{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["aTr7NB-e7onn","cbqDr-mu05n7"],"authorship_tag":"ABX9TyNQRnrVXpRjjVNA6R8uCmA7"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"markdown","source":["# Traduction Anglais-Français via un Transformer \n","\n","Certains professeurs du département IPLN (Informatique Pour Les Nuls) ont des lacunes dans la langue de Shakespeare. Plutôt que de perdre leur temps à apprendre une langue, ils sont à la recherche d'un outil de traduction automatique offline. Ils proposent donc un projet à leurs étudiants.\n","\n","![meme.jpg](data:image/jpeg;base64,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)\n","\n","Dans ce TP, nous allons construire un modèle seq2seq, que nous entraînerons sur une tâche de traduction automatique de l'anglais vers le français.\n"],"metadata":{"id":"x3vCZfXyCYP-"}},{"cell_type":"markdown","source":["# Prérequis\n","\n","Assurez vous que le runtime utilise un GPU \n","**(Runtime/Change Runtime Type/Hardware accelerator/GPU)**\n"],"metadata":{"id":"aTr7NB-e7onn"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"cWxk2psowaFD"},"outputs":[],"source":["import pathlib\n","import random\n","import string\n","import re\n","import numpy as np\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","from tensorflow.keras.layers import TextVectorization"]},{"cell_type":"markdown","source":["# Dataset"],"metadata":{"id":"cbqDr-mu05n7"}},{"cell_type":"code","source":["text_file = keras.utils.get_file(\n"," fname=\"fra-eng.zip\",\n"," origin=\"http://storage.googleapis.com/download.tensorflow.org/data/fra-eng.zip\",\n"," extract=True,\n",")\n","text_file = pathlib.Path(text_file).parent / \"fra.txt\"\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gA_MuOlsxDI0","executionInfo":{"status":"ok","timestamp":1665145486178,"user_tz":-120,"elapsed":438,"user":{"displayName":"Sandratra RASENDRASOA","userId":"17781711258222429413"}},"outputId":"35d7894c-0705-4bc5-af99-c965200abc3e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from http://storage.googleapis.com/download.tensorflow.org/data/fra-eng.zip\n","3424256/3423204 [==============================] - 0s 0us/step\n","3432448/3423204 [==============================] - 0s 0us/step\n"]}]},{"cell_type":"markdown","source":["Permet de vérifier que le dataset a bien été téléchargé et que le fichier de travail existe à l'adresse : /root/.keras/datasets/fra.txt"],"metadata":{"id":"OF4sfASR1Jni"}},{"cell_type":"code","source":["%ls -la /root/.keras/datasets/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3FHgq8beywij","executionInfo":{"status":"ok","timestamp":1665145486178,"user_tz":-120,"elapsed":4,"user":{"displayName":"Sandratra RASENDRASOA","userId":"17781711258222429413"}},"outputId":"18fb6b0c-dca8-4dd7-cae0-2547cbb7770c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["total 14756\n","drwxr-xr-x 2 root root 4096 Oct 7 12:24 \u001b[0m\u001b[01;34m.\u001b[0m/\n","drwxr-xr-x 1 root root 4096 Oct 7 12:24 \u001b[01;34m..\u001b[0m/\n","-rw-r--r-- 1 root root 1441 Oct 7 12:24 _about.txt\n","-rw-r--r-- 1 root root 3423204 Oct 7 12:24 fra-eng.zip\n","-rw-r--r-- 1 root root 11669748 Oct 7 12:24 fra.txt\n"]}]},{"cell_type":"markdown","source":["# Exercice 1 : Prétraitement"],"metadata":{"id":"9tyRchiYrYQD"}},{"cell_type":"markdown","source":["# Exercice 1.1 :\n","\n","Chaque ligne contient une phrase anglaise et sa phrase française correspondante. La phrase anglaise est la séquence source et la phrase française est la séquence cible. \n","\n","**Ajouter le jeton \"[start]\" et le jeton \"[end]\" aux phrases en français.**\n","\n","Utiliser le code suivant pour vous aider :\n"],"metadata":{"id":"dyjfmrcI1iNK"}},{"cell_type":"code","source":["with open(text_file) as f:\n"," lines = f.read().split(\"\\n\")[:-1]\n","text_pairs = []\n","#@title insérez votre code ici"],"metadata":{"cellView":"form","id":"SUtgBuHEkp7f"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Résultat Attendu (avec des phrases aléatoires)\n","for _ in range(5):\n"," print(random.choice(text_pairs))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MaKGRBuaxQY0","executionInfo":{"status":"ok","timestamp":1665146819842,"user_tz":-120,"elapsed":224,"user":{"displayName":"Sandratra RASENDRASOA","userId":"17781711258222429413"}},"outputId":"c1083223-26c2-4c70-a7d4-b28fe29c5be8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["('I was careless.', \"[start] J'étais insouciante. [end]\")\n","(\"You're too trusting.\", '[start] Vous êtes trop confiants. [end]')\n","(\"You don't have to lie.\", \"[start] Vous n'êtes pas obligée de mentir. [end]\")\n","('I like to look at old pictures.', \"[start] J'aime regarder de vieilles photos. [end]\")\n","('Have you ever been stuck in an elevator?', '[start] Avez-vous jamais été coincé dans un ascenseur\\u202f? [end]')\n"]}]},{"cell_type":"markdown","source":["# Exercice 1.2 :\n","\n","Réaliser une séparation en train/val/test avec le ratio suivant sur le dataset : 80/10/10\n","\n","N.B. : pensez à modifier l'ordre des lignes dans le dataset avant la séparation \n","\n","Résultat attendu\n","\n","* 167130 lignes au total \n","* 133704 en train\n","* 16713 en validation \n","* 16713 en test\n","\n"],"metadata":{"id":"QsOUumoJ2tj7"}},{"cell_type":"code","source":["#@title Insérer votre code ici \n","text_pairs = []\n","train_pairs = []\n","val_pairs = []\n","test_pairs = []\n","print(f\"{len(text_pairs)} au total \")\n","print(f\"{len(train_pairs)} en train\")\n","print(f\"{len(val_pairs)} en validation \")\n","print(f\"{len(test_pairs)} en test \")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_Tn2oj_ij9Hn","executionInfo":{"status":"ok","timestamp":1665148207096,"user_tz":-120,"elapsed":221,"user":{"displayName":"Sandratra RASENDRASOA","userId":"17781711258222429413"}},"outputId":"aef512b7-81b0-4766-c28d-1855561b1be3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0 au total \n","0 en train\n","0 en validation \n","0 en test \n"]}]},{"cell_type":"markdown","source":["# Exercice 1.3\n","\n","L'objectif est de transformer les séquences originales en séquences d'entiers où chaque entier représente l'index d'un mot dans un vocabulaire. (cf section du cours sur la tokenisation)\n","\n","Pour cela, nous allons utiliser la classe [TextVectorization](https://www.tensorflow.org/api_docs/python/tf/keras/layers/TextVectorization), en indiquant les informations suivantes : \n","\n","* taille du vocabulaire = 15000\n","* longueur max de la séquence = 20\n","* taille du batch = 64\n","* une fonction de normalisation \"customisée\", que nous allons implémenter\n","\n","La fonction de normalisation \n","\n","```\n","def custom_standardization(input_string):\n","```\n","\n","des séquences doit avoir les critères suivants : \n","\n","* elle prend en entrée une chaine de caractères\n","* Tous les caractères en sortie seront en minuscule (vous pouvez vous aider de la méthode de [tf.strings.lower](https://www.tensorflow.org/api_docs/python/tf/strings/lower)\n","* Suppression des ponctuations, à l'exception de \"[\" et \"]\" qui sont utilisés pour les tokens [start] et [end]. (voir les méthodes [string.punctuation](https://docs.python.org/3/library/string.html), [tf.strings.regex_replace](https://www.tensorflow.org/api_docs/python/tf/strings/lower), et [re.escape](https://docs.python.org/3/library/re.html))"],"metadata":{"id":"l_-L7ESF4BGj"}},{"cell_type":"code","source":["#@title Décommentez les lignes correspondant à eng/fra_vectorization pour créer vos instances de vectorisation \n","\n","#insérez votre code ici\n","\n","# eng_vectorization = TextVectorization(\n","# max_tokens=vocab_size,\n","# output_mode=\"int\",\n","# output_sequence_length=sequence_length,\n","# standardize=custom_standardization,\n","# )\n","\n","# fra_vectorization = TextVectorization(\n","# max_tokens=vocab_size,\n","# output_mode=\"int\",\n","# output_sequence_length=sequence_length+1,\n","# standardize=custom_standardization,\n","# )"],"metadata":{"cellView":"form","id":"9NfQIDLglJwG"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Exercice 1.4 \n","\n","Appliquer la transformation sur votre dataset d'apprentissage (train), via la méthode [TextVectorization.adapt()](https://keras.io/api/layers/preprocessing_layers/core_preprocessing_layers/text_vectorization/)\n","\n","Attention à bien distinguer la vectorization faite sur l'anglais et le français !"],"metadata":{"id":"Kvn4xV_YlzwV"}},{"cell_type":"code","source":["#@title insérez votre code ici"],"metadata":{"id":"RuHavY9QmpJu"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Construction du dataset après tokenisation\n","\n","Nous allons maintenant formater nos jeux de données.\n","\n","À chaque étape de l'apprentissage, le modèle cherchera à prédire les mots cibles N+1 (et au-delà) en utilisant la phrase source et les mots cibles 0 à N.\n","\n","L'ensemble de données d'apprentissage produira un tuple (entrées, cibles), où :\n","\n","* *inputs* est un dictionnaire avec les clés encoder_inputs et decoder_inputs.\n","* *encoder_inputs* est la phrase source vectorisée et encoder_inputs est la phrase cible \"jusqu'ici\", c'est-à-dire les mots 0 à N utilisés pour prédire le mot N+1 (et au-delà) dans la phrase cible.\n","* *target* est la phrase cible décalée d'une étape : elle fournit les mots suivants dans la phrase cible -- ce que le modèle va essayer de prédire."],"metadata":{"id":"VgjLNe7uoLht"}},{"cell_type":"code","source":["def format_dataset(eng, fra):\n"," eng = eng_vectorization(eng)\n"," fra = fra_vectorization(fra)\n"," return ({\"encoder_inputs\": eng, \"decoder_inputs\": fra[:, :-1],}, fra[:, 1:])\n","\n","\n","def make_dataset(pairs):\n"," eng_texts, fra_texts = zip(*pairs)\n"," eng_texts = list(eng_texts)\n"," fra_texts = list(fra_texts)\n"," dataset = tf.data.Dataset.from_tensor_slices((eng_texts, fra_texts))\n"," dataset = dataset.batch(batch_size)\n"," dataset = dataset.map(format_dataset)\n"," return dataset.shuffle(2048).prefetch(16).cache()\n","\n","\n","train_ds = make_dataset(train_pairs)\n","val_ds = make_dataset(val_pairs)"],"metadata":{"id":"YoILXxsm0DzL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Si tout s'est bien passé, le résultat est le suivant\n","\n","for inputs, targets in train_ds.take(1):\n"," print(f'inputs[\"encoder_inputs\"].shape: {inputs[\"encoder_inputs\"].shape}')\n"," print(f'inputs[\"decoder_inputs\"].shape: {inputs[\"decoder_inputs\"].shape}')\n"," print(f\"targets.shape: {targets.shape}\")"],"metadata":{"cellView":"form","id":"kdnsH_QSqjLM"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Exercice 2 Construction du modèle\n","\n","Notre modèle de séquence à séquence consiste en un TransformerEncoder et un TransformerDecoder enchaînés ensemble. \n","\n","Pour que le modèle soit conscient de l'ordre des mots, nous utilisons également une couche PositionalEmbedding.\n"],"metadata":{"id":"fK2giXTXq6Q8"}},{"cell_type":"markdown","source":["# Exercice 2.1 \n","\n","Finaliser la construction de l'encodeur en complétant le code ci-après et en s'aidant des informations suivantes :\n","\n","* __init__ est la méthode qui définit les attributs de la couche que l'on souhaite créer (cf [doc](https://keras.io/api/layers/base_layer/#layer-class))\n","* **call** est la méthode permettant d'appliquer les différentes couches à notre entrée (cf [doc](https://keras.io/api/layers/base_layer/#layer-class))\n","* Notre encodeur contiendra les couches suivantes :\n","\n"," * une couche [MultiHeadAttention](https://keras.io/api/layers/attention_layers/multi_head_attention/)\n"," * deux couches denses et deux couches de normalisation"],"metadata":{"id":"hKfdFVTZr1PD"}},{"cell_type":"code","source":["#@title Complétez le code ici\n","\n","class TransformerEncoder(layers.Layer):\n"," def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\n"," super(TransformerEncoder, self).__init__(**kwargs)\n"," self.embed_dim = embed_dim\n"," self.dense_dim = dense_dim\n"," self.num_heads = num_heads\n"," # insérez le code manquant\n","\n","\n"," self.dense_proj = keras.Sequential(\n"," [layers.Dense(dense_dim, activation=\"relu\"), layers.Dense(embed_dim),]\n"," )\n"," self.layernorm_1 = layers.LayerNormalization()\n"," self.layernorm_2 = layers.LayerNormalization()\n"," self.supports_masking = True\n","\n"," def call(self, inputs, mask=None):\n"," if mask is not None:\n"," padding_mask = tf.cast(mask[:, tf.newaxis, tf.newaxis, :], dtype=\"int32\")\n"," attention_output = self.attention(\n"," query=inputs, value=inputs, key=inputs, attention_mask=padding_mask\n"," )\n"," proj_input = self.layernorm_1(inputs + attention_output)\n"," proj_output = self.dense_proj(proj_input)\n"," return self.layernorm_2(proj_input + proj_output)"],"metadata":{"cellView":"form","id":"1907M-8AsI2z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Le reste de l'architecture est donné ci-après. Notre objectif est maitenant de les assembler !\n","\n","class PositionalEmbedding(layers.Layer):\n"," def __init__(self, sequence_length, vocab_size, embed_dim, **kwargs):\n"," super(PositionalEmbedding, self).__init__(**kwargs)\n"," self.token_embeddings = layers.Embedding(\n"," input_dim=vocab_size, output_dim=embed_dim\n"," )\n"," self.position_embeddings = layers.Embedding(\n"," input_dim=sequence_length, output_dim=embed_dim\n"," )\n"," self.sequence_length = sequence_length\n"," self.vocab_size = vocab_size\n"," self.embed_dim = embed_dim\n","\n"," def call(self, inputs):\n"," length = tf.shape(inputs)[-1]\n"," positions = tf.range(start=0, limit=length, delta=1)\n"," embedded_tokens = self.token_embeddings(inputs)\n"," embedded_positions = self.position_embeddings(positions)\n"," return embedded_tokens + embedded_positions\n","\n"," def compute_mask(self, inputs, mask=None):\n"," return tf.math.not_equal(inputs, 0)\n","\n","class TransformerDecoder(layers.Layer):\n"," def __init__(self, embed_dim, latent_dim, num_heads, **kwargs):\n"," super(TransformerDecoder, self).__init__(**kwargs)\n"," self.embed_dim = embed_dim\n"," self.latent_dim = latent_dim\n"," self.num_heads = num_heads\n"," self.attention_1 = layers.MultiHeadAttention(\n"," num_heads=num_heads, key_dim=embed_dim\n"," )\n"," self.attention_2 = layers.MultiHeadAttention(\n"," num_heads=num_heads, key_dim=embed_dim\n"," )\n"," self.dense_proj = keras.Sequential(\n"," [layers.Dense(latent_dim, activation=\"relu\"), layers.Dense(embed_dim),]\n"," )\n"," self.layernorm_1 = layers.LayerNormalization()\n"," self.layernorm_2 = layers.LayerNormalization()\n"," self.layernorm_3 = layers.LayerNormalization()\n"," self.supports_masking = True\n","\n"," def call(self, inputs, encoder_outputs, mask=None):\n"," causal_mask = self.get_causal_attention_mask(inputs)\n"," if mask is not None:\n"," padding_mask = tf.cast(mask[:, tf.newaxis, :], dtype=\"int32\")\n"," padding_mask = tf.minimum(padding_mask, causal_mask)\n","\n"," attention_output_1 = self.attention_1(\n"," query=inputs, value=inputs, key=inputs, attention_mask=causal_mask\n"," )\n"," out_1 = self.layernorm_1(inputs + attention_output_1)\n","\n"," attention_output_2 = self.attention_2(\n"," query=out_1,\n"," value=encoder_outputs,\n"," key=encoder_outputs,\n"," attention_mask=padding_mask,\n"," )\n"," out_2 = self.layernorm_2(out_1 + attention_output_2)\n","\n"," proj_output = self.dense_proj(out_2)\n"," return self.layernorm_3(out_2 + proj_output)\n","\n"," def get_causal_attention_mask(self, inputs):\n"," input_shape = tf.shape(inputs)\n"," batch_size, sequence_length = input_shape[0], input_shape[1]\n"," i = tf.range(sequence_length)[:, tf.newaxis]\n"," j = tf.range(sequence_length)\n"," mask = tf.cast(i >= j, dtype=\"int32\")\n"," mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))\n"," mult = tf.concat(\n"," [tf.expand_dims(batch_size, -1), tf.constant([1, 1], dtype=tf.int32)],\n"," axis=0,\n"," )\n"," return tf.tile(mask, mult)"],"metadata":{"cellView":"form","id":"-zcoIoXf0WSi"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Exercice 2.2 \n","\n","Assembler l'encodeur, en intégrant les informations suivantes :\n","\n","* l'encodage *x* des positions de la séquence d'entrée, obtenu via la couche *PositionalEmbedding*\n","* la sortie de l'encodeur après le passage dans la couche *TransformerEncoder*\n","\n","Les dimensions de l'entrée et des vecteurs latents, ainsi que le nombre de têtes (pour la MultiHeadAttention) sont donnés ci-après\n","\n","\n"],"metadata":{"id":"-hH305Qyw3X6"}},{"cell_type":"code","source":["#@title Décommentez et complétez le code ici\n","\n","# embed_dim = 256\n","# latent_dim = 2048\n","# num_heads = 8\n","\n","# encoder_inputs = keras.Input(shape=(None,), dtype=\"int64\", name=\"encoder_inputs\")\n","# x = \n","# encoder_outputs = \n","# encoder = keras.Model(encoder_inputs, encoder_outputs)"],"metadata":{"cellView":"form","id":"CF2-cniKyeNS"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Exercice 2.3\n","\n","Assembler le décodeur, en intégrant les informations suivantes :\n","\n","\n","* l'encodage des positions de la séquence en entrée du décodeur (donc la séquence en français), obtenu via la couche PositionalEmbedding\n","* l'entrée du decodeur inclue à la fois les sorties de l'encodeur et les sorties précédentes du décodeur\n","\n"],"metadata":{"id":"70kYWRR01G75"}},{"cell_type":"code","source":["#@title Complétez le code ici\n","\n","decoder_inputs = keras.Input(shape=(None,), dtype=\"int64\", name=\"decoder_inputs\")\n","encoded_seq_inputs = keras.Input(shape=(None, embed_dim), name=\"decoder_state_inputs\")\n","# x = \n","x = TransformerDecoder(embed_dim, latent_dim, num_heads)(x, encoded_seq_inputs)\n","x = layers.Dropout(0.5)(x)\n","decoder_outputs = layers.Dense(vocab_size, activation=\"softmax\")(x) # permet d'obtenir une distribution de probabilité sur l'ensemble des tokens de notre vocabulaire\n","# decoder = \n","\n","# decoder_outputs = "],"metadata":{"cellView":"form","id":"479fs1p_0M-Z"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Transformer Assemble !"],"metadata":{"id":"q8aPAwYA01Hx"}},{"cell_type":"markdown","source":["![avengers_assemble.jpg](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEASABIAAD/2wBDAAEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQH/2wBDAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQEBAQH/wAARCAEAAQADASIAAhEBAxEB/8QAHwAAAgIDAQEBAQEAAAAAAAAACAkGBwQFCgMCAQsA/8QAQBAAAQQBAwQBAwIFAQYEBgMBAwECBAUGBxESAAgTIRQJIjEVQRYjMlFhJAoXM0JxgSWRocEYNENS0fAZJmKx/8QAHAEAAgMBAQEBAAAAAAAAAAAABQYDBAcCCAEA/8QAOxEAAQMCBQIEBAQFBAIDAQAAAQIDEQQhAAUSMUEGURMiYXEUMoGRB6Gx8CNCwdHhFRZS8TNyJENiNP/aAAwDAQACEQMRAD8A5kKqLOS0qpzZRlbJJbskt88j04bzORFRSL7d/Zybe/abb9EDOxyXOwqxjAguLJ/TojURqK4qijhhyFKNyb7k2cqp7TdU9Jui7CVPVCSiAGIwEZJkOaRJZHORylRCuY4bka172oqNVd27qm+6fmY0eR5LEkBHXXluESECiNWYwnEY3KPgvkRW+xxxKvNFRUciLujkXo60WQJIMji2/l55I/fOFPNsvqqpaXE1i1aFBUAGDpKVXgQPlufU/S5LKDZCwXC7ikcdkmquLKT5B7PeXxsCNiyBKqq9/mIwbmbcuLt9vwvV0Y9Ng3WJXtgerrh1t1Y0Q58N8Vo5tVkADbTmxiN9giGcjSLz4NIV2zVVW7dU1TTrqYRIbZzWhHOW1+OyMiDdKkESOZxET7FRowCI4A0ajXPYVU2ROitwAGMpg+WhyCJyjz8kiVVyQKII0R898pYVgBPStUMoQVVy+lY5+yr76M0q2FjTpMm20m8CAOd/cesnCDnLr7NMpK/O4Hjp0gn6wJIFt4ItY2xU3c1pze43a4WOmEqQ5WLRZjzwVVgXimBjkUBET06S1V+5f+yqvQ9JjOQumgK10pIDnhZwcVWuRzYsdqvaiJsrVfyTl+N2qiKqoux9atHyvGyYBS2kqHZuZi0WOSUUTSBK0bY7YxAkfu1XKJzVerf+f9+h7vKHIsfyFjJclH1Ji+IYhsG4iPJHWQrFVqKjEZ5U4ou3pVXfdPXbjLCyUxvEgd0wY2NzFxf9BjjJ87fRRMMqWlCipUa1BMQJjzEdveMEn2QYRDuYGdtmR40qZVZ3hRnPlQ4dnJNVlh3LbquEOcwo3R5lUkkT2CGpfMUJmu4CcqF/qdpzj2OaaakT1qYMwYMAup9NZArgxzOeDFJtlVsI5RqxizKt5YcojOIR2MSO37XJska+l/i1RkUfVWLajmxrexz3B1wy2bHYaMK8qKTLbN1VKV3BHjymriWmMx2NdslnPhIiqZGtU/NbMNbN7a9YbOnxjhVYlpFlJG2CgPzNR3uHXRqZ7m/eIEiptmy66zKRdwpZQ27NeNrV/N1TDR8DgHSocpnSL/8AHnc3jnFGtp6qpzKnfclSQoOIKR8xHylMWI3kifcAnHOTQBsSvg28mPHSumkarnFEjSvjMRWoMLmo1qIJ6eNqInprWb7Kq9Mj7dcCoC0gYmR1QDuyzEoNtjswlaA/6UBs6NOnOP4Y7TSCS/mxniK573MT7nvX8qI2Dw5ZK+kQmNR0iRmxljNe2zaG1I3fnIkAeNWCl8o6+QYF8SORyuburkR62jemVXZUunR/4evF8Gm1mCc58UESPTzW3UQz207gD+Wd02ttnMN8xm6sjMUA2NjoiXX009IKdaSDrXJ2MeVJE+hjvHc7gjKtVVmLtRTIQoFtcmUqEiyeR3JP3m2BKhaPUFXcAdEgVUgKk8uzqyE9xGI9U4r5RorXuYu3v2iqqrv0TWnnb1h1jl8CzBjVcEJpSqGNMrIJ0C8xERo2t8L0ewDnIrUVr2uREbxci7Om+odbjlORsethTh2G6K80kKAJIY1qKrWgeNHCUbFRznpt9mz3cWvRepRptlH6RLqZB4MqSkY4z7HEJoGiAUZCEQjCJ5mjbxUiMci8V2VWqqb2GX6apfCNSUktKuVAXOkXJIsCOJ/LAPL6F+nrNFQCQl5uN73JPHeNhJkk73vzvm7bMROumr4WIYxCYPTjGVkij4/UwWkVYx/9T5IkAZCPcqo72RiqjkXf8p0m697ccbgWcs3jr2uQrkaFkUSjYrl9NVCNVqKv4aqpum/L/q5juA7krHVK6yfHMghRamZgdyPE8SkQI44EO6x9YMaRUxZbjOVX3LEMgQHCvi4teNRoT8K+1KgXsks14a20BIMqFc5qHe9HIjxfb4h7LuQRGIiN+943o3dzV6tro00zDC1uN+dGpJS4lQuqIBMX7je/a+CGar1VbjdN46FeXVpbUbwBqMAwNhftfFCR9NotEUJSUMGQ0hybjJAhFe5gkcrHruLZrH7Jsv5VVaiel6l5q6mlxxBFjlXFK9Gq5HVUHZNnKjkXYaqi8UTdF2/PWiq4uoH3NUc+Qzy+BqnjnRWEGrUc3yuYrd2I5EexV32+7ZEa7ay6HGsuMx45tZIAr3qT5Jop/GwXr+by8SIg/e/kcvD+zk/cS/WMNkoUpJVYbp5j+9/rOKtJQZkqFkuLHClIUny2iJHEx9LGcQydQ4hHZ/Op6tjk8aJwqoao5VREcvJGel3X8L+feybb7RSeGgAMg4dJXOI5zla4ldA3TduyOTcf43Tb/Cov7/ghLrC5tZEGSSJ88r2se6OyEZTNE/dPKgkZ5HMRWqnJGKn2r+6KnXrjuNYbcSEr5EaQ2aVGhRCgeNiFcRwWtarmptyOjxNRf6ntc1vJUXoc7mTbSZUNbZB0hMq5AIESTeT+7HE5ZVrLSXKlTeqwSQQAZBgEi5MyBM9owCU+rVZBv1OogT6k6uQoGQggQSpujXK8SIiK1NuKbIrk2/K9bDA9Dm5pltU/EY8ckdIeRzbQUgCFfAjVWO3Ni9WjRvkMMwYqgC1qKrSqjV9qidMEt+3gVDDkvmBI1sphCR2FjP8AEjU2cvLmz7lYjmr6T0i7r63312kWJQNPcf1r1Pj/ACGT8VxN+I0T5UIbKxMlzgiRSK4Z9hkOGijT/jPcxxlLJYqw5W7XNpjM6d8xSDQuYWVAjz21JvYQePvfBz/TqmiCC8orBAi0qIABmIme/wBMKAZVS8alvHGYWDYrFUbyh+x7gWEdBniv9Lt5Y67E32Rr/Sr+3RG6UYPR6qHiYHe4o6VeWLZQ6K8qa4BrJj2RyT5L5To3jf444AEMVHNVyBG9yorUcvV3xsHxLVV7zmrj43k8sp0FKAA5Yticb0aQThOG9AK0r05N5KViLsRd0d1tcVxq00IzlczsccsQGw0yZFWQo86zNFkpXD8ZKGPY04UYVMnYQzZRSFY6IFTC5cWu2suPNOABRisAA1myeJAJtH68ibCk0Klb9PpSsiVAwk+WSIJsYAETP9ZwLV/oZfYjkMSgPUCmitigi09k2EORBsh2BkBEkgO9rmBlc12Gqqqo9u227durKyftyxfG2AEebLdIEM0izeKPEagEiiOpVRPGrjKHxHI3hunLg7+n31fOkmvwsSzmxrtQMJu8ixXILOA3GMWjxYzEwuvvbCQ0s5km8lWs20n1EmyjjimimFJhRxTheP5ThOZJ9Sa6BV39lc1rbHLMAqrSLFt7CI4L7XH5EspCObdwxNKZIQnKm70RoyNC9CL4kenQPM86zOlQtKEqJaSVtwCSpSRICYFz6SZkAYa8j6eoqqrQ1VVZSHHEIUpfkQlKylJKlmyUifMSYgEzbCkMyx0jbGw0/wAbjDdMy3L2oVzwDHOSjgoxtSJSjG3/AEkpSEny0crWcY7Hu2RE3k97p0mpWqOnWj2ClDJ01w+igjNklGyQke7nyYcfItSLyVOVWLYnrLKxLjonkc4DWwRCHuqtRSwyvSm6nYprjqlC8EK/h5FZYxhrmt+NNNSKGRGsiQK4iMM1goyMGETWGNDLIdIejhqxW1/CxLLNCtGkxiJh5LLULVuFAmz8isXXdDY4Zpy+IR0SpjzUkSYdI+1OAk06z3PdbFV7FkxRR3x1zHMeo3asuN0bgYqqEGlW4YH/APQQtagTuEk+Y8cwAZ37Jui3cuFO/XDxqHMYqmACCla6dISy3O38RWkTyD2jHlKwij7ntVMb03xmBHxzSXTmDJpa09JWRa+xm1o6+DHvLa5kCa18wpzwgRK9CuV8VWOlCT+WReo93hZGfN51Po7pHjdBW6bYpfU1fiUCmx6IO71FyapgzkkAsLd6Nmy2NmxFjshBKMdmyRIPKc9eTUx9PMhutObORjFFVEBe5vBkVZJmN3MRYUaL4WLIhilh+R4iRiOlpIHGe8NgRznjM1ROYpTYJovj1hh1PqDd28ysPik+1gPu6V0FMtwW1iilSa9yQZbn1xn5S+UaqjZB7FT/ACZMqQ5SAVqZ1V5qxltY2/V12qiZUU0KZB1Zm8UoqVpIMFKkmARIJ2BmRp1HQ1uY5SKWloQH6kOpzF0AlARTQtFCFDykNJAWm9yIjeLl7CO03SvBosyNcTKjUTVifWWOO30xI759DjdvGiR7nIqiDLlBNWDsIcmerJFgxzXs+GsOErSCfzXzl/1DdOMfybLcUJ2/10k9He32OknQ5WPtFMPTW86sLKAj6d5EBLLEdIAhSON4Xt8jlIj16c79OC0w+tBQaf8AKYmS5m2VmWVzJ1fOJYeS2EWydDjCSUCotS1Sf+HzbGt3LZq8coi+Mok65TNccMtIup+rN1EjyZtQDWLPqf5ZIpBNE7+IrebCIsfkR4gzoxDsDzKvAkczGLsNE6ZvwxzGpruourHswfW4kv0vw6Vgp3RCQmYkxMAXgn6KX4zZDky8u6YaLTTaG6JKXyHy2UvaQClRbkgkavKYFu4wYs7v6wRwpMF2hPxicVRzSTadCC2TfjxbXj2REVq/axqIiIu235ihO97BCRlAPR4ibvaZy/LpNkVmzV/qh/4/Hpf3Xdd06X9LrpA4zlOHg55ZDnOIGQKaLcDPEhZh1Vhwv4qjY6IhXbbIvrfrTsC57SbNV3PjtxYrUVz/AMbN33byX8NX8fhV369HU9XUNtBI4CdIPaB+cfuceaD0J0u+tTqE6lGNSkPOvzEcLAABE3STMDbH5LRqGK/7UXySN/6/SeRP/VP7f4/6dfdcqtK1WOT25N9lci7fn1uie0/P/wCNuvKxejSka1rlcpZCcGpu5eRET03fdVXf0n5X8J+3WJGKvkY1itdureKI9qK5jlRGvRHOReDnI4bXLsilaoVRC7MVeStKbFQBJsCYnYYfS0iDqKYi/mO3P5YLvSmlbZWEUDpihIsckhCqhXOaQZRva7+lUXgqsdsnpyIrU3/HV7YoNI99JqXSFdX3FyIUwMtUVJEiFNK6K8nHk1Djc8iMe37FabZXbtVEoLSeeJtqBAuapAVhSl3K1Nm+UCOTfdPu2Y9OO22/r902tjFJn6jfGLHdtxuDnGnk2c/lLd42b78RueXgmyuRVX9tt1QpSFSQFJ+YEke4IxleeUvjVFWgKLDWlULNpBiAk3vv/g7G7rdhTbyfhYiNWM3+FK9rHPE540J4Y6ORXJuu26IiKzfffffb30PTsasKm3lBuxul0hTidI8AXKWM5zFjIrTvRWMQiPZsqoqbp6ROK74WtWomsrUow1GBWeY2cCpihPcQKWWGqqAMKkWLGLYhRIkgqlA7dCla5Goj3tarkatD4l3CW8vOI+LamUlfSSDOSK6Sw6vZHlHY9kUU4CEexRukNGhFa/kJ3HbdVXjYRm1G26lkuNB1S0hapnQSR6Wm425jbC9T9M525l5caXTqbbUpaELAL602jSk77CTBMSeDjpD7MtVNAsB7dsdw9trTV2pcKvqa51qU0IcCLX2vcBU5pBnZNOfC3rswgwKKdj9JGK1J0ymvLMwUeCIidW3N1exgOnF9FyPXXTDFsVpq6tZbVFPqDh9rKv5776mLl2LW0V0ufUZfQWcQ4rMMvHZL7bGpUGcSawcSVIEIMfp0TH0kHUbF8ix2BeYvlmo+CVV0OzixyurhHq79tTeRznVrhCo72TW2MhHm5ugCK1io1HblR3IaV4rh+hmtUedj8RrpuledzaeXAh05w0WXVGnV6awY1sQMgkeFllW9s9JjXB8bq2uXkivc54auKPjHkJcUkPqBSpvde1hYTN/seMMWV5u6G6ZFTSMqXR+R0qSDouPmFymY5G8e+ATDqpQTdRe3u3k636S2uk0bPdLZEfAqy3xiwt4MqqlZSbMsktVgyJ8vG8dECRGS0i3ghjmEPGlBHLArDvYNp93HUdfp7gESNqRMrMwy4mWXi1WX6iwpz63Gcs1SxUlVkU62LTVAfnMra6AChYxItdXYzMvRO8xYh2j5h8DlwqSxiNHWgYVyHG0jxiV6hKFzXqiKNVI1zXNduRHDK1ybsRqMRryc0w39I0z7P8+h1UdttnWN6PUNdZAjNsWHrXY5bClQfB43DaaFPrDiWLJa16knPL40EURHGMxZS1T0ZS+5qKAkh02B0gwJ/ffcY4azRS66sdaoW4VdJCLKAULA3uBuAJjeMGdkmvujb9NbGtv8yo8z1ggYNqvUY3dU1/W5RXAvMo0PwnHsll217UPk1hZVNYVxZNBMbLVx8jsFj1qGMh2siVJrho9pngWkdJkuX0c+TjGjeoUTUHDUBaRcqIbVzC3cjRrSTGHXWmSVZ4zjY5BqZ0+dX2T4YpYIxJIWE9sYxSppnz0PRwIUTYT3DZCjR1VGyWqYDgsjjE3dNyuXb75IhOdsjE21sjEam+vzvDDhyhMex8eQkOO8yKitajXchqnII2sa0iO+5RhXZvD2NaDZKlOPKkIVpUgm0xMwN9ova9r4+qr0POsuJy9rX4kq8kHUkp32252ubenh3Oax6fOl2Lwat0mS10jWHHh4xS45kOOvyC9bj2t+cT7i7HStY20qoFnhq00qxtZ8RK5IEhsCCY7xINlyZV3HaT5SsWLgmY4NByaBktfW21tbZFFq6Sywa/1b7hrMzouWMhCrabJcfq49aOsnNeaICBkeMrLRa8ISOGTvKr681Zp5CrYdTBspGJFhSzxKuE2xVoGiJIjJJZGYQLo7mOa1wnNd40ax+/vaX6OY4/KNINP5oaCvsBMx2NDdxhwSENIgnmwJL5SljuZzM4LEMqJuMjCIiKnBGkXqcryyldXUvkIdkiTZEJud7Xi/13BxQRnjKs2q6c0TBd8KEggTq1fLbn057XOJXD1rwgV+7CqHMZWoNePF81vcw1KJe4MPAccNQz9bR16yI8ACyZ2U5Q+7xqsA+JJfWSauJDlQ0aRUauky3WnGbnAsoqqTUqmMQhNO2GLdau49glg8MTKLpc0gRiz4Mf5+HwKlK8kTHKvzXV22QKPGU7mq1ufM0vkRmmix49TTpKE8JwQwRgco7lc3wF8TBodFY96qwzXDGUvmG1HBEvVKXWNYDQObDt4tTJJCQxRBlDGrxijuR0mWMZEe7YTn+QRlc5AEUSqiIiL1QDSKglLZU4qbBQ8xFrx2NoFrnkjE689dpm5dpGkJQIJUBa4Mbd7fUj2u0erONytX8xkzNScdyr9M097ip459RqZitfXxVvdVryZptWt1EQcnGcdnyMPWMtGlg50mlhvZFm/HkPI0dA6t6vUV5AoK3Gcxx+dbyO5vXYt/k2MzwGfL00wG8lZRplGr1hDUP6E691CyEILxnFtiOkhRhs+JCBth6UWVPqpbphkKnw3ESSZlkK5sbZ8SJjddTxHkaGwlqjeNlMLGQZwxVa5PLsLf1utF634xp9SXKY7pbmBswNCVjTWGPwP00KH5E88dEaJqmANyA3anAZWoiru5V6Dqzegoc4ayOpZqF1L8BSUAnwEqg6zawggz6/dnocqzjNenazqClTQMUTTiSVVZQFOEQAmkKiSSY4PzHtg5v/il02k5rqHDvNS6+4kF1H1oj4tbWOd10Ojx7H4ehulMKII0CREOCdEsrwlpBpJD5EN0G7iz3jFIM6Vw22l+tmjWH4F3Kn1tuYk3Dsx1ZqLSRkkHU2m1Puyurc/p7PDA1txj44tcKAgYEOvn18AO9Rjt5OiWBPJHexAP030dVr/17KMdDIe1oSRI5osdGF5o6RKdME5nGQ1HsjNaNWf8RZB3OVX7KQerdPj8XRTKK6rp6CqEKPGeSNFiwYYWnfc4ihpLwjYFFktaskw3bqZ40erEIjVb0QzCmZbrKWio1afEflS5uQSDqJHfcmOfsEYz59VM4/UUqFmmlA8RIKjoIA1AzJtf7RGJd/v5oLHUqFZUGruBV1Na6yzplbRY9mGKElRfka66x2t291BGcS1j1DMOlUSyLg8ENfJqEgDimexG7bDENdsUr8T0crs+1qwWCOLpvgdhnMPM80x6qs7W7tcAzor3Saa0mgsL2ULJ7KvDKBCiyZcEqRvlxh+J3Fb+hNNDZrtVufDjNQxr9rirEjCMx71K1ELLLGb4/OjWNIwjmMKJXiRERy9FRr5oTHz1tTDZQ08u3kugWtABwAogRpLmkiQpjBgc7jaMr5JUmgbwRxFV7Boxeu8wb/0+taaqXkaHEjSpWxJCTYk7jbYe+LmT5yc5p1vUdAyhaFEEJAuQd7byR672m2JLrFr5oS/uDx3UzEMgO/CaPtS13tJo8Gk1LrbEdTrTN9TSQ6uHInCLWxbCwIaALEbC0aUJI1nXNh+Mjgi6zNS+4o+X4ullp1rPpnoHeW+NMyfUWnzPL8QfbV2JSNONXlm4zZ1w0EuQ5PMky6SDDj10OXKg5FYU84jQx4rzhWHnt5b43q7MrJFItWVJseDdVXxBQaB8FhoDgQxRYUdsKZFWWMr4x5avmNlxgtYjEapUsWhudMrjV8FlMw503ImT4b5TJ/yHw5z1ZECAwlUBI0goXKJzxqqML4WoTZjn7RZs2pvL3atlz4rwmXHWy2ZBUhOoARzxyfc4PdNVRqeoaPKcxYZokVNSwwtbiBKW3XUIU5oVY6QSSCBtpOxJYqzVfAMnzPULIp2tGP2lZhU/HKM8XJsgoqz9VPY9v2E1449JWyZA5FxZz8xfYy8hiVceaeNdsnNlAGJPJ1I9Y8rx/WDT0+NUWX0OVfxBmGBB/UMn1WxjGbu2qZWX65Fymtu4B4cKbkON4tlEyvx7Fsdo2EPNxdMRvUceMdHEr7/cvpzrBlWTVcQdXjN3T3XyZ1wkMsQSW/ySTA2KiKxgGnroJGBDOaJ6DI8yHK9uz0LDS/SzHqLK9O5GeR6TL6TF7KVjNfPUVRdVdvH+QsyqnTIw43+kOYk6R5ZDicxThSmjcaOxrQ+NOufxjy3JaDNGaXLIzVsPAlSSCXflkk73MzcX98e/On/wRzenSxmj+cMOUKadFXl1Kt0PoqksgPJb8P5WApKCLRFhF4wvHVzQc+nyynRcHvyNxWPEW0fjpizsdqmtBB8lkyQavEz9LGk6FbMs4M57BsrpHyo4jOlDZd+kmmNxilVlOl0XFKjMMl1cxLIotPSXuS09VCyB9RjdrNjzqwt9MgxJ5WWJxEj1ta8FoU7vKrXsRWo4HWSFnWTTm0+G1cPFMYixobY091aWQoUqYpJUeJGgSIx6E1HPjI+tmQmkEI6LEOVpHIjRq077MfwekxWZlub3NTkmZFqrK3qLjF6qwx6djl8KMiQMjgAZbl+JJhTQwgJ8UXxGxI8hqRtnqq+dOmfxKzHq8ZfkmYskvvVjimktJJeYUFBTJtYQYme9piManVLpnsqqHGcqoqKrbyh+u00IT4NMtDeh5yrCAAlTiQSPNJUReTIrDtSvdS8e7mtNtIc/xGpra3t90ovcVjTKEZHXYJVgNywK3MyfFYeRNiVIFSBMglNWHjtU7Zb2FEQgJdyXaNr4mI3zsT7QdYoiXN9OuUyoGaxreksSzcqlT2XVhj7KeOpC2A5nKMgrKSONGOgnnag3I07+xeyxvVTMcv1dvJwUy/PraxocSxWou/vrKAVMhHDhM8SWAylUJuM3l4wAe5nyFIRBJBdUvqRaT4DDyqhuKjVCNluN5G7FLWri3VZYRXf6aziTZIGfrhQqoZcISuIm7hPI0bmNMj0T2/0DTJGZ1bSA82/Tpo0vqdBHxDqGwC4LQopJImeSNseO/wAWcszRzLaKtYQ1UJcpVvuIRB0qIlsq2iRqj/M4TvqH9PrvVrR0rbHtx1Yhyb6wqKiCCbU1jI0ifYtQUUA5DLJ8Zvke9rfISQ0Q0cjjEYiovVZt7BO82mv6mnte3LUqNNntLKixCVlevyItd4iSiCI2e8SuGOQFWh8imLzVAjI5j2sYlZfVF0ysoxIwY2sZHtdHcLlac2skJMGonjV1094ytYIahePi9HPciPbsi9Qe/wDqI4MUzQ7auQ5UaSqK49wchogyNbu0BH3Tyi8jvtKrXp/Qiqip6T0khglCFrdSFFIETe2n+1u25x5EGedTNamW+nXn20LUAsS0DsLFJ80gRHvfCTZ/Eksyqn/1zI/dEcitUqIqcF3R3pP6V9L+F9L1JUv7K6r4FKkanrK6L8KUVIMAADTbmHAJWns5U4vksCyLOK4azwNkgrDyhMltrRSGqR0fPGOV8uSgSOiikuGYzVZsN5ivUTHbuRUc9rN90RyNVdve265MatIcqoJjhhVGPVqvV6tVvpVR38vdXKv9k/f/AKKDQhKoKrEEG9+Qe17j9zjWFjQNS7Jjc3tbtPcYsnExWkKQha7isxRoHiPhurDGYicvu9IiKruTtmtXZzl4oq9GHolj0Mf8SBubklVkArunY/HDLGA+XEmPU57Bry7v8g45FRGx1VWqRrnom7FUadG6Jk3JHDspfxo4YhDPecBpAjqH0KO8YHoXhIJwE5U2REIvNzGI56WpVKCPlN5LmzpMhUm+RPB4iryVz2iRr3OIUbhuWMJ3EhUSOFNxtVvs/SMyEkwAqIJvvEc2vA+oO4xnue+G98UhtRKtB4O/l5MX+nMYLXv0i/KFovGBlWSJSWFRGvLOjg2RKutBcEjDCV0SOGIsH5ti0bZEo73vVhmKJrWse5elm5DhtjkVvBm1AyNjxZr4kuVKkOkSocTZCNn2ctWNAPkgjfHKqMVxhN25Im6GTq/hmXak4jgEmhuKirDHhW0ioWzhzzT3MNOUjmFnie8bFjlRwBjfD4sErHNf93FBfxDSTU6TkF3STHuSvrSANfyXzEZSDjlJxikb8hqJJIr3nXYwSNZzVGDTk5VzvOKOtoaioenw0lYKFwFA+ZJvpJP6AztE4K9PVlOWWkMvpccZQUuNqBEAphcahBgSbAzE2G3SZ2X6iaP4hiYbfKstx1R5jMw8cuJNdJSesWroJNaaeovAo3SltFjg8Q1Vxoc+VKbugkXpmeRwQW+gl9YY/XPy+LlWnWU4K97SRLCJl0oeJWkPHxQeDWNNKkj442aIr2SJKxoLHpxkOcnKZheoOI4ktVVwoxbq8r5cYEeZZOZJghVvJu8as8TgARUGxqmVTI0Lt2MZzVremLs27stBsjwHSnRjPiYlUakZCWzDk8c4RV1Fl1WQSspqmdDHEhVE23I9yfDnQfgWECUCJJEQpGmBKVMw6pzemU28pAeaZKYIKQq2x3nj2JnmMMvT/SPT2cPVqPFqaOufcLnitKABSNOpICyEnUSP/wBGBAgGEI6idp+RUGSJeYfieWJhko7WjkXOM2VJMqJDxN+QEgXx3sA1s0c0Qo7SPcwYWjRNlai9AGiOC4ze9sWn2O5nGlW8nH8B04yjGq2YhmNqp1birI13ZwSDGKZEsUFDqJvLdrGBr5guW7pCuv7NdIs3xRuq2gF5k5L7RbVfHLay7f8AL7ic2Zk+neodeJ9hExm2t1RTuLAJyAE8x5j2dd9phtmtkEdznaWfVA1D7eIOWYHeRY+U5PVWN3jzn5Tzs4+N2cEsuhtIFaABADJAsI7HsOxCDcNUc8RlQj0Ug3+Iz+atsMO0JV8OQtRQUggCEkkTJ9hJxLW/he3kbqqs5pVFl8KaaFSgOJ1/NCQ1qUn5blQAiBNrtwyC+i2copCyPIMpHoMI2uTcbHK1yOY1EVFXbl7VU9L7/K9T3DbnGYENgCQxDcZOHmRu5HK71x3/ACm+/wDhPyv7dKm0l7x5OrJpYm6eY1AtGMYSBU02YF+RkAVZMdNWoprGIWRKnRjgE9a+JNlHLBKWSP8AnB+KZl9fCh0qQyTVUBTwRTHskPaJYzTRQymucw6jVg2fIENTP4DEVeJlEqbdOGX5lRVrKVU4WlbjaiUKSok6CkGPKCdNhHracZ5VZRU5W8DUrSppL0hchMJWU6SVW+aJFrRftipu6V9fKybBJIkGyOv8WDK9qcgjcSaJjGPT0/mqPQTeO6cmuRdk99enanmAKvTKXismQ4Zccy/JITWNa5VSNNJEtQN/KKjUPNmPbuiIjHtRPwvVO9x2YVZbXDLBjOUGeLJjBJLckf47nZTYMjy3kaQgEc8YRo4SIRrWvYqEcmyJ8drM6Pax9W1MV7mVpqO+QLRvLJmum1axHJHYN3NVeaOPyqxr/EhGbo5PfTySEdONLKCTEibQkATKVXEEXBuAPQYz1kJd6rqEtEKKoiCIJkWBJg87Hc74M2xs6aWRTlTm/lyYZEcipx9vX8rvs3ddl/q2Tb2idVTqNgw8qiJV0f6PVZBJrplfMy6wjIU9HV2aNIeur4ytcw82yEOOkgxmoMAUY5j+fJEuWPpnbyoMUs0jUmOE0xHRwEjiRGPG4YXCcUir4nseF5F4+Zqc1YPmrE1x9OcnPOKUhnvQi8mvY5zd2p6RFVFajlRuzVVfwxEaiN2VVRz1BS0h8QOthwGANUGQRwL9p9gZ3h8e6cratlxJpVq1FJkXGkRyDH5+84FTSXtutaRL5uQOqChsxx1HChGWVXPfEe5xbEgzMaoJElqDVWBdxaqvRU/HVw4joGKTOVtbAgAC1fJzixhMNuj3I/m/7lVyr+F3RePH163W98b0zyc0pnx1IqCXcgxucMKt/wDuejnmd/1RE2dvvyb1cbcKyvHAxZkakedhCgHLMCSEJGsJ5FIaOFXOWQ8asHyAvhVzXo7nu1GqMq87Dji6xtlldY4pKUvFKSpDelIJ1kSdN4Gom22DdFkbhp2svX8WijSA46ylzQ0pSSJBTMAza4ueO4+2Wjx4URZBJcWCOMMjlk2Lgxoo3KowxxEPIcwTXHK5oxtcqcnNVE33TcIdVZ9hd5TlOEfBx47Jv6bLrDVNdHJNm3karkxI7JFm0xWvYJGqQFc+O0akRp3v9IvTDe7fGru27cM7C1k00h9lhgwrEjvkHVGXzJZGo2Kx0luwoz1eVvJiMVWO2Rqqq89AsHtp+e5jk9mCSkHEMfyDJ4/ljEEItqDC5FTU893PY8n6jOUqKr2IN4lVWK5iql/L3gpt7MnagOOUziGgT5TrX8mlCrkKMXAgDeBgPn9A27Us5VRMOMpepzUueVWlDKEgrUp27chMkyokgc8grAtHYdmFZbTzGFBBYRpsgjCGM90Zkh7DMKBPcoTkYrOBFVF/uv56MDXbOS3mYUtpVMjuDGxDDFgOVsoUpWFh2ZeUc0QwHR1OWZIJIE1NkfwYJeLl6CjL4b7UU+XA/mGg1AnhBKRfOZQ30uK+GDxu4vO5zHMcv4RW+uae+rBzbJBS5FSWrkkYsPDcNiOKNxF4z4UIvkaPfZWvEUqpts/Zw/ft27WmvaGYGjU42C4hKSoSCRIF7GDHaT2iL4S8sW3lwW2wHW0BZGtKvmIIgwLwd9ib2O4wHuZZffSc/wAghS88kpD/AF15JtTOmWjW+cUiK5UOh/OJTucSXBHDYXx8HsX+rdUwbnIbGszWWevzpamINWNQBXSyqNwY4ZDwiCMLkjPe9jRIdj/fkRN/ap1H8smDu8+t5qYoJxpttHPLmzW2p3TJ0eVG8k5CIZsdpDnVZCM8PAaqjXK9EVetXmTTRs1tBuwZDMSaKOWYQlwRwEUMZXOcaLNZEMzyt86tYx7kExo2cylY1J1UraKEtqgtwU6SRcECRp3iBEXMWI7HaStdVmlNU+KouFCShwhQKFeXSo8jSYO02vO+GI4t3xZRiGVXeCZ7cnnGzSfOg02WMawjsExmKgBRVmwIoxGsGWD92leUhHui+QbmuI5qK+zT1E0vxjDpOVCBkmm5QxsttMnxgbIxq+zlykuxQbuEV5iSIch0uTI+EJvlg+c0WOigRU65IMVpael1Rz7V7OxSrDBNPsj846rk8M6+yAUMZaTGBtkvkmjDWcnkP5BSwI37TRyoNwXHHp93J644LVWGV5xmIrPCdTplBnUSqhkjX2M1RckhHdCGyJHWU6js6msqn44WuktV0JIkdpIx/J5B+Tfxm/CnLM+o1ro6UM1LpWpZb0oLkwfmMJuQAQo8XN4Pvf8ABb8UKt+ip8h6kqnaphwimdPiHxKekUAPEQu4CgRsklUTAOOlrI9XZOQYjU3dBkmWUmRY+l9NoSVVQ5It1XkIP9OpItXI5VNhZAr5YvhDkM5TOfNzkQDdkh96Njk2dCgzLSxDHvcukxYVFW5DNTGkHaGeMafrUmukIkQ9shXRwQXMSK0yuEZWCRXdS2b9RHI4+jWYU0yVPlFp0mLRWT8cpwHnwjAG/H6+HMhSoZIMmqKyTEHYvGHzDWOIg1LwZ0qTK9Zbi1zDT/VSyo7bIKqsyiK6We1kWNpUus7HnFZiMNsVrKJtvBoCyLhjpoJEpZLxnMB42NYzDfwi/B3N8nzGora5LTSaWoU5TB3wlKITGk60qUEwIhJImY7Y1zqbrPprpPJ8wy/LVipczU/C3UtbqaFZBKgtSBaVKlvcz8uDS+nzZ1kTW/SXHDyYcG7HO1qnWdvT/JOOTX0i11DCE1ZLWlr48QwjGjwwPeMhP9a9qcxqROvcVUFpNQs/izadYnys1yZYhrGENJspr8rsJCzo6jc4ros56+WM+Q1pWiJsT2vt2n09dOzWGYXOv9rjFziVLX4zkVRicWVcSp0HKZ95NeG3yWvDIjuLBrSwIkfxFhyJwbIzBuiCGB6qEjs17itLrusLX317232DHvjvkIbFtPA3kWXClyWkAeQn+uFIZLi+aQ171IbdVkcSnerfYXRVMtWY1L4CHdg7CdOlaVNgGVABQgHabn0x5A/E3qJ+ky3LS61Uo8VLiCwkqWssAfwl6EyvSUneIB3AMY5TYuKPyaxx+prpNRXnu5460Mq0UdPXwJBzjVsqxnuaiAhxWOeV8l6cQtaPkieVm8cu6RlY5GKYMyYsyRHPNY9xmSfAV4/ko5UR745EY4jSK3ZBK1ztuSb9PGf9yunmSJT/ACjdsDyY3b1d/WPhYZgkZyzK1x0EyY6K9GzormnTyRjqQRVG1TtM1GNZTt93I4lZXlbdPb23usKaPPjxjx8HwYITCsxRwyklxubgyVRkdPjuc1rgue527lXrZ00r6lFQgpUE6QXECNhsVSN/7bY81v8AXDbavCayytUhtRVq8B7zzptdEza/+Mc25XDZKMhCOG3zSURrFVzXkYV3hRzG7seic/HyexXNT2ioqb9SbHZDjSmjVuyIrGu9qqb7/j2nv1v6/f1/21kOKRtgpZICJDGeY5zgsGRXbPXxi3VVVHPciN8m27VXfb9+ttXyzRi8vC9m73kVi+LdjGr9iOcmyqqJ+fwvv10AZBI83kuLKIB4sNM3Pv8AbnWKtwrbbSj+JrBUnSCRAgkmYgx6XnBHYXEc2acoW+P/AEjWo5qJyVGzAuVFT8KjkTZ6fhWq78fnrf0UyHSWjpbqiusJT5cx53yhcmNR6oxo28l+0bmOdu1uyq5Gq1U49VBj+amrmvc1EUjmuAnJyK3+cqIiKjXKu/NzdtkX36/C9WpSClXzVkwxvGioiHRBfa7jt5VVzvSbqrd9k/t+2/V12rDTYCSoaYm3oLC/p/nCqqi1OOqd0wsQOCJji8f25xe9Tnc6yhQIpI8dkGtYeLGTxjEMZJD0I9RbKqKrntbu3bijW7om6qqwfV3N5NXjQ6wDkDMtrBk48mMofGSNAE5gQvcxqOcnlkKRRuVUc8LFVNk2WXY9URxBirGjjdLY/lwc3cL3o1UVXcl2eqb+2ptv+UXqi9fKKxqY9IFYhysKSYgjNTk1fO4TkEiNRVQiOI5BIqe+KoiLsuwbPatNRly0KA1KLcKO/wA6Lzc8E+sxivluVJpcxKmnIKQtShJCCFIKdvc9txfEIx6snSTRpz5xIjX8HkM17kkIDixyqNV3ajjLuNqqitRUVE/ujB9DtQ8DsrqFiOpGOx8cx2TXlhVGoEWxnPzfD7tqf+E5PWpFMV7o8SYkd1lGMF4iwvIgwK/ZzQYwLF8iZj4i2rDr4ScowitcySsRUZwEXZFR5WkUj2CcjVRrmvTdF68szunVUdHq0JPAdqMr3PYcpSuVNnFGqKqcUaiPG7b0iIu3rrNHW2n1GnIkqBEEW9DPG94n+7ZTOPUlQ24yVoKHErChIBUCCEmP5bX7R9cdbNf3hYjqbieOabS9VKKRqLg5MEg5pOFGsKytNkdeJ9NX57j0u5iwiyqq/I2vj2AHQhEr7R5mueaFNZJZzqa6drWsue626u5bhmlmQW9DMzm/s5culrXrBhyyyZEu0UiK5qR0Yf5j3sVHcm/HUS7FYrq67eJP8TV+os+fLsZucXdDJhUYY4zFkxX0qx7uIZSKRBxmGJBSFE3RWjaN6cXc16fb2lZ1Py/Gr69tGO+Le4y2ZYxWtRs2fZTcWcA1uMyq2OcMadCHyKqP+TLiHRzuW+6/kWUJV1A8wHlNNrSWplITqCgYuQAOZH2w99U9S1j/AE0zUqaSt1hwFXhhSzpCANSQkEkxwQO1scyNuTIqI9ckQ1hAs8dKJak0U5Y8qsNDlLLEgJIFCWI+LNR52Iv9JuWzkRd+ne4D3VZZrT2qV2TZfaWFhl+O3UbTrU2VXjatlOrrGKxcYs5kYDVKJ94ysbFPMG5g3TIbmvT5ckb1rzUjtdx7K8JvMhWZj1Hk8At1askWc4dY22q6qNNmy4TnFGIJrIyxfBWiBydKkkaqIsdqvQWu1vVil0LzqyHmNC7INI87p0xzUHHE8rJJqpZkeyr7SoONFWLf01jHbLrikavAvkA9WjM9Ucqeoc6VzmnDqlPtNOkrEJWjwlqQVaYME2vMbRczhKfokdVdOrUlKmn6psBpS0ltQeSkhOqx0pM+vJMDBT5NKs8gpKeBGcwYG1sYsZ1vLOyc4cgyy3+YLBNURDvVyFai+moitRF3VdVgGc5xpXqZXZDWOR1gVY7itjy2AiGhBc0siLNWYzwvacAvExiL5XKrWhTyOTe5s1i4RkuTnu9NbyFcacXEaHMwyeGTyfIq2haEcWUB+xodtCI14LSEZW/HmMejN2OG50Sl0DxTBnYjGudCVjxve0ilej3DXZHPds1G+9kb+3+PfoR1VLmWUfEMFJpa1gOtBI8zaikJIWkWTJExJMG0Wx5fSmtyTPvh3pFXQ1QZfVcoWnUlQKVGCsERwLg+s9aOgGm1LrNp3gGYXrP0c2ZY5S3pK50I0aVGLZtRzoLUkiYYwhtRzlkNG8P3ARSqr28iFzDtMoQCjgoowBcE2eedNjha5VXZFTkg3NRVRUXZH/n9vSrX2l/cJjuC6C6QVTaYc+bXadYYyFsMMUKyW1AOZnlF7GjCKrSu2c4jnMX8Jv1YOKas5hrYWVDgHraOfCc1YKrJALk96vUnibI3LIRqNanJBqiKqbqiKi9eVcwaaarKxKtTi0vuoSdRCRCoAkGIGxtxMTj2dlag/llG54jDafhmXHU/z+dIMwAQTP8ATnFN2miR8MkPkpAjOjjVFcWBNhTvKgWuIViNEdhmuVjFVEViov5XdE6Qp2Fd/Wr3eP8AUlvtBpk6FV6VUuB6q2zsTBVpIGkvGZ4v4YsySwwfnQZS1xYLpbTSDDO6SZQtEwKoj3e6bJca7WtEc31r17zSBTxKyJLDUQRmZ+vZLkEsJhV1RTtY4QXTJhHN4PG17BBQhTKjGL1wP0ut+sHax3LWOsvb1qvFxa9PiUeRW5lAohXQrqBmVDSXE7FprZgJMJJhPM6psFaJqRpNeUQiqjVXork1PWuBJcb/AIPCgSpESNlR5rC/Yz6nDTRZMzU5NmbiUJU4KhpFM4QU6kKQgrvp/wCRIASCNuZx0gfVi75cO7eZNX27U0yCXOcjLX3mc2BKw9pH0/xgI5/6VGHAY6EttnV+N82fU0EiTCDCqa0dpYymNsIkXqh+xvMMzy3RzXQ2oEYsG+bDky6GRJhAg3QsYsao8ilh29cGCNkaTKEoZp2kIZAsmtY0r+DCdIIsdU7DVXVouRdzd5eZqPIcnsNQMqtQgQsm/wA1nGBJhEunLyLFh8mR6tGxEYsatDX1lYIQlPsW+C9wmvF7qDn2m+g8ezopV1ghjyoAaqPmMp8aNBXjYZRYSDMgY3URF4hsJMoh5EIQPiiEVyrxPONN0+tLi3QtyopnmkNyptTbWkq1k6SCQDAi5EYXs26cqH26XK6OlYbZo6V6szHOH1oZDyXxr/01DiwNQbnQRaw2nF0SKAETToGUymDfJs8plVY3A2ewIa+RbSzcpDdmDUkownO4psqK5U2XZUX9Glxi5LCY3ORTZDL5xCxGsyJinR5y8YLEfCaFVGn2pycjNkVXPRN16Pi5lWtf254j+qlFIsnZhYsmTIjRhjSZAQEBNPGAFOA45paleJisTgNRs2Tjt0GosSx2FMbcMrWrJjTPksI87/ZHOc5CbI5v5c5Ucvr7f3T89aRlDqn2i6tJKkiUyLBIAIBJNrW97e3l7NAxS5m9StQ54aiFIYIWEXAABMTBkc8RMYgFgzzZTImxMiVAxLxAyo0eBfyYwVJKA4USSVkL4fySva4So2T4uTmo5yJuvXxmklxL+cIuYCiIh3qevFDs/GHkMDVRhoyCG5CsQDGFEMbWOVHKik+5D61lxLBMK7YKNlLp/MptQ4eCf70M1zGflZ4MG8oc+yRwcWiU9Y4bATrVUgvKkGKQsmJCE8jtlWYkYdu0Ptpzju5tMYhxKePGmEgzLXPMts5M/wCFRY5TnFFFfS40NhDSXHCjBwocdvnsZyCjjYry7oHa6spah/MRUsJp2KFK9bi7JBSJlNzqAifvacPFR0XW0VFktbTOrqanOW0ttMI8yh4xCBNhpMq7nvOBE1fyaXYZFkWOjcgqmFkE2QNYXkcyXMOqvkSzjGMaPK5XboNd1YjVI3ZUdvNG2uW4ZplTzXWIVqsiNdQZWO/Jb8idTsSJLBYNaRiMUR7CPYRYj2CeYMgSom0eWNyOo1N7XPpmaGyCTtbshyewyh1iadYkuM8raAV3PG4byhgYbVjn3lHGGjFRgLtgZchhUYRBK5N4nE7Cez7uzn5Pmfa/rq7UaBV4dkOQTtIK+yEbOsPmOitfWNrMbn/pl46BUkaAb4jofjmQmtICYqoikT8x6r6fzVxLaFuKS2QFrLRCFzAhCpIVJtJAtzjUMm6czjJENKfX8M88ylCQXR4iF2I1JBkSRaJJ4AAGFaQc5FF08fCNjUyTNvKFtJLizitM2dGlWMq9rVixS82LYwyrHfWmbLjIWGE7pAiFdEawwNBdPMOwXRyk1H7u7KPjuhuIZRkOqmHYNKjZBX5LqVkV1jVXT0dkWCWMAk6uHKjxYGO08CP+o5IX5ZCS4tUMxTUtkeFG0PwW91o1fnQqzK6udXP0E0zvAtdKsLain0jTFzHExI4Mmt+DXnI4MoZIkAluiWTlM8MXoMNf+4bVbuYyuPmOczvBEr/jpi+L1TJcemxaM6e2IvxoKtG55SNQYjWLmtnOA1RRUFHRGJ2xlTeYQ1RoS1RFaFvPkaFFKSlXlsQQYgyoWsJ4OfGry8B7OTUVNYtKmqJhfnBW4NKHTKiQGz5wfSww4ftB7stQe4fu+oqNkR+CaKVemOWSMA09iSYjo/6VRqOip7C7+EqsNOhghljQ4isACCMSIGMiO8pEAZpKNA1KzMsZgWkbl+XjCRQCenAt3Yj5OYRj0c5rVarXORVRzUVFTdUVyn0xtBc2xTMZ3cFb19ji2BwMEn09S3IociLkWVysgUkhJ2P15xi89JGHCU0iwc1ontOAQVIdXotu5t9PrspthS8ir8h14r8jsp36xaJYhpCV6EsJzZlsICMM0rXjfIkDjI5itRGsVXKn5Z8hoqWlqalvLkLfaEIW8lIKfE1JBGoHeN/a0Xxn/WPVyKarpms7e1ut0qkIedRZKVKQQ0EmxtebQExzjnjFFgI6Y2YckV6VxyRHBgJNbInCViiilYIw1ijc17ldIKnFPSo1E9dYbUZu5HMam+yKqMauyp6VEVEVF2VNt/wvpUXZOnsZn2KdkES/qCY7lGujaBkvjkDLWNQ/OfHfXTmoSveGQicElChqnkRf5fyVcn8vqupPZ92fw8hM1mT6ylxV8Byw2rBp2275qSBp6RsnxOZ4VJ9rlTd2y/hETptNLVpQVpQ8QIJEG22wnvf/ADIwiDrvpuNALK1gkkhhMn5R+UXG4I9sLTyZ2g7NFcQTHDZouvhMsvX5wpzccRBi6KT9MDBC3ZqyXbR0c5UUvyvlI57hNYxtHBeVVVf5it33Xiq8lX+3vdV/b1+P3VPXUovKN8KOOxYCa2LNkT2NkFC1IMmTGkO5JFl/Hb5HjjGY6XH5facivXZV9aOJ/Si7Iv37L723T1uiqntE9qvr30j5Imn0n4WrVWaX3GVGofb1N+EkKKleYhJgEpSSFE7C+NfzN52pcpiqkbpFJpkPhLbZaC0uFIEBQBhO54gGJGJliaDi2UaVLiLJjjVj/Fza1ObXcnEI7ZeDGMRXuX8I1FVEVyJ0U+nUoEGTGPkFfYPrZxLA8V8GSYQ1AiPc2UPdVFJEAisE9vi2Vw0au6udvR+BVA3uHYTYz3VqyGiaVUEMZSIiuVg2uTyEdv6VURVVF5Kioi9E1SiZfZBXU6EcOKWQtfXlObcVcwxXkRFRU4MGp9nI1uyeRWuXd3pWENNPeZTqQixKSLmwte3I98I9eFoW+hTaipSSGlggJSqQASQQYAPYnnBa6bVtW2xntsBU1jGHHGsWYwhfC8HySR3HGJXxk8jjMRj1Yj3KjmqiNa5dxg15BkUjUPLbCuitbhmJ5E6KKY6OVICQiOYKrbEbIb/PI5UO/wAiOMVvi+56NciLdWIEtIkpsWR8ZHgEMcYJQNI1rBvVrTNR26IRzgIRi77omypv+ettneJZlqXYVeKlHMrsNieKwsrUIYQQyDbbi4IjHvOdisegd04s5Krt/WwzNKVl5lSGjqPlASfVSZ78Xi4EbzgTSOOM1anH1HQtET/xIAIvuZ2EX9+A107yq/rr28srmX5K1gWtqq8kJZYLG1VjEC16KYT47JLmhAQrX7jQxOKN8bOOV3B0eDnn4ZbYgw0XI59G+Rm1UvIsCDZK9EH8JXta/wAhWrzc1XP4uVU5O9Khbn7TmKCRKx20lBBAgpYkr7pVMspsJqyJJH2MZjOLlG9hWDZux23iVd2r0JtzSCFOM4KoVwyFa6QREV73Dev3tau7mj5JsNFXfZqb7++kxzJHWn0u6wNHnVIMkCLbb7cenrhoo69upbCBtqAAiFTsCTGwjaeQbnEj0Io4yQsxty2MenssMg12WAm2TxAhmZBksa6rKdE5hfPYpBBCqqMxOTVZu9yrZJPqe6lafY/A020JxKkqKPG/1pDZJe4xU5TlJYtlKLJWp/18WT8Coq5cgj4cYI/OCMcYPM5zpHMUT5OlfR51jCgcszJ200ANiFpWvgObNI9sl7V2G5jTvE0iJ6UTnNVPz11W9r/Zf28v7OaQULEgRswFTQBZFk1vTQ4hplwow2xps+zlDa4jJSSzPVykVrmMa1PTURFLP8yoel36d+sol1y6p6GC2VJDayASVEdxuP6iMar0V04/1W1V0jda3SN07WpwOAK1iQNICpExeR37Tjl5xL6gWslYTIazUiW3UTHspLLfkVLkdfHYcJ5STGtfTlBEi/w4o2njCjpSpXqCPFdHRfCaSwxo9qeI4D3FRv45rqYN2GlvZeP5zgmTzTQnwaa5q1ZEvaO7pSAmSbWulPdKitkPWP8AJExp2OC1zOgS7x9OcXr9d7LHsMyihtHScokUD5lfLjmrI5RzEFGMQ8MIIjRRlKop3N8vdrka/wDC9Mv7EsLrtBNR7bt81Bs4tZm9nAmT0hV8iPKgXZauNIll/TZxhGHz+Ef5bwgIhDREkNa5PDu1+oWMvzqlpnnKZtpb7YI8Na1KGrSR4gUZBBsIjdRPGEfqv47IEVNNSPuVCaKoSmChKAEzCi3piQY815gDk2n+P6X3WjkGHiVhLGWony7myxS05NmknUwpiR3WlpH+x8eyLJGoXR3CcwijaZrGqm6Tayt8bdMilD8jg2PsQroSja1xdtyjeqqiNarSu2cioqelT2m1s65wRgvMKDHkONFjYxcthsQTAvC599Je5CGZ9yjVzlciJ65ojtkXqhp8MZHcfj8RCaNiNbzI5HCC5682cx77q5you6KiuVURV63Xp7L6ZjKWKILlttskhXMoAhPI9J59MeReqMzfqM3ralSkqdW+lZSkGUwUyDA3G9pGHsaSZrIzzTDC7GmsS30SspodCWQSuSuOkikiDGSMsYbWBC5WqEAycGpJ/r3R+zkVZmf1ob7QjWPLMLutDJIpOGWVxUFk/wAbTKi9cyOpmw2vCWqbHjEl8Q8ki2B3K04XM3aj1awDsGyGCPTqyxw0gTpIcomyoMUpBeUw1rqwxnPGRwSeLyK1rmikOe9xEGrHjITYQPqp4v2GauvfjebzcjxfuZrKgsLGcv09r6+waWxYEZayh1IdJNXU1tUtMrRGmpLHdUIpBzsfLHGJHfgXUuW0lBnS2XEkoff1pAiQHVC8m3tb6AjHsP8ACQvZ3lrCvgXsxSmmQh1bbZIYDIA8+ogEDeBeb8jC0e9Lve1L+o3rLpZHyEV/hum1TW18TT3EL48m1aCbO/09hbzSiatdMu5tnHNUhmRCnFDANEG5jjSGqT+nXYe67xOsiS68VjXUt1Z5RVLMr8dAllPnsCoo57WPTjuLIFY+J+lTYdlaLBjH+UsFgVVFRV3b33CZMDNdJtGdVbKPExLSrI5IMWFFo6xlvBnyZ5pLhyLzxMlWUMNweYauHOIZRCIktpEQ4mt/oV9iLdDq3t3rbazZjHz3AkCsXWzIko7YhGtMNg3yWo97Zzivlke96K6RIM1NmMYq7J0/kmTZR06jNjTvZmtTiWfhW0hekrJhV5hIvPPpcHDB1r1fX5SqnyPJ22qNKlB8vPIJbSEEakLSgFeokHSSLDe+OE3ve7c7jRW0ub2+gMrrTLJNWCobi0d4cbqYJ1ctywanIdXDAQgISuZMVTMQrkVB7NSQ6F96WMdqmkGcY3g2kzbuTndDVVNrkdtkkCvNNnRI5QSbU8KEGxtZjYzJK+L5CiQCjGkXwtR3TGPrQZng+c6kC0e0wE2ysZuQBLW49AGgI9S94nvkmdLQ5wpDHBe6e5IvhBHHJRxlaaKq9absl0z0OrtKe4uZjuBVLrevwCtrzX1tWEsLAc81NYvsZ0aVcOlIJyzQlMhBK1FiPRGrw26VutMvpmXW36XQ0VtJfW3pKfB1gKLShbSpEwRsLj/2GVvVeU1OV5Y5nlMrMKqqLhay4PFhl15qJW6EEKLLxugAFQBAMHYS7nUDG870NxGBjuO5JjZq+0jTblL6PGNFJb3sabaTB01tEa1lnFA1rPKF8cMqCV3xpD/Ix6dUSmlGfWMury79YxyjwuzcD9KffZJT1Ui6BHM6HNk1lVL/APEp8FktiuWUJo0exzXhVWb79C2g3atOyr6aupeZDwGhm5rMqzxcAtMsZBNYA4WNhJlSsfQkVza+3nRFZDBLY4avenBHKzZOuRPuV1J1PzW8qIucTmimaR45ExKLVuhRKibV1WPTCQIMOUCMJqypsMapCklREc5rBzN08iL0DybqSlZJy1atThTIfBhtUkAgA3nvaN/omZx+FdTXms6kb+Co0soQ5V5Sw9FVQoWhC2lLUfK4ghSQEplfcHfBW9x+uAbur0v0qkZLiUyTiESLU2QaPFbo4rVtdYS2VNdcXhprhThNgnbEcyBXxIUxJEkBX+VXG6dp2yYFm8HsJyQWjLwYNkWqmcWUC+zgtklCldQY/Gjgp6WIWGUEmIJsk8+UtPAcYs4z2hkjK1BtHyz6UagupM0x3JrOUywMa2hxJprBz5sdI8sRUJJkjMqlY+K17nIQatIP25jkcqddbeIaq4EXQnRbPok6lptPMauEJf8AAE6Nj0XMJKVkmTJmyWHG8sRp4kyS6ZY/c2VIAiuVj13VPxMYYyzpmoqaNzw3a18BxbYlaULABJ7kTMbWvbDd0CirzDPMry7MNJYoaIqQVyE+IgakhJ3SqRHpfAo90f07q7UjDcZy3VfULLlFiVWVlRp5Sgr6qljR4UYSuspNpJWZNscovHALYXVmZglYaWsNr0SK170r9pmbZ72n99uHZxpiIJ4GLXoj51CsLAFlBNhaK8ljWzrZ5mFdKdT7vH4VcqGGwLWOY9zHPV7xtMscsMG1v1EdrBnseHZaf5cynwpmV2RMSHlbYsiwh2tSevb4kTioGVowSk/UWzTRiI9EVOuWXM8zx3FRY/nOliSq6zyTH4sK6AVziRotlCDGg3MVsQyopwldHMpSF3+W+S9XclX3lv4fqqa1qqbq6xVW0kENILCBEaY8VUAmO6ZOwicaz13SU2X01I8xlzTDp0E1L7q3Cv5QS0ATBiY1ffDM/qTY9N1Q17xLWzTw97menHdDUR820UaKOkp8StnST0tlp3DqY+4Ytri+UBkw5dfFa4ctrIc14vK1zkJ/tU+n1hmmtXA1s7qQY7YPx6AO9h6a3UqI/EYEcgp5JEvUx0hBMKeDJJHm/wAPqdrJhoww2BuG4ltP6eZGXfZdi4autFm+oWneJ5fn+nmN1cSNKy+njzbiIbN8LxSbYI5gL/KS1NPmdU6pfHLV1NZe1ivRspBGURrx3Taq9wGTwcfyo0nFsEi5NDFGwGC+UCvI59q6KU+RSXvSTbTwvQjjvkjUYZCuGIY2sRF2ujdrc5y9jKcsWxSsUTiUV7wMOupKkgIaCfMJ2JVHbtjJUVNNldfV1uYGpq1PISqi+JAcSyrchpIJ8pP23OHaae95+Ea56/ztGtN4jp+K0uA3A0zWQRINcrMbHGgUtfi9abiFtCkZAtDzYrBoESQhvc1xSJJyrvD7kqy0v4wtQIMCogXdxXQw/oFG2U6PFsZEUQWDbXsMrUaBjCEL95HtcYmznr1AO37Jr3DMjyKxx+wkUQW0J4cy7jhjuQNe20epa6VZT4c2HASeMH8uQRBlUjBqi7sbtYK9lHdhqzjUDWDFNJ7/ACLAsinSQU+ThFVxYk8prY8Z5QMbKgllhdJeMS2yxRAM9XOarxua9z3leSsZBTKao33Sh5xLiwpWpfiRc6gPlN49Y3xm+cV9DnuardzmlpwhKCG1PtltuykhITI+eJIA/lExY4rt3dvrxPjylk54FCBcnjiDoahhJXk/qQTmxGoiBT/jI/dOLyNZshSI+OH7k9Y5KcX5ONyNdvt+kVaIjkRW8k2AioqovtUX2uyru5N+iGkfTJ75cPkAlXHb/coF02qgNi2R6Q8Q57mW2tgikPh2imaQ084BCe1VGJX/AM9UTqJTvp+92tNeAxix0itoltKWwYKK+yqC7Eqo5TzhtmAnuArQsRF3c5XekVV36uGvqghaEuuJkJE6DptF/X93gXHnK+j5kMZYhYNtCgJPlsb95kSCeMOQ+qBgHab2Wdu+A6FYthBrLUnV7FabUXGpUxkyyfgdbl1XSkzC5WylsYh3XMiC0FPBhIRsUPmORzUc7fnBLRxwU6XsOxYWO47gDaUbmP8AlpuhA8f+XYXFUcqbI78pv12R/wC0N6GDP2c9sGqsCiHLkwQ4NAsLtKwqzK2mNptXtFXfqLRufFrCTWpJaM7mM+Y97FVXJt1xjEcwcl6RBkEHYTWjJ9zmvXdCckXdGqRyKrEdty2dsi7b9ea/wFyipybpeupM1XU1Gdf6s4vPP9SfVVOrcdGqldp1KCCylaYWUpSoApKeZxt3WdcvMqjK69RbZpRlTLLCmGkoFQ6CNRc0wGy3vB1yBc4leN3kwnxobCl8EYykFF8iKxCqx3pF2XZSqvjau+/J6J0xPQvA4WWY/fXcosmNOx5lS+Dy2isNImTQsMjppd2NdHG567vT2uyo7dPQL4EKOdSEeb4Z3PY1klAMeReDmuchBL7ROKO3VURGp9y7bb9GrppTZLZusYjpc4FeoCPnQhPcJLAEdBKnOMVwwOernDdwMqM4buT7kb1t7hQ2oJB3gdt+bRAn7dzjNa4l4EAOK0iQ6hrU2qI3UCIHckGI7mQVMTCAhQtwcxJUdtsKnQnkYxwWxhq9ntPZ92K5fO1OD902/PsnMPoseSxkUNgdWQhyyx1kOKiMHHZWLMMRSekYo/tRn91cqIvJOhR0rxy1vHsl3dxOhBE8j2AgtV/3BREDxOV7lcRQbbsbuvtPHuzbre6/ZjIxaFXYti9gY9lk8yNFGiI/9VlFleKO2M1WopEeZSOa1ERG8mor3tcxOpoT4S1AQUoUsK3+UTH1ggERvIHGF0MOv1XwxVaUBRJ0ghSkjcciZ9dp3xm6wa30tWa1w3Tk0o0KXFlVFhcGepxvhlaxsiNFexv8lGPaxd/SopF3dxXboHpNDNPKYUo3+aSxVcNibo30qJuv4322V359r726szvbzDIdAtb6nTfRq2FeVGM4niA86pJ9ZXrU3uXyayLZ5hSViiAKfY18WeqRpkh04pHWEaQ/mUasE2J6aay4nrLDe6NTfw1lNW0y2UGO974VqxxSOMsDyI1wDjerhNjKxovsRpG7/lRTnSqpYQ5TIaS4VNJId1GQUwY0jeLCe4nfGgM9OCiYLiVhRaQlwwLEQJJkkiCQeQJtGKrssEtIbjTX175ACo4bmk2RpGK9jnjRd1VHMVrSNVPaKq7InXTDpPobO1px3SzX2JnWZwMUs9Iv1PJ9MJOQnj4PImxMLlYbOksjIjYhBPyiHNnPb8mO6uHwhI1r2IqIat8lfOEldKekUFaMngEwI2N8PJxPKdo15yJSc0a9xUVo0RGMXxo1ej87N+6KfaaP592p1OU1OLahWluK10asb8sZsaQy+mRmZ5g0EsxP01s6SgR5TQwZrfimt0s4Y1eeyY1y91dltXXZeioYbQTRPILyVtB3U1ATqSSQUQd4Cjhw6Bzaiy3MvhapbhTWNq8F1DpZSl0QvSsAKCkxtMAW3FsKwk2Omea9uuRaY5lS1kPMe13Ns/mxpmMuF+rZPX5JbuHHyC0vYwyktamBPhgrp+5nEB4YRBqiSSOca/b1V0Oq83sg1ax2vtCZhAfrVjmqkyFLS2ESdhuFgdGnPgPe2TCDLpZpZkuYc3ieJyjG9nNel9932Bx9IdTNS8Xl2V3UzAkLW3FjMvop3ZrJe2FJlRnVtKjwx5ElqOkyotokJwDBQbvuTilPdsveXqh285Bd2GnkoVdKtsck4kx/xFkfCrrsMaLfChwzEeJsy9gxAVcqUQaqgHKSM17kROimVsqoaduqbPihTbCg3JR5gklaQBMaZva/ad/ucstZ3Vv0L7zdEHVPoFQ3FSpAJTpU4mWrq4BUDvjp6yrHdMDCqMy1W1Zx7CMdxehPHlVq21ILLrclhdSyiSsh2ltGr48V7URrLCbJEGU5dh8eG67XQr/dL3jzMlx7su7Zo+olBh0QzMw1N181wiadWNjaRxujW0bSvHMZJNn5kavktmSI02TFpaGTHHDIOxjtnj83KnrDrVkevmXUZM7yO9sK5g6NMomxxCM2CQsgg5b4sNSxhzpFRVkjQKaI9WohYzmFViyTK2+tMNeqnRnGsyxzTmhzm6ifOn8JttlNhQgn0DR+KCydU0kxi147CC4Ei0qw2T2/MfJiPK4PkGjnVdWONULKaclt4pAKQu4NhCjpEW39xEYSqH8NumKV54VrYrU6ShdYpoJK3CdWvwgpcTIlIcN+wx1Gava06MfTOxadg2XZ/prqXmOpLTWVXRaX5hFPmmmQT1AaiZMzeqvCHtKttdbtjjqEq5E2ztLhrhyIcONCc6RyE6g641dpmTr6yvz31mazMWfeQAjPczgsYRnzWXM+S5WJKcriHWTFGdXOe1ibp6p/K9RT5JYXNn+hU0GVfJVvmRaeECKIMiqmSJXlrGtcxYQ3MR7XCTmx73IVTFIXl1UJqUD5BZYHHAF5OSvl8WNGrvvVrmue7mRFcq7ptyT8b/sp17xzNxp2sMPpCDqHmOkRpE29TvzvtjQcmDXSWU1FF05TNFysUUOPEuJU00SPlSlYBMTNwR3OLFt8qU2WFvgqaOqWDLCITzHVxWFUDvLIlPahHE9ciEAjk8ybpt66bTiH1FtVKfSqjx9mdTZESyBPqLSqiWMpZtbEhFjRozpQZQwMX50ZomRiAI4jUjORERXe041kKVcTq+opBGnSylVEOxm7lc1qq4jWKn2sYzl9ztk2/dP2fh2pfTnkR9HrbW3XCpGGpqsdh5Pi2NIgI1tkH62T4NLPJvwM+odKRZr3ha9Cx4r15OGdjnOWQ9Z5jkjSsuofDdU/p0+MnxEtKATcIMhXfcbfeGry/K3qV/Os6KWaGgbC1urBCnXgkEssJWsqWFqBBVqMKJgbYu/spxu2yLS3uE1bzHGa7Kcjm05YWO5RmsuyhZBRYnZY1lUdpMZjGrrH5cmdcCiElFcsVUq424zOa7dbj7Q7c1NpXr9UmYSOeZRRwuEVP5b3Cx+ZDaBvJznqIZDMVquVFeJUVU2VW9SqkvHV2j2r9MyPHiujaWYIJHPYSOo2vpsp4CRqNbsJGkG1SOTdXq1Hfe7boWO3jJFo8F1gUckchzIkV5XC3RXqtTJVWv3XcrmL9ok3VznIiJ+duuqpyqzRjNn8wIcqnvEcUtI0JDirq0NyoJTMwkWHtjzfnvUFHmfVeX11I0uny/xWEUlOpRIYaaKR5pCRLqYJGkadiSBhhPdl3w5do720ErtM7o8Sj091DxzTvGamGNxa+1mYFjwGZSG1gMTxzI13fvlmsRkK03xX+QSscqIiVPqDab0GVWXbv3MYgKsBjfdFhUQd9Hgyfhhr9SxtbByUU6IdEdWkaWRCgvaRzxK6mOb257ndMZzLM9Psv+nT3SYvjY8cq9WKDX2wv/1bJpEd9vl+PZK1cmliwSVLa1kKyZjBmEmVsbiWfGiyUjkkGI4KLh1YuDZH9M3t2ZbCX9ax3WbOQUB3MM8hagFxkQlYE3HiGO08gZRqq8SO4oirunWbPZeigGWPFvU6laitU6S7qIAB3iD72E49MUOaVGYKfV4dKhPw7YH8ErNSkNpADp1gLKRsbwRtzgBdGsCxym19LpXrBTSjurZdhWyItdbcGNuoxQCjSAzIiEDJhnEkt2yrtx22T310eaZY/QY99OHuRg1zpd/XAzw1FU1BkNMWpiyCBrrAwXlY0ZBAr3SCtMVyNEdgnv8AtYqdct1HeltMpssovbWcPIj3HnW3RHqRHOniAYhlCimakcLj8FanJFexGpye3frS+mvPqtcOyXK9OY0oVpktxkV5QXFPWujmvpkvJJIIdYsmFKenimWbijACcdrWhCV0gjkEJ7uuOuaarqOnkvoSag/EJimNgIgRqvvt8tt7Til0pVUbOaVLD4DLhCkIqNUFKVWISkD1mJkb+uAixfP8ot+2jPtOtZKS3pnaVTLfGKLLI9dbW8SRXRjyCUljkVbWr8spIsYo64cyH5YBS8HSHjRrF65yM1rgSss/h2rdKaCJaHKY1rHWrcJh5jSNCo3yDDr471ejQtmNGuxWOe7kip13J696P43pVpRikvTSUPUaTqTrYujeVixWvLllbWw4EUxNQrnKbePDmCRuE2axIzI8gtc6XHQKRWWEqbEY/kI1606uMz7n9SsdxM5MhspWYW1Mx8WtNWxyghTDwxR21RmNkRgihxYrpoHM8gH+QkjaWhHkW+hsuzCidfL7LdOy+jWSW50AwYE/N2Ex3F5wf6szjLs1oKVKKxVTUUz5ZDKyUBMDSlZVJkEgWKQL77DBjdkPdpQdvN3j0CKzIc1i0MolvhrMdr3w7vGcilpDDbVfwkIgryBMSIjX2LDfBcxjvMg/I1XmxqtW9lHdpnOKZiSs1C7bMioLG2ts0xaPh1aFmpJpxi3qLjtqC+sIWOXxb4Ymy1uBQaolSSwLHkOmbRUQFZZazRx1zWYzK8uVsIWpkWsKS5nxwMIWMvik78wvKYEhQI1wjSRJycVo2DV9B2GU5VOmPnTZ05CGahyHQhfKVEVXjM2W/wAZSKpF58mOVqO9K967u6cKDL1UGZPZjRLdT4mouDWQ2s2vouN9jMid+8GZVHR/+2MtyvMRUvZ+tt4vKp0htVISkFkh6FlySRMhNjaDMdWhKzSDGdNcx0Q05xbE6B+Q4FHl2VAAcGyyvN0pogI/8R3cq4Kr0dOdIPKVW/pleIsljxiQqhUor413s6W4HgVXp1f5hqPCvMSrf4bnVEClu0q66VTyHxFgxFi2goTxQ/jiEhAhZ5XBaR6KVVXpU3a93OZjplm7L+Y5uTkm08zF7Qd0V8ic+gsmtSRGrbEpnSYUmMg2Sa9HPNFfIYrPCxz93YWpEqJb6jZRbxGqatuL015Gcg+DTR7hWzBkcxGo0bXFMQSDTi0ZBvA3Zw1amm5bnSahkAFAqU/M2q4KBEqneZItE334x5tzP8OcxZqHKmuq6uvyV5Knm6hb5QqmfKk+GyWkg6gGys6ysCRGiTOGfWHfXgM0JlFnuq55DhKrVLHyVuyDkjKMyPJcubzEZrCjeiIrHo17VR6NXqt7Tu7wmYi+LLtRlktMRxDvHbvK1p28SuaR1zzapWKrTKmyvRdn+t+gDbj8ywWubRwHS5F1LiVkGvgCMWXLnypLIzK+OITXrJkTHshjY1WuYN7m8lTdN/zLsGv8CsLKkySGWryCmsjVVxXFUTy18yK1FMAxY6q0ivVFAYbd1aZrhK1HJshJWZqKQjS2FCNQ0g22/f8AU4EU/QWTKh1Tz6tSiG0F87CCVAzedthscf1b840Iw7uR7OKjSHUGDDmUeZaL4nVmkSIceWtcpsSqk/UIiSGub8uM5FNCd9iiVWqu6IqL/MT7w+37EO2vXzUHTLGswiZ9UYvf3FVGn1YTQFrnjmSwRosj5Ef4zCxkAF0toHfz3Skcx6e0T+o7jd5iNtoRhOITsvoqqys9J8OrxxT3USLJbJkYbXMHyA2QKYiKrk8owo0yM3Rio/ivX82Dvs0VNpj3JasYpb5NTZlMjZxexnXNHNNZw5qqd0gAWTXq8pCDGdwlEZrkdKjHjrI8oStTBcqbqP8Ae1N8NUAUFTlDqataf/uzCmfQlBWPkK0NkoHlkBR7Y9CvLap+k8ydrGnVON16F0NPU07lOWGXjoWlvxYUW44BEaZ5JwGumbq+SEx0IGDPgmGVp1lTFJLN5GNWHErG7AaBBOe8kkib7IqtTfZOmaaCYiTUJsesHYUWNSD3SUy+Fh7SXOCKBKs22NqhCIjzEWOaONGK1hGvRGudx2RdeK10ejmwCRYIzmhWoJsqttYD5b5o4zxyFiSxhViFrpSIgJIWO5ljkIxjXucjFKTF9UckxjNbfPaKtrsfGtu+6TH6eG0FFjoJMhzxxKmLKKUwYkVTGgRATpEg6xDsF5nPYwTNWey5xXn1K1C4BiLdxwO/5bYzx7PFeCWKfwW2iIKECSpJjykkk7bxfvO2CRvrldELLUOKa1bZWmBXkugpgrH3iyZwlbGbMCNF/wBOVrSrwARH/wDCVF/Cr0MWnN5O1X180yrsuOzx5Hn+NsPMsJBY43w1tESc75wyICNHM5nxyIxipyEgWkF5Ho4nsw04n6qaYapd2Gc2WJ6eaTAtZl1Mt8lsbYBtSMzIsWUfT7SOjqEWzvLokcrlsLOa5KajV6LLZN3bEcBWeZ92YBxr+LsAyTWOdeQrosAWDZJKpKqzFHJBDNiZDCvqyolRpgRTnmrJohrVyFJBYUYCNlGLDFu5knw3aNHzqQUKUJJSYEgXt2PeZxPT5TUFaK51CQhxTSimSBpSpChESYNpv3xduLac3epHcZMqciusg/h3JNVJ9A+zMsW9dApv4hlxZBYDJrViVUaHDM14UjoJGCjvKZXPQxFI6T2p1Gm0/OpuGwWSaR1tGHjmQntGTHLjFwSRGjXsiIBrGBsMgjxiNjRwK9oor2S0axCMV6jNLe4TJ5mXNqJFpPbVWc4zY8qI8grKMF4+UkDSIdqkOsMZmRfC1NjncIznhI4jm+5dr3qhKwbEbXN9Ma3TXHsxoaU2G1URkmFBZVYnXOhBjxpjzFSRMJVQgFvHSi+aIvF8ZrQEY5yDRZRmP+rsvPuLTSMrLiBsFLCklIXNuTMAcbjGjZ1n+X0uQONUdAqozB1CGnA2CShspOtYM7JISJM3InsBx7jsZl6YPrpBCmcC2gKaOGQNwpAJkYYkmRZXpPud5WKrU3b9zVark99LylW879WSxhypAjoQUiOcEmQGWGS1yqJ8aSAjTAMPgqo4KoX0i7bOaqty7nGxtfMCqLbHRxaa1xecMi45YTVmyJEOZCjNN+nSmuSRKG58dTJ8pVcJrkY7ZjW9V39O7td05zvUWFqvrdZrUaO4Bqph9PetmxyyIcmQKvtMsvWFQDCvcrKWjh1KRmsc4sq8rxDRxXo1X95fiullDSnQ8jSG0AFKyIML1A+UbgiLxjNMhcerVobR/CfQ4khJnxG06hqJPFhFj6kYqbWPs61IttJMD15zi8iYHVZPiQbHJpWohCRMhyGaHk+HPxau4vvMj/XoDBsfJkCBHJNdImWZVFHCx69rjFMfgx620xq4dDjih8yEmu5Tm3Di8EC6HEcjoIToi/HeHeMbdGlGMar06L6uNhlWvuqE7Lqv9Tp8XwjFqytx/GQx54qQeBVjgxsbkVdGUYjwT20ABiWL56yohEZwYwJkRvSQKSXDMCPR27Q/AW0iWsqQ1qim+MKOakL5rV5NikRUV3PZURV+7fqlT5ZUNoSurUKVpK16WzsQSO87C3bm0DGvuJoW/CoqWmaNU+lsOVaitSnFpACjOogFM8ex3GN9gt02jyS2npBizY9rAeCQ17kiPQsmI1qlDPGnzI740lz3EfEXk9vMPJGkXoqsJuMLotObatspFHAsblOKzlqp0yxawbV4th2Ay+QQ3l9uFI5uciKquVFVOhRyYBKK1hmr6qbCo7iA6dTENH8wJ0ARzxZEus8iIR8EcqLJCUmyopgkRFVG9ZFTOBLOEFoUjYZI3zYpCvKIRoq77mG1rVb4kcjmqTdGNcmzlTbbqtXZY06pBYW48AArUyUwIULXF5sfrxvioy8MurTTuoadHiBtSXwopKlJT506VC9wBuNjsMeRqmiLYWhZEx6SAI/9MVomvbYOUzFa2Ucz2vhtcNHfd7RXK0eyucnW3otFdU9RrcFbjWF2N3KCFpWljxllsSKVUeyU9qNTlHjjI1qvRm7l2b9y7J1YWmWP1J9SMEdPiK2jzTKomM1cx4QzTCkfOqxyrThNVIrYcBs8T/KZrmke7xia97Ho3ol0kwbGdKLiDX1la+O2vuSRLm1h2DyXNyMLiRpIZIji+GRDDYU1dBdH+NCk7OGhSOYzqWjpEVCXtQWF0yUapgmIF1biBFtoj6YH9TZ9SdNU1MX21qqMxqVtUSGAC24vUkBCwqVQmYsbie04Czsp+l3nqalwqbVbF7HFIFeJ1xlsG9itrpX6dWsbOsGSE2Q8COQCsHyKgntccTdtnonTfNZNV5svBLKvnVcGmiio6Onr40COkOKDGqY44GOV1aLdrXxY8QLClIjEcY5iu2VF3XS9q3fVQ9xutHdDC1IwqZiWR2dJlA9NpY8usVyE8GvCIMmsydPGEGQMeCOCQYsH9MUL0HGcIzuKtq/u6uGsddBLXProdPT0DI5mua+JZEnKwIghIJqvZOgkGLytI4nEZYw3EcXdEKZOxRv1yfDKi4gpI2vZNttgfWbXjbGQ/idnufeFljOYrapqP4Ylmip9YblXmSt6VnUuVTvBHGKShZISVgXcCU5HI9uEYfFa5zlIqoCJcqVEXb0g2kHs31xarURVVF2EPRm9kNxDVlo2jkNLHY0yyBva5rFqDuY4XH0rhtcj1cqbM/KqibqlqYbLtJ+JdwmLsYkmetFiDxmGQrikWQ6bFQTRqquc5yMVr0an9bV9e06pejLo7gdPk+PZznljCuo9g2VbVuKWgQBsnwIJAlxewmPqZwpMkoDqQwmyBgh8mKUz3Io0N5pmVBlKqwVatBU2TCSke8H2I9D9cJHTPTObdUeAqhbDqmSFKdiCVSkk+WwBk7CRi08r0Yn6w9h1nmWk0ZLfUDGbLF5+bU/6hGYa2HBsT4qCzZGlEYnwxY+eujSTiR+7BmcqqxVXqK95WoGDaednPaB2u0EiMfUfCX3uTaiMPG+M+NMuiTbiZIGRr3MkQ5lteTVryc3DfFgCRv8AQ1OtdpJlWSxcYu7TRzKLyHCHWurLuNarFOEUOYU6DnDmxYooc0ZVM1TPl0bzVpRNUUcrEY5y9NStI9YwJf6rai3A7aPEsa+kk3BLT5sx6WojHqI0IXD48eIaII6wInBrYgmk8vjKqIidTURzdHjUj3xlLTrStOgguJKlJUQoQRA2sATEWIvu2X54ciFHS5w2KTMG0raRRrCtb0ANhe/yqGlQi8HfAxmimcU00XNj5Vi5yDa5RkYYxiHRRqz2jUJHT2iKibJ10EfQNxPJrzVzWV9fkwsVPC09cwdlY1s23HAisJNyXIZ9dVMMKpZkJaXFz0kCyuUUEVbIpxq8rBjek+nx2YSrfkDIBXR4xCtGjRIZoWOAqRzOGqoj0UiSmo5+ychvarkVF6K/sy7hMm0K1cjZNTTJHKbWXcK7hglShAkxiUNpAdBkCjkCx8KSG0lMaOSGQ1hHsKNd2tejlV5a27lzlOU6tUqTIBhekaQkR32HHN8LLmZldct8OeF5gl0oMaVqIhIkzO25M3jHRxgOsNjpn2g3FvnpiYFjeP5Pb5PkGFychhUU2Rn+Z6VyskxejqrS3aJZd1jMfK11Ayu3mskqa1m09Ag3FhCXrkLwvXpdPCazZSGPNkZ1qBW2dLjmRlkRjJjcrJrYq5VkR2vGvzr09O4kGteNGBriySTGM+R4FUru57uiuO5a90rwzUjJ1w7T2ibPzHLZUKKty6Zk2UFgVuQ5Ietc5HTZMKooK2ogQxoiir4fhE4SE5NAjXLFcAx7MbWj01zqNqFhgIsSxx/K4ldNowzIdjEGcQZlZbTJ1gG4iPWRX2UVxxiiPAJ4WmDIGVq4ul8EJQ4EpOhLekWkSnedz6fpg61oeAc0iNIuJGsiCFKk/NzawtbDRPpH9umg/cLl9+XUKkq7/J6idDhYth+TH8uN2sW5KCtsrOzpgfCsrWxrIjlHXnrbxtwfIrSjra+LGq0sWyg+7stIh6X5FLhrCbACa7zWpYF0KHFHEbjeSWNYkd74hHx0keMI3Fa1gEcVytChlRSLV/adqdqxpdq9g9xpNZxomXPtAR6ePMMOLCklnOHDLWzpBiMGCHPAU8Q0t3/CIYBGPCRjDicJ9QbT27ybtdg5xPhPxObUZGfMsowA1OGOTG7bLgBq8imVNmepW1t8f+bEY+tuIeSz8WgRZcWsrKp9mhyvhChTq8NIBQvfVuDA2g3ne4M3vid5Ty9C/EUXEESohJUsCISSRMDiCOfTHOVXzTVliGWxUY4B0IqIrm+kdsquT0v2IiKifsqqn532cx2GL2zZvfZ1p9r5pnMzibcYzVZZpXdwrg0CxxwzZzZl7SEE2dDFYVVpFdPjqBX+SJJAMw1TydJekDY0xV4INHEM1UQSCYicyMco2I5yNYrkVGqnHyJ/NVjXPXq69OM3vcYyDE7Wht5VXcw6mfEjT4TkBIE9pSNE3zOYVCNaNrmtYrNt1X877rSShTeYsLQtTTalgOqRY+HvIJtcgSbccScFg78X03nGVrdSqoqmSqmW6bofBTCEwYjSVE7kRAtjqoyfQj6fFpicjIMU7Y83xSPj0yHbSpIc5lJ5BV1vXNPGGxL7mKwt4hS0laono9HmmzN0WJzaM2Yafdj58kgXsbQHNA41Hl3D5tLKzE0+TLHNhFi1j1mSLMh0lQpr2yjnM8j3jZu5XO3XpUU3un17lY1FxF2oeTjrY0wthJc2e/5FjaR3GQHz+Axo8MYMyWgBNGxqOIR6t3QfUGNrVqnJbxJqDlDxrsvEli9zlT87v2Tirl33dt63X16Xp4ZapwlTjjrkqA0AFIlII0mIPmIN+OcYU9071GHWA1WMpVTgpWlKlaDcRJ1TMj8+Iw6HJdM8GNg1DY5V3K6sT9U8mo6Cwob2zmZPArawRkbIio2gky6qyjPl1TgBx5rpoaxWOAYp/EGUwIY0dNcizTKsC1WJOtpFHit1m0OwubOrdbT8egTJT5rLexiyraOtlEHBNJY8Fif45HmGaS051Z1V1T3JUFpV1FblhrSDIpIlfSgPGHIs7aRXwIhgyTHNLMRlvZzprAHbNKUQQgACK0TQNVFsyJQYXqVdS9TanVWRkWKHxtMFs8avKlcNy2Hb2QTWtjVZNLWQlOWquZCnvv1yilyASQDkV0qJClIBkxbaocuy3wDRpSX/AIhxetaNZBqVpLl+EA2SB8vGNJrc56l6jcqz1HWuPUgZcUlBUhsJIRKVAICQSIEW3jBMdu+immmr0U94SS6OWBZRIvhBdMkTJjH/ABnsY4cCPYPHHA1BtkunyYzneVo4TCHexGE/iH018q1eyoGOVeS1VOWwyQNLT1TF/UINnEnWAlrpY5bVjGX45io8zJaHOJYysKNjHI5Qew7tV04M6ssq3M7jFUmTa5xjYrnML9VZFaJGyzNBEmutTMciPUbxAeg0YIhVaIT3IdnZ3jV5ol3FYzqDjGtWpGYUengL/KoGnFpPGSbn982nsItBhtd+pGihU11k0mljo95Efu95lcjWOXopVVFSy04suJhKFEjSJsBzP09JPrhAy+no3cxp6dmodWXHkICCSdUlJg27g+nobwCv1HNbsAzDU6LohoVBk3WgvaVDmaN4VXFR1VU2s7G1fCzzUZIm5mGs8ry2NaTGTXuPIbESKxyiGjWIiW2hukSZ1gIDIBCTDGLCfu0onF+5Bs5sR7AePxKxGs8SvUrhuejlVCkzhL6sDrJaTDIlqmQXNRdsDISUjbefayv1VATAkeOUx8lJjmFARfMIKGaqfcqifLnHKo3HM+SVUaJHkcj1YEY2tjj8qbKQTAeNwnvVVVSParlVFVUbKwpb1W84dSlL1JvIAKh9PSPe3J2muhtiiYSNOhuFJHfSnc+hH325xJcWuIVD4J6gU0uHJGQTNnEa1xFQfIZEYvhciu5PK4bl8fJGtVUTk1rAe4TOLasoY+R4DTawUEqhOzHMHvYxy1cRbGCwHkrmhOJsa4IyM6OCyQgVEd7CnagWva5TFVBLMBDiiGwZ7KzSOhmKqt4jj7ta7blxYpSNcV/4Yxvv8oqN/wBE7uTohoFItgWuGX8m4LIonQh3UY1rXla7yPNXB8ZZQRhChYx3BeJVa/caqqL0arn3PhtYQFlsFelB0KISgmx9wLc3xFQtB14oLqWEFP8AFcIElsrQlSAdxIM2vbBLd0OjOU9rdlp1Q3sY1YTN9PqPPY1XIsGWRqJ09xY9piVreCiQ41lYY85rRy5gwRxkUghR2yFRyo0H6XmEYFnOX9ruk1mOEeDkOb57qTldC6EF8e8sqhlfcndZmVELJf4LnGYbYipyjjgoNGoJCOQCfqL65fx/Qdqz7JsOfnkrRyuyiORd0iV8XNWxLBGHZJVBnJWFhyRQfnOaiuehVb7VOq20r7iMW7erHRmwg5dkWHas4Fmd9kE/MqeXGtImS4bk2IU1HWVDgHOkKrmx81qhRsg8D386maBRPQm6ir5J1A43lFFnVZSOIQPigsI/8hUmUoVybEexPrY95P001VdX5zk9HWNsuilS5SOOAAhKkpUSokjVMkg7/ng+v9odzSlwTW3OaPBS11XdkwnGoMwFaIEEKVEuKZ0qDMGaPHHIlzwjtErHwSyGxSQHxpTSyigA7kmwkZLTLKOolVzZCWc6NBBClxzgGVtqYUUU1HubCHHixXHaQcgjnv2Y87AFEErmuc+oz3Q4X3RduGkuctyGFk+vWQap2GP5/j8yM8mSRy4+6TMZEjGY10YdHEm2Dogw/Je8jHiOwivG7kue7HlegF1VZVMu6K+zS8iXMMdJMpDyo1RQXONviAOwNrFjQhxpEW2PAixgna6vfEcqo0kkaEG/7lXmjUutOJQ+64EIeB1BCSgIKTEgmxNr2OHyv6aVlCKGlbq0VSqZhD7zyCFLDz0FYLgJOmUSlP8ALxJnEoyWGDKcSx7EB1rIEbEIeSgwwcKOYVzmVW+0fJyGcOcyIUJ62uINrKFPkpBkxRyWDO6UQ7WUzZ6eWFGlkF9x4o0avrKZYT40ybcpWyrBZEiRUvjMPVP8iNKF4HShka1HIiNcjkbf+impeM1d5idVq9agrKt1lkVYlnlkWYVuNzJMFrBwZQhDDaw8dLasgT7IBooY1ax6HWKVzzAfZfdPLxKlyudCw+rhUMC/oqCwl09XPkXFPX5B+nDjXLcdyMzXsssdt5cU+SUUkJXK2tvRMjIyI5jlgOZ1FKpphlLgDqgJAkBNhfe9999sHMoy3Kc5LrVXpFVTo+IS6VAGUwmFbyCU7ScRPSTS4+s2ptbi1daRNP8AEMOpyPqJ15LGGaSZyJZBmGd5ObLS+sK+TZGc/wASsiRjMGJfANj3zXkWux7QXO9VrmvlGxjF9P4mbTZMaaWNKbYzoEWVTwY9kVHc59pdmix47BtK47pCoNXI17mos7IoUzUjWus0kbZvpYWRWdlmtvLjy2xZBanFsJyirfFgPO0nlOhbv5MUJFcsh7XBGiPej0Y19Q0+tmmugeiuA3uf27NLtSNNMWLYUAYNSONLvsWg1xAWZDwg/NOwLXDIopB3JGIDyDYjld1YybNvg6rMqarcAFShCGyuCQpQsATAjt/fGY9d5Ic0zDK3Kf8A8GVuKcQSZbLgI8QgQQTMGJtJ3vIJ4d3U4hpTm+b6p4vH/iXI7edLi4DUT3yGWFVEvCO+W2zmtO8JYseErgzY8ZrfNMUT1c7wo7q/BfUBh60yBYRqViFbikS8tqemJm0K/ljj0TVtQjESwrZleclmjPirIe8UqMjRiRiN5IikStaR3QLgrGERWvKcoHRieQUdrmkUQGPT8gGhWlG/fmbyv5Kqs36klrOGGjDMSQqySqCQMbF3KpUIQT2OIv3o1F3OxzdntJx2X87tdBQLy+Xm1+YgLCiZusBQ7d/oANrDCfm1NSdQBLdayHVNpLYUv5oEJsSODYRt3Ix0U4PST8Ft9eP9YAsguN4rFBYqIsdslJEycyDYQQyFYbi4MgMwKtDwZyehXsc1EXbdl1fUUvb3rcXMNMdMcxnxc3zDJcosM1tgMyMEWNCgmx/46EprZTQrKS00sxQlZKhvE8pIpUawJIVh2uekpOzXA9cdQjlt5GYDiaQZPjQJhiZfkuVYa6wO+cj5hRJ+lQaB1Vc3NmL+VCLNhV4kec6IMPck7n6vOZVFpbo7S2GAplmQmobqNZWEqUHIQ2yNjV0OxIAqFaWK8YpMN70cBhOQ3CVHp0r9VU7+eNrcYJSttPhvLM6VKSACYuLmCDPtgn+Haj0dVv0TtIHGKl1SqVxJgpZUQWwpXfTvO5n3xizdXr/RjNNSMVosVx8kG6tJdsevqTtySmrgEiOZLroEsY4AZoxvF8740Matb9/ha5rWcofr9nNXaaOY0ognYmoGYByQda3yhbXkxumkwZ4PjSY4lLBPKslPBeqq6OPiNGNfyakV0r1WJg+quXYtk2LxMgO3LZSkt1U8e5qFpgloTjqTlRzRkkJ5SHV4nq97t3Irva2dq7p3Wamm00zHF74bdMK8kzB76DYFQt1hMqEiXljY2go/uTEvll2cqpnhANhoEBQuc10dOZjpuibyYsPt1immahtIfbT8ilJSnVAEASRfn6kDEfVNSM1qBUPUAeqqd1fh1KjLyUBUhIUbxpsBGwxGdN8Fms0C1ByCwSKB8ulZY0UEr0NaIAMkBYUzwLxcBqnNOG7lvHRJCIpduW1c6IUOP11bmWU3wlmWc6RS4hiFRH3JJfNsp45N7Zqxnt3waiIsWK1V5Pkz47Ea1jnlYfOdYFi9VoPmuR48sWVXf7ua6ixq4hHinbZVh7aoQTwyQmQZClcR7XRXf6wS7Mc3k7boW9DlxTCMBynUjI4ZZEXT25mahNORshGSHYlQvSspGGWMSOsmyyc9SxscjxrJYB6MQjmuZ09tVtPUU/iNLSkJ8xnexvvyY5Eesi2a06H311rSmXkKqK1pbYVIhsOJCosTESOYv6DAP57gUlJwbMbhOp4GWZLiB2vKn6kP9KtppxkLFe3dozNEQLV5q9ytIiDVNlXz7lL/AEnu7jGF0u09LhMCJgePVWUMIbmS9y6vCUdxk7Qv4fDSwe8bPjAQjXtEwruDvs6jup8i9hzK+Qa3ZOTNKSlzxxwIVwUlZGM9hIYZovI9thXznz4krjx+5eTmojkb1TMmfZ3NgMDjuK9FaIkh6EOg4yJ/ML5CtRWbKjERi7Kqrx3RV6VaxHiVAeWVBKTKIJAm1iIuP33xolFLbCGBfRAVbzWgb8/rf2xJ9Op8ipy/FbuEV8QtXb1ksc0rEaMH6fMHMaVVIxRtckgMZqPNsxrFMiMVytILpu+o9rFML2Z4Qcw6fK6fWHT2hrrm0o5kBREtqbIq67qZtnAppwogxhGGdGIe5q6LyLPK0DpdmZ743KwhJgZDXgc9h4hhkQsZJJ3j4lErUkFjseyM1SI1CNHvIa1vlaiCGYojg1S1P1EJpHgmJW+U5NJwejiHLDxDIpqy66rmTxo6TWRJDGPfIGAhiPFHO9joDDOaAIzIhVHvaFqBvKSCONo37x/X6Yuwob+1x7f2P39MAjl6wX3c0lcRpIJTuJEaxDsGKORzihAjZTWSWpHE5gdzCGZ/BHkYjnuVfXFpDnW1U0Tdnx3HRqMdwVznK9yKjlR2/wDxW/lqfhfa/jqOSHved7nuVytKXdXKqvdsV6bvVVVUcv5VF9+91/PW1xsjWXtYqvYxHy+Cue77BMcgd3PRqI5UTdXcU3Xim/7qnUKwVD0ICVxvoBSbHcXAv6nH5rS2oeUrKpgEk3ix+nbuZ4wZuOYPqFl2TU8PGqV13kdnPi1dVXgkV6yLObYokGPE+OaSFpDGcVWtfuh3EYu4UVN1uOV2M92cPIo+Jy9B84iZHMBOlRac0euHJMGtGEs5w1dZIHaMyQFXo4rV+9ERF97SLFVTHJNdb0uo9DUWUOZWWUWfCddR5UA1aR0qFMhTErHjZIHIJyVI71c7ZGoRV99Mkb3kVl3DrL8/cVagyGI10N0i2s74VlEU6CgyjElPrSiHEnmExXHcZoyDGzyKN3JEO0z7tRAbKIaQhMLMCAQLzufeN+wxmlfm+YUTlQy3RKqz8Q4pJSDqbmPMo/zJOyRAAg3vOEygoqwkQV6/BoBa0xCMUw7+5jQTkZIK1HLDbPcqcl/LGGYi77bpvv1ZtFeSWsjCCMAq6K2QOHUVkFgKesDJMJZAmRRcl/1AWtNIJIIZzyhCUpnqELmDaCJOSXAZ4AGSZLX4wI7wlc5TySuEPaMR/l5fy0V40RV5LxErPvS8Y9hmOKWEW/dEk4+SceUkJyBGIKqJ4wGix0MhBljgG30QkYiyFe4DXI9OXXxx9TDY0BDji/KlRCICjASSYECSLg7RtGGaqy01DgbU8ttKY8RCVGSkRqAHJIG1toxc9nAtIsMVneVMQcJY7wRH2cWvIWQ8C7JGBFI0skjHsesoUl8dscYwc1KiuEimn2nz8808xDWLuFbheFixKiwJ+MUl3nUyLT0RMin5FWSWFroE1pX5BHHIgRYRVp4YUceUCCW1gLIKqBDc6v55kVNjoMtydL3EIllXRpMOVWVVfHqCxHIaNDs5MCqhSRw7GOMxK+aYzxFbHkBO10kUPpgOoAKPUKDk+u2nk6HnOl2Z6BD09g6e5TlEOrw7Q7JsNsqS7jnroDACBExwo4NgyKGIE9q+1lukJJKs55hrWcZwunYUzUNL8Z1OgeECpvUYgqN4G4J/7wR6a6TZTmaK5DiFMMOB0B2ELtEaRYk/TefYJWv7OLX6YZzX346w1lmFpVWdDKpzo5kqyiSJD5bIo27DjRQSCnccqK5DEd8blxIjuhMbFLzGwQ15o1GtE5jm8Xclcg3ck+0Y0Jw5f0o9r2Iq8U6IzUOLKXIIsV60xEhNlkkupmzVqZT1kva2cAEoQGxxuQAxuENrmq0DCKUpXuM6vZEaDBlwpjo6TI0cgZEqvfIfHWQ1p0OTxzWiOYYJJUI5dl8gnOd4yNa5ESeholIoE1ABBcSk6ReSrT+Qmfoe84OZjUpNb4CiLFWldoEAdrGdrzvuIxk4iKFWXOESrm6YGqklcl0OtiEdPp4kictfZvcEzRjlS2wvHIjMGRGlYj2DJ5Ua1zdant2xrUbAKuq7dYuRZJkMmALK8Xwy4rYtPcX0egkmg3VlJaY7zsgzmzGTqqVJIFkgUaQjGPY1j3LzBN/jm2Jk1fhONRoorHHoUTGpZZclY8IL2rMkEmpICluKS4wxSYU1plmOLD4OG6Ii9Mo02yjPNHdS4+RssAOyQUaFIsJvlr47ARworgVcNoCNjrXQxOQAqoTAxRoBgnhdsTmOzZOYfBuro9brzY1pZSLwAQdgCYBiDIvi1llRk/xLFHmq1UzNU4llytblRZSSFhagfIEgpGoxtG0xiY93+DaiD0/7WbfO9OrDBszodOLTE7ensormnWPjGVEHTmlMahmlbJr5LSCMqSA+MrXDVROaiAjqzRLe4jVy2PSJk9BNsTBhR0mimXcWdQypVmOYwbkhPAJ9NGKjACBHGJsaRGYE5CeV0uq/cjH1yh0gLauv5NXhFFBrKawn1kaZGsJ1kc0u8JOsDWqSYowv+JGrIwAGjkYMzFINPE0Ia55R4pkuI5gXGIEWtv7TGbeljk8ZldXyjjMTzx47pRHCkv8AjPCFwioNsd7Q8HtcquZ8hoH6vpZLDtMGKtTX8NlQKtHnClqAVIveZHNsJmY9VU+W/iAVMOF+jQfgk5kk6FPNKSEBRCNKYIgAiL/XCO4zJ1faxJEmY6O+LafrEWISSQbDSJYBfHksD5FjIfcPA6P4yCPcpFYqt2W3ouo+YWObRcmuZMG7tccs6C+x2LkZz3NVCLT5BAvGkBXTSS4RgyDQR/qMeSIgCw1NHIBw+CDrm8j3U+HNl3gh1t5Ad8S0rAU568sQCSmPi2U9SuEEDDMVngIJXyHc2/zladrW1nKuCmOzxMUUlGKyQ2G4rhF4CVj/AGj2ka4rd0Jxfx5Lu0bdtugr1C06hOoIS9T+RCgNMKBHiSlOkG4G49h30leaO0ikhC1vMVbYUXFK1hSBBbuZUI1q+WCSTMxA22UW8/Icrur2/ujWVzeXlxaWlhIkGNJly7ywm2NlJNMeqHMeVLmGM8rv/qkIRqMcV69b2Lml/GUIZdtYWFZGkMM2vnSXFhMaKLGgNdHE9GghtWDDix0bHYAAmBY0IwAa0PULhNYJqyZkYa83bta5zjEXdd91RzuTHIn7q5ffpdure06dQWNqrJuPmt5w1YsOK5jCwTNF97mHhrxLJc5rV2G2QJrlREcnHdOuVNtoabKwlTiCIXyIA7d/Xe8b2sZEh+urlUzdcaRypBQ05KZgAHQNQIjYz/ecWL2l6kzaDut0Qy05xwIws7x6nsHoXi2PU2M8dPPaRE4ojXx5r9nKjhK9GvXdWoqdgNvopindx9OTXRlhb1UvINLspFH0VScyICRMfh1eR9lWVk8jhuEPJ6ppkMwkhwjmhiYNriEROuMe9kS5GrMW+myoQBfxHUyGmrYMetFDAGY1IcVYsQKshOqzNYhRPcVStbzeVCMa/rp87YO3yLd9qNLqtQ5Vm1hcXee12ARagepNXjWBPtJE4VcsmLRygWE+wspMnxi5OeJgFVDkcMPkY8VXZCjNip9twU66dVK4pwKMrlwhSYnsIkflitnuZu5WyKV8eM1SPVTbjsJGtzQ2UmRE3JO8SbbCOYvUTBLXEcikU8iLLgHhHfHNELHewsMLSE8IiNXdu42OExUc5FY1yKqJuidRNARLeKrZBliya+MVXB3cxr0jPV+6Ob+Oe/8AVsq7+tk2Tdu31I6LF6nXGxw7hXI2npMbhYpmFOeNKk5FWmo4vyDZWkMI0kyjWS2Bg3bkam3iaRkobh8VzO0Wt5VnjTnzwx4mRxGFCUiur48gUdxBmgMkvDIjJMKMSFUpyiYRVeqMY1yMY20VUj4CmL7mouMNpgmJKQlJNtO5STYc7cFXNOp5x7wxAStSgIiEqVqSNuEEX+vmxW1je3Y8cw7HzWsxlOwDpcKD8kz2I8s6wMjxxy+QYFeddnKEIXzHOjkkEI2OJnVpYxkw6qTh91wAG/rL2gtq2QU/3kDU28aSR0x8VFKLysiOGwhF8iNe5qt4fmX4Fp/W5F3DxBWTYkrBcHuK+FOQTYyHtI8IsWuq4g1Yd/kfPs5UYDpAuAiNU5BsE3ZB0RlENMc1Ry2saJ8P9PyO1jwITToRsRrpBfHGKUvm5DG0iNVr3kVipsjk2ROuEaHm3KdseG04o6if55IvqMkSb2/XES5bIUCQpIEHUbEWtJ9NucXXJ1aorvLcyzrKoz33WT3WQnaOM7wsgfq1gedHcFjUbxaxxUE46Ly9cl3X0pW9iWU2s7U+/oaKL80OQU9pWRX/ABmOkUZQEjyoUorpvOOVHwhugG5ic8AVF41RGuaQMNLsFx/UzUvTvT+1V1P/ABJkkGksrJhAvc0ZT+QjAtRXoZ5htegFQfrcaOa5UVFudbNugncvCtKVsmuppllHmCYV3JYgicKqwQtk7xPkPINwZchjgBEN72q1jGtViyVgIphSMwC2g6ISCQdMAiZPM3PMbYko1JTUCrq/4tOlQ1pJMESmRYjcTcnYiDbDt9e8SzzHO3mZimVRrO4i5xk2IPiS0iVzasOPQXWc+3ZitbAjhjwCW9sWsBI+UB7pC1qKxyDKqsRf3N6u1mFYY7t/wqympBl39bmefr8aPDbYviMMSkoSvEJUmMrUUNk/gowGsHyHP+5WtRgXdR3Z5jTdusqHNlXwbq3fAx3CpSnA2FCspzpDZ9vVFYf5vwQ49HnyIMhxXsSUYKR+Bmo9eem3kyZ9hJkzDkmy5Cuc8z0ehZStGitYYg1a5EcRn3PHwf8AzCK57kVOIfKU1LFOluocUpcyomxO1oGkG08euLGaKyqsqi/ltOqnSlJ8NKUkjaw8wJ3Mf5wR2eVBoVfgtTKY/wDUImPwzzSKJpBR3WCOnBG1E5tIgocoCOc3/wCup2KxFT3d2Sdt+D4lpVpvf3WYXGN5PrLTWuVYlPlmq5GNW2O0d6bHbNDVtYJtvXlPZxZLYaT2MQoBskNYqPRUrHNrilsy0drVyrEtBAjVkSUliA0pYtmCDAlzYYd2j5wxyDKILyqUqBciNLunPqMZbksds7HxlW0uR11UWBTUk2cSQHHQzpMiY6PWPC8JGxWFlENFjPIRR82jM4xdiKeqWn3BTlCiG58wA3kSDcTHP0+w1hwjWAghabKVck3SSTuBYbRce2JxoFpDaau6+4Dp7ikKqlVtXbVhpsW0av6NJSBIdMtLi6chBr8R8GDIO9EOKa5rfjxjAMZHOIP6m2o2BZPqnU6a4RjY6un0YpUwudYw7fjU2uQC5LcSa7FqoUaix4cKe58VAQ3TDHMEsmdLkyikM7B7S80rNCcP1C14t6cJroiSKvCoUgxmClWC1dnXx3SDglMbNgpmU2gnyo7xcJDMLng5sCczHrpyTIJ19Z2M20lklzLCbJnWc4+75EqXMM+VIOUqOVpCvkEfycjGjRE2Yxibp0GCHA6oqJKRIA7bdhB99sEXFoUlGgRbzmTcwI5gHuPvfEOM1zSPTiqqiqvtUVVT88nORE3Vf3Vff916y6Z423FYQrneNssHkRHK1w2oZFb4Xbfy381Rz3bO5Juxdk68HOe8kh6IiqrHv/C7btc1G7e91arVRf77+99uvqtGj7CEz3u+aBip622UzP8AH7/j8/n/AK9SHYgnSCDJtwJ59sfGiUrBAlQjTPCioAH8zuD7c4cLj2cyHYNhN3YV2CWxrqpfNHClYvVSpAYMIAhDmTXIxqI+SdWjR6uU++7nCRVVOpgHOQUwcefbYZhTMWDcwLy0wyLj8KBHyKSklpRu/XSIW2ixhq1Ckr48psA73OR8NqIiJYfaDpdqNlHZzYanntNIb3AsPzGzwakwXN8OlZRlFrZRbSOe4jwD03xpdIGPCs6l8eZKkfzHSUeziNqubVusOYabUk6RiWpui+YafZNXWY4cGdhWZmHVDqWDD8yXGpctjWIjWOz1WBDBbuj7cf5qomyhMpzKiqKxVG87VVKmnHAtFM4lCkpsE6zYkcJ9Qd+JMz6VzemjNaZinYbdbS5qqELWlZMElIEib3BEbEROLRuP9weT4zBop2IUuL6t1JYl/Hy6rBkPwJx/jAItSOjpYEirqCz1f82fMMoHlskGMphi5OSmMopcrlGiVzAyretcx0REbi7Qww+V6SOQJ9icA/F4nLMkcZAvCVuzHNV6Iu60+zzTrNKEhZtywFqgpI6+RLe2DY2ZIwW19hUSSHezymryNe4M0nFpWC5ornoi9MG7ZdLtINRLOBjuX62Y5SuV5Tz3319CqodejRteSOI0+SIUiWQCNG1YzmDVeKqqMVy9OdYKSmy9VQtD6khtRiTJAANuJmwPfnCwlT7uZpQSpJcWlGoyQnUUgGOACZ327SMA5gXabqtm8PIJuJ0cfL8YIKRV38OTNrKg0iMCPGspbD117b1suRD4O+SKwhFP8cte44zMEwrusTQzSSFTZDqFRyLc4tOcFpLqwkvj3SXcfLcnk0lvGgYUPHChFCj28CU50zIcjnzBxoVTWlZCivlyxER3/eVZ6M9knYtNttPMgr8q1L1uyedi2nMmNJrLOQle2skw7XJ62xqZNjFSNjFS6WyeVliYa2NtVogxnQaJz+do+rknTmzyWFkhWRK7LJg7IN66vfeSqu3RyR2S59UrSPt65ITzyCRXNO/lHY9zHtUzX5TltR1Bm9FmmYMU80LTy6ambqBrccUCICNQmYmCJNvpjVCrpujqcrpalS01YDfiVDatLRXA8zxFlJvKtXfnHz3PaT/wLiGhOaFFHamcYzfPlIjRMeMkaeF8GPKEwhGKP9PeOTGGu6M8pRv2c3bpfl5Cb8xqhQg0eiFcNVRzWj9+2K3dWhkL7WP/AEM8TdkT30xnURuqHcvq1Y4bis2TkdFTT50arpLF8eprMSiVryDr4oBHa9KqPYxmEOaabcshz1Zs3cbG0hqF2o62UuSWdXX4NbyoUCJHVCxT0UuXcTTjU8gtdDW3BLlwopHrE8cOOZzGsAR6scZqLomWVKqfKaMVagHFWeU4dQZGkG07Xgekdowk5wlt7OqlFC4y+gAFCUjmArUkR6SbDYjmMVVorfWtXkgYdeOCM0mRCYgzhikI9WTBFG+H86bXRvmjc1DxmMkqcr4yia1jXcnHHlGOS5uRNj2FhLqmHkjFPWwBYEmwzypDv9HKGV51gywvc5sgZJUqP96PDJKjt0AuNgmpGEX0WZlOAZxjB62zjseW1xi9hRBpHM1DuJMWAkUbiIFXNRDuTZURSKiqiPZxnGNP9XqKBmd1cQYx5jyKczbEDEI2IGOsJk2IyW1XPETyKjeLVRVXmz9up6etYaqkqpnmqhpwEOLEFSZKSUjfymJ7e2KdayE5e4p9tRUSEFKZCikgyDyRIkjvF8B9UOhY1NSHYyDtx4z27uIZ7hzo68hN3CZ26tIrd90REVyI5v8Ayr1JIEigfOdGqw2BJc4gXQXDMRy8jIgWR1Y5is2RHiI071VU+Q9qO+zq6MyxbS29nwoUS1oJZoKCrXsFYskmhDEuzZMqHCIY4WN9OHya9V5oq78kakfybJMU0hpslObGn27kwi7kVF5FsFqmwJlVD8oyBhWjRSJvgnzK1COjtRyOnRG7I16tG1rzegpKddQpyFpa0JggRrEbj3B3wgt9Ov5zV06GWVtJL7fzA6S0FJ8XiCW0DWIuD3my/e8XJ8CzvKJdZRY4LCct0oo6rBptkvzPl6kAimcCfZWwWV7gR7qnsiRhRVMu06AWK3k6VEjcFuEAyHYCeQZmeKR5Dpx4Oawbke9q8dnbq1q7bbKu3r+/VnWky3s78k7exlWdjYo8iSHyDyZ9kaYx+0hxVc+ZyVEfvuqo5rC8kINj0zdWMRusVyOLEyOnSss7ith2aihPe+EckhV5rFcqKwHJGuZ4VeRRGCVj3fcnSSdZAIGrUpTpWRJh0hQk2MD1+u2Nrq0U4ZYSlQQugTT0zFOLIqGgmPGWnlSiIJJvbFRzmkDNIg3I5nncjlVj2+nI17GuVybb8VX2v5/Cqu3qa4tKdDuYT22QKRqvTaxKOQQcT0nM5GRGFkPY1FXm0Q1dt+FT89aiHWmtyAWQdGyTiZIMBzEbsVrlEzm/dGq9zN12bt+y7dbuwpYQJ1eAY3qfmguKKrmOe1PuVdl2/H777evSL+Ur1CEoZOkzKgTzBOm327Yky6lqm6tuubQQEVISABAaWpCDCY+WQQY/TGxySzR19Yy617ZrGWHyUnRBzAwJMluyfIAycAEgITPdzep+Sc+DUHs/kx1vZ33k1Wmmn8jLtRIWRWFXp0tjlun2JLOnwoOQar2kePFpKunryOWseOMd8u9vLAhnMgVcVfCIck8ZHJeuKl1bGcQ8REK543j2Zuzi5Ue5SO3V3HdiLuq7bo1EVE36LXtpyOoyyhv9D8gnVkceSWES5xGVbWMaOGry1HDhDZVNnki16EuYhFrpUNZ0IktqBeFx5EdgSWqFDb1JUNkwshN+YSqU+3JB4nmcfs/y16rdebeUVEK8dbJIKF6gkgqSbEqAEnm04x86yHKdR7+vye5kiLa2g5J5k8ZSPAxpLeeSLFjoVFckONDMCJDjqiKGJHAhWtK569XBSa4pjGJW2CWun+OZGKiiT5VJdynS2y66bMC80icwYyMHNYj3MYCG1RkC7mnLgRjW6bGcNubGbjgUUUeYttYQpdc6MpBslhko/wCFHYjGGU7WtcGSx4/KwzSK4aN/ES1bx4wqhBRhx2WmZ5NDo44nOGJ8eLECw0orkGqMIIx5cJivHuz7niXYgyIkyqOnap/CUR/CACbCE/KRAi1jb07Wwtt1KkPr1SkuQmNgICUD9Bf332EZrMZnYtgVDmpypRy8vuJGZzp0Y7HGh0VCsmFSRWRAOdJiSZFkllbNAdV5oyucR7+AFQV7WWS3u7K0my0nSLaTInSZKN2Mc8l7jKcjf+V5OaOcxFRGqqtTZE26O7XCAuN6dyaVYRoUUFNQ4/BKNWsLJlEm05JRGI5OXxSgqStV6bIjFfwc3yEVwRsowxqslgN6vRDkTgi7lT2qpvxXfgn99l3RPz69dUiUkI2IAF+42/t+mIqhaQ4lJA4B4nm8++/fHlj1/bYxcVV/SzzxLigsgWNXKY7xHjzoZEIEoyIjlY5jmI316c3dq/a7bpp+d6F3mtnbrA7gAZFjl1fEDMy6RXxYawMjnNbIkBuyE80trkAUbzNj1g2oQK1wyDEiKiqpX9VYHkNrHqYrmsRXMR6EeT2iNTiuyfsm3vb89MUxfO5WL4NiOFSoLY0eqgRZ02BMBJhS2zJsYE6SCV5laqDcKS4jfX3cl/KOXq8lDLtRZQ1JF7xsBb/PptihWJql0oW3qS1rSClJgEFQvAIkxM++BT7gsvJkeFaPQC3MifOrKCzfZ/JK5zBJWWK0dK1oX/YIaxEJIZxaiM2Idd3OcvVSaWYNEzuwrq4tZfzZUmbKGCDUxGSjTRwYZ7I3BX7jr4YBAV1vbWW8KmiJ8qUjmGGx1qagYLatz2mrS2kevhTsWHJpvNHDImBiWwrCYOAeGiKirHCUzFeREe1qBeqb++vbSqptsZj2Kt+ZGk2cSwhnOriRDRqo6LGPAdJ24+K9IwZJCsYRZIQxgE5i+zoYE+O8ptMAokkgXgQT/Qept7GShVJSNuKIlelI1RAJAAP3gxPrGGRaadkdHqf27mzmjlmgSbSzyIQaye80dYk2IORXhcJ0iO3z1jyVER0aX5lUo5aPQI2PYvS1sa7f87k5TfX2U4tldbp7putdP1KyePUWRIdFVyJ0cSQ0sviPgQLa5RHx6dk8oBMKoZTi+Lk1zL8owW10zvcB09zTUfMMOxmriVmVWGF08gUe0h5EXG2SJA0ikkIOwrcpA0tTBPHOaO2wiMRj5BSsM2Edwfc/mVxV3On2INlRdIZA6qlFQzXAbNy7Ma+ujpc2N/LGpGXP8HQCLXc5BpHhsXEO9scjxjZ1mVfUZe0w0hGvxSEpUqCU7GQSeR9778y9PZd8Y9U1NTUtFtIKltCPMIAAAmPax27YX13GanacZJlkyp0RBf1WmdfGiwcepL6HGq58EMcKKRhokSdYseVjjFeWcaYaTYSzzJZVY46oosSpCsikjsGFRSHOeUjkRrkc5yq7ht7Ym6r6Rfx7Tqwszw9KCbGOr3QRzYseasNyNX4fzlIWOJrkcu73R0Ecm+y8zbL+yJXM1A+0Rzirz2e5U2bsioiv9Jt+U29flfz1AFlxtLigEKUAVJAgKMD9P198V3GvBfeQlRUgqOmTYCRAA42/XHlCRjWn3cip4URNlVURHL7Tff2q8U3/APb31/opxRzx5AlIsocwZhNe1PCrGNY5zkd+/Eqpuip//ld069WAfHa/mxECUaeNyJ6d+/7p+f7/APb/AKdar2jV/vu9Eaq/08fG5eKb7purWu29ov8A365McgFJsoHbSQQfbePrHOPraglaFHhQIHcgiBjom+kjqjpYLDNbdNNctS8rwrTquz/EdRgV2M20SLMnzJkSxp5X6ZFMN0sr5AauMKyWOcLFCsdFaqjYqbDu7yOn1u1XiV/ajpvaS9PNOqP40651Lk4wZ08757SzrOZKtp5nMgSwuQQ4rnLKcqcxojl6TT2/yrSbl1xBp4/y5FnTt5xVQaNIoCMkc2eV4Uc4QQSSkXls1HO3RU99FsfIs2j0LKqorlnwnzlHKuKudMfFmEGVJDYExgP9OV0NBvcjVc/j/Sj1YibLGU9GtNdRPZqzVOJaqkAKZStQbSpJKiqAQBM3m5tYYaM768zFWSIy1tCG/AIAcUJKkqCU6QSOAmbenAnFQ4phdvUgZHr42MwXJIbJmyztIRJZ5TjtAMkp/wDJGP71OgGuRjiIjtkeqr1euKaF5zaWo7GBnsuna4T/AJMmVHPPqJxkb8Y5TDdKRjPDyajNlapmNRd12TqlLg2T2x4uP1U4UitjRkJMGY0Jw3ThGR6/MKxrSWUlERfM5rWI4fJGbK7qR1eoWcU0iFhzM/IOiSMxsyuYIDGhKcLkeIE832sBvsnxn8tv6VXfbfTCKdSdBJUmwhV0z/6kkXj9b4zh0lP/AMkSNtt5t2FjO0Xn6EWpqnpdklZXY/hljqHJzc1LV3d9RyZNgRlJDdayItlcRK2rU5gVoLIcSOwskKCdLmxoPyFI4IuOVobgos3oXzodiGPGQZS2dkUb5kbGoEZypJmThRnMaaT5AqGHEa9h55HtZycxqtcNusupRKOlfjsS5BYy58RgYsqEUiqz5QnCfGO6Q56ugDRXFT4jmt842qu7EVvVFac5be4wCyrI+Y5BQOkiQqiop0lGyTAPzRbAQkTZykc18Nzl8Q2te56KvHqspxhA8BKUNMtqC1JQlKEyOdIAGr1AJI98fUs1DrS3m3Sl9xCg0twlQQojchU2E+x3OOjvSXFdJNGMOdOsCFxZpZ0eUebZQD2c80VAPlf+JOhRiySW8+QoZMp0ZIwY5JA4AVYOG9zQo7q9TMT16wpgKkE2TIwKXImQJMqE+ESDXTiIybGr/Onyo4EmNEbxKqOYVWEUhCORWCppj3W5uYhcVtr+VZ1MyMSSGwtLJp7LGDRROQnkvCN81uMsnjHGFw2qxB/arm7uWw7TUSpswWcGxyYbD2VfJqyVxKmVDLYSi1zSik8HKRx2LZKrvMJrGuReTk2Vu1x1NFVUK9CUhSC2QCkebzpJttESb9r8YXaGmzGjzZCqlC3NfiaqpKlACUHcCxBPlEdxMzGB50+1cz7HZ8eor9RtQK2okSVVa9mU3RYgiu8m0lsAs18EjkauxmnjPa9HjVyuVNkelhH6G7FMXjZVQsuJ9/AHaxMkngdKsgD4i5Bs1IwYeTkVyr/pzsVj0am37c+GHWdXBzTH5lisZIbr2IkxSsUwHCJPY9w+K8XiaQLDMV2y8V3/AH9I/O9TK624g2uQ5bQvxTLqOQXHQ43lksEnHWmixmQhmHFEoBPZDY3aJZI4BVRyNc3b1SoKKlerU+IhKGktqENoCUlQKYJCQAebk3+2CPUFa9TUCkNulC1LSdajJiCSPWbW97cY11RplhOA6iqeuoqOtuMiBMtXTCwWQpaRPkMSKIZgAKR8UhEcVhBO8rXoOOqNE5BoMXcbneKag4zrDWSCUt9rmPVvC9C9Mh2CvtbPG8HBjdllOoWR1BJjHtjWuVZ5Z0eNSLpgVmJCoBV0Q0VEONxQYVh2cSpeR2eMW2m+X0tFW2cM0qxs8tnTLeM9hZzoYJjwHrmWj1MkNBQXxIozsVOSOY9/SRCTbKnzWrk2tdPprMOUS7fJZxIpm5JEGt46cWLGhSZ6vlSgy5MyVAa+PHVysYxbAypz6O1lDQqWwh1CTTBz+IdI+WBAVAvfnbfEPQlVVOVNQtVUHn2mtdK3qH/kKgFeWeRvYiCZtJJh4J226vac52eBrJgtxWZbh9dR2ONQ8rqFivlOu1mEfYuEaMrZ80rYnJkud5Qo0Z1ReT1VYx3oYzJNpzh9qQIySaO/u4LAokEMyrj3UUlqGtdGivYkmHDPCncntargq4RC/v043Ou87/8AkRzGRllLhUzG9NNG8DwHTqvy3Jqg8SyyGyrwSXqO6JBNYsJc/Nac6RYBB1ogSRLNVCPa5yuO9XJKXEMQPh58biutbKxB+nRbCSWY2tC7zSQX08LT+V0qQwMpYUVNwGK8TSu4BTl8zRjLmstX4OkJUhPhRBV5UgKEwDExFgIPeMRNZrnb3WYRUJBQFFDgSSUAJ06QACUiBNo3tGFBx5JIVlHOo1WJHkL4Aua1FNxVEcr90XdjFVVRF/pbujdupQSVKe6S6wqfHInJClVh2rKCSEFHke6REZuiHZNazxPebfii8xruibRexlqRsbY7XeBRsI/ZWD+7k5RjD+Y6tX/LttkavWwJkMmYYBZj2mNHjNijdMerhNhj/wCGNrGN9Lv69uTZfeybr0g7oTMRH3nv9dsbDT1jbBcBqFtkupcKSCoaglACgNh2n0jEttDxiR4Y/k2T5RXDGcEpxHM2Ub905PVVc1HI1fH7aqN32+1No1ZzyVrIgRyEbLjHUrTCajlGF6eNGo30n8tzmEK7dF8Iyt34uci/n6oWXCkxXI1isVkkDx7cAtE9qvYm+7uJBc27rvs1He/S7x+1nvkrBLuELUQnEQ/ZQhRd3sc5f6tuCuRPSq1zv2T1206pskJKQDFyReCLe3pxwLnE9fXodUt4FbpWyhsqCf8AikAiw4nm31jDXu3vMR5rimESZBTR8nBYzh5HZkluLLlSYpgiFPDLKRxyPmQGRHEepFcpWPcqq5eq2qMDttVtVMlCebdHr8Ty2X+jSgMkWJgxoknm+pHBQisaOQjI7glYxFCOOqN2axES3u0mLhuF6Diya5x5cyzC1uZJcfrIzhx6TGnOI2EyZkCvT5Ess1jAkBEC9jFY5SO9OavVsRdXsf0UESlw7GI0nUDMZrbnIpZZ0qyR+XXHie5FqUlRyV0QTZIIMGBFe4bHwTG5OcZ6NNNNtOtpW4ZUoSbgixgfzbQB+fGM5qXn233lU9E88Equ5pURJSkjgxvEWPa0Aj13Dx75+KWx5dLLkSKybApJUm5iTiSKqFZPkErbKqbKcVslhA15atzmrvTmdGJHUS2BeYRQ3RmUryI87zi5hlBRjGgQSN4IRqNRH8lci7ucqucu+6rvv0U2smrOqGq9se1y/NVmgDkcGvl1zHyYEWFOEJeEMYDkljeIEOE1qr5HsVzXETjy6Ds6SaLKbOnknFKiMnvCYYiMKMwzGY1phlG1rX+EZkInBNnK1fSLv1IEIagogptBA2mIFpuLAfpiw2pa2Ct5HhuKAUULEFBJkpggER7AWE4vLs/7Xsx7tNdse0rxdJFfVtlss8wydjUQWK4vFMP5tm8r08TZbk5wqsT9lPNeES+kMnTz+5f6YGi2i+nuQajP1hy6KtLCJPsrLK3wbV9iKvC2KCvcoTRDTpE5BggwVepjAexWP3C1W9Kx7S+4LuC7aw5+LtpTDJp0yebHzN+V1dHdT7+vhSfHQ/AQ7Kq7j14AslqYcea4aT5SPQjmNa9dl3L93vdJ3C08dNWcarqzDMI2u5sWhi2UeturBi8K0Nm2VMkcxR5rnHKg1Vvi9qqpsvSxUuZkiuBFQhCOwABCSQY2AJ078yNpnF1DbRpk+Yn5YBmCZSdu8jcWuTOKmuqbTSw1CwTL5GZGkgPi8W2zE9NXzpkmH8UclC1hqaa4Mw2T20d9TGiRo74MWKwF0UTlifHemfJzWviu/wB41GSalHiEWLYYaO4SGaHMcwzwYx5aUzpIVe/wiLIgyXkCdwTDMqNeR/QjY9lt0O6tQFnq2VkjhSpRPHtyfttGKFXbIMLGESHEYickG5HORUdv1YuqUSwxPTrBKggzCBmI7DIxEVhGxy1cGSWsiRYj1RGPjiMI5SoiucM7xoqN5+zVBTtNlyuKyfEQQASSJ09r+n3+/FRUu1KkUigIaKVGbWEGNz/TtiO3GtdxkF7UnzWwnWlfVZBFkAsJlhYTZVbRDnily6FnyZDos2r3R6QRy+cqOoGR3FIIhmqTWUaYlnaT4fqxgsiflmI5THuoV1kXwiRAYpmRLolUTGro5nuiVcg9YL9VgedG+WqvY8td5UmQ966pTEIIKIirHcm7Uc3k1ytdzcj/ANv7qqp+FX+69Me+nXrtA09t9S9E8zAmT6b624fIrpWMzgRrWO7MquM6XRXFVBnfyh27ogjV4uDmuM4iBcq7oiVn2kVLqA6SUFaYubCU7dgY+xO2JUPfBoUpII0i4AiZI3737z9cAjmCvfPsxmkHlmGWKRFONw3gZFjjjsikR6uV6xhsaNCMXxERPMP2R6rXEpVMA72D5eEXMytREQY3u/Com39+Xr/8dXDqpXw6bOclhV5kfWMtZsaE+Ojhs+Kr/LEESM5V+OQQiNE8abOGrPA5NgMVaUkDY1o3tLyeR5GkEm6IJGq5GscnpHboiO/dN1/x1LVMpZfWlBJb8NGme40iRb7kb44DhePiG0zb7fuB798fTTuUQguI57GN+xHK5U47boqIqqm/7Kuyf+W3X3XR4hnFWW7gjVVRe9uSqmzt9vzuqI3+/pE226xxCciC5flWqvr8rvvt+37e/wBv8dYysXmnv1yXbdVTbdf7fvv/ANP/AC6g4V7f1GJWyEq1EA6bwRIsRi+e3y5l0WsOOErreFSLMmpVLcWQwyIMCFZBkVlkeWAzCB8YokokhiEA9ZDhLuYSpy6Lr/dqt9mciXjFzeYxjNdOlGk5rDiT/DCNsMLjx6+kM6QBxGn47wpQioNeDlXZU6XVjU1tdkNTNKJhmx7GI54jNUoDx/kN80Yrd2qgJLVVJSbqqtVeHTgr7T7N5mN4td4W7TPF3ZDXElxm4vIikdF+XHe0IbFyyp8DyyhqwDo9qBSG4IEgxK3fovldP4yFRZSQCI3lUbmx3Fx37xhfzt1CXG3FveElxRA2IkBH8uwA7xtf2W7aX9nW2MuqDXS4kmLNchYklx41qI5vtZxc1qOIiq5HNYvp26NRF39frAnl1inCqTQSXCdIJYuOwkaSwzmmJ6TyGYF3p6M3cmy7/wBkhmalISbWzwlK5rCmhl8gTeUEmNLK2PwYdpFU7gDY/i4nnYuyK1rvS7vE6UWQ7lvb51FjZyEIxjStLJIdU8ZQfpjii8ZOSKVCkINiqv8AV72WsHZdLZ/8YBg/y7CL97Wj9cEAzDSbQQQe8XF4P752nEaySceTKNaRBDPAhEZUOK0JHVrGIxHbjIrXlV7eEhE8aK5rzD5bI7rzCgiCjSlgThhfWljyTTZ4xikzl5tBIA1jGGWGLm17+bvKiiY5E4NeqbTKcaiVR7KPUWEi3qIzmHHYNA53FgwvVrZkdGOaFzDKxfMwrmNRN3PViL1Doqy7hwxFmbJG8TGIXyEMZeY1eyI9jOA1UDnuY4vEDkaqIVXIiLUeBK0lPBTfgepO0d++JwfIb3n68YndZNFVjqYYlqpZIpI0znGCyQ2Y1jXPBHt5SE8L2ML7I2MyRxGrfINFXkhA4hkZ8vy8Utg49JJHKrAjsIrIE34QVirEnOap5KlQUoz3EfGj1ZrCOwUZQBcwz3IO0OsxuAhLS4uCQ2DM9I1XEYxZ0wLSo1ivKNhxMeRiL/W5nBPeyqm3W9Hl+PV98OTh9FLiS0d8t0WZPNYhnkEIbgsKQLBvVCqhXPC8bxsRqqrdkVUs05/5rBRuRqB4EfnE3/XEZStQIQQlW4JiLGee4EfXFr6k6ay8StHllWtdNaOxiod1cOTGaqS5Ko2QLzN+E9o3sdGkMCUbo7pAnnEIpVarN9Dct07mYrBi5NdughlVSQZMWTDLPY6fWx5MdYVfLAyQjZcpRoRP9ZGQyuTi16J1jfT1+mx3T/VYubSdi4Z+nehVOaRHv9Y8ghyD4bW34nxzHqMGrz/66/u4qjQUxsXlXVb+H6jNjuc2O17tfov9E/6ZlJO021O1Dkd2uu+OxbCdZUSWETJCxclfHSUOFLi1EiBimPHNYAawcWZaTrCOPysfER3JOi9FWtMvWSbNqEgWjy+h5nbaL84XM+y9dfRwpaG1pdQpS1KCRoSCFXUQIMiNjPbALduGllZqznuD6NaO3Ul99mdqRjITiHsA1GNFlkFa3nxVroNdCjx4MSxK9HGstpIFVxPJIViNm7hPon9p2P4HmesF9e51eahYHg+R3UZXSauPVzj08EkqMGzFFoBFlw4niYsYrrWPJI5xubWvR3V6/RK7U7OFRaj95eo+K0mK5Z3GW9xY6XYdj1XHiY7p9pN84iV0Kgjj/nhZaCb8WHIa5r5ddBfJVEfY+2hd1GP2ANCdaWFHHWHJ0xzkZJrv5O8l1HPIzyFIvJrQMT+Y56o1yovFzvSrO9XuOKCytIZb8ziCRqUiAAIufXvHe+IcoyKmoWDVUrikV6k6Kd5ElOpQgmRIMAmTyY9Z4wKfufxXRjtN1QlzpVPjuLjPWQqSqp4uIyrDNc3u40x0DG6anSE40BXQKyTc5XkNhJVQ1FQyEAi2BE65o8+1IyTUnL7/ADPI5pp9hcySSHGMrvCASOVkWBCju/8AlocIG6BaiI3cLWpspWop35to73C9xAWv0B7fdVtbcKxSzvMXWfp9p5lOV0LMgZHgsm25bPHQGihtWkcxIsNVIqxeKSPKktBqDubaUao6cZWXANR8CzHB88gyayEXBsmxyzqMvHLuFEyqjpQy4w7F8uwecLIMZI/llHKIA2OK9rVXqnMDWPKCG1JZbENpIIgqgHexmB+V8M+T5QzlNOl5x41WYvLU9UuAhQ1KUCmImCAVTyD74jlnYhuIdZVQqGtq3V1cwUiRD+S+dZykVzy2V3IKnxlO9eLAMhuczguzvfUSChYEnnKa90qOJJEUDAIUBztXdoZLHq1qCIn2lVVRWNVVTZejkuuznu10l05yfLNSO1TXzA6DH+UvIcuy7TDJqHGqSD5ECk2xurOvBFho4jmtYpStQjnt8SPRyKvhinaT3VZbi1FqZjXa1rhn2H3cRpMeuqfSjLJNHfR2qqgnMuv0pkWREKv3xJkExBnbxJHI9jmvUBrrAtxLdPrSVTKgR2EC1xEGxjjucMSHQ4tLzzymyEQEpiIBBM8SZJIN9rYDKQltKFLltqnVzJ8lORxBdHgsCbzc4gnPcpFcjUfxVn8tEfuqpum/Ux9Gv6ffa733dvGS57rbhtZNsNNtW8fxEto6zZSfO0yqcbkTMkDISHJjjPLmS5EN77FqvkjGwftrCOVefPVvt77xccxlco1d0D1c0+wHGnxQmssn07usYxyjBazQx64EmwmQ4445ZMtwhRiyykkSzbNZsivTpgPbLqd9Tnss0OySRp9otqnp/o4GTH1KyDKch0mt2VMmO8MJgbq0vLSAyJFpJcVkIzjPRI8qI5R8v5zVWCoo6moQNSQlSDqCEgxFuAJvB9vXfB3pyupWK55T9cGWihADj7fjNyIkBvkzvA3P36ecG+lL2nYXU1Uh+B3kmsg4e+fnbx51PxgdJZkuIs9cohynSCwz/oeGFhvJWuEYE4kFRBE4iu6wct+lx2OVbL+e7TSvyudkOXYtleM3F3qbd4vYE0zzu0jV2KzqicOUNlxeSixLmXArnMrXzJRJLGq1sQbjIx07+pH9YbXLCLbN9P8ATbP9SsVuMkg2JchxbSCxv8R+bjYkr5FdWGr4roEOoixgRQWlZHOkPkUrSuer1Rsd0k+oZ9VTU+TbxNMtLL3U5MGqsLwa2ZT6Lz8zrsTiaeyrJ2LRJ0lkItbWWNXKlzyiNIMlhvIUzXNEbfrkUGavMEMpqRIhKk1KWkmLCEkyJO/1Pu5M9R9L01Q2qozGncCXg+8wzQOIpqtSS2QladCQkADR5oslJA8xgMdX6fG8U1+zbS/EKGZFBjlpksfEY1MwM6XJyCRyqqGJkEqxlpF/h0DXyBSbUZJuRKpBjDXOiFNJjgFlvmh5a8EoLo8utKCDKY4QwSFmRuAXocAUQY1a5uyNaiorNlR5Ecr1ZvrBoj3+avZ9k2q2YdpGt0PPcmyCLezrXFtIMkxivj/BrnifGhQAwgQhRjuVk+SKOFXPkCJ4WkXfpemp2l+r9BquDCc301zzGtTclk1LY2E3WK3EXMbGdbuYKtHVUZIbLCwJYEG5IfxoxVkq16i58VVGiibcpcspGnSfHbKA6CoLXqGjUVKE6p5VsfbGX9T5hRZx1HmVfSU6Kelq3nnmGmUw0024sqS2gbJSkEAJm0RbbEv0RyMdNn+T19qWTEi39baRJ6fzEGEjZqMU8hWI08Jo/XM7kaxFXflsqdE1h1YeovLrF5J2TcCyypkVVnAmKee2MpuSx5lcQ/IwTeZ6ta3fZ8VjH+2Ki9R+k+n732ktszVvaJ3MsJayCxo0yRo7nCskgRzZQ2cVrWvGGVwY1SsagmI7+Y9u3W3/APhi7r8UvK/ALbQ7X3F9S8mX5GB4rkmL3FfbZFXVyIk5lJVTYo51oyM9HISTFCWKio5vlcrXInz4X4vUVjSPNc2vM2O3vP0B5ALcLWkJSf5fKN/5dxF7R/0cCRT6LZNZarXeEFcQY8YJLnyrEyOc1lYIT31vjc9WtkLZOjQhRWo9EQKmVqojHbWr3Z2zELo7hYJZHVuO6T4k2zriI4o6i9mecl2sZhmjkQ5ElhUkSxhQkaS9EMqK5qOQ4oHZp9QuTDBlE3tb1tkOnggBvnRtHsnS1s4Nd5AwgL4694SnY5EaKQBWL7Qr1aNefQZ91Hbv3PYPkOWahas9t2r+lmLWL1ix7nO8EyGkoRSYEaMArIFtPhChFkn+J/pQtOh5AzFZFCXd20raWG6bwE/yg6feB6ze/fsT2kRT1hcVUuoZ+HUPI3qHiINgFFMzb7G/OAdnsg/LOODMcYYJcneT9zBHjkejgPFGdu0Tne3Paibt5eP8iVUz6y9l4xZVmRV8n48uosINjDMNHNMOTCkikDc3gvkVP5ftGIq8VXl9u69EJp12Qd4mq+KVmc6YdsOvGbYZdkIGsy3GtN8jsMetCBKoXurLRIDIVgFCJwbJhmMB/pWEc331u8j+nT9QSsr5ttcdl3ctWVFTEfNm2EzSPLQR40ELSvLOO/8ATVRoBjjme567I0bFfvwVFWBCihMxcbCLyVAbexn23tjtXyn984r3uJsIV/qVkF9Vs4wcgDV5IFyCCJpn3FfGnFeFgU2eFTGIgCqm5Rojt12XYbZjeKqxfbnLybt7/LN1T/qi7pt+f79XllTz2WH6d3hBkU0Wil4dbMcF7Dx7DEZ5oTByRK1CskLWyIHkY5vJipxds9rmtqgVJbXMk4YFdJkvCkh50YPZogR4z5JiOc9WpuIIiEe1FV6MYruKoqbz1P8AGWyY/lMjt5U8xsY94Ft8coWkgDUmZIiRvO3vfEfe5whx91/ImIvvbZFVdt1/b3/5r/ndOvIq/wA1m/rk5u2/rddm77f3/wC3Vt02T12OUsyuLRU9nL8ombyoyHez4zfLIA57GrzYUbk5tUrPGrVRmxEc1LZ0g0fx7Va6kXMyBOr4InqRcXpDvZKsUY9qH+A1rXzPjR0dxUYlL9jXKitY1UbCKcuHShQ1WJuDaQL+52jm22PriwyApSTYggQZMdv8X7YE5y+OQhF9K143/wBtlY5EYi+vXJfSf3X9/wCzMtO+5/J6XA6KBMBP+HWBFX1cgdZAfWmIFjHRmmWRVz4hClMqRzlJ/rGM4cUTfqvtU+z6wZkpW6cV0qvgqpd4dzIc1I7ChAoivKUbnoxTm4R0G4xDP/8AmGjb763GCae5TURo2Gmy26lNqbIpxU8OueWrZOKxa+WSFFtWvhSpjhuY4fJGxivH43qJuzlI5ezU0rhVeFBIJ7RF5j3/AFudxdYikrmwtaGlruNDhAKdr3O5/T0wKjamaclnkNJMjjg/KtZa0UojJj4oIk8rBITzE8hWvRFaIwkWTuu72qnWgQQSDVZ0VsZJI/lunQXJKYFpXuRWHjIvkDsqfdyTdP8A/kPnSyDnzhc37JOmuRHP8jkISUVSGa9jAO8r37K9ybNci+mtX8bmPKEKK0hXgjuefw8ZykUTwPaiMejwtQo1Qi/c4rpCIi/0r76HYLEjTJPlPPvH9xxjeUl0SumN8RSSYkpyAmiC9EbKE5UaqeEiK3k1uxEY77XOYjXelVeszUd2NALVWmIfKiRrCKZ80T2uYFk0ZURUDuxqNf43tSQxrnsY9EYzZEXfGi1lUBATrKTV2jZZHjWLVWzWSoSMG56SI4yNGvlGrUePyJx5tTmx6IrVxJWLWZIch4WzCNTySRRbRr3IkN6tI88WxHIJGIR7v+MIcaO3bgqru3r4oagQeccpSiR5uRz7en7jEBez7EO4nlMmzeSo9/p/tGoiIqq9yrs1P/1WdfSJ7B7z6hvedgGiwCzazAqlS51qxfwOaHrMBx6TG/UWRJTEVoJ93LNGpawrlThIlqRrVUadLSV0BjSbR/vT7GMKQjm80RU8wyCe3iolRV2XdXJunFEXdO1r/ZZTY7p72/fUE7kEo6eLllePHcVqzw4rg/Aq8H0zsMu+KFxCFKjba5lR51k1qjZIcIBHIrwsZ1w2hwrCEJJEKJjsBMgewv8A947WUNp1FcARv6kD6b/uDjf/AFo/qMt0Oxyf9Mb6dOR0ejlFo5jjaXVeywxz6e1mxYdV5ZGl+HXNco2VloZvklZLNRW3t7LSXXhsozVkpKQD9JHs11C7+O83B9OoVvkNdiNZaQsm15yaM9hiB0vqDNmzYRTmCRW2uWyv/wCvQGFI5zlnFKr08BVRcuS6zZhe6y5zqzYSluMiz3M8murV85Cy3zpV7ZzzHVeT1dIlhZJEKMZWK6IpVfyVr0RP6P30GOySr7N+0qLqtqBRtqNbO5MNRnmaNlxwtscUxFBOkYTjB3eOK2LFhwJH65btO7myRZmYZeAlIKZkKdUfCJJQfOUg2EwQbeuKa9JT5tBSUmzmyrg6R6kD7TzGCi+qb3U0nZt2q1uD4CWHQag6pRwaPaSU1cUUN+OVEWoUFlY1bGIIgYuN45FSHVIJjWx5B4z2u5IiqUup8Ud32N5eKzGSbPL20Wb5fNCm8khdM3Ge8pVR7nmeRznOIVzXvcq+1Vd+uAH6wPf7I7xu+PJsgxSRl8zSvRObK030ekDrLllfMZU2BG3maBQcd7pkjLboUhRz0GxzaaLEYIaDciL116d9+mJasdomd6VZFALo/rnhfbTYVuSaZ544gbWxgj02aoclxoxZsUOS09tFcOVCnR3SpMARxikiadHsS+60hbASgkOSSomYUBxMCe/HPriuS6mtQorbTTFlKG2UWKXQoFRgCIIsY/WxDv8A2VOuLM7I9ZUJPEadF7ob+U+PHf5XVon4rirRQXyD+VWHKiOkbINEa9Q+9wKvQpYd2WSe8X/aVtfs9yuqSdo92qZDh2qOSrPjuk1FpnFfjdazTqhKyTHNGl+O+C3IJMNxXD8FW1Va1Cp1bn+yU5IKt7QO7Mr3EKQfc8F4wjaWQYhZGBY6/k0Q0eVqvcxdnKiJszkqr+eugwMTRjsqwnum7ncxtB14cotcg111UyCWKNFmQoVDjo0rsailYivlfFh1ba2vQivJJl2HBo2+dqrRQ2PDCgkhIJBUBYm3YGLkx/XFwvIDhQFjWRMc2jbjCRvrfaoyu6Hu07M/pJ4DbGmrq/qfiWpPceKFJcaTC05pbL9RDjtigSuSO2dEr7KwKIwnNa1sRPGgFb05zvX7qND/AKcHbNG1T1MxWfkWE0U7GNPsdwXFoVOKZKZK8EOprq6GdYVWCHQ1MNTPRzBsjV7Xk2c9E35mPoLfN72fqHd2/wBTzVeNfLkgjnqMSrrBwSUuNWWcqgK7H4DHqhFXFsLq49WNw5CKN8g8l7Vedd+h7vr1J+mVnGKvwnvosNGMqj6fVpNS4uluoVyA99D81TIHXTgYyydFPLlWVWMzaoUiDJV7jBeJVc5rkkVRuqTqQFAkSASAD2I2v/bH01BHlkRN472tvNxE+lu+OafuZ+pNpD9crXTsj7Be3TTLPdPcau+6CkzzWyTkcKlHDt8BwKnsLQrIYKg8ljokGK+7lSlsBuah2xXiajlG5OkP6wmmdLH+ln3tRKaA0ba3t3yUdZGixxqgBU0eoWvhB8XCQQEYUMIhRxuEpEYNu3prekD/AEGcD0t1j+oN3Zd9eBaJYboZozpjiKaK6F4JhlASDX41Z5xZgnzr6eiElPk3MjTzGmOurH5JAtNlzgRkGNrUJ1y3VrpJr9phklDaLSaiacZG22xTI4QCeWts/wBIskh29VIcFXuV8SdCfElsaxzVULmcl264pqd1cyCVK1JEi50xI4Pv/wBT+U6lOqFABIClcwFC07xtsMJrj4pRfS3+h3j9WaNDr89x/QOPCIbxx22EnV7WMD5M8rm+EZJUyss8ikNaRCuVW1Edy7cPRG/Tt0fxnsb+mRp7ZZ2rI5qjTq7161UmkhgizzX2XgkZtafLKVzgukwqmTVY3HSUr2qGrYDdilVEB36w2R2ndR3PfT1+ntiMh8aFnOqYdc9YBwyo90DTDTqZ5gQJwkaQIQyljzYwkI0gXERqORWrzS3frwdw0bR/sLfp9UWT67Itdb2p0phxwIMckdFWIW4yJRLGjsMow1kGPWOYgGweMoA9hqR5FssA2bUCkJJGxI+a/wCZN/TFaoWlqHlLBCwClIMzEbD2H+cFh2Pd/fbp38UufZljWCS8Hq9Pb6qoB2epcrHIf65ZzK2bOktx9v6inMdZFlRvlkXkEZpwgDa3xuYxfelmnWOd5v11NV9YUr6afpp2R6FQNM8Nta4EGfF/3lZopFk2kY6MMA1nSVh7V9fMGV8iMRwnRXC4tVcLRr6fvYtpT9PCk1s1z0RxhmdVWgRtT9RbNmSZ7WmnXEagJZw5M4UPOAwoM+SE0SHLBFjoEkywMgkb/LahTfQz0hx3STs3h6iMhAqL/X/LrbU6fG4mE6NTzyfCw6qRZ0iVLWPWV0VqAHJmyeA/5pioMz3pK60DsDfaxuB/T6+3OIGqom4EJMwJAja3HB7372MWN31fVR0Q+n/muK6S5dgGoOf3+S4+uREfg0ekKlWB0g0aMyyLcSHudJsWxjkhjFuxgh7k3aiOVevYNqHU/U++qnqJ3rgxm8pNJe2fRLG9M9IMfyEUZ06rybMgmfkMycKIpK4U106TlEwT4jmuWOyA5URBNTqVfVR7ofpSY3ppqDr/ACsV7e+5XuUsoQcLwiAWS3NZosigslQaqZkNbFszRodNihPLKnBYKMtiYKVpHI47ndEP9DrTCHoX2JYrnmYhBR5Z3A3V9rXljyV4KOLCo7RSV+JQnR2kcODWwMZqGWcRSFcKOO3lFc7dUTrphtSmXEgElA84HAgjb7bXv64kcdJdbVNlwEnvb0tvM+04LrJ/qF6I4T3xYT2B1dDkl9q/ldZR5A5tLHhrR45WXVNlOQSrG6cZ/wAiOKjpsf8AlTEE5rmDuq9jWK5WIoNf7QieDlHaLpz2k4rEr5Gpfe73GaP6M4XEPHHJfCQmUQLa+yeAMiSFiyKaEOM5s8DWMEIxGlco1fuxHTLTnsfyfWfJe8TSXGdJMt1untlYplOt2KzouRX7ROqIcYlBKugT5oobCVoYQSRBKIbY4QhfHRstydcjff19ZLOce+q5p3d5XoFWGoeyPUawo4GndheDyXJ7+FbQpILrL6u3gAlVlZPsKu2i2tG0cdBU/wCmw4c6bHYaQQNMMmUSlULGpMixAiTt67Wxb8YnUPFSrSQlQHCrWMmOfYHnaepzui7qNDfpdaCaK41PwO2yTHq6FUaVYViWLDq4UqJU4hjsYMixEGSsSC0bGBYhRxxumzp53kVCP5OTexu+XQy57P5/c3bWePYNKstI7vPRaf5tkuKtzumZFr7L9Np7Wqi2JJ/ypagin+A0biIKWMT2u8jmpEsT1d7IPq49t0uKAcHNcZtasS3GPTpcan1N0tyGRE8aEYrCSbXHckrVa8QbSIRYM1GoRCSY5SMX+fr9YX6aGpv059Wqwb9R7PVLRHVOdaJpbm0+QVMhipTBinl4vmtLzMxb+vZMEx9iEjqq0iJFkwAV6ukQ1+uNlttTmidIB+WOQD62n9wcch6VBE3VwSDwLX/fpvA0XkANzc5tnlteSJMbPswyTJpjq6piyhFs50s0lSq1Y7WVUMMYwxETyK1UAjuKO9OH7ImRZU6VT40Sb8yZONDHEZF+IWUOYBsWcjix3sQZmje+Oqkc1HBO7ZeKdfeN6j19Rj86jm2l2FJFaxRUgYrImPNkDNFQUWSWNFfOerGBfczZ8t6EfLVsMYfiKUi/NcmIzflzXzVjleR7mvFOZJHDC4bnsJKleVkgyobipUaNCeHjs1zk49WCW3UtnRpUU6hNiZAnbae0++KyJbcUVCJJI2M/LHfmMQIGLGJZJTQFmybYpJyTgHjhGwkth1GoADIRieNFLJVToRUciNXkvrYm9P8AHrrDcYJlON30ilvMYm/FLJB/MJAkFYg57Hv5qwfGIQfAqmSPzmbPehWOa2vcEpsD1KvmVJZOVjmljeEba1/+lNJj7uUcF/xjHjMkkRz2FKRHKheZGjRqr0Z3/wAD3cMzSma6BjLse0tsZrrWUi3ENmYXEkrP5TZlvNkPDJiKnBFjDhJFVkcDWsO5HSSCV5vl2X1SWnqhCXXCG0NELlRUQqAQnSJ094+4huy7ozP+oaB/MKKldfpqUanloW2kISmASQtxJEExYE+mPjB7SktqOXd5/qfMnaguLIgxaCJNIR1PWmdHUHkkI74s6ZKRjmzZLVeOOu6hX3v1D7Gu/QUO3FCWlzYRxLLdCDZPlRhKYqHc8qIT4wnt4vTylM0hVTm1m7l6FK+0n1ewEwiSY0xo2ENXR5ZJg5ipHjvAMziBjiRRoBh2K5zmsRXNdw3Tiim/pHQ1c7CY8KosKp9vFG590eSZQWdjOGxzmjKAmzVju57x5r4/8pmyPbui7tLDxcbugpBSlSVQAFAwbcwB32+uEWqp10zqmwJWhZSvggpgGZME3j1udsKIsS+KysHMRjUbPlo7l9zkd8ou6N2T25P/AF/t+etlGlQjiE2WrnPHukh6cfsa5dmuaMvBz1RERdhc1/si779fs+BMrJYb6ZWskVs6ztyQvkvG6NOdDsXCkMIMZVMwYnkEJzXhYpVLuxeDXKmkO5yEcRA/GaV5TKJicQIhFVWDAxeXBBouzXIu6/vsvSil0yJncc+uGNxCW20oVE2JiLSUm5FveN9+2JA2FWqc3iKj/sRo3RVYxUcn3oR6PH9r02+1vPi52zX8mKrVyBzHOM1jMgkU5GDVHMlR3tjFQezlVo4ziCUhnNRr/wDTta9FVd27bOhzCKxHP5OREYrFRF9Ocqt4ueibb7OVP7bfn9uvXcbh8nukeT8ORzNmo71uJCbuXkz057fSfc3b9+pvESDBJkxx3/f5YhCUzYHvPmjg77cjEteKMSokW8hkaXYy5CSDIIT2/Hax3iQbGr4GuaVm7kVg2uRzvbVRN16fP9nG7hMXqsZ7te2jJJDYn+8+rqc4p4wiDEsmBBx+wwfMgMbIcxFkjr7KksEVHcHRoUprla5rHLypKV6s/qcrUXdWqrtl/ZN05KnpF/b/AB/bq3NCNYNTNDdTcV1E0kspcDOqiaWPUeBvyWz1uWfpRqk8J7VDLiWQ5axDRiooyoVrePLi5s9O+lp1KzJ/l2n5/LsP/b127b/Fo8RCkxP8x9kkKO/oMOO+mN9OYWtH1KMxxXOwR7jQjtWzi3znUCwjOGemyk9VkMpMCwYMxryhcmUWTBTLuMiulCoqCaIsdqvaRnbl3S/UX7aO0nF8SN3Fah0unEXOpJ6nGKc1feXZrCBViAs9XQKCosiR4sUJQB2cAQd3uivI5rVai/vpv9uFP2jdqdJVZXcVU3WXUw8rVzWuW2ZHbKLm2WR32YYUuQpl5DxSsaOnSKR/IafqsjijWSXO49/qsdw2oPeB3AZZrBAxzMDaC4JYWelWm+VLT3BcNT9BlkFcnHkUaGapHY21p5JThpKWR8dIrSK1j0TpjUlvLKQPBpC3HiISmFHzCSYFwQR/NAAIvgKUrrXghtZSy2brMJMiALGCRyODvjrsX6330mxzrIkjOdKZsBhnSIciv0cyjzMr2CG8TLCKPBwsjGjEcZDNhkIjwIiqgCK4bBd+qv3Cdv3cdhFTpnpzieXak93YMYZlem6aMCk1GR6bY1bVS3Dsh1Ny/wCO2LjGBfwlKCSwx3KXtnGgkj+GvCjxmdwwnKwTnKskKvcQio8kkCoqoTfiqNkI5GuI3kQLRkYTdzXt2cqdOn7Ju8bvKkYtP0J7S+2TF84mZdCfVZvmFfi17d5DfPkgkxpV5qFqDLmJGixIiyHOClpLiV1aMIxMhsaNdxacwW+lVOulUjxAUhSU6igmOAP+/fF1NIlt1Cy5rAgXXsfKbD7/AKWjD6P9laySfTdvPdXVsJEI4XcTjcsqOmfcV4sCjil/GeBeR1e0fnavkYitYibLy5J4f7Sx3+/o+m+E9m2GWpVttSJbM51SjRjoI4cPpit/QKWd/NRXMubcRJr2IRzJESoANRK0nNFBYC36mn0gtHtQclkQNCsBwjUTMR38x+S6gY3f3t9k8esHA+BiFHEmtl3ZRw0cklII1BFRzlkSAM48lAa89zepPcZqjkureqEsFvmGU+BppI2OjxK4II7YcWvrR8npBgxgfaMYVb41UhW/e53XXxzdJlyKBtguPha1KUpBkglKpMgbbW+t4mJunU7VrWpxtGlBUgKVBWExqKRBkgxInVe1jj+gP9E/Suq7fuwrSNk+M+jzHV10/WbM46Ed8xG5YZP4XiyCOEu7f4Uj150axznjdPcqCGiK5yKfq6/Th7tb7Uzuw+ojqRqzovjeBgIe7o4MbJcnu8ogYbj0OtxXT3DwV7MaHBj2MqsgQH8CTFix5UyRII8qOc3oAMI+u13l4Fi2F4XUh0rkwMLxqkxSvk2eLyEsZVRRVsSmrpk5RTGx3nSthAaVRoNio1XIicl6rHur+sz3Xd3Okx9C9Tv93lfg8q+x7ILOFjdGaomWxqGx/UI1fOlOnmEWqkEHGG4D27KNicVRXbpJU5lljuXtsGoDT6FJLhAMpUAmUfa/H3me6Wgr11Idp2viPGJShpStJISNSl6FQYSAQVbCQCSTbrJ+mJZA+n/9Iz/fBnDwtuLbBM+181AlEY9LUdpa04ZeGUYhR2Kkk0SifSVZByXsL8iZHbEGZojtaZ30j87n2nYBohcWd4+5uMpfnmbWksSsbPlS8hz6/vZj5EeSf5KMBJOQR03M54x8WNYjvt4otU/q9dyHcL29zu1jLaHCarTq7jUMA0LBqo0SzWFjMuLYiq3zVs5JRV8ta6tLIEETRPSKokc1iqN0+0F+rV3laOaRU2gmC1eCxcXwyrlVEOXOoyMugVp5E075pZQpkZUmxyyTyI6pyapmqrmORzkXqnqqJTjT7bqS202EriLqgHVCiCqd5vEY/Loa5NK64qmqElZS4PEZcQVon5WgU+cgoKNKf51ITdSgcdJ3Yder3UfVH76+9aSdZenukRIHaxo0uziCIymc5+UWlW9eAFbILCmSVUL3KrJ40IRFeiul/wBSLs37hu9zuv7dMkiZXppUdtujNdHblOKZHkVqDN720u8lBZZbY09JDoZFcR4qWkpa2M+bcx1kbzNlG2S1OubvtP8Aqia99sOl2QYJphR40J+QWS5hJJaY1+oNtL0cMIbSbKkukhMwx4MVzylc0jzmG1GuV73I4z9H/rf5dhunea5P3B1xczzzIMz+Zp9X4AlPVxI2JtxoJVuLUkyyJZQQy7BoILHJHWIJlO5jmJKlOd0RaeoG/DDqm0rdClpTKSYKrEwTFrkGT9oxA/RZi/UJaTTuIZHgpHipU0Qt1IASQoTrCgoaTcGxEmzgvq1arOt9JdJ+0/EGGi3XcrqthOjtdXV5m1xzYVUFjXGRQmMKFqeB5YWNxZxkGSI0BDsfLa1+yMEty5RV9tUzAtII1TiWZwdJ5eBYBXXtuOLQ0V4zHT4/UGm3NRFsWf6N7lnPmR4ZUKRERVG56cOC7V/6kWr+sfcxpT3A0LZuGQtIpZbLDX2Z52YBjTba0CS8uJjSkGR4JcEIAECJ4wibHG6MvFEVSFyz6xPcXgorm8wHUukvJkkI28LqrBMirMkxEeR0aCcpSVwmE2UAFe432IiuVr1RYf8AVaFCjqXTlHiKbTCkkmCBcTIM723AvviJ7Jq5PhkM1TReSpSdba0BIRrBClEAJMtLgKuQkxaTjca8/Te1s031V7fcC1F1M0YuAal5YXFKKpwa1tmXD6ls8BMlvZqWuL1EcphV5XPjNjllSTlG2wIUDFcR3Sd9QXuvj9tP0+NRqHFwxKWcfTqm0i01rYMhonQmX8QePwRMcwTDIStpATXPexH7rElO4tRdl482fUm7t9ZNYKHV7LLLC5F5iGN32M0JJFHVw8boz3jSEsLNGnnAbFtDMKUaSEUcgCvVnL7EYmbq53ha7awDxKo1cyfTzJanG7UFtSYxUGkSI0qzhx0YJ86RBkSq2QBQOMiuLNcNpJkpqIiuVOqrVVTlytS04lSlRASZ+bTAm/PpPpi4acpao3DKm0BoeKAstuKes2htYGlbioJKEyU3mLDHZb9MEGL9lf08sNg5sypqLg2FW2t2p55HKKWRcZTCk5CVpCOUTzBiVFfT1kdHsCVp4ot2AGZXtEv6a3bDoj3K6fasd33cToliGaZz3RaqZTmtW/MIKWEyPp1X2cqtx+DxI8Cw41vHjPNIWARzVjhAgjOOwbXITyr6jXdTq1pjk+B5bk9FVYhk1BKxq7g1OPw47EqjDaF0AFuNziNcsIQ4LXAVrEit8aJs7qmNOvq095GIRKDS7T3J8PqcVwuqLjFBJZjjPOympPI4UuVICVgzFGR5N1A0bneRzla5U93F1jCPAC20+RvS4UjUAVReBvvMxeBO+KDTLq11CUE61rBSCQgmIP8AMZFyPyHt0PdqvYTrp22d/Oe9zmR2Onmm2i06dnMjHdP9IsvkRo8umsGErcFx66xh1JWxDwqqpYKaUDikkPmordile8vSrvrbdzNjr73QC09BJFPx3QqgbSOKL+fBHkeblHf29hHhS0KeTfMq20tc5GjDEgqI0cb2+PxvhWEfVd79M9xSwiQLzHrKAgSV8q2JjbZN3KKJznzjx5hz7x1eUvlheRqq2Mgd0VHdKA121Qsm5BfWMkEwuUZDMkWNzcy3tSyLYyCENPLKMEavIQst6vCL2wTWjGFzGMaqCHc8yt152kYqGHHmkypgKGsCQLpncGDESNokYZHukM/o8ups4raKoZoKlWlmqKSUlW4uJgG19jeDjTytVcdqL2JTBx2I6KIJKOxESNHcWYCe18SUaQbwoR9u7drncVa2MiMaxHKTdKWtsaIKLMGYCVVcGwOOLa2qyI0c4wv5PDEajDSp0lAPErYEMJiO2e9dmJy6jtdDR7B5PffIdVvmvHFcJ7HSbu0HwlqKIbk5/CK5Glsp6IgmiVoBvedPtvWNWT9R86w6lPHI+TkhIcs8ubzkRqasgNaWyjwoY9gcYsZVaeWdhObiq9qo9u/Vcr+I3/hqBBSEi0SNyNhG533HIxQbSadQlPiAG/e8Xj9PzIxmaf4xbRsWj3dFmUXCrFk0c2jm2gJp77MJoF4eYEKti2Z6igiNRQxRHcFZZFIcqK8bBsJnIO83XXHsSFjc/PhZpQBjiEeitKydBI2QEjXmHDso3xTyWtI8oklyYrOSDVhUReSpvspbNxGKkjCYOP1tGE44IpbYcmNNNHhDUXgQkmQ10grH8S+cPFWoRrmquyp0Kmo2cAt6v44KSWtzEd8YlvbmdPfEaR5ZE5sBXK5UHKlnMfkVxCNRzWtVG7IkbmVZbVo+IdLbj7DiYKSlRDmkkAxJHNzEbb4LUGd9Q5Q3V0jJqqWlrUkJS4lbaHWyRKm9UBQmxgd98Wja93cHMoUiNbVdnUnKKOxKCOQUevaUT4qq9bIISTpjJKRnKWA47WPR+yldsqrI8NOzJ2mtwyR4+WYpguSqcgHDiSf6ByxjIMqhYhGIjfKj0bz3cnBdwCnXjRy401IA1LHOkgj2gcxkhUA4SIqImyIir5Ec1N1cifhOr30zyW2mx0j1UR6qx6P+bx5LHkOjmLtJZuvkE4icNnJsiE3/AC1vVylqnDKFiAkBKNJsYG1p2j63wv1DahC1EqWtalKURyQPvfnm2BssAI+zs1aqNeybNVN0Tlv8o6oqP4q9F9qzbkiORU5o5yNcmG+U9ysR7fIwaI1Ucm+ztv8Al2VNvx633Tb/ADv1vJgJrpdtOQLpEYEySsswmtb8ZCSy+HytVN1eRHhR6DRGtR7l2RU60YmNKR+8jwtcu7WcGOTl+3tWuVE3329/36DhCTJBTa9pm0drcT/3cusOf/YJnkTbbvH7HOMdzvaOa1E3c1OLtvu3ciI3ZVRHK5dmtaior1VGoqOVF637yRpdORYONgYavBCFLuGFkFK97jHV0kjHKgGOlMeMB27cucJFarWqqLoSKiOeiP3e3dWORqL96J9j0RU2arXcXIqJyaqcmq1zUVMg8sh1Y1rARUHDjQytgtQLJTQe2PlKxGOKVF3VziK5xHOc4iucu/XBSSoG1o/XHSXFoSUAJKVCCT8wEXItvbvjDRUdyRE4t5PRP2VUa7ZF2X8J/wC/RUdmmpGkOjevWI6qay0lvk2PYGsm/osdqYcWellmMdiJjpbMEsgo/wCm1Ut62r1JzQkmHGjoxVNugtfuq7Juqq5V2RFVVXdfe34397J63/CdewjoFCoohlQwnCXyDGVGormuRzEI13AiPaxzCM4kY5qKxyL1IklJChEggidrY4/sR9xGOiTuV+s9iOfaL6h4Donj+cY9mudRHU8OztItTFi42G55QsqsIBK55ppLCwr5s4YCFIqR1kE8fBrniUcuzb6zOp/atpRjWhM/SfAM50rxhLNsarGEtDbykuzFNYHuyvBZ113MOpVc8s+GhGog2NKxrU6TE1XtVq7ua5u/rk5W/d+d2qqtVF/Oyptvu7bkqr1+I1E/b/la3/s1qNan/ZqIn+fyvtVXq2quqFLSskK0p06VfLFvQ3EW9zfECadsJIuCpWolI5+49eMdIsj6y3ZzaKy4suxPDiZIMqP5pg2jx0V6MTi9JjsXJLUiKjdzuRpHL9xEc/dVr3VP6+uq07Fy4ZoVo5iWl9G4LRR3TlAcFa4DUHGkw8fx6LTUwpQGMan3teJzmqhRk398/wDz9bKMbk22Xkr/AGn+UReK7fsu3Xo0oWt2dFar/wDleiu2bv8AhG/ds1qfhE2TZPx66/Kr6gpISUtk7FInta4EWHvxbj6KZrcqXaCPew7/ALgD1xbWreu+qWvuVPzHVvNcjzjJVYQTJ1xYFLFrHEcqsBR1rXNrqaExq8WBr4oEYi8kdyVzlrWFDLPOCtiiLInSzNZBFHTzFLJc9GoAbFaiEIZ6oMDF47kc1F5b7dahURU29/hU9Oci+/8AO+6r/ZVVVT9l69o8mTCMOVCkSYsqO5pY8iNIKA4TDcjxlCYb2EEVj2tcMo3NIx6I5rkVEXoe6H1oK01C/iIIBI8kGAbzIsLQP1xZp/CQ40H0OO0qXW1vMtO+A46hCgdPi6FqSIkaRAPKjhj+Gac1ODYPm+JHhUFxlx9N8hyTLZzywLOyorVYT0r6KBCb5pUccMDEWWdRo1JD0RrmfjqDaS43d1mBUWFSMcm4le6h27cnxnPIlJX5AybXfGiAjVl+AsQ0mlqivYVpDtIqbv8AIqMRFToNId/OBMnTz2NiE9oEw580MyQ2XKbJT/UDlFaRCSRyFVEMMznse1VV7V2262o8syRoY8OsyXJITI4fAOOG9sxRxR09jEAbJTWBCn9TQia0bXe0ai++lb/btWouxXalvueMsuLWNJMApTCSCkBIkQJ7Y9DI/GDphK8p0dKmnTlWWPZW0poUhcU1UO061OuKUypTjyQl4JWpQ+YCwJODkxK3zF2XZvkOS4jBdVuAPSduVYHEgy3UVrDcqlt6qoLFM+zkSmEZ8mUxgFj+Hx+0cqLc1RCmByyDSZKasuYuB47My2ysWVwv1e6iWAUWJHvIgoQlFKE5XKCErFVxubeS7L0qqHfZZFQkKBkl5Xo8pTmFCt7CIB5ivQhpDxRzjG8xXo1SFe1XvXZXPXinXyDJspBJPLDkFzHlnRoZcplvPGaU0HsaHK2QjzsaqqrWE5MaqqqNTdeuP9vVZS4BUsSsJEqS6uCnczqTvOwSIA74novxmyGmGXl7p9deKOvVVBqq+FSkNOOla2h4bYCtJbYdQCmCsLBIESdWF3lhmOowXXNtkcrGcXS9zawg3GLRsajxq+ucc0IUdjADlyYQCuGgxOO0ZHtYpWEaqtW6/jYOLLMpzk8mqtKl+GVeaw8aiVtZ8GpAOrjtgTLvwgaKZOJYGmJUwTptEeWxmtY43B41Y/xBkm5pUi+tpp58MlcchLWYUxoD1dzjSHlM9xwKrl3GRXN/CL6RqJ510q6hCkxa6wtAR5wwgnCiyXtBLDGH4wBksa5rDsCNytYwjXI1r3bf1O35e6brXn2VozNxsNthtSE6kpBIHyJ80DveSTEgTP3Jvxq6cypmoo6noyizZLmb12bsV9a20usYqHRopJjyuNoagKZlKEL8ySojDNIVfPFT6XYvGNcY/d5zbnyu/tKfFYdoCRDtTSXsgW8qeGQKvCkMjSu8ryqJoX8mucqbQXKZpmYbrTnNdjFaSVbXwMBxWPUUTZscK1b1HZ3Afi1zRpIIMMpUOIDGNMqbKrW/cEIsmy17TPmZNk7mDAowgJfWDWfa5ERGhSQjGoxE4t2bsjFc1E4vci6UN/ksIY4sO/yGtiKpXNFEtpwQkJIVyle9gjtZyerncncd3K5VX25euGumKxp1tRrStKHStcuLkgqkwNMT7+3tbrfxs6araZ2lHSXhtKo30sqSmh1t1TlCmjaI1MKIZbUXn51KXqdVYzODN0dZYYTpFledWsayn3mSZCKjqVZiaZNIhvrXqSxluo5IPjIqSUJAVSIiq4blVPztQ93mFhllxd2uQHkWsuAJtZUuk00Kqs31bZb5iISBGb4ojxlI4ThePypFY1nNFTfqt4uSZfDjjgwcjuokcZDFL4bmxCx5JDnPO94GSWiWQV73kKbipXkVxXOV6qq7inswUFpcR5MR9lKs2jMlpLdJPYRXLtJVFIyYgyvIAjIilO0j92q5yq9XOVkyjLqiifqHHnvES9GjSpSlAWidQSBHEc9ucl6s6wy/Och6dyHKMrXlrOS5e00894jaXKnMEOlZrFFDZKilB0JkpsD5RbHxOuySzMAebaRohHMiDi/LktE5vjRznMjBI0St3/LnOa/j6Rv79WTpxDpaOeCxyaYkCsmz4scktzTEZHCIRZA44GCapG/JkDCh1UbkJvs96tV28N/VBz5D3x41ZVwQse4zpYPKd5EYqG8bV3ioV7UVrSkE7g5UciovvrzFNuYAQuWW04XNGyOTxj3iuM/+U8ymYRTtEjkF/ZGv+5Fam3TCk+Ek6ZWQCQF7Ei8GCbHm0xzjNfBMtrEBxC0LMbHSQbwBNx9sNH7d+4fSzTqrKEz4R4VwAtdf17A+eQMsN5IsK8ji2VzXSYLIrZbVc1CuajmKLbigX9wVnh+Z5Za5Jj5KyRDMZ7gxBWceIGLwcrxNJFU8aWV5Gt5lEwrhbqrCPe1Va4Wb25hxJEUFCj4hoQnBl2YpBeVjLcrXP5sV6tYgnIqDcNrE/PvqMlsTSk5zWNOVd2sOrneRFd7VXObtyRd1VVcqqq9I9N0xR0OfVGdtKWX6xMONqUShCjBOgTESfT2xr2d/ivmWc9D5f0a9Q0rbFCof/IQhIcKABEKABKpF5gcycSmXYikynXVl8mxnI5gIw2jiR4EcjGo0Twx4rWxwDjINihANGCI5xHPYR7yq679M8lsMPx6Rk8aLMlGBEnVbJ8goozqussTNNZJzepnuZJenBeCj2G5zG7N22GyqDLmzY8KIzkY7vErF4OArHr9xTsIiiawP5SQ9qpHVy/uROr/g1xKF9dCtJkWfTyUWCeMBm8FyFRFU0kXpxVa9fGjyK5rkTmNGtXp0pHIcAj5kKQqYsCADzvEx+o3xkbzSloUoLKBKTKbrsRIA2k7b/wB8TS+7hf4lwGbiRKd1ldy5bnNsVY4pYsVd1ZGjO2VAxxsfuhGbEcit5EXgidDnBh5OxrQghyys5ORGK5xBtT7moquftsqtTd6uRfuV356Isza6Mx8ePFhRz+RqKSJEEB7wI1GoiIxifliNRFRE/HrbdetU9vgNyYxzhq704i+JqNVu6boJWqTbf3zVy/336ipMpboviFpeWoPveJpUZgHcD8ov37YI5z1NX521lrb7bTaMqpvg2dCdJcb8p1uQLrlIEyoQTecVUDBrecJx7VFGLivjCxz5DRoxN3Kd4WoMaKn9LHqxU/HU2xuKuLyYhauKeK1U4SnOK9yTikRHjdsio1GNTZET3s31uvUjHdja0Y1/qRpSvQL0Aq/HI1GtRSIrUQjftRrERq/nbf315T7JyqrnhQUZyIVqc+RBqROeyO/O7d9t2qiL729fgk022gkpnVF57DY/vbjAAuLXAcgxtpne3c8/9RimI2G3uWTLB2O1NpIrx2EgU04gSH1wFWURu7itKkhzWJ9y7s5IjftXdETrJXSiySNdSmWle79Ebylw3EeyYxGs5KqRHMZIaz9/Obkz87r1Ybsts9O8gfDx46CrbkZDGichFEco5hFcRpm8Gq5ybornORFT+p23Vm3bqfM6N9o48CptI8ZDuuCzgRZE9JbPG6B8cr1NaGa5OMqGwZARwKwgiNVyqtEMtJSReSDEm0wL3vx/b1IKddWsIkGACY9xb9J5uSMBK98dAJwc5SMexXeRV4O2Y5OKbIq/c5EVqJ/zIn9+vx7BoKOVkkRHlQivjtY9CB2ciIj3KiIn7+l/Prb8dT3MKiMFseVDr4cNXIEDn18lxoppUVFYUrgFVSRyEY5z3Jug+eyN9qnUbpIdbKt4sa7lpVwH/dIkF5I30qbcnNa5fuVdt9kX0vtP3qEEECNyBPYHn1H27Y7JgT2xHnqqJ6Xb3/8Anr6VEVNl9pui7f5T8dWRb0mGyreHDqLtkWEZ3gk2JmFNBCZVVBHcqsa7xquyu2cqNbu5yI1OoteY+SklyY36hX2KxycPlVklsuDKY9NxSBFaqoikTdHC3VROb93567KCBM/u3+f2bRtOh0EgERG/rjQ/3X91Xdf8r+P/AG6/3X6qOaqtc3ZUXb/r/n/2/Cfjr8VheBDIP+SxEG86p9o1f/f+6/2X9vfUZMRzJj9f7YlJhJPbTbvqWlH5ap52x/usn4M1YC2bQO+B8xK75DCBcNZbmebio1cklpURVaqqNApsvF6t9rvcSwvIs6tn4/jUJkyzZW2tu8b5LIw0rqivNPnHdJJuMb2RIpiMCqK939Tf6uou9yt5MX0r1RzkR2+7mpxRVVPTlRv2ov8A9qbJ62TqE1DRcDSVhTltSdikEgAnfkgfuMSlh5LCahbZS2tbraCT8ymlBJ9pBn02vj6/H5/P7/8A6nr/AMuv915sVffv+3/v1lxIpJchANc1rUenMj9+LWbt5Ofsu/BqKqv2VF4ouyoqeplHSQDyoJ9pMT9MRX0FXIQpYT30xInjf19sYyon7Im6qiJ6VVVVXbbZqK5VXfb0nv8A6dSCLjF7IF5AwiAY1WvaU5UG1yLtsqNcqE23VP2/7dXlpHhlZfHSNEAGbZIvDkrflSuSu2akcLd2BRf6WlK1VVF/uvUi1bwu603uJFPk9TZ19gGOhliyA/zhPlxflVfEPJrXAkCViv8Aw1rVc7bqqcyoGK74J5wfElMoQD8yCY1el5F8MbfSGfVOQnqJikWqhSIWZ06VQFFOv5Z0kHYCIwP38NTYhRpYSxBGZFe44WqbiibJxVWu9J7Xff1unvbfrbpjOOhA8km0lPcjeblc1gQO3RVbxeJzi/cu/L1+Nt/8xuRd2ExqsKRqtTZviE+ONqL7V6PY1yu3aqtRW8dvzvsuydaQrnK5He9/7Ij27bfj71VWqn9uO235XbfoiFIRYIMG++4MX29I/dlgNmElflUQCUgzG1ptMR+xM7dzIo5Q/jPV49noNiryRG8k/DnJzei+t+aIv9t036ksYg4zxvI8Y2p9yo9URqo1N1bwTZV3/ddl32T9+oKJzgladuzHMRXK56cuW37Kuyqqe/7r+y/4SVVFK6zYKwnSGEETmQbV5qiIi+mbIqoibovr8e/xsqdRpGpxZSFWIPBna3vJOOwIAHbH5HA20mypHv4zFVR/aqLsq7r4m/szf8+l/wC/7eU+Iu7Wt29orla3+ndF9f8ARfx1I4rnqd3hjtYNqrGarEVrVV34VE9f9N1/ZV9+9uvSXAIJwlcNiq5Nt90/9dl/x7/v/wBV6tJSClJg3AMT3AP79z6RwFmSCIExJ7W/Pcdp9jiHgYRmyPTfj79/svrf87e/z/8Av4y5CSGSDvUB3tYrHu4ePZWeuLVcnvZF2X/Cev8AHW2PAKm5BpwXdeSOVFTf/G/7bp6/8/x1IqKgdZmYN70Qhtuaek5NRf3/ACi+v77+kXbZfzXW5oQVlNgSN+294/Zti1SU7lY+ywykqW8YBFwni+0/p64ilPMG6U4UkeyOG9rWK7fbn6XZvvdV/wC/7ov56y7m7SuFJAwnjkSmI0bUXyqjebHudyTfxK5rFRU2T/7U6kmUYktG4kmMNiEGjHsVFT05q7ouye9l4+99t/7fjqm5piyJZnOVSFITk57ETZzWtVODU2XZWrs5U3/CKv8AjrhuoDiAsAweJ9vf69o5xYr8vfy58sPiFjexHb33nGM5eb3EcqK9y7/hP+6+kT2i/v8A56/He02X2npPf9t09dfSCJv7Y9P8uaif+qJv/n9/39deroxeLXNRV+5qqiJvum/4/wAp/nb+39+uHDJ1diDH2G/07Yo4mWFRK5HTZcmSqGGzxxojRlcstpeXkYpROb4RsUYuaOXZ/kRfXBV6saW408TAqgosgwwgCNWvezlyTxIm6ru9qJ/L4on39VLUDZHiK+QI7DEIR8ZW7t5+Lj5W8URN0Vr27p739b/36vXS6k/jLIaCPB87mvtK8KKQaMY0vyW8k9Jt9rlXZd9+pnqyloaJ6rqFeGhhCnFL/wDym5t9D+t8XcqyiszvNKLLKIBdRW1CGG0kwJcIBJMiyR2vjZSrAKtCR4Z6OaAY3OI0cdyKNvjej2EbyVyua5Vf+FRUVF9L1ilso5gbC+8wnIViEdsoxJ6ehFa3gRVcjlajd1a1UavtPTW9V+ye4xzAWZnKQTgkAySvjVEJxVnJU2Tb3t/23/f89KzvaP4c9wY73M2KVjmqRvkb/Md+WuRFRPaqifhEVFT89DOmursp6qYedyt/xUsOFC+YUkweBF+NpnDb19+GOffh+aBectoSiuSrw9EiCINwSomUgkxFwcQ2ZKQjnORdxoi7EVqtVPfLx7KiKibp/Sqfnb87+tdIsjFRHK5VYiIiuVfabJsqft+6bfhP+/UmnV6DCimMzg96jVjWo4xHDX7nO/P3uRfa/lff7e11Yq6I7cEhhBscqub4xOVVa5VVqr+26tX9vwqL0z+YghM3j157YzfyyCLjv32tf7nvMW4//9k=)"],"metadata":{"id":"6eDTMGsd0b7Z"}},{"cell_type":"code","source":["transformer = keras.Model(\n"," [encoder_inputs, decoder_inputs], decoder_outputs, name=\"transformer\"\n",")\n","transformer.summary()"],"metadata":{"id":"vouHwS1sZg-o"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Exercice 2.4 : \n","\n","Entraînez votre transformer jusqu'à convergence du réseau. \n","* Employez la fonction de coût suivante : *sparse_categorical_crossentropy*\n","* Employez la métrique suivante : *accuracy* (à noter que pour les traductions, le score BLEU est recommandé, mais il n'est pas nativement disponible sur Keras)\n","* Optionnel : vous pouvez utiliser la classe [EarlyStopping](https://keras.io/api/callbacks/early_stopping/) pour stopper l'entraînement dès que la métrique observée ne s'améliore plus.\n","* Le choix de l'optimiseur est libre"],"metadata":{"id":"vbsby1gA5ZQP"}},{"cell_type":"code","source":["#@title Insérez votre code ici"],"metadata":{"cellView":"form","id":"zNFPTRlf7chf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Exercice 2.5\n","\n","Nous souhaitons maintenant tester notre modèle sur les phrases de tests.\n","\n","Complétez la fonction suivante, qui prend en entrée la séquence en anglais et qui vous permettra d'obtenir la séquence décodée en français.\n"],"metadata":{"id":"kmRDpjtE8d_2"}},{"cell_type":"code","source":["#@title Complétez la fonction\n","\n","fra_vocab = fra_vectorization.get_vocabulary()\n","fra_index_lookup = dict(zip(range(len(fra_vocab)), fra_vocab))\n","max_decoded_sentence_length = 20\n","\n","\n","def decode_sequence(input_sentence):\n"," # tokenized_input_sentence = \n"," decoded_sentence = \"[start]\"\n"," for i in range(max_decoded_sentence_length):\n"," tokenized_target_sentence = fra_vectorization([decoded_sentence])[:, :-1]\n"," # predictions = \n","\n"," # sampled_token_index = TOKEN LE PLUS PROBABLE \n"," sampled_token = fra_index_lookup[sampled_token_index]\n"," decoded_sentence += \" \" + sampled_token\n","\n"," if sampled_token == \"[end]\":\n"," break\n"," return decoded_sentence"],"metadata":{"id":"UsRPLwjT_GT1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title Testez votre fonction en lançant cette cellule\n","test_eng_texts = [pair[0] for pair in test_pairs]\n","for _ in range(30):\n"," input_sentence = random.choice(test_eng_texts)\n"," translated = decode_sequence(input_sentence)\n"," print(f\"ENG : {input_sentence}\\n FR : {translated}\")"],"metadata":{"id":"Dst8CfH8bMs4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Exercice 2.6 (optionnel)\n","\n","On constate que le modèle n'est pas très performant dans ses traductions. Donnez un ou plusieurs éléments qui pourrait expliquer cela."],"metadata":{"id":"LDJDkkwz90c2"}},{"cell_type":"code","source":[],"metadata":{"id":"Femzlxi3-hFc"},"execution_count":null,"outputs":[]}]}