GAN_MNIST_Word_to_Number_generation.ipynb

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  "metadata": {
    "colab": {
      "name": "GAN_MNIST_Word_to_Number_generation.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "X42vzR3g0NPP"
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      "toc_visible": true,
      "include_colab_link": true
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    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GiridharaSPK/Advanced-Analysis-and-Design-of-Algorithms/blob/master/GAN_MNIST_Word_to_Number_generation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wOCmwH43uDb"
      },
      "source": [
        "import os\n",
        "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "from tqdm import tqdm_notebook\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXanfBu53uDo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c2f6aa45-ddc9-46ac-ef85-93fbf8a7ba21"
      },
      "source": [
        "from keras.layers import Input\n",
        "from keras.models import Model, Sequential\n",
        "from keras.layers.core import Reshape, Dense, Dropout, Flatten\n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "from keras.layers.convolutional import Conv2D, UpSampling2D\n",
        "from keras.datasets import mnist\n",
        "from keras.optimizers import Adam\n",
        "from keras import backend as K\n",
        "from keras import initializers\n",
        "\n",
        "K.set_image_dim_ordering('th')\n",
        "\n",
        "# Deterministic output.\n",
        "# Tired of seeing the same results every time? Remove the line below.\n",
        "\n",
        "\n",
        "# np.random.seed(1000)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzVT9a6vHBzg"
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "\n",
        "american_number_system = {\n",
        "    'zero': 0,\n",
        "    'one': 1,\n",
        "    'two': 2,\n",
        "    'three': 3,\n",
        "    'four': 4,\n",
        "    'five': 5,\n",
        "    'six': 6,\n",
        "    'seven': 7,\n",
        "    'eight': 8,\n",
        "    'nine': 9,\n",
        "    'ten': 10,\n",
        "    'eleven': 11,\n",
        "    'twelve': 12,\n",
        "    'thirteen': 13,\n",
        "    'fourteen': 14,\n",
        "    'fifteen': 15,\n",
        "    'sixteen': 16,\n",
        "    'seventeen': 17,\n",
        "    'eighteen': 18,\n",
        "    'nineteen': 19,\n",
        "    'twenty': 20,\n",
        "    'thirty': 30,\n",
        "    'forty': 40,\n",
        "    'fifty': 50,\n",
        "    'sixty': 60,\n",
        "    'seventy': 70,\n",
        "    'eighty': 80,\n",
        "    'ninety': 90,\n",
        "    'hundred': 100,\n",
        "    'thousand': 1000,\n",
        "    'million': 1000000,\n",
        "    'billion': 1000000000,\n",
        "    'point': '.'\n",
        "}\n",
        "\n",
        "decimal_words = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']\n",
        "\n",
        "\n",
        "\n",
        "def number_formation(number_words):\n",
        "    numbers = []\n",
        "    for number_word in number_words:\n",
        "        numbers.append(american_number_system[number_word])\n",
        "    if len(numbers) == 4:\n",
        "        return (numbers[0] * numbers[1]) + numbers[2] + numbers[3]\n",
        "    elif len(numbers) == 3:\n",
        "        return numbers[0] * numbers[1] + numbers[2]\n",
        "    elif len(numbers) == 2:\n",
        "        if 100 in numbers:\n",
        "            return numbers[0] * numbers[1]\n",
        "        else:\n",
        "            return numbers[0] + numbers[1]\n",
        "    else:\n",
        "        return numbers[0]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def get_decimal_sum(decimal_digit_words):\n",
        "    decimal_number_str = []\n",
        "    for dec_word in decimal_digit_words:\n",
        "        if(dec_word not in decimal_words):\n",
        "            return 0\n",
        "        else:\n",
        "            decimal_number_str.append(american_number_system[dec_word])\n",
        "    final_decimal_string = '0.' + ''.join(map(str,decimal_number_str))\n",
        "    return float(final_decimal_string)\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "function to return integer for an input `number_sentence` string\n",
        "input: string\n",
        "output: int or double or None\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "def word_to_num(number_sentence):\n",
        "    if type(number_sentence) is not str:\n",
        "        raise ValueError(\"Type of input is not string! Please enter a valid number word (eg. \\'two million twenty three thousand and forty nine\\')\")\n",
        "\n",
        "    number_sentence = number_sentence.replace('-', ' ')\n",
        "    number_sentence = number_sentence.lower()  # converting input to lowercase\n",
        "\n",
        "    if(number_sentence.isdigit()):  # return the number if user enters a number string\n",
        "        return int(number_sentence)\n",
        "\n",
        "    split_words = number_sentence.strip().split()  # strip extra spaces and split sentence into words\n",
        "\n",
        "    clean_numbers = []\n",
        "    clean_decimal_numbers = []\n",
        "\n",
        "    # removing and, & etc.\n",
        "    for word in split_words:\n",
        "        if word in american_number_system:\n",
        "            clean_numbers.append(word)\n",
        "\n",
        "    # Error message if the user enters invalid input!\n",
        "    if len(clean_numbers) == 0:\n",
        "        raise ValueError(\"No valid number words found! Please enter a valid number word (eg. two million twenty three thousand and forty nine)\") \n",
        "\n",
        "    # Error if user enters million,billion, thousand or decimal point twice\n",
        "    if clean_numbers.count('thousand') > 1 or clean_numbers.count('million') > 1 or clean_numbers.count('billion') > 1 or clean_numbers.count('point')> 1:\n",
        "        raise ValueError(\"Redundant number word! Please enter a valid number word (eg. two million twenty three thousand and forty nine)\")\n",
        "\n",
        "    # separate decimal part of number (if exists)\n",
        "    if clean_numbers.count('point') == 1:\n",
        "        clean_decimal_numbers = clean_numbers[clean_numbers.index('point')+1:]\n",
        "        clean_numbers = clean_numbers[:clean_numbers.index('point')]\n",
        "\n",
        "    billion_index = clean_numbers.index('billion') if 'billion' in clean_numbers else -1\n",
        "    million_index = clean_numbers.index('million') if 'million' in clean_numbers else -1\n",
        "    thousand_index = clean_numbers.index('thousand') if 'thousand' in clean_numbers else -1\n",
        "\n",
        "    if (thousand_index > -1 and (thousand_index < million_index or thousand_index < billion_index)) or (million_index>-1 and million_index < billion_index):\n",
        "        raise ValueError(\"Malformed number! Please enter a valid number word (eg. two million twenty three thousand and forty nine)\")\n",
        "\n",
        "    total_sum = 0  # storing the number to be returned\n",
        "\n",
        "    if len(clean_numbers) > 0:\n",
        "        # hack for now, better way TODO\n",
        "        if len(clean_numbers) == 1:\n",
        "                total_sum += american_number_system[clean_numbers[0]]\n",
        "\n",
        "        else:\n",
        "            if billion_index > -1:\n",
        "                billion_multiplier = number_formation(clean_numbers[0:billion_index])\n",
        "                total_sum += billion_multiplier * 1000000000\n",
        "\n",
        "            if million_index > -1:\n",
        "                if billion_index > -1:\n",
        "                    million_multiplier = number_formation(clean_numbers[billion_index+1:million_index])\n",
        "                else:\n",
        "                    million_multiplier = number_formation(clean_numbers[0:million_index])\n",
        "                total_sum += million_multiplier * 1000000\n",
        "\n",
        "            if thousand_index > -1:\n",
        "                if million_index > -1:\n",
        "                    thousand_multiplier = number_formation(clean_numbers[million_index+1:thousand_index])\n",
        "                elif billion_index > -1 and million_index == -1:\n",
        "                    thousand_multiplier = number_formation(clean_numbers[billion_index+1:thousand_index])\n",
        "                else:\n",
        "                    thousand_multiplier = number_formation(clean_numbers[0:thousand_index])\n",
        "                total_sum += thousand_multiplier * 1000\n",
        "\n",
        "            if thousand_index > -1 and thousand_index != len(clean_numbers)-1:\n",
        "                hundreds = number_formation(clean_numbers[thousand_index+1:])\n",
        "            elif million_index > -1 and million_index != len(clean_numbers)-1:\n",
        "                hundreds = number_formation(clean_numbers[million_index+1:])\n",
        "            elif billion_index > -1 and billion_index != len(clean_numbers)-1:\n",
        "                hundreds = number_formation(clean_numbers[billion_index+1:])\n",
        "            elif thousand_index == -1 and million_index == -1 and billion_index == -1:\n",
        "                hundreds = number_formation(clean_numbers)\n",
        "            else:\n",
        "                hundreds = 0\n",
        "            total_sum += hundreds\n",
        "\n",
        "    # adding decimal part to total_sum (if exists)\n",
        "    if len(clean_decimal_numbers) > 0:\n",
        "        decimal_sum = get_decimal_sum(clean_decimal_numbers)\n",
        "        total_sum += decimal_sum\n",
        "\n",
        "    return total_sum"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWRR1o3V3uD3"
      },
      "source": [
        "# The results are a little better when the dimensionality of the random vector is only 10.\n",
        "# The dimensionality has been left at 100 for consistency with other GAN implementations.\n",
        "inputDim = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F7IBOYb03uEA"
      },
      "source": [
        "# Load MNIST data\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "X_train = (X_train.astype(np.float32) - 127.5)/127.5\n",
        "X_train = X_train[:, np.newaxis, :, :]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0G2m39r3uEW"
      },
      "source": [
        "# Optimizer\n",
        "adam = Adam(lr=0.0002, beta_1=0.5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gLiW4uu3uEr"
      },
      "source": [
        "# Generator\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_yoYfJ33uEv"
      },
      "source": [
        "generator = Sequential()\n",
        "#input: 10 dimension vector\n",
        "#output: 128*7*7 vector\n",
        "#initial weights: random weights with standard deviation 0.02 \n",
        "generator.add(Dense(128*7*7, input_dim=inputDim, kernel_initializer=initializers.RandomNormal(stddev=0.02)))  \n",
        "generator.add(LeakyReLU(0.2))       # giving low priority to inactive nodes\n",
        "generator.add(Reshape((128, 7, 7))) \n",
        "generator.add(UpSampling2D(size=(2, 2))) # increasing the size of each image with factor two\n",
        "generator.add(Conv2D(64, kernel_size=(5, 5), padding='same'))\n",
        "generator.add(LeakyReLU(0.2))\n",
        "generator.add(UpSampling2D(size=(2, 2)))\n",
        "generator.add(Conv2D(1, kernel_size=(5, 5), padding='same', activation='tanh'))\n",
        "generator.compile(loss='mse', optimizer=adam) #mean square error\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nC7I8jTH3uE4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "1e7cc43d-201b-44e5-cac1-617c129c8d4b"
      },
      "source": [
        "generator.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 6272)              68992     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 128, 7, 7)         0         \n",
            "_________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2 (None, 128, 14, 14)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 64, 14, 14)        204864    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)    (None, 64, 14, 14)        0         \n",
            "_________________________________________________________________\n",
            "up_sampling2d_2 (UpSampling2 (None, 64, 28, 28)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 1, 28, 28)         1601      \n",
            "=================================================================\n",
            "Total params: 275,457\n",
            "Trainable params: 275,457\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0RoneNdp3uFD"
      },
      "source": [
        "# Discriminator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7T730W4k3uFG"
      },
      "source": [
        "discriminator = Sequential()\n",
        "# giving an image for input\n",
        "discriminator.add(Conv2D(64, kernel_size=(5, 5), strides=(2, 2), padding='same', input_shape=(1, 28, 28), kernel_initializer=initializers.RandomNormal(stddev=0.02)))\n",
        "discriminator.add(LeakyReLU(0.2))\n",
        "discriminator.add(Dropout(0.3)) #drop 30% nodes in each layer\n",
        "discriminator.add(Conv2D(128, kernel_size=(5, 5), strides=(2, 2), padding='same'))\n",
        "discriminator.add(LeakyReLU(0.2))\n",
        "discriminator.add(Dropout(0.3))\n",
        "discriminator.add(Flatten()) # converts any input to 1 dimensional array\n",
        "discriminator.add(Dense(inputDim + 1, activation='softmax'))           # why softmax \n",
        "discriminator.compile(loss='categorical_crossentropy', optimizer=adam) # cross entropy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k564SdnZYcRa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "7d29821a-60b4-4e48-bb26-5699a6be67da"
      },
      "source": [
        "discriminator.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 64, 14, 14)        1664      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)    (None, 64, 14, 14)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64, 14, 14)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 128, 7, 7)         204928    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_4 (LeakyReLU)    (None, 128, 7, 7)         0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 128, 7, 7)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 11)                69003     \n",
            "=================================================================\n",
            "Total params: 275,595\n",
            "Trainable params: 275,595\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EN07QrVf3uFN"
      },
      "source": [
        "# Combined network\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYb4TMQN3uFQ"
      },
      "source": [
        "discriminator.trainable = False\n",
        "ganInput = Input(shape=(inputDim,))\n",
        "x = generator(ganInput)\n",
        "ganOutput = discriminator(x)\n",
        "gan = Model(inputs=ganInput, outputs=ganOutput)\n",
        "gan.compile(loss='categorical_crossentropy', optimizer=adam)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJcKV-cv3uFa"
      },
      "source": [
        "import os\n",
        "image_folder = 'images_numb_Gan_mse_sftmx_cc_cc'\n",
        "\n",
        "if not os.path.exists(image_folder):\n",
        "    os.mkdir(image_folder)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncAvmp3c3uFp"
      },
      "source": [
        "# import os\n",
        "model_folder = 'model_numb_Gan_mse_sftmx_cc_cc'\n",
        "\n",
        "if not os.path.exists(model_folder):\n",
        "    os.mkdir(model_folder)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cHGoZWQ3uFy"
      },
      "source": [
        "dLosses = []\n",
        "gLosses = []\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0bt57Zi3uF6"
      },
      "source": [
        "# Plot the loss from each batch\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWqyvFDv3uF9"
      },
      "source": [
        "def plotLoss(epoch):\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    plt.plot(dLosses, label='Discriminitive loss')\n",
        "    plt.plot(gLosses, label='Generative loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    print ('-'*15, 'Epoch %d' % epoch, '-'*15)\n",
        "    plt.savefig('{}/dcgan_loss_epoch_%d.png'.format(image_folder) % epoch)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vM4b8fHI3uGG"
      },
      "source": [
        "# Create a wall of generated MNIST images\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDKSaDAz3uGK"
      },
      "source": [
        "def plotGeneratedImages(epoch, examples=100, dim=(10, 10), figsize=(10, 10)):\n",
        "  \n",
        "#     noise = np.random.normal(0, 1, size=[examples, randomDim])  #C\n",
        "#     pickup_batch = np.random.randint(0, X_test.shape[0], size=examples) #C\n",
        "#     generatedImages = generator.predict(noise)#C\n",
        "    \n",
        "    #UC\n",
        "    pickup_batch = np.arange(0,examples)\n",
        "    our_input = to_categorical(y_test[pickup_batch],inputDim)\n",
        "    generatedImages = generator.predict(our_input)\n",
        "\n",
        "    plt.figure(figsize=figsize)\n",
        "    for i in range(generatedImages.shape[0]):\n",
        "        plt.subplot(dim[0], dim[1], i+1)\n",
        "        plt.imshow(generatedImages[i, 0], interpolation='nearest', cmap='gray_r')\n",
        "        plt.axis('off')\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('{}/dcgan_generated_image_epoch_%d.png'.format(image_folder) % epoch)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GqIjD7d3uGZ"
      },
      "source": [
        "# Save the generator and discriminator networks (and weights) for later use\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1KQsr8r3uGd"
      },
      "source": [
        "from keras.utils.np_utils import to_categorical"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BCrSl-mA3uHN"
      },
      "source": [
        "def saveModels(epoch):\n",
        "    generator.save('{}/dcgan_generator_epoch_%d.h5'.format(model_folder) % epoch)\n",
        "    discriminator.save('{}/dcgan_discriminator_epoch_%d.h5'.format(model_folder) % epoch)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bELiRn-P3uHW"
      },
      "source": [
        "def train(epochs=1, batchSize=128):\n",
        "    batchCount = X_train.shape[0] // batchSize\n",
        "    print ('Epochs:', epochs)\n",
        "    print ('Batch size:', batchSize)\n",
        "    print ('Batches per epoch:', batchCount)\n",
        "\n",
        "    for e in range(1, epochs+1):\n",
        "        print ('-'*15, 'Epoch %d' % e, '-'*15)\n",
        "\n",
        "        for _ in range(batchCount):\n",
        "            # Get a random set of input noise and images \n",
        "\n",
        "            pickup_batch = np.random.randint(0, X_train.shape[0], size=batchSize) #selecting 128 random images out of 60k\n",
        "            our_input = to_categorical(y_train[pickup_batch],inputDim)            #classifying each image into 0-9  \n",
        "            imageBatch = X_train[pickup_batch]\n",
        "\n",
        "            # Generate fake MNIST images\n",
        "            generatedImages = generator.predict(our_input) #UC\n",
        "#             generatedImages = generator.predict(noise) #C\n",
        "            X = np.concatenate([imageBatch, generatedImages])  #original image and generated image\n",
        "\n",
        "            # Labels for generated and real data\n",
        "#             yDis = np.zeros(2*batchSize)\n",
        "            \n",
        "            image_outputs = to_categorical(y_train[pickup_batch],inputDim+1)    \n",
        "            gen_image_outputs = to_categorical([inputDim]*batchSize,inputDim+1) # 11 is for fake.\n",
        "            \n",
        "            yDis = np.vstack((image_outputs,gen_image_outputs))\n",
        "\n",
        "        \n",
        "            \n",
        "#             # One-sided label smoothing\n",
        "#             yDis[:batchSize] = 0.9\n",
        "\n",
        "            # Train discriminator\n",
        "            discriminator.trainable = True\n",
        "            dloss = discriminator.train_on_batch(X, yDis)\n",
        "\n",
        "            # Train generator\n",
        "#             noise = np.random.normal(0, 1, size=[batchSize, randomDim]) #C\n",
        "            our_input = to_categorical(y_train[pickup_batch],inputDim) #UC\n",
        "            yGen = to_categorical(y_train[pickup_batch],inputDim+1) \n",
        "#             discriminator.trainable = False #C\n",
        "\n",
        "            discriminator.trainable = False #UC\n",
        "#             gloss = gan.train_on_batch(noise, yGen) #C\n",
        "            gloss = gan.train_on_batch(our_input, yGen)  #UC\n",
        "\n",
        "        # Store loss of most recent batch from this epoch\n",
        "        dLosses.append(dloss)\n",
        "        gLosses.append(gloss)\n",
        "\n",
        "        if e == 1 or e % 2 == 0:\n",
        "            plotGeneratedImages(e)\n",
        "            saveModels(e)\n",
        "#         print(\"done\")\n",
        "\n",
        "    # Plot losses from every epoch\n",
        "    plotLoss(e)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuC5q18h3uHv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "lM7W77nB3uH7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6467
        },
        "outputId": "d483945d-259a-4ef6-93fa-1ee6dec9157d"
      },
      "source": [
        "if __name__ == '__main__':\n",
        "    train(14, 128)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epochs: 14\n",
            "Batch size: 128\n",
            "Batches per epoch: 468\n",
            "--------------- Epoch 1 ---------------\n",
            "--------------- Epoch 2 ---------------\n",
            "--------------- Epoch 3 ---------------\n",
            "--------------- Epoch 4 ---------------\n",
            "--------------- Epoch 5 ---------------\n",
            "--------------- Epoch 6 ---------------\n",
            "--------------- Epoch 7 ---------------\n",
            "--------------- Epoch 8 ---------------\n",
            "--------------- Epoch 9 ---------------\n",
            "--------------- Epoch 10 ---------------\n",
            "--------------- Epoch 11 ---------------\n",
            "--------------- Epoch 12 ---------------\n",
            "--------------- Epoch 13 ---------------\n",
            "--------------- Epoch 14 ---------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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weDxJPRVZU87IU25urhmR+N73vsfvf/97Lr/8csDuYUciEbZt2wbYo6L5+flm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\nzPr1602e7LBhw1w9xevm+6Ymw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Q7LsnjooYcAeyGM1+s105zPPfdcNIsWdbpvzmVZFp06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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde822df190>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OfvrTnwJgt4dE0/m9fPzxxwAsW7aM/Px8nResHuTqAW+z2ejcuTM/+9nPAIiP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VbN26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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde80245790>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oUJvrYp7M5/ORkJBghmJSU1PN98aajIwMKisrzY5iS5YsoV+/fnz55ZdAMC9p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\nCrkvKG1zapcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde6ba1f090>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0XXjhhXb5rS3mGiQlJREdHU16ejoQGX3jWBpTJ+f8889nzZo19nf89re/HeIW\nBZfL5bIzw+bf+9577wVg8uTJnHHGGd32gICTpXtN61JSUnjllVeAjv2eixYtalZaDLAnkEXadSop\nKbElUo9NiTieTz/9lAsvvLArmhUSN9xwAytWrGDdunX2MzNeXn31VbZs2WJXpy677DIOHjxoDzGJ\nj48nMTHRrt6ZEyBP1b0o5AGxx+MhIyOj2fKkKVmTn5/fqRJPxcXF1NXV2dOlxo4dGxEDy3SW9moz\nbt++nRUrVtjreOwyTFRUFGeddRbbtm0Djlzj7s6UwvJ4PMcdFHV1dUBgmTM+Pp5BgwYBgVQU8+IQ\nCTSmTl5CQgIul8v+3k47pS41NdWOJ7M8ae4p2dnZDB061PaD2tpaUlJS2LFjB3DqlzHDhe41x9fR\ne0NpaSn33ntvi9PYIvWI+Ndee832iY4y9+krr7ySjRs3cuaZZ3ZF04IuJiaG1atXc+eddwKBU/sq\nKyuBwL2moKCAyZMn2++PjY21482cinn//fcDgfKgXq/XTlicbGAc8oB40qRJzR7caWlptrZuRzcg\nmBv1woULKS8vt/lapjB8d3fPPfcc93vy8vKorq5uM8fT5/ORkJDQ7OjMSGB2La9bt46qqiog8DA/\nVkNDA3v37gUCOZHx8fG2f7ndbttnIoHG1IkzL58bNmzA7/fbnGKn1SBOTk62fSgnJ4fi4mK7+vST\nn/wEgJdeegmAf//73xQVFdlZ0KKiooh54T6a7jUnzwSFgwYNahYMp6ens3TpUi699NJQNa1L/c//\n/A+33XYbEHgWH50/W1VVxa5du+zGsby8PD766CO+/PJLIFAhKCsry+ZkR8Lm+ZiYGH7zm98Agdrv\nL7/8MkCrVUo8Ho/NyTfXzQTQI0eObPbyfrKUQywiIiIijhayGWJTh84cwWeWB957771OlaYpKiri\nZz/7GRA4uS02NtYeq3n22WefyiaHzP/+7/+2+TWzROXz+YiPj29Wv/BYf/rTnyJuudvs8v7oo49s\njnV+fj7x8fH2bbKwsJCbb77ZVlaor6/H7XbbfhYps1kaUyfPHMFrZiBGjRoFBGp8O03fvn0B7Lg5\n1q233grAjBkzeOONN+yu+R/+8Ic8//zzwWlkEOlec3IqKirsLLtZyTQpSfv374/4nHRTnu5YCQkJ\npKen853vfKfZ5+ZeNHv2bPx+vz0d0ev1RsS1Mtfj/vvvt1VbzAl9Jk6Ji4vD7/fbmWOv10tqairP\nPvss0Ho5v5Ph8oeoUvjTTz8NwO233w5gNwB98sknHQ7aKisrGTVqlE1Sd7lcjBs3zuYh9erV61Q3\nOyRMYNPaP1VmZiYACxYs4LQaEULwAAAgAElEQVTTTrO5NR988EGz74uNjaW2tjbi8vvMQzgjI4PG\nxkYAHnzwQaZNm2YfSo8++ih5eXm2PwwYMICKigp+/OMfAzBz5syIuMFoTJ2cqqoqWwOzsrKS9PR0\nm7cfyb/3yaqoqKBfv372/pSSksLu3bvthpdIoXvNiWtoaGD06NHN9sFERUXZAxfaChadzASBqamp\nVFdX22tUX18fcRNbpixocXExGzduZNKkSUBgkmfOnDl8+OGHQCCO2bhxY7sFBk5GyGaIj53JnDp1\nKtB+Ur7f76exsZGtW7cCgfzGsrIym5N1wQUXsGDBgoh7eJnZBXM4wtFMztX48eNJTEzkpptuAgIH\nCRydj2M2CUUak8O3detWm1i/ePFinnzySfs1r9fLiBEjGDx4MBDY0FFRUWEDxkh5QGlMnZxzzjnH\n5obGxcWxfft2R/zeEMjtXL58uT390mwC64jevXvTp08fqqurgcD9qrCwMOICYt1rOs+8JC1cuJA9\ne/Y0+1pZWZkC4XaYibBvfvObfPjhh3ZTXSQ+x81KQWZmpp3kg8BeBrfbbVcUsrKyuiwYBuUQi4iI\niIjDhWyG+KqrrgKwpTf+/Oc/A4F8mbS0NAD27dvHXXfdZevV+Xw+YmJi7Ftljx49SE1NZcaMGUCg\nvt3IkSOD+nsEgylBYmo8Hu2jjz6yX0tMTOTRRx8FWu7WTElJicg3S2PkyJF8/fXX9s+1tbV2iXP/\n/v243W6bW7tr1y7cbnfEldLSmDpxtbW1lJSU2DFy6623RtwMZ3sSEhJ49tln7dL+0qVLmTJlSod+\ntr6+noaGBnvthg8fzumnn95lbQ013Ws6zqxqLlq0qNkzaezYsa1W6JAjTD7tBx98gN/vj6haxB11\n4MABdu7caWeIu7oKSchyiI0RI0aQn59vc1v79u1rk8tXrFjR7MhZwxwu8YMf/ICbb77Z5v21dYxm\nd2eugSkBdbSjc4K9Xm+LQNikW+zcudORG4MgcF3ef/99W75u3759XHrppbz++ushblnX0JjqvE8+\n+YRvfOMb9prt3bu3U2kDkaC8vNwGssnJyTzwwAMAzJo1yy5pHs08OmbNmsXLL79sr90111zDkiVL\nWr1fRTqn3WuOx9SmNsGceWlqamrqVD10p6muriYrKwsIBIXJyck2rW3EiBGhbFpQXX755XYzIcD7\n77/fpUFxyJ92S5YsITc3125SKCkpsYMoISEBn8/X7JCJ1NRUli1bBsCECRMcMajMwygvL8/O3JWV\nleHz+eymBPMmfvTuzAkTJthZQqflrh3N5XLx4osvkpeXZ/88e/bsELeq62hMdd7mzZtxuVw2d9rM\nqDtJ37597W7vhx56iNdeew0I5KZ/61vfsrl9NTU1NDU1sXz5ciDwkhUbG0tOTg4QmA10YjAMzrvX\nHM+SJUua/fmOO+4AcMQ9xufz2efx8fZx1NXVsXHjRgDmz5/Pp59+ams2x8bG8uabbzoqEDbMQWJG\nV1c5ivxeKSIiIiLSjpCnTPj9frKysigqKgICMzPr168HArOc9fX1fPrpp0DgrXLChAmOnX1ojVn+\nNkvkZnezWQIXOHToEJmZmbYKw5lnnsnu3bsjdpZCY6rjzPiZOXMmq1at4qKLLgICp5FFav/oiJqa\nGjsD/Oijj7J//367qnDsaZgul4v09HS2bNkCwJAhQ4Lb2DDitHtNe5qammwt6+rqagYMGNDsBM1I\n5vV6Wbt2rZ31nTZtGueee679enV1tV36P7oUnREVFcV5550HBOrKm+voNH369LH5+QCNjY1dWjo2\n5CkTLpeL/Px8ezRfazmLJpdGWjLpFKasj7T0xBNP0NDQYIviL1u2LKIfUBpTHeP1eu2Lwueff86Q\nIUNYuHAh4Iwl3fYkJSXZEo4JCQn84he/oLS0FAgEOi6Xy957brzxRn7/+99HXI3zE+G0e017Fi9e\nbF84o6KiKCwsDG2DgigqKoqSkhKee+45AJ555hm7Sbempoa6ujo7niBwzzYlHv/2t7+Rk5MT0Zvg\nOyo3N5eVK1faMdTVe1qcOVJFRERERP6/kKdMiHSV+vp6ANLT02loaODiiy8GAktQTt5k6HRm5rys\nrIzFixcDgYMpbrrpJrtxRbMzzXk8Hrtxd+XKlYwfP54BAwYAtFqBwml0r2muoKCA7Oxsm15z5ZVX\nOq7Sht/v57333gMCG09LSkoAcLvdjBkzxh6CM2zYMEdu4u2IPXv2MG3aNObOnQsEUtu6kgJiiVhm\nOfzaa68lMzPT7pp3co6jHOH3+23pMKcua8upoXtNgDny/brrruOdd94hJSUFgMLCQu1rkbAX8hxi\nka7g8/lsmajp06czffr0iD4sQDrP5XJpJlhOmu41AX6/3x7fnZWVRV5eHg8++CCgTd7SPWhaRERE\nREQcTSkTErFM/tqhQ4fo3bu3PZ5YRORUitR7jd/v79QqisnPd7vdVFRUMHDgQMA5J15K96aAWERE\nREQcTSkTIiIiIuJoCohFRERExNEUEIuIiIiIoykgFhERERFHU0AsIiIiIo6mgFhEREREHE0BsYiI\niIg4mgJiEREREXE0BcQiIiIi4mgKiEVERETE0RQQi4iIiIijKSAWEREREUdTQCwiIiIijqaAWERE\nREQcTQGxiIiIiDiaAmIRERERcTQFxCIiIiLiaAqIRURERMTRFBCLiIiIiKMpIBYRERERR1NALCIi\nIiKOpoBYRERERBxNAbGIiIiIOJoCYhERERFxNAXEIiIiIuJoCohFRERExNEUEIuIiIiIoykgFhER\nERFHU0AsIiIiIo6mgFhEREREHE0BsYiIiIg4mgJiEREREXE0BcQiIiIi4mgKiEVERETE0RQQi4iI\niIijKSB2EL/fH+omhC1dGxEREedSQBzhDhw4wIEDBxg2bBhjx47F7XbjdrtD3aywoGsjIiIiAC6/\npsYiSm1tLQA33HADK1euxOfzNfv6pEmTAHj77beD3rZQ07UROXm1tbXk5+eTnZ0NQExMTIhbJNJ9\naTy1rbGxkdWrV1NZWQnAjBkziI2N7bL/nmaIRURERMTRutUMsc/nw+Vy4XK5Qt2UsPTLX/6SX/3q\nV21+3eVycfDgQQD69u0brGaFBV2bU8/v93e7sejz+YiK6vw8gFlNOJGfjRQjRowA4PPPPweOzGS9\n8sorTJs2zdHXxqk0nk6cxtPxTZgwgU2bNpGWlgbAtm3b6NOnT5f998I+IO7fv78NVI51xRVX8NJL\nL3HaaacBzh5ct99+O08//XSbXz/jjDPYuXMnKSkpQWxVeNC1OTmlpaX89Kc/BaCpqckuWV1yySVM\nnjyZAQMGAIHxF84B8siRIykoKGDMmDEALF++nMGDB3foZ+vq6qirq6Nfv35d2cSwNXToUL788ss2\nv56RkUHPnj0BOHToEBMnTuSFF14IVvMkBDSeTpzGU8ekpqZSVVVFcnIyAJ999pmN97pCWAfEU6dO\n5Y033mjxuXno9uzZk6SkJG688UYAHnroIaKjo4PaxlBramoCaDWvZujQoezbtw8grAOVrqJr0zYz\n7L/++mvKy8s544wzAPB6vdTU1LB8+XIAnnjiCaqrq+3PnX322cTHxwNw/fXXc/XVV9sXiXC9jsf2\nA/P/f/e73zF79uwO/R11dXUUFBTYPD+niY6ObpFzb8TExDB8+HDKy8sBqKioICYmhry8PADOOuus\noLUz2NxuN3fccQdr1qwB4PDhw8TFxdlgBuDgwYM0NDQA4PF4iI2N5d133wXg8ssvD36jT5LG08nT\neOqYoUOHUlRURFxcHAD/+te/GDZsWJf995w7pSoiIiIiAoTldsaJEycCsH79enr06MFTTz0FYGeC\nTWmsV199lXnz5vHmm28CcPfdd3dpfkk4ysrKavXz0aNHs3PnziC3Jrzo2rTO6/Vy/fXXA/CXv/wF\nn89HYmIiAA0NDTQ1NdlZIJfLRXJyMhdffDEQqNBhUpguueQSevbsGbYzw8bRv4vf78fr9QLw1Vdf\ndfjv2LJlC4888ghr167tkjaGs6amplZnsxISEgBYsmQJ48ePt9VZ7rrrLmpra/nZz34GwKpVq4LX\n2C7gdrupr6+3/Xz//v3MmjULgI8//rjTf5/H47EzqSZ/tDvReDo5Th9PHWGuz7H7VLqywgSEYUBc\nXl7OP/7xDyCwPPvBBx/Y/BHDPLzHjBlDdXW1XdItLi52VED87rvv2mV/4xvf+AYAH330USiaFDZ0\nbdpWVlZGfX09EHhpGDVqFA888AAAlZWV1NXVUVpaCkBubi5paWnN8vNNukW4B8LG0Uu7jY2N9ma7\nevVq+3u3VerIPOznzp1Lfn5+1zc2DD388MMtPktLS6OgoADApsxMnz4dgNdff50NGzawfv164MQ3\nXoWLHj16AFBTUwPAypUr2b59e7s/07t3bwBOP/109u7da8ebkZOT0wUtDQ6Np5Pj9PHUEYcPHwYC\n+1d8Pp9NOfriiy/IyMjosv9uZF9VEREREZHjCLsZ4okTJ+LxeAB4/vnnW8wOH624uBi322030oXx\n/sBTLi8vjwkTJjT7bPjw4Y6f/QRdm/ZUVVWxYcMG5s6dC8BFF13U6ULw3WVm2DCzKb169WpWseaf\n//wnCxYsAGD+/Pmt/l4mPWvv3r32vuQUBw4cALCzfsbo0aPZtm1biw3MZsl3zpw5bNq0yc6o7tix\no1vPiEZHRxMfH29/34qKijY3RMXHx1NYWEj//v3tZ4WFhVxxxRX2f7tcrm69MUrj6cRoPHXcrl27\nAOzKiont1q1bR25ubpf9d8OmysR//vMfAM4880y7Q7e0tLTdh/WUKVNYuXKlXdL6/PPPyczM7PrG\nhpDJl/7e977X7HOXy8WXX37J6aefHopmhQVdm9b5/X7mz58PwFtvvUVDQwN//etfATpcJqk7M7e4\nQYMG8fXXXzf7mnno7N69m6FDh7b42S1btgCB1BG/309jYyPQ/V4KOqupqYmkpCQAu1w5ZMgQILBs\n2d7vX1RURE5ODmVlZQBce+21/PGPf+ziFgfPBx98wLe//W0gcJJWYmIiRUVFAK2m7Hk8Hq699log\nsPwNgRdRONK/uhONp87TeOocEwib/mRcccUVNnWkK4TNDLHZZNCrVy8b2BwvD6mqqorY2Fibr2Xq\noUaqhoaGFsGekZubG/G/f3t0bdpWVFTE448/DhzJg1yxYgWArS8cyczDZuHChdxyyy3NVpLMBqF5\n8+bx3HPPtdi0YfIczebB9kr5RQIza5eRkWEf3BDIcfziiy+A4wcv/fv3Z9CgQVRUVABHrnGkyM7O\n5uqrrwbgtttuY9y4ce1+v8fjYffu3c0+M5vpuuPhNhpPHafxdGJMac/09HQ7sw60mzFwKiiHWERE\nREQcLWxmiI0LL7yQCy64oN3vMW+kSUlJREdHk56eDkT+ssvo0aPb/FpOTg67d+9m4MCBAPaIa7Pk\nYK5ZV79hhYquTdtcLpedGTa/67333gvA5MmTOeOMMxxxoM0NN9zAihUrWLdunf3MXI9XX32VLVu2\n2Jn0yy67jIMHD9ol3vj4eBITE+1shTktKZKum8fjsQcllJSU2M8HDhzI/v37O3V/NWMMArOB3a0y\nSXtSUlJ45ZVXgI79PosWLaKwsLDZZ+aEtu58PTSe2qfxdPLOP/981qxZY39Pk6rUVcImIDalNDwe\nz3EHRV1dHRBIqYiPj2fQoEFAYNn82JyTSGA6f3s1K7dv386KFSvsDebYDQtRUVGcddZZbNu2DThS\nuq6707U5vtTUVDumzHKbuQbZ2dkMHTrU3nBqa2tJSUlhx44dQGQtZcbExLB69WruvPNOAJYuXUpl\nZSUQuC4FBQVMnjzZfn9sbKy9buYEv/vvvx8IlETyer32Bt3dH+Qej4eMjIxmy5NmHOTn53eqzFNx\ncTF1dXX2dKmxY8dG3IO7o79PaWkp9957b7NNeDExMRFxDK/GU9s0nk6NhIQEXC6X/d278pQ6CKOA\n2OzKXbduHVVVVUDgQX6shoYG9u7dCwTyIePj422yutvttvnEkeSee+457vfk5eVRXV3dZn6Rz+cj\nISGh2ZGikUDX5viSk5PtjTknJ4fi4mI7W/6Tn/wEgJdeegmAf//73xQVFdlVl6Kiooh6QYiJieE3\nv/kNEKh1+fLLLwO0WjXA4/HY/Qrmxcs88EeOHNnsRaO7mzRpUrOHd1pamq3jbe6vx2NeshYuXEh5\nebm9F5tDXZzETNoMGjSoWd9KT09n6dKlXHrppaFq2iml8dQ6jaeTY/rHhg0b8Pv9Nqe4K2sQg3KI\nRURERMThwqbs2sKFCwFYsGCBnb3Kz88nPj7evi0UFhZy8803292Z9fX1uN1upk6dCsD//d//2VNe\nIokpK9da3UazdNKrVy+amppsrmhr8vPzu3X9y9bo2pwaZkZrxowZvPHGG/bazZw5k+effz6UTesy\nRUVFXHjhhQC2pJH5vePi4vD7/Xamy+v10q9fPz777DOg9fJa3ZGpI2tW6MxS7r/+9S9GjBjR4b+n\nqKjIHi377rvvEhsba+/Lf/jDHzpd67o7q6iosNfTrEqZJd/a2tqImQU9lsaTxtOp8uSTTwKBGsyA\nXVFZu3atnS3uCmETEJuSIhkZGbY24YMPPsi0adNsAPzoo4+Sl5dHr169gECZtYqKCn784x8DgYd3\nJN5szKBq7Z/K1F1esGABp512ms3J+uCDD5p9X2xsLLW1tRGVEwq6NqdaRUUF/fr1s9czJSWF3bt3\n200vkcaUQiouLmbjxo1MmjQJCDzY5syZw4cffggE+sjGjRvb3bzZHT399NMA3H777QB2E9Ann3zS\n4TzFyspKRo0aRW1tLRAIgsaNG2fzZM392gkaGhoYPXp0sz0NUVFR9kAK8wIfqTSeNJ5OVlVVla1h\nXVlZSXp6uh1PXf27h81rhskX3rp1q02sX7x4MU8++aT9mtfrZcSIEfYwgdLSUioqKmyni8RgGI7k\nHJnzvY9m3pzGjx9PYmIiN910EwCbN29ulsdlktMjja5NS3V1dSxfvtye1mc2nXZE79696dOnD9XV\n1UDg+hYWFkZsQGxm7jIzM5sd6pOcnIzb7bYzfFlZWRH38AZarJqYWaj2xoM5UGHr1q1AYHWvrKzM\n5jhecMEFLFiwIOIf3EczL5ALFy5kz549zb5WVlYW8YGwofGk8XSyzjnnHLuPLC4uju3btwftd1cO\nsYiIiIg4WtjMEBsjR45sdhxkbW2tTafYv38/brebtWvXAoHzrt1ud5eX4gg1U7rG1L482kcffWS/\nlpiYyKOPPgq03OWbkpISUbOghq5NSwkJCTz77LM2lWjp0qVMmTKlQz9bX19PQ0ODvR7Dhw935JHX\nBw4cYOfOnXZGK1KqAhzrqquuArCls/785z8DgZND09LSANi3bx933XWXrTfr8/mIiYmxs549evQg\nNTWVGTNmAIH6tCNHjgzq7xFqZoVq0aJFze4vY8eObbVaktNoPGk8dURtbS0lJSX2+XPrrbcGdXUy\nbHKIO8Lr9fL+++/bUlv79u3j0ksvtefDRyqTl9VaMvnRea9er7dFsGdSCnbu3Mnw4cO7sJWhoWvT\nuvLychvIJicn88ADDwAwa9Ysu6x5NHMbmDVrFi+//LK9dtdccw1Llizp0o0M4ejyyy/nH//4h/3z\n+++/H7EPcYARI0aQn59v/9379u3Ld77zHSBwzPfRx84a5iCbH/zgB9x888027y/SN/y0xtTtNpvK\nzAO9qampUzVnI5XGk8ZTR3zyySd84xvfsNdt7969nUr5O1nd6kq7XC5efPFF8vLy7J9nz54d4lZ1\nPRPA5OXl2bfGsrIyfD6f3axhZiiO3tU7YcIE+4YaqfnVujat69u3L2+++SYADz30EK+99hoQyHH7\n1re+ZfP7ampqaGpqYvny5UDgZh0bG0tOTg4QmPFyWjAM2ENajLPPPjtELQmOJUuWkJubazc0l5SU\n2CAvISEBn8/XrJJLamoqy5YtA2DChAmOD/qWLFnS7M933HEHgOOvi6HxpPHUEZs3b8blctn8aTOr\nHizOvOoiIiIiIv9ft0qZOHToEJmZmXYn55lnnsnu3bsd+zYFR1IGzPKMqcBhll+cTNcmoKamxs4A\nP/roo+zfv9/OThx7ep/L5SI9PZ0tW7YAMGTIkOA2Nkz06dPH7l0AaGxsjOiyfH6/n6ysLIqKioDA\nzMz69euBwIpKfX09n376KRCY9ZwwYYIjVw5a09TURN++fQGorq5mwIABzU4pE40njaf2mWf1zJkz\nWbVqFRdddBEQOLk4mPFdt0qZeOKJJ2hoaLBHyS5btszRwTAcSRkwpefkCF2bgKSkJFtyLiEhgV/8\n4heUlpYCgYf50WfF33jjjfz+97+P6IdVR+Tm5rJy5Up7f4n0PD6Xy0V+fr49Wre13zcrKyvYzeoW\nFi9ebB/oUVFRFBYWhrZBYUjjSeOpLV6v174sfP755wwZMsQe1Bbs+M7Z0aSIiIiIOF63SJmor68H\nID09nYaGBi6++GIgcIxfJG6IEulKHo/HbjRcuXIl48ePZ8CAAQCtVqBwoj179jBt2jTmzp0LBJby\nRI5VUFBAdna2TT268sorI77q0YnQeJJjmdnzsrIyFi9eDAQOlbrpppvsMdfBLofaLQJiM51+7bXX\nkpmZaXfMOzW/UUREQsccq3vdddfxzjvvkJKSAkBhYaHj9iiInAy/32/LfoY6BTbsE3l8Pp8tETV9\n+nSmT5/uyIMCREQk9Px+vz3aPCsri7y8PB588EHAeRt2RU6Wy+UKm4OxlEMsIiIiIo7WLVImTH7W\noUOH6N27tz3iUERE5GT5/f5OzVKZ/Ee3201FRQUDBw4EIr96gkgk6xYBsYiIiIhIV1HKhIiIiIg4\nmgJiEREREXE0BcQiIiIi4mgKiEVERETE0RQQi4iIiIijKSAWEREREUdTQCwiIiIijqaAWEREREQc\nTQGxiIiIiDiaAmIRERERcTQFxCIiIiLiaAqIRURERMTRFBCLiIiIiKMpIBYRERERR1NALCIiIiKO\npoBYRERERBxNAbGIiIiIOJoCYhERERFxNAXEIiIiIuJoCohFRERExNEUEIuIiIiIoykgFhERERFH\nU0AsIiIiIo6mgFhEREREHE0BsYiIiIg4mgJiEREREXE0BcQiIiIi4mgKiEVERETE0RQQi4iIiIij\nKSAWEREREUdTQCwiIiIijqaAWEREREQcTQGxiIiIiDiaAmIRERERcTQFxCIiIiLiaAqIRURERMTR\nFBCLiMhx+Xw+fD4fhYWFNDU1hbo5It2axlP4CZuA2O/34/f7W/18ypQpTJkyBZfL1ez/0tPT+da3\nvsXgwYMZPHgwSUlJXH/99SFofeg1Njbyxhtv8MILL/DCCy/g8XhC3aSgUL8R6XqPPPII0dHRREdH\nk5mZSWxsLD169KBHjx689957oW6ehDmnPp/aovHUMcHuN2ETEIuIiIiIhILL39r0WhDV19dzzjnn\nUFhYCEBsbCwDBw4EoKKigqqqqk79fUlJSVRXVwPgcrlOaVvD2YQJE9i0aRNpaWkAbNu2jT59+oS4\nVV1H/aZ9Pp+PqKjOv+/6fD6AE/pZiUxffvklQ4cObfd7pkyZwuuvvx6cBkm347TnU3s0njou2P0m\nZAGx1+sFID09nbKyslP297pcLhobGwGIiYk5ZX9vuEtNTaWqqork5GQAPvvsM0477bQQt+rUU785\nvpEjR1JQUMCYMWMAWL58OYMHD+7Qz9bV1VFXV0e/fv26sonSjYwYMYLPP//8uN938cUXA7B58+au\nbpJ0M055PnWExlPHBbvfhOzJbwKbmJgYXC4X0dHRAAwePJhnnnnGft/SpUvZsmULAEVFRS1m7xob\nG+2sFkB0dHREzPB1Vq9evaiqqrJBndvtDnGLuob6TdvMxozPPvsMgK1btwLw1ltvMXv27A7/PcXF\nxY4LiN1uN3fccQdr1qwB4PDhw8TFxdGzZ0/7PQcPHqShoQEAj8dDbGws7777LgCXX3558BsdJHV1\ndR36vh07dgBQW1tLYmJiVzYpbKjfdIxTnk8dofHUccHuN1oXFRERERFHC9kMcY8ePQB47733OHz4\nMKNHjwawM37G+PHj2/w7ysvLycnJsXmkEJhiP/bviGRmltPv9zeb4YyNjQ1Vk7qU+k3bzAyxy+XC\n7/fb2fSvvvqqw3/Hli1beOSRR1i7dm2XtDGU3G439fX1dpzs37+fWbNmAfDxxx93+u/zeDx25r0j\nS6DdTX19PQCVlZXAkdz63r1722t5NJNq5Ha7I2pGS/3mxDnt+dQejaeOC1W/CfkM8VlnncWFF15o\nS5B0RlxcXIs80rvvvvtUNi/sHT58mMOHD1NaWorP56OhoYGGhga++OKLUDetS6nftBQbG2v/D47U\nuVy9ejVNTU3t1rr0er14vV7mzp3L+++/H6wmB1WPHj2Ij4/H4/Hg8XhYuXIl27dvZ/v27W3+TO/e\nvenduzfnnnsu8fHxLb6ek5NDTk5OVzY7ZMy9ZMqUKaxatcpet0OHDuF2uzlw4AAHDhwgOzsbl8tl\n+1t5eXmom35Kqd+cOKc+n1qj8dRxoeo3IQ+IRURERERCqVtupzeFMSZOnEhNTY39PDk5mZ/+9Keh\nalZI7Nq1CziyHGOuzbp168jNzQ1Zu8JRpPcbUyqtV69eHDx40H7+z3/+kwULFgAwf/78VjcPms0K\ne/fujdii+dHR0cTHx9sVhYqKimYbK48WHx9PYWEh/fv3t58VFhZyxRVX2P/tcrk466yzur7hIZKS\nkgLAiy++2OrXBwwYAMAzzzzD2LFj7QrEjh07yMrKCkobg0H95sTp+XSExlPHharfdKuA2OfzsX37\ndrszt7a2ttnX//73v4eiWSHV1rKbk0u1HMtp/ebYsnE+n4/HHnsMgFmzZrVaA9PcgJqamoiKirI3\noO5eeeNYLpfL5qFPmVOnDlkAACAASURBVDKFRYsWAYGqI4mJiRQVFQG0Wuty8ODBXHDBBUAgsPH7\n/ba6wLx584LR/KDq6L/9fffd1ywd59///ndXNSlk1G9OjJ5PR2g8dVyo+k1YBsRmhmrNmjXceuut\nVFRUNPv8WGZm7JxzzglOA8OIyU9LT0/nwIED9nNTt89JnN5vzA134cKF3HLLLc2OtDY32Hnz5vHc\nc8+12JyQn58PQM+ePXG5XPb7I3nzS3Z2NldffTUAt912G+PGjWv3+z0eD7t37272mdkUdezmDycw\nfWTDhg3Akf4X6cegq990nJ5PHefU8dSaUPUb5RCLiIiIiKOF3QxxSUkJw4YNA1oubR/Pp59+yoUX\nXtgVzQp7559/PmvWrLFvld/+9rdD3KLgUr854oYbbmDFihWsW7fOfmZmi1999VW2bNnC448/DsBl\nl13GwYMH7SEm8fHxJCYm2rdycypQdy9J15qUlBReeeUVoGPLmYsWLWpWqg+wB5hE8ixfa/x+PyNH\njgSOzGydd955AAwZMiRk7QoG9ZvOc/rz6XicPJ7aE+x+E3YB8Wuvvdbhk1wMs/R95ZVXsnHjRs48\n88yuaFpYS0hIwOVyERcXB2CDQ6dQvzkiJiaG1atXc+eddwKBU/tM7cumpiYKCgqYPHmy/f7Y2Fgb\n8Hq9Xmpqarj//vsBmD59Ol6v196IIi0w7mhAUlpayr333ttsM1VMTAwvvPBCVzUtbG3evJlx48a1\nSEXatGlTiFoUfOo3neP051N7NJ7aFux+4/IfnWgYJswxfT6fr1keZFVVFbt27bIbgPLy8vjoo4/4\n8ssvgcBOeZfLZXO6TC5OJDPXp0+fPlRWVtK7d28A3n//fUaNGhXKpgWd+k1z5nrMnj2bl19+GaDN\nWsTm5cDv9xMTE8OVV14JwB/+8AdSU1NbraXqBOYlq1evXs2uXXp6OkuXLm33AJhI4fF4WLFiBffd\ndx9Ai9lOCMyaVlVVBbll4Uv9JkDPp5Y0no4vVP1GOcQiIiIi4mhhOUPcWU899RSAPQ7TLGd5PJ6I\nW+I91pNPPgnAnDlzALj00ksBWLt2rWNn9TrKKf2mqKjI5kibE/rM7xoXF4ff77dLul6vl379+vHZ\nZ58BrZeRcoqKigpbT9bM8pmlu9ra2ojqIwAFBQUsXboUgNNPP93u6F62bBlr166loaGhzZ/NysqK\n+GOIO8pp/aY9Tn4+aTyduFD1m4gIiM3DPDU1lerqalsv8ujz5yNRVVWVrSlbWVlJenq6HUS9evUK\nYcu6Byf1G3PzLS4uZuPGjUyaNAmAgwcPMmfOHD788EMgkE+8ceNGRo8eHbK2hoOGhgZGjx7d7KEU\nFRVlDzAxfSVSfP3111x11VW2nu7QoUPtQQF///vfqa6ubvfnx40b16JclBM5rd+0x8nPJ42nExfK\nfhN2m+pOhMl//OY3v8mHH35oN0dFekc655xzbJ5RXFwc27dvj/gbzankpH5jZqgyMzPJzMy0nycn\nJ+N2u+1MVlZWlqODYTM/sHDhQvbs2dPsa2VlZREb0Pj9fqKiouxL4r59+/j0008BOHz48HF//sMP\nP+TnP/85AD/60Y9ISkoiMTERODLOIplT+017nPx80ng6caHsN5F9ZUVEREREjiMiZohNXuQHH3yA\n3++PqJqybamtraWkpMTOZt566622Zqx0jBP7zbEOHDjAzp077QyxydVyKjN7s2jRomalssaOHUtq\namqomtXlBgwYwPTp0/ntb38LBPpFezmOx6qvr+fXv/41AI899hh9+vTh4YcfBgLHhUc6p/abtjj9\n+aTxdGJC3W+6fUBcXV3NueeeCwQ6UXJyMnPnzg1xq7re3r17gSNL4ffcc08om9PtOLXfHGvmzJm2\nPBvAtddeG8LWhF5BQQGArdtsbswbN24MWZuCITo6mtmzZ5ORkQHA97//fUpKStr9GXOsd0xMDH37\n9qWmpgYIbEq96qqruOaaa7q20WHEqf2mLU5/Pmk8nZhQ95uwCIh9Pp+9gbSXv+n3+6mrq7M3mfnz\n5/Ppp5/amo+xsbG8+eabjBgxousbHWKbN2/G5XLZ+nxpaWkhblHwqd+cvG3btjX789lnnx2iloSH\nJUuWNPvzHXfcAUR+3h4EHuLf/e53ATj33HNZtWoVEDgFMicnh9zcXCBQecQJ16MznNxvWqPnk8bT\niQh1v9G/goiIiIg4WshniL1eL2vXrrWzd9OmTbNL2RBY2jZ5ja3V5YuKirJnfq9du5a+ffsGodWh\nY/KQ3n//fWJiYuyMXkxMyP8pg0r95tQwy3SGU3aBt6apqYnly5fbPw8YMIDHH388hC0KPrPSkpmZ\naWc5pX3qN0fo+dScxlPHhEu/CXkvjYqKoqSkhOeeew6AZ555xiZR19TUUFdXR2lpqf1+l8tlH9p/\n+9vfyMnJicgyWa3xer2sX78eCAR5Q4YMYeHChYDzlubUb06N3NxcVq5cafuPUx9cAIsXL7Y35qio\nqFaPVBU5lvpNgJ5PciLCqd+ol4qIiIiIo4XFSXV+v5/33nsPgBUrVtjdmG63mzFjxjBhwgQAhg0b\n5sjkfK/XCwTKhC1evBiAuro6brrpJrsRzImzneo3J2/Pnj1MmzbNVtiYOXNmaBsUIgUFBWRnZ9vy\nc1deeSWvv/56iFsl4U79Rs8nOTHh2G/CIiCWjvH7/fZEJC1BiZy82tpaAK677jreeecdUlJSACgs\nLCQ5OTmUTZMwpn7Tkp5PciLCqd84N2GwG3K5XHrTFjlF/H4/1dXVQODI6ry8PB588EEAxwY1cnzq\nN63T80lORDj1G73GiYiIiIijKWVCRCKG3+/v1GyDyWNzu91UVFQwcOBAwNnVNpxI/UZEFBCLiIiI\niKMpZUJEREREHE0BsYiIiIg4mgJiEREREXE0BcQiIiIi4mgKiEVERETE0RQQi4iIiIijKSAWERER\nEUdTQCwiIiIijqaAWEREREQcTQGxiIiIiDiaAmIRERERcTQFxCIiIiLiaAqIRURERMTRFBCLiIiI\niKMpIBYRERERR1NALP+PvXsPr6q68z/+PklO7gEChCQQSRA0XBSpZkRFBZX2yaB1BJk+naKIWq0y\nHWu1VXvRKUqtrR1nxlKsUEWLDoNOFaiWWi6WiNzKpSnlEkkxpBRJSAgJSU6Sc9m/P85vrxByIQGS\nc9mf1/P41Jxzkq6z3fu7v3ut71pLRERExNGUEIuIiIiIoykhFhERERFHU0IsIiIiIo6mhFhERERE\nHE0JsYiIiIg4mhJiEREREXE0JcQiIiIi4mhKiEVERETE0ZQQi4iIiIijKSEWEREREUdTQiwiIiIi\njqaEWEREREQcTQmxiIiIiDiaEmIRERERcTQlxCIiIiLiaEqIRURERMTRlBCLiIiIiKMpIRYRERER\nR1NCLCIiIiKOpoRYxIECgQCBQICysjJ8Pl+omyMiUUhxRiJJRCXELS0tvPvuuyxZsoQlS5bg9XpD\n3SSRiPPcc88RGxtLbGwsI0aMwO12Ex8fT3x8PBs2bAh183qdZVlYltXh69OnT2f69Om4XK42/2Rl\nZXH99deTk5NDTk4Oqamp3HnnnSFovYQz3aNaOT3OSOSJqIRYREREROR8c1kddZWEqcLCQj7++GMy\nMjIA2L59OwMHDgxxqyRUAoEAMTE9f6YLBAIAZ/W7ke7QoUPk5eV1+Znp06fzzjvv9E2D+lBTUxPj\nxo2jrKwMALfbTXZ2NgA1NTXU1tb26O+lpqZSV1cHgMvlOq9tjVaWZUX1sdI9KsjJccam+1PnwvXY\nRFRCnJ6eTm1tLWlpaQDs27ePoUOHhrhVEgpjx46ltLSUiRMnArBs2TJycnK69buNjY00NjYyePDg\n3mxiWBozZgz79+8/4+euvvpqADZt2tTbTep1fr8fgKysLKqqqs7b33W5XLS0tAAQFxd33v5uNKis\nrOTRRx8FwOfz4Xa7Abjmmmu49dZbyczMBII3tmhKkHWPCnJinDmV7k+dC+djE1FRvH///tTW1pqb\nkMfjCXGLQsfj8fDwww+zevVqAE6ePElCQgLJycnmM8eOHaO5uRkAr9eL2+3mgw8+AOCGG27o+0af\nB/bEjH379gGwdetWAH7zm9/w4IMPdvvvHDlyJGoDTlcaGxu79bmdO3cC0NDQQEpKSm82qdfZCXFc\nXBwul4vY2FgAcnJyWLx4sfnc0qVL2bx5MwDl5eXtErWWlhbTQwEQGxsbVcncmdh9J5999hnV1dVc\neOGFQPD41tfXs2zZMgBefPFF03MOMHr0aBITEwGIj48nJSXF9PBE2/HTPSrIiXEGdH/qSiQcm+jt\nkxcRERER6YaI6CG2e2VOrz+zh+GilcfjoampyXznw4cPc8899wDwxz/+scd/z+v1miex7gxnhSP7\nKdPlcmFZlun9+/vf/97tv7F582aee+451qxZ0yttDEdNTU0AnDhxAmjtmRswYIA5z05llwB4PJ6I\n77mJj48HYMOGDZw8eZIJEyYAmJ5i29SpUzv9G9XV1RQUFJj6YwgOj5/+N6KV3+83q2r8+te/JhAI\nmPOiubkZn8/X5tpMS0szw+GzZ8/m2LFjQLBkIjk5Oep6hp16jzqdk+MM6P7UlUg4NhGREJ88eRII\n1qUFAgFTBvDpp58yfPjwUDatV9k38vr6egBWrFjBjh07uvydAQMGAHDBBRdw4MCBdgGooKCgF1ra\nd+wbjNvtbjOE/f777/ODH/wA6Lye074AH3nkEUpKSnq/sWHEvmamT5/OjBkzuPnmm4HWpLCiogKA\nL3zhC+zevdsc1+rq6qgZurv44ovP+ncTEhLa1R8//vjj59qkiFFVVWViSX5+Ppdccom53k6cOEFj\nYyOVlZUATJ48mYyMjDYTX+xyi2hLhG1OvUedzulxRvenzkXCsVHJhIiIiIg4WkT0EO/evRtoHY6x\nexvWrl3L5MmTQ9au3hYbG0tiYqJ5uq6pqWkzqedUiYmJlJWVMWTIEPNaWVkZN954o/l3l8t1Tr1k\n4cDuderfv78ZhgX485//zNNPPw3AvHnzOuyJsie4HDhwwHEL5vfr1w+A1157rcP37Vn/ixcvZtKk\nSWZ4a+fOneTn5/dJG8ORHWumTZtmRmoA0tLSzCoK0a62tpb169fzyCOPAHDVVVf1eFWNaO0Ztjn1\nHnU6p8cZ3Z86FwnHJiIS4s6G+aNtqZaOuFwuUzoxffp0FixYAARnvKekpFBeXg7Q4VqXOTk5XH75\n5UAwIbYsy6xK8dRTT/VF83vN6TfkQCDAT3/6UwDuueeeDtfAtG9aPp+PmJiYqB/GPVV3v+OTTz7Z\nZovVv/71r73VpLAWCATYsWOHWY2loaGhzfsffvhhKJrVZyzLYt68eUBwFnhzczO/+93vAC0x1xEn\n36NOpTgTpPtT58L52EREyURiYiKJiYlkZWW1ed1e69Epxo8fz+23387tt9/Ohx9+SH19PQMHDux0\n4Xev18vevXvZu3eveW3//v3s37+/w61rI4G9le78+fPbXQz2xJ6nnnqqw6fIkpISSkpKSE5OJjU1\ntc1EIKezj8X69euB1uMc7dsTe71evF4vq1atIjMz02wtGxsby5VXXklDQ0ObZDgmJoaYmBjGjRsX\nwlb3vvLycl544QVeeOEFdu3axd69e1m+fDnLly8PddPCku5R3RPtcUb3p85FwrGJiIRYRERERKS3\nRNTY1+c+9zlWr15tni5uuummELeob/Xr148333wT6N5QwYIFC9osEwWYmbyRPgwze/Zsli9fztq1\na81rdq/3W2+9xebNm3nhhRcAuO666zh27JjZdCExMZGUlBSOHj0KYHaScsoSWqezLIuxY8cCrUvj\nXHbZZQDk5uaGrF29raKigpEjRwLtSyLOZM+ePVxxxRW90ayw4HK5TM20fV195zvfAeDWW2/lwgsv\ndOz10hWn36O64qQ4o/tT58L52ETU1s2333477777LgkJCUBwPcxp06aFuFXhqbKykuzs7DaT8OLi\n4kzt47XXXhuqpp03Pp+Pb33rW0BwlzF77cuOJh663W5z0fj9fuLj4/nSl74EwKxZs/D7/ebmFS2B\npzs2bdrElClT2g1T2QniqTsfRpsFCxbw0EMPAXS7hMiufxs8eDBFRUVcdNFFvda+UDp58qQpxTp9\naDIxMZG8vDyT9DU0NNCvXz+z65jT1t49le5RHXNinNH9qXPhemwioofYvlmtX78ey7LMNqBOWt+x\nu+wtM4cNG9bm5MrKymLp0qVRkQjb4uLi+MlPfgIEb+C/+tWvgI4vKq/Xa9YytM8n+yIcO3asYzZZ\n8Hq9LF++nCeffBKg3QgCBEciovEGdbqvf/3r3H///UDwnDk1Ka6trWX37t1mMkdxcTHbtm3j0KFD\nABw9epT8/HymTJkCYGoio0VaWprphSkoKODIkSNkZ2cD8M1vfhOA119/HQhOiCovLzf1s+Xl5VGx\nyUJP6B7VluKM7k9dCddjoxpiEREREXG0iCiZeOmllwCYO3cu0Drcv2bNGvMkLsF1iu11iO1hTnvo\nrqGhIaqeME9XXl5uajrtHcXsId2EhAQsyzJPn36/n8GDB7Nv3z6g4yXrIl1paSlLly4FgrsW2rPd\n33jjDdasWWN2lOpIfn5+xG7t3dt+8YtfAJgt0O1zzOv1RvX11RF7NOqOO+7g3XffNcdizpw5vPrq\nq6FsWp9z6j1KcaZ7dH/qXDgdm7BPiGtra826dCdOnCArK8tcRP379w9hy8JLc3MzEyZMaBNgYmJi\nzILW9lrG0cwOvkeOHKGoqMhsG3rs2DHmzp3Lli1bgGBNUlFRERMmTAhZW3vTZ599xm233WbWqM7L\nyzML4n/44YfU1dV1+ftTpkxpsyyStLIDc3p6OnV1dea6ampqcuyxqqmpYfDgwWY4s1+/fuzdu9dM\neIl2Tr1HKc70jO5PnQuXYxP2NcTjxo2jtrYWCD4t7NixI6qDTE/ZN6H58+fzySeftHmvqqrKEYmw\nze4NHzFiBCNGjDCvp6Wl4fF4TK95fn5+VAcby7KIiYkxydvBgwfZs2cPEKzXOpMtW7bw3e9+F4CH\nHnqI1NRUUxNq7zbkVPb3v/LKK9myZYuZVBfpN/TGxkaWLVtGYWEhEJyD0F0DBgxg4MCBJgFKTU2l\nrKzMMQmxU+9RijM9o/tT58Ll2ETfWSciIiIi0gNhnRA3NDRQUVFhdji57777HNPr0F0nT57k5MmT\nLFiwoM0MzUmTJpGenh7CloWPo0ePsmvXLrO7TTSttNGRzMxMZs2aRXJyMsnJydTV1VFfX2/WlT2T\npqYmfvzjH/PjH/+Y4cOHM2rUKF577TVee+213m14BKiqqqKqqoqNGzfi9Xq54ooromI94qSkJH75\ny1+Sn59Pfn4+7777brd/t6mpiebmZhOnR40axQUXXNCLrQ0fTr5HKc6cH067P/VEXx+bsC6ZOHDg\nANDanf7EE0+EsjlhqbS0FGhdhsQeui0qKgpZm8LNnDlzaGlpMT9/+ctfDmFrel9sbCwPPvigWfLp\na1/7GhUVFV3+jr12bFxcHIMGDTI3Na/Xy2233WbWfXSyuro6Lr30UiB4M09LS+ORRx4JcavOD5fL\nxXvvvWcS2QceeMAsu3bPPfeYGHwqu1xr7ty5NDQ0mHNo+PDhZGRk9FHLQ8vJ9yjFmfPDafennujr\nYxPWCfGmTZtwuVwMGDAAwDFBticWLVrU5ueHH34YiM4arLO1ffv2Nj+PHj06RC3pO7GxsXzxi18E\n4NJLL2XlypVAcHe2goICJk+eDARn6TrpXAkEAuahsau6X8uyaGxsNA+W8+bNY8+ePWZlBbfbzapV\nqxgzZkzvN7qPDBo0iFWrVgHw7LPP8vbbbwNQX1/P9ddfb2r76uvr8fl8LFu2DIDly5fjdrspKCgA\nghueRPPKCqdy+j1KcebcOfH+1F19fWx0hoqIiIiIo4Xlsmv2Ehxz5sxh5cqVXHXVVQCsXbtWT5mn\n8Pl8DBo0CAgO52ZmZpphTmk1cOBAampqzM8tLS2O3l7Wqfx+P2vWrDG9vjNnzjQlEBC8huwatY7W\nR42JieGyyy4DguvL2tdeNKqvrzc9wM8//zyHDx822+6evpWzy+UiKyuLzZs3A5Cbm9u3jQ0B3aPk\nfNH9qXN9fWzCrmTC7/ezbt06IHhTys3NZf78+YDKAE63cOFCE5hjYmI63B5TYPLkyaxYscKcP3Fx\nYXfaSx+IiYmhoqKCV155BYDFixebCVD19fU0NjZSWVlpPu9yuczyWb///e8pKCiI+OXVuis1NZW7\n774bCE64+973vmeOjc/nw+VymbrZu+66i5/97GeOuYnrHiXnk+5PnevrY6OrV0REREQcLWxKJvx+\nPxBc1mjhwoVAcLH4u+++20xccUrvTHeUlpYyfvx4M3x5yy238M4774S4VeHpk08+YebMmWZFgDlz\n5oS2QRIylmWxYcMGIDgZzJ4V7/F4mDhxotmYYuTIkY6bINUVr9drNltYsWIFU6dONbuSdbQCRTTS\nPUp6g+5PnevrYxM2CbHNsiyznI+Gn9praGgA4Ctf+Qq//e1v6devHwBlZWVmH3kREekdukeJRKew\nK1axFziX9izLMtuj5ufnU1xczDPPPAOgZFhEpA/oHiUSnfR4KyIiIiKOFnYlE05jWVaPehvsOjaP\nx0NNTQ3Z2dmAZqaKiIiInC0lxCIiIiLiaCqZEBERERFHU0IsIiIiIo6mhFhEREREHE0JsYiIiIg4\nmhJiEREREXE0JcQiIiIi4mhKiEVERETE0ZQQi4iIiIijKSEWEREREUdTQiwiIiIijqaEWEREREQc\nTQmxiIiIiDiaEmIRERERcTQlxCIiIiLiaEqIRURERMTRlBCLiIiIiKMpIRYRERERR1NCLCIiIiKO\npoRYRERERBxNCbGIiIiIOJoSYhERERFxNCXEIiIiIuJoSohFRERExNGUEIuIiIiIoykhFhERERFH\nU0IsIiIiIo6mhFhEREREHE0JsYiIiIg4mhJiEREREXE0JcQiIiIi4mhKiEVERETE0ZQQi4iIiIij\nKSEWEREREUdTQiwiIiIijqaEWEREREQcLeoSYsuyQt0EEXEAxRoRkegRFQnx0aNHOXr0KCNHjmTS\npEl4PB48Hk+omyUiUUaxRkQkOrmsCOzmaGhoAGD27NmsWLGCQCDQ5v2bb74ZgPfee6/P29Yb7P9E\nLper3eszZswAYMWKFW3ey8zM5OKLL+bgwYMAnDhxgunTp7N06dI+aLFIdHBarOmOhoYGSkpKGD9+\nPABxcXEhbpGEu5aWFt5//31OnDgBwB133IHb7Q5xq0TaiooeYhERERGRsxVxPcTf//73+eEPf9jp\n+y6Xi2PHjgEwaNCgvmpWr2hqamLcuHGUlZUB4Ha7yc7OBqCmpoba2toe/b3U1FTq6uqA9r3N0jHL\nsnSsOhHtx8ZJsaY7xowZA8D+/fuB1p7hN998k5kzZxITE739K4FA4Ky+nz2iEM3HpjsKCwv5+OOP\nycjIAGD79u0MHDgwxK0KD4FAAJfLFdWxNBTO5v4UUQnxAw88wMsvv9zp+xdeeCG7du2iX79+fdiq\n88/v9wOQlZVFVVXVefu7LpeLlpYWQMOcp6usrOTRRx8FwOfzmeG8a665hltvvZXMzEwgeGNzWuBy\n4rFxSqzprry8PA4dOtTp+8OHDyc5ORmA48ePM23aNJYsWdJXzetVY8eOpbS0lIkTJwKwbNkycnJy\nuvW7jY2NNDY2Mnjw4N5sYthLT0+ntraWtLQ0APbt28fQoUND3KrQGTJkiHmYPt2NN97I66+/bo6P\n0x+mzuR83p8iIivy+XwAHd6g8vLyTJ1stNyM7YQ4Li4Ol8tFbGwsADk5OSxevNh8bunSpWzevBmA\n8vLydt+/paWlTc1jbGxs1Byj7rCf9T777DOqq6u58MILgeDxra+vZ9myZQC8+OKLpuccYPTo0SQm\nJgIQHx9PSkqKCUrRcvx0bDrmtFjTXX/72986fS8uLo7k5GSqq6uB4OjV//7v//Kd73wHgIsvvrhP\n2ni+2efCvn37ANi6dSsAv/nNb3jwwQe7/XeOHDni+IS4f//+1NbWmg4ZJ09EnTFjRofJsB1Ttm7d\nSkFBAXfddRcAzz77rMkBnCJU9yc9eoiIiIiIo0VED3F+fn6Hr0+YMIFdu3b1cWt6X3x8PAAbNmzg\n5MmTTJgwAaDdU+LUqVM7/RvV1dUUFBSY+mMIDls55UnT7/dz5513AvDrX/+aQCBASkoKAM3Nzfh8\nPtMD5HK5SEtL4+qrrwaCKwrYT/DXXHMNycnJUdUjqGPTOafFmu7w+XztVtcASEpKAmDRokVMnTrV\nrLTx2GOP0dDQwLe//W0AVq5c2XeNPY9OvQYsyzIjd3//+9+7/Tc2b97Mc889x5o1a3qljeHOPm9O\nr+d04goT06ZNA2DdunXEx8fzi1/8AsD0BNu95m+99RZPPfUUq1atAuDxxx93VL11KO9PYZ8Qf/DB\nB2aY0vYP//APAGzbti0UTeoz5zLUmJCQ0K7++PHHHz/XJkWMqqoqmpqagGCSc8kll/CDH/wACC5B\n19jYSGVlJQCTJ08mIyOjTa1WZ0vdRQMdm445OdZ05Uc/+lG71zIyMigtLQUwddSzZs0C4J133mH9\n+vWsW7cOOPsJaaFmJ21ut7tN+dn7779vrpfO5mLYyfMjjzxCSUlJ7zc2TJ08eRII1nkGAgGam5sB\n+PTTTxk+fHgom9anqqur+cMf/gAEh/U3btxo6qltdtI3ceJE6urqTCnAkSNHHJUQh/L+FHlRSkRE\nRETkPArrVSaKi4tNuYBt1KhRHDhwIEQtCn/2f87Jkyfz0UcfmdfT0tLaFJ9Hs9raWn77299ywQUX\nAHDVVVdpVY3/T8emY4o17R09ehSAYcOGtSmZmDBhAtu3b++0/Oq9995j1qxZ1NfXA62ThCKNHUsz\nMzPbTIKKiYnhEpT/RAAAIABJREFUe9/7HgDz5s3rsCfK/u5DhgyhubnZ9Bg7zcaNGwG47rrr2rz+\n/e9/n2eeeSYUTQqJiRMnsnPnTgC2bNnCFVdc0eln165dy7Rp08z1tW3bNi699NI+aWeohfr+FJZ3\nQrt25p/+6Z/avO5yuVi/fn0omhT2AoEAO3bs4IYbbgBad9iyffjhh6FoVp+xLIt58+YBwVngzc3N\n/O53vwO0xJyOTecUazrm8/nIy8sDWutAc3NzAdi5c2eXw5Hjx48nISHBDJe/8MIL/M///E/vNrgX\nnX6NBAIBfvrTnwJwzz33mON0qt27dwPB4xgTExO1ZUZn0tmD0KZNm/q4JaFhr85SXFxsSiQuu+yy\nLn/n5z//OV6v15wrqampvdvIEAun+1PY3Q2bm5vb3ZxskydPNmvKOZXX6wVg9erV3HfffdTU1LR5\n/XR2bc24ceP6poEhUl5ezgsvvAC09s4sX74cwKxR6FQ6Nh1TrGnPjiPDhw839Z4QrBn+9NNPgTMn\ndUOGDGHYsGEmNtkTYCKN/T3nz5/PV7/6VU4dTLW/01NPPcUrr7zSbpKYXTdsT+qxP++0yWT2ElhZ\nWVlmxAFoVz8brezl+fr3728evs9Ud15bW4vb7WbAgAEAUR+Hwun+pBpiEREREXG0sOshPr2O71QF\nBQXs3bvXbF9sb3doL/9jP8FH69NnRUUFI0eOBNqXRJzJnj17uqxbinQul8s8Xdrngb0xwK233sqF\nF17omCXnTqdj0zHFmra8Xi/jx48HgrHGlp2dzeHDh3s03H/qVrTJyckRXTIwe/Zsli9fztq1a81r\n9vd566232Lx5s+nhuu666zh27JjZMCkxMZGUlBTTO2rvPua06+1zn/scq1evNv/9b7rpphC3qG9d\nccUVXH755V1+xj6nUlNTiY2NJSsrC4jMa6Ynwun+FDYJsX0g9u/f3+lnduzYwfLly01wOb1MICYm\nhosvvpjt27cDrcuYRIu3336bxsbGHv2OXTJxyy23UFRUxEUXXdQbTQu5U9dYtocn7fNj/Pjx5OXl\nmcDS0NBAv379zCSHaB/G1LFpS7GmPa/Xy/Dhw9sMa9vfqaSkpEfLph05coTGxkYSEhIAmDRpUkTf\n1OPi4nj//ff51re+BQR3CD1x4gQQvJ5KS0u59dZbzefdbre53uydtf793/8dCC5N5/f7TULolMQ4\nKSkJl8tlzgm7Yyfa2UvLeb3eM/63tu/tcXFxJCYmMmzYMCBY2mU/iEejcLo/hU1C/MQTT5zxM8XF\nxdTV1XVakxYIBEhKSiI5Ofl8Ny8sfP3rX+f+++8Hgt/11Jq22tpadu/ebSZzFBcXs23bNg4dOgQE\nZ4zn5+czZcoUgKibMJSWlmZu5gUFBRw5csT07n3zm98E4PXXXwfgr3/9K+Xl5eYJvLy8POITmq7o\n2LSlWNPezTff3CYZzsjIMGsyd3dSj30Tmz9/PtXV1aYG0l40P5LFxcXxk5/8BAiurfurX/0KoMMN\nS7xer6kHtWO0nUCPHTvWURsk2d9//fr1WJZlaoqdsgbxkCFDgODKEbW1tUAwATxdc3OzWdGmvr6e\nxMREc915PB5zLUWjcLo/qYZYRERERBwtbNYhtrcr7mi1BLu7vH///vh8PlNv0pGSkpJz2uEt2tjb\nQ9qzXe1j2Z0hnGhjD0ndcccdvPvuu+ZYzJkzh1dffTWUTQs5Jx0bxZpW9vq6dk+WXRrxl7/8hTFj\nxnT775SXl5utmj/44APcbjczZswAgstIRdPyfuXl5WY+hr0bqH3eJCQkYFmW6Tn2+/0MHjyYffv2\nAThqxzGAl156CYC5c+cCcO211wKwZs0a01sczebPnw/A008/bXo9S0pKSExMNL3nZWVl3HvvvWYV\nl6amJjwej7l+/uu//svsBulUfXV/CpuE2A7EHTVnxIgRQPCkGjp0qKnHshf9trndbhoaGqKy7vFs\n2YE5PT2duro6kww0NTVFdF3fuaipqWHw4MHmXOvXrx979+41E16czAnHRrGm1csvvwzAAw88AGAm\n1f3pT3/qdnw4ceIEl1xyiZno63K5mDJlCkuWLAGCDxfRxl6S7siRIxQVFXHzzTcDwQeMuXPnsmXL\nFiB4nhQVFXU5gTNa1dbWmjWaT5w4QVZWlqnbj8ZzoiP20oPDhw+npaUFgGeeeYaZM2eaBPj555+n\nuLjYHJPMzExqamr4xje+AQSTPqd1XnWmt+9PYfPYbtfL2Iu5n8p+qpw6dSopKSncfffdQHBx71Nr\nuOzCfWll3/yvvPJKtmzZYibVRfpxamxsZNmyZRQWFgKYCQjdMWDAAAYOHGh27ktNTaWsrCxqkj4d\nm64p1rQ6vQfc7pXq6rtZlkVLSwtbt24Fgr1gVVVVps7x8ssv5+mnn47qpMeeHDZixAjzEAXBekiP\nx2Nqz/Pz8x2ZDENw7Xu7bjYhIYEdO3ZE9TnREbteeOvWrWYi5cKFC3nppZfMe36/nzFjxpCTkwNA\nZWUlNTU15uE02pLhcL4/qYZYRERERBwtbHqI7WVr3nzzzXbvbdu2zbyXkpLC888/D7Sf4duvX7+o\n6LU5n+wat40bN2JZVtSsRZyUlMQvf/lLM6y0dOlSpk+f3q3fbWpqorm52Zwro0aNMnunRwMdm64p\n1rS67bbbAMySYv/3f/8HBOccZGRkAHDw4EEee+wxsw5vIBAgLi7OlF/Fx8eTnp7OHXfcAQTX7R07\ndmyffo9wcfToUXbt2mV6iO0RB6dpaGigoqLCXCP33XdfVI0y9dTYsWP57LPPzM8NDQ2mnOLw4cN4\nPB7WrFkDBLf99ng8Ubs0XVjfn6ww0dTUZDU1NVlAu3/cbrf5JyYmpt37qampVmpqqnXgwIFQf42w\nUltba2VlZVlZWVkWYKWlpVl79+619u7dG+qmnRdVVVVWUlKSlZSUZA0ZMsRauHChtXDhQqupqanD\nzwcCASsQCFhz5syxYmJirISEBCshIcG68847LY/H08et7106Np1TrGlv9OjRlsvlsuLj4634+Hgr\nOzvbmj17tjV79mwrISGhw2OVlpZmpaWlWU888YR14MABy+v1Wl6vN9RfJaSmTJnS5hh99NFHoW5S\nSOzatcuKi4szMejw4cOhblLY8vl81ocffmhNnDjRmjhxopWRkWFNnz491M3qVeF6fwqbHmK7Jqu4\nuNj0NFRVVREIBPB4PEBrzd+pM3oLCwtNr0a01dpAsDfG/r5nqutrbGykqKgIgHnz5rFnzx4zO9Pt\ndrNq1aoezRwPd4MGDTL7wz/77LO8/fbbQLAu8vrrrze1ffX19fh8PpYtWwYE90l3u90UFBQAsGDB\ngqib8axj0znFmvYWLVrE5MmTzcSfiooKs/h9UlISgUCgzaoc6enpvPHGGwAUFhb2aOOOaGZv1GIb\nPXp0iFoSWps2bcLlcpm6cnu0QdpzuVy89tprFBcXm5/tVaGiVbjenxTFRERERMTRwmbZta7YS9yU\nlJTgdrvNbMy0tLRQNqvX+f1+1qxZY3p9Z86cyaWXXmrer6urMzVqHW1DGxMTw2WXXQYE130cNGhQ\nH7Q6NOrr681T5PPPP8/hw4dNj9bpu425XC6ysrLYvHkzALm5uX3b2D6mY9N9To01lmWRn59PeXk5\nEOzRW7duHRDsHW9qamLPnj1AMK4UFhZG3cjB+TBw4EBTGwrQ0tIS8Uvz9YR9/cyZM4eVK1dy1VVX\nAcGd2jSK0LHjx48zYsQIs+LLRRddxN69ex1zvMLp/hQ2JRNdsYc47WVInCImJoaKigpeeeUVABYv\nXmwmJtTX19PY2EhlZaX5vMvlMsva/P73v6egoCAqJv50R2pqqlkiKykpie9973vm2Ph8PlwulzmP\n7rrrLn72s5855kalY9N9To01LpeLkpISs+VwRxtp5Ofn93WzIs7kyZNZsWKFSWaiaUOSM/H7/eYh\nav/+/eTm5pqNKZyS3J2NF198kebmZrMF8RtvvOGo4xVO9yfnHHURERERkQ5ERMmEk1mWxYYNG4Bg\nQXlFRQUAHo+HiRMnmsWtR44cqYkLp/B6vWZi1IoVK5g6dSqZmZlAay+gU+nYiPSOTz75hJkzZ/LI\nI48AwdKBaGePKlRVVbFw4UIguPnC3XffbSZxO2WksieampoAyMrKorm5mauvvhoIljdG26Tdngjl\n/UkJsYiIiJwTy7LMlrpOGvI/W3Z5yZe//GVGjBhhVlpw2ryNcOKcAicRERHpFS6XSz3B3RQIBMzS\nYrNmzWLWrFlRtwFSJNJjnIiIiIg4mkomRERERPqQvaTY8ePHGTBggNkKXUJHCbGIiIiIOJpKJkRE\nRETE0ZQQi4iIiIijKSEWEREREUdTQiwiIiIijqaEWEREREQcTQmxiIiIiDiaEmIRERERcTQlxCIi\nIiLiaEqIRURERMTRlBCLiIiIiKMpIRYRERERR1NCLCIiIiKOpoRYRERERBxNCbGIiIiIOJoSYhER\nERFxNCXEIiIiIuJoSohFRERExNGUEIuIiIiIoykhFhERERFHU0IsIiIiIo6mhFhEREREHE0JsYiI\niIg4mhJiEREREXE0JcQiIiIi4mhKiEVERETE0ZQQi4iIiIijKSEWEREREUdTQiwiIiIijqaEWERE\nREQcTQmxiIiIiDiaEmIRERERcTQlxCIiIiLiaEqIRURERMTRlBCLiIiIiKMpIRYRERERR1NCLCIi\n0kcsywp1E0SkA0qIRUREetHRo0c5evQoI0eOZNKkSXg8HjweT6ibJSKnUEIsEqUsy+qwN8qyLKZP\nn8706dNxuVxt/snKyuL6668nJyeHnJwcUlNTufPOO0PQepHI1dDQQENDA7fffjuxsbFkZ2eTnZ3N\nwYMH2bx5M//8z//MP//zP4e6mdLLFIPPTUNDAzt37sTn8+Hz+Xr9/08JsYiIiIg4mstSQVNE83q9\nAOzevRuv18vYsWMBSElJIRAI8Le//Q2Al19+mYcffpisrKyQtTVUAoGAefp2gqamJsaNG0dZWRkA\nbreb7OxsAGpqaqitre3R30tNTaWurg7AMcfQiQKBADExPe8jCQQCAGf1u9Ho+9//Pj/84Q87fd/l\ncnHs2DEABg0a1FfN6hU6ZzqmGHxuxowZA8D+/fsBiIuLA+DNN99k5syZvXbeKCGOYLfeeiu/+c1v\n2ryWkJAAQF5eHhUVFeYiCgQCpKenU1FRAQQv0Gg2ZMgQc9M53Y033sjrr7/O0KFDgegJyn6/H4Cs\nrCyqqqrO2991uVy0tLQArYFJosvYsWMpLS1l4sSJACxbtoycnJxu/W5jYyONjY0MHjy4N5sYER54\n4AFefvnlTt+/8MIL2bVrF/369evDVvUOnTPtKQafu7y8PA4dOtTp+8OHDyc5ORmA48ePM23aNJYs\nWXJe/r/DOiH2eDw8/PDDrF69GoCTJ0+SkJBgDgbAsWPHaG5uBoK9pW63mw8++ACAG264oe8b3Uee\nfPJJfvjDH5r6pKSkJG666SYef/xxAIYNG8b//u//8otf/AIITuoYOnQoe/bsAWhzDKPNjBkzePfd\nd9u9bj9ZJycnk5qayl133QXAs88+S2xsbJ+2sTfYATM3N5eKigrznXJycli8eLH53NKlS9m8eTMA\n5eXl7XocWlpaTA8OBANwU1MTQFQcp444NdbYdXn2A7L9v//93//Ngw8+2K2/0djYSGlpKePHj++d\nRkaA04/jqfLy8jh48CAQHb17Omc6pxh87mJjY9t891PFxcUxatQoqqurgWBve1xcHMXFxQBcfPHF\n5/T/HR1dYyIiIiIiZynkPcQej4empibzhHT48GHuueceAP74xz+e1d/Mz88HWutPooldk3TxxRfj\n9XoZMmQIAAcPHiQlJaXNZwOBABs3bgTg3/7t30hOTmblypUA5veiybRp0wBYt24dgOkdt3uC7WWO\n3nrrLZ566ilSU1MB+Pjjjxk4cGBfN7fXfPLJJ5w8eZIJEyYAPetRqK6upqCgwJxnABkZGVRWVp7v\nZvY5xZr27F6n5ORkLMsy5UPf+c53mD9/frf+xrp163juuedYs2ZNr7Uz3I0cORLA9ATbJkyYwK5d\nu0LRpF6jc+bMFIPPjs/n63CUJSkpCYBFixYxdepU3nvvPQAee+wxGhoaKCwsBDD5zdkKeTFKfHw8\nAPX19QCsWLGCHTt2dPk7AwYMAOCCCy7gwIED5gK1FRQU9EJLw8O+ffuA4NBbQkICf/rTnwDaJcMQ\nrI0dPXo0EBzC8fl8Ubv2ZXV1NX/4wx8AGD16NBs3biQtLa3NZ+xjNHHiROrq6kx99ZEjR6IqIT6X\nYaOEhIR2tW92GU6kU6xp79Rh71OHad9//31+8IMfAJ3XLNr1ko888gglJSW939gw9cEHH7RLhP/h\nH/4BgG3btoWiSb1K58yZKQafnR/96EftXsvIyKC0tBTA1N7PmjULgHfeeYf169ebTrCzneRpU8mE\niIiIiDhayHuIY2NjSUxMNEMKNTU1nRZUJyYmUlZW1ma4v6ysjBtvvNH8u8vlOufC6nB24YUXAsEh\nhH79+pGRkdHl5+1JdDt37mTUqFHtek2jxbRp08wSdK+++mqX3/PIkSN4PB5zzoXxvNI+Yx+DadOm\nmR5UgLS0NB599NFQNeu8Uqxpz+5N6d+/f5tVWf785z/z9NNPAzBv3rwOJ4PZo00HDhww157TFBcX\nm+Fa26hRo6KyZ9imc6Z3OCEGd+bo0aMAZoTBNmHCBLZv396u5MQuoZg7dy4ff/yxOV47d+48t1E7\nK8x89NFHVnx8vBUfH28BVkpKilVdXW1VV1d3+PmWlhZrxowZ1owZMyzAAqyrrrrKuuqqq/q45X2j\nsrLSqqystNLS0qyEhARr586d1s6dO9t9LhAIWEVFRVZ+fr6Vn59vZWZmWjt27AhBi3tXeXm5VV5e\nbiUkJFjp6elWenq65fV6u/yd2267zQLMeXbw4ME+am348fv91rZt26yUlBQrJSXFXEP2P9u3bw91\nE3uNYk0wTgQCASs7O7vdf/ukpCQrKSnJ+vTTTzv83U2bNlmbNm2y3G63FRcXZ/6WE6xcudJauXJl\nu2Pmcrms8vLyUDevV+mcOb+cHIMty7K8Xq+VkJBgJSQkmO+cm5tr5ebmnvHcOHTokJWRkWG5XC7L\n5XJZ//Iv/3JObQl5D/Hpxo8fz+233w7A/fffz5QpU7r8vNfrZe/evW1esye4WJYVFcvcnMquoUlN\nTeXYsWN897vfBYJrQPbv39/0eP30pz9l3rx55qnzmmuu4fLLLw9No3uRvcxP//79WbVqFXDm+rXa\n2lrcbrepD83MzOyDloaW3RuzevVq7rvvPmpqatq8fjq7F2jcuHF908AQUKxpXQZs/vz5fPWrX20z\nWmIvr/XUU0/xyiuvtJvsYteAJicn43K5ulx6LJo0NzfzT//0Tx2+N3ny5KiPJzpnzo5icFv29x4+\nfLhZzhKCNcOffvopcOZlCocMGcKwYcPMsTzX7Z1VQywiIiIijhZ2PcT9+vXjzTffBLq3iPmCBQva\nLE8CmN1vIrHH5kzsHmDLsvD5fHz00UdAsJbmoYceMk9I//M//0MgEDDLvtjLlESrK6644ow94HZP\nRmpqKrGxsWYb62g8T05VUVFhloVqaGjo0e/u2bOHK664ojeaFXKKNa1mz57N8uXLWbt2rXnNvl7e\neustNm/ezAsvvADAddddx7Fjx8zGAomJiaSkpJg6QHsHyGjdQMCOqR0pKChg7969Zptee8t4u+bR\nPqbRMJdD50z3KQa35fV6zaYs9u65ANnZ2Rw+fLhH8dS+xqB1KUD79Z4K+TrE56KyspLs7Ox2O7p8\n+OGHAFx77bWhalqvsb/rL3/5Sx577DEz1JCQkEBSUpK5cFwuFxdddBE//elPgejZnvh0c+fOBYIX\nmL1lamff1V5mbc6cOXz44YdcffXVQPDhwS6fiEYLFizgoYceAro/gdAuOxk8eDBFRUVcdNFFvda+\nSOCEWOPz+fjWt74FBHfSOnHiBECHEw/dbrdJXvx+P/Hx8XzpS18Cgksi+f1+brrpJiB6khz72ukq\nlt5www2UlpaaRO/04fCYmBguvvhitm/fDnS8XGYk0TnTPYrBrbxeL8OHDzfXCLReB5999lmPHhYP\nHjzItGnT+Pvf/w7ACy+8wH333XfWbQu7HuLuaGxsBILbE5964WVlZbF06dKouDl1xg7GX/3qV0lP\nT+fJJ58Egk9Zfr/fnAz/+I//aNZdjWb2KgBr166ltrYWgPT09Hafa25u5sCBA0BwHdrExESzMYfH\n44nqhPjrX/86999/PxC8UZ0akGtra9m9eze7d+8GgrPmt23bZvaSP3r0KPn5+aa+dv369X3b+BBz\nUqyJi4vjJz/5CRDcuvpXv/oV0HFy4/V6TU2+fT7ZydDYsWNJT0+PuqTmiSeeOONniouLqaur67SW\nMRAIkJSU1GZL8Eimc6Z7FINb3XzzzW2S4YyMDLOOt31PPhP7QXP+/PlUV1eb+7fdyXW2orPbUERE\nRESkmyKuZKKmpsb0CtpP4QkJCUCwNidanzA7UlVVZYaYDh48SExMjFl3OCcnJ5RN6zP2VqFPP/20\nqdsrKSkhMTHRPIWXlZVx7733mpmrTU1NeDweZsyYAcB//dd/mdU7JMje9tpexcOux/J6vY65xpwc\na8rLy035lb1rln0OJCQkYFmW6QX0+/0MHjzY7KIZTbs+nsoecetoVQD72PTv3x+fz9dmHdnTlZSU\nRPz61R3ROXN+RVsMttestmOqPdr9l7/8hTFjxnT775SXl/Ptb38bCO4S6Xa7zb385z//eaerTHVH\nRCXEzc3NTJgwwSx1BMGDai/27YQSgVPV1tZy2WWXAcHam/j4eLPfuT2JI9rZy60MHz6clpYWAJ55\n5hlmzpxpEuDnn3+e4uJi+vfvDwSXWaupqeEb3/gGEKwpjsQA05vsG1d6ejp1dXXm2mpqaor4CWTd\noViDmZ9w5MgRioqKuPnmm4HgjW3u3Lls2bIFCNaGFhUVdTnZLBrYN/CObpkjRowAgg/mQ4cO5d//\n/d8B2LhxY5vPud1uGhoaonaZMZ0z50+0xWB7js8DDzwAYCbV/elPf+r29zlx4gSXXHKJmZjocrmY\nMmUKS5YsATD3+LMVETXEdgCaP38+n3zySZv3qqqqHHFzOl0gEODuu++mvLwcCB6j1NTUqA20nbHr\nhbdu3Wp6yxcuXMhLL71k3vP7/YwZM8b0mldWVlJTU2MuSCXD7dk3/yuvvJItW7aYCR2RGIh7QrGm\nld0bPmLECJPwQXCFBI/HY3rN8/PzHZHY2PWNJ0+ebPeeXUs+depUUlJSuPvuuwHYtGlTm3rapKSk\nqL6GdM6cP9EWg08fNbF7dbv6PpZl0dLSwtatW4FgXK6qqjI1w5dffjlPP/30OSfCNtUQi4iIiIij\nRUQPsf1EvmDBgjZP25MmTepwRQEneP/991m9enWb4buYmBgzZHUudTSRaOzYsXz22Wfm54aGBlNO\ncfjwYTweD2vWrAFg9+7deDwesy6ktGfXAG7cuBHLsqJuHczOKNac2dGjR9m1a5fp7YumlTa6cuut\ntwKYtatPtW3bNvNeSkoKzz//PNB+tYV+/fpFbA/fuXDqOXMuoi0G33bbbQBmmb7/+7//A4I10hkZ\nGUBwLtRjjz1m1rYOBALExcWZkbn4+HjS09O54447gOBa2GPHjj1vbYyIGuKdO3cCtFljF4ITXaJ1\nfd3O2AGlqKiIhx56yGyFmZCQwIUXXsiKFSuA4JCVEwNvR/x+Px999JFZNungwYNce+21vPPOOyFu\nWXiqq6sjPz8fCN7I0tLSzJBVTyY/RCLFmjO74YYb+MMf/mB+/uijjxyR4NidDYmJie3eO7VUze/3\nt0uE7XKLXbt2MWrUqF5sZXhy6jlztqI5Bo8ZM4aSkhJzzQwaNIjPf/7zACxfvrzNNs42e23if/3X\nf+Xee+8lLy8POP8dfxHRjbho0aI2Pz/88MNA9G420Rm/32/WcczIyCAvL4/S0lLzXn19vVmjcNas\nWY6ZWHcmLpeL1157jeLiYvOzPXM3WgUCAZPMnalGq7GxkaKiIgDmzZvHnj17zPq7brebVatWRXwQ\n7i7FmjOzN5WwjR49OkQt6Vt2fWxxcbHpoaqqqiIQCJjJlvYIw6mrKxQWFpreMKfOV3DiOaMY3LFF\nixYxefJkMwm+oqLCdEQkJSURCATarOSSnp7OG2+8AUBhYWGvxmJFeRERERFxtLAvmfD5fAwaNAgI\nDiNkZma22eXECeynJZ/PZ3opamtrWbp0qdmprqmpif79+/PFL34RgP/8z//U2rr/3/HjxxkxYoSZ\n5XrRRRexd+/eqO318/v9rFmzxvQ4zJw5k0svvdS8X1dXZ4YrT11WzBYTE2OW81uzZo25/qKdYk33\nDBw40NTnA7S0tDhudZvT2cO89lCwvaJNT7ahjWZOO2cUgztnWRb5+flmhayMjAzWrVsHBEdUmpqa\nzH4KMTExFBYWdlim1BvCvmRi4cKFJtjExMRQVlYW2gaFgJ3IeTwes4D58ePHOXr0KJmZmeZzjz76\nKLNnzwacsw5xd7z44os0Nzeb/dLfeOONqE2GIXidVFRU8MorrwCwePFihg4dCgTPpcbGRrNeNQSH\n8+xla37/+99TUFDgyPpzxZrumTx5MitWrDDXkNMm8HbE7qiwl3KUtpx2zigGd87lclFSUmLKPzs6\nF+z66b4WvVmBiIiIiEg3hHXJRGlpKePHjzcrK9xyyy2OXBmgqakJCO72Yw/BVVRUUF5ebmYsZ2dn\n99mwQqSwj1tWVhbNzc1cffXVQHAIKtont1iWxYYNG4DgzN2KigogOMowceJECgsLARg5cqRZ8sbJ\nFGu675NPPmHmzJk88sgjQHCnR5GuOPGcUQyOPGGZENvb8n3lK1/ht7/9ramFLSsrU02WdJtdl/Tl\nL3+ZESPbvIUzAAAgAElEQVRG8PbbbwOQm5sbymZJGFGsERERCMMaYsuyqKurA4J1JMXFxTzzzDOA\nJihI9wUCAbN16KxZs5g1axYXXHBBiFsl4USxRkREbKohFhERERFH65OSCcuyejRj0p596PF4qKmp\nITs7G4j+malyftn1oMePH2fAgAFm+0eJXoo1IiJyNsKyhlhEREREpK+oZEJEREREHE0JsYiIiIg4\nmhJiEREREXE0JcQiIiIi4mhKiEVERETE0ZQQi4iIiIijKSEWEREREUdTQiwiIiIijqaEWEREREQc\nTQmxiIiIiDiaEmIRERERcTQlxCIiIiLiaEqIRURERMTRlBCLiIiIiKMpIRYRERERR1NCLCIiIiKO\npoRYRERERBxNCbGIiIiIOJoSYhERERFxNCXEIiIiIuJoSohFRERExNGUEIuIiIiIoykhFhERERFH\nU0IsIiIiIo6mhFhEREREHE0JsYiIiIg4mhJiEREREXE0JcQiIiIi4mhKiEVERETE0ZQQi4iIiIij\nKSEWEREREUdTQiwiIiIijqaEWEREREQcTQmxiIiIiDiaEmIRERERcTQlxBK1AoEAgUCAsrIyfD5f\nqJsjIuIoisESSZQQR4mGhgZ27tyJz+dT4AGee+45YmNjiY2NZcSIEbjdbuLj44mPj2fDhg2hbp6E\nuZaWFt59912WLFnCkiVL8Hq9oW6ShJhlWViW1eHr06dPZ/r06bhcrjb/ZGVlcf3115OTk0NOTg6p\nqanceeedIWh931MMlkijhFhEREREHM1ldfTIKxFjzJgxAOzfvx+AuLg4AN58801mzpxJTIzznnkO\nHTpEXl5el5+ZPn0677zzTt80KIwFAgHTmyWtCgsL+fjjj8nIyABg+/btDBw4MMStklBoampi3Lhx\nlJWVAeB2u8nOzgagpqaG2traHv291NRU6urqAKL2ulMM7j7F4PChhDiC5eXlcejQoU7fHz58OMnJ\nyQAcP36cadOmsWTJkr5qXsiMGTPGPCB05eqrrwZg06ZNvd2ksDJkyBCOHTvW4Xs33ngjr7/+OkOH\nDgVw5AMVQHp6OrW1taSlpQGwb98+c0zEGfx+PwBZWVlUVVWdt7/rcrloaWkBWjswoo1icNcUg8NT\nWCfEHo+Hhx9+mNWrVwNw8uRJEhISTJIHcOzYMZqbmwHwer243W4++OADAG644Ya+b3Qfio2NJRAI\ndPheXFwco0aNorq6Ggj2ZMTFxVFcXAzAxRdf3Gft7Gu5ubmUl5ef8XMJCQkAVFdXk5KS0tvNCgsz\nZszg3Xffbfe63TuRnJxMamoqd911FwDPPvsssbGxfdrGcJCXl0d5ebk5R/7yl78wcuTIELcqNJwa\nh+2kNTc3l4qKCnMd5OTksHjxYvO5pUuXsnnzZgDKy8vb9fS1tLS0idNxcXE0NTUBRO21pRjcOcXg\nzoU61ujRQ0REREQcLeQ9xB6Ph6amJvN0dPjwYe655x4A/vjHP57V38zPzwfo1pBNpPL5fLjd7nav\nJyUlAbBo0SKmTp3Ke++9B8Bjjz1GQ0MDhYWFAKxcubLvGttH7F6XzMxM6urqzDk1YMAAc56dyu6R\nKCsrY/DgwX3b2D42bdo0ANatWwfAL37xCwDTC+HxeAB46623eOqpp0hNTQXg448/dlTtrN2TN2LE\nCA4fPkx8fDwAJSUlDB8+PJRN61WKw5375JNPOHnyJBMmTAB61qtbXV1NQUGBqT8GyMjIoLKy8nw3\nMywoBndOMTgonGNNyAuY7BtOfX09ACtWrGDHjh1d/s6AAQMAuOCCCzhw4EC7i6ygoKAXWhpefvSj\nH7V7LSMjg9LSUgD69esHwKxZswB45513WL9+vbkYA4FA1NUm2cMo06dPZ8aMGdx8881A6w2soqIC\ngC984Qvs3r3bJD/V1dVRHYyrq6v5wx/+AMDo0aPZuHGjqY212TemiRMnUldXZyb9HDlyJKqC8Zmc\nPHkSgMrKSgKBgDmnPv3006hOiBWHO3cu5WUJCQnt6o8ff/zxc21S2FIM7phicKtwjjXRlRGJiIiI\niPRQyHuIY2NjSUxMNE+QNTU1nU4US0xMpKysjCFDhpjXysrKuPHGG82/u1yuqJ4wdvToUQB+8IMf\ntHl9woQJbN++vd1wnl1CMXfuXD7++GPzVLZz586o6cGx2b3ir732WofvZ2ZmArB48WImTZpkNjDZ\nuXOnGXKJRtOmTTMbS7z66qvteiZOdeTIETwejzmPwnjOba/YvXs30Dr0a3//tWvXMnny5JC1q7cp\nDp9f9nkzbdo0E3MB0tLSePTRR0PVrF6nGNwxxeBW4RxrwqKH2OVymR1spk+fbv4dgsMI1dXVVFdX\n4/F4yMzMbLMTUE5ODpdffjmXX345EDx5Vq9ebWYpRhOfz0deXh55eXnmBMrNzSU3N5edO3d2Wds2\nfvx4EhISzG5LL7zwQl81u890dy3HJ598Ep/Ph9frxev18te//rUPWtf3/va3v/G3v/2N4uJi0tLS\nSEtL47LLLuvyd37+85/j9XrNlqt2HZtTFBQUdPig6IRloRSHz10gEOCPf/yjud4++uijNu9/+OGH\nIWpZ31AMbksxuGPhGmtC3kN8uvHjx3P77bcDcP/99zNlypQuP+/1etm7d2+b1+zCasuyomKxa/vJ\ncvjw4aZGC4I1w59++ilw5gXehwwZwrBhw6ipqQFw5PbO9ndev3490HrMonUr1QcffBCA/v37s2rV\nKqDzdU/tNVdra2txu92mZsvu0XGKxMREILj2rD0aA3TZoxONFIe7Zsfk1atXc99995m42tkW3/Z8\njXHjxvVNA8OUYrBi8OnCKdaERQ+xiIiIiEiohF0Pcb9+/XjzzTeB7m1ruWDBgjZL2gBmtmo09Ep4\nvV7Gjx8PtM7QBcjOzubw4cM9+o6nDmclJyeb2qRoOE5nYlkWY8eOBVp7Keyhq9zc3JC1qy9cccUV\nZnipM/a5kJqaSmxsLFlZWYAzzo2OfO5zn2P16tXm+990000hblHfUhzuXEVFhdmkpaGhoUe/u2fP\nHq644oreaFbYUwxWDO5IOMWasEuIoftfqrKyku985zvtdgGKlu2JvV4vw4cPbzN0ay/NUlJS0qNl\n044cOUJjY6PZGWjSpElRf6HZNm3axJQpU9oNZ3788cchalHfsJcJ83q9Z1w7tbGxEQheP4mJiQwb\nNgwILqNkT8x0kqSkJFwul7lenLhLneJwx95++21zvXSXHatvueUWioqKuOiii3qjaWFLMVgxuCvh\nEmvCMiE+E/vEGTZsWJsDk5WVxdKlS7n22mtD1bTz6uabb26TDGdkZHDw4EGAbhfa2wFo/vz5VFdX\nm7okew/5aOT1elm+fDlPPvkkQLunSQg+lZ66HWQ0smfmrl27ltraWgDS09Pbfa65uZkDBw4AwbUh\nExMTzfnl8XjMOeMEdi/N+vXrsSzL1BRH8xrEZ8spcfh0X//617n//vuB4CS6U1cBqK2tZffu3Wa1\nkuLiYrZt28ahQ4eA4CpB+fn5pk7SrqWNNorBQYrB50dfxRrVEIuIiIiIo0VcD3FNTY156rLrkOxh\nzcOHD/doW81wdezYMQDWrFkDtA63bdiwoUdLsJSXl/Ptb38bgA8++AC32222jxw9evT5bHJIlJaW\nsnTpUiC4g429EsAbb7zBmjVr2qzIcbrs7Ow+aWMo2bOZt23bZradLSkpITEx0fRqlZWVce+995rV\nSpqamvB4POY8s0t0nMLeTvXEiRMAXHLJJQCMGjUqZG0KR06Iw12xl4g6XVJSEllZWXz+859v87p9\nXj344INYlmV2LfP7/RF9rBSDu6YYfO76MtZEVELc3NzMNddc02bJsJiYGLPFYSQHllO98847bX62\nb8o9SWJPnDjBNddcYyZ9xMTEcN111/GTn/wE6Hzpl0jx2WefMWvWLMrLywHIy8szy9MUFRV1GYgh\nGIyjfVLhv/7rvwLw4x//2JTevPjii8ycOdME3+eff559+/bRv39/IFgaUFNTw6RJkwBnBePa2lq+\n+93vmp+zsrJ47733gNbl2MQ5cfh8skssHn/8cerq6nC73QA9mgcSbhSDz0wx+Nz0dayJiKzIvmjm\nz5/PJ5980ua9qqqqTp/WI9WpOxsBzJgxA+g6aFiWRUtLC1u3bgWCx6qqqsrUHl1++eU8/fTT5qKL\ndJZlERMTY+qJDh48yJ49ewA4efLkGX9/y5YtJvl56KGHSE1NNYEnkm9Sp7Jr1bZu3WpWSVi4cCEv\nvfSSec/v9zNmzBhycnKA4KSFmpoas7KJk5KbcePGmTq/hIQEduzYETXXy/ngtDh8Ptkx5corr2TL\nli1mUl0kJ4KKwWemGHx2QhVrouOsExERERE5Sy4rAjbKtrvHc3NzTW0fBJcO27hxY6ia1WvsbSzt\nukW7ZGLdunVkZGQAwafxxx57jLVr1wLB2c5xcXHmySk+Ph6fz8cdd9wBwOzZsxk3blzUPHn7/X5e\neukl/uM//gMIzt62h+i6e0rbvTOxsbEMHDiQH/3oRwDcc889vdDi8NHQ0GB21jp8+DAej8fUq69e\nvZr6+noz0jBw4MCQtbMvNTQ0MGDAANPbNXfuXH72s5+FuFXhxWlx+HyqqqoCgnW2lmWZndkWL14c\nymadE8Xgs6cY3LVQxZqIKJkoLS0FWie62BdRUVFRyNrUm+w1T0ePHk1JSYkZMpgwYYKZrLF8+fIO\na7TsSQ333HMP9957L3l5eUDk1wyfLjY2lgcffNAsh/W1r32tzcYlHbHr9uLi4hg0aJApTfF6vdx2\n22186Utf6t1Gh4mUlBQzNJmdnc1HH31kln/6+9//zrXXXuu4IGwveWRP1njiiSdC2Zyw5LQ4fL7U\n1dVx6aWXAsEJU2lpaTzyyCMhbtW5Uww+e4rBXQtVrImILGnRokVtfn744YeB6Kkz6syiRYuYPHky\nLS0tQHCHpJ07dwLB2cyBQKDNQufp6em88cYbABQWFkb98YmNjeWLX/wiAJdeeikrV64EgsepoKCA\nyZMnA8En7Gg/FmfL5XLx2muvUVxcbH5+8MEHQ9yqvrdp0yZcLpepubdHYqSVU+MwBEfg7JvymeZy\nNDY2mhv3vHnz2LNnj1lH1e12s2rVKsaMGdP7je4DisHnTjG4vVDFGp2hIiIiIuJoYV9D7PP5GDRo\nEBAcesrMzGyze1s0syyL/Px8s6xNRkYG69atA4JDu01NTWZWb0xMDIWFhVoeSnrk+PHjjBgxwgxd\nXnTRRezdu9cxvTl22dGcOXNYuXIlV111FRDcWcopx6A7nByH/X4/a9asMb2+M2fONCUQEDwe9k5Z\n+/fvb/f7MTExXHbZZUBwbXn7OIqAYvDpQhlrwr5kYuHCheamFRMT0+EWkNHK5XJRUlKC3+8HOq4D\nzs/P7+tmSRR58cUXaW5uNvVsb7zxhmMCsd/vNw+Y+/fvJzc3l/nz5wPOKAPoCSfH4ZiYGCoqKnjl\nlVeA4ES4oUOHAsElMhsbG6msrDSfd7lcZrm+3//+9xQUFET08mrSu5wcgzsSyljj3KMuIiIiIkKY\nl0yUlpYyfvx4s0vJLbfc0m4XNxHpuaamJiC4G1tzczNXX301EBzSjfaF4O0Rl6qqKhYuXAhAY2Mj\nd999t5nspB69VorDwfK1DRs2AMEVfuzVFDweDxMnTqSwsBAIrhCkCZnSHU6OwZ0JdawJy4TY3m74\nK1/5Cr/97W/p168fENzz215WTETOnl0q8OUvf5kRI0bw9ttvA8F1H53CsiyzXqqThyg7ozgs0nsU\ng1uFS6wJuxpiy7LMosz5+fkUFxfzzDPPACgIi5wHgUCAESNGADBr1ixmzZrFBRdcEOJW9T2Xy6We\n4E4oDov0HsXgVuEUa9QtIiIiIiKO1iclE5Zl9agnxq7x83g81NTUkP3/2rv34Cir+4/j710SkpCQ\nEC4SbnInXCqiBtGiAwjtpGi1INPpCKVChapjKYNO0Y6iIO3PW3WmQ/GCtI6RVtTaiCKDQZSA3ORi\nZEC5FCMiJgTIBZLNbXd/f+w8BzYXSIDsbvZ8Xn/J7iYc1uc85/uc8z3f060bEH2nrYmEi5OjderU\nKTp06GCO/JbopfuwSOSI5ntwa73XRGQOsYiIiIhIqChlQkRERESspoBYRERERKymgFhERERErKaA\nWERERESspoBYRERERKymgFhERERErKaAWERERESspoBYRERERKymgFhERERErKaAWERERESspoBY\nRERERKymgFhERERErKaAWERERESspoBYRERERKymgFhERERErKaAWERERESspoBYRERERKymgFhE\nRERErKaAWERERESspoBYRERERKymgFhERERErKaAWERERESspoBYRERERKymgFhERERErKaAWERE\nRESspoBYRERERKymgFhERERErKaAWERERESspoBYRERERKymgFhERERErKaAWERERESspoBYRERE\nRKymgFhERERErKaAWERERESspoBYRERERKymgNgifr8/3E0QERGRZtDYHRoKiKNcQUEBBQUF9O/f\nn9GjR+PxePB4POFuVkj4fD58Ph/5+fnU1taGuzkiIiJNYvPYHS4uvx49okp5eTkA06dPJzs7G5/P\nF/T+rbfeCsAHH3wQ8raF0lNPPcUjjzwS9FpsbCwAOTk5jBkzJhzNEhGJKk4I4XK56r0+efJkALKz\ns4Pe69q1K4MGDeLw4cMAlJSUMGnSJLKyskLQ4siksbu+6upqVq9eTUlJCQDTpk0z43hL0AyxiIiI\niFhNM8RR5NFHH+XPf/5zo++7XC6KiooA6NSpU6iaFXLffvstffr0Oe9nJk2axLvvvhuaBkmrUVNT\nA8CePXuoqalh6NChACQmJuLz+fjuu+8AePnll5k7dy5paWlha+vl5vP5cLubP0fizGRdzM9K61VZ\nWcmwYcPIz88HAitw3bp1A6C4uJjS0tJm/b6kpCTKysqA+rPN0U5jd8MyMzP57LPP6NKlCwA7duyg\nY8eOLfb3KSCOEvfeey8vv/xyo+/369eP3bt3k5ycHMJWhceQIUP4+uuvL/i5G2+8EYDNmze3dJOk\nFbj99tt5//33g16Li4sDoE+fPhQWFpoB2+fzkZqaSmFhIUCLLuOFwtChQzl06BCjRo0C4N///jc9\ne/Zs0s9WVFRQUVFB586dW7KJEiG8Xi8AaWlpnDhx4rL9XpfLRXV1NQAxMTGX7fdGOo3djUtNTaW0\ntJT27dsD8NVXX9G9e/cW+/si+qrzeDzMnTuXNWvWAHD69Gni4uJo166d+UxRURFVVVVAYHYnNjaW\ntWvXAjBu3LjQNzrEnM1iDXWoPn36mBwtm564KyoqmvS5Xbt2AYHcrcTExJZsUsRQn2rYY489FpSb\nl5CQwPjx45k/fz4APXr04M033+Sll14CAhteUlJSzIxyaw2InfvHV199BcC2bdsAeP/997nvvvua\n/HuOHTtmZUBsY39yAuKYmBhcLhdt2rQBoGfPnixbtsx8Lisriy1btgBw5MiRemNQdXV1UJ5smzZt\nrBqnNHZfWEpKCqWlpeZBqaU3FWqNS0RERESsFvYZYo/HQ2VlpXkKOnr0KDNnzgTg888/b/bvq6mp\nMTMbTVk2b+3S09MbfH3EiBHs3r07xK0Jr8rKSgCzI9W5pjp06GCus3M5y3IejyeqZojVp5rOyX98\n+umn8fv9XHHFFQAcPny43jUxf/58Ro8eDcDvf/972rVrx5kzZwCCZgRbE2eWyuVy4ff7zezf999/\n3+TfsWXLFp566ilycnJapI3hpv4UrG3btgBs2LCB06dPM2LECAAzU+yYMGFCo7/j5MmTZGRkmP4H\ngeXxur8jmmnsbpyzcuD3+4NmyFt6JS7sAbHTuZyBJTs7m507d573Zzp06ABAr169OHjwYL1AJyMj\nowVaGnnWrl1rllUcI0eOBGD79u3haFJYOcuSkyZNYvLkyaZMjXOTdfI9f/rTn7Jnzx7T6U6ePBlV\ny73qU03npAq4XC7i4uL44osvABp8QHK73QwePBiA3r17U1tb2+rrgjoDTGxsbNAS9urVq3niiSeA\nxvM5neB53rx57N+/v+UbGybqTw0bNGjQRf9sXFxcvfxjJz3JBhq7z+/06dMAHD9+HJ/PZ8b2b775\nhiuvvLLF/l6lTIiIiIiI1cI+Q9ymTRvi4+PNLF5xcXG9gtSO+Ph48vPzzbImBJY8b7nlFvPfLpfr\nkp5cW4u8vDwyMzODXhswYIDVT5fOLtzXXnutwfe7du0KwLJlyxg9erRZLt61a1ejy1etkfpU0/Xr\n1w8IbKJLTk425X0as3fvXiBwzQwYMMDsfm6tnFJpKSkppqwTwJdffsmiRYsAWLhwYYMbe5zZ8YMH\nD5rNhdFI/enycYpaTZw40cy4A7Rv354HH3wwXM0KKY3dF7Znzx7gbBqkc92sW7euRQ/VCntADIHl\nSmdZatKkSSxZsgQI7EJNTEzkyJEjAA3Wn+vZsyfXXnstELjZ+P1+s+N3wYIFoWh+SK1atQqAO+64\nI+h1l8vF+vXrw9GkiNHU3biPPfZY0FHO//vf/1qqSWGjPtU0zr/f5/Nx/PhxcyO+5pprgj7n9/vZ\ntGmTyf2sra3l+eefb9GamKFUNy3C5/Px3HPPATBz5swG63o731VtbS1ut7vRE8uigfrTpfH5fOzc\nudNU1XBOZXN88skn4WhWSGnsbrrGUopaukRqRATE5xo+fDh33nknALNnz2bs2LHn/XxNTQ379u0L\nes3ZqFA3Ibu1q6qqqteZHGPGjDEzoNIwJwh2bj7OtfHrX/86bG0KBfWpxjmrCklJSRQVFfGnP/0J\nCNThTUlJMTOBzz33HAsXLjRB349//GMT5LRmzv/LxYsXc88993BuWXqnvyxYsIDly5fX29Di5A23\na9cOl8tlPt9aS9A1lfpT45yVgjVr1jBr1iyKi4uDXq/LWaEYNmxYaBoYJhq7myc+Ph4I1LouKCgw\nr7f0ipxyiEVERETEahE3Q5ycnMyKFSuApi29LVmyJKh0C2AqBkTTkzdgyts0JCMjg3379pmjM10u\nFy6Xi4SEBOBsDk5rz3m8WH6/3xzD68xkXX311UCgYkA0U59q3LnlfWpra9m4cSMA999/P3PmzDHX\nyr/+9S98Pp/pg+ce4hENpk+fzsqVK1m3bp15zblnvPXWW2zZsoXnn38egJtvvpmioiJz6EJ8fDyJ\niYlmJsc5SSpaS2ipPzWssLCQ/v37A/VTIi5k7969XHfddS3RrIigsfviXHPNNaxZs8b0k/Hjx7fo\n39eqj24+fvw43bp1C9rgEBMTY/KRbrrppnA17bJy/hc5y0sNGTduHIcOHTKDUt0lKrfbzaBBg9ix\nYwfQcFmpaLV582bGjh1b7ztxbtqttYZsS7ClTzmcf+err77KH//4R1PeJy4ujoSEBDNIu1wuBg4c\naPJqz9cXW6va2loeeughIHDKmFPPu6ENZLGxsSbg9Xq9tG3bll/+8pcATJ06Fa/XawavaA2Mm8Km\n/rRkyRLmzJkDQFPDCid3vXPnzuTm5jJw4MAWa184aOy+NHfeeSf//e9/iYuLA+A///kPEydObLG/\nL+JmiJvCOZq3R48eQTeatLQ0srKyouomA/Dwww9f8DN5eXmUlZUFbRY7l8/nIyEhwYrgr6amhpUr\nV/LYY48B1JudgcAsjw3fRVPZ1qcczkB1zz33kJqaaq6ZwsJCvF4vs2bNAuBnP/uZ2VQVrWJiYnjm\nmWeAQB3Q119/HWg4IK6pqTF1iJ1B3wmghw4dat0hC3XZ2J8eeOABZs+eDQSumXOD4tLSUvbs2WM2\nYubl5bF9+3a+/fZbIHAUenp6usnHjpZNZhq7L45z7axfvx6/329yiluyBjEoh1hERERELNfqUiaK\ni4tNjUfnicqZTi8vL4/KWQlnZqqhnbpObk1KSgq1tbVBtR3r2r9/f1TVvzx06BBZWVlA4EQoJ8fq\njTfeICcnxyx/NyQ9Pb1VHpvaEmzsUw05ceKEWeY/fPgwbrfb1B3u2bNnOJsWckeOHDHpIs6JYs69\nJi4uDr/fb2Y+vV4vnTt3Nqf+RUspuoul/tR0L730EoApZ+hcYzU1NVHxPWnsvjgvvvgiENjLAWdT\ni3JycsxscUtoVQFxVVUVI0aMCApk3G63KRAfrUuazrJuQ/+r+vbtC8CiRYvo3r07jz/+OACbNm0K\n+lxsbCzl5eVRUxLphx9+4Be/+IWp/9mnTx9TuuaTTz6hrKzsvD8/duzYeuXXbGRrn2pIaWmp2Wj5\nww8/0LZtW44fPw5gNrjYxHmgPHbsGLm5ueYo9KKiIu6//362bt0KBO4tubm55904ZAv1p+ZxHqpS\nU1MpKysz309lZWVU3Jc1djdfaWmpqXteUlJCWlqa6U8pKSkt+ne3ihxi52JavHgxBw4cCHrvxIkT\nUX+TSUpKAs6e730u58lpwoQJJCYmMmPGDCCwkezc3LWEhISouME4/H4/brfb/BsPHz5sZvMa+p7q\n2rp1q6k5O2fOHJKSksxmhWjcMFWX7X2qLp/Px4wZM8wDlt/vJykpyZpBqCHOrGbfvn3N4A2B3e4e\nj8fMfqanp1sfDKs/XRznXnv99dezdetWs6kuWsYqjd3NN2zYMEpLS4HAPWjnzp0tHgg7on/kFxER\nERE5j1YxQ+w8XS1ZsiToyWn06NGkpqaGq1khc/vttwOY2pfncs4/X7FiBYmJiTz77LNA/Z3hycnJ\nUfWU2bVrV6ZOncpf//pXILBL+Xw5w3VVVlby9NNPA4FTyDp27Mj//d//AYGjaqOd7X2qrtWrV7Nm\nzZqgpU23222uqbpHG9usoKCA3bt3mxniaKyY0FzqTxfHyU/ftGkTfr8/6moRa+xunvLycgoLC82/\nd9asWaaueSi0ihziXbt2AQTVBIXAhgUblredQbmhZPJzl3S9Xm+9zuQs2ezevZsBAwa0YCtDz+v1\nsmlm9wAAABTKSURBVHr1agB+97vfUVhYCDReA9P5rmJiYujUqZPZxFBTUxMUXDvfWTSzvU85nKAu\nNzeXOXPmmOOI4+Li6NevH9nZ2UAgbcCWQelCxo0bx6effmr+vHHjRuuDYvWn5isrKyM9PR0IPGS1\nb9+ebdu2ATBkyJBwNu2y0djdPF988QUjR440383Bgwfp0aNHyP7+VjHt8corrwT9ee7cuYAduZ5w\nNpcvLy+PadOmAYEna5/PZzZrODMU5+4Ez8zM5J133gGiszh+mzZt+PnPfw7AVVddxXvvvQcEashm\nZGQwZswYILDr3ZZrpals71MQGIScWrpdunShT58+HDp0yLx35swZs/Fy6tSpVm6sa4hzQIBj8ODB\nYWpJ5LC1P/l8PjPmnO+B0e/3U1FRQW5uLgALFy5k7969pl5zbGwsq1atippA2KGxu3k2b96My+Wi\nQ4cOQOC+HErR3VtFRERERC4g4lMmamtr6dSpExBYYunatas54lDOLsns37+f2NhYUy/VxnPPpWls\n71NOTdDa2lozg1NaWkpWVpY5qa6yspKUlBSzAvHCCy+QnJwcngZHmI4dO1JcXGz+XF1dbXU1Dlv7\nk9frJScnx8z6Tpkyhauuusq8X1ZWZlJpGqr57na7TZnDnJwc8x3aQmP3Wc53cffdd/Pee+9xww03\nALBu3bqQrrJEfMrE0qVLzZfldrsbPIbXZs6APnz48DC3RFoL2/uUkzvu8XjMIRKnTp2ioKDA1LIG\nePDBB5k+fTpgZx3ixowZM4bs7GwzUNm+4dDW/uR2uyksLGT58uUALFu2zGyAOnPmDBUVFaaONwRS\nApzyWR999BEZGRlW5+Vr7A7wer18/PHHQODBqXfv3ixevBgIfcqRUiZERERExGoRnTJx6NAhhg8f\nbnaC33bbbbz77rthbpVI66U+FUiHgMCJa87yZGFhIUeOHDG7ubt169aiR4S2ZgcOHGDKlCnMmzcP\nCCxz2sr2/uT3+9mwYQMAK1euNJV+PB4Po0aNIjMzE4D+/fuHfIOURDZnQ/OJEydYunQpABUVFcyY\nMcNsrgz1CkJEBsTl5eUA3HXXXXz44Ycmdy8/P9/K/BqRS6U+JXL5qD+JXB5+v9+USg13VZaIS/7y\n+/2UlZUBgSNB8/LyePLJJwE7k81FLpX6lMjlo/4kcvm4XK6IySVXDrGIiIiIWC0kKRN+v79ZTwBO\nbonH46G4uJhu3boB2s0s4lCfErl81J9EJCJziEVEREREQkUpEyIiIiJiNQXEIiIiImI1BcQiIiIi\nYjUFxCIiIiJiNQXEIiIiImI1BcQiIiIiYjUFxCIiIiJiNQXEIiIiImI1BcQiIiIiYjUFxCIiIiJi\nNQXEIiIiImI1BcQiIiIiYjUFxCIiIiJiNQXEIiIiImI1BcQiIiIiYjUFxCIiIiJiNQXEIiIiImI1\nBcQiIiIiYjUFxCIiIiJiNQXEIiIiImI1BcQiIiIiYjUFxCIiIiJiNQXEIiIiImI1BcQiIiIiYjUF\nxCIiIiJiNQXEIiIiImI1BcQiIiIiYjUFxCIiIiJiNQXEIiIiImI1BcQiIiIiYjUFxCIiIiJiNQXE\nIiIiImI1BcQiIiIiYjUFxCIiIiJiNQXEIiIiImI1BcQW8fv94W6CiIiINIPG7tBQQBzlCgoKKCgo\noH///owePRqPx4PH4wl3s0RERKQRGrtDz+WPkEcPpxkul6ve65MnTwYgOzs76L2uXbsyaNAgDh8+\nDEBJSQmTJk0iKysrBC2OTOXl5QBMnz6d7OxsfD5f0Pu33norAB988EHI2yahpT518aqrq1m9ejUl\nJSUATJs2jdjY2DC3SiKdrhu5WBq7w08zxCIiIiJitbDPEFdWVjJs2DDy8/MBiI2NpVu3bgAUFxdT\nWlrarN+XlJREWVkZUH9mLNo9+uij/PnPf270fZfLRVFREQCdOnUKVbPCzufz4XK5rLke1KcuXWZm\nJp999hldunQBYMeOHXTs2DHMrWpZPp8Pt7v5cyTOTNbF/Gy0sfG6kUunsbthoR67w3YH83q9eL1e\nevXqxeHDh/H5fPh8PqqqqsjPzyc/P7/ZAzcElh2c322Te++997wdql+/fpSUlNCpUycrOtQVV1xh\nOlKbNm1wu93mz+PHj+fo0aPmmosW6lOXz7Zt2ygvL+fkyZOcPHmSysrKcDepRQ0dOpT4+Hhuvvlm\nbr75Zo4ePdrkn62srOTUqVMt2LrWw7brRi6dxu5g4Ry7Yy77b2wiZ3CNiYkx/3CAnj17smzZMvO5\nrKwstmzZAsCRI0fqPSlUV1cHfTFt2rSxZhYLoLa2FoCXX3653nt9+vQxuaA2fSeTJ082T9Pncr6D\nbdu2kZGRwW9+8xsA/vKXv5jrrzVTn7p8UlJSKC0tpbq6GiBqN7M494+vvvoKCPQNgPfff5/77ruv\nyb/n2LFjdO7c+fI3sJWx5bq5EI/Hw9y5c1mzZg0Ap0+fJi4ujnbt2pnPFBUVUVVVBUBNTQ2xsbGs\nXbsWgHHjxoW+0SGmsbu+cI/dWuMSEREREauFbYa4bdu2AGzYsIHTp08zYsQIgHrR/oQJExr9HSdP\nniQjI8PkSgKkpqZGxWxfU6Wnpzf4+ogRI9i9e3eIWxNeEydOBODjjz+mbdu2vPTSSwDmadKZrXnr\nrbdYsGABq1atAmD+/PlRkeenPnXpnJlxv98fNDMTrZUCnFkql8uF3+83qwzff/99k3/Hli1beOqp\np8jJyWmRNrYGtl03ELifVlZWmn/v0aNHmTlzJgCff/55s39fTU2NWZX4+uuvL19DI5TG7rMiZewO\nW0DsGDRo0EX/bFxcHCdOnAh6bf78+ZfapFZj7dq1ZlnFMXLkSAC2b98ejiaFzcmTJ/n0008BGDx4\nMJs2baJ9+/ZBn0lMTARg1KhRlJWVmY1ix44di4qA2KE+dfFOnz4NwPHjx03+NcA333zDlVdeGc6m\ntQgnYIuNjQ1KlVm9ejVPPPEEEEjBaYgTPM+bN4/9+/e3fGMjmG3XDZx9AD9z5gwQKOG4c+fO8/5M\nhw4dAOjVqxcHDx6sl2OdkZHRAi2NPBq7z4qksVspEyIiIiJitbDPEF8Mp1LcxIkTzdMpQPv27Xnw\nwQfD1ayQysvLIzMzM+i1AQMGWPd06Zg4cSI1NTUA/OMf/6j3hHmuY8eO4fF4TBpAhJxNE1bqUwF7\n9uwBMDNXzveybt06xowZE7Z2tRSnVFpKSkrQZpYvv/ySRYsWAbBw4cIGN/Y4y5gHDx40fc9Wtl03\nEEjFio+PN/fR4uLiRnf+x8fHk5+fzxVXXGFey8/P55ZbbjH/7XK5Lml1q7XQ2B0sksbuVhUQ+3w+\ndu7caXagOie7OD755JNwNCuknNyZO+64I+h1l8vF+vXrw9GksPruu++AwE3G6UhXX331eX/m73//\nOzU1NWaQT0pKatlGRjD1qWCNLdlu3rw5xC0JrbppET6fj+eeew6AmTNn0qdPn3o/4wSBtbW1uN3u\nRk9GtIGt143L5TKpE5MmTWLJkiVAoFJNYmIiR44cAWhwWbtnz55ce+21QCAg9vv9pirFggULQtH8\nkNLYHSwSx+6IDIidp4U1a9Ywa9YsiouLg16vy5nlGDZsWGgaGCZVVVX1OpNjzJgxdO3aNcQtCj9n\nE0ZKSoq54Vwo57G0tJTY2FiTz2bD96Y+1TTx8fEApKWlUVBQYF4/36xFa+YMLIsXL+aee+4JmnFx\nNtwtWLCA5cuX19sg5uQNt2vXDpfLZT4fzRvJGmPbddOQ4cOHc+eddwIwe/Zsxo4de97P19TUsG/f\nvqDXnM10dTcntnYau+uLxLFbOcQiIiIiYrWImyEuLCykf//+QP3l2wvZu3cv1113XUs0KyI4ZbQa\nkpGRwb59+8wRvc7JLgkJCcDZXJtonbG47rrrzPJbY5zvICkpiTZt2pCWlgZE/xKv+lTzXXPNNaxZ\ns8ZcG+PHjw9zi1rW9OnTWblyJevWrTOvOf3lrbfeYsuWLTz//PMA3HzzzRQVFZnDXeLj40lMTDQz\no927dwfql/uzgW3XzbmSk5NZsWIF0LR76pIlS4LKOwLmcJdouydr7G5cJI3dERcQv/3221RUVDTr\nZ5zl3dtuu43c3FwGDhzYEk0LG+diOF9txp07d7Jy5UozKNVdCne73QwaNIgdO3YAZ8uYtHZOSaOa\nmpoLDsDOdRUTE0N8fDw9evQAAstZzs0nGqlPNV9CQgIul4u4uDgA80ARrWJiYli9ejUPPfQQEDjN\nsKSkBAikThw6dIjbb7/dfD42Ntb0N6/Xy5kzZ3j88ccBmDp1Kl6v1wSDNgXGtl03dTU1QDl+/DiP\nPPJI0Ca8mJgY/vnPf7ZU08JCY3fjInHsjriA+IEHHmD27NlAYGPHuTltpaWl7Nmzx2zmyMvLY/v2\n7Xz77bcAFBQUkJ6ebnKXoiVR/eGHH77gZ/Ly8igrKzN5fHX5fD4SEhKCjs6MBs6u5XXr1lFaWgoE\nDpKoq6qqioMHDwKBupnx8fEmId/j8ZicpGikPtV0znezfv16/H6/yQ2N1lqy54qJieGZZ54BAnV1\nX3/9dYAGKwfU1NSYvD7nO3MC6KFDh1p1mAvYfd00hxPY9OjRI+i6SktLIysri5tuuilcTWsRGrsb\nF4ljt3KIRURERMRqETdDDGdPwKkrISGBtLQ0fvKTnwS97hzzd9999+H3+82pJ16vNypmKV544YVG\n33OWqHw+H/Hx8UE1ZOt68803oy43y9mVun37dpOntX//fuLj482sTX5+Pr/97W/55ptvgECtUI/H\nY54yo2UJ6nzUp5rG+Xc7s50/+tGPgECdUBs418njjz9udn47Jxc69464uDj8fr+Z4fN6vaSmpvLq\nq68CDZfYina2XzdNUVxcbGYFndlQJ7Xk6NGjUXlf0djduEgcu13+KDiVwLkxp6amUlZWZm7q556z\n3po5+ZwN/a/q27cvAIsWLaJ79+4mj2/Tpk1Bn4uNjaW8vDzqSiI55cOuvPJKqqurAXjyySeZMmWK\n6UTPPvsseXl5pKSkAIFSLcXFxfzhD38A4O67747Km/GliPY+1ZDS0lJTb7ekpIS0tDST++dcOzZx\njh8+duwYubm53HrrrQAUFRVx//33s3XrViBwb8nNzT3vxqFopuvmwqqqqhgxYkRQLq3b7TaHuzT2\nwN7aaexuXCSO3RE5Q9xczkV3/fXXs3XrVrMBKFoGbudp6PTp0/Xec3KuJkyYQGJiIjNmzAACBeHP\nzdFyNntEGyfnaNu2bWYTz9KlS3nxxRfNe16vlyFDhtCzZ08gsKGjuLiY4cOHA3Zt+mmqaO9TDRk2\nbJjJZYuLi2Pnzp1WBzTO7F3fvn3N4A2B3e4ej8fM8qWnp1sbDIOum/NxAsHFixdz4MCBoPdOnDgR\ntYGwQ2N34yJx7FYOsYiIiIhYLSpmiJ0ct02bNuH3+6OubqpT7sip8Xgu5/zzFStWkJiYyLPPPgvU\n3xmenJwclU+ZjqFDh/LDDz+YP5eXl5slmaNHj+LxeMjJyQECR856PB7rSiI1R7T3qbrKy8spLCw0\nfWTWrFmmnq4EKygoYPfu3WaGONoqAzSHrpvzc2ZGlyxZEjQmjR49usGKAtFGY/eFRdLY3epziMvK\nykhPTwcCN+r27duzbds2AIYMGRLOpl02Ti6fU8rnXOfmFXm93nqdyVmy2b17t7UbPLxeLxs3bjQl\ncA4fPsxNN93Eu+++G+aWRSYb+lRdX3zxBSNHjjT96eDBg6bWpQQbN26c2WQJsHHjRmuDYl0357dr\n1y4A80DtBHa1tbUmLSuaaey+NKEeuyNihtjn85mOcr4nIb/fT0VFBbm5uQAsXLiQvXv3mtqGsbGx\nrFq1KuoGbSeXLy8vj2nTpgGBGTyfz2c2JThP4ufuBM/MzOSdd94B7M6TdblcvPbaa+Tl5Zk/O+eo\nRyv1qebZvHkzLpfL1LTs0qVLmFsUuZwDAhyDBw8OU0vCT9fN+b3yyitBf547dy6AFcEwaOy+VKEe\nu+24KkVEREREGhH2lAmv10tOTo6ZoZoyZQpXXXWVeb+srMwsxzV0/KHb7ebqq68GICcnh06dOoWg\n1ZHDWZLZv38/sbGxZjdmtJ57fjFOnTpF3759TZ3HgQMHsm/fvqidpVCfajqn/9x9992899573HDD\nDUDg9KRovT4uVceOHU2OH0B1dXXUlYS6EF03F1ZbW2vuHWVlZXTt2tUcTywau5si1GN32FMm3G43\nhYWFLF++HIBly5aZTQlnzpyhoqKC48ePm8+7XC5T0uajjz4iIyMjqhPOL8RZknHKkEh9f/vb36iq\nqjJFvN94442oHrTUp5rG6/Xy8ccfA4EHg969e7N48WLAniXdizFmzBiys7PNd+QU2LeFrpumWbp0\nqQn63G43+fn54W1QhNHYfWGhHrvVe0VERETEamFPmYDAxp4NGzYAsHLlSgoLCwHweDyMGjWKzMxM\nAPr3769NC9JklZWVAKSlpVFVVcWNN94IBNIAon2jgvpU47xeLxDY3LJ06VIAKioqmDFjhtk8aMMM\n+cU6cOAAU6ZMYd68eUAgbcAGum6a7tChQwwfPtyU5rvttttU1UeaLFxjd0QExCItwVnW/NWvfkXf\nvn15++23Aejdu3c4myURwu/3m5O0tNQtTaXrpnHl5eUA3HXXXXz44YckJycDkJ+fr9xYabJwjd12\nJX+JNXw+nzludurUqUydOpVevXqFuVUSSVwul2b0pNl03TTM7/dTVlYGBI7zzsvL48knnwS0UUya\nLpxjtx5vRURERMRqSpmQqOXkr506dYoOHTrQtm3bMLdIRKT18Pv9zZoNd/KsPR4PxcXFdOvWDbCv\nEolcmnCN3QqIRURERMRqSpkQEREREaspIBYRERERqykgFhERERGrKSAWEREREaspIBYRERERqykg\nFhERERGrKSAWEREREaspIBYRERERqykgFhERERGrKSAWEREREaspIBYRERERqykgFhERERGrKSAW\nEREREaspIBYRERERqykgFhERERGrKSAWEREREaspIBYRERERqykgFhERERGrKSAWEREREaspIBYR\nERERqykgFhERERGrKSAWEREREaspIBYRERERq/0/bMeVpfiH84gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde69789110>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a6Hn44YdZsGCBORdMmzbNTG+3ti2qx+PhvffeMyWS7GVZ8UTnms5h12f2er1k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FhERERErKaEWERERESspoRYRERERKymhFhERERErKaEWERERESspoRYRERERKymhDhJ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\nEbGaEmIRERERsZoSYhERERGx2v8BoRvy+H1Z/LgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde67b62610>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g2s/WrVvJzc3lm2++Aey1U/PmzeOBBx44Lb/f3U80EREREZGTiJoux6233trg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\nERERX1NALCIiIiK+poBYRERERHzt/wHAw0mCSxEuTgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde65800210>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mhpKSElMWav369cTFxbF+/XoAkxsZ6TSmWs7v9zN9+nQTCNvsx6FHczAMgVJP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Jf9Fs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\nmzYNgNzc3Dr/nJ/97GcmJSfe6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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde63a9e090>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rZaQSXTAY5Prrr28zEDZuuOEGbr311jbryKY6t59rmjKfrSOfcdq0aWH/fsaM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\nE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xBfcsklkqS9e/cOuDZhwgTt2LFDkvTXv/7Vrs2SpLa2Nn3jG9/ITSERaB9++KEuvvhi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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde61cb7d90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hE4nxiRrSazuq2VEZ+Lv5sbtiv8NsbCtsb2vpLsxWM9+ZtJJYn2il4zgkmxVf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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde822df0d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iefjxeE43uIH"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmFTRUl53uIa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "436ee5ee-0695-4ad9-a859-0fb8be88efc4"
      },
      "source": [
        "from keras.models import load_model\n",
        "discriminator=load_model('{}/dcgan_discriminator_epoch_12.h5'.format(model_folder))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python2.7/dist-packages/keras/engine/saving.py:327: UserWarning: Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
            "  warnings.warn('Error in loading the saved optimizer '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PW3kmk7R3uIy"
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YLoOhgqM3uI8"
      },
      "source": [
        "from keras.models import load_model\n",
        "generator=load_model('{}/dcgan_generator_epoch_14.h5'.format(model_folder))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3cKLoLsDzxR"
      },
      "source": [
        "#INPUT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpmQTus-HHLU"
      },
      "source": [
        "l = []\n",
        "\n",
        "x=word_to_num(\"four thousand seven hundred twenty three\")\n",
        "\n",
        "while x!=0:\n",
        "  t = x%10\n",
        "  x/=10\n",
        "  l.append(t)\n",
        "  \n",
        "l=l[::-1]\n",
        "l=np.asarray(l)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8IMujZ6CD3BJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UeUG7Iu3D3ok"
      },
      "source": [
        "#OUTPUT\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZWywJ1s3uJL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "outputId": "8d019d1e-cba5-4f4b-9d8c-98070b530e81"
      },
      "source": [
        "generatedImages = generator.predict(to_categorical(l,inputDim))\n",
        "dim=(28,28)\n",
        "\n",
        "# plt.imshow(generatedImages[0, 0], interpolation='nearest', cmap='gray_r')\n",
        "## To plot ###\n",
        "dim=(10, 10)\n",
        "figsize=(10, 10)\n",
        "\n",
        "plt.figure(figsize=figsize)\n",
        "for i in range(generatedImages.shape[0]):\n",
        "    plt.subplot(10,10,i+1)\n",
        "    plt.imshow(generatedImages[i, 0], interpolation='nearest', cmap='gray_r')\n",
        "    plt.axis('off')\n",
        "    \n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAASEAAABMCAYAAAAiAAErAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYRV/wPvmKFjsVgbjAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde61a11a50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Fn9QU9t0Mbw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X42vzR3g0NPP"
      },
      "source": [
        "##Nothing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RD7v2IZ4bF-8"
      },
      "source": [
        "batchSize =128\n",
        "\n",
        "pickup_batch = np.random.randint(0, X_train.shape[0], size=batchSize) #selecting 128 random images out of 60k\n",
        "our_input = to_categorical(y_train[pickup_batch],inputDim)   "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kiOjBKRMn3YG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "11fd3338-089c-4938-eee0-ef6d31392f51"
      },
      "source": [
        "our_input.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuG_uo1_n5pY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "5aae417d-cdc6-40c6-d23e-ad55ca636ac4"
      },
      "source": [
        "plt.imshow(our_input)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fde892c6290>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAADcAAAD7CAYAAADO8QyJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLlWyuFTJ4lLlf4a+LLWAaZTtAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde61990810>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQ_YWf80znXT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "90bec362-2e4b-4846-d934-201d79c0a7ef"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 1, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xywiBiTiog82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8c81435a-e59f-413a-9c4c-db3b3ae4b6c2"
      },
      "source": [
        "imageBatch = X_train[pickup_batch]\n",
        "# plt.imshow(imageBatch[0])\n",
        "imageBatch.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 1, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFFT-V_xqBOQ"
      },
      "source": [
        "# Generate fake MNIST images\n",
        "generatedImages = generator.predict(our_input) #UC"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFiPDY0JqBoH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "outputId": "9d26a130-f0fc-40e1-94af-37c4f380233e"
      },
      "source": [
        "plt.imshow(generatedImages)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
            "\u001b[0;32m<ipython-input-50-812069806ac9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeneratedImages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3099\u001b[0m                         \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilternorm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilterrad\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3100\u001b[0m                         \u001b[0mimlim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimlim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3101\u001b[0;31m                         **kwargs)\n\u001b[0m\u001b[1;32m   3102\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3103\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1715\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1716\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1717\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1718\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1719\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[1;32m   5129\u001b[0m                               resample=resample, **kwargs)\n\u001b[1;32m   5130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5131\u001b[0;31m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5132\u001b[0m         \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5133\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    620\u001b[0m         if not (self._A.ndim == 2\n\u001b[1;32m    621\u001b[0m                 or self._A.ndim == 3 and self._A.shape[-1] in [3, 4]):\n\u001b[0;32m--> 622\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid dimensions for image data\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_imcache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Invalid dimensions for image data"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAAD8CAYAAABzYsGzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fde801fd750>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahHs9n97Lwfq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "425c8f12-0570-4ec2-e28d-08a3ddad1855"
      },
      "source": [
        "x1 = np.linspace(-5,5,100)\n",
        "y = 2*x1+1\n",
        "img=plt.plot(x1, y, '-r', label='y=2x1+1')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HD0avXr1w3333SR2BiMhrqLPPwRg/Cvo9u+AKDYUwdzlKBgzkdE0VUmXntLOzs1G9enXU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\nVSZRRMCmjTAkJ0J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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff72be3d890>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}