAMAxu3LhBnjx5sNyHbwImJCRw6NAhSpUqlerPUEavPVWJLiIiD7z0eqhbLGYhYeHCULHizfHHj8P//mdfve7qalab+/nBlSv2tz//hBdeSN3iZds26NEDXn8d6taF55+Hli3tq9Mz23ddREQc6MKFm4+9vR0VhYiIiIjkMEqii4hIrhAUlPEkdVAQTJuWuno9+Xhvb/vcSdWqMH06dO9ukJRkwdXVYMQICy4usHixWdm+dq1569MHatUyE+pJSfD++2ay3sXFfM3OnbP6nYuISJZJmURXOxcRERER+YeS6CIi4pTSq16/3finnzbYsuU8jz3mTYkS5lfMBgyAo0fNvuyLF0NUFGzcaN5SslpRH3URkZwuuR86KIkuIiIiIjZqHCUiIk4rKAjq1ctcBXvt2tdSjS9RAvr2hXXrzJ7rU6aY1eu3SkqCdu1gxgw4duxeoxcRkSynJLqIiIiIpEGV6CIiIlkoIMDskd6sGZQsad93HW62fQGoUMGsTA8PhyefhLx51UNdRMShlEQXERERkTSoEl1EROQ+SO677upqPnd1hYEDYdgwePxxs0f6nj0wfjw0bgxFisCjj5pV7fXrmwn4mTMd+hZERJxPyiS6FhYVERERkX+oEl1EROQ+Sa/v+tCh8NdfEBEBK1bAypVmG5jff795rNUKXbrAli3QoAHUrAnFi4PFkvp1VL0uIpJFtLCoiIiIiKRBSXQREZH7KCgo7cR2kSLwwgvmzTBg9mwz6X6rqVPNG4Cvr5lMr1kTatQwb0uXQrduZtLdxcWsfk/rPCIikgFq5yIiIiIiaVASXURExMEsFmjUyEyCp+yh7uICbduabV9++w1On4Zly8xbWqxW6N7drH5XRbqIyF1QEl1ERERE0qCe6CIiIjlAWj3Up02D//4Xtm2DS5dgwwaYOBHatTNbt6QlKQmqVYPmzc3+60uXwpEjZrV7suPHYfVq815ERFJQT3QRERGRTLFYLCxdutTRYdx3SqKLiIjkEJ07w+HDZoL78GH7tix580JYGPTubSbW9+83q9Nd0viX/MwZ+P57GD4cnnsOgoPBx8fsrR4ersVLRUTSpZ7oIiIikkXi4+Pp27cvJUuWJG/evNSuXZvNmzfbjTEMgyFDhhAQEEDevHlp2LAhBw4csO1PTEykffv2FCxYkHLlyvHzzz/bHf/xxx/zxhtvZMv7Sc+pU6do0qSJQ2PIDmrnIiIikoOk10M9LY8+alard+9uVqC7usL48VC5MuzYAdu3m7fffzcXMv3lF/vjkxcvnTkTQkPN6vbkW+nS4Ol5c2xmFy/VYqci8kBKrkR3cYECBRwbi4iIiDzQunTpwu7du/nyyy8JDAzkv//9Lw0bNmTPnj0UK1YMgLFjxzJp0iTmzp1LqVKlGDx4MOHh4ezZswdPT0+mTZvG1q1biY6O5scff+Sll14iLi4Oi8XCoUOHmD59Olu2bHHI+7t27Rru7u74+/s75PWzmyrRRUREHmC3Vq+/8QY8+aRZsT57tplMv3wZtm6Ft99O+xzR0WYyvn9/aNECKlYELy+zgv3pp+Ff/7pZvV6iBHTtCl9/bVa7r1hhJufXr4dNm2DnTvjwQ7PKPTPV7moxIyI5QnIS3ds77a/6iIiIiGTA33//zeLFixk7dixPPvkkZcuWZdiwYZQtW5bPPvsMMKvQJ0yYwKBBg2jevDmVKlXiiy++4OTJk7b2KHv37qVZs2ZUrFiRnj17cubMGc6ePQtAjx49GDNmDAULFrxtLD/99BOenp5cSPmNO6BPnz7Ur18fgHPnztG2bVuKFSuGl5cXoaGhfPXVV3bj69WrR69evejbty8+Pj6Eh4cDqdu5DBgwgHLlyuHl5UXp0qUZPHgw169ft+0fNmwYVapU4csvvyQ4OJhChQrRtm1b4uPjbWOsVitjx46lbNmyeHh4UKJECUaOHGnbf+zYMVq3bo23tzdFihShefPmHD58+M4Tcw9UiS4iIvKAu1P1uoeH2Sfd1xfGjUu9eOm4cXDunFk5nny7dMnspX7kiP25DANmzDBvGZFc7T5pkpmADwgAf/+b9/7+sHYtvPeeOdbFxUzop2xlkx5Vu4tIlktOoquVi4iISM5340r6+yyu4OqZsbG4QJ68dx6bJ1/GQ7txg6SkJDxTfr0XyJs3L+vXrwfg0KFDxMbG0rBhQ9v+QoUKUatWLaKjo3nxxRepXLkyX375JX///TcrV64kICAAHx8f5s2bh6enJ88999wdY2nQoAHe3t4sXryYzv980EpKSmLhwoW2xHRCQgLVq1dnwIABFCxYkOXLl9O+fXvKlClDzZo1beeaO3cuPXr0ICoqKt3XK1CgAHPmzCEwMJDffvuNrl27UqBAAd555x3bmJiYGJYuXcqyZcs4f/48rVu3ZuzYsYwaNQqAgQMHMn36dMaPH88TTzzBqVOn+OOPPwC4fv064eHhhIWFsW7dOvLkycOHH35I48aN2bVrF+7u7nf8mdwNJdFFREScRPLipSnbv3z+eeqEtWGYfdUPHDCrzceOTX2uSpXMavVr127eEhMhPt5sHXOrXbvM250kJ91nzAA/P7MY1NsbChW6+djbG6KizNY1mU28i4iky2q92RNdi4qKiIjkfIvyp78v8Bmot/zm88W+kHQ17bG+T0HDyJvPvwuGxLOpx71kZDi0AgUKEBYWxogRI3jkkUfw8/Pjq6++Ijo6mrJlywIQGxsLgJ+fn92xfn5+tn2vvvoqu3btokKFCvj4+LBo0SLOnz/PkCFDiIyMZNCgQSxYsIAyZcowa9YsW5uYlFxdXXnxxReZP3++LYkeERHBhQsXeP755wEoVqwYb6f46vIbb7zBypUrWbRokV0SPSQkhLFpfUBMYdCgQbbHwcHBvP322yxYsMAuiW61WpkzZw4F/mmf9/LLL7N69WrA7CU/ceJEJk+eTMeOHQEoU6YMTzzxBAALFy7EarUyY8YMLBYLALNnz8bb25vIyEgaNWp02/julpLoIiIiTqRzZ3Nx0YMHoWzZtCu4LRazat3X12zH8u9/21evu7rC8uVpH3v8uHnMrdXuM2bAjRtw6hTExt68P3QI4uJSn+fXXzP+npIT7ytXQvXqUL48PPwwlCkDbm72salyXUTSdfnyzb+8VIkuIiIi9+jLL7/k1VdfpVixYri6ulKtWjXatm3L1q1bM3wONzc3pkyZYretU6dO9O7dm+3bt7N06VJ27tzJ2LFj6d27N4sXL07zPO3atePxxx/n5MmTBAYGMm/ePJo2bYr3P4UDSUlJfPTRRyxatIgTJ05w7do1EhMT8fLysjtP9erV7xjzwoULmTRpEjExMVy+fJkbN26kajkTHBxsS6ADBAQEcObMGcBsYZOYmEiDBg3SPP/OnTs5ePCg3fFgVtPHxMTcMb67pSS6iIiIk8nM4qXpVa+nd3x64zt1Snt8ekn3Tz81j7140SwMvXDh5uMjR8zFUm/19dfmLVmePGYi/eGHzSr5lSvNKntVrotImpJbuYCS6CIiIg+C1pfT32dxtX/+/OnbnOiWdVCaH77biOyUKVOGNWvWcOXKFS5dukRAQABt2rShdOnSALYFOePi4ggICLAdFxcXR5UqVdI85+rVq/n999+ZMWMG/fv355lnniFfvny0bt2ayZMnpxtLjRo1KFOmDAsWLKBHjx4sWbKEOXPm2PZ//PHHTJw4kQkTJhAaGkq+fPno27cv165dsztPvny3b2kTHR1Nu3btGD58OOHh4RQqVIgFCxbwySef2I1zS1nthNlX3frPh8K8efNyO5cvX6Z69erMmzcv1b6iRYve9th7oSS6iIiI3FZGqtfvdnxGW8yklF7i/c03zQr3ffvgjz/gyhXz8b599scnV65PngyhoWZlerly5n1ICKQsaDh+HDZvdqdGDbOnu4jkYkqii4iIPFgy0aP8vo3NgHz58pEvXz7Onz/PypUrbe1QSpUqhb+/PxEREbak+aVLl9i4cSM9evRIdZ6EhAR69uzJvHnzcHV1JSkpCcMwW8xcv36dpKSk28bRrl075s2bR1BQEC4uLjRt2tS2LyoqiubNm/Pyyy8DZruV/fv3U6FChUy91w0bNlCyZEnef/9927Yjty60dQchISHkzZuXiIgIunTpkmp/tWrVWLhwIb6+vndcVDUrKYkuIiIid5SZ6vXMjs9skj4jiXfDgBMnzGT6//5nLmx6qx07zNut/P3NpLphwPr1FgyjCC4uBsOHQ8+eZqvkf1rvpaKWMSIPsJRJdPVEFxERkXu0cuVKDMOgfPnyHDx4kP79+/Pwww/T6Z+v6VosFvr27cuHH35ISEgIpUqVYvDgwQQGBtKiRYtU5xsxYgTPPPMMVatWBaBOnTr079+fTp06MXnyZOrUqXPbeNq1a8ewYcMYOXIkrVq1wsPDw7YvJCSEb775hg0bNlC4cGHGjRtHXFxcppPoISEhHD16lAULFlCjRg2WL1/OkiVLMnUOT09PBgwYwDvvvIO7uzt16tThzJkz/P7773Tu3Jl27drx8ccf07x5cz744AOCgoI4cuQI3377Le+88w5B9+mDmJLoIiIi4nCZTdLfKfFusdw858MPm1Xnt1auf/YZnDtnJr337zdvZ86Yvdr/WccHMLPlVquFwYNh8GBwdzcT7X5+5n3yLSYGvvpKLWNEHliqRBcREZEsdPHiRQYOHMjx48cpUqQIzz//PCNHjrRrZfLOO+9w5coVunXrxoULF3jiiSdYsWIFnp6edufavXs3ixYtYkeKKqBWrVoRGRlJ3bp1KV++PPPnz79tPGXLlqVmzZps2rSJCRMm2O0bNGgQf/75J+Hh4Xh5edGtWzdatGjBxYsXM/WemzVrxptvvkmvXr1ITEykadOmDB48mGHDhmXqPIMHDyZPnjwMGTKEkydPEhAQwGuvvQaAl5cXa9euZcCAAbRs2ZL4+HiKFStGgwYN7mtlusVIrvsXO5cuXaJQoUJcvHjxnibAarVy+vRpfH19cXFxufMB8kDTfDsfzblz0Xw/uGbOzFjLmAsXzKT60qXw0Uf39poWCyxcCC1bmq8pOZ8jf8ez6tozN8jun4Vt3pctw6VrV3Pj1KnmXxqSa+jfcOehuXYOmmfnYbVaOXHiBPHx8ZQuXTpVcllyB8MwuHHjBnny5MGS3td+70FCQgKHDh2iVKlSqf4MZfTaU5XoIiIikutltGWMtzfUqAEBATB6tH31uqur2R7Gze1mtXpsLMTFwdat8P339ucyDGjdGnx9oUULeP55+Ne/zONFJAe6cOHmY1Wii4iIiEgKSqKLiIiIU8hMy5ibfdcNkpIsuLoafP65hbJlzf0lS9qPP34cli2zT7pbLFCwIJw+bZ5r2jQzL9esGbRqBU8/DR4e6qMuklNY1BNdRERERNKh77yIiIiIpKFzZ/jzT4PFi//izz+N2/Y3T066J7dtcXWF6dPNHus//QTdupkV6efPw9y58OyzULQo1KoFJUpA/fpmYn7mzDvHdfw4rF5t3otIFlJPdBERERFJhyrRRURERNIRFATu7tfw9b3z2PRaxjz9tHn7z39g/XpYvBi+/RZOnIBNm24eb7VCly4wahQUL262lPH3t7/fsAGGDzfHZmbxUlW7i2SAkugiIiIikg4l0UVERESyyO1axri6wlNPmbcJE+Czz6BXr9TjYmLM250kJ93nzDGT7r6+ZnV78n3y4xUroG/fzCXelXQXp6Se6CIiIiKSDiXRRURERLKZiws0bw69e9v3UXdxga++gqQkOHXKXLg0+f7gQTh8OPW51q/P+OsmJ96/+cZMvD/0UOrb6tUwdOj9r3ZXol5ynJRJ9EKFHBaGiIiIiOQ8SqKLiIiIOMDNxUvNpLmrK3z+ObRunfb448fNvum3Jt3Hj4cbN8z+66dP29+fPAkJCanPtWJFxmJMTrpPnmy2lEmZbC9S5Objdevgo49uJt6nToWuXdM/78yZZp/4zCbqRe6r5HYuBQpAHn1MEhEREZGbdHUoIiIi4iDp9VFPS3pJ99sln9NLvH/wgbnt3Dn72/HjZuL9Vjt2ZPw9Wa1mgrxXL8iXD7y87G8Wi9nb/dbxNWtCaOjt34sq1+W+Sk6iq5WLiIiIiNxCSXQRERERB7pdH/VbZSbpnnzuzCTe00u6T59uJr//+it14v3IETh0KPW5rl0zbynXakyP1QqVKoGfH1SsePNWoYJ5v2TJ3VWuK/EuGWYYSqKLiIiISLqURBcRERF5gGQm6Q5ZU+3+6qvpH5NW4t3VFaKizLbSV6+at7//Nu+PHTN7wRtG6nPFxZm3X35J//WSK9evXYNy5cDHx2wp4+MDnp43x91Ny5jjx2HzZndq1IASJW4/VnKZv//Gcv26+VhJdBEREZEMs1gsLFmyhBYtWjg6lPtKSXQRERGRXM4R1e61aqV/TN68afeC37sXfv8d9uwx73//HY4eTX281Qqvv556u5eXmUwvUMA8NuX4rl3hyhWzKj1lb/dChcwqezPpbsFqLYKLi6E+7U7G5eLFm0+8vR0Wh4iIiOQeSUlJDBs2jP/+97/ExsYSGBjIK6+8wqBBg7BYLAC88sorzJ071+648PBwVvyziFFiYiJdunThu+++w9/fn//85z80bNjQNvbjjz/m6NGjfPrpp9n3xm5x6tQpCjtBEYKS6CIiIiJi535Wu99ufM2a5i2lP/4wW7qkrHS3WMxx8fE328rcuGFWuqeVdAez8r1Pn9TbXV2hYMHkTh7mhxmr1UL37maMagPjHOyS6E7wIVBERETuvzFjxvDZZ58xd+5cKlasyJYtW+jUqROFChWid+/etnGNGzdm9uzZtuceHh62x9OmTWPr1q1ER0fz448/8tJLLxEXF4fFYuHQoUNMnz6dLVu2ZOv7Snbt2jXc3d3x9/d3yOtnNxdHByAiIiIiD76gIKhXL+NJ54yOf/hhs9Ld1dV87upq9mj/9Vez2jw21mztcuECxMTApk3wxRdmoj0liwWeeAKqVjVbteTLZ25PSkq7b3tSkpnkF+dguXDh5hMl0UVERCQLbNiwgebNm9O0aVOCg4Np1aoVjRo1YtOmTXbjPDw88Pf3t91SVnXv3buXZs2aUbFiRXr27MmZM2c4e/YsAD169GDMmDEULFjwtnH89NNPeHp6ciHl9Q7Qp08f6tevD8C5c+do27YtxYoVw8vLi9DQUL766iu78fXq1aNXr1707dsXHx8fwsPDAbOdy9KlS23jBgwYQLly5fDy8qJ06dIMHjyY68lt84Bhw4ZRpUoVvvzyS4KDgylUqBBt27YlPj7eNsZqtTJ27FjKli2Lh4cHJUqUYOTIkbb9x44do3Xr1nh7e1OkSBGaN2/O4cOHb/tzuFdKoouIiIhIjta5Mxw+DKtXm/e3tlmxWMy2LKVLQ40a0L69mWi/NfG+bh1s22Yuhnr5MiQkwIkT8NNPZt/0lFxdzSp5cQ5G/vwYzZrBk0+a/3MjIiIiOd61K9fSvd1IuJHhsdf/vp6hsZlVu3ZtIiIi2L9/PwA7d+5k/fr1NGnSxG5cZGQkvr6+lC9fnh49enDu3DnbvsqVK7N+/Xr+/vtvVq5cSUBAAD4+PsybNw9PT0+ee+65O8bRoEEDvL29Wbx4sW1bUlISCxcupF27dgAkJCRQvXp1li9fzu7du+nWrRvt27dPlfCfO3cu7u7uREVFMXXq1DRfr0CBAsyZM4c9e/YwceJEpk+fzvjx4+3GxMTEsHTpUpYtW8ayZctYs2YNY8eOte0fOHAgo0ePZvDgwezZs4f58+fj5+cHwPXr1wkPD6dAgQKsW7eOqKgo8ufPT+PGjbl2LfPzlFFq5yIiIiIiOd79aDHj4QGBgebN7OtukJRkwdXV4PPPLWrl4kRuPPooxpIlWG793xQRERHJsUblH5XuvpBnQnhp+Uu25//2/TfXr15Pc2zJp0rySuQrtucTgydy9ezVVOOGGkMzFd+7777LpUuXePjhh3F1dSUpKYmRI0faEtdgtnJp2bIlpUqVIiYmhvfee48mTZoQHR2Nq6srr776Krt27aJChQr4+PiwaNEizp8/z5AhQ4iMjGTQoEEsWLCAMmXKMGvWLIoVK5YqDldXV1588UXmz59P53+qUSIiIrhw4QLPP/88AMWKFePtt9+2HfPGG2+wcuVKFi1aRM0U/RZDQkLskt1pGTRokO1xcHAwb7/9NgsWLOCdd96xbbdarcyZM4cCBQoA8PLLL7N69WoA4uPjmThxIpMnT6Zjx44AlClThieeeAKAhQsXYrVamTFjhq23/OzZs/H29iYyMpJGjRrdaWruipLoIiIiIpIrZXZB1aefNtiy5TyPPeZNiRKWOx8kIiIiIpKORYsWMW/ePObPn0/FihXZsWMHffv2JTAw0JYcfvHFF23jQ0NDqVSpEmXKlCEyMpIGDRrg5ubGlClT7M7bqVMnevfuzfbt21m6dCk7d+5k7Nix9O7d267aPKV27drx+OOPc/LkSQIDA5k3bx5NmzbF+58F1ZOSkvjoo49YtGgRJ06c4Nq1ayQmJuLl5WV3nurVq9/xfS9cuJBJkyYRExPD5cuXuXHjRqqWM8HBwbYEOkBAQABnzpwBzBY2iYmJNGjQIM3z79y5k4MHD9odD2Y1fUxMzB3ju1tKoouIiIiIYCbc3d2v4evr6EhERERE5E4GXh6Y7j4XV/tvl719+u10RoLFxb54os/hNFajvwv9+/fn3XfftSXKQ0NDOXLkCKNGjbIl0W9VunRpfHx8OHjwYJpJ5NWrV/P7778zY8YM+vfvzzPPPEO+fPlo3bo1kydPTjeWGjVqUKZMGRYsWECPHj1YsmQJc+bMse3/+OOPmThxIhMmTCA0NJR8+fLRt2/fVO1R8iUvLJSO6Oho2rVrx/DhwwkPD6dQoUIsWLCATz75xG6cm5ub3XOLxYLVagUgb968t32Ny5cvU716debNm5dqX9GiRW977L1QEl1EREREREREREQeKO753B0+9nauXr2Kyy2t4lxdXW3J4rQcP36cc+fOERAQkGpfQkICPXv2ZN68ebb2MIZhAGaf8KSkpNvG065dO+bNm0dQUBAuLi40bdrUti8qKormzZvz8ssvA2a7lf3791OhQoUMv18wF1MtWbIk77//vm3bkSNHMnWOkJAQ8ubNS0REBF26dEm1v1q1aixcuBBfX987LqqaldT0T0RERERERERERCQLPfvss4wcOZLly5dz+PBhlixZwrhx42yLgV6+fJn+/fvz66+/cvjwYSIiImjevDlly5YlPDw81flGjBjBM888Q9WqVQGoU6cO3377Lbt27WLy5MnUqVPntvG0a9eObdu2MXLkSFq1aoWHh4dtX0hICKtWrWLDhg3s3buX7t27ExcXl+n3HBISwtGjR1mwYAExMTFMmjSJJUuWZOocnp6eDBgwgHfeeYcvvviCmJgYfv31V2bOnGl7Hz4+PjRv3px169Zx6NAhIiMj6d27N8ePH890zBmlSnQRERERERERERGRLPTpp58yePBgXn/9dU6fPk1gYCDdu3dnyJAhgFmVvmvXLubOncuFCxcIDAykUaNGjBgxwi7BDbB7924WLVrEjh07bNtatWpFZGQkdevWpXz58syfP/+28ZQtW5aaNWuyadMmJkyYYLdv0KBB/Pnnn4SHh+Pl5UW3bt1o0aIFFy9ezNR7btasGW+++Sa9evUiMTGRpk2bMnjwYIYNG5ap8wwePJg8efIwZMgQTp48SUBAAK+99hoAXl5erF27lgEDBtCyZUvi4+MpVqwYDRo0uK+V6RYjue5f7Fy6dIlChQpx8eLFe5oAq9XK6dOn8fX1TfUVDsl9NN/OR3PuXDTfzkdz7lwcOd9Zde2ZG2T3z0K/585B8+w8NNfOQfPsPKxWKydOnCA+Pp7SpUvj6enp6JDkPjAMgxs3bpAnTx4sFsudD8ikhIQEDh06RKlSpVL9Gcrotaf+phERERERERERERERSYeS6CIiIiIiIiIiIiIi6VASXUREREREREREREQkHTkiiT5lyhSCg4Px9PSkVq1abNq06bbjv/76ax5++GE8PT0JDQ3lhx9+sNtvGAZDhgwhICCAvHnz0rBhQw4cOHA/34KIiIiIiIiIiIiI5EIOT6IvXLiQfv36MXToULZt20blypUJDw/n9OnTaY7fsGEDbdu2pXPnzmzfvp0WLVrQokULdu/ebRszduxYJk2axNSpU9m4cSP58uUjPDychISE7HpbIiIiIiIiIiIiIpILODyJPm7cOLp27UqnTp2oUKECU6dOxcvLi1mzZqU5fuLEiTRu3Jj+/fvzyCOPMGLECKpVq8bkyZMBswp9woQJDBo0iObNm1OpUiW++OILTp48ydKlS7PxnYmIiIiIiIiIiMi9sFgsAFitVgdHIg+qrPizkycL4rhr165dY+vWrQwcONC2zcXFhYYNGxIdHZ3mMdHR0fTr189uW3h4uC1BfujQIWJjY2nYsKFtf6FChahVqxbR0dG8+OKLWf9GREREREREREREJMu5urri4uLCyZMnKVq0KO7u7rbEuuQOhmFw48YN8uTJk6VzaxgG165d48yZM7i4uODu7n7X53JoEv3s2bMkJSXh5+dnt93Pz48//vgjzWNiY2PTHB8bG2vbn7wtvTFpSUxMJDEx0fb80qVLgPk/FffyvxVWqxXDMPS/ZU5C8+18NOfORfPtfDTnzsWR860/YyIiIiJps1gsBAcHExcXx8mTJx0djtwHydfgLi4u9+U/SLy8vChRogQuLnfflMWhSfScZNSoUQwfPjzV9jNnztxTL3Wr1crFixcxDOOeJkoeDJpv56M5dy6ab+ejOXcujpzv+Pj4bH09ERERkQeJu7s7JUqU4MaNGyQlJTk6HMliVquVc+fO8dBDD2X5dbirq2uWVLg7NInu4+ODq6srcXFxdtvj4uLw9/dP8xh/f//bjk++j4uLIyAgwG5MlSpV0o1l4MCBdm1iLl26RPHixSlatCgFCxbM1PtKyWq1YrFYKFq0qD58OwHNt/PRnDsXzbfz0Zw7F0fOt6enZ7a+noiIiMiDxmKx4Obmhpubm6NDkSxmtVpxc3PD09Mzx37ucmgS3d3dnerVqxMREUGLFi0A84cWERFBr1690jwmLCyMiIgI+vbta9u2atUqwsLCAChVqhT+/v5ERETYkuaXLl1i48aN9OjRI91YPDw88PDwSLXdxcXlnifPYrFkyXnkwaD5dj6ac+ei+XY+mnPn4qj51p8vEREREZGcy+HtXPr160fHjh157LHHqFmzJhMmTODKlSt06tQJgA4dOlCsWDFGjRoFQJ8+fXjqqaf45JNPaNq0KQsWLGDLli1MmzYNMD/49O3blw8//JCQkBBKlSrF4MGDCQwMtCXqRUREREREREREREQywuFJ9DZt2nDmzBmGDBlCbGwsVapUYcWKFbaFQY8ePWpXmVO7dm3mz5/PoEGDeO+99wgJCWHp0qU8+uijtjHvvPMOV65coVu3bly4cIEnnniCFStW6GuyIiIiIiIiIiIiIpIpFsMwDEcHkRNdvHgRb29vjh07ds890c+cOaNeqk5C8+18NOfORfPtfDTnzsWR8528Hs+FCxcoVKhQtr52TpNV1+EZpd9z56B5dh6aa+egeXYemmvn8CBchzu8Ej2nio+PB6B48eIOjkREREREnEV8fLzTJ9F1HS4iIiIi2e1O1+GqRE+H1Wrl5MmTFChQAIvFctfnSf7fjOyqpBHH0nw7H825c9F8Ox/NuXNx5HwbhkF8fDyBgYFOX2WVVdfhGaXfc+egeXYemmvnoHl2Hppr5/AgXIerEj0dLi4uBAUFZdn5ChYsqF92J6L5dj6ac+ei+XY+mnPn4qj5dvYK9GRZfR2eUfo9dw6aZ+ehuXYOmmfnobl2Djn5Oty5y1xERERERERERERERG5DSXQRERERERERERERkXQoiX6feXh4MHToUDw8PBwdimQDzbfz0Zw7F82389GcOxfNt3PSvDsHzbPz0Fw7B82z89BcO4cHYZ61sKiIiIiIiIiIiIiISDpUiS4iIiIiIiIiIiIikg4l0UVERERERERERERE0qEkuoiIiIiIiIiIiIhIOpREFxERERERERERERFJh5Lo99GUKVMIDg7G09OTWrVqsWnTJkeHJFlk7dq1PPvsswQGBmKxWFi6dKndfsMwGDJkCAEBAeTNm5eGDRty4MABxwQr92zUqFHUqFGDAgUK4OvrS4sWLdi3b5/dmISEBHr27MlDDz1E/vz5ef7554mLi3NQxHKvPvvsMypVqkTBggUpWLAgYWFh/Pjjj7b9mu/cbfTo0VgsFvr27WvbpjnPXYYNG4bFYrG7Pfzww7b9mm/noev13Ee/37lTVnz++uuvv2jXrh0FCxbE29ubzp07c/ny5Wx8F5IRd5rrV155JdXveOPGje3GaK5zvqz6jH306FGaNm2Kl5cXvr6+9O/fnxs3bmTnW5HbyMg816tXL9Xv9GuvvWY3JqfMs5Lo98nChQvp168fQ4cOZdu2bVSuXJnw8HBOnz7t6NAkC1y5coXKlSszZcqUNPePHTuWSZMmMXXqVDZu3Ei+fPkIDw8nISEhmyOVrLBmzRp69uzJr7/+yqpVq7h+/TqNGjXiypUrtjFvvvkm//vf//j6669Zs2YNJ0+epGXLlg6MWu5FUFAQo0ePZuvWrWzZsoX69evTvHlzfv/9d0DznZtt3ryZzz//nEqVKtlt15znPhUrVuTUqVO22/r16237NN/OQdfruZd+v3OfrPj81a5dO37//XdWrVrFsmXLWLt2Ld26dcuutyAZdKe5BmjcuLHd7/hXX31lt19znfNlxWfspKQkmjZtyrVr19iwYQNz585lzpw5DBkyxBFvSdKQkXkG6Nq1q93v9NixY237ctQ8G3Jf1KxZ0+jZs6fteVJSkhEYGGiMGjXKgVHJ/QAYS5YssT23Wq2Gv7+/8fHHH9u2XbhwwfDw8DC++uorB0QoWe306dMGYKxZs8YwDHN+3dzcjK+//to2Zu/evQZgREdHOypMyWKFCxc2ZsyYofnOxeLj442QkBBj1apVxlNPPWX06dPHMAz9judGQ4cONSpXrpzmPs2389D1eu6k3+/c724+f+3Zs8cAjM2bN9vG/Pjjj4bFYjFOnDiRbbFL5tw614ZhGB07djSaN2+e7jGa6wfT3XzG/uGHHwwXFxcjNjbWNuazzz4zChYsaCQmJmbvG5AMuXWeDcOw+9yVlpw0z6pEvw+uXbvG1q1badiwoW2bi4sLDRs2JDo62oGRSXY4dOgQsbGxdvNfqFAhatWqpfnPJS5evAhAkSJFANi6dSvXr1+3m/OHH36YEiVKaM5zgaSkJBYsWMCVK1cICwvTfOdiPXv2pGnTpnZzC/odz60OHDhAYGAgpUuXpl27dhw9ehTQfDsLXa/nbvr9di4Z+fwVHR2Nt7c3jz32mG1Mw4YNcXFxYePGjdkes9ybyMhIfH19KV++PD169ODcuXO2fZrrB9PdfMaOjo4mNDQUPz8/25jw8HAuXbpk+wax5Cy3znOyefPm4ePjw6OPPsrAgQO5evWqbV9Omuc82fpqTuLs2bMkJSXZTTCAn58ff/zxh4OikuwSGxsLkOb8J++TB5fVaqVv377UqVOHRx99FDDn3N3dHW9vb7uxmvMH22+//UZYWBgJCQnkz5+fJUuWUKFCBXbs2KH5zoUWLFjAtm3b2Lx5c6p9+h3PfWrVqsWcOXMoX748p06dYvjw4dStW5fdu3drvp2ErtdzL/1+O5+MfP6KjY3F19fXbn+ePHkoUqSI5v4B07hxY1q2bEmpUqWIiYnhvffeo0mTJkRHR+Pq6qq5fgDd7Wfs2NjYNH/vk/dJzpLWPAO89NJLlCxZksDAQHbt2sWAAQPYt28f3377LZCz5llJdBGRTOjZsye7d++2660puVP58uXZsWMHFy9e5JtvvqFjx46sWbPG0WHJfXDs2DH69OnDqlWr8PT0dHQ4kg2aNGlie1ypUiVq1apFyZIlWbRoEXnz5nVgZCJyr/T7LZK7vfjii7bHoaGhVKpUiTJlyhAZGUmDBg0cGJncLX3Gdg7pzXPK9QpCQ0MJCAigQYMGxMTEUKZMmewO87bUzuU+8PHxwdXVNdWqwXFxcfj7+zsoKskuyXOs+c99evXqxbJly1i9ejVBQUG27f7+/ly7do0LFy7YjdecP9jc3d0pW7Ys1atXZ9SoUVSuXJmJEydqvnOhrVu3cvr0aapVq0aePHnIkycPa9asYdKkSeTJkwc/Pz/NeS7n7e1NuXLlOHjwoH7HnYSu152Hfr9zv4x8/vL390+1aPCNGzf466+/NPcPuNKlS+Pj48PBgwcBzfWD5l4+Y/v7+6f5e5+8T3KO9OY5LbVq1QKw+53OKfOsJPp94O7uTvXq1YmIiLBts1qtREREEBYW5sDIJDuUKlUKf39/u/m/dOkSGzdu1Pw/oAzDoFevXixZsoRffvmFUqVK2e2vXr06bm5udnO+b98+jh49qjnPRaxWK4mJiZrvXKhBgwb89ttv7Nixw3Z77LHHaNeune2x5jx3u3z5MjExMQQEBOh33Enoet156Pc798vI56+wsDAuXLjA1q1bbWN++eUXrFarLWEjD6bjx49z7tw5AgICAM31gyIrPmOHhYXx22+/2f2nyapVqyhYsCAVKlTInjcit3WneU7Ljh07AOx+p3PMPGfrMqZOZMGCBYaHh4cxZ84cY8+ePUa3bt0Mb29vu9Vk5cEVHx9vbN++3di+fbsBGOPGjTO2b99uHDlyxDAMwxg9erTh7e1tfPfdd8auXbuM5s2bG6VKlTL+/vtvB0cud6NHjx5GoUKFjMjISOPUqVO229WrV21jXnvtNaNEiRLGL7/8YmzZssUICwszwsLCHBi13It3333XWLNmjXHo0CFj165dxrvvvmtYLBbjp59+MgxD8+0Mbl0lXnOeu7z11ltGZGSkcejQISMqKspo2LCh4ePjY5w+fdowDM23s9D1eu6k3+/cKSs+fzVu3NioWrWqsXHjRmP9+vVGSEiI0bZtW0e9JUnH7eY6Pj7eePvtt43o6Gjj0KFDxs8//2xUq1bNCAkJMRISEmzn0FznfFnxGfvGjRvGo48+ajRq1MjYsWOHsWLFCqNo0aLGwIEDHfGWJA13mueDBw8aH3zwgbFlyxbj0KFDxnfffWeULl3aePLJJ23nyEnzrCT6ffTpp58aJUqUMNzd3Y2aNWsav/76q6NDkiyyevVqA0h169ixo2EYhmG1Wo3Bgwcbfn5+hoeHh9GgQQNj3759jg1a7lpacw0Ys2fPto35+++/jddff90oXLiw4eXlZTz33HPGqVOnHBe03JNXX33VKFmypOHu7m4ULVrUaNCggS2Bbhiab2dwaxJdc567tGnTxggICDDc3d2NYsWKGW3atDEOHjxo26/5dh66Xs999PudO2XF569z584Zbdu2NfLnz28ULFjQ6NSpkxEfH++AdyO3c7u5vnr1qtGoUSOjaNGihpubm1GyZEmja9euqf7zU3Od82XVZ+zDhw8bTZo0MfLmzWv4+PgYb731lnH9+vVsfjeSnjvN89GjR40nn3zSKFKkiOHh4WGULVvW6N+/v3Hx4kW78+SUebb886ZEREREREREREREROQW6okuIiIiIiIiIiIiIpIOJdFFRERERERERERERNKhJLqIiIiIiIiIiIiISDqURBcRERERERERERERSYeS6CIiIiIiIiIiIiIi6VASXUREREREREREREQkHUqii4iIiIiIiIiIiIikQ0l0EZEc6vDhw3z44YdcvnzZ0aGIiIiIiIiIiDgtJdFFRHKgxMREXnjhBXx8fMifP3+2vGZkZCQWi4ULFy5ky+uJiIiIiDjCnDlz8Pb2dtjrHz58GIvFwo4dOxwWg4iIZI6S6CIi/3jllVewWCyMHj3abvvSpUuxWCzZGsubb75Jo0aNeO2117L1deX+slgsLF261NFhiIiIiDhUTrrultwpODiYCRMmODoMEclFlEQXEUnB09OTMWPGcP78eYfG8Z///IeRI0fecdy1a9eyIRoRERERkayVU667M0rX3SIizk1JdBGRFBo2bIi/vz+jRo1Kd8ywYcOoUqWK3bYJEyYQHBxse/7KK6/QokULPvroI/z8/PD29uaDDz7gxo0b9O/fnyJFihAUFMTs2bPtznPs2DFat26Nt7c3RYoUoXnz5hw+fDjVeUeOHElgYCDly5cH4LfffqN+/frkzZuXhx56iG7dut2xl/oPP/xAuXLlyJs3L//617/sXifZ+vXrqVu3Lnnz5qV48eL07t2bK1eu3Pa8//vf/6hRowaenp74+Pjw3HPP2fadP3+eDh06ULhwYby8vGjSpAkHDhyw7U/+au2yZcsoX748Xl5etGrViqtXrzJ37lyCg4MpXLgwvXv3JikpyXZccHAwI0aMoG3btuTLl49ixYoxZcoUu7iOHj1K8+bNyZ8/PwULFqR169bExcXZ9u/cuZN//etfFChQgIIFC1K9enW2bNkCwLlz52jbti3FihXDy8uL0NBQvvrqK7vz16tXj969e/POO+9QpEgR/P39GTZsmF2MAM899xwWi8Xuz8t3331HtWrV8PT0pHTp0gwfPpwbN27c9ucsIiIi8iDLyHU3wOLFi6lYsSIeHh4EBwfzySef2O0PDg7mww8/pEOHDuTPn5+SJUvy/fffc+bMGdu1X6VKlWzXdSktXbqUkJAQPD09CQ8P59ixY7Z9ydf8M2bMoFSpUnh6egJw4cIFunTpQtGiRSlYsCD169dn586dt30PmzZtomrVqnh6evLYY4+xffv2VGN2795NkyZNyJ8/P35+frRv356zZ8/e9rxRUVHUq1cPLy8vChcuTHh4uO0/JRITE+nduze+vr54enryxBNPsHnzZtuxya0cV65cSdWqVcmbNy/169fn9OnT/PjjjzzyyCMULFiQl156iatXr9qOq1evHr169aJXr14UKlQIHx8fBg8ejGEYtjF3uuY/cuQIzz77LIULFyZfvnxUrFiRH374AYCkpCQ6d+5MqVKlyJs3L+XLl2fixIl27zv5M9G///1vAgICeOihh+jZsyfXr1+3xXjkyBHefPNNLBaL3bcb7vT55j//+Y/tz4Sfnx+tWrW67RyIiPNQEl1EJAVXV1c++ugjPv30U44fP35P5/rll184efIka9euZdy4cQwdOpT/+7//o3DhwmzcuJHXXnuN7t27217n+vXrhIeHU6BAAdatW0dUVBT58+encePGdpUvERER7Nu3j1WrVrFs2TKuXLlCeHg4hQsXZvPmzXz99df8/PPP9OrVK93Yjh07RsuWLXn22WfZsWMHXbp04d1337UbExMTQ+PGjXn++efZtWsXCxcuZP369bc97/Lly3nuued45pln2L59OxEREdSsWdO2/5VXXmHLli18//33REdHYxgGzzzzjO2CF+Dq1atMmjSJBQsWsGLFCiIjI3nuuef44Ycf+OGHH/jyyy/5/PPP+eabb+xe++OPP6Zy5cps376dd999lz59+rBq1SoArFYrzZs356+//mLNmjWsWrWKP//8kzZt2tiOb9euHUFBQWzevJmtW7fy7rvv4ubmBkBCQgLVq1dn+fLl7N69m27dutG+fXs2bdpkF8PcuXPJly8fGzduZOzYsXzwwQe2GJI/tMyePZtTp07Znq9bt44OHTrQp08f9uzZw+eff86cOXMy9E0EERERkQdVRq67t27dSuvWrXnxxRf57bffGDZsGIMHD2bOnDl248aPH0+dOnXYvn07TZs2pX379nTo0IGXX36Zbdu2UaZMGTp06GCX6L169SojR47kiy++ICoqigsXLvDiiy/anffgwYMsXryYb7/91ta//IUXXrAlmrdu3Uq1atVo0KABf/31V5rv4fLly/zf//0fFSpUYOvWrQwbNoy3337bbsyFCxeoX78+VatWZcuWLaxYsYK4uDhat26d7s9vx44dNGjQgAoVKhAdHc369et59tlnbYUm77zzDosXL2bu3Lls27aNsmXLEh4enirOYcOGMXnyZDZs2GAr6JkwYQLz589n+fLl/PTTT3z66ad2x8ydO5c8efKwadMmJk6cyLhx45gxY4Zt/52u+Xv27EliYiJr167lt99+Y8yYMbZ1oKxWK0FBQXz99dfs2bOHIUOG8N5777Fo0SK7GFavXk1MTAyrV69m7ty5zJkzx/bn4ttvvyUoKIgPPviAU6dOcerUKeDOn2+2bNlC7969+eCDD9i3bx8rVqzgySefTHcORMTJGCIiYhiGYXTs2NFo3ry5YRiG8fjjjxuvvvqqYRiGsWTJEiPlX5dDhw41KleubHfs+PHjjZIlS9qdq2TJkkZSUpJtW/ny5Y26devant+4ccPIly+f8dVXXxmGYRhffvmlUb58ecNqtdrGJCYmGnnz5jVWrlxpO6+fn5+RmJhoGzNt2jSjcOHCxuXLl23bli9fbri4uBixsbFpvteBAwcaFSpUsNs2YMAAAzDOnz9vGIZhdO7c2ejWrZvdmHXr1hkuLi7G33//neZ5w8LCjHbt2qW5b//+/QZgREVF2badPXvWyJs3r7Fo0SLDMAxj9uzZBmAcPHjQNqZ79+6Gl5eXER8fb9sWHh5udO/e3fa8ZMmSRuPGje1er02bNkaTJk0MwzCMn376yXB1dTWOHj1q2//7778bgLFp0ybDMAyjQIECxpw5c9KMPS1NmzY13nrrLdvzp556ynjiiSfsxtSoUcMYMGCA7TlgLFmyxG5MgwYNjI8++shu25dffmkEBARkOBYRERGRB0lGr7tfeukl4+mnn7Y7tn///nbXsSVLljRefvll2/NTp04ZgDF48GDbtujoaAMwTp06ZRjGzWvOX3/91TZm7969BmBs3LjRMAzzmt/Nzc04ffq0bcy6deuMggULGgkJCXYxlSlTxvj888/TfK+ff/658dBDD9ldP3/22WcGYGzfvt0wDMMYMWKE0ahRI7vjjh07ZgDGvn370jxv27ZtjTp16qS57/Lly4abm5sxb94827Zr164ZgYGBxtixYw3DMIzVq1cbgPHzzz/bxowaNcoAjJiYGNu27t27G+Hh4bbnTz31lPHII4/YfWYZMGCA8cgjjxiGkbFr/tDQUGPYsGFpxp6Wnj17Gs8//7ztefJnrRs3bti2vfDCC0abNm1sz0uWLGmMHz/e7jx3+nyzePFio2DBgsalS5cyHJuIOA9VoouIpGHMmDHMnTuXvXv33vU5KlasiIvLzb9m/fz8CA0NtT13dXXloYce4vTp04DZTuTgwYMUKFCA/Pnzkz9/fooUKUJCQgIxMTG240JDQ3F3d7c937t3L5UrVyZfvny2bXXq1MFqtbJv3740Y9u7dy+1atWy2xYWFmb3fOfOncyZM8cWS/78+QkPD8dqtXLo0KE0z5tcEZPea+bJk8fudR966CHKly9v93P28vKiTJkytud+fn4EBwfbqlOStyX/3NKLPywszHbevXv3Urx4cYoXL27bX6FCBby9vW1j+vXrR5cuXWjYsCGjR4+2+5knJSUxYsQIQkNDKVKkCPnz52flypUcPXrU7jUrVapk9zwgICBVnLfauXMnH3zwgd3PuWvXrpw6dcruq7MiIiIiudHtrrv37t1LnTp17LbVqVOHAwcO2LX2S3kN5ufnB2B33Z28LeV1WZ48eahRo4bt+cMPP2x3bQhQsmRJihYtanu+c+dOLl++zEMPPWR37Xbo0CG7a8db30OlSpVs7WAg7evu1atX253z4YcfBkj3vLe77o6JieH69et2Pzs3Nzdq1qyZ6ud868/Oy8uL0qVL22279Xr28ccft2uREhYWZpuTjFzz9+7dmw8//JA6deowdOhQdu3aZXf+KVOmUL16dYoWLUr+/PmZNm1aquvuihUr4urqanue0evu232+efrppylZsiSlS5emffv2zJs3T9fjImKTx9EBiIjkRE8++STh4eEMHDiQV155xW6fi4uL3VdBAbt2JMmSW4Eks1gsaW6zWq2A+VXP6tWrM2/evFTnSnnxnjJZfj9dvnyZ7t2707t371T7SpQokeYxefPmvefXzezPLasMGzaMl156ieXLl/Pjjz8ydOhQFixYwHPPPcfHH3/MxIkTmTBhAqGhoeTLl4++ffumWmDqbuK8fPkyw4cPp2XLlqn2pfywJSIiIpIb3e66O6NSXoMlJ3fT2pbZ68dbr7svX75MQEAAkZGRqcZ6e3tn6ty3nvfZZ59lzJgxqfYFBASkeUxWXHdD6p9Tdlx3d+nShfDwcFu7mFGjRvHJJ5/wxhtvsGDBAt5++20++eQTwsLCKFCgAB9//DEbN25MN+6Mxnmnzzfu7u5s27aNyMhIfvrpJ4YMGcKwYcPYvHnzPc2viOQOSqKLiKRj9OjRVKlSxbZ4Z7KiRYsSGxuLYRi2C/LkHon3olq1aixcuBBfX18KFiyY4eMeeeQR5syZw5UrV2wX+lFRUbi4uKSKPeUx33//vd22X3/9NVU8e/bsoWzZshmOpVKlSkRERNCpU6c0X/PGjRts3LiR2rVrA+aCnfv27aNChQoZfo303Br/r7/+yiOPPGJ77WPHjnHs2DFbNfqePXu4cOGC3WuXK1eOcuXK8eabb9K2bVtmz57Nc889R1RUFM2bN+fll18GzA9g+/fvz3Tcbm5udlVTYP6c9+3bl6mfs4iIiEhukt519yOPPEJUVJTdtqioKMqVK2dXhXw3bty4wZYtW2zr9+zbt48LFy7Yrh/TUq1aNWJjY8mTJ4/dIvG388gjj/Dll1+SkJBgK5BI67p78eLFBAcHkydPxtI0ydfdw4cPT7WvTJkyuLu7ExUVRcmSJQGz6Gfz5s307ds3Q+e/nVsT2r/++ishISG4urpm+Jq/ePHivPbaa7z22msMHDiQ6dOn88YbbxAVFUXt2rV5/fXXbWPTq8a/HXd39zSvu+/0+SZPnjw0bNiQhg0bMnToULy9vfnll1/SLHgREeeidi4iIukIDQ2lXbt2TJo0yW57vXr1OHPmDGPHjiUmJoYpU6bw448/3vPrtWvXDh8fH5o3b866des4dOgQkZGR9O7d+7aLnLZr1w5PT086duzI7t27Wb16NW+88Qbt27e3fXX1Vq+99hoHDhygf//+7Nu3j/nz56daoGnAgAFs2LCBXr16sWPHDg4cOMB3331324VFhw4dyldffcXQoUPZu3evbaEggJCQEJo3b07Xrl1Zv349O3fu5OWXX6ZYsWI0b9488z+wW0RFRTF27Fj279/PlClT+Prrr+nTpw8ADRs2tM3ntm3b2LRpEx06dOCpp57iscce4++//6ZXr15ERkZy5MgRoqKi2Lx5s+1DVEhICKtWrWLDhg3s3buX7t27ExcXl+kYg4ODiYiIIDY2lvPnzwMwZMgQvvjiC4YPH87vv//O3r17WbBgAYMGDbrnn4mIiIjIgyC96+633nqLiIgIRowYwf79+5k7dy6TJ09OtTDn3XBzc+ONN95g48aNbN26lVdeeYXHH3/cllRPS8OGDQkLC6NFixb89NNPHD58mA0bNvD++++zZcuWNI956aWXsFgsdO3alT179vDDDz/w73//225Mz549+euvv2jbti2bN28mJiaGlStX0qlTp1SJ4GQDBw5k8+bNvP766+zatYs//viDzz77jLNnz5IvXz569OhB//79WYQSRisAAAPtSURBVLFiBXv27KFr165cvXqVzp073/0P7R9Hjx6lX79+7Nu3j6+++opPP/3Udt2dkWv+vn37snLlSg4dOsS2bdtYvXq13XX3li1bWLlyJfv372fw4MFs3rw50zEGBwezdu1aTpw4wdmzZ4E7f75ZtmwZkyZNYseOHRw5coQvvvgCq9WabmGSiDgXJdFFRG7jgw8+SPW1wEceeYT//Oc/TJkyhcqVK7Np06YsuZD38vJi7dq1lChRgpYtW/LII4/QuXNnEhISbluZ7uXlxcqVK/nrr7+oUaMGrVq1okGDBkyePDndY0qUKMHixYtZunQplStXZurUqXz00Ud2YypVqsSaNWvYv38/devWpWrVqgwZMoTAwMB0z1uvXj2+/vprvv/+e6pUqUL9+vXZtGmTbf/s2bOpXr06//d//0dYWBiGYfDDDz+k+jrm3XjrrbfYsmULVatW5cMPP2TcuHGEh4cD5tc7v/vuOwoXLsyTTz5Jw4YNKV26NAsXLgTM/vTnzp2jQ4cOlCtXjtatW9OkSRNbZc+gQYOoVq0a4eHh1KtXD39/f1q0aJHpGD/55BNWrVpF8eLFqVq1KgDh4eEsW7aMn376iRo1avD4448zfvx4W9WQiIiIiDNI67q7WrVqLFq0iAULFvDoo48yZMgQPvjgg7tu+5KSl5cXAwYM4KWXXqJOnTrkz5/fdm2YHovFwg8//MCTTz5Jp06dKFeuHC+++CJHjhxJt3glf/78/O9//+O3336jatWqvP/++6natgQGBhIVFUVSUhKNGjUiNDSUvn374u3tbbfGUkrlypXjp59+YufOndSsWZOwsDC+++47WyX76NGjef7552nfvj3VqlXj4MGDrFy5ksKFC9/FT8tehw4d+Pvvv6lZsyY9e/akT58+dOvWzbb/Ttf8SUlJ9OzZk0ceeYTGjRtTrlw5/vOf/wDQvXt3WrZsSZs2bahVqxbnzp2zq0rPqA8++IDDhw9TpkwZW2vMO32+8fb25ttvv6V+/fo88sgjTJ06la+++oqKFSve649MRHIBi3FrY18REZEHTHBwMH379s2Sr6eKiIiIiEja6tWrR5UqVZgwYYKjQxERyVaqRBcRERERERERERERSYeS6CIiIiIiIiIiIiIi6VA7FxERERERERERERGRdKgSXUREREREREREREQkHUqii4iIiIiIiIiIiIikQ0l0EREREREREREREZF0KIkuIiIiIiIiIiIiIpIOJdFFRERERERERERERNKhJLqIiIiIiIiIiIiISDqURBcRERERERERERERSYeS6CIiIiIiIiIiIiIi6VASXUREREREREREREQkHf8PP2rSwkFgrj4AAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","📈 Nombre de composantes nécessaires pour conserver:\n","  85% de variance: 33 composantes\n","  90% de variance: 40 composantes\n","  95% de variance: 52 composantes\n","  99% de variance: 119 composantes\n","\n","📊 Variance des premières composantes:\n","  Composante 1: 0.0925 (9.25%)\n","  Composante 2: 0.0741 (7.41%)\n","  Composante 3: 0.0665 (6.65%)\n","  Composante 4: 0.0488 (4.88%)\n","  Composante 5: 0.0423 (4.23%)\n","  Composante 6: 0.0399 (3.99%)\n","  Composante 7: 0.0371 (3.71%)\n","  Composante 8: 0.0328 (3.28%)\n","  Composante 9: 0.0323 (3.23%)\n","  Composante 10: 0.0288 (2.88%)\n"]}]},{"cell_type":"code","source":["# 2. RÉDUCTION DIMENSIONNELLE AVEC DIFFÉRENTS NOMBRES DE COMPOSANTES\n","print(\"🔧 2. Réduction dimensionnelle\")\n","\n","# Tester différents nombres de composantes\n","n_components_options = [25, 50, 75, 100]\n","reduced_embeddings = {}\n","pca_models = {}\n","\n","for n_comp in n_components_options:\n","    print(f\"\\n🔄 Réduction à {n_comp} composantes...\")\n","\n","    pca = PCA(n_components=n_comp, random_state=42)\n","    embeddings_reduced = pca.fit_transform(embeddings)\n","\n","    # Stocker les résultats\n","    reduced_embeddings[n_comp] = embeddings_reduced\n","    pca_models[n_comp] = pca\n","\n","    # Calculer la variance conservée\n","    variance_preserved = np.sum(pca.explained_variance_ratio_)\n","\n","    # Calculer la taille\n","    size_mb = embeddings_reduced.nbytes / (1024**2)\n","    reduction_factor = embeddings.nbytes / embeddings_reduced.nbytes\n","\n","    print(f\"  ✅ Variance conservée: {variance_preserved:.3f} ({variance_preserved*100:.1f}%)\")\n","    print(f\"  📦 Taille: {size_mb:.1f} MB (réduction ×{reduction_factor:.1f})\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rzko_PpEWefr","executionInfo":{"status":"ok","timestamp":1757335529977,"user_tz":-120,"elapsed":5914,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"e47ef08c-a818-42fb-b7f6-7b5fbc9eb5c0"},"execution_count":71,"outputs":[{"output_type":"stream","name":"stdout","text":["🔧 2. Réduction dimensionnelle\n","\n","🔄 Réduction à 25 composantes...\n","  ✅ Variance conservée: 0.777 (77.7%)\n","  📦 Taille: 34.7 MB (réduction ×10.0)\n","\n","🔄 Réduction à 50 composantes...\n","  ✅ Variance conservée: 0.945 (94.5%)\n","  📦 Taille: 69.4 MB (réduction ×5.0)\n","\n","🔄 Réduction à 75 composantes...\n","  ✅ Variance conservée: 0.980 (98.0%)\n","  📦 Taille: 104.2 MB (réduction ×3.3)\n","\n","🔄 Réduction à 100 composantes...\n","  ✅ Variance conservée: 0.987 (98.7%)\n","  📦 Taille: 138.9 MB (réduction ×2.5)\n"]}]},{"cell_type":"code","source":["# 3. COMPARAISON DES TAILLES DE FICHIERS\n","print(\"💾 3. Comparaison des tailles de fichiers\")\n","\n","# Calculer les tailles estimées après sauvegarde\n","print(f\"\\n📁 Tailles estimées des fichiers embeddings:\")\n","print(f\"  Original (250D): {embeddings.nbytes / (1024**2):.1f} MB\")\n","\n","for n_comp in n_components_options:\n","    size_mb = reduced_embeddings[n_comp].nbytes / (1024**2)\n","    reduction_ratio = (1 - size_mb / (embeddings.nbytes / (1024**2))) * 100\n","    print(f\"  Réduit ({n_comp}D): {size_mb:.1f} MB (-{reduction_ratio:.1f}%)\")\n","\n","# Test de sauvegarde/chargement pour vérifier la taille réelle\n","print(f\"\\n💾 Test de sauvegarde (exemple avec 50 composantes):\")\n","import tempfile\n","import os\n","\n","# Sauvegarder l'embeddings réduit\n","with tempfile.NamedTemporaryFile(delete=False, suffix='.pickle') as tmp_file:\n","    pickle.dump(reduced_embeddings[50], tmp_file)\n","    file_size_mb = os.path.getsize(tmp_file.name) / (1024**2)\n","    print(f\"  Taille fichier pickle (50D): {file_size_mb:.1f} MB\")\n","    os.unlink(tmp_file.name)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lOtOs-B-Wvnv","executionInfo":{"status":"ok","timestamp":1757335530187,"user_tz":-120,"elapsed":196,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"7271132f-8af2-4588-8fbe-3dc37be796b9"},"execution_count":72,"outputs":[{"output_type":"stream","name":"stdout","text":["💾 3. Comparaison des tailles de fichiers\n","\n","📁 Tailles estimées des fichiers embeddings:\n","  Original (250D): 347.2 MB\n","  Réduit (25D): 34.7 MB (-90.0%)\n","  Réduit (50D): 69.4 MB (-80.0%)\n","  Réduit (75D): 104.2 MB (-70.0%)\n","  Réduit (100D): 138.9 MB (-60.0%)\n","\n","💾 Test de sauvegarde (exemple avec 50 composantes):\n","  Taille fichier pickle (50D): 69.4 MB\n"]}]},{"cell_type":"markdown","source":["**Méthodologie d'évaluation des performances**\n","\n","**Objectif :** Mesurer l'impact de la réduction dimensionnelle sur la qualité des recommandations.\n","\n","**Protocole de test :**\n","1. **Échantillon :** 20 utilisateurs tests (données cohérentes entre tous les tests)\n","2. **Baseline :** Recommandations générées avec embeddings originaux (250D)\n","3. **Comparaison :** Recommandations avec embeddings réduits (25D, 50D, 75D)\n","4. **Métrique :** Pourcentage d'articles identiques entre baseline et version réduite\n","\n","**Calcul de similarité :**\n","- Pour chaque utilisateur : intersection des 2 listes de 5 recommandations\n","- Score = (nombre d'articles identiques) / 5\n","- Résultat final = moyenne des scores sur les 10 utilisateurs\n","\n","**Interprétation :**\n","- 100% = recommandations parfaitement identiques\n","- 60% = 3 articles identiques sur 5 en moyenne\n","- Plus le score est élevé, plus les recommandations sont cohérentes\n","\n","**Limites du test :**\n","- Échantillon réduit (10 utilisateurs, 5 recommandations)\n","- Mesure la cohérence, pas la qualité absolue\n","- Articles différents peuvent avoir des scores de similarité très proches"],"metadata":{"id":"trXIYJpLYk1B"}},{"cell_type":"code","source":["# 4. ÉVALUATION DE L'IMPACT SUR LES PERFORMANCES\n","print(\"🎯 4. Évaluation de l'impact sur les performances\")\n","\n","def evaluate_content_based_performance(embeddings_to_test, embeddings_name, n_test_users=10):\n","    \"\"\"\n","    Évalue les performances du content-based filtering avec des embeddings donnés\n","    \"\"\"\n","    print(f\"\\n🧪 Test avec {embeddings_name}:\")\n","\n","    # Utiliser les mêmes utilisateurs tests que précédemment\n","    if 'user_articles_dict' not in globals():\n","        print(\"❌ user_articles_dict non trouvé, relancer la section Content-Based\")\n","        return None\n","\n","    test_users = list(user_articles_dict.keys())[:n_test_users]\n","\n","    all_recommendations = {}\n","\n","    for user_id in test_users:\n","        clicked_articles = user_articles_dict[user_id]\n","\n","        # Vérifier les articles valides\n","        valid_articles = [art for art in clicked_articles if art < len(embeddings_to_test)]\n","\n","        if len(valid_articles) > 0:\n","            # Stratégie mean\n","            if len(valid_articles) > 1:\n","                reference_embedding = np.mean(embeddings_to_test[valid_articles], axis=0).reshape(1, -1)\n","            else:\n","                reference_embedding = embeddings_to_test[valid_articles[0]].reshape(1, -1)\n","\n","            # Calcul similarité\n","            similarities = cosine_similarity(reference_embedding, embeddings_to_test)[0]\n","\n","            # Candidats (exclure articles déjà vus)\n","            candidates = []\n","            for article_id, similarity in enumerate(similarities):\n","                if article_id not in clicked_articles:\n","                    candidates.append((article_id, similarity))\n","\n","            # Top 5\n","            candidates.sort(key=lambda x: x[1], reverse=True)\n","            recommendations = candidates[:5]\n","            all_recommendations[user_id] = recommendations\n","\n","    return all_recommendations\n","\n","n_users = 30\n","\n","# Test avec embeddings originaux\n","print(f\"📊 Comparaison des performances:\")\n","original_recs = evaluate_content_based_performance(embeddings, \"Embeddings originaux (250D)\", n_users)\n","\n","# Test avec embeddings réduits\n","for n_comp in [25, 50, 75, 100]:\n","    reduced_recs = evaluate_content_based_performance(\n","        reduced_embeddings[n_comp],\n","        f\"Embeddings réduits ({n_comp}D)\",\n","        n_users\n","    )\n","\n","    # Calculer la similarité entre les recommandations\n","    if original_recs and reduced_recs:\n","        similarity_scores = []\n","\n","        for user_id in original_recs.keys():\n","            if user_id in reduced_recs:\n","                orig_articles = [art_id for art_id, _ in original_recs[user_id]]\n","                reduced_articles = [art_id for art_id, _ in reduced_recs[user_id]]\n","\n","                # Intersection des recommandations\n","                intersection = len(set(orig_articles) & set(reduced_articles))\n","                similarity = intersection / 5.0  # Sur 5 recommandations\n","                similarity_scores.append(similarity)\n","\n","        if similarity_scores:\n","            avg_similarity = np.mean(similarity_scores)\n","            print(f\"  📈 Similarité moyenne avec original: {avg_similarity:.3f} ({avg_similarity*100:.1f}%)\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"imV7Ium9W2S_","executionInfo":{"status":"ok","timestamp":1757336239311,"user_tz":-120,"elapsed":118340,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"cd4d15b5-c015-4ca2-a72b-6c29ee2f557f"},"execution_count":77,"outputs":[{"output_type":"stream","name":"stdout","text":["🎯 4. Évaluation de l'impact sur les performances\n","📊 Comparaison des performances:\n","\n","🧪 Test avec Embeddings originaux (250D):\n","\n","🧪 Test avec Embeddings réduits (25D):\n","  📈 Similarité moyenne avec original: 0.380 (38.0%)\n","\n","🧪 Test avec Embeddings réduits (50D):\n","  📈 Similarité moyenne avec original: 0.600 (60.0%)\n","\n","🧪 Test avec Embeddings réduits (75D):\n","  📈 Similarité moyenne avec original: 0.580 (58.0%)\n","\n","🧪 Test avec Embeddings réduits (100D):\n","  📈 Similarité moyenne avec original: 0.587 (58.7%)\n"]}]},{"cell_type":"code","source":["# 5. SAUVEGARDE POUR AZURE FUNCTIONS\n","print(\"\\n💾 5. Sauvegarde pour Azure Functions\")\n","\n","# Configuration finale retenue\n","recommended_n_components = 50\n","recommended_embeddings = reduced_embeddings[recommended_n_components]\n","recommended_pca = pca_models[recommended_n_components]\n","\n","print(f\"\"\"\n","📋 CHOIX FINAL POUR AZURE FUNCTIONS :\n","\n","**50 composantes retenues** basé sur l'analyse :\n","- Variance conservée: {np.sum(recommended_pca.explained_variance_ratio_):.3f} ({np.sum(recommended_pca.explained_variance_ratio_)*100:.1f}%)\n","- Réduction de taille: ~80% (de 347 MB à ~70 MB)\n","- Cohérence des recommandations: 60% avec embeddings originaux\n","- Compatible avec les limites Azure Functions gratuites\n","\"\"\")\n","\n","# Sauvegarde des fichiers pour le projet Azure Functions\n","print(\"\\n💾 Sauvegarde des fichiers...\")\n","\n","# Embeddings réduits\n","with open(drive_folder + 'embeddings_azure_50d.pkl', 'wb') as f:\n","    pickle.dump(recommended_embeddings, f)\n","embeddings_size = os.path.getsize(drive_folder + 'embeddings_azure_50d.pkl') / (1024**2)\n","\n","# Modèle PCA (pour cohérence si besoin)\n","with open(drive_folder + 'pca_model_50d.pkl', 'wb') as f:\n","    pickle.dump(recommended_pca, f)\n","pca_size = os.path.getsize(drive_folder + 'pca_model_50d.pkl') / (1024**2)\n","\n","print(f\"✅ Fichiers sauvegardés:\")\n","print(f\"  embeddings_azure_50d.pkl: {embeddings_size:.1f} MB\")\n","print(f\"  pca_model_50d.pkl: {pca_size:.1f} MB\")\n","print(f\"\\n🚀 Prêt pour l'implémentation Azure Functions dans VS Code\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cvY9725aVLNw","executionInfo":{"status":"ok","timestamp":1757337605533,"user_tz":-120,"elapsed":286,"user":{"displayName":"Grégoire","userId":"01249298206117462766"}},"outputId":"26404dde-fedb-412c-e25b-386cc70303d3"},"execution_count":82,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","💾 5. Sauvegarde pour Azure Functions\n","\n","📋 CHOIX FINAL POUR AZURE FUNCTIONS :\n","\n","**50 composantes retenues** basé sur l'analyse :\n","- Variance conservée: 0.945 (94.5%)\n","- Réduction de taille: ~80% (de 347 MB à ~70 MB)\n","- Cohérence des recommandations: 60% avec embeddings originaux\n","- Compatible avec les limites Azure Functions gratuites\n","\n","\n","💾 Sauvegarde des fichiers...\n","✅ Fichiers sauvegardés:\n","  embeddings_azure_50d.pkl: 69.4 MB\n","  pca_model_50d.pkl: 0.1 MB\n","\n","🚀 Prêt pour l'implémentation Azure Functions dans VS Code\n"]}]}]}