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%% Cell type:markdown id: tags:



# Classification d'Images

[Énoncé du TD]("https://gitlab.ec-lyon.fr/edelland/mod_4_6-td1").

## Introduction

Ce TD a pour objectif d'appliquer les méthodes de classification abordées en cours. Nous allons travailler sur la base de données [CIFAR-10]("https://www.cs.toronto.edu/~kriz/cifar.html"). Dans un premier temps, nous mettrons en œuvre la classification par les k-plus proches voisins en utilisant la distance euclidienne. Ensuite, nous explorerons la classification à l'aide de réseaux de neurones. Pour chaque méthode, nous évaluerons le taux de réussite.





Afin que ce document puisse rester interactif, les fonctions longues à exécuter ont été désactivées et les résultats sont fournis manuellement.





## Importation des Données

Nous importons les bibliothèques nécessaires pour charger et traiter les fichiers.



%% Cell type:code id: tags:



``` python

import numpy as np

import pickle

import os

import matplotlib.pyplot as plt

```



%% Cell type:markdown id: tags:



Nous extrayons ces données et développons des fonctions qui nous permettront de créer des listes d'images d'entraînement et de test à partir de ces données. De plus, nous récupérons les listes de libellés de classe associées à chaque image, ce qui nous permettra d'attribuer un libellé à une image de test et de la comparer ensuite au libellé réel de l'image.



%% Cell type:code id: tags:



``` python

def read_cifar_batch(batch_path):

    with open(batch_path, 'rb') as file:

        batch_data = pickle.load(file, encoding='bytes')

    data = np.array(batch_data[b'data'], dtype=np.float32)

    labels = np.array(batch_data[b'labels'], dtype=np.int64)

    return data, labels

```



%% Cell type:code id: tags:



``` python

def read_cifar(path_folder):

    data = np.empty((0, 3072), dtype=np.float32)

    labels = np.empty((0), dtype=np.int64)

    for filename in os.listdir(path_folder):

        if filename.startswith("data_batch") or filename == "test_batch":

            batch_path = os.path.join(path_folder, filename)

            d, l = read_cifar_batch(batch_path)

            data = np.concatenate((data, d), axis=0)

            labels = np.concatenate((labels, l), axis=None)

    return(data,labels)

```



%% Cell type:code id: tags:



``` python

def split_dataset(data, labels, split_factor):

    num_samples = len(data)

    shuffled_indices = np.random.permutation(num_samples)

    split_index = int(num_samples * split_factor)



    data_train = data[shuffled_indices[:split_index],:]

    labels_train = labels[shuffled_indices[:split_index]]

    data_test = data[shuffled_indices[split_index:],:]

    labels_test = labels[shuffled_indices[split_index:]]



    return data_train, labels_train, data_test, labels_test

```



%% Cell type:markdown id: tags:



Afin de vérifier le bon fonctionnement de ces fonctions, nous les appliquons à une liste de 10 images. Nous vérifions que les images d'entraînement et de test sont correctement sélectionnées de manière aléatoire à partir de la base de données.



%% Cell type:code id: tags:



``` python

if __name__ == "__main__":

    #read_cifar_batch("data/cifar-10-batches-py/data_batch_1")

    d, l = read_cifar("data/cifar-10-batches-py")

    print(l[0:10])

    d_1, l_1, d_1, l_2 = split_dataset(d[:10,:], l[:10], 0.9)

    print(l_1,l_2)

    d_1, l_1, d_2, l_2 = split_dataset(d[:10,:], l[:10], 0.9)

    print(l_1,l_2)

```



%% Output



    [6 9 9 4 1 1 2 7 8 3]

    [4 3 7 8 6 9 1 2 1] [9]

    [8 9 3 7 6 9 1 4 1] [2]



%% Cell type:markdown id: tags:



Afin de vérifier l'association correcte entre les images et les libellés, l'algorithme suivant affiche les libellés et les images correspondantes, tout en indiquant si chaque image sera utilisée pour l'entraînement du modèle ou pour les tests.



%% Cell type:code id: tags:



``` python

def affichage(d_train, l_train, d_test, l_test):

    long, large = 5,2



    with open("data/cifar-10-batches-py/batches.meta", 'rb') as file:

        batch_data = pickle.load(file, encoding='bytes')

    liste_names= np.array(batch_data[b'label_names'])

    fig, axes = plt.subplots(large, long, figsize=(12, 5))

    fig.subplots_adjust(hspace=0.5)

    for i in range(len(l_train)):

        im = np.array(np.reshape(d_train[i, 0:3072], (32, 32, 3), order='F'), dtype=np.int64)

        im = np.transpose(im, (1, 0, 2))

        name=liste_names[l_train[i]]

        axes[i // long, i % long].imshow(im)

        axes[i // long, i % long].set_title(f"Train : {name.decode('utf-8')}")

        axes[i // long, i % long].axis('off')

    for i in range(len(l_test)):

        im = np.array(np.reshape(d_test[i, 0:3072], (32, 32, 3), order='F'), dtype=np.int64)

        im = np.transpose(im, (1, 0, 2))

        j = i + len(l_train)

        name=liste_names[l_test[i]]

        axes[j // long, j % long].imshow(im)

        axes[j // long, j % long].set_title(f"Test :  {name.decode('utf-8')}")

        axes[j // long, j % long].axis('off')

    plt.show()



if __name__ == "__main__":

    d, l = read_cifar("data/cifar-10-batches-py")

    d_1, l_1, d_2, l_2 = split_dataset(d[:10,:], l[:10], 0.5)

    affichage(d_1, l_1, d_2, l_2)

```



%% Output



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">
 


%% Cell type:markdown id: tags:



## Classification par les k Plus Proches Voisins



Dans cette section, nous allons développer un algorithme de classification en utilisant la méthode des k plus proches voisins, choisis en fonction d'une distance euclidienne.



Pour ce faire, nous commençons par écrire la fonction de calcul de distance. Cette fonction prend en entrée les images d'entraînement et de test, et pour chaque image de test, elle calcule sa distance par rapport à chaque image d'entraînement. En plaçant les résultats dans une matrice, nous obtenons une image de test associée à chaque ligne et une image d'entraînement associée à chaque colonne.



Pour une base de données composée de N images d'entraînement et M images de test, la matrice de distances en sortie est la suivante :



|                  | Image entrainement 1 | ... | Image entrainement n | ... | Image entrainement N |

|------------------|----------------------|-----|----------------------|-----|----------------------|

| Image test 1     | dist(im_e1,im_t1)    | ... | dist(im_en,im_t1)    | ... | dist(im_eN,im_t1)    |

| ...              | ...                  | ... | ...                  | ... | ...                  |

| Image test m     | dist(im_e1,im_tm)    | ... | dist(im_en,im_tm)    | ... | dist(im_eN,im_tm)    |

| ...              | ...                  | ... | ...                  | ... | ...                  |

| Image test M     | dist(im_e1,im_tM)    | ... | dist(im_en,im_tM)    | ... | dist(im_eN,im_tM)    |





%% Cell type:code id: tags:



``` python

def distance_matrix(data_train, data_test):

    dist_mat=[]

    for image_test in data_test:

        dist_mat.append([])

        for image_train in data_train:

            dist_mat[-1].append(np.sum(np.square(image_train-image_test)))

    return(np.array(dist_mat))

```



%% Cell type:markdown id: tags:



Avec cette matrice de distances, nous allons rechercher pour chaque ligne (image de test) les k plus petites valeurs de distance et récupérer les libellés des images d'entraînement associés à ces valeurs. Enfin, nous comptons les libellés les plus fréquents dans cette liste pour associer un libellé à l'image de test.



La fonction `knn_predict` prend donc en entrée la matrice de distances, la liste des libellés des images d'entraînement et le nombre k de plus proches voisins considéré, et renvoie en sortie le libellé associé.



%% Cell type:code id: tags:



``` python

def knn_predict(dist, labels_train, k):

    resultat=[]

    for image_test in dist:

        k_max = np.argpartition(image_test, k)[:k]

        val, count = np.unique(labels_train[k_max], return_counts=True)

        indexe = np.argmax(count)

        resultat.append(val[indexe])

    return (resultat)

```



%% Cell type:markdown id: tags:



Une dernière fonction va appeler les deux dernières fonctions et comparer les résultats du modèle avec les vrais libellés des images de test. En sortie, nous obtiendrons le taux de réussite du modèle, c'est-à-dire le rapport entre le nombre de classes correctement attribuées et le nombre total de classes testées.



%% Cell type:code id: tags:



``` python

def evaluate_knn(data_train, labels_train, data_test, labels_test, k):

    dist_matrice = distance_matrix(data_train, data_test)

    res = knn_predict(dist_matrice, labels_train, k)

    return(np.sum(labels_test == res) / len(labels_test))

```



%% Cell type:markdown id: tags:



Pour visualiser ce modèle, nous traçons les taux de réussite en fonction du nombre de k voisins choisis.



%% Cell type:code id: tags:



``` python

if __name__ == "__main__":

    x = range(1, 20)

    d, l = read_cifar_batch("data/cifar-10-batches-py/data_batch_1")

    d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)

    dist_matrice = distance_matrix(d_train, d_test)

    y = []

    for knn in x:

        stat = 0

        res = knn_predict(dist_matrice, l_train, knn)

        y.append(np.sum(l_test == res) / len(l_test))

    plt.plot(x, y, label='Précision')

    plt.xlabel('nombre de plus proche voisins concidérés')

    plt.ylabel('taux de bonne calification')

    plt.xticks(range(1, 20))

    plt.yticks(np.arange(0, 1.1, 0.1))

    plt.legend()

    plt.show()

```



%% Output



img 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">
 


%% Cell type:markdown id: tags:



Pour évaluer l'efficacité de cette méthode, j'ai effectué 10 itérations, chaque fois sur un nouvel ensemble de test et d'entraînement. J'ai tracé les 10 courbes sur le même graphe, ce qui a donné le résultat ci-dessous.



%% Cell type:code id: tags:



``` python

%%script false



if __name__ == "__main__":

    nbr_knn = 20

    nbr_val = 10

    x = range(1, nbr_knn)

    d, l = read_cifar_batch("data/cifar-10-batches-py/data_batch_1")

    for essai in range(nbr_val):

        d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)

        dist_matrice = distance_matrix(d_train, d_test)

        y = []

        for knn in x:

            stat = 0

            res = knn_predict(dist_matrice, l_train, knn)

            y.append(np.sum(l_test == res) / len(l_test))

        plt.plot(x, y, label=f'Précision knn mesure {essai}')

    plt.xlabel('nombre de plus proche voisins concidérés')

    plt.ylabel('taux de bonne calification')

    plt.xticks(range(1, nbr_knn))

    plt.yticks(np.arange(0, 1.1, 0.1))

    plt.legend()

    plt.show()

```



%% Output



    Couldn't find program: 'false'



%% Cell type:code id: tags:



``` python

from IPython.display import display, Image

display(Image(filename="result/knn_1_20_valid_10test.png"))

```



%% Output



img 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3Um3TSHyMhIbt68SVhYGMePH6eoqIjU1FQmTZpETU0NR48eZfny5eTk5FBSUsKuXbu4fv26dojWx8eHlJQUUlJSOHfuHDNmzODWrVsPbTc2Npbs7GyioqI4efIkhYWF7N69u8FJINOnT+ebb77hL3/5C+fOnWP9+vV88sknzJ49u7kuR4sjCaAQQohmZ25uzqFDh+jYsSNBQUG4u7vzxz/+kcrKSm2PYEREBGvWrGH9+vX06NGDESNGUFhYqLc+Kysr3nrrLfr27Uu/fv24ePEie/fu1TuUbG5uTmpqKjdv3qRfv34EBwfj6+tLfHx8s52fpaUl+/btQ1EUhg8f3uASJk3x/fffk5yczHfffUevXr1wdnbWvrKzs5uljVouLi5kZWVRU1ODv78/Hh4ezJo1C1tbW4yMjLC2tubQoUMMGzaM3/zmNyxcuJBVq1ZpF9yePHkyERERhIeHM2jQIJ577rlG9eb27NmTL7/8kvPnzzNw4EB69+5NXFwcLi4u9R7TqVMnUlJSSEtLw9PTk1WrVrFx40ZZA/AxqJTHXQugBbt9+zY2NjaUlZU16v4MIUTLVllZSXFxMZ06ddI701EI0Xwa+n2T72/pARRCCCGEaHEkARRCCCGEaGEkARRCCCGEaGEkARRCCCGEaGEkARRCCCGEaGEkARRCCCGEaGEkARRCCCGEaGEkARRCCCGEaGGeqgTw3Xffxc3NDVNTU1544QWOHTtWb1m1Ws2SJUvo3LkzpqameHp6sn///icYrRBCtBwXL15k6dKlzfrYNCHEo3tqEsDt27czZ84cXnvtNXJzc/H09CQgIIBr167pLb9w4UL++c9/sm7dOs6cOcP06dP5/e9/z9dff/2EIxdCiKdbVVUVY8eOxcHBAUtLy0Yfp1Kp+Pzzz5u97KNavHgxvXr1+lnbEOJJeWoSwNWrVzN16lQmTZrEb3/7WzZs2IC5uTmbNm3SW/6jjz7ib3/7G8OGDeO5555jxowZDBs2jFWrVj3hyIUQ4pdt4sSJqFQqVCoVJiYmdOnShSVLlnD37t1GHT979mz8/f2ZPn16k9q9cuWK9rmzzVn2l0ytVhMbG4uHhwcWFha4uLgQHh7O5cuXDR2aQV25coVx48bxm9/8BiMjI2bNmmXokH71nooEsLq6mhMnTuDn56fdZ2RkhJ+fH1999ZXeY6qqquo8G9DMzIwjR478rLEKIcSvUWBgIFeuXKGwsJC5c+eyePFi3n77bb1lq6urdbbXr1/PsmXLmtymk5MTbdq0afayv2R37twhNzeXRYsWkZuby65duygoKOCVV14xdGjN4sHPRmNVVVXh6OjIwoUL8fT0bOaoWqanIgG8ceMGNTU1tG/fXmd/+/btKS0t1XtMQEAAq1evprCwEI1GQ1paGrt27eLKlSv1tlNVVcXt27d1XkII0RK0adMGJycnXF1dmTFjBn5+fiQnJwP3eghHjx7NsmXLcHFxoVu3bgB8++23hISEYGtrS9u2bRk1ahQXL17UqXfTpk306NGDNm3a4OzsTFRUlPa9+4d1q6uriYqKwtnZGVNTU1xdXVmxYoXesgCnTp3Cx8cHMzMz7O3tmTZtms79h7Uxr1y5EmdnZ+zt7YmMjEStVjf6mhQVFfHcc88RFRWFoigkJiZia2tLamoq7u7uWFpaahPnxrZrY2NDWloaISEhdOvWjRdffJH4+HhOnDhBSUlJvbG8/PLLREdHM2vWLOzs7Gjfvj3vv/8+FRUVTJo0CSsrK7p06cK+fft0jjt9+jRDhw7F0tKS9u3bM2HCBG7cuKF9f+fOnXh4eGivo5+fHxUVFdo2H+yJGz16NBMnTtRuu7m58frrrxMeHo61tTXTpk0D4MiRIwwcOBAzMzM6dOhATEyMtl593Nzc+Mc//kF4eDg2NjYN/2BEozwVCeCj+Mc//kHXrl3p3r07JiYmREVFMWnSJIyM6r8kK1aswMbGRvvq0KHDE4xYCPHUURSorjDMS1EeK3QzMzOd3pz09HQKCgpIS0tjz549qNVqAgICsLKy4vDhw2RlZWkTotrjEhISiIyMZNq0aZw6dYrk5GS6dOmit721a9eSnJzMJ598QkFBAUlJSbi5uektW1FRQUBAAHZ2dhw/fpwdO3Zw8OBBneQSICMjg6KiIjIyMtiyZQuJiYkkJiY26vzz8/MZMGAA48aNIz4+HpVKBdzrwVu5ciUfffQRhw4doqSkhHnz5j1Wu2VlZahUKmxtbRuMacuWLTg4OHDs2DGio6OZMWMGY8eOpX///uTm5uLv78+ECRO4c+cOALdu3cLHx4fevXuTk5PD/v37uXr1KiEhIcC9YdewsDAmT57M2bNnyczMJCgoCKWJn52VK1fi6enJ119/zaJFiygqKiIwMJAxY8aQn5/P9u3bOXLkSJ2fj/h5tTJ0AM3BwcEBY2Njrl69qrP/6tWrODk56T3G0dGRzz//nMrKSv7zn//g4uLCX//6V5577rl625k/fz5z5szRbt++fVuSQCHEo1PfgeUuhmn7b5fBxKLJhymKQnp6OqmpqURHR2v3W1hYsHHjRkxMTAD4+OOP0Wg0bNy4UZscbd68GVtbWzIzM/H392fp0qXMnTuXmTNnauvp16+f3nZLSkro2rUrAwYMQKVS4erqWm+MW7dupbKykg8//BALi3vnGB8fz8iRI3nzzTe1o0V2dnbEx8djbGxM9+7dGT58OOnp6UydOrXBa5Cdnc2IESNYsGABc+fO1XlPrVazYcMGOnfuDEBUVBRLlizRKdOUdisrK4mNjSUsLAxra+sG4/L09GThwoXAve+rN954AwcHB229cXFxJCQkkJ+fr+1Z7N27N8uXL9fWsWnTJjp06MD58+cpLy/n7t27BAUFaa+3h4dHgzHo4+Pjo3OdpkyZwvjx47W9h127dmXt2rUMGjSIhISEOrdniZ/HU9EDaGJiQp8+fUhPT9fu02g0pKen89JLLzV4rKmpKc888wx3797l008/ZdSoUfWWbdOmDdbW1jovIYRoCfbs2YOlpSWmpqYMHTqU0NBQFi9erH3fw8NDm/wB5OXlceHCBaysrLC0tMTS0pK2bdtSWVlJUVER165d4/Lly/j6+jaq/YkTJ3Ly5Em6detGTEwMBw4cqLfs2bNn8fT01CZ/AN7e3mg0GgoKCrT7evTogbGxsXbb2dm53pUjapWUlDBkyBDi4uLqJH8A5ubm2uSvvjob265arSYkJARFUUhISGgwLoCePXtq/21sbIy9vb1Owlab+Na2lZeXR0ZGhvbnY2lpSffu3YF7w9uenp74+vri4eHB2LFjef/99/nhhx8eGseD+vbtq7Odl5dHYmKiTrsBAQFoNBqKi4ubXL94NE9FDyDAnDlziIiIoG/fvjz//POsWbNGe+8DQHh4OM8884z2npGjR4/y/fff06tXL77//nsWL16MRqPhL3/5iyFPQwjRkrQ2v9cTZ6i2m2Dw4MEkJCRgYmKCi4sLrVrpfn3cn2wBlJeX06dPH5KSkurU5ejo2ODtNvp4eXlRXFzMvn37OHjwICEhIfj5+bFz584m1XO/1q1b62yrVCo0Gk2Dxzg6OuLi4sK2bduYPHlynY4AfXU+OGTamHZrk79Lly7xxRdfNKrDQV+99++r7Ymtbau8vFzbK/ogZ2dnjI2NSUtLIzs7mwMHDrBu3ToWLFjA0aNH6dSpE0ZGRnXOTd89lPo+G6+++ioxMTF1ynbs2PGh5ymax1OTAIaGhnL9+nXi4uIoLS2lV69e7N+/X/sXT0lJic5/OJWVlSxcuJBvvvkGS0tLhg0bxkcfffTQeyyEEKLZqFSPNAxrCBYWFvXen6ePl5cX27dvp127dvUmL25ubqSnpzN48OBG1WltbU1oaCihoaEEBwcTGBjIzZs3adu2rU45d3d3EhMTqaio0CYfWVlZGBkZaSeoPCozMzP27NnDsGHDCAgI4MCBA1hZWT1WnQ+qTf4KCwvJyMjA3t6+Weuv5eXlxaeffoqbm1udhL6WSqXC29sbb29v4uLicHV15bPPPmPOnDk4OjrqTHCpqanh9OnTD/15enl5cebMmSZ9nkTzeyqGgGtFRUVx6dIlqqqqOHr0KC+88IL2vczMTJ2bbAcNGsSZM2eorKzkxo0bfPjhh7i4GOheHCGEeMqMHz8eBwcHRo0axeHDhykuLiYzM5OYmBi+++474N7CyqtWrWLt2rUUFhaSm5vLunXr9Na3evVqtm3bxrlz5zh//jw7duzAyclJ7x/t48ePx9TUlIiICE6fPk1GRgbR0dFMmDChzmoRj8LCwoKUlBRatWrF0KFDm/XpJmq1muDgYHJyckhKSqKmpobS0lJKS0sfeQmV+kRGRnLz5k3CwsI4fvw4RUVFpKamMmnSJGpqajh69CjLly8nJyeHkpISdu3axfXr13F3dwfu3duXkpJCSkoK586dY8aMGdy6deuh7cbGxpKdnU1UVBQnT56ksLCQ3bt3P3QSyMmTJzl58iTl5eVcv36dkydPcubMmea4FC3SU9MDKIQQ4pfD3NycQ4cOERsbS1BQED/++CPPPPMMvr6+2h7BiIgIKisreeedd5g3bx4ODg4EBwfrrc/Kyoq33nqLwsJCjI2N6devH3v37tU7lGxubk5qaiozZ86kX79+mJubM2bMGFavXt1s52dpacm+ffsICAhg+PDh7N27t1nq/f7777XL6zz41JGMjAxefvnlZmkHwMXFhaysLGJjY/H396eqqgpXV1cCAwMxMjLC2tqaQ4cOsWbNGm7fvo2rqyurVq3SLrg9efJk8vLyCA8Pp1WrVsyePbtRvbk9e/bkyy+/ZMGCBQwcOBBFUejcuTOhoaENHte7d2/tv0+cOMHWrVtxdXWts7SQaByV0tT53ELr9u3b2NjYUFZWJhNChBAPVVlZSXFxMZ06dZKZjkL8zBr6fZPv76dsCFgIIYQQQjycJIBCCCGEEC2MJIBCCCGEEC2MJIBCCCGEEC2MJIBCCCGEEC2MJIBCCCGEEC2MJIBCCCGEEC2MJIBCCCGEEC2MJIBCCCF+dhcvXmTp0qXN+tg0IcSjkwRQCCHEz6qqqoqxY8fi4OCApaVlo49TqVR8/vnnzV72US1evLjO49mE+LWSBFAIIUSDJk6ciEqlQqVSYWJiQpcuXViyZAl3795t1PGzZ8/G39+f6dOnN6ndK1euaJ8725xlf8nUajWxsbF4eHhgYWGBi4sL4eHhXL582dChGdSuXbsYMmQIjo6OWFtb89JLL5GammrosH7VJAEUQgjxUIGBgVy5coXCwkLmzp3L4sWLefvtt/WWra6u1tlev349y5Yta3KbTk5OtGnTptnL/pLduXOH3NxcFi1aRG5uLrt27aKgoIBXXnnF0KE1iwc/G4116NAhhgwZwt69ezlx4gSDBw9m5MiRfP31180cYcshCaAQQoiHatOmDU5OTri6ujJjxgz8/PxITk4G7vUQjh49mmXLluHi4kK3bt0A+PbbbwkJCcHW1pa2bdsyatQoLl68qFPvpk2b6NGjB23atMHZ2ZmoqCjte/cP61ZXVxMVFYWzszOmpqa4urqyYsUKvWUBTp06hY+PD2ZmZtjb2zNt2jSd+w9rY165ciXOzs7Y29sTGRmJWq1u9DUpKiriueeeIyoqCkVRSExMxNbWltTUVNzd3bG0tNQmzo1t18bGhrS0NEJCQujWrRsvvvgi8fHxnDhxgpKSknpjefnll4mOjmbWrFnY2dnRvn173n//fSoqKpg0aRJWVlZ06dKFffv26Rx3+vRphg4diqWlJe3bt2fChAncuHFD+/7OnTvx8PDQXkc/Pz8qKiq0bc6aNUunvtGjRzNx4kTttpubG6+//jrh4eFYW1szbdo0AI4cOcLAgQMxMzOjQ4cOxMTEaOvVZ82aNfzlL3+hX79+dO3aleXLl9O1a1f+7//+r+EfkqiXJIBCCGEgiqJwR33HIC9FUR4rdjMzM53enPT0dAoKCkhLS2PPnj2o1WoCAgKwsrLi8OHDZGVlaROi2uMSEhKIjIxk2rRpnDp1iuTkZLp06aK3vbVr15KcnMwnn3xCQUEBSUlJuLm56S1bUVFBQEAAdnZ2HD9+nB07dnDw4EGd5BIgIyODoqIiMjIy2LJlC4mJiSQmJjbq/PPz8xkwYADjxo0jPj4elUoF3OvBW7lyJR999BGHDh2ipKSEefPmPVa7ZWVlqFQqbG1tG4xpy5YtODg4cOzYMaKjo5kxYwZjx46lf//+5Obm4u/vz4QJE7hz5w4At27dwsfHh969e5OTk8P+/fu5evUqISEhwL1h9bCwMCZPnszZs2fJzMwkKCioyZ+dlStX4unpyddff82iRYsoKioiMDCQMWPGkJ+fz/bt2zly5Eidn09DNBoNP/74I23btm1SLOK/Whk6ACGEaKl+uvsTL2x9wSBtHx13FPPW5k0+TlEU0tPTSU1NJTo6WrvfwsKCjRs3YmJiAsDHH3+MRqNh48aN2uRo8+bN2NrakpmZib+/P0uXLmXu3LnMnDlTW0+/fv30tltSUkLXrl0ZMGAAKpUKV1fXemPcunUrlZWVfPjhh1hYWAAQHx/PyJEjefPNN2nfvj0AdnZ2xMfHY2xsTPfu3Rk+fDjp6elMnTq1wWuQnZ3NiBEjWLBgAXPnztV5T61Ws2HDBjp37gxAVFQUS5Ys0SnTlHYrKyuJjY0lLCwMa2vrBuPy9PRk4cKFAMyfP5833ngDBwcHbb1xcXEkJCSQn5+v7Vns3bs3y5cv19axadMmOnTowPnz5ykvL+fu3bsEBQVpr7eHh0eDMejj4+Ojc52mTJnC+PHjtb2HXbt2Ze3atQwaNIiEhARMTU0fWufKlSspLy/XJqui6SQBFEII8VB79uzB0tIStVqNRqNh3LhxLF68WPu+h4eHNvkDyMvL48KFC1hZWenUU1lZSVFREdeuXePy5cv4+vo2qv2JEycyZMgQunXrRmBgICNGjMDf319v2bNnz+Lp6alN/gC8vb3RaDQUFBRoE8AePXpgbGysLePs7MypU6cajKOkpIQhQ4awbNmyOsOfAObm5trkr7bOa9eu6ZRpbLtqtZqQkBAURSEhIaHBuAB69uyp/bexsTH29vY6CVvtedfGk5eXR0ZGht6Z2UVFRfj7++Pr64uHhwcBAQH4+/sTHByMnZ3dQ2O5X9++fXW28/LyyM/PJykpSbtPURQ0Gg3FxcW4u7s3WN/WrVv5+9//zu7du2nXrl2TYhH/JQmgEEIYiFkrM46OO2qwtpti8ODBJCQkYGJigouLC61a6X593J9sAZSXl9OnTx+dL/lajo6OGBk17Q4kLy8viouL2bdvHwcPHiQkJAQ/Pz927tzZpHru17p1a51tlUqFRqNp8BhHR0dcXFzYtm0bkydPrtMrp6/OB4dMG9NubfJ36dIlvvjii4f2/tVX7/37antia9sqLy/X9oo+yNnZGWNjY9LS0sjOzubAgQOsW7eOBQsWcPToUTp16oSRkVGdc9N3D6W+z8arr75KTExMnbIdO3Zs8Bz/9a9/MWXKFHbs2IGfn1+DZUXDJAEUQggDUalUjzQMawgWFhb13p+nj5eXF9u3b6ddu3b1Ji9ubm6kp6czePDgRtVpbW1NaGgooaGhBAcHExgYyM2bN+vcB+bu7k5iYiIVFRXa5CMrKwsjIyPtBJVHZWZmxp49exg2bBgBAQEcOHCgTi/n46pN/goLC8nIyMDe3r5Z66/l5eXFp59+ipubW52EvpZKpcLb2xtvb2/i4uJwdXXls88+Y86cOTg6OupMcKmpqeH06dMP/Xl6eXlx5syZJn2eAG3S/a9//Yvhw4c36VhRl0wCEUII0ezGjx+Pg4MDo0aN4vDhwxQXF5OZmUlMTAzfffcdcG9h5VWrVrF27VoKCwvJzc1l3bp1eutbvXo127Zt49y5c5w/f54dO3bg5OSkd2LE+PHjMTU1JSIigtOnT5ORkUF0dDQTJkzQDoM+DgsLC1JSUmjVqhVDhw5t1qebqNVqgoODycnJISkpiZqaGkpLSyktLX3kJVTqExkZyc2bNwkLC+P48eMUFRWRmprKpEmTqKmp4ejRoyxfvpycnBxKSkrYtWsX169f1w7R+vj4kJKSQkpKCufOnWPGjBncunXroe3GxsaSnZ1NVFQUJ0+epLCwkN27dzc4CWTr1q2Eh4ezatUqXnjhBe01KSsra67L0eJIAiiEEKLZmZubc+jQITp27EhQUBDu7u788Y9/pLKyUtsjGBERwZo1a1i/fj09evRgxIgRFBYW6q3PysqKt956i759+9KvXz8uXrzI3r179Q4lm5ubk5qays2bN+nXrx/BwcH4+voSHx/fbOdnaWnJvn37UBSF4cOHN7iESVN8//33JCcn891339GrVy+cnZ21r+zs7GZpo5aLiwtZWVnU1NTg7++Ph4cHs2bNwtbWFiMjI6ytrTl06BDDhg3jN7/5DQsXLmTVqlXaBbcnT55MREQE4eHhDBo0iOeee65Rvbk9e/bkyy+/5Pz58wwcOJDevXsTFxeHi4tLvce899573L17l8jISJ1rcv8EItE0KuVx1wJowW7fvo2NjQ1lZWWNuj9DCNGyVVZWUlxcTKdOnRo101EI8ega+n2T72/pARRCCCGEaHEkARRCCCGEaGEkARRCCCGEaGGeqgTw3Xffxc3NDVNTU1544QWOHTvWYPk1a9bQrVs37bMIZ8+eTWVl5ROKVgghhBDCMJ6aBHD79u3MmTOH1157jdzcXDw9PQkICKizAnutrVu38te//pXXXnuNs2fP8sEHH7B9+3b+9re/PeHIhRBCCCGerKcmAVy9ejVTp05l0qRJ/Pa3v2XDhg2Ym5uzadMmveWzs7Px9vZm3LhxuLm54e/vT1hY2EN7DYUQQgghfu2eigSwurqaEydO6DwWxsjICD8/P7766iu9x/Tv358TJ05oE75vvvmGvXv3MmzYsCcSsxBCCCGEoTwVj4K7ceMGNTU1dVZ4b9++PefOndN7zLhx47hx4wYDBgxAURTu3r3L9OnTGxwCrqqqoqqqSrt9+/bt5jkBIYQQQogn6KnoAXwUmZmZLF++nPXr15Obm8uuXbtISUnh9ddfr/eYFStWYGNjo3116NDhCUYshBC/XhcvXmTp0qXN+tg0IcSjeyoSQAcHB4yNjbl69arO/qtXr+Lk5KT3mEWLFjFhwgSmTJmCh4cHv//971m+fDkrVqxAo9HoPWb+/PmUlZVpX99++22zn4sQQjxtqqqqGDt2LA4ODlhaWjb6OJVKxeeff97sZR/V4sWL6dWr18/ahhBPylORAJqYmNCnTx/S09O1+zQaDenp6bz00kt6j7lz506dZ0gaGxsDUN/T8dq0aYO1tbXOSwghnnYTJ05EpVKhUqkwMTGhS5cuLFmyhLt37zbq+NmzZ+Pv78/06dOb1O6VK1e0z51tzrK/ZGq1mtjYWDw8PLCwsMDFxYXw8HAuX75s6NAM6siRI3h7e2Nvb4+ZmRndu3fnnXfeMXRYv2pPxT2AAHPmzCEiIoK+ffvy/PPPs2bNGioqKpg0aRIA4eHhPPPMM6xYsQKAkSNHsnr1anr37s0LL7zAhQsXWLRoESNHjtQmgkIIIe4JDAxk8+bNVFVVsXfvXiIjI2ndujXz58+vU7a6uhoTExPt9vr16x+pzfpGcB637C/ZnTt3yM3NZdGiRXh6evLDDz8wc+ZMXnnlFXJycgwd3mN78LPRWBYWFkRFRdGzZ08sLCw4cuQIr776KhYWFkybNu1niLQFUJ4i69atUzp27KiYmJgozz//vPLvf/9b+96gQYOUiIgI7bZarVYWL16sdO7cWTE1NVU6dOig/OlPf1J++OGHRrdXVlamAEpZWVkznoUQ4mn1008/KWfOnFF++uknQ4fSJBEREcqoUaN09g0ZMkR58cUXdd5funSp4uzsrLi5uSmKoiglJSXK2LFjFRsbG8XOzk555ZVXlOLiYp16PvjgA+W3v/2tYmJiojg5OSmRkZHa9wDls88+UxRFUaqqqpTIyEjFyclJadOmjdKxY0dl+fLlessqiqLk5+crgwcPVkxNTZW2bdsqU6dOVX788cc65/T2228rTk5OStu2bZU//elPSnV1db3X4bXXXlM8PT212xcuXFA6deqkREZGKhqNRtm8ebNiY2Oj7N+/X+nevbtiYWGhBAQEKJcvX36sdo8dO6YAyqVLl+otM2jQICUqKkqZOXOmYmtrq7Rr10557733lPLycmXixImKpaWl0rlzZ2Xv3r06x506dUoJDAxULCwslHbt2il/+MMflOvXr2vf37Fjh/K73/1Oex19fX2V8vJybZszZ87UqW/UqFE637Wurq7KkiVLlAkTJihWVlba9w4fPqwMGDBAMTU1VZ599lklOjpaW29j/f73v1f+8Ic/1Pt+Q79v8v2tKE/FEHCtqKgoLl26RFVVFUePHuWFF17QvpeZmUliYqJ2u1WrVrz22mtcuHCBn376iZKSEt59911sbW2ffOBCiBZJURQ0d+4Y5KXUc6tLY5mZmVFdXa3dTk9Pp6CggLS0NPbs2YNarSYgIAArKysOHz5MVlYWlpaWBAYGao9LSEggMjKSadOmcerUKZKTk+nSpYve9tauXUtycjKffPIJBQUFJCUl4ebmprdsRUUFAQEB2NnZcfz4cXbs2MHBgweJiorSKZeRkUFRUREZGRls2bKFxMREne+JhuTn5zNgwADGjRtHfHw8KpUKuNeDt3LlSj766CMOHTpESUkJ8+bNe6x2y8rKUKlUD/1+2rJlCw4ODhw7dozo6GhmzJjB2LFj6d+/P7m5ufj7+zNhwgTu3LkDwK1bt/Dx8aF3797k5OSwf/9+rl69SkhICHBvWD0sLIzJkydz9uxZMjMzCQoKavJnZ+XKlXh6evL111+zaNEiioqKCAwMZMyYMeTn57N9+3aOHDlS5+fTkK+//prs7GwGDRrUpFjEfz01Q8BCCPFro/z0EwVefQzSdrfcE6jMzZt8nKIopKenk5qaSnR0tHa/hYUFGzdu1A7vffzxx2g0GjZu3KhNjjZv3oytrS2ZmZn4+/uzdOlS5s6dy8yZM7X19OvXT2+7JSUldO3alQEDBqBSqXB1da03xq1bt1JZWcmHH36IhYUFAPHx8YwcOZI333xTu2SYnZ0d8fHxGBsb0717d4YPH056ejpTp05t8BpkZ2czYsQIFixYwNy5c3XeU6vVbNiwgc6dOwP3OiaWLFmiU6Yp7VZWVhIbG0tYWNhD7zv39PRk4cKFwL1Ji2+88QYODg7aeuPi4khISCA/P58XX3yR+Ph4evfuzfLly7V1bNq0iQ4dOnD+/HnKy8u5e/cuQUFB2uvt4eHRYAz6+Pj46FynKVOmMH78eGbNmgVA165dWbt2LYMGDSIhIQFTU9N663r22We5fv06d+/eZfHixUyZMqXJ8Yh7JAEUQgjxUHv27MHS0hK1Wo1Go2HcuHEsXrxY+76Hh4fOvV15eXlcuHABKysrnXoqKyspKiri2rVrXL58GV9f30a1P3HiRIYMGUK3bt0IDAxkxIgR+Pv76y179uxZPD09tckfgLe3NxqNhoKCAm0C2KNHD517vp2dnTl16lSDcZSUlDBkyBCWLVumTWDuZ25urk3+aut88JGkjW1XrVYTEhKCoigkJCQ0GBdAz549tf82NjbG3t5eJ2GrPe/aePLy8sjIyNA7M7uoqAh/f398fX3x8PAgICAAf39/goODsbOze2gs9+vbt6/Odl5eHvn5+SQlJWn3KYqCRqOhuLgYd3f3eus6fPgw5eXl/Pvf/+avf/0rXbp0ISwsrEnxiHskARRCCANRmZnRLfeEwdpuisGDB5OQkICJiQkuLi60aqX79XF/sgVQXl5Onz59dL7kazk6OtZZheFhvLy8KC4uZt++fRw8eJCQkBD8/PzYuXNnk+q5X+vWrXW2VSpVvcuA1XJ0dMTFxYVt27YxefLkOr1y+up8cMi0Me3WJn+XLl3iiy++aNSqE/rqvX9fbU9sbVvl5eXaXtEHOTs7Y2xsTFpaGtnZ2Rw4cIB169axYMECjh49SqdOnTAyMqpzbmq1uk5d+j4br776KjExMXXKduzYscFz7NSpE3DvD46rV6+yePFiSQAfkSSAQghhICqV6pGGYQ3BwsKi3vvz9PHy8mL79u20a9eu3uTFzc2N9PR0Bg8e3Kg6ra2tCQ0NJTQ0lODgYAIDA7l58yZt27bVKefu7k5iYiIVFRXa5CMrKwsjIyO6devW6HPQx8zMjD179jBs2DACAgI4cOBAnV7Ox1Wb/BUWFpKRkYG9vX2z1l/Ly8uLTz/9FDc3tzoJfS2VSoW3tzfe3t7ExcXh6urKZ599xpw5c3B0dOTKlSvasjU1NZw+ffqhP08vLy/OnDnTpM+TPhqNRufpXKJpnqpJIEIIIX4Zxo8fj4ODA6NGjeLw4cMUFxeTmZlJTEwM3333HXBvYeVVq1axdu1aCgsLyc3NZd26dXrrW716Ndu2bePcuXOcP3+eHTt24OTkpHdixPjx4zE1NSUiIoLTp0+TkZFBdHQ0EyZMqPPI0EdhYWFBSkoKrVq1YujQoc36dBO1Wk1wcDA5OTkkJSVRU1NDaWkppaWlOpNumkNkZCQ3b94kLCyM48ePU1RURGpqKpMmTaKmpoajR4+yfPlycnJyKCkpYdeuXVy/fl07ROvj40NKSgopKSmcO3eOGTNmcOvWrYe2GxsbS3Z2NlFRUZw8eZLCwkJ2797d4CSQd999l//7v/+jsLCQwsJCPvjgA1auXMkf/vCH5rocLY70AAohhGh25ubmHDp0iNjYWIKCgvjxxx955pln8PX11fYIRkREUFlZyTvvvMO8efNwcHAgODhYb31WVla89dZbFBYWYmxsTL9+/di7d6/eoWRzc3NSU1OZOXMm/fr1w9zcnDFjxrB69epmOz9LS0v27dtHQEAAw4cPZ+/evc1S7/fff09ycjJAnaeOZGRk8PLLLzdLOwAuLi5kZWURGxuLv78/VVVVuLq6EhgYiJGREdbW1hw6dIg1a9Zw+/ZtXF1dWbVqlXbB7cmTJ5OXl0d4eDitWrVi9uzZjerN7dmzJ19++SULFixg4MCBKIpC586dCQ0NrfcYjUbD/PnzKS4uplWrVnTu3Jk333yTV199tdmuR0ujUh53LYAW7Pbt29jY2FBWViZPBRFCPFRlZSXFxcV06tSpwZmOQojH19Dvm3x/yxCwEEIIIUSLIwmgEEIIIUQLIwmgEEIIIUQLIwmgEEIIIUQLIwmgEEIIIUQLIwmgEEIIIUQLIwmgEEIIIUQLIwmgEEIIIUQLIwmgEEKIn93FixdZunRpsz42TQjx6Az+KLj09HTS09O5du0aGo1G571NmzYZKCohhBDNpaqqirFjx/LHP/4RS0vLRh+nUqn47LPPGD16dLOWfVSLFy/m888/5+TJkz9bG0I8KQbtAfz73/+Ov78/6enp3Lhxgx9++EHnJYQQwvAmTpyISqVCpVJhYmJCly5dWLJkCXfv3m3U8bNnz8bf35/p06c3qd0rV65onzvbnGV/ydRqNbGxsXh4eGBhYYGLiwvh4eFcvnzZ0KH9YmRlZdGqVas6z0oWTWPQHsANGzaQmJjIhAkTDBmGEEKIhwgMDGTz5s1UVVWxd+9eIiMjad26NfPnz69Ttrq6GhMTE+32+vXrH6lNJyenn6XsL9mdO3fIzc1l0aJFeHp68sMPPzBz5kxeeeUVcnJyDB3eY3vws9FUt27dIjw8HF9fX65evdqMkbU8Bu0BrK6upn///oYMQQghRCO0adMGJycnXF1dmTFjBn5+fiQnJwP3eghHjx7NsmXLcHFxoVu3bgB8++23hISEYGtrS9u2bRk1ahQXL17UqXfTpk306NGDNm3a4OzsTFRUlPY9lUrF559/Dtz7voiKisLZ2RlTU1NcXV1ZsWKF3rIAp06dwsfHBzMzM+zt7Zk2bZrO/Ye1Ma9cuRJnZ2fs7e2JjIxErVY3+poUFRXx3HPPERUVhaIoJCYmYmtrS2pqKu7u7lhaWhIYGMiVK1ca3a6NjQ1paWmEhITQrVs3XnzxReLj4zlx4gQlJSX1xvLyyy8THR3NrFmzsLOzo3379rz//vtUVFQwadIkrKys6NKlC/v27dM57vTp0wwdOhRLS0vat2/PhAkTuHHjhvb9nTt34uHhob2Ofn5+VFRUaNucNWuWTn2jR49m4sSJ2m03Nzdef/11wsPDsba2Ztq0aQAcOXKEgQMHYmZmRocOHYiJidHW25Dp06czbtw4XnrppYeWFQ0zaAI4ZcoUtm7dasgQhBDCYBRFQV1VY5CXoiiPFbuZmRnV1dXa7fT0dAoKCkhLS2PPnj2o1WoCAgKwsrLi8OHDZGVlaROi2uMSEhKIjIxk2rRpnDp1iuTkZLp06aK3vbVr15KcnMwnn3xCQUEBSUlJuLm56S1bUVFBQEAAdnZ2HD9+nB07dnDw4EGd5BIgIyODoqIiMjIy2LJlC4mJiSQmJjbq/PPz8xkwYADjxo0jPj4elUoF3OvBW7lyJR999BGHDh2ipKSEefPmPVa7ZWVlqFQqbG1tG4xpy5YtODg4cOzYMaKjo5kxYwZjx46lf//+5Obm4u/vz4QJE7hz5w5wrzfNx8eH3r17k5OTw/79+7l69SohISHAvWH1sLAwJk+ezNmzZ8nMzCQoKKjJn52VK1fi6enJ119/zaJFiygqKiIwMJAxY8aQn5/P9u3bOXLkSJ2fz4M2b97MN998w2uvvdak9oV+Bh0Crqys5L333uPgwYP07NmT1q1b67y/evVqA0UmhBA/v7vVGt6b+aVB2p72j0G0bmPc5OMURSE9PZ3U1FSio6O1+y0sLNi4caN2eO/jjz9Go9GwceNGbXK0efNmbG1tyczMxN/fn6VLlzJ37lxmzpypradfv3562y0pKaFr164MGDAAlUqFq6trvTFu3bqVyspKPvzwQywsLACIj49n5MiRvPnmm7Rv3x4AOzs74uPjMTY2pnv37gwfPpz09HSmTp3a4DXIzs5mxIgRLFiwgLlz5+q8p1ar2bBhA507dwYgKiqKJUuW6JRpSruVlZXExsYSFhaGtbV1g3F5enqycOFCAObPn88bb7yBg4ODtt64uDgSEhLIz8/X9iz27t2b5cuXa+vYtGkTHTp04Pz585SXl3P37l2CgoK019vDw6PBGPTx8fHRuU5Tpkxh/Pjx2t7Drl27snbtWgYNGkRCQgKmpqZ16igsLOSvf/0rhw8fplUrg89ffSoY9Crm5+drb+I8ffq0znu1/2EIIYQwvD179mBpaYlarUaj0TBu3DgWL16sfd/Dw0Pn3q68vDwuXLiAlZWVTj2VlZUUFRVx7do1Ll++jK+vb6PanzhxIkOGDKFbt24EBgYyYsQI/P399ZY9e/Ysnp6e2uQPwNvbG41GQ0FBgTYB7NGjB8bG/02CnZ2dOXXqVINxlJSUMGTIEJYtW1Zn+BPA3Nxcm/zV1nnt2jWdMo1tV61WExISgqIoJCQkNBgXQM+ePbX/NjY2xt7eXidhqz3v2njy8vLIyMjQOzO7qKgIf39/fH198fDwICAgAH9/f4KDg7Gzs3toLPfr27evznZeXh75+fkkJSVp9ymKgkajobi4GHd3d53yNTU1jBs3jr///e/85je/aVLbon4GTQAzMjIM2bwQQhhUKxMjpv1jkMHaborBgweTkJCAiYkJLi4udXph7k+2AMrLy+nTp4/Ol3wtR0dHjIya1r6XlxfFxcXs27ePgwcPEhISgp+fHzt37mxSPfd7cNRJpVLVWY7sQY6Ojri4uLBt2zYmT55cp1dOX50PDpk2pt3a5O/SpUt88cUXD+39q6/e+/fVdqzUtlVeXq7tFX2Qs7MzxsbGpKWlkZ2dzYEDB1i3bh0LFizg6NGjdOrUCSMjozrnpu8eSn2fjVdffZWYmJg6ZTt27Fhn348//khOTg5ff/21dphYo9GgKAqtWrXiwIED+Pj46L0mon6/mH7U7777DoBnn33WwJEIIcSToVKpHmkY1hAsLCzqvT9PHy8vL7Zv3067du3qTV7c3NxIT09n8ODBjarT2tqa0NBQQkNDCQ4OJjAwkJs3b9K2bVudcu7u7iQmJlJRUaFNPrKysjAyMtJOUHlUZmZm7Nmzh2HDhhEQEMCBAwfq9HI+rtrkr7CwkIyMDOzt7Zu1/lpeXl58+umnuLm51TusqlKp8Pb2xtvbm7i4OFxdXfnss8+YM2cOjo6OOhNcampqOH369EN/nl5eXpw5c6bRnydra+s6PaTr16/niy++YOfOnXTq1KlR9QhdBp0EotFoWLJkCTY2Nri6uuLq6oqtrS2vv/76Q/8KE0II8cs1fvx4HBwcGDVqFIcPH6a4uJjMzExiYmK0f/AvXryYVatWsXbtWgoLC8nNzWXdunV661u9ejXbtm3j3LlznD9/nh07duDk5KR3YsT48eMxNTUlIiKC06dPk5GRQXR0NBMmTNAOgz4OCwsLUlJSaNWqFUOHDm3Wp5uo1WqCg4PJyckhKSmJmpoaSktLKS0t1Zl00xwiIyO5efMmYWFhHD9+nKKiIlJTU5k0aRI1NTUcPXqU5cuXk5OTQ0lJCbt27eL69evaIVofHx9SUlJISUnh3LlzzJgxg1u3bj203djYWLKzs4mKiuLkyZMUFhaye/fueieBGBkZ8bvf/U7n1a5dO0xNTfnd735Xp4dRNI5BE8AFCxYQHx/PG2+8wddff83XX3/N8uXLWbduHYsWLWpyfe+++y5ubm6YmprywgsvcOzYsXrLvvzyy9qFTe9/DR8+/HFOSQghBPfuhTt06BAdO3YkKCgId3d3/vjHP1JZWantEYyIiGDNmjWsX7+eHj16MGLECAoLC/XWZ2VlxVtvvUXfvn3p168fFy9eZO/evXqHks3NzUlNTeXmzZv069eP4OBgfH19iY+Pb7bzs7S0ZN++fSiKwvDhwxu1hEljfP/99yQnJ/Pdd9/Rq1cvnJ2dta/s7OxmaaOWi4sLWVlZ1NTU4O/vj4eHB7NmzcLW1hYjIyOsra05dOgQw4YN4ze/+Q0LFy5k1apV2gW3J0+eTEREBOHh4QwaNIjnnnuuUb25PXv25Msvv+T8+fMMHDiQ3r17ExcXh4uLS7Oen2iYSnnctQAeg4uLCxs2bOCVV17R2b97927+9Kc/8f333ze6ru3btxMeHs6GDRt44YUXWLNmDTt27KCgoIB27drVKX/z5k2dv6b+85//4OnpycaNG3XWMGrI7du3sbGxoaysrFH3ZwghWrbKykqKi4vp1KmT3pmOQojm09Dvm3x/G7gH8ObNm3Tv3r3O/u7du3Pz5s0m1bV69WqmTp3KpEmT+O1vf8uGDRswNzev93nCbdu2xcnJSftKS0vD3NycsWPHPtK5CCGEEEL8Whg0AfT09NTbJR8fH4+np2ej66murubEiRP4+flp9xkZGeHn58dXX33VqDo++OAD/vd//7fBewmqqqq4ffu2zksIIYQQ4tfGoLOA33rrLYYPH87Bgwe1j3X56quv+Pbbb9m7d2+j67lx4wY1NTV1bu5t3749586de+jxx44d4/Tp03zwwQcNlluxYgV///vfGx2XEEIIIcQvkUF7AAcNGsT58+f5/e9/z61bt7h16xZBQUEUFBQwcODAJxbHBx98gIeHB88//3yD5ebPn09ZWZn29e233z6hCIUQQgghmo/B1wF0cXFh2bJlj1WHg4MDxsbGXL16VWf/1atXcXJyavDYiooK/vWvf9V5VI8+bdq0oU2bNo8VqxBCCCGEoT3xBDA/P5/f/e53GBkZkZ+f32DZ+x9r0xATExP69OlDeno6o0ePBu6tMZienv7Qh0vv2LGDqqoq/vCHPzSqLSGEEEKIX7snngD26tWL0tJS2rVrR69evfQ+JgfurT5eU1PT6HrnzJlDREQEffv25fnnn2fNmjVUVFQwadIkAMLDw3nmmWdYsWKFznEffPABo0eP/tlWWhdCCCGE+KV54glgcXExjo6O2n83l9DQUK5fv05cXBylpaX06tWL/fv3ayeGlJSU1FkwtKCggCNHjnDgwIFmi0MIIYQQ4pfOoAtBHzp0iP79+9d5BuHdu3fJzs7mf/7nfwwUWePIQpJCiKZoyQtBX7x4kY8//phZs2ZhaWlp6HBECyALQTfMoLOABw8erHfB57KyskY/HFwIIcQvW1VVFWPHjsXBwaFJyZ9KpeLzzz9v9rKPavHixfTq1etnbUOIJ8WgCaCiKKhUqjr7//Of/8jDnYUQ4hdi4sSJ2uelm5iY0KVLF5YsWcLdu3cbdfzs2bPx9/dn+vTpTWr3ypUr2ufONmfZXzK1Wk1sbCweHh5YWFjg4uJCeHg4ly9fNnRoBpWZman9DN7/Ki0tNXRov1oGWQYmKCgIuPcX28SJE3WWVqmpqSE/P5/+/fsbIjQhhBB6BAYGsnnzZqqqqti7dy+RkZG0bt2a+fPn1ylbXV2NiYmJdnv9+vWP1ObDlvF61LK/ZHfu3CE3N5dFixbh6enJDz/8wMyZM3nllVfIyckxdHiP7cHPRlMVFBToDNm2a9euOcJqkQzSA2hjY4ONjQ2KomBlZaXdtrGxwcnJiWnTpvHxxx8bIjQhhBB6tGnTBicnJ1xdXZkxYwZ+fn4kJycD93oIR48ezbJly3BxcaFbt24AfPvtt4SEhGBra0vbtm0ZNWoUFy9e1Kl306ZN9OjRgzZt2uDs7KyzdNf9w7rV1dVERUXh7OyMqakprq6uOqs6PDgEfOrUKXx8fDAzM8Pe3p5p06ZRXl6ufb825pUrV+Ls7Iy9vT2RkZGo1epGX5OioiKee+45oqKiUBSFxMREbG1tSU1Nxd3dHUtLSwIDA7ly5Uqj27WxsSEtLY2QkBC6devGiy++SHx8PCdOnKCkpKTeWF5++WWio6OZNWsWdnZ2tG/fnvfff1+7GoaVlRVdunRh3759OsedPn2aoUOHYmlpSfv27ZkwYQI3btzQvr9z5048PDy019HPz4+Kigptm7NmzdKpb/To0UycOFG77ebmxuuvv054eDjW1tZMmzYNgCNHjjBw4EDMzMzo0KEDMTEx2nob0q5dO5ycnLSvByd3isYzyJXbvHkzmzdv5rXXXuODDz7Qbm/evJl//vOfzJ8/HwcHB0OEJoQQT4yiKKgrKw3yetz5f2ZmZlRXV2u309PTKSgoIC0tjT179qBWqwkICMDKyorDhw+TlZWlTYhqj0tISCAyMpJp06Zx6tQpkpOT6dKli9721q5dS3JyMp988gkFBQUkJSXh5uamt2xFRQUBAQHY2dlx/PhxduzYwcGDB+usC5uRkUFRUREZGRls2bKFxMREEhMTG3X++fn5DBgwgHHjxhEfH6+9nenOnTusXLmSjz76iEOHDlFSUsK8efMeq92ysjJUKhW2trYNxrRlyxYcHBw4duwY0dHRzJgxg7Fjx9K/f39yc3Px9/dnwoQJ3LlzB4Bbt27h4+ND7969ycnJYf/+/Vy9epWQkBDg3rB6WFgYkydP5uzZs2RmZhIUFNTkz87KlSvx9PTk66+/ZtGiRRQVFREYGMiYMWPIz89n+/btHDly5KHr9sK9peScnZ0ZMmQIWVlZTYpD6DLok0Bee+01QzYvhBAGdbeqirURwQZpO2bLTlo/wkxkRVFIT08nNTWV6Oho7X4LCws2btyoHd77+OOP0Wg0bNy4UZscbd68GVtbWzIzM/H392fp0qXMnTuXmTNnauvp16+f3nZLSkro2rUrAwYMQKVS4erqWm+MW7dupbKykg8//FB7P3l8fDwjR47kzTff1C4PZmdnR3x8PMbGxnTv3p3hw4eTnp7O1KlTG7wG2dnZjBgxggULFjB37lyd99RqNRs2bKBz584AREVF1XnSVFParaysJDY2lrCwsIfOVvX09GThwoXAvUeXvvHGGzg4OGjrjYuLIyEhgfz8fG3PYu/evVm+fLm2jk2bNtGhQwfOnz9PeXk5d+/eJSgoSHu9PTw8GoxBHx8fH53rNGXKFMaPH6/tPezatStr165l0KBBJCQk6J0h7+zszIYNG+jbty9VVVVs3LiRl19+maNHj+Ll5dXkmMQv4FFwO3fu5JNPPqGkpETnr0mA3NxcA0UlhBDifnv27MHS0hK1Wo1Go2HcuHEsXrxY+76Hh4fOvV15eXlcuHABKysrnXoqKyspKiri2rVrXL58GV9f30a1P3HiRIYMGUK3bt0IDAxkxIgR+Pv76y179uxZPD09dSYTent7o9FoKCgo0CaAPXr0wNjYWFvG2dmZU6dONRhHSUkJQ4YMYdmyZXWGPwHMzc21yV9tndeuXdMp09h21Wo1ISEhKIpCQkJCg3GB7tOzjI2Nsbe310nYas+7Np68vDwyMjL0zswuKirC398fX19fPDw8CAgIwN/fn+DgYOzs7B4ay/369u2rs52Xl0d+fj5JSUnafYqioNFoKC4uxt3dvU4d3bp1095aANC/f3+Kiop45513+Oijj5oUj7jHoAng2rVrWbBgARMnTmT37t1MmjSJoqIijh8/TmRkpCFDE0KIn12rNm2I2bLTYG03xeDBg0lISMDExAQXF5c667c+uHJDeXk5ffr00fmSr+Xo6Njke7e8vLwoLi5m3759HDx4kJCQEPz8/Ni589GvX+vWrXW2VSoVGo2mwWMcHR1xcXFh27ZtTJ48uU6vnL46HxwybUy7tcnfpUuX+OKLLxq1Vp2+eu/fV9sTW9tWeXm5tlf0Qc7OzhgbG5OWlkZ2djYHDhxg3bp1LFiwgKNHj9KpUyeMjIzqnJu+eyj1fTZeffVVYmJi6pTt2LHjQ8+z1vPPP8+RI0caXV7oMmgCuH79et577z3CwsJITEzkL3/5C8899xxxcXF61wcUQoiniUqleqRhWEOwsLCo9/48fby8vNi+fTvt2rWrN3lxc3MjPT290eu+WltbExoaSmhoKMHBwQQGBnLz5k3atm2rU87d3Z3ExEQqKiq0yUdWVhZGRkY6vUiPwszMjD179jBs2DACAgI4cOBAnV7Ox1Wb/BUWFpKRkfGzParUy8uLTz/9FDc3tzoJfS2VSoW3tzfe3t7ExcXh6urKZ599xpw5c3B0dNSZ4FJTU8Pp06cf+vP08vLizJkzTfo86XPy5EmcnZ0fq46WzKDTZ0pKSrTLvZiZmfHjjz8CMGHCBLZt22bI0IQQQjyG8ePH4+DgwKhRozh8+DDFxcVkZmYSExPDd999B9xbWHnVqlWsXbuWwsJCcnNzWbdund76Vq9ezbZt2zh37hznz59nx44dODk56Z0YMX78eExNTYmIiOD06dNkZGQQHR3NhAkTtMOgj8PCwoKUlBRatWrF0KFDdWYXPy61Wk1wcDA5OTkkJSVRU1NDaWkppaWldW6TelyRkZHcvHmTsLAwjh8/TlFREampqUyaNImamhqOHj3K8uXLycnJoaSkhF27dnH9+nXtEK2Pjw8pKSmkpKRw7tw5ZsyYwa1btx7abmxsLNnZ2URFRXHy5EkKCwvZvXt3g5NA1qxZw+7du7lw4QKnT59m1qxZfPHFFzJa+BgMmgA6OTlpe/o6duzIv//9b+DeM4IN+IQ6IYQQj8nc3JxDhw7RsWNHgoKCcHd3549//COVlZXaHsGIiAjWrFnD+vXr6dGjByNGjKCwsFBvfVZWVrz11lv07duXfv36cfHiRfbu3at3KNnc3JzU1FRu3rxJv379CA4OxtfXl/j4+GY7P0tLS/bt24eiKAwfPrxRS5g0xvfff09ycjLfffeddsZr7Ss7O7tZ2qjl4uJCVlYWNTU1+Pv74+HhwaxZs7C1tcXIyAhra2sOHTrEsGHD+M1vfsPChQtZtWqVdsHtyZMnExERQXh4OIMGDeK5555rVG9uz549+fLLLzl//jwDBw6kd+/exMXF4eLiUu8x1dXVzJ07Fw8PDwYNGkReXh4HDx5s9D2koi6DPgt4ypQpdOjQgddee413332XP//5z3h7e5OTk0NQUBAffPCBoUJrFHmWoBCiKVrys4CFeNLkWcANM+g9gO+99572ZtTIyEjs7e3Jzs7mlVde4dVXXzVkaEIIIYQQTy2DJoBGRkY63ff/+7//y//+7/8aMCIhhBBCiKefQe8B3Lx5Mzt27Kizf8eOHWzZssUAEQkhhBBCPP0MmgCuWLFC7yPf2rVrp7MyuRBCCCGEaD4GXwamU6dOdfa7uro2+NBrIYQQQgjx6AyaALZr1478/Pw6+/Py8n62hS+FEEIIIVo6gyaAYWFhxMTEkJGRQU1NDTU1NXzxxRfMnDlTJoMIIYQQQvxMDDoL+PXXX+fixYv4+vpqH0Oj0WgIDw+XewCFEEIIIX4mBk0ATUxM2L59O6+//jp5eXmYmZnh4eGBq6urIcMSQgjRzC5evMjHH3/MrFmzsLS0NHQ4QrR4Bh0CrvWb3/yGsWPHMmLECEn+hBDiKVNVVcXYsWNxcHBoUvKnUqn4/PPPm73so1q8eDG9evX6WdsQ4kl54j2Ac+bM4fXXX8fCwoI5c+Y0WHb16tVPKCohhBD1mThxonZt1tatW9OxY0fCw8P529/+pr19pyGzZ8/G39+f6dOnN6ndK1euYGdn1+xlf8nUajULFy5k7969fPPNN9jY2ODn58cbb7zR4LNyW4KqqiqWLFnCxx9/TGlpKc7OzsTFxTF58mRDh/ar9MQTwK+//hq1Wg1Abm4uKpVKb7n69gshhHjyAgMD2bx5M1VVVezdu5fIyEhat27N/Pnz65Strq7GxMREu71+/fpHatPJyelnKftLdufOHXJzc1m0aBGenp788MMPzJw5k1deeYWcnBxDh/fYHvxsNEVISAhXr17lgw8+oEuXLly5ckX7OFnxCJQnLC8vT6mpqXnSzf4sysrKFEApKyszdChCiF+Bn376STlz5ozy008/GTqUJomIiFBGjRqls2/IkCHKiy++qPP+0qVLFWdnZ8XNzU1RFEUpKSlRxo4dq9jY2Ch2dnbKK6+8ohQXF+vU88EHHyi//e1vFRMTE8XJyUmJjIzUvgcon332maIoilJVVaVERkYqTk5OSps2bZSOHTsqy5cv11tWURQlPz9fGTx4sGJqaqq0bdtWmTp1qvLjjz/WOae3335bcXJyUtq2bav86U9/Uqqrq+u9Dq+99pri6emp3b5w4YLSqVMnJTIyUtFoNMrmzZsVGxsbZf/+/Ur37t0VCwsLJSAgQLl8+fJjtXvs2DEFUC5dulRvmUGDBilRUVHKzJkzFVtbW6Vdu3bKe++9p5SXlysTJ05ULC0tlc6dOyt79+7VOe7UqVNKYGCgYmFhobRr1075wx/+oFy/fl37/o4dO5Tf/e532uvo6+urlJeXa9ucOXOmTn2jRo1SIiIitNuurq7KkiVLlAkTJihWVlba9w4fPqwMGDBAMTU1VZ599lklOjpaW68++/btU2xsbJT//Oc/9ZZ5UEO/b/L9rShP/B7A3r17c+PGDQCee+45/vOf/zzpEIQQ4hdBURQ01TUGeSmK8lixm5mZUV1drd1OT0+noKCAtLQ09uzZg1qtJiAgACsrKw4fPkxWVhaWlpYEBgZqj0tISCAyMpJp06Zx6tQpkpOT6dKli9721q5dS3JyMp988gkFBQUkJSXh5uamt2xFRQUBAQHY2dlx/PhxduzYwcGDB4mKitIpl5GRQVFRERkZGWzZsoXExEQSExMbdf75+fkMGDCAcePGER8frx21unPnDitXruSjjz7i0KFDlJSUMG/evMdqt6ysDJVKha2tbYMxbdmyBQcHB44dO0Z0dDQzZsxg7Nix9O/fn9zcXPz9/ZkwYQJ37twB4NatW/j4+NC7d29ycnLYv38/V69eJSQkBLg3rB4WFsbkyZM5e/YsmZmZBAUFNfmzs3LlSjw9Pfn6669ZtGgRRUVFBAYGMmbMGPLz89m+fTtHjhyp8/O5X3JyMn379uWtt97imWee4Te/+Q3z5s3jp59+alIs4r+e+BCwra0txcXFtGvXjosXLzZr9+27777L22+/TWlpKZ6enqxbt47nn3++3vK3bt1iwYIF7Nq1i5s3b+Lq6sqaNWsYNmxYs8UkhBD1UdQaLsdlG6RtlyX9UZkYN/k4RVFIT08nNTWV6Oho7X4LCws2btyoHd77+OOP0Wg0bNy4UZscbd68GVtbWzIzM/H392fp0qXMnTuXmTNnauvp16+f3nZLSkro2rUrAwYMQKVSNThhcOvWrVRWVvLhhx9iYWEBQHx8PCNHjuTNN9+kffv2ANjZ2REfH4+xsTHdu3dn+PDhpKenM3Xq1AavQXZ2NiNGjGDBggXMnTtX5z21Ws2GDRvo3LkzAFFRUSxZskSnTFParaysJDY2lrCwMKytrRuMy9PTk4ULFwIwf/583njjDRwcHLT1xsXFkZCQQH5+Pi+++CLx8fH07t1bZ9m1TZs20aFDB86fP095eTl3794lKChIe709PDwajEEfHx8fnes0ZcoUxo8fz6xZswDo2rUra9euZdCgQSQkJGBqalqnjm+++YYjR45gamrKZ599xo0bN/jTn/7Ef/7zHzZv3tzkmIQBEsAxY8YwaNAgnJ2dUalU9O3bF2Nj/f8JffPNN42ud/v27cyZM4cNGzbwwgsvsGbNGgICAigoKKBdu3Z1yldXVzNkyBDatWvHzp07eeaZZ7h06dJD/8ISQoiWaM+ePVhaWqJWq9FoNIwbN47Fixdr3/fw8NC5tysvL48LFy5gZWWlU09lZSVFRUVcu3aNy5cv4+vr26j2J06cyJAhQ+jWrRuBgYGMGDECf39/vWXPnj2Lp6enNvkD8Pb2RqPRUFBQoE0Ae/ToofP94+zszKlTpxqMo6SkhCFDhrBs2TJtAnM/c3NzbfJXW+e1a9d0yjS2XbVaTUhICIqikJCQ0GBcAD179tT+29jYGHt7e52Erfa8a+PJy8sjIyND78zsoqIi/P398fX1xcPDg4CAAPz9/QkODm7yZJu+ffvqbOfl5ZGfn09SUpJ2n6IoaDQaiouLcXd3r1OHRqNBpVKRlJSEjY0NcG+iaHBwMOvXr8fMzKxJMQkDJIDvvfceQUFBXLhwgZiYGKZOnVrnP4hHsXr1aqZOncqkSZMA2LBhAykpKWzatIm//vWvdcpv2rSJmzdvkp2dTevWrQHqHU4QQoifg6q1ES5L+hus7aYYPHgwCQkJmJiY4OLiUmf27/3JFkB5eTl9+vTR+ZKv5ejoiJFR09r38vKiuLiYffv2cfDgQUJCQvDz82Pnzp1Nqud+tf/311KpVA8dlXJ0dMTFxYVt27YxefLkOr1y+up8cMi0Me3WJn+XLl3iiy++eGjvX3313r+vtie2tq3y8nJtr+iDnJ2dMTY2Ji0tjezsbA4cOMC6detYsGABR48epVOnThgZGdU5t9pJnvfT99l49dVXiYmJqVO2Y8eOes/N2dmZZ555Rpv8Abi7u6MoCt999x1du3bVe5yon0EWgg4MDATgxIkTzJw587ETwOrqak6cOKEzG83IyAg/Pz+++uorvcckJyfz0ksvERkZye7du3F0dGTcuHHExsbW2yNZVVVFVVWVdvv27duPFbcQomVTqVSPNAxrCBYWFvXen6ePl5cX27dvp127dvUmL25ubqSnpzN48OBG1WltbU1oaCihoaEEBwcTGBjIzZs3adu2rU45d3d3EhMTqaio0CYfWVlZGBkZ0a1bt0afgz5mZmbs2bOHYcOGERAQwIEDB5qlE+N+tclfYWEhGRkZ2NvbN2v9tby8vPj0009xc3OrdzkflUqFt7c33t7exMXF4erqymeffcacOXNwdHTkypUr2rI1NTWcPn36oT9PLy8vzpw506TPk7e3Nzt27KC8vFzbY3n+/HmMjIx49tlnG12P+C+DLgS9efPmZvnFuXHjBjU1Ndru7Vrt27entLRU7zHffPMNO3fupKamhr1797Jo0SJWrVrF0qVL621nxYoV2NjYaF8dOnR47NiFEOJpNH78eBwcHBg1ahSHDx+muLiYzMxMYmJi+O6774B7CyuvWrWKtWvXUlhYSG5uLuvWrdNb3+rVq9m2bRvnzp3j/Pnz7NixAycnJ7237YwfPx5TU1MiIiI4ffo0GRkZREdHM2HChDrfE4/CwsKClJQUWrVqxdChQykvL3/sOmup1WqCg4PJyckhKSmJmpoaSktLKS0t1Zl00xwiIyO5efMmYWFhHD9+nKKiIlJTU5k0aRI1NTUcPXqU5cuXk5OTQ0lJCbt27eL69evaIVofHx9SUlJISUnh3LlzzJgxg1u3bj203djYWLKzs4mKiuLkyZMUFhaye/fuBieBjBs3Dnt7eyZNmsSZM2c4dOgQf/7zn5k8ebIM/z6iJ94DGBQURGJiItbW1gQFBTVYdteuXT9bHBqNhnbt2vHee+9hbGxMnz59+P7773n77bd57bXX9B4zf/58ncWrb9++LUmgEELoYW5uzqFDh4iNjSUoKIgff/yRZ555Bl9fX22PYEREBJWVlbzzzjvMmzcPBwcHgoOD9dZnZWXFW2+9RWFhIcbGxvTr14+9e/fqHUo2NzcnNTWVmTNn0q9fP8zNzRkzZkyzPlzA0tKSffv2ERAQwPDhw9m7d2+z1Pv999+TnJwMUOepIxkZGbz88svN0g6Ai4sLWVlZxMbG4u/vT1VVFa6urgQGBmJkZIS1tTWHDh1izZo13L59G1dXV1atWsXQoUMBmDx5Mnl5eYSHh9OqVStmz57dqN7cnj178uWXX7JgwQIGDhyIoih07tyZ0NDQeo+xtLQkLS2N6Oho+vbti729PSEhIQ122oiGqZTHXQugiSZNmsTatWuxsrLS3q9Xn8bO7Kmursbc3JydO3cyevRo7f6IiAhu3brF7t276xwzaNAgWrduzcGDB7X79u3bx7Bhw6iqqmrUQpW3b9/GxsaGsrKyRt2fIYRo2SorKykuLqZTp056ZzoKIZpPQ79v8v1tgB7A+5O65pq6bWJiQp8+fUhPT9cmgBqNhvT09Hq7lL29vdm6dSsajUb7F+T58+dxdnZ+5FXKhRBCCCF+DQx6D2BzmjNnDu+//z5btmzh7NmzzJgxg4qKCm0vY3h4uM4kkRkzZnDz5k1mzpzJ+fPnSUlJYfny5URGRhrqFIQQQgghnogn3gPYu3fvRj/nNzc3t9H1hoaGcv36deLi4igtLaVXr17s379fe8NvSUmJzr0iHTp0IDU1ldmzZ9OzZ0+eeeYZZs6cSWxsbNNOSAghhBDiV+aJJ4D336PX3KKiouod8s3MzKyz76WXXuLf//73zxaPEEIIIcQv0RNPAOubYSuEEEIIIZ6Mp+YeQCGEEEII0TgGeRJIrZqaGt555x0++eQTSkpK6ixyefPmTQNFJoQQQgjx9DJoD+Df//53Vq9eTWhoKGVlZcyZM4egoCCMjIx0HjIuhBBCCCGaj0ETwKSkJN5//33mzp1Lq1atCAsLY+PGjcTFxcnkDCGEeIpcvHiRpUuXNutj04QQj86gCWBpaSkeHh7Avce8lJWVATBixAhSUlIMGZoQQohmUlVVxdixY3FwcMDS0rLRx6lUKj7//PNmL/uoFi9eXOfxbEL8Whk0AXz22We5cuUKAJ07d+bAgQMAHD9+nDZt2hgyNCGEEP/PxIkTUalUqFQqTExM6NKlC0uWLOHu3buNOn727Nn4+/szffr0JrV75coV7XNnm7PsL5larSY2NhYPDw8sLCxwcXEhPDycy5cvGzo0g7r/M3j/q0ePHoYO7VfLoAng73//e9LT0wGIjo5m0aJFdO3alfDwcCZPnmzI0IQQQtwnMDCQK1euUFhYyNy5c1m8eDFvv/223rIPTuhbv349y5Yta3KbTk5Oje4MaErZX7I7d+6Qm5vLokWLyM3NZdeuXRQUFPDKK68YOrRm8eBno7H+8Y9/cOXKFe3r22+/pW3btowdO7aZI2w5DJoAvvHGG/ztb38D7j3J4/Dhw8yYMYOdO3fyxhtvGDI0IYQQ92nTpg1OTk64uroyY8YM/Pz8SE5OBu71zowePZply5bh4uJCt27dAPj2228JCQnB1taWtm3bMmrUKC5evKhT76ZNm+jRowdt2rTB2dlZZzH/+4d1q6uriYqKwtnZGVNTU1xdXVmxYoXesgCnTp3Cx8cHMzMz7O3tmTZtms79h7Uxr1y5EmdnZ+zt7YmMjEStVjf6mhQVFfHcc88RFRWFoigkJiZia2tLamoq7u7uWFpaahPnxrZrY2NDWloaISEhdOvWjRdffJH4+HhOnDhBSUlJvbG8/PLLREdHM2vWLOzs7Gjfvj3vv/++9pGoVlZWdOnShX379ukcd/r0aYYOHYqlpSXt27dnwoQJ3LhxQ/v+zp078fDw0F5HPz8/KioqtG3OmjVLp77Ro0czceJE7babmxuvv/464eHhWFtbM23aNACOHDnCwIEDMTMzo0OHDsTExGjr1cfGxgYnJyftKycnhx9++EH7uFfRdL+odQBffPFF5syZw8iRIw0dihBC/OwURaG6utogL0VRHit2MzMznd6c9PR0CgoKSEtLY8+ePajVagICArCysuLw4cNkZWVpE6La4xISEoiMjGTatGmcOnWK5ORkunTpore9tWvXkpyczCeffEJBQQFJSUm4ubnpLVtRUUFAQAB2dnYcP36cHTt2cPDgwTpPisrIyKCoqIiMjAy2bNlCYmIiiYmJjTr//Px8BgwYwLhx44iPj9c+4vTOnTusXLmSjz76iEOHDlFSUsK8efMeq92ysjJUKhW2trYNxrRlyxYcHBw4duwY0dHRzJgxg7Fjx9K/f39yc3Px9/dnwoQJ3LlzB4Bbt27h4+ND7969ycnJYf/+/Vy9epWQkBDg3rB6WFgYkydP5uzZs2RmZhIUFNTkz87KlSvx9PTk66+/ZtGiRRQVFREYGMiYMWPIz89n+/btHDlypN4neenzwQcf4Ofnh6ura5NiEf9l0HUAV6xYQfv27esM927atInr16/Lc3mFEE81tVrN8uXLDdL23/72N0xMTJp8nKIopKenk5qaSnR0tHa/hYUFGzdu1Nb58ccfo9Fo2LhxozY52rx5M7a2tmRmZuLv78/SpUuZO3cuM2fO1NbTr18/ve2WlJTQtWtXBgwYgEqlavCLf+vWrVRWVvLhhx9iYWEBQHx8PCNHjuTNN9/UPiPezs6O+Ph4jI2N6d69O8OHDyc9PZ2pU6c2eA2ys7MZMWIECxYsYO7cuTrvqdVqNmzYQOfOnYF7jyhdsmSJTpmmtFtZWUlsbCxhYWFYW1s3GJenpycLFy4EYP78+bzxxhs4ODho642LiyMhIYH8/Hxtz2Lv3r11PoObNm2iQ4cOnD9/nvLycu7evUtQUJD2etdO3GwKHx8fnes0ZcoUxo8fr+097Nq1K2vXrmXQoEEkJCRgamraYH2XL19m3759bN26tcmxiP8yaA/gP//5T7p3715nf48ePdiwYYMBIhJCCKHPnj17sLS0xNTUlKFDhxIaGqqzXquHh4dOQpmXl8eFCxewsrLC0tISS0tL2rZtS2VlJUVFRVy7do3Lly/j6+vbqPYnTpzIyZMn6datGzExMdpJg/qcPXsWT09PbfIH4O3tjUajoaCgQLuvR48eGBsba7ednZ25du1ag3GUlJQwZMgQ4uLi6iR/AObm5trkr746G9uuWq0mJCQERVFISEhoMC6Anj17av9tbGyMvb29TsJWm/jWtpWXl0dGRob252Npaan9Ti4qKsLT0xNfX188PDwYO3Ys77//Pj/88MND43hQ3759dbbz8vJITEzUaTcgIACNRkNxcfFD69uyZQu2traMHj26ybGI/zJoD2BpaSnOzs519js6OurcMyGEEE+j1q1ba++DNkTbTTF48GASEhIwMTHBxcWFVq10vz7uT7YAysvL6dOnD0lJSXXqcnR0xMioaf0PXl5eFBcXs2/fPg4ePEhISAh+fn7s3LmzSfXc78FroFKp0Gg0DR7j6OiIi4sL27ZtY/LkyXV65fTV+eCQaWParU3+Ll26xBdffPHQ3r/66r1/X21PbG1b5eXl2l7RBzk7O2NsbExaWhrZ2dkcOHCAdevWsWDBAo4ePUqnTp0wMjKqc2767qHU99l49dVXiYmJqVO2Y8eODZ6joihs2rSJCRMmPFIPtvgvgyaAHTp0ICsri06dOunsz8rKwsXFxUBRCSHEk1G7rMqvgYWFRb335+nj5eXF9u3badeuXb3Ji5ubG+np6QwePLhRdVpbWxMaGkpoaCjBwcEEBgZy8+ZN2rZtq1PO3d2dxMREKioqtMlHVlYWRkZG2gkqj8rMzIw9e/YwbNgwAgICOHDgAFZWVo9V54Nqk7/CwkIyMjKwt7dv1vpreXl58emnn+Lm5lYnoa+lUqnw9vbG29ubuLg4XF1d+eyzz5gzZ06dzpqamhpOnz790J+nl5cXZ86cadLnqdaXX37JhQsX+OMf/9jkY4Uugw4BT506lVmzZrF582YuXbrEpUuX2LRpE7Nnz37oPRhCCCF+ucaPH4+DgwOjRo3i8OHDFBcXk5mZSUxMDN999x1wb2HlVatWsXbtWgoLC8nNzWXdunV661u9ejXbtm3j3LlznD9/nh07duDk5KR3YsT48eMxNTUlIiKC06dPk5GRQXR0NBMmTNAOgz4OCwsLUlJSaNWqFUOHDm3Wp5uo1WqCg4PJyckhKSmJmpoaSktLKS0tfeQlVOoTGRnJzZs3CQsL4/jx4xQVFZGamsqkSZOoqanh6NGjLF++nJycHEpKSti1axfXr1/H3d0duHdvX0pKCikpKZw7d44ZM2Zw69ath7YbGxtLdnY2UVFRnDx5ksLCQnbv3t2oSSAffPABL7zwAr/73e8e9/RbPIP2AP75z3/mP//5D3/605+0H2xTU1NiY2OZP3++IUMTQgjxGMzNzTl06BCxsbEEBQXx448/8swzz+Dr66vtEYyIiKCyspJ33nmHefPm4eDgQHBwsN76rKyseOuttygsLMTY2Jh+/fqxd+9evUPJ5ubmpKamMnPmTPr164e5uTljxoxh9erVzXZ+lpaW7Nu3j4CAAIYPH87evXubpd7vv/9eu7zOg08dycjI4OWXX26WdgBcXFzIysoiNjYWf39/qqqqcHV1JTAwECMjI6ytrTl06BBr1qzh9u3buLq6smrVKu2C25MnTyYvL4/w8HBatWrF7NmzG9Wb27NnT7788ksWLFjAwIEDURSFzp07Exoa2uBxZWVlfPrpp/zjH/9olvNv6VTK464F0AzKy8s5e/YsZmZmdO3a9VezmOft27exsbGhrKysUfdnCCFatsrKSoqLi+nUqdNDZzoKIR5PQ79v8v1t4B7AWpaWlvVO/RdCCCGEEM3rF7UQtBBCCCGE+PlJAiiEEEII0cJIAiiEEEII0cJIAiiEEEII0cIYPAH86KOP8Pb2xsXFhUuXLgGwZs0adu/ebeDIhBBCCCGeTgZNABMSEpgzZw7Dhg3j1q1b1NTUAGBra8uaNWsMGZoQQgghxFPLoAngunXreP/991mwYIHOg7H79u3LqVOnDBiZEEIIIcTTy6AJYHFxMb17966zv02bNlRUVBggIiGEED+HixcvsnTp0mZ9bJoQ4tEZNAHs1KkTJ0+erLN///792mcNNsW7776Lm5sbpqamvPDCCxw7dqzesomJiahUKp2XrMwvhBDNr6qqirFjx+Lg4IClpWWjj1OpVHz++efNXvZRLV68uM7j2YT4tTJoAjhnzhwiIyPZvn07iqJw7Ngxli1bxvz58/nLX/7SpLq2b9/OnDlzeO2118jNzcXT05OAgACuXbtW7zHW1tZcuXJF+6qdhCKEEOK/Jk6cqP1D2cTEhC5durBkyRLu3r3bqONnz56Nv78/06dPb1K7V65c0T53tjnL/pKp1WpiY2Px8PDAwsICFxcXwsPDuXz5sqFDM7ikpCQ8PT0xNzfH2dmZyZMn85///MfQYf16KQb28ccfK126dFFUKpWiUqmUZ555Rtm4cWOT63n++eeVyMhI7XZNTY3i4uKirFixQm/5zZs3KzY2No8atqIoilJWVqYASllZ2WPVI4RoGX766SflzJkzyk8//WToUJokIiJCCQwMVK5cuaJcvHhRWb9+vaJSqZTly5frLV9VVfWEI3wyXnvtNcXT0/NnbePWrVuKn5+fsn37duXcuXPKV199pTz//PNKnz59ftZ2n5RH/WwcOXJEMTIyUv7xj38o33zzjXL48GGlR48eyu9///t6j2no902+vxXF4MvAjB8/nsLCQsrLyyktLeW7777jj3/8Y5PqqK6u5sSJE/j5+Wn3GRkZ4efnx1dffVXvceXl5bi6utKhQwdGjRrF//f//X8NtlNVVcXt27d1XkII0RK0adMGJycnXF1dmTFjBn5+fiQnJwP3eghHjx7NsmXLcHFxoVu3bgB8++23hISEYGtrS9u2bRk1ahQXL17UqXfTpk306NGDNm3a4OzsTFRUlPa9+4d1q6uriYqKwtnZGVNTU1xdXVmxYoXesgCnTp3Cx8cHMzMz7O3tmTZtms79h7Uxr1y5EmdnZ+zt7YmMjEStVjf6mhQVFfHcc88RFRWFoigkJiZia2tLamoq7u7uWFpaEhgYyJUrVxrdro2NDWlpaYSEhNCtWzdefPFF4uPjOXHiBCUlJfXG8vLLLxMdHc2sWbOws7Ojffv2vP/++1RUVDBp0iSsrKzo0qUL+/bt0znu9OnTDB06FEtLS9q3b8+ECRO4ceOG9v2dO3fi4eGhvY5+fn7ae/RffvllZs2apVPf6NGjmThxonbbzc2N119/nfDwcKytrZk2bRoAR44cYeDAgZiZmdGhQwdiYmIavPf/q6++ws3NjZiYGDp16sSAAQN49dVXG7zVSzTM4AlgLXNzc9q1a/dIx964cYOamhrat2+vs799+/aUlpbqPaZbt25s2rSJ3bt38/HHH6PRaOjfvz/fffddve2sWLECGxsb7atDhw6PFK8QQgAoikJNzR2DvBRFeazYzczMqK6u1m6np6dTUFBAWloae/bsQa1WExAQgJWVFYcPHyYrK0ubENUel5CQQGRkJNOmTePUqVMkJyfTpUsXve2tXbuW5ORkPvnkEwoKCkhKSsLNzU1v2YqKCgICArCzs+P48ePs2LGDgwcP6iSXABkZGRQVFZGRkcGWLVtITEwkMTGxUeefn5/PgAEDGDduHPHx8ahUKgDu3LnDypUr+eijjzh06BAlJSXMmzfvsdotKytDpVJha2vbYExbtmzBwcGBY8eOER0dzYwZMxg7diz9+/cnNzcXf39/JkyYwJ07dwC4desWPj4+9O7dm5ycHPbv38/Vq1cJCQkB7g2rh4WFMXnyZM6ePUtmZiZBQUFN/uysXLkST09Pvv76axYtWkRRURGBgYGMGTOG/Px8tm/fzpEjR+r8fO730ksv8e2337J3714UReHq1avs3LmTYcOGNSkW8V+tDNn41atXmTdvHunp6Vy7dq3Oh6p2XcCfw0svvcRLL72k3e7fvz/u7u7885//5PXXX9d7zPz585kzZ452+/bt25IECiEemUbzE5lfehik7ZcHncLY2LzJxymKQnp6OqmpqURHR2v3W1hYsHHjRkxMTAC0f1hv3LhRmxxt3rwZW1tbMjMz8ff3Z+nSpcydO5eZM2dq6+nXr5/edktKSujatSsDBgxApVLh6upab4xbt26lsrKSDz/8EAsLCwDi4+MZOXIkb775prazwM7Ojvj4eIyNjenevTvDhw8nPT2dqVOnNngNsrOzGTFiBAsWLGDu3Lk676nVajZs2EDnzp0BiIqKYsmSJTplmtJuZWUlsbGxhIWFYW1t3WBcnp6eLFy4ELj3ffXGG2/g4OCgrTcuLo6EhATy8/O1PYu9e/dm+fLl2jo2bdpEhw4dOH/+POXl5dy9e5egoCDt9fbwaPrn1cfHR+c6TZkyhfHjx2t7D7t27cratWsZNGgQCQkJeidkent7k5SURGhoKJWVldy9e5eRI0fy7rvvNjkecY9BE8CJEydSUlLCokWLcHZ21v4n0VQODg4YGxtz9epVnf1Xr17FycmpUXW0bt2a3r17c+HChXrLtGnThjZt2jxSjEII8Wu2Z88eLC0tUavVaDQaxo0bx+LFi7Xve3h4aJM/gLy8PC5cuICVlZVOPZWVlRQVFXHt2jUuX76Mr69vo9qfOHEiQ4YMoVu3bgQGBjJixAj8/f31lj179iyenp7a5A/uJRAajYaCggJtAtijRw+dNWidnZ0fugZtSUkJQ4YMYdmyZXWGP+HeaFZt8ldb54OTERvbrlqtJiQkBEVRSEhIaDAugJ49e2r/bWxsjL29vU7CVnvetfHk5eWRkZGhd2Z2UVER/v7++Pr64uHhQUBAAP7+/gQHB2NnZ/fQWO7Xt29fne28vDzy8/NJSkrS7lMUBY1GQ3Fxsd5VQM6cOcPMmTOJi4sjICCAK1eu8Oc//5np06fzwQcfNCkecY9BE8AjR45w+PDhx55Wb2JiQp8+fUhPT2f06NEAaDQa0tPTG+xSvl9NTQ2nTp2S7mQhxBNjZGTGy4MMs+i9kZFZk8oPHjyYhIQETExMcHFxoVUr3a+P+5MtuHePdZ8+fXS+5Gs5OjpiZNS0O5C8vLwoLi5m3759HDx4kJCQEPz8/Ni5c2eT6rlf69atdbZVKhUajabBYxwdHXFxcWHbtm1Mnjy5Tq+cvjofHN1qTLu1yd+lS5f44osvHtr7V1+99++r7WSpbau8vFzbK/ogZ2dnjI2NSUtLIzs7mwMHDrBu3ToWLFjA0aNH6dSpE0ZGRnXOTd89lPo+G6+++ioxMTF1ynbs2FHvua1YsQJvb2/+/Oc/A/eSXQsLCwYOHMjSpUtxdnbWe5yon0ETwA4dOjz2fSi15syZQ0REBH379uX5559nzZo12ptfAcLDw3nmmWe0Nw0vWbKEF198kS5dunDr1i3efvttLl26xJQpU5olHiGEeBiVSvVIw7CGYGFhUe/9efp4eXmxfft22rVrV2/y4ubmRnp6OoMHD25UndbW1oSGhhIaGkpwcDCBgYHcvHmTtm3b6pRzd3cnMTGRiooKbfKRlZWFkZGRdoLKozIzM2PPnj0MGzaMgIAADhw4UKeX83HVJn+FhYVkZGRgb2/frPXX8vLy4tNPP8XNza1OQl9LpVLh7e2Nt7c3cXFxuLq68tlnnzFnzhwcHR11JrjU1NRw+vTph/48vby8OHPmTJM+T3fu3KkTY20vanPlES2NQSeBrFmzhr/+9a91ZoU9itDQUFauXElcXBy9evXi5MmT7N+/X9vlXVJSovNB/eGHH5g6dSru7u4MGzaM27dvk52dzW9/+9vHjkUIIVq68ePH4+DgwKhRozh8+DDFxcVkZmYSExOjnWy3ePFiVq1axdq1ayksLCQ3N5d169bprW/16tVs27aNc+fOcf78eXbs2IGTk5PeiRHjx4/H1NSUiIgITp8+TUZGBtHR0UyYMKHOZMFHYWFhQUpKCq1atWLo0KHN+nQTtVpNcHAwOTk5JCUlUVNTQ2lpKaWlpTqTbppDZGQkN2/eJCwsjOPHj1NUVERqaiqTJk2ipqaGo0ePsnz5cnJycigpKWHXrl1cv35dO0Tr4+NDSkoKKSkpnDt3jhkzZnDr1q2HthsbG0t2djZRUVGcPHmSwsJCdu/e3eCI3ciRI9m1axcJCQl88803ZGVlERMTw/PPP4+Li0tzXZIWxaA9gKGhody5c4fOnTtjbm5ep/v65s2bTaovKiqq3g9QZmamzvY777zDO++806T6hRBCNI65uTmHDh0iNjaWoKAgfvzxR5555hl8fX21PYIRERFUVlbyzjvvMG/ePBwcHAgODtZbn5WVFW+99RaFhYUYGxvTr18/9u7dq3co2dzcnNTUVGbOnEm/fv0wNzdnzJgxrF69utnOz9LSkn379hEQEMDw4cPZu3dvs9T7/fffa5fXefD2qIyMDF5++eVmaQfAxcWFrKwsYmNj8ff3p6qqCldXVwIDAzEyMsLa2ppDhw6xZs0abt++jaurK6tWrdIuuD158mTy8vIIDw+nVatWzJ49u1G9uT179uTLL79kwYIFDBw4EEVR6Ny5M6GhofUeM3HiRH788Ufi4+OZO3cutra2+Pj46B2+Fo2jUgzYd7ply5YG34+IiHhCkTya27dvY2NjQ1lZWaPuzxBCtGyVlZUUFxfTqVMnefSkED+zhn7f5PvbwD2Av/QETwghhBDiaWTQBBDuzUa6cOEC165dqzML6n/+538MFJUQQgghxNPLoAngv//9b8aNG8elS5fqzOJRqVQ/60LQQgghhBAtlUETwOnTp9O3b19SUlIeayFoIYQQQgjReAZNAAsLC9m5c2eT1gISQgghhBCPx6DrAL7wwgsNPnpNCCGEEEI0P4P2AEZHRzN37lxKS0vx8PCosw7g/c81FEIIIYQQzcOgCeCYMWOAe4tJ1qp9bqJMAhFCCCGE+HkYNAEsLi42ZPNCCCGekIsXL/Lxxx8za9YsLC0tDR2OEC2eQe8BdHV1bfAlhBDi16+qqoqxY8fi4ODQpORPpVLx+eefN3vZR7V48eI6j2cT4tfKoAkgQFFREdHR0fj5+eHn50dMTAxFRUWGDksIIcT/M3HiRFQqFSqVChMTE7p06cKSJUu4e/duo46fPXs2/v7+TJ8+vUntXrlyRfvc2eYs+0umVquJjY3Fw8MDCwsLXFxcCA8P5/Lly4YOzeDeffdd3N3dMTMzo1u3bnz44YeGDulXzaBDwKmpqbzyyiv06tULb29vALKysujRowf/93//x5AhQwwZnhBCiP8nMDCQzZs3U1VVxd69e4mMjKR169bMnz+/Ttnq6mpMTEy02+vXr3+kNp2cnH6Wsr9kd+7cITc3l0WLFuHp6ckPP/zAzJkzeeWVV8jJyTF0eI/twc9GYyUkJDB//nzef/99+vXrx7Fjx5g6dSp2dnaMHDnyZ4i0BVAMqFevXkpsbGyd/bGxsUrv3r0NEFHTlJWVKYBSVlZm6FCEEL8CP/30k3LmzBnlp59+MnQoTRIREaGMGjVKZ9+QIUOUF198Uef9pUuXKs7Ozoqbm5uiKIpSUlKijB07VrGxsVHs7OyUV155RSkuLtap54MPPlB++9vfKiYmJoqTk5MSGRmpfQ9QPvvsM0VRFKWqqkqJjIxUnJyclDZt2igdO3ZUli9frresoihKfn6+MnjwYMXU1FRp27atMnXqVOXHH3+sc05vv/224uTkpLRt21b505/+pFRXV9d7HV577TXF09NTu33hwgWlU6dOSmRkpKLRaJTNmzcrNjY2yv79+5Xu3bsrFhYWSkBAgHL58uXHavfYsWMKoFy6dKneMoMGDVKioqKUmTNnKra2tkq7du2U9957TykvL1cmTpyoWFpaKp07d1b27t2rc9ypU6eUwMBAxcLCQmnXrp3yhz/8Qbl+/br2/R07dii/+93vtNfR19dXKS8v17Y5c+ZMnfpGjRqlREREaLddXV2VJUuWKBMmTFCsrKy07x0+fFgZMGCAYmpqqjz77LNKdHS0tl59XnrpJWXevHk6++bMmaN4e3vXe0xDv2/y/a0oBh0CPnv2LH/84x/r7J88eTJnzpwxQERCCPHkKIpCRU2NQV7KA4/fbCozMzOqq6u12+np6RQUFJCWlsaePXtQq9UEBARgZWXF4cOHycrKwtLSksDAQO1xCQkJREZGMm3aNE6dOkVycnK9DwZYu3YtycnJfPLJJxQUFJCUlISbm5veshUVFQQEBGBnZ8fx48fZsWMHBw8eJCoqSqdcRkYGRUVFZGRksGXLFhITE0lMTGzU+efn5zNgwADGjRtHfHy89klWd+7cYeXKlXz00UccOnSIkpIS5s2b91jtlpWVoVKpsLW1bTCmLVu24ODgwLFjx4iOjmbGjBmMHTuW/v37k5ubi7+/PxMmTODOnTsA3Lp1Cx8fH3r37k1OTg779+/n6tWrhISEAPeG1cPCwpg8eTJnz54lMzOToKCgJn92Vq5ciaenJ19//TWLFi2iqKiIwMBAxowZQ35+Ptu3b+fIkSN1fj73q6qqwtTUVGefmZkZx44dQ61WNykecY9Bh4AdHR05efIkXbt21dl/8uRJ2rVrZ6CohBDiybij0dD50CmDtF30Px5YGBs3+ThFUUhPTyc1NZXo6GjtfgsLCzZu3Kgd3vv444/RaDRs3LhRmxxt3rwZW1tbMjMz8ff3Z+nSpcydO5eZM2dq6+nXr5/edktKSujatSsDBgxApVI1OFFw69atVFZW8uGHH2JhYQFAfHw8I0eO5M0336R9+/YA2NnZER8fj7GxMd27d2f48OGkp6czderUBq9BdnY2I0aMYMGCBcydO1fnPbVazYYNG+jcuTMAUVFRLFmyRKdMU9qtrKwkNjaWsLAwrK2tG4zL09OThQsXAjB//nzeeOMNHBwctPXGxcWRkJBAfn4+L774IvHx8fTu3Zvly5dr69i0aRMdOnTg/PnzlJeXc/fuXYKCgrTX28PDo8EY9PHx8dG5TlOmTGH8+PHMmjULgK5du7J27VoGDRpEQkJCnUQPICAggI0bNzJ69Gi8vLw4ceIEGzduRK1Wc+PGDZydnZscV0tn0ARw6tSpTJs2jW+++Yb+/fsD9+4BfPPNN5kzZ44hQxNCCHGfPXv2YGlpiVqtRqPRMG7cOBYvXqx938PDQ+ferry8PC5cuICVlZVOPZWVlRQVFXHt2jUuX76Mr69vo9qfOHEiQ4YMoVu3bgQGBjJixAj8/f31lj179iyenp7a5A/A29sbjUZDQUGBNgHs0aMHxvclwc7Ozpw61XBCXlJSwpAhQ1i2bJk2gbmfubm5NvmrrfPatWs6ZRrbrlqtJiQkBEVRSEhIaDAu0H14grGxMfb29joJW+1518aTl5dHRkaG3pnZRUVF+Pv74+vri4eHBwEBAfj7+xMcHIydnd1DY7lf3759dbbz8vLIz88nKSlJu09RFDQaDcXFxbi7u9epY9GiRZSWlvLiiy+iKArt27cnIiKCt956CyMjg89n/VUyaAK4aNEirKysWLVqlfZGYhcXFxYvXkxMTIwhQxNCiJ+duZERRf/T9B6V5mq7KQYPHkxCQgImJia4uLjQqpXu18f9yRZAeXk5ffr00fmSr+Xo6NjkL20vLy+Ki4vZt28fBw8eJCQkBD8/P3bu3Nmkeu734NOnVCoVGo2mwWMcHR1xcXFh27ZtTJ48uU6vnL46HxwybUy7tcnfpUuX+OKLLx7a+1dfvffvq+2JrW2rvLxc2yv6IGdnZ4yNjUlLSyM7O5sDBw6wbt06FixYwNGjR+nUqRNGRkZ1zk3fcKy+z8arr76q93u+Y8eOes/NzMyMTZs28c9//pOrV6/i7OzMe++9h5WVFY6OjnqPEQ0zaAKoUqmYPXs2s2fP5scffwSo89eiEEI8rVQq1SMNwxqChYVFvffn6ePl5cX27dtp165dvcmLm5sb6enpDB48uFF1WltbExoaSmhoKMHBwQQGBnLz5k3atm2rU87d3Z3ExEQqKiq0yUdWVhZGRkZ069at0eegj5mZGXv27GHYsGEEBARw4MCBZv/eqk3+CgsLycjIwN7evlnrr+Xl5cWnn36Km5tbnYS+lkqlwtvbG29vb+Li4nB1deWzzz5jzpw5ODo6cuXKFW3ZmpoaTp8+/dCfp5eXF2fOnGnS56lW69atefbZZwH417/+xYgRI6QH8BH9Iq7atWvXOHnyJCdPnuT69euGDkcIIcRjGj9+PA4ODowaNYrDhw9TXFxMZmYmMTExfPfdd8C9hZVXrVrF2rVrKSwsJDc3l3Xr1umtb/Xq1Wzbto1z585x/vx5duzYgZOTk96JEePHj8fU1JSIiAhOnz5NRkYG0dHRTJgwQTsM+jgsLCxISUmhVatWDB06lPLy8seus5ZarSY4OJicnBySkpKoqamhtLSU0tJSnUk3zSEyMpKbN28SFhbG8ePHKSoqIjU19f9v787jpKjOxf9/au1lpqdn32AAwQVFWQQkuAQTUTRel2gS9JqIqPklXjUabogxUTHxRlyiwUSjV6MmXxOjWdQYjStXNIm4IGJc2VT2GWDWnl5rOb8/eqaZgQFmoGGQed68ilq66pxTXV3TT59zqoqZM2fieR6vv/46N954I4sWLWL16tU89thjbNq0KddE+8UvfpGnn36ap59+mo8++ohLLrmElpaWneZ71VVX8eqrr3LZZZexZMkSli9fzl//+tcdXgSybNkyfve737F8+XLeeOMNzjnnHN57771u/RdF3/RrABiLxfjGN75BbW0tU6ZMYcqUKdTW1vL1r3+d1tbW/iyaEEKI3RAOh3nllVcYMmQIZ511FoceeigXXXQRqVQqVyM4Y8YM5s2bx69+9StGjRrFf/zHf7B8+fIe04tEItxyyy1MmDCBiRMn8umnn/L3v/+9x9qfcDjMc889R1NTExMnTuQrX/kKJ5xwAnfeeWfe9q+wsJBnnnkGpRSnnnoq8Xg8L+muW7eOJ598krVr1zJ27Fhqampyw6uvvpqXPDrV1tbyr3/9C8/zOOmkkzjiiCO48sorKS4uRtd1ioqKeOWVV/jSl77EwQcfzDXXXMNtt92Wu+H2hRdeyIwZMzj//POZMmUKw4cP71Vt7ujRo3n55ZdZtmwZxx13HOPGjeO6666jtrZ2u9t4nsdtt93GmDFjOPHEE0mlUrz66qvbvRJc7JymdvdeALth+vTpvP322/zyl79k8uTJACxcuJArrriCsWPH8sgjj/RX0Xqlra2NaDRKa2trr/pnCCEGtlQqxSeffMIBBxzQ45WOQoj82dH5Jt/f/dwH8KmnnuK5557j2GOPzS2bNm0a9913HyeffHI/lkwIIYQQYv/Vr03AZWVlRKPRbZZHo9E+X2YuhBBCCCF6p18DwGuuuYZZs2ZRX1+fW1ZfX8/s2bO59tpr+7FkQgghhBD7r73eBDxu3LjcvYgAli9fzpAhQ3L3/lm9ejWBQIBNmzbxrW99a28XTwghhBBiv7fXA8Azzzxzj6V91113ceutt1JfX8+YMWP45S9/yVFHHbXT7R555BHOPfdczjjjDJ544ok9Vj4hhAB2+zm8Qoidk/Nsx/Z6ADhnzpw9ku6jjz7KrFmzuOeee5g0aRLz5s1j2rRpLF26dIfPFf7000/53ve+x3HHHbdHyiWEEJ06n8qQSCQIhUL9XBoh9m+JRALY9gkpIqtfbwOTT5MmTWLixIm5+zz5vk9dXR2XX345P/jBD3rcxvM8Pv/5z3PhhRfyj3/8g5aWlj7VAMpl5EKIvtqwYQMtLS1UVlYSDoe7dYkRQuw+pRSJRIKNGzdSXFxMTU3NNuvI93c/3wYmXzKZDG+99VbuecIAuq4zdepUFi5cuN3tfvKTn1BZWclFF13EP/7xj53mk06nSafTufm2trbdK7gQYsCprq4Gsk9AEkLsOcXFxbnzTWxrvwgAN2/ejOd52zzip6qqio8++qjHbf75z39y//33s2TJkl7nM3fuXH784x/vTlGFEAOcpmnU1NRQWVmJ4zj9XRwh9kuWZWF8Rp6z3V/2iwCwrzofQXffffdRXl7e6+2uvvpqZs2alZtva2ujrq5uTxRRCLGfMwxDvqCEEP1mnwgAM5kMn3zyCSNGjMA0+16k8vJyDMOgoaGh2/KGhoYeq39XrlzJp59+ymmnnZZb5vs+AKZpsnTpUkaMGLHNdoFAgEAg0OfyCSGEEELsS/r1RtCJRIKLLrqIcDjMqFGjWL16NQCXX345N910U6/TsW2b8ePHM3/+/Nwy3/eZP39+7hnDXY0cOZJ3332XJUuW5IbTTz+dL3zhCyxZskRq9YQQQgixX+vXAPDqq6/mnXfeYcGCBd0e1Dx16lQeffTRPqU1a9Ys7rvvPn7729/y4YcfcskllxCPx5k5cyYA559/fu4ikWAwyOGHH95tKC4uJhKJcPjhh2Pbdv52UgghhBBiH9OvTcBPPPEEjz76KJ/73Oe63Qph1KhRrFy5sk9pTZ8+nU2bNnHddddRX1/P2LFjefbZZ3MXhqxevRpd79d4VwghhBBin9Cv9wEMh8O89957DB8+nEgkwjvvvMPw4cN55513+PznP09ra2t/Fa1X5D5CQgghxGePfH/3cxPwhAkTePrpp3PznbWAv/71r3vsuyeEEEIIIXZfvzYB33jjjZxyyil88MEHuK7LHXfcwQcffMCrr77Kyy+/3J9FE0IIIYTYb/VrDeCxxx7LkiVLcF2XI444gueff57KykoWLlzI+PHj+7NoQgghhBD7rf3mWcD9QfoQCCGEEJ898v3dD03AfXl+7kA9KEIIIYQQe9JeDwCLi4u73fJlRzzP28OlEUIIIYQYePZ6APjSSy/lpj/99FN+8IMfcMEFF+Su+l24cCG//e1vmTt37t4umhBCCCHEgNCvfQBPOOEELr74Ys4999xuyx9++GHuvfdeFixY0D8F6yXpQyCEEEJ89sj3dz9fBbxw4UImTJiwzfIJEybwxhtv9EOJhBBCCCH2f/0aANbV1XHfffdts/zXv/41dXV1/VAiIYQQQoj9X7/eCPrnP/85Z599Ns888wyTJk0C4I033mD58uX85S9/6c+iCSGEEELst/q1BvBLX/oSy5cv5/TTT6epqYmmpiZOO+00li1bxpe+9KX+LJoQQgghxH5LbgS9G6QTqRBCCPHZI9/f/VwDKIQQQggh9j4JAIUQQgghBhgJAIUQQgghBhgJAIUQQgghBph+DQA/+uij7b723HPP7cWSCCGEEEIMHP0aAB555JHcdddd3Zal02kuu+wyzjjjjH4qlRBCCCHE/q1fA8Df/OY3XHfddXzpS1+ioaGBJUuWMG7cOF588UX+8Y9/9GfRhBBCCCH2W/0aAH7ta1/jnXfewXEcRo0axeTJk5kyZQqLFy9m4sSJ/Vk0IYQQQoj91j5xEUgmk8HzPDzPo6amhmAw2N9FEkIIIYTYb/VrAPjII49wxBFHEI1GWbZsGU8//TT33nsvxx13HB9//HF/Fk0IIYQQYr/VrwHgRRddxI033siTTz5JRUUFJ554Iu+++y6DBg1i7Nix/Vk0IYQQQoj9ltmfmS9evJhDDjmk27KSkhL++Mc/8tBDD/VTqYQQQggh9m+aUkr1dyE+q+Rh0kIIIcRnj3x/93MN4IUXXrjD1x944IE+pXfXXXdx6623Ul9fz5gxY/jlL3/JUUcd1eO6jz32GDfeeCMrVqzAcRwOOugg/vu//5tvfOMbfcpTCCGEEOKzpl8DwObm5m7zjuPw3nvv0dLSwhe/+MU+pfXoo48ya9Ys7rnnHiZNmsS8efOYNm0aS5cupbKycpv1S0tL+dGPfsTIkSOxbZunnnqKmTNnUllZybRp03Zrv4QQQggh9mX7XBOw7/tccskljBgxgu9///u93m7SpElMnDiRO++8M5dOXV0dl19+OT/4wQ96lcaRRx7Jqaeeyg033NCr9fdUFfLmxGauXnAT/zNlNlUFVXlLVwghhBDSBAz9XAPYE13XmTVrFscff3yvA8BMJsNbb73F1Vdf3S2dqVOnsnDhwp1ur5Ti//7v/1i6dCk333zzdtdLp9Ok0+ncfFtbW6/K11eXPXcD77f9H9P+/DKXjr2UmUd8HVPf5w6VEEKIHvgZD7/dwWvPdBt3TivHxywNYlaEMMuzg1EUQNO1/i66GED2yahi5cqVuK7b6/U3b96M53lUVXWvLauqquKjjz7a7natra0MGjSIdDqNYRj86le/4sQTT9zu+nPnzuXHP/5xr8u1qw53j2XIvz5h0WHr+MWS23h8+ZPccvxPOLz88D2e975OeT5eawavJYXbksZrSeO1ZdACBkbExiiyMaKB3LRm7hP3Ohdiv6WUwmtK4cUdjEIbvdBCt43+LlZeKaVQKS8byMUcvHhHYBfrDPAc/PZMx9hBZby+Z2LqWOXBXEDYddALLDRNgkORX/0aAM6aNavbvFKKDRs28PTTTzNjxow9nn8kEmHJkiW0t7czf/58Zs2axfDhwzn++ON7XP/qq6/uVua2tjbq6uryXq5hnyxjRdUYpq6oYXXgUxaNXMm5T/8nXz1oOt+d8B0idiTvee4LlFKopLslsGtNZ6ebU9n5ljReLANbdVpIkMbV/B7T1EPGli+lQhs9YmMUWhiFFlrYQNngmh5eJo2TyeCm07jpVHY6k8ZJp3EzadxMBg0wTBPTNDENA8MwMUwD3TDQ9OxY13U0w0DXDXRdQ8dAc3R0R0N3dfSMBhnQHNAyCtLZQaV9SCuMsIVVHsYsDWGWBjFKg9lxNIBmyBeA6H9eW5rMmnYSq5uJr25CrU9hpLt/Nn0LKNDRC2ysoiBWJNhx3mXPRaPjfDQKLbSQucPgJp1Os3HjRhoaGkin01RVVVFdXU1hYWGfy66UQmU8/JSHSrn4KQ8/5aKS3ae7B3TZMV4fe0uZWpf97T7WTA23MYW7OZkdmlLg+jj1CZz6xDZJaUEDszyE1VlbWBZEFZu4hRoZ5ZJKpUin093Gvu9TUlJCWVkZpaWlfX7CllIK5fgd74277XuW8lCuj1EcyNZmlgbRI7bUYn6G9GsA+Pbbb3eb13WdiooKbrvttp1eIdxVeXk5hmHQ0NDQbXlDQwPV1dXb3U7XdQ488EAAxo4dy4cffsjcuXO3GwAGAgECgUCvy7WrgsOGEli5hLRRQ7Vfyenvbea9sg/4k3qEF1a9wI8m/4BpQ6d95n4RKk/htaVzwVw20Et1mU6j0jv/5ZzRXVbZDXxCAw20kqbn4A8AH2jrGPYATYGuwEBDVzoGOiZGbsj+r2GQfc3oWGd782abgV1vYisTmy1jUzMxozZ2eRizrGtwmJ3WQ/tkZb7Yip/2cDclcBoSubHyVPaLvWtzYLT/mwN95bOpsYFNK9eQWN2CVu8Q2WxTmN4SSGTr1zUczaHJaCPqRQgqG90BWnxoSeGsS+HsKB9d4QZ93DC0BzO0miliJGlx4zQmWmhN9HzyFoYLqCquoDJSTmW4lIpAMYWEUGkP1RGkeEkHvyOAUWkPlfbRdqPXe2dLg17QEcR2Tkesbj80jUILLWD0+Dfa9xXJdBrdyeCnUjjpNKlEikRTjERTO8nmdpKxBKl4klQiSdrJkPFdMhuzg6O5ZPBQfdyRgmCYkoIoJaEoxYEIxWYhUa2AIhVGz6gtAV66M9Bz2dGf1h6ZGmZJcJu/T2ZZEKMkiB7Yec2w5ysa29PUt6VoaEvT0JZibF0xhw+K9rEwYmf69VvjpZdeyks6tm0zfvx45s+fz5lnnglkLwKZP38+l112Wa/T8X2/Wx+//jL5GJuS8seJtw9m7bohNFpDOMyZwsHvbeTNQe8x++XZPF77BNd87kfURfJfA5kPSikyn7YRX9SQ/YXbksJv27b2rieOliGtJUl4MWJOM23pRprcVlosl/aQiWOH6fYQG6XA7+EvlQYaGp3/58aatvWSne8P4OPjb/VHV2ngaeChAK9j2NHX3a6zkgb2GhN7tYnVERwGMLGUianraLaOFtIhrKMVGlBkoBUbaCUWdsjGtm0swyJkhigOFOcGy7D2SHkHGieTJhWLkWqPkdzcSqY+hrspBS0uWkzDSpnYXgCFIoNLUsuQJIPSILTcIqQCBDDR0PDwSAfSOAU+lJgEKgspHFxOyfBa7OJw3n78JZwE69rXsTa2lg3N60iubcWs94g2hRjcVk61U04xUEwICAHg4bM6sIFlwVWsKqwnVp5GrwxSECykNdVKIhFHxV20uIeZ1ClyCyh2Cyn2iihxIxQ5hYT9MMrTSeLQrLfT5LbTEkvgtfcccYSVTalfiIVJk9ZOq5agPRGnPRFnJZ/m1gsok1I/QpmKUOYXUq4iRFUYPReqZrl4xI0kCT1JXE8SN1Id4wRxPUmzGaPFjNFitNFixmg220iaHphBbC2CpRViqgKMeCF6ewHahjB4BfhuGOWH8DJBzLSO4XrYbhLbSxLyk4RIUUiagNaHJuIdxEya0rAxtvnBaGOC0ojpSVq1BEktQzyVIJ5KsJYN3RNRUKiCRFWYqApT1DGOqhCFBNE1HS1ooodM9ICRnQ4a6EETDC37A74phdeSAlfhbkribkr2XN4CC7fIIhk2abE0Npmwxvf42HFYkUhTH8uwqT2N53f/O/vfJx4sAeAesN9UG8yaNYsZM2YwYcIEjjrqKObNm0c8HmfmzJkAnH/++QwaNIi5c+cC2f58EyZMYMSIEaTTaf7+97/z0EMPcffdd/fnbgCQSKwAoKBwLYccshb/wDfYtLmOjQ3Dmdx8PO2bN/J68h3OrD+Tb4/5NheMumCf+RJXviL5fiOtL63CW79tU4anPBJuW7ch3jntZceeclG6gVsQwSuI4hYXoazuzT2am8Fyk0QMqCwsIhAM4Rg+ad0hoWWIk6RNtdPsxWj2WtnstZAxXFxD4RgK1/RxDYWvQ5EfocwrpswtZjDV1FFLtV9OmVtMUbqAUNLCyGS/OtyAImO7JAMOCTNNwkzTrieI6QnatDgx2mlV7cRUO60qRsxvx1EeBka2hlD1PO6ctj2TgGtj+RaWb2FgomnZbwBH83DwiGvb+ZHiArGOoaHnVTRAaT6e5uNqLo7u4OoemqFhmAaGYWCZFpZlYVs2QStI0A4SskOE7TDhQAGmEcLQg7hKJ+ND2ssOSVeRcCDpgW5aRCKFRAtCFAUtIkGTSNCiqGMcCZqE7Z5rSJTvk0mlSCfiZBJx0okE6WR2nJtPxPE9D13XuzXBax3zum6gG3rHcr1jfsvrmq6D40I6DalUxziNSqUglUQlk5BI4XkuGUOno+WetPLIeB5pN0M6k8FPg+2EsL0QASuKZRdhWgV4hk5Sy5DSnFyglzQyJM0MKTLb/JDIHR8FQWzCyibo24RiNqE2m9CnNqHX1xDkfUwPXC9B2o8T09ppM+K0BzMkwx4UBQlGighFiokUlVIYiVIQKMDFYVOmgfrkBtan1rEhvg690aGyrYiDU0M5ODmUYzN16AzdpkwNgSY2RluIV7hQGyRSV8ag0sM5u/BkooHoTptvV29Yzer1q9nQsIGGjY2saNqAm3Z7fhCpptB0hY1OIQHK/AjVfgkVbjFRrxAdHRePFqOdBr2ZTXobLVqCdlKk8EhrLhuMZjaw5RZjPj4pK0HcjtMWaKMxGKPFbsfTdXzfxHF1HNfA9w1Q2a9EzUjkBvQUWufx8tqAjaDA8i0K3AIKnAIK3AIK3cLcdMgN5YLObidfFz4KR1M4mt8xeDiaT0bzcHSvY97F0d3stO7g6A74LrrnYLouxWmTOqeEwW4ZJV4pFaqMckop0Uux9SC+8smoNO2kaNLaaTGSxPQMcSNDQsuQJIWnebRrKdpJsY6mbmXUdZ1ItJiysjLKy8uoLC+nrCxCWVkZkUgkd+xTjsfGlhSb1sdoq28nvTmJak5hxlwKki4ljiKiNFTcwYg7FAKFwGBgXEdeGRT1GKwnxAZ8WgM6mUITPxrggGhou58xsev6/TYwf/7zn/njH//I6tWryWQy3V5bvHhxn9K68847czeCHjt2LL/4xS+YNGkSAMcffzzDhg3jN7/5DQDXXHMNjz76KGvXriUUCjFy5EiuuOIKpk+f3uv89tRl5H7KJd74KZud56ivf5xE4pPca+l0mI0NB9DQMJx1qTiLat6norKCaz93LROqJ+StDH2lHI/4Ww20LliNasnWgHm+y6ft71Gf/DQb3HkxfMvHDofRAiZp0yeup2khRpPfhrLCBMxSQnopYYrRuv4B1aGsuoRRhxzIwSNqiUR0XLcFx8kOaidtFb7vE3faacu00ZZuoy3TRmsmO27LtBFLt5H2tl/7qyudFg82eBqbHA2vlzWHnUzdJGJFKLQLidiR3HShlZ3vnDY0A095uL6Lp7zstONmh5SL25pAtabx29IQd1EJDyMNlmtjEEA3bdBNlGHgaeDqCgcPbzt9JPc0X4Hrg6PA8cDzFMpT4LoYnovpZwh4GWw3jeUksbw0upshoAcIGGGCRgEBPTvODuHcck3TcH0HVzl4HWPXz3SMHVyV2XZ5D695atsLzhSArqN0E2Wa2IEoth3FtArRrSDKtPAMnbSWrclL4WzzBb8zmq+j+xYo8A0Hpff9GAWUSUjZBLEJKRvb19BcF+Wm8ZwETrqdtJNCVxYlVjkldhnFdilRK5r7YdFV0kvQ4rXR6rYRU+20k0AZKhtI6zq6aaIbBoahZ4Nps7MfrA6WgWtCGo+kl6E9laA9ue2PQABN0ygtLaWqqorKykqqqqqoqqqiuLgYXddxfZfWdGt2yLTSkmqhJdaIm4hjRAqwrAABIztYukXa0WmKuWyqj9G0qY1EUxtuLIaVbsfo4W+DUpB2wXF8VNpFS2cwUynCKo2Fj29Y+KaNHwhAMIBvaWBqoGvZlgW189YDD4+4FSduxreMO6YTZgJXczt+kUHA0QmlDYJpg1BG7xgbBNN6x3jLvOn37sI2QzN7/Gx3ex8AZZj4dhBlB3ACIbxACN8Kolk77s/noZPSgrT7JjHXIKksksoioczstG+RxqDzxChAowaDEQGbEbZNnWFRpXRKXShIK/QdRCLhyVWUnnFwr/a7t+Q2MP0cAP7iF7/gRz/6ERdccAH33nsvM2fOZOXKlbz55ptceuml/PSnP+2vovXKnvoAtb++gZbHV2ANKiQ0pgLvwA00xP5GQ8NTuG5rbr1YrIyG+uF80BpgUckKvnjYF5k1fhYlwZK8lWVnvLhDfOF62v65BlLZj1LaS7Ki7W1aSpoZfdopeDUFLE9+yoexpbzf+AEfNX2E56cpwaLOKaXWKaLSLyBsKEwrjWWmMa004bBPYaGGbTtoWhzXbUWpXbi6Ls98BQmtgKRWjGNWoKxqzGAdgeBQIsGybJDXJdgrtAoJGIFuNSUZ1yeWcoil3I7BoS3lknY90o5P2vNJOx5p1yfj+l3G3rbznk/a8XHTKYx4M2a8iUCyhWCqhXC6hUrXoUozKLCLCFhF2GYhthnGNsNYRhClaXj42UHzt0zj42s+Lj6OcsioNGmVJqMcHOXgdvzLbqdA01Canh0bJuh9uwJbUxpBZRIiQFgFCGETVBYhlQ1uQh1BTjbgsTqaSn1csjWjrrZl7OLj4OJqfnYZ2RqVrutumXZx8LLr4+NqXnZ/+kqBqSx03wbPQvOz03rH2HAUgbRLIONgp1IEMgksN47hOdljoBs4lo5jabiWhtMxuBa4JjiWwjUVvqlwDdXngDP/FKaZwbaT2HYC09xO1wfPQ3cyaE6235vmZjB8haGb6IaJYRgYuo6h6RiArnx010V3Hcg46Jk0eiqNkcmgueC2W6QSQVJegJRhkTQMHMPENQx8vTM66+yOAcoO4AXD+IFwdhwMo8yeW0y0TArdcfAtG2XZsJOmds3JoDtptEx237ZMp9FcB9O2MWwLzTLBNlCmjmdmm9FJOhDPQCKTjUj7QLctrEgBdqQAO1KIHQwSRMNKu1jxFHZLO1p7OzFLo9VWtBoeMc0hQYa0cvB8D9PTCTgGAUfHzujoW32gFKBMG98O4AeC+FYQPxDIBotWYKfvTTYRH8110BwH3c1kp12n43PQMe1m0H1FyIxQaBZTaBZTYHWOoxSaxcSHpRl96el9eo92RgLAfg4AR44cyZw5czj33HOJRCK88847DB8+nOuuu46mpqbcTZ33VXvqA9T67KfEXlmzpQOuBoERxQTHRInXvkv95ifYvHkBWketju9rNDUNYmlzCW8E0lx0zP/HmQeeuUcvEnEbk8ReWUv7ono6u7O0Oy0sa3uT5NAM/kSDDdYHxOPLCWkZwrqiQIdCTaNAV1jGrtdIZTybWKaAuBOm3cmOPbVtbYYGmIaOqWtYht4xZKfNrmN9y2tm13X07DyAUh6p1Hri8eV4Xvv23xeqSao6Yu4gmtOD2JiqYUO8is2JQJeAzyHl7OUaOaUo8BIUuW2EyRA1NEo1n4hSVCidUt8gqkwiyqQAi7BmEzKCFBhBAsbOL3zylU/Sa8816yvANENoRgDPNPB0SOlOtilU6zKQbSbNaFvXVCh03cWy0l1+FGRyPw4sM42m+Sg0lNJRKjtGablppbSdv97DunRZpimNABa2srB8E02Z4Jt4SiODRspQKNPCMC0MM4itW1iGgaY88DPofga8NIabzAYLmTSak8FwHHQnjZ5J42d8Eu0aiZhGJqnBTmp4dE0jaBvYQQszbGEELAiYYNv4doiMaZHSdJIaJDWflOb2OaDVdTcX2NmBJAE7kZ0PJLLTgexrhtF/P8i8jE6m3cRpt8i0W7lxpt3CiZlkEhb4GmCAFkDTbNACoAVQZgA/GMQLWHgBHS8Afk8donzQXQ3dUeiuwnC8bDCTyQZ7+A7ggHJQKjve1T7AwcII4Wgx4WiUcLSEgmgx4aIo4eJiwgURbMfFjLVjNjVDfT2ZNWtx1qwhs3Yt/q7ck9Yw8IsjZKIh4hGblhBsCnpsNjI06mniho9jGPiaAejYrkEgo3cEjCY2IQwjhG9ZKNPKBoumhbKy09sLsnvkeTsMEEd94XhOnXFJ3/dxByQA7OcAMBwO8+GHHzJ06FAqKyt54YUXGDNmDMuXL+dzn/scjY2N/VW0XtlTH6CNGzexYX0jB8QjpP69mcyqLie3qREaWYo52qTBeI4VKx8iULjlfXIcm1VNVXwarOai4/+HA0sOzFu5ADJrYrQuWE3q/cZsDYyRZFPwbdYHXyM1eCOUNFNspenNHUuU0nDcIBmvgLgXoSUToTEZIpYpoN0J054pIO4U0N4xdAZ8rp/9w2LqGpWRABWRAK6vcsFVLOXi+vn5WBu6RmHAJBI0c7V2Ab2JmoIGagvrqSmop7agnprCBors7QeGLaki1ser2RCvZn17VXYcr8YnSiRod/SRMwnZBgHTwDZ0ApZOwNSxTT27zMzOd53esiy7fOv1LQ3STWli6+O0rovTtKadts0pMsmd32dT1xRh26PQTFNEmgLlUOBnCHg+lg8mFppZiGYXoXX0QfWANksjboKja6R1yBgeSTtN0kqQMuIkjBRJyyFpuaQsn5QFGdMjY3g4JriGwtM1HM3ExcLpcbAJkqSczZSxqWPcOb2JItr6v4JsV2kB0ApwVZiUFybhBIllAjQnAzQlLRJOkKQbIumGSLjZ6YQTIuluWR4NhxhUHKK2OEhNUYCikEXG88m4DlpmE8HEKkKpNYTdekJsJGi0ELDbMINJjMIMhHv/A0WLA606JA1M30PrcqWXQkNpBko38DUdpRn4ut4xraM0DV/T8TUNpWUv2Mpu7dNRB5UdVHZetzzsghRmKLNtQbaifA2VLEBrL4C2AHqzjr5JYW5ysDe72JtS2LF0Lse0bdNSUkwqGKSgPU5BPE4wlUJpJhm7kIxdhBOK4hWV4RSW4oSLcQJR0mYRaS1MWgVJOwbZPz3ulqCwpwBR+aCH0LQCdCNMoLCIUNjCNjxslcZ04pjJFoy2RoymevTG9ViZGJYTx3LimG4cw+9+DhsV5diD67DqBmMPrsMoK8VrbcXb3Ijb2IjXmB27jY27FDBmCgPEC01aCzQaQy4bA2naQhpJG9I2pCxI2ZC2INVRe+2bQdBD6FoQkxAhL0TQ6xi7QUJeCEttP1DM/ghMYQ2J8L3z5vS5zDsiAWA/B4DDhw/nL3/5C+PGjWPChAl885vf5Fvf+hbPP/8855xzDk1NTTtPpB/tqQ/QbX98llsrqilvbeHYlk18PlzI0WYNwU893I1b+tRoQYPQ4eVsjPybt1b+itKh9dihLk8qSRbQbh/OaZP/h+LC4btcHuUrUsuaaXz5HeJtH5AqWk06sor2whWowuYet8m4NvFYKfH2UtLpMI4bwHUCpNJBmuKFNMSjrEsVEydIRrdwDANHt3B0E0c3KCsMUFkUpKooQHVRkMqiINUd81VFQaqKgpQV2Og99FFRSpFy/FyzatdxrNvYpW2rZW1dXtv6SrSthW0jd3FDJGhSHk5SU9BAZWgDJfY6Cs21BFmNyebtpmGaxRQUHEhBwQgKCg7CMovRNKNjMLtM97wMD1QyjUqk8GJJYhsTNG7I0LzJo6nFoCUWwPPNjpotHaUMNM1D011s1UrQbcR2NxP0mrC9FgJ+C5bXhkUbBgkwFY5t0FIYpbkwSktBlOZwlJZQlJZgxxAoptWO0mpFaTOL8LX+v/m26buUujFKvDbK/VbKtRiVWhsVRhuVRhuVRishy0U3Fbrhg+ahlIdSPkq5HdNOr65a312+yuC6MTwvnrc0057VEQxmg0INRXGglWigDb2XtYEZzySWjJCMh3HiNn6bidECgWafcGOG6OYUZRvjBFLb/phIBcIkC4pIF0bJFBXjRkvwoyVQUopWWopZVopZVkGgopxwcSEh2yJsG4QDBkHTwHAUTtIlFXe2DO0OnusTLLCwwx5moAnN3ozSN+JRTyZTTyq9nlRqHalUPUrtPEg0HLDjHnZcYcUUVisYbaC1admgtkVHtWj4LTq4O7+FiULDsQrI2EVk7EK8gkKcUAQ3FCUTLCJjdQaMhaRVGE+ze3Useiy77hMI6oQKLIIlFsFohkAkhR2OYwYTWCGXcGERhdFSCotLCQSimGYEw4iguT5uUxPu5ka8pkbczY24jZvxGptwN2/C21iPu3kjblMLXmt7n5uoe35vwLU13CIdt0TDKQG3WCMT1XCKDNyIBgUKLeShB1zMgINpZmuYG5uP4mtn/2G3y9CVBID9HABefPHF1NXVMWfOHO666y5mz57NMcccw6JFizjrrLO4//77+6tovbKnPkCXPvAif60rwzW3BDe65zFq7acck8zweSo4eHMY4luaX/SIxVqzng/bfk/wkA2U1GzMNc8oBZ5xEEcc8v9RWTEN0yzYYf5KKVKpNbS1vEfzykW0bHqHVOBTvGBLj+sn0yHisTLi7aW0t5fSHi8lkw4DGprvU9rYRE39Bqo31FPS3Izei4+cZttogUDH2Ea3A92W6QE720k5EEALBDDLyrBqa7FqazBrarBqazGKi3e5GVwpRdLxugWHlqETDWWDvcKA2aV5OHsJiqsUrlLZCx86pj0FaSdGLLGa9uQqYonVxBNraU+uI5nZhIeGj4GHgY+OhkLvWLJl3Pnqtss619t6HX0nkUuKIK1EaSNKK8U7nE5ofb/hbkAlu9XXmTjYmk9A87F1jYCuYes6QV0noJsEDYugaREyAgTNIGEzRMgMYWHhx128mIPTliHTkiHTlCbVnCZlQLLUpr3IpLVApzmgsdlQNOH3Km6rsE0GBWwGBS0GB2wGB7PTg4I2gwI2ZVbPVynvKb7v4nntuG5s28HrOt/WZTq7vtexjuf1fNFFV0rpuFopvlaGr5WjGRVoRgWGVYFpVWDZ1QQDldhWNNvUbOgELANL13ANaPN8WjyfJtdlY9phU3ucjW3tpF2P0oIQpYUFlIWClFgGJZZJsWlQapmUWAZ2H/uF7iqlfDLtq0mte4XUpkWkWj8klVhDmnZSAYNUUMexel8WDQ1T2ZiuienoGCnQ46C3++gxD63Vh1YfmhWqSUNr0dDjoLk7uVBEN3HNAhwrO3jhIH6pjVdi4BfZeEUWboGBHwDfcvHNNJgJDDuOYbdjBOLodjuG1cfbl/k2ugpiKAvL17B9D9tJY6ZT6KkkjmeTVGFSKkzCDxP3Cmh3C2j3Coj5hcSI0KYXkDBCaJ6PpqmOH5Y+muGhGQrN9LKD5aPZPrrtogV8DGPrv3o9/a3r/JvmYbg+h672Oeniv/ZtH3dCAsB+DgB938f3fUwz2/nikUce4dVXX+Wggw7iW9/6Fra967+O9oY99QFa8dZG3vy/1bwZT7Ky2mJFjUVjUfdfn9FYG0etWcexTiGfa41Sltzyh6ZVxVieeI3GYe9RfMAGios3dtkyQHX1l6ipPouSks+hlE8isZJY7ANi7R8Qi71Pe+wDXC+2TbmUgmSiiPZ4NtDrDPhcN9tHLJhMUtzSQrS1lWhLK5FYO2ZxBQWFYWzfxfScbAfrTBqVcVDpNCqTyY6dXes342sG6UAxuu9gOe3oqqNfpKaRLori1dXhDBqMU1NDpqqKTEUlmZIyMiUlpAojJNFI+D5xzyfRMcQ9Lzed8HwSfnac8RU+qiO4A69LkLev0VT3oHBLoOiTIkRa69tTAQx8ivUkJXqaEiNDqeFQZriUGj5lpqLMUpRZOuWWRpllELAKsaxiLLMkO97OVad7Qsb32ZB2WJdyWJvOsC6VYV3KYV06w9pUhrUph2RP943cSkjXGBy0GRIMMDRkZ4eO6SFBmwJz33vc2ZYgsq1bAAkagUAlgUA1tl2WOxa+UjQ7Hpsch80Zl80Zl00Zl00Zh81Odnpzxs29ntrNrhUFhk6JZVBqmtngsCNILOkSJJZ0jEs7gsci00DfWSDupqHhPVi3GNa/nR1vXtrRdLyV0uFQeyRe7RGkKgaTihSR8ps7ag7Xk0434DqtOE4zjtvSq6B6e3RsTD+E4VoYKRM9AXrMR0v7qIhChT28oINnObimi2v4qF39zaEAN4DrFNLmVdHmlxDXTRKGQcowSVkmKd0mQZgEYZKESVDQZTo7nySUvZhrH3LZ+kVcc97FeU1TAsB+DgBXr15NXV3dNr+ylVKsWbOGIUOG9FPJemdPf4BaNyVY+lo9S1+vZ3Uiw8cdweCnVSbprX69HrShnqMbXY6JRxndCmbHUV2T/pR3C5ZgHPAxVdWfEAptCewsqwzPa8f3t/316Ps68Xhxt0AvHi/G9y0M1yXa2poN9lpac9NxO0L8oEMpnXAkB31xMkWjDiNtmCT8bPCU9n3SPYwzviLleaQdh1TGIeU4ZByXpOuSdl0SKY/2lEd72iPhKBIeJH1IK400Gq6h4RjgmBqOCRlD69Ov+z1J9zwM38Pw/ezgZad13+9Y7mF4Pobys/0mDQPXtHHMAI5u4mkGrpa97YyvZwNbX8v2bfd1encl3naEdI0K26LCNim3TSqs7HSZbVLRMV/eMV1s7t3asJ3y3Oy+630PwpRSNLteLjDsDBLXdgSJ61IZGjI77yNZbpkdgWGAoUGbIV0CxOqAhdEP75frK5odh+bmdbRsXEFz42qaWhto9DQ2B8rYbJWwySxisx5mkxakUZl9vqVR2NCpsMzcZ6PCtii3TAK6RrPr0ey4NDtdxq5Li+P1+aESnQwNoh0BYsQwMDUw3CRmOoaZbkVPt2KmWjB9F0Nlf/SYysNQHqYZwAiXYhSUYRZWYBRWYlpBTE1D18DUNExNw9C0bLqahqVphA2dsKFTYOgENQ9bxbH9GJbfiuW1YXpN+G4LjtucuxWV42yZdt2W3bpjgaGH0PUIjlZCUhWT8AtJ+gXE3QDtXoCYaxHzbNpUgFatgFajiBYzQrMZJW6GdznfbmVQHiE/RSgXKrYT1joGEoRIECSF6qjP8wmjtILcGC2E0kL4egilBVFaALQgvm5n19c6b52fbUHpnO68fttTCk/5eMpnRnURMw/IbzwgAWA/B4CGYbBhwwYqKyu7LW9sbKSyshLP6/9bfuzI3voAKaXYsKKVpa9tYMVbG0mmPdaWm6ystlhVa7C2uHtNaTidYWJDO0fHQhy92aMmpYirFK/zLpsqV1BV9THlFauwrGwfGde1iMdLss237aXE20tIJIpRvoaZTlHW1ERFYxPRllaKW1soaI+T0U1WlA1l0+FjUUeNp2DCESSiRR1foE72CzWdocnp/2NoOQrbVdiewnKz05YLtuMSymQIptMEMynCqQThZIJwqp2CZIyCRBsRN06J6VIUAjOZRDW1olrj4GabhTSlo6nslYYaOnrntKYDJko3IGii2ToEDLBNsAyUZaIsA2Vk7zPnaRaeCtDk1tHiDepxP0qqAlQOK6FyWITKoUWUDy5Et3S8jiZnr2PoVjvJlunO2spCQ6fcNikw9r0aLJSCRBPENmwZ2jqn6yG2Pjtu35gN/opqIVoH0cEdQ133+UDfm68B0h21iGuSGValMqxKpnPj1ckMze6OP9e2plHXGRR2BIidweKQoE1kJ7WHSinink+T43YLqpo6xi1ux3zGoTmVoDmdotmDtl3sU1bitFKeaaHcaaLCjVGuUlRoGSp0n3JTy/5ICAQoDxVQEC6GcCmESiFcAqESCES33PbH97I1cl46O3bT+E6KtkyK5oxDcyaT249mz6fZVTR50OxrtPg6zcqgSZk0Y5PQ9t1nFQT1bKAY0nUKDKNb0BjWdYK6R0jLECBNQKUIkMD241gqhq7SxCminQgxCoipAK2+Tatn0OJmA+ndCZp1FMUqQ0TzKTI0iiyLIjtAJBimyLaJGNma1SLTIGIaFABm3EOPuWhtGfzmDJmmFO1NaWJNadqbU/huR6iguRhWEt1KohkOXqYQL10IPdyJIV8mnDqMSaftej/2nkgA2M8BoK7rNDQ0UFFR0W35qlWrOOyww4jH89cpek/YYx+g1a/BW7+Bg6fBiC9CcMsjcNyMxyfvbOaj1zaw5oMmlIL2gMantTYbhlt8GNVotbpfVTW0LcXRTTqTG11GNaVYpn3K+9anhKObyKRDpFIRQipAiV9I0PFItK2lYO0HDNuwiWQowsbScpZWD2Vp7QFsHlRHqqaSVKSARs8n3cuPjwlYmoatwFBgegrDUWiOj57xMTwwfZUbm13GhqcImVBg+UTMNBE9QZHWQpG/iWJ3LZHUGkJOHDOj0B0dLWOCE4RMCM8tIOVHSXjFJP0oSb+YhB/Fp29PTtF9B5TC103YC80jEaOBSmsFldYKqszlVFgfY+tJiNRA1SioPCw7rhoF5QeDueefUb3b0u3dg7i2jnHXYC9WD97OO+/3WrAYiuu2HyQWVvX5foUArY7L6lSGVV0CxNXJDKtSadakMrg7OS1KLSNXWxg29FyNWVOX4M7ZjT/NUSdGsUpRoitKbJsyy6Tcj1PhtlHuNFOR2kxFsp7y+DrK4muw45sg1dJzk2lvaAaYwWzQ5++89rS30ppFs1VEsxWlySyi3QxnOzRYYbzS4bglw3FLhuEVD8ULleV+5PTcH7fjxxHdfyh17crhKoXjq46uID4J39sy3THs7Vuqhw29hybybLN51/lSc0vzea+azftA+YpELNMREKaINaVob0oRb83gez7KV/h+9ik+vq/wPYXys889Vp3zKjv2/ezN4LvN+1uW+x3TXTvyHnXaAUw89YC87Q9IAAj9FADOmjULgDvuuINvfvObhMNbqqw9z+P111/HMAz+9a9/7e2i9cke+wA99yNY2HEPRN2EIZOzweDBJ0PZgblmv3hrmmWvN/DRaxtoWp8NlhXQPChA45giPgxneE/X8br8IbA9n/HNPkdtzjCoeTO6bpGww3yit7BCbyJuQFthlPrSStoLd157ogPVAYtBAYtqw6TMhWjSp6DFI7g5jbkhSbo+iZ/Z8Z9N0/SJFqYoCrRRZG6iSFtLkf8xUWcpEb0eU8vT83U1A6UZZLQiEqqUpF9CQhWT9LKBYdIvIuEVkXSLSLiFJN1CHLX94ErXfAxDYZoKwwDD1DoGHcMyOgYTM2BhWCZGwMouMzVMS0c3dUxLz65v6hSVh6gcHCLkrMn2aWr4ADZ+kJ1uWb2dQpjZz8XWgWG0breaiHfITUOqDdJt2cAhN90xbt/YPdiL1WeX91a4HIpqsgFvpBoitdlxUcc4UgOeA61roXVNx7jr9BpIte48H93Kplk8pEuA2DkMgYJysAvA2PlNgXNvja/YkHG6BIUdNYgdAWJfasUD+JT4SUoyLZSkNlHitFDqtFHitlHstFHitFGqUpQUVVBSOpiSihEU147CrDqs7z8KfB/Srdka2GRzx7hp++NkS3ba2cEPdU3PBoaGnR2bgS2DEehYZm+1Tpd1t14nWAy1Y7Of911o/t9dSinSvuoIDrfTZ7jbaz4Jb9sg0lWKqGVQYm7p79gZvHUu65wP7KULZ/Y1ylf4KhsUanr2b2o+SQDYTwHgF77wBQBefvllJk+e3O1iD9u2GTZsGN/73vc46KCD9nbR+mSPfYDWLoL3H4dlz0Hj8u6vlRyQDQQPngZDjwHTRinF5rXtLF1Yz7I360nGtgRLoSEFxCeWsaJc45WWVjb0+ADO7Qvreu7qyLqgTbVhUJxSFLZ7hBsdjIYk7Q1JWjYmd3JvOUVhIEGR3UiRvoGoWpUN8owGiowGQnrr9r9fNT1bUxPpCAiKtgoKItVghbOBkG5mvxi6jrUu87sQEDkZj2RbBjQwtgrYdvSopLxLtcGmj7YNDLcX6ASKoPLQ7oFh5WHZ9yrdlt2uM2jrOp0bt27/tR08Nm+H7MiWY5YL5rYK7gqrs1/4uyvV1nNg2LmsbT30tp+WboJVAHY4GxBaHeNdmI7pIVarAKs8k08djXQ6Tkl8AyVtn1DatJSSTf+muPFDSpw2wn6qew+9UCnUjIbq0VAzJjsuG9EvwVCOk8oGjG6yI1jrEswZ+24TrhjYJADs5ybgmTNncscdd3xm3/y98gFqXAnLn88Gg5/+s+PO8x3sQhjxBThoGhx0EkSq8DyfNe838dFr9Xzy7025fhuaBoMPKyV4VDmvhX3+sHwNbYU2uuOhewa1AZtDi8NMqCrigKBNUUJR0OLgbUrSujFJS0OC1o1JEm07ap5TFIbTFFsbKfaXU8ynRM31FBv1RIyNGNs87YFs/6HOwK7H4K4GCiv79wtuX6ZUNpDZ+AE0vJ8dNn4Am5Z2/6zsKXYEgkXZYDMY3TJdUN5zcBeI7Pky9ZbnQns9tGwdHHaMW9ZAZtur4feaosFdgr2OgK9o0J6r1RViAJEAsJ8DwM+6vf4BSsfg4wWw7FlY/gK0N3R/vXbcltrB6jGkkh4r3trI0tfqqf94Sy2RHTQ4YFwF3uAQxSGLYEplg7yNCVoaEsSaUju8AW6o0KQ46lAc2Eyx+oRo8m2KnQ+ImvWYWpcA0bCh6vDsbRe2br7rbNqzQvl9j0SW58Dm5dsGhq1rtqxjF3YEbh3BW+d0t3F0++sEIvt/YO45kImDk4BMAjLtPUzHu6yzvekets2dZBqUH7Ql0KvuGArK+nPPhdivSQAoAeBu6dcPkO/DhiUdtYPPZu991VVhVbZW8OBpMPx4WloMlr5ez9LX6rMB3k7YQYPiqjDRcoviYAvF2iqK0/8m2vIPArGl226g6dnmxdqxUHskDDoSKkflpylP5E+qLdvsGSja/4O3fZlS4KaywaAVzDYPCyH2GgkAJQDcLfvUByhWn60VXP4crHypo4ahg2Fn+wsefDLqwBNZ31TK0tfqWfV+I8ECi+KqMMWVIaJlFsXmBooz7xNqehNt/WLYvIweqwNLR2SDvM5gr3p0tn+UEEIIsY/bp76/+4kEgLthn/0AuWlY9Wq23+CyZ6H5k+6vlx+crR0cfnz21hvrFsP6xdlmwp5u4VA0GAaNywZ7teOytXyhkr2xJ0IIIUTe7bPf33uRBIC74TPxAVIKGldsCQZXL9zxfbrCZVtq9TrHhZXbX18IIYT4jPlMfH/vYXKN/v5O6+hgXn4QHH1Z9pYeK/8Plj2fDQajgzuCvY4avuIhcpWhEEIIsZ+TAHCgCUZh1JezgxBCCCEGpIF5i3EhhBBCiAFMAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFmvwoA77rrLoYNG0YwGGTSpEm88cYb2133vvvu47jjjqOkpISSkhKmTp26w/WFEEIIIfYX+00A+OijjzJr1izmzJnD4sWLGTNmDNOmTWPjxo09rr9gwQLOPfdcXnrpJRYuXEhdXR0nnXQS69at28slF0IIIYTYuzSllOrvQuTDpEmTmDhxInfeeScAvu9TV1fH5Zdfzg9+8IOdbu95HiUlJdx5552cf/75vcpTHiYthBBCfPbI9/d+UgOYyWR46623mDp1am6ZrutMnTqVhQsX9iqNRCKB4ziUlpZud510Ok1bW1u3QQghhBDis2a/CAA3b96M53lUVVV1W15VVUV9fX2v0rjqqquora3tFkRube7cuUSj0dxQV1e3W+UWQgghhOgP+0UAuLtuuukmHnnkER5//HGCweB217v66qtpbW3NDWvWrNmLpRRCCCGEyA+zvwuQD+Xl5RiGQUNDQ7flDQ0NVFdX73Dbn/3sZ9x00028+OKLjB49eofrBgIBAoHAbpdXCCGEEKI/7Rc1gLZtM378eObPn59b5vs+8+fPZ/Lkydvd7pZbbuGGG27g2WefZcKECXujqEIIIYQQ/W6/qAEEmDVrFjNmzGDChAkcddRRzJs3j3g8zsyZMwE4//zzGTRoEHPnzgXg5ptv5rrrruPhhx9m2LBhub6ChYWFFBYW9tt+CCGEEELsaftNADh9+nQ2bdrEddddR319PWPHjuXZZ5/NXRiyevVqdH1Lhefdd99NJpPhK1/5Srd05syZw/XXX783iy6EEEIIsVftN/cB7A9yHyEhhBDis0e+v/eTPoBCCCGEEKL3JAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhgJAAUQgghhBhg9qsA8K677mLYsGEEg0EmTZrEG2+8sd1133//fc4++2yGDRuGpmnMmzdv7xVUCCGEEKIf7TcB4KOPPsqsWbOYM2cOixcvZsyYMUybNo2NGzf2uH4ikWD48OHcdNNNVFdX7+XSCiGEEEL0n/0mALz99tv55je/ycyZMznssMO45557CIfDPPDAAz2uP3HiRG699VbOOeccAoHAXi6tEEIIIUT/2S8CwEwmw1tvvcXUqVNzy3RdZ+rUqSxcuDBv+aTTadra2roNQgghhBCfNftFALh582Y8z6Oqqqrb8qqqKurr6/OWz9y5c4lGo7mhrq4ub2kLIYQQQuwt+0UAuLdcffXVtLa25oY1a9b0d5GEEEIIIfrM7O8C5EN5eTmGYdDQ0NBteUNDQ14v8AgEAtJfUAghhBCfeftFDaBt24wfP5758+fnlvm+z/z585k8eXI/lkwIIYQQYt+zX9QAAsyaNYsZM2YwYcIEjjrqKObNm0c8HmfmzJkAnH/++QwaNIi5c+cC2QtHPvjgg9z0unXrWLJkCYWFhRx44IH9th9CCCGEEHvafhMATp8+nU2bNnHddddRX1/P2LFjefbZZ3MXhqxevRpd31LhuX79esaNG5eb/9nPfsbPfvYzpkyZwoIFC/Z28YUQQggh9hpNKaX6uxCfVW1tbUSjUVpbWykqKurv4gghhBCiF+T7ez/pAyiEEEIIIXpPAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFmvwoA77rrLoYNG0YwGGTSpEm88cYbO1z/T3/6EyNHjiQYDHLEEUfw97//fS+VVAghhBCi/+w3AeCjjz7KrFmzmDNnDosXL2bMmDFMmzaNjRs39rj+q6++yrnnnstFF13E22+/zZlnnsmZZ57Je++9t5dLLoQQQgixd2lKKdXfhciHSZMmMXHiRO68804AfN+nrq6Oyy+/nB/84AfbrD99+nTi8ThPPfVUbtnnPvc5xo4dyz333NOrPNva2ohGo7S2tlJUVJSfHRFCCCHEHiXf32D2dwHyIZPJ8NZbb3H11Vfnlum6ztSpU1m4cGGP2yxcuJBZs2Z1WzZt2jSeeOKJ7eaTTqdJp9O5+dbWViD7QRJCCCHEZ0Pn9/Z+Uge2S/aLAHDz5s14nkdVVVW35VVVVXz00Uc9blNfX9/j+vX19dvNZ+7cufz4xz/eZnldXd0ulFoIIYQQ/SkWixGNRvu7GP1ivwgA95arr766W62h7/s0NTVRVlaGpml5zautrY26ujrWrFmzx6qnJQ/JQ/KQPCQPyWMg5qGUIhaLUVtbm9d0P0v2iwCwvLwcwzBoaGjotryhoYHq6uoet6muru7T+gCBQIBAINBtWXFx8a4VupeKior2eP8EyUPykDwkD8lD8hhoeQzUmr9O+8VVwLZtM378eObPn59b5vs+8+fPZ/LkyT1uM3ny5G7rA7zwwgvbXV8IIYQQYn+xX9QAAsyaNYsZM2YwYcIEjjrqKObNm0c8HmfmzJkAnH/++QwaNIi5c+cCcMUVVzBlyhRuu+02Tj31VB555BEWLVrEvffe25+7IYQQQgixx+03AeD06dPZtGkT1113HfX19YwdO5Znn302d6HH6tWr0fUtFZ5HH300Dz/8MNdccw0//OEPOeigg3jiiSc4/PDD+2sXugkEAsyZM2ebJmfJQ/KQPCQPyUPykDzE7tpv7gMohBBCCCF6Z7/oAyiEEEIIIXpPAkAhhBBCiAFGAkAhhBBCiAFGAkAhhBBCiAFGAsB9zCuvvMJpp51GbW0tmqbt8NnEu2Lu3LlMnDiRSCRCZWUlZ555JkuXLs1rHnfffTejR4/O3bxz8uTJPPPMM3nNY2s33XQTmqZx5ZVX5i3N66+/Hk3Tug0jR47MW/qd1q1bx9e//nXKysoIhUIcccQRLFq0KK95DBs2bJt90TSNSy+9NC/pe57HtddeywEHHEAoFGLEiBHccMMNeX/OZiwW48orr2To0KGEQiGOPvpo3nzzzd1Kc2fnnFKK6667jpqaGkKhEFOnTmX58uV5zeOxxx7jpJNOyj1VaMmSJXndD8dxuOqqqzjiiCMoKCigtraW888/n/Xr1+d1P66//npGjhxJQUEBJSUlTJ06lddffz2veXT17W9/G03TmDdvXl7zuOCCC7Y5V04++eS878eHH37I6aefTjQapaCggIkTJ7J69eq85dHTOa9pGrfeemve8mhvb+eyyy5j8ODBhEIhDjvsMO65555ep9+bPBoaGrjggguora0lHA5z8skn9/kcFNuSAHAfE4/HGTNmDHfdddceSf/ll1/m0ksv5bXXXuOFF17AcRxOOukk4vF43vIYPHgwN910E2+99RaLFi3ii1/8ImeccQbvv/9+3vLo6s033+R///d/GT16dN7THjVqFBs2bMgN//znP/OafnNzM8cccwyWZfHMM8/wwQcfcNttt1FSUpLXfN58881u+/HCCy8A8NWvfjUv6d98883cfffd3HnnnXz44YfcfPPN3HLLLfzyl7/MS/qdLr74Yl544QUeeugh3n33XU466SSmTp3KunXrdjnNnZ1zt9xyC7/4xS+45557eP311ykoKGDatGmkUqm85RGPxzn22GO5+eabd2kfdpZHIpFg8eLFXHvttSxevJjHHnuMpUuXcvrpp+ctD4CDDz6YO++8k3fffZd//vOfDBs2jJNOOolNmzblLY9Ojz/+OK+99touPcqrN3mcfPLJ3c6ZP/zhD3nNY+XKlRx77LGMHDmSBQsW8O9//5trr72WYDCYtzy6ln/Dhg088MADaJrG2Wefnbc8Zs2axbPPPsvvfvc7PvzwQ6688kouu+wynnzyybzkoZTizDPP5OOPP+avf/0rb7/9NkOHDmXq1Kl5/d4akJTYZwHq8ccf36N5bNy4UQHq5Zdf3qP5lJSUqF//+td5TzcWi6mDDjpIvfDCC2rKlCnqiiuuyFvac+bMUWPGjMlbej256qqr1LHHHrtH8+jJFVdcoUaMGKF8389Leqeeeqq68MILuy0766yz1HnnnZeX9JVSKpFIKMMw1FNPPdVt+ZFHHql+9KMf5SWPrc853/dVdXW1uvXWW3PLWlpaVCAQUH/4wx/ykkdXn3zyiQLU22+/vUtp9yaPTm+88YYC1KpVq/ZYHq2trQpQL774Yl7zWLt2rRo0aJB677331NChQ9XPf/7zXUp/e3nMmDFDnXHGGbucZm/ymD59uvr617++R/PY2hlnnKG++MUv5jWPUaNGqZ/85Cfdlu3OObl1HkuXLlWAeu+993LLPM9TFRUV6r777tulPESW1AAOcK2trQCUlpbukfQ9z+ORRx4hHo/vkcfsXXrppZx66qlMnTo172kDLF++nNraWoYPH855553Xp+aZ3njyySeZMGECX/3qV6msrGTcuHHcd999ec1ja5lMht/97ndceOGFaJqWlzSPPvpo5s+fz7JlywB45513+Oc//8kpp5ySl/QBXNfF87xtakhCoVDea2Y7ffLJJ9TX13f7fEWjUSZNmsTChQv3SJ57S2trK5qm7bHnmWcyGe69916i0ShjxozJW7q+7/ONb3yD2bNnM2rUqLylu7UFCxZQWVnJIYccwiWXXEJjY2Pe0vZ9n6effpqDDz6YadOmUVlZyaRJk/Le5aerhoYGnn76aS666KK8pnv00Ufz5JNPsm7dOpRSvPTSSyxbtoyTTjopL+mn02mAbue9rusEAoE9dt4PFBIADmC+73PllVdyzDHH5P0JKO+++y6FhYUEAgG+/e1v8/jjj3PYYYflNY9HHnmExYsX5x7vl2+TJk3iN7/5Dc8++yx33303n3zyCccddxyxWCxveXz88cfcfffdHHTQQTz33HNccsklfOc73+G3v/1t3vLY2hNPPEFLSwsXXHBB3tL8wQ9+wDnnnMPIkSOxLItx48Zx5ZVXct555+Utj0gkwuTJk7nhhhtYv349nufxu9/9joULF7Jhw4a85dNVfX09QO6JQp2qqqpyr30WpVIprrrqKs4991yKiorymvZTTz1FYWEhwWCQn//857zwwguUl5fnLf2bb74Z0zT5zne+k7c0t3byySfz//7f/2P+/PncfPPNvPzyy5xyyil4npeX9Ddu3Eh7ezs33XQTJ598Ms8//zxf/vKXOeuss3j55ZfzksfWfvvb3xKJRDjrrLPymu4vf/lLDjvsMAYPHoxt25x88sncddddfP7zn89L+iNHjmTIkCFcffXVNDc3k8lkuPnmm1m7du0eO+8Hiv3mUXCi7y699FLee++9PfIr6pBDDmHJkiW0trby5z//mRkzZvDyyy/nLQhcs2YNV1xxBS+88EKf+sz0Rdfaq9GjRzNp0iSGDh3KH//4x7z9ivZ9nwkTJnDjjTcCMG7cON577z3uueceZsyYkZc8tnb//fdzyimn7FLfqe354x//yO9//3sefvhhRo0axZIlS7jyyiupra3N63489NBDXHjhhQwaNAjDMDjyyCM599xzeeutt/KWx/7OcRy+9rWvoZTi7rvvznv6X/jCF1iyZAmbN2/mvvvu42tf+xqvv/46lZWVu532W2+9xR133MHixYvzVnvdk3POOSc3fcQRRzB69GhGjBjBggULOOGEE3Y7fd/3ATjjjDP47ne/C8DYsWN59dVXueeee5gyZcpu57G1Bx54gPPOOy/vfy9/+ctf8tprr/Hkk08ydOhQXnnlFS699FJqa2vz0jJjWRaPPfYYF110EaWlpRiGwdSpUznllFPyfpHZQCM1gAPUZZddxlNPPcVLL73E4MGD856+bdsceOCBjB8/nrlz5zJmzBjuuOOOvKX/1ltvsXHjRo488khM08Q0TV5++WV+8YtfYJpm3n6pd1VcXMzBBx/MihUr8pZmTU3NNkHxoYcemvem5k6rVq3ixRdf5OKLL85rurNnz87VAh5xxBF84xvf4Lvf/W7ea2dHjBjByy+/THt7O2vWrOGNN97AcRyGDx+e13w6VVdXA9nms64aGhpyr32WdAZ/q1at4oUXXsh77R9AQUEBBx54IJ/73Oe4//77MU2T+++/Py9p/+Mf/2Djxo0MGTIkd96vWrWK//7v/2bYsGF5yaMnw4cPp7y8PG/nfnl5OaZp7rVz/x//+AdLly7N+3mfTCb54Q9/yO23385pp53G6NGjueyyy5g+fTo/+9nP8pbP+PHjWbJkCS0tLWzYsIFnn32WxsbGPXbeDxQSAA4wSikuu+wyHn/8cf7v//6PAw44YK/k6/t+ri9HPpxwwgm8++67LFmyJDdMmDCB8847jyVLlmAYRt7y6tTe3s7KlSupqanJW5rHHHPMNrfhWbZsGUOHDs1bHl09+OCDVFZWcuqpp+Y13UQiga53/3NiGEaupiPfCgoKqKmpobm5meeee44zzjhjj+RzwAEHUF1dzfz583PL2traeP311/dIn9Y9qTP4W758OS+++CJlZWV7Jd98nvvf+MY3+Pe//93tvK+trWX27Nk899xzecmjJ2vXrqWxsTFv575t20ycOHGvnfv3338/48ePz2tfTMh+phzH2WvnfjQapaKiguXLl7No0aI9dt4PFNIEvI9pb2/v9ivzk08+YcmSJZSWljJkyJDdTv/SSy/l4Ycf5q9//SuRSCTXjykajRIKhXY7fYCrr76aU045hSFDhhCLxXj44YdZsGBBXv9ARyKRbfotFhQUUFZWlrf+jN/73vc47bTTGDp0KOvXr2fOnDkYhsG5556bl/QBvvvd73L00Udz44038rWvfY033niDe++9l3vvvTdveXTyfZ8HH3yQGTNmYJr5PfVPO+00fvrTnzJkyBBGjRrF22+/ze23386FF16Y13yee+45lFIccsghrFixgtmzZzNy5Ehmzpy5y2nu7Jy78sor+Z//+R8OOuggDjjgAK699lpqa2s588wz85ZHU1MTq1evzt2XrzMwqK6u7nVN447yqKmp4Stf+QqLFy/mqaeewvO83LlfWlqKbdu7nUdZWRk//elPOf3006mpqWHz5s3cddddrFu3rk+3G9rZe7V14GpZFtXV1RxyyCF5yaO0tJQf//jHnH322VRXV7Ny5Uq+//3vc+CBBzJt2rS87cfs2bOZPn06n//85/nCF77As88+y9/+9jcWLFiQtzwg+4PlT3/6E7fddluv0+1LHlOmTGH27NmEQiGGDh3Kyy+/zP/7f/+P22+/PW95/OlPf6KiooIhQ4bw7rvvcsUVV3DmmWfm7UKTAatfr0EW23jppZcUsM0wY8aMvKTfU9qAevDBB/OSvlJKXXjhhWro0KHKtm1VUVGhTjjhBPX888/nLf3tyfdtYKZPn65qamqUbdtq0KBBavr06WrFihV5S7/T3/72N3X44YerQCCgRo4cqe69996856GUUs8995wC1NKlS/Oedltbm7riiivUkCFDVDAYVMOHD1c/+tGPVDqdzms+jz76qBo+fLiybVtVV1erSy+9VLW0tOxWmjs753zfV9dee62qqqpSgUBAnXDCCX1+D3eWx4MPPtjj63PmzMlLHp23l+lpeOmll/KSRzKZVF/+8pdVbW2tsm1b1dTUqNNPP1298cYbeX2vtrYrt4HZUR6JREKddNJJqqKiQlmWpYYOHaq++c1vqvr6+rzvx/33368OPPBAFQwG1ZgxY9QTTzyR9zz+93//V4VCoV0+T3aWx4YNG9QFF1ygamtrVTAYVIcccoi67bbb+nSLqZ3lcccdd6jBgwcry7LUkCFD1DXXXJP3vy0DkaaU9KIUQgghhBhIpA+gEEIIIcQAIwGgEEIIIcQAIwGgEEIIIcQAIwGgEEIIIcQAIwGgEEIIIcQAIwGgEEIIIcQAIwGgEEIIIcQAIwGgEEKIAeuOO+5g4cKFe207IfYVEgAKsQdomsYTTzzRb/kff/zxXHnllXs0j08//RRN01iyZMkezac/XH/99YwdO7a/i7GNvrzn+/Px6dSb43TBBRds97F9t912G4899hhHHnlkn/Ld1e2E2JfIs4CFEOIzoq6ujg0bNlBeXp7XdT+rvve973H55Zfv0rb/+te/eOihh1iwYAGBQGCPbyfEvkYCQCH2EY7jYFlWfxfjM29/fh8Nw6C6ujrv635WFRYWUlhYuEvbHnPMMb2qHVVK4Xkepmn2aTsh9nXSBCz2W8cffzzf+c53+P73v09paSnV1dVcf/313dZZvXo1Z5xxBoWFhRQVFfG1r32NhoaG3OudTUwPPPAAQ4YMobCwkP/6r//C8zxuueUWqqurqays5Kc//ek2+W/YsIFTTjmFUCjE8OHD+fOf/5x7rbN57tFHH2XKlCkEg0F+//vfA/DrX/+aQw89lGAwyMiRI/nVr361w/2Mx+Ocf/75FBYWUlNTw2233bbNOul0mu9973sMGjSIgoICJk2axIIFC3aYrqZp3H333dvdh6395je/obi4uNuyJ554Ak3TcvPvvPMOX/jCF4hEIhQVFTF+/HgWLVq0y2XY3vvo+z4/+clPGDx4MIFAgLFjx/Lss892S3vt2rWce+65lJaWUlBQwIQJE3j99de7rfPQQw8xbNgwotEo55xzDrFYLPea7/vMnTuXAw44gFAoxJgxY3b4/vzwhz9k0qRJ2ywfM2YMP/nJT3Jp7qjcWzfrNjc3c95551FRUUEoFOKggw7iwQcf7HHdBQsWoGka8+fPZ8KECYTDYY4++miWLl26y8enpaWFb33rW1RVVREMBjn88MN56qmncq//5S9/YdSoUQQCAYYNG7bNZ3PYsGHceOONXHjhhUQiEYYMGcK9997bbZ0dHaetm4A9z2PWrFkUFxdTVlbG97//fbZ+3P3Ojlvn+/TMM88wfvx4AoEA//znP3e63Y6OhRD7JCXEfmrKlCmqqKhIXX/99WrZsmXqt7/9rdI0TT3//PNKKaU8z1Njx45Vxx57rFq0aJF67bXX1Pjx49WUKVNyacyZM0cVFhaqr3zlK+r9999XTz75pLJtW02bNk1dfvnl6qOPPlIPPPCAAtRrr72W2w5QZWVl6r777lNLly5V11xzjTIMQ33wwQdKKaU++eQTBahhw4apv/zlL+rjjz9W69evV7/73e9UTU1Nbtlf/vIXVVpaqn7zm99sdz8vueQSNWTIEPXiiy+qf//73+o//uM/VCQSUVdccUVunYsvvlgdffTR6pVXXlErVqxQt956qwoEAmrZsmXbTbe3+/D2228rpZR68MEHVTQa7ZbG448/rrr+mRk1apT6+te/rj788EO1bNky9cc//lEtWbJkt8uw9ft4++23q6KiIvWHP/xBffTRR+r73/++siwrt7+xWEwNHz5cHXfcceof//iHWr58uXr00UfVq6++2u24n3XWWerdd99Vr7zyiqqurlY//OEPc2X7n//5HzVy5Ej17LPPqpUrV6oHH3xQBQIBtWDBgh735b333lOAWrFixTbLli9frpRSOy331u/5pZdeqsaOHavefPNN9cknn6gXXnhBPfnkkz2u+9JLLylATZo0SS1YsEC9//776rjjjlNHH330Lh0fz/PU5z73OTVq1Cj1/PPPq5UrV6q//e1v6u9//7tSSqlFixYpXdfVT37yE7V06VL14IMPqlAopB588MFcGkOHDlWlpaXqrrvuUsuXL1dz585Vuq6rjz76qNfHacyYMbn0br75ZlVSUqL+8pe/qA8++EBddNFFKhKJqDPOOKPXx63zfRo9erR6/vnn1YoVK1RjY+NOt9vRsRBiXyQBoNhvTZkyRR177LHdlk2cOFFdddVVSimlnn/+eWUYhlq9enXu9ffff18B6o033lBKZb9gwuGwamtry60zbdo0NWzYMOV5Xm7ZIYccoubOnZubB9S3v/3tbnlPmjRJXXLJJUqpLV/O8+bN67bOiBEj1MMPP9xt2Q033KAmT57c4z7GYjFl27b64x//mFvW2NioQqFQLgBctWqVMgxDrVu3rtu2J5xwgrr66qt7TLcv+9CXADASiewwmN3VMmz9PtbW1qqf/vSn3ZZNnDhR/dd//ZdSSqn//d//VZFIRDU2NvaYb0/Hffbs2WrSpElKKaVSqZQKh8O5QKTTRRddpM4999zt7s+YMWPUT37yk9z81VdfnUuzN+Xe+j0/7bTT1MyZM3vMa3sB4Isvvphb5+mnn1aASiaTSqm+HZ/nnntO6bquli5d2uPr//mf/6lOPPHEbstmz56tDjvssNz80KFD1de//vXcvO/7qrKyUt19991Kqd4dp64BYE1Njbrlllty847jqMGDB+cCwN4ct8736Yknnsi93pvtdnQshNgXSR9AsV8bPXp0t/mamho2btwIwIcffkhdXR11dXW51w877DCKi4v58MMPmThxIpBtpopEIrl1qqqqMAwDXde7LetMt9PkyZO3md+679CECRNy0/F4nJUrV3LRRRfxzW9+M7fcdV2i0WiP+7dy5UoymUy3psXS0lIOOeSQ3Py7776L53kcfPDB3bZNp9OUlZX1mG5f9qEvZs2axcUXX8xDDz3E1KlT+epXv8qIESN2uwxd38e2tjbWr1/PMccc022dY445hnfeeQeAJUuWMG7cOEpLS7eb79bHvetnZ8WKFSQSCU488cRu22QyGcaNG7fdNM877zweeOABrr32WpRS/OEPf2DWrFm9LvfWLrnkEs4++2wWL17MSSedxJlnnsnRRx+93fyh+zlRU1MDwMaNGxkyZEifjs+SJUsYPHjwNp+rTh9++CFnnHHGNvsyb948PM/DMIxtyqNpGtXV1bn3uTfHqVNraysbNmzodi6YpsmECRNyzcB9OW5dP1O92W5XjoUQ/UkCQLFf2/piAE3T8H1/t9PIR7oABQUFuen29nYA7rvvvm36inV+We6K9vZ2DMPgrbfe2iadXe1A3xNd17fpb+U4Trf566+/nv/8z//k6aef5plnnmHOnDk88sgjfPnLX96tvLu+j70RCoV2us6OjnHnsXr66acZNGhQt/V2dGXoueeey1VXXcXixYtJJpOsWbOG6dOn96nsXZ1yyimsWrWKv//977zwwguccMIJXHrppfzsZz/b7jZd96uzf2bnfvXl+PTmPeyNHb3P+cqjU1+OW0/n5o6225VjIUR/kotAxIB16KGHsmbNGtasWZNb9sEHH9DS0sJhhx222+m/9tpr28wfeuih212/qqqK2tpaPv74Yw488MBuwwEHHNDjNiNGjMCyrG4XLzQ3N7Ns2bLc/Lhx4/A8j40bN26T7s6uEu3LPlRUVBCLxYjH47llPdUWHnzwwXz3u9/l+eef56yzztppR/m+vo9FRUXU1tbyr3/9q9vyf/3rX7njOnr0aJYsWUJTU9MO896eww47jEAgwOrVq7d5T7vWKG9t8ODBTJkyhd///vf8/ve/58QTT6SysrLX5e5JRUUFM2bM4He/+x3z5s3b5iKKvurt8Rk9ejRr167t9lnr6tBDD+1xXw4++OBe/6Dpy3GKRqPU1NR0Oxdc1+Wtt97Kze/qcevtdvk+FkLsSVIDKAasqVOncsQRR3Deeecxb948XNflv/7rv5gyZUq35p9d9ac//YkJEyZw7LHH8vvf/5433niD+++/f4fb/PjHP+Y73/kO0WiUk08+mXQ6zaJFi2hubs41FXZVWFjIRRddxOzZsykrK6OyspIf/ehH3ZqnDz74YM477zzOP/98brvtNsaNG8emTZuYP38+o0eP5tRTT83LPkyaNIlwOMwPf/hDvvOd7/D666/zm9/8Jvd6Mplk9uzZfOUrX+GAAw5g7dq1vPnmm5x99tl5fx9nz57NnDlzGDFiBGPHjuXBBx9kyZIluSutzz33XG688UbOPPNM5s6dS01NDW+//Ta1tbXbNDn3JBKJ8L3vfY/vfve7+L7PscceS2trK//6178oKipixowZ2932vPPOY86cOWQyGX7+85/3qdxbu+666xg/fjyjRo0inU7z1FNP7TA43pG+Hp8pU6bw+c9/nrPPPpvbb7+dAw88kI8++ghN0zj55JP57//+byZOnMgNN9zA9OnTWbhwIXfeeedOr2rvqq/H6YorruCmm27ioIMOYuTIkdx+++20tLTkXt/V49ab7fJ5LITYK/q5D6IQe8yUKVO6XQmrlFJnnHGGmjFjRm5+1apV6vTTT1cFBQUqEomor371q6q+vj73+tadzJVSasaMGd2uKuwpL0Dddddd6sQTT1SBQEANGzZMPfroo7nXt+6g39Xvf/97NXbsWGXbtiopKVGf//zn1WOPPbbd/YzFYurrX/+6CofDqqqqSt1yyy3blCeTyajrrrtODRs2TFmWpWpqatSXv/xl9e9//3u76e7KPjz++OPqwAMPVKFQSP3Hf/yHuvfee3MXgaTTaXXOOeeouro6Zdu2qq2tVZdddlnuAoR8lUGp7BWq119/vRo0aJCyLEuNGTNGPfPMM93W+fTTT9XZZ5+tioqKVDgcVhMmTFCvv/66Uqrn4/7zn/9cDR06NDfv+76aN2+eOuSQQ5RlWaqiokJNmzZNvfzyy9vdH6WUam5uVoFAQIXDYRWLxfpU7q3394YbblCHHnqoCoVCqrS0VJ1xxhnq448/7nHdzosbmpubc+m9/fbbClCffPLJLh2fxsZGNXPmTFVWVqaCwaA6/PDD1VNPPZV7/c9//rM67LDDlGVZasiQIerWW2/ttv3QoUPVz3/+827LxowZo+bMmZOb78txchxHXXHFFaqoqEgVFxerWbNmqfPPP7/b+bqz49bT+9Sb7XZ0LITYF2lKbdVpRwghyPbFevzxx7f7GK2BUgYhhNgfSR9AIYQQQogBRgJAIYQQQogBRpqAhRBCCCEGGKkBFEIIIYQYYCQAFEIIIYQYYCQAFEIIIYQYYCQAFEIIIYQYYCQAFEIIIYQYYCQAFEIIIYQYYCQAFEIIIYQYYCQAFEIIIYQYYP5/aaOlDRxwC9AAAAAASUVORK5CYII=">
 


%% Cell type:markdown id: tags:



On remarque une chute à k=2, cette chute est due à l'introduction d'un nouveau label parmi les choix possibles. L'algorithme développé ne traite pas la situation où dans ses k plus proches voisins, deux labels apparaissent le même nombre de fois, et il choisit naturellement le plus petit des deux.



Pour réduire les erreurs lorsque deux labels apparaissent le même nombre de fois parmi les k plus proches voisins, l'algorithme ci-dessous choisira celui des deux dont la somme des distances est la plus petite.



%% Cell type:code id: tags:



``` python

def knn_predict2(dist, labels_train, k):

    resultat=[]

    for im in dist:

        dico={}

        kmax=np.argpartition(im, k)[:k]

        for indexe in kmax:

            if labels_train[indexe] in dico:

                dico[labels_train[indexe]][0]+=1

                dico[labels_train[indexe]][1]+=im[indexe]

            else:

                dico[labels_train[indexe]]=[1,im[indexe]]

        dico = sorted(dico.items(), key=lambda item: item[1][0], reverse=True)

        max_value = dico[0][1][0]

        dico = [item for item in dico if item[1][0] == max_value]

        if len(dico) > 1:

            dico = sorted(dico, key=lambda item: item[1][1])

        resultat.append(dico[0][0])

    return(resultat)

```



%% Cell type:markdown id: tags:



Pour comparer les deux méthodes, j'ai tracé les résultats des deux méthodes sur le même graphique en utilisant les mêmes ensembles d'entraînement et de test.



%% Cell type:code id: tags:



``` python

if __name__ == "__main__":

    nbr_knn = 20

    x = range(1, nbr_knn)

    #d, l = read_cifar_batch("data/cifar-10-batches-py/data_batch_1")

    #d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)

    #dist_matrice = distance_matrix(d_train, d_test)

    y1 = []

    y2 = []

    for knn in x:

        stat = 0

        res = knn_predict(dist_matrice, l_train, knn)

        res2 = knn_predict2(dist_matrice, l_train, knn)

        y1.append(np.sum(l_test == res) / len(l_test))

        y2.append(np.sum(l_test == res2) / len(l_test))

    plt.plot(x, y1, label=f'Précision knn')

    plt.plot(x, y2, label='Précision knn2')

    plt.xlabel('nombre de plus proche voisins concidérés')

    plt.ylabel('taux de bonne calification')

    plt.xticks(range(1, nbr_knn))

    plt.yticks(np.arange(0, 1.1, 0.1))

    plt.legend()

    plt.show()

```



%% Output



img 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">
 


%% Cell type:markdown id: tags:



On remarque que la nouvelle méthode améliore sensiblement la précédente et ne présente pas de chute à k=2.



### Résultat

Finalement, on constate que la méthode des k plus proches voisins a une efficacité d'environ 30% sur la base de données CIFAR-10, quel que soit le nombre de plus proches voisins choisis. Cela représente une amélioration par rapport à un choix aléatoire qui aurait un taux d'environ 10% compte tenu des 10 classes, mais reste relativement faible.



## Réseaux de Neurones Artificiels



Dans cette partie nous allons développé un réseau de neuronne pour la classification de la base de donnée CIFAR-10. Pour cela nous prenons la fonction d'activation $\sigma$ et les matrices de poids et de biais respectant les relations suivantes:



$$Z^{L+1}=W^{L+1}A^{L}+B^{L+1}$$



$$A^{L+1}=\sigma(Z^{L+1})$$



La fonction de coût donné C est la fonction de coup MSE :

La fonction de perte donné C est la fonction de coup MSE :



$$C = \frac{1}{N_{out}}\sum_{i=1}^{N_{out}} (\hat{y_i} - y_i)^2$$



### 1



Pour entraîner notre modèle nous devons effectuer la descente de gradiant, qui consiste à trouver la relation qui permet d'avoir l'impacte des matrices de poid et de biais sur la fonction coût.

Pour entraîner notre modèle nous devons effectuer la descente de gradiant, qui consiste à trouver la relation qui permet d'avoir l'impacte des matrices de poid et de biais sur la fonction de perte.



$$\sigma(x)=\frac{1}{1+e^{-x}} $$



$$\Rightarrow \sigma'(x)=\frac{-e^{-x}}{-(1+e^{-x})^2} $$



$$\Rightarrow \sigma'(x)=\frac{1}{1+e^{-x}}(\frac{1+e^{-x}-1}{1+e^{-x}}) $$



$$\Rightarrow \sigma'(x)=\sigma(\frac{1+e^{-x}}{1+e^{-x}}-\frac{1}{1+e^{-x}}) $$



$$\Rightarrow \sigma'(x)=\sigma(1-\sigma)$$



### 2



$$\frac{\partial C}{\partial A^{(2)}} ? $$



$$\Rightarrow \frac{\partial C}{\partial a_i^{(2)}}=\frac{1}{N_{out}}\sum_{i=1}^{N_{out}} (a_i^{(2)} - y_i)^2 $$



$$\Rightarrow \frac{\partial C}{\partial a_i^{(2)}}=\frac{2}{N_{out}} (a_i^{(2)} - y_i) $$



$$\Rightarrow \frac{\partial C}{\partial A^{(2)}}=\frac{2}{N_{out}} (A^{(2)} - Y)$$



### 3



$$\frac{\partial C}{\partial Z^{(2)}}=\frac{\partial C}{\partial A^{(2)}}\frac{\partial A^{(2)}}{\partial Z^{(2)}} $$



$$A^{(2)}=\sigma (Z^{(2)}) $$



$$\frac{\partial A^{(2)}}{\partial Z^{(2)}}=A^{(2)}(1-A^{(2)}) $$



$$\Rightarrow \frac{\partial C}{\partial Z^{(2)}}=\frac{\partial C}{\partial A^{(2)}}[A^{(2)}(1-A^{(2)})]$$



### 4



$$\frac{\partial C}{\partial W^{(2)}}=\frac{\partial C}{\partial Z^{(2)}}\frac{\partial Z^{(2)}}{\partial W^{(2)}} $$



$$Z^{(2)}=W^{(2)}A^{(1)}+B^{(2)} $$



$$\frac{\partial Z^{(2)}}{\partial W^{(2)}}=A^{(1)} $$



$$\Rightarrow \frac{\partial C}{\partial W^{(2)}}=\frac{\partial C}{\partial Z^{(2)}}A^{(1)}$$



### 5



$$\frac{\partial C}{\partial B^{(2)}}=\frac{\partial C}{\partial Z^{(2)}}\frac{\partial Z^{(2)}}{\partial B^{(2)}} $$



$$Z^{(2)}=W^{(2)}A^{(1)}+B^{(2)} $$



$$\frac{\partial Z^{(2)}}{\partial B^{(2)}}=1 $$



$$\Rightarrow \frac{\partial C}{\partial B^{(2)}}=\frac{\partial C}{\partial Z^{(2)}}$$



### 6



$$\frac{\partial C}{\partial A^{(1)}}=\frac{\partial C}{\partial Z^{(2)}}\frac{\partial Z^{(2)}}{\partial A^{(1)}} $$



$$Z^{(2)}=W^{(2)}A^{(1)}+B^{(2)} $$



$$\frac{\partial Z^{(2)}}{\partial A^{(1)}}=W^{(2)} $$



$$\Rightarrow \frac{\partial C}{\partial A^{(1)}}=\frac{\partial C}{\partial Z^{(2)}}W^{(2)}$$



### 7



$$\frac{\partial C}{\partial Z^{(1)}}=\frac{\partial C}{\partial A^{(1)}}\frac{\partial A^{(1)}}{\partial Z^{(1)}} $$



$$A^{(1)}=\sigma (Z^{(1)}) $$



$$\frac{\partial A^{(1)}}{\partial Z^{(1)}}=A^{(1)}(1-A^{(1)}) $$



$$\Rightarrow \frac{\partial C}{\partial Z^{(1)}}=\frac{\partial C}{\partial A^{(1)}}[A^{(1)}(1-A^{(1)})]$$



### 8



$$\frac{\partial C}{\partial W^{(1)}}=\frac{\partial C}{\partial Z^{(1)}}\frac{\partial Z^{(1)}}{\partial W^{(1)}} $$



$$Z^{(1)}=W^{(1)}A^{(0)}+B^{(1)} $$



$$\frac{\partial Z^{(1)}}{\partial W^{(1)}}=A^{(0)} $$



$$\Rightarrow \frac{\partial C}{\partial W^{(1)}}=\frac{\partial C}{\partial Z^{(1)}}A^{(0)}$$



### 9



$$\frac{\partial C}{\partial B^{(1)}}=\frac{\partial C}{\partial Z^{(1)}}\frac{\partial Z^{(1)}}{\partial B^{(1)}} $$



$$Z^{(1)}=W^{(1)}A^{(0)}+B^{(1)} $$



$$\frac{\partial Z^{(1)}}{\partial B^{(1)}}=1 $$



$$\Rightarrow \frac{\partial C}{\partial B^{(1)}}=\frac{\partial C}{\partial Z^{(1)}}$$



%% Cell type:markdown id: tags:



### 10



La fonction de coût MSE n'est pas adapté à la classification, pour la suite nous prendrons donc la fonction loss cross-entropy qui prend la matrice target $Y$ et la matrice de prbabilité de prédiction $\hat{Y}$

La fonction de perte MSE n'est pas adapté à la classification, pour la suite nous prendrons donc la fonction loss cross-entropy qui prend la matrice target $Y$ et la matrice de prbabilité de prédiction $\hat{Y}$



$$loss=\frac{1}{N_{out}}\sum_{i=1}^{N_{out}}-(y_i*log(\hat{y_i}))$$



Pour calculer cette fonction de perte il faut avoir $\hat{y}$ qui est une prédiction sous forme de probabilité, donc que la somme des probabilité soit 1 et que chaque probabilité soit comprise dans $[0,1]$. Pour cela on remplace la signoïde $A^{(2)}=\sigma(Z^{(2)})$ par la fonction softmax qui donne $\hat{Y}=A^{(2)}=softmax(Z^{(2)})$.



$$softmax(x_i)=\frac{e^{x_i}}{\sum_{j=1}^{n}e^{x_j}}$$



%% Cell type:code id: tags:



``` python

def learning_methode(k,dk,learning_rate):

    k2=k-learning_rate*dk

    return(k2)



def learn_once_mse(w1,b1,w2,b2,data,targets,learning_rate):

    a0 = data # the data are the input of the first layer

    z1 = np.matmul(a0, w1) + b1  # input of the hidden layer

    a1 = 1 / (1 + np.exp(-z1))  # output of the hidden layer (sigmoid activation function)

    z2 = np.matmul(a1, w2) + b2  # input of the output layer

    a2 = 1 / (1 + np.exp(-z2))  # output of the output layer (sigmoid activation function)

    predictions = a2  # the predicted values are the outputs of the output layer



    # Compute loss (MSE)

    loss = np.mean(np.square(predictions - targets))



    dc_da2=(2/data.shape[0])*(predictions-targets)

    dc_dz2=dc_da2*(a2*(1-a2))

    dc_dw2=np.dot(np.transpose(a1), dc_dz2)

    dc_db2=np.dot(np.ones((1,dc_dz2.shape[0])),dc_dz2)

    dc_da1=np.dot(dc_dz2,np.transpose(w2))

    dc_dz1=dc_da1*(a1*(1-a1))

    dc_dw1=np.dot(np.transpose(a0), dc_dz1)

    dc_db1=np.dot(np.ones((1,dc_dz1.shape[0])),dc_dz1)



    w1=learning_methode(w1,dc_dw1,learning_rate)

    b1=learning_methode(b1,dc_db1,learning_rate)

    w2=learning_methode(w2,dc_dw2,learning_rate)

    b2=learning_methode(b2,dc_db2,learning_rate)



    # Compute loss (MSE)



    return(w1,b1,w2,b2,loss)

```



%% Cell type:markdown id: tags:



### 11



La fonction one_hot va passer le vecteur label en matrice de 1 et de 0 qui servira de target à la matrice de prediction de probabilité rendu par softmax.



%% Cell type:code id: tags:



``` python

def one_hot(label):

    nbr_classe=9

    mat=np.zeros((len(label),nbr_classe))

    for label_indexe,label_im, in enumerate(label):

        mat[label_indexe,label_im-1]=1

    return(mat)

```



%% Cell type:markdown id: tags:



### 12



%% Cell type:code id: tags:



``` python

def softmax(y):

    y=np.exp(y)

    v=np.sum(y, axis=1, keepdims=True)

    return(y / v)



def learn_once_cross_entropy(w1,b1,w2,b2,data,labels_train,learning_rate):

    targets = one_hot(labels_train)

    a0 = data

    z1 = np.dot(a0, w1) + b1

    a1 = 1 / (1 + np.exp(-z1))

    z2 = np.dot(a1, w2) + b2

    softa2=softmax(z2)

    predictions=softa2



    loss=np.mean(np.sum(-targets*np.log(predictions),axis=1))



    dc_dz2=(predictions-targets)/data.shape[0]

    dc_dw2=np.dot(np.transpose(a1), dc_dz2)

    dc_db2=np.sum(dc_dz2, axis=0, keepdims=True)



    dc_da1=np.dot(dc_dz2,np.transpose(w2))

    dc_dz1=dc_da1*(1-a1)*a1

    dc_dw1=np.dot(np.transpose(a0), dc_dz1)

    dc_db1=np.sum(dc_dz1, axis=0, keepdims=True)



    w1=learning_methode(w1,dc_dw1,learning_rate)

    b1=learning_methode(b1,dc_db1,learning_rate)

    w2=learning_methode(w2,dc_dw2,learning_rate)

    b2=learning_methode(b2,dc_db2,learning_rate)



    return(w1,b1,w2,b2,loss)

```



%% Cell type:markdown id: tags:



### 13



La fonction accuracy va mesurer le taux de réussite du model, elle sera utile pour mesurer l'efficacité du modèle sur une base de test et pour suivre l'évolution du modèle en fonction des epoques d'entraînement (et donc vérifier que le modèle s'améliore)



%% Cell type:code id: tags:



``` python

def accuracy(w1,b1,w2,b2,data,labels):

    a0 = data

    z1 = np.dot(a0, w1) + b1

    a1 = 1 / (1 + np.exp(-z1))

    z2 = np.dot(a1, w2) + b2

    softa2=softmax(z2)

    predictions = softa2

    prediction_2 = np.empty(predictions.shape[0], dtype=int)

    for i, ligne in enumerate(predictions):

        prediction_2[i] = np.argmax(ligne)+1

    indices_egalite = np.where(prediction_2 == labels)[0]

    nombre_indices = len(indices_egalite)

    return(nombre_indices/len(labels))



def train_mlp_cross_entropy(w1,b1,w2,b2,d_train,labels_train,learning_rate,num_epoch):

    train_accuracies,loss_evo=[],[]

    for k in range(num_epoch):

        w1,b1,w2,b2,loss=learn_once_cross_entropy(w1,b1,w2,b2,d_train,labels_train,learning_rate)

        t=accuracy(w1,b1,w2,b2,d_train,labels_train)

        train_accuracies.append(t)

        loss_evo.append(loss)

    return (w1,b1,w2,b2,train_accuracies,loss_evo)

```



%% Cell type:markdown id: tags:



### 14



%% Cell type:code id: tags:



``` python

def test_mlp(w1,b1,w2,b2,d_test,labels_test):

    test_accuracy=accuracy(w1,b1,w2,b2,d_test,labels_test)

    return(test_accuracy)

```



%% Cell type:markdown id: tags:



### 15



%% Cell type:code id: tags:



``` python

def run_mlp_training(data_train, labels_train, data_test, labels_test,d_h,learning_rate,num_epoch):

    d_in = data_train.shape[1]  # input dimension

    d_out = max(labels_train)  # output dimension (number of neurons of the output layer)



    w1 = np.random.randn(d_in, d_h)  # first layer weights

    b1 = np.zeros((1, d_h))  # first layer biaises

    w2 = np.random.randn(d_h, d_out)  # second layer weights

    b2 = np.zeros((1, d_out))  # second layer biaises



    w1,b1,w2,b2,accuratie_train,loss=train_mlp_cross_entropy(w1,b1,w2,b2,data_train, labels_train,learning_rate,num_epoch)

    test_accuracy=test_mlp(w1,b1,w2,b2,data_test, labels_test)

    return(accuratie_train,loss,test_accuracy)

```



%% Cell type:markdown id: tags:



### 16



%% Cell type:code id: tags:



``` python

if __name__ == "__main__":

    d, l = read_cifar_batch("data/cifar-10-batches-py/data_batch_1")

    num_epoch=1000

    dh=64

    learning_rate=0.1

    d_train, l_train, d_test, l_test = split_dataset(d, l, 0.9)



    train_accuracy,loss,test_accuracy=run_mlp_training(d_train, l_train, d_test, l_test,dh,learning_rate,num_epoch)

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))



    ax1.plot(range(num_epoch),loss,label="cross-entropy")

    ax1.set_xlabel('epoque')

    ax1.set_ylabel('loss')

    ax1.set_title('evolution de la fonction loss par epoque')

    ax1.legend()



    ax2.plot(range(num_epoch),train_accuracy,label="cross-entropy")

    ax2.set_xlabel('epoque')

    ax2.set_ylabel('accuracy')

    ax2.set_title('evolution de la accuracy')

    ax2.legend()

    plt.tight_layout()

    plt.show()



```



%% Output



    C:\Users\Utilisateur\AppData\Local\Temp\ipykernel_26032\2793120310.py:10: RuntimeWarning: overflow encountered in exp

      a1 = 1 / (1 + np.exp(-z1))

    C:\Users\Utilisateur\AppData\Local\Temp\ipykernel_26032\1996586622.py:4: RuntimeWarning: overflow encountered in exp

      a1 = 1 / (1 + np.exp(-z1))



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">
 


%% Cell type:markdown id: tags:



On remarque que la fonction de perte de perte tend vers 2. Cette limite peut être un minimum local, en effet le modèle part d'une matrice généré aléatoirement et tend vers le minimum local le plus proche, ainsi avec d'autres matrices de poid et de biais initiale nous pourions potentiellement arrivé à un meilleur modèle. De plus il est possible le le learning raite soit trop grand et que 2 ne soit pas réellement le minimum mais la limite vers laquel notre nodèle tend car il tourne autour du minimum avec un pas trop grand pour l'atteindre.



## Conclusion



On a vue dans ce BE deux méthodes de classification : la méthode par k-plus proches voisins et par réseau de neurone. Chacun de ces modèle pourrait être améliorer pour augmanter leurs taux de réussite.

- k plus proches voisins : on peut choisir une autre métrique de distance qui serait plus efficace ou ajouter une étapes de prétraitement pour

chatgpt.py

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import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import OneHotEncoder

from sklearn.metrics import accuracy_score

from read_cifar import read_cifar_batch, split_dataset

import matplotlib.pyplot as plt



# Charger CIFAR-10 depuis votre source de données

X,y = read_cifar_batch("data/cifar-10-batches-py/data_batch_1")



# Diviser les données en ensembles d'entraînement et de test

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)



# Prétraitement des données

# Vous devrez redimensionner les images, les normaliser, etc.



# Définir l'architecture du réseau de neurones

input_size = 32 * 32 * 3  # 32x32 pixels et 3 canaux (RGB)

hidden_size = 64  # Nombre d'unités dans la couche cachée

output_size = 10  # 10 classes dans CIFAR-10



# Initialiser les poids et les biais

np.random.seed(0)

weights_input_hidden = np.random.randn(input_size, hidden_size)

bias_input_hidden = np.zeros((1, hidden_size))

weights_hidden_output = np.random.randn(hidden_size, output_size)

bias_hidden_output = np.zeros((1, output_size))



# Hyperparamètres

learning_rate = 0.1

num_epochs = 100

y_print,x_print,y2_print=[],[],[]

# Entraînement du modèle

for epoch in range(num_epochs):

    # Forward pass

    hidden_input = np.dot(X_train, weights_input_hidden) + bias_input_hidden

    hidden_output = 1 / (1 + np.exp(-hidden_input))  # Fonction d'activation (sigmoid)

    output_layer = np.dot(hidden_output, weights_hidden_output) + bias_hidden_output



    # Calcul softmax

    exp_scores = np.exp(output_layer)

    probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)



    # Calcul de la perte (cross-entropy)

    num_examples = len(X_train)

    corect_logprobs = -np.log(probs[range(num_examples), y_train])

    data_loss = np.sum(corect_logprobs) / num_examples



    # Calcul du gradient

    dprobs = probs

    dprobs[range(num_examples), y_train] -= 1

    dprobs /= num_examples



    dweights_hidden_output = np.dot(hidden_output.T, dprobs)

    dbias_hidden_output = np.sum(dprobs, axis=0, keepdims=True)



    dhidden = np.dot(dprobs, weights_hidden_output.T)

    dhidden_hidden = dhidden * (1 - hidden_output) * hidden_output

    dweights_input_hidden = np.dot(X_train.T, dhidden_hidden)

    dbias_input_hidden = np.sum(dhidden_hidden, axis=0)



    # Mise à jour des poids et des biais

    weights_input_hidden -= learning_rate * dweights_input_hidden

    bias_input_hidden -= learning_rate * dbias_input_hidden

    weights_hidden_output -= learning_rate * dweights_hidden_output

    bias_hidden_output -= learning_rate * dbias_hidden_output



    x_print.append(epoch)

    y_print.append(data_loss)

    predicted_class = np.argmax(output_layer, axis=1)

    y2_print.append(accuracy_score(y_train, predicted_class))

    # Affichage du loss à chaque époque (pour le suivi)

    if (epoch + 1) % 100 == 0:

        print(f'Époque {epoch + 1}: Loss = {data_loss:.4f}')



# Évaluation du modèle

hidden_input = np.dot(X_test, weights_input_hidden) + bias_input_hidden

hidden_output = 1 / (1 + np.exp(-hidden_input))

output_layer = np.dot(hidden_output, weights_hidden_output) + bias_hidden_output

predicted_class = np.argmax(output_layer, axis=1)

accuracy = accuracy_score(y_test, predicted_class)

print(f'Précision sur l\'ensemble de test: {accuracy:.4f}')



fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))



ax1.plot(x_print,y_print)

ax1.set_xlabel('epoque')

ax1.set_ylabel('loss')

ax1.set_title('evolution de la fonction loss par epoque')

ax1.legend()



ax2.plot(x_print,y2_print)

ax2.set_xlabel('epoque')

ax2.set_ylabel('accuracy')

ax2.set_title('evolution de la accuracy')

ax2.legend()

plt.tight_layout()

plt.show()
 
 No newline at end of file

test.py

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Original line number Diff line number Diff line 
import numpy as np



# label=np.array([1,2,2,3,3])

# dist=np.array([[10,25,10,42,3],[75,63,87,64,1]])

# for im in dist:

#     dico={}

#     kmax=np.argpartition(im, 3)[:3]

#     for indexe in kmax:

#         if label[indexe] in dico:

#             dico[label[indexe]][0]+=1

#             dico[label[indexe]][1]+=im[indexe]

#         else:

#             dico[label[indexe]]=[1,im[indexe]]

#     dico = sorted(dico.items(), key=lambda item: item[1][0], reverse=True)

    

#     max_value = dico[0][1][0]

#     dico = [item for item in dico if item[1][0] == max_value]

#     print(dico)

#     if len(dico) > 1:

#         filtered_dict = sorted(dico, key=lambda item: item[1][1])

#     print(dico[0][0])

def one_hot(label):

    nbr_classe=9

    mat=np.zeros((len(label),nbr_classe))

    for label_indexe,label_im, in enumerate(label):

        mat[label_indexe,label_im-1]=1

    return(mat)



mat=np.array([1,8,6,4,7,8,5,2,4,6,4])

print(one_hot(mat))

# mat_exp=np.exp(mat)

# v=np.sum(mat_exp,axis=1)

# print(v)

# mat_exp_norm=mat_exp/v[:, np.newaxis]



# vrai=np.array([[0,0,0,1],[1,0,0,0],[0,0,1,0]])

# print(-np.log(np.max(mat_exp_norm*vrai,axis=1)))

# L=np.mean(-np.log(np.max(vrai*mat_exp_norm,axis=1)))

# print(L)
 
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