Face-Emotion (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Face Emotion Detection<h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1. Data Loading\n",
    "#We have been provided with 213 images of 7 facial expressions in a single folder structure in below format\n",
    "#[PersonName].[ExpressionName][Number of the respective expression][ImageID].tiff\n",
    "#First separate the images based on the labels to respective folders.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the Data from local and copying the files into 7 different folder structures.\n",
    "IMG_DIR=\".//Expression Data//\"\n",
    "import os\n",
    "from shutil import copyfile,move\n",
    "for filenames in os.listdir(IMG_DIR):\n",
    "    if 'tiff' in filenames:\n",
    "        if \"HA\" in filenames.split('.')[1]:\n",
    "            copyfile(os.path.join(IMG_DIR,filenames),os.path.join(os.path.join(IMG_DIR,\"extract//train//HA//\"),filenames))\n",
    "        elif \"SA\" in filenames.split('.')[1]:\n",
    "            copyfile(os.path.join(IMG_DIR, filenames), os.path.join(os.path.join(IMG_DIR, \"extract//train//SA//\"), filenames))\n",
    "        elif 'SU' in filenames.split('.')[1]:\n",
    "            copyfile(os.path.join(IMG_DIR, filenames), os.path.join(os.path.join(IMG_DIR, \"extract//train//SU//\"), filenames))\n",
    "        elif \"AN\" in filenames.split('.')[1]:\n",
    "            copyfile(os.path.join(IMG_DIR, filenames), os.path.join(os.path.join(IMG_DIR, \"extract//train//AN//\"), filenames))\n",
    "        elif \"DI\" in filenames.split('.')[1]:\n",
    "            copyfile(os.path.join(IMG_DIR, filenames), os.path.join(os.path.join(IMG_DIR, \"extract//train//DI//\"), filenames))\n",
    "        elif \"FE\" in filenames.split('.')[1]:\n",
    "            copyfile(os.path.join(IMG_DIR, filenames), os.path.join(os.path.join(IMG_DIR, \"extract//train//FE//\"), filenames))\n",
    "        elif \"NE\" in filenames.split('.')[1]:\n",
    "            copyfile(os.path.join(IMG_DIR, filenames), os.path.join(os.path.join(IMG_DIR, \"extract//train//NE//\"), filenames))\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "#now when we have our folder structure ready, randomly move 20% (approx) of the images to our test folder\n",
    "train_folder=\".//Expression Data//extract//Augmentation//train//\"\n",
    "test_folder=\".//Expression Data//extract//Augmentation//test//\"\n",
    "for folders in os.listdir(train_folder):\n",
    "    count=round(len(os.listdir(os.path.join(train_folder, folders))) * 0.2)\n",
    "    # print(count)\n",
    "    for filenames in os.listdir(os.path.join(train_folder,folders)):\n",
    "        if \"HA\" in folders and count>0:\n",
    "            # move(\"C:\\\\Users\\\\test\\\\PycharmProjects\\\\Assignment\\\\Expression Data\\\\extract\\\\train\\\\AN\\\\KA.AN1.39.tiff\",\"C:\\\\Users\\\\test\\\\PycharmProjects\\\\Assignment\\\\Expression Data\\\\extract\\\\test\\\\AN\\\\KA.AN1.39.tiff\")\n",
    "            move(os.path.join(os.path.join(train_folder, folders+'//'),filenames), os.path.join(os.path.join(test_folder, folders+'//'),filenames))\n",
    "            count-=1\n",
    "        elif \"AN\" in folders and count>0:\n",
    "            move(os.path.join(os.path.join(train_folder, folders + '//'), filenames),\n",
    "                 os.path.join(os.path.join(test_folder, folders + '//'), filenames))\n",
    "            count -= 1\n",
    "        elif \"DI\" in folders and count>0:\n",
    "            move(os.path.join(os.path.join(train_folder, folders + '//'), filenames),\n",
    "                 os.path.join(os.path.join(test_folder, folders + '//'), filenames))\n",
    "            count -= 1\n",
    "        elif \"FE\" in folders and count>0:\n",
    "            move(os.path.join(os.path.join(train_folder, folders + '//'), filenames),\n",
    "                 os.path.join(os.path.join(test_folder, folders + '//'), filenames))\n",
    "            count -= 1\n",
    "        elif \"NE\" in folders and count>0:\n",
    "            move(os.path.join(os.path.join(train_folder, folders + '//'), filenames),\n",
    "                 os.path.join(os.path.join(test_folder, folders + '//'), filenames))\n",
    "            count -= 1\n",
    "        elif \"SA\" in folders and count>0:\n",
    "            move(os.path.join(os.path.join(train_folder, folders + '//'), filenames),\n",
    "                 os.path.join(os.path.join(test_folder, folders + '//'), filenames))\n",
    "            count -= 1\n",
    "        elif \"SU\" in folders and count>0:\n",
    "            move(os.path.join(os.path.join(train_folder, folders + '//'), filenames),\n",
    "                 os.path.join(os.path.join(test_folder, folders + '//'), filenames))\n",
    "            count -= 1\n",
    "\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <!-- step 2. Preprocessing and Label Generation -->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2. Preprocessing and Label Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Since we have very less number of images for training, we will use keras augmentation process to generated more images\n",
    "#Example- Zooming, shifting the height,rotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#A separate utility class was created for this task, however attaching it here for display purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End of the Augmentation Task.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing import image\n",
    "from keras.preprocessing.image import ImageDataGenerator, load_img\n",
    "import os\n",
    "from shutil import copyfile\n",
    "train_datagen=ImageDataGenerator(rotation_range=40, width_shift_range=0.2, height_shift_range=0.2,rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)\n",
    "test_datagen=ImageDataGenerator(rotation_range=40, width_shift_range=0.2, height_shift_range=0.2,rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)\n",
    "\n",
    "train_folder=\".//Expression Data//extract//Augmentation//train//\"\n",
    "test_folder=\".//Expression Data//extract//Augmentation//test//\"\n",
    "\n",
    "#Data Augmentation for default training set\n",
    "for folders in os.listdir(train_folder):\n",
    "    print(f\"Currently into {folders} folder for training data\")\n",
    "    for images in os.listdir(os.path.join(train_folder,folders)):\n",
    "        img = image.load_img(os.path.join(os.path.join(train_folder,folders+'//'),images), target_size=(224, 224,3))\n",
    "        copyfile(os.path.join(os.path.join(os.path.join(train_folder, folders + '//'), images)), os.path.join(\n",
    "            os.path.join('..\\\\Expression Data\\\\extract\\\\Augmentation\\\\train\\\\',folders + '\\\\'), images))\n",
    "        x=image.img_to_array(img)\n",
    "        x=x.reshape((1,)+x.shape) # API requires input to be of rank 4, that is (1,224,224,3)\n",
    "        i = 0 #default batch size is 32, but we want to restrict the task at 20\n",
    "        for batch in train_datagen.flow(x, save_to_dir=os.path.join('C..\\\\Expression Data\\\\extract\\\\Augmentation\\\\train\\\\',folders+'\\\\'), save_prefix=f'emotion_{folders}', save_format='tiff'):\n",
    "            i += 1\n",
    "            if i > 20:\n",
    "                break\n",
    "\n",
    "#Data Augmentation for test set\n",
    "for folders in os.listdir(test_folder):\n",
    "    print(f\"Currently into {folders} folder for test data\")\n",
    "    for images in os.listdir(os.path.join(test_folder,folders)):\n",
    "        img = image.load_img(os.path.join(os.path.join(test_folder,folders+'//'),images), target_size=(224, 224,3))\n",
    "        copyfile(os.path.join(os.path.join(os.path.join(test_folder,folders+'//'),images)),os.path.join(os.path.join('..\\\\Expression Data\\\\extract\\\\Augmentation\\\\test\\\\',folders+'\\\\'),images))\n",
    "        x=image.img_to_array(img)\n",
    "        x=x.reshape((1,)+x.shape)  # API requires input to be of rank 4, that is (1,224,224,3)\n",
    "        i = 0 #default batch size is 32, but we want to restrict the task at 20\n",
    "        for batch in train_datagen.flow(x, save_to_dir=os.path.join('..\\\\Expression Data\\\\extract\\\\Augmentation\\\\test\\\\',folders+'\\\\'), save_prefix=f'emotion_{folders}', save_format='tiff'):\n",
    "            i += 1\n",
    "            if i > 20:\n",
    "                break\n",
    "\n",
    "print(\"End of the Augmentation Task.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3. Train/Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#We have already splitted our original data into 80:20 ratio. Then we have executed Augmentation Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#We used python to split the original data into test and train data set. No libraries used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libs imported\n"
     ]
    }
   ],
   "source": [
    "# import libs\n",
    "from keras.layers import  Input, Dense, Flatten\n",
    "from keras.models import Model\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.applications.resnet50 import preprocess_input\n",
    "from keras.preprocessing import image\n",
    "from keras.preprocessing.image import ImageDataGenerator, load_img\n",
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "print(\"Libs imported\")\n",
    "\n",
    "#We are using resNet50, expected input size is [224,224]\n",
    "IMAGE_SIZE=[224, 224]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.3\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import keras\n",
    "print(keras.__version__)\n",
    "# import tensorflow as tf\n",
    "# print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using a pretrained ResNet50 CNN model which is already trained on 1000 object categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "resnet = ResNet50(include_top=False,  pooling='none', input_shape=IMAGE_SIZE +[3],  weights='imagenet')\n",
    "cfg = resnet.get_config()\n",
    "cfg['layers'][0]['config']['batch_input_shape'] = (None, 224, 224, 1)\n",
    "resnet_model = Model.from_config(cfg) \n",
    "\n",
    "\n",
    "for i, layer in enumerate(resnet_model.layers):\n",
    "    if i == 1:\n",
    "#         print('up')\n",
    "        new_weights = resnet.layers[i].get_weights()[0].sum(axis=2, keepdims=True)\n",
    "        resnet_model.set_weights([new_weights])\n",
    "        layer.trainable = False\n",
    "    else: \n",
    "        break\n",
    "        \n",
    "       \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#it is a 50 layer model and we will be adding an additional dense layer of size 7 \n",
    "#marking layers to non-trainable. We are trusting the existing weights to work so no need to retrain those.\n",
    "for layers in resnet.layers:\n",
    "    layers.trainable= False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking the output of the last convolution block in ResNet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=Flatten()(resnet.output)\n",
    "#useful for getting number of output classes\n",
    "folders=glob('../input/newfile/Augmentation/train/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a fully connected layer having 7 neurons\n",
    "# prediction=Dense(len(folders), activation='softmax',name=\"Expression\")(X) \n",
    "prediction=Dense(len(folders), activation='softmax')(X) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model to be trained\n",
    "model=Model(inputs=resnet.input, outputs=prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 55, 55, 64)   0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 55, 55, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 55, 55, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 55, 55, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 55, 55, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 55, 55, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 55, 55, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 55, 55, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 55, 55, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 55, 55, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 55, 55, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 55, 55, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 55, 55, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 55, 55, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 55, 55, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 55, 55, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 55, 55, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 55, 55, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 55, 55, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 55, 55, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 55, 55, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 55, 55, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 55, 55, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 2048)         0           avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 0)            0           flatten_2[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 0\n",
      "Non-trainable params: 23,587,712\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We have successfully added a new layer to our model. Layer name Expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set what cost and optimization method to use for our model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using the image data generator to import the images from the dataset\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input)\n",
    "test_datagen=ImageDataGenerator(preprocessing_function=preprocess_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4518 images belonging to 7 classes.\n",
      "Found 929 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "training_set=train_datagen.flow_from_directory(train_folder, target_size=(224,224), batch_size=5,  class_mode='categorical',shuffle=False)\n",
    "test_set=test_datagen.flow_from_directory(test_folder, target_size=(224,224), batch_size=5, class_mode='categorical',shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Found 4518 images belonging to 7 classes.\n",
    "# Found 929 images belonging to 7 classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step 4 fitting and training the Model (used kaggle kernel for execution) (NVidia K80 GPUs)\n",
    "<!-- r=model.fit_generator(training_set, validation_data=test_set, epochs=400, steps_per_epoch=141, validation_steps=len(test_set)) -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIQAAAHBCAYAAAAYf2BOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3wP9x/A8ddlD4kRJCRIJCHUjBRRMWtUzbaKVlWpWXu2RlG0Vbv8qq1Ssyil9qxRKkaMoIQgRpQISSQSiYz7/fFNTr6SyDeRhffz8fCQu/vc3fvue9/7fu/9/QxFVVWEEEIIIYQQQgghxKvDKL8DEEIIIYQQQgghhBB5SxJCQgghhBBCCCGEEK8YSQgJIYQQQgghhBBCvGIkISSEEEIIIYQQQgjxipGEkBBCCCGEEEIIIcQrRhJCQgghhBBCCCGEEK8YSQgJIYQQQgghhBBCvGIkISSEEEII8RwURbmmKMqb+R2HEEIIIURWSEJICCGEEC8kRVH2K4oSriiK+XNs4wtFUf5OZ35xRVEeK4pS5fmiFEIIIYQomCQhJIQQQogXjqIozoAPoAJtn2NTy4F6iqK4PDW/M3BWVdVzz7FtIYQQQogCSxJCQgghhHgRdQOOAEuAj1NmKopiqSjKTEVRriuK8kBRlEOKolgmL6uvKMphRVEiFEW5qShKd1VVg4G9wEfpbH9p8nquiqLsVRTlvqIo9xRFWakoSpGsBKsoSm1FUXyT931bUZT5iqKYpVr+mqIouxVFCVMUJURRlDHJ840VRRmjKMoVRVGiFEU5oShKmayfLiGEEEIIfZIQEkIIIcSLqBuwMvlfC0VR7JPnzwBqAfWAYsAoIElRlLLAdmAeUAKoAZxOXmcpqRJCiqJUTF6+KmUW8A1QGqgElAEmZjHeRGAoUBzwBpoC/ZP3ZwPsAXYk78MN+Ct5vWFAF6AVYAv0AGKyuG8hhBBCiDQUVVXzOwYhhBBCCIMpilIf2AeUUlX1nqIoAcBPwFwgGqirqqr/U+t8AdRWVbVDOtuzAu4ALVVVPawoylSgiqqq7TLYf3tggqqqNZOnrwGfqqq6JwvHMARoqKpqB0VRugCjUrb3VLmLycs2GrptIYQQQghDSA0hIYQQQrxoPgZ2qap6L3n6t+R5xQEL4Eo665TJYD6qqsYAa4FuiqIowIckNxcDUBSlpKIoqxVFuaUoSiSwInlfBlMUpYKiKFsURbmTvI2vU20jw9gyWSaEEEIIkW2SEBJCCCHECyO5P6D3gYbJyZU76JpiVQdKAbGAazqr3sxgfoqlydttBtgAW1It+wZd59XVVFW1Bbqia0aWFQuAAMA9eRtjUm3jWbFlFrcQQgghRLZIQkgIIYQQL5L26PrjqYyun58a6Pr1OYiuX6HFwCxFUUond8jsnTws/UrgTUVR3lcUxURRFDtFUWqk2u5BIAL4GVitqurjVMtsgIdAhKIojsDIbMRtA0QCDxVF8QD6pVq2BXBQFGWIoijmiqLYKIpSJ3nZL8BkRVHcFZ1qiqLYZWP/QgghhBB6JCEkhBBCiBfJx8CvqqreUFX1Tso/YD66pl6fA2eB40AYMA0wUlX1BrqOmYcnzz+NrlYRAKquU8VlQLnk/1ObBHgCD4CtwPpsxD0C+ACIAhYCa1LtOwpdzaQ26PoyCgQaJy+eBfwO7EKXUFoEWGZj/0IIIYQQeqRTaSGEEEIIIYQQQohXjNQQEkIIIYQQQgghhHjF5EpCSFGUloqiXFQU5bKiKJ/nxj6EEEIIIQoSRVG2K4ryMJ1/Y/I7NiGEEEKIp+V4kzFFUYyBS+jawgeja8PfRVXV8zm6IyGEEEIIIYQQQgiRLSa5sM3awGVVVa8CKIqyGmgHZJgQKl68uOrs7JwLoQghhBBCCCGEEKIgefw4DgAzM/N8juTld+LEiXuqqpZIb1luJIQcgZuppoOBOk8XUhSlN9AboGzZsvj5+eVCKC+2qKgoAGxsbPI5kpylqiqXLl6kQsWKKIqS3+GkUdDjE0IIIYQQQogX2fVrQQCUc3bJ50hefoqiXM9oWW70IZTeE3Sadmmqqv6sqqqXqqpeJUqkm6x6qUVEhNO/Ty8mjh/HsaNHtH/+p0/z+PFjAJISE0lKTNTWWTB/HrGxsfkVMgBdO7/P/76fqxfzsaNHCLhwnsRUsT5twfx5RESEa9MPH0bpLe/fp5fe8tywZPEv9OvVk9DQu2mWXQ68xO6dOzOMr6DbvHEjnd7twOaNG7XXZOmvi+nc8V3tenoRPH78mLatWvJZ396Eh2d+PaS8jxbMn/fMck9ffy+bzRs30rXz+wRdvZpm2VcTxmd6flILunqVrp3fz8nwhBBCCCGEEAVQbtQQCgbKpJp2Av7Lhf28FOzt7aldp266ywoXKZLH0RimbDnnDGM2hKIo1PJ6PQcjMszrteuwdfNmLgdepkSJknrL/E+fxvuNN/I1vudlampKlapVcSlfHoDadepSurQj14KuUqGiRz5HZ5gb16/x3vud2LNrFwEXLuBdr15+hySEEEIIIYTIYYqikNP9GYusy40aQscBd0VRXBRFMQM6A5tyYT8vvX/PneXmzRsZLg+4cIEvx46hY4d2fPRBZ8Lu38/D6DKWmJDA7p07ad+6FSOGDibggn73UXFxcaxYthSAyMhIJowby+3btxk1fBgjhg555jE/D5fyrlT0qMRR38N6tZliYmI4dfIk9vYOaeJLcfSILyOGDqZD29b06tGdR48eacuGDhrAxYAAbXrzxo0MGfgZV69cAXRN0JYt+ZVvpk7OleN6Fjd3d65cvgzojuv7ObPTlPl+zmzi4nRteEcMHcLZM/7s3rmTXj26M37M51y6eDHP4vU/fZpmzVvg5u6G3/GjaWqdPYiIYOFPC+jc8V06dmhHRHhEmm1kdv1lRlVV7X3VueO7ad5XhpyjsPv3WfjTAjq0bc3oEcOzHENuUlVVu3d89EFnFv604Jn3jhXLlvL9nNl8P3uWdq+Jjn6YhxELIYQQQoiXjampWX6HIMiFhJCqqgnAAGAncAH4XVXVf3N6Py+LuMePiYgI1/7Fx8c/WRb3mIT4hHTXCw29y8G/DzBh0les3bCR5b+tZsK4sSxZ/EuuZ1pjYx/pxZw67suBl+jW9QPi4mL5c8s2Zsyei6IY8feBA9r6qqoSFRkJgK2tLZOmTKVUqVJ8N3MWM2bPoUyZsrkSt5WVFT179cbv+DEO7NurzV+7ZjXR0Q8xNjZOE1/KMYXevcv0WXPYsGkLCxcvodcnH2tNzCp6eLBm1UqtOd/2bVtITExk7e+riY+PJ/DSRXbt2E7rNm1z5bgyoqoqfseP4V6hgjYdER6WplxEeJh2zdy5/R9/rF3LGz4+LFy8hO49PuWrCePZuGF9rsd79ow/a39fQ6nSpenxaW9O+Pmxd88evTKDB35G0zebs3rtH6zdsJHjx44SfPNJl2WGXH/PEhp6l/69P9XeV6vX/sHUyZOY/u03WtO7zM6R37FjDPysH02bNWfDpi1MmjIV/9P+OXSWMqaia+qY0Xsz9fGl3DuW/7aaDu+8x4RxYzO8b0RFRrJn104GDhmq3Ws+69uHP9b+nuvHJIQQQgghXk6lSpfGzMyc2//9R0xMjNQWyie50WQMVVW3AdtyY9svm717drN3z25teuLkqXjWqpXpepcuXqRdh3cwNnnyEtZv0JBjR315+PBhrnZEvXzpEpYvXaI3LyXuE34nKFKkCG/4NNCWlXd1pYZnzVyLJyscnZx4rWpVjh49QpM3m/HgwQNOnzpJ9Ro1MlznhN8J2rRtq9fBtNfrtTl50o9GTZpQtVp1Fv70I/dCQ3EqUwZLS0vatG3PbyuXEx4ezsWAixQuUgSnXEp0pYiNjWX9urXYFi4MwOXAQC4GXOCd9zpmaTsNGjbEysoKAEdHR1zd3Pnv1q0cjzc1VVX559BBHB0dAbB3cKBS5cr4+R2jWYsWAMRER1PX21trEgfwho8P27dt1aYNuf4SExM54Xdcb/+1vF7H2NiYSxcvEhUVpfe+ert1G1YsX8a9e6GULq2L71nnyNTMjMdxcYTcvkP58q5YWFjQqUuXHDlPzxIVGcnYz0enuywl7ksXL5KYmKR37yheogT1GzR85n3DwaGU3vVf19sbv+PHeavV21hZW+fwkQghhBBCiFdBiZIluRV8k9C7IfkdyisrVxJCwnBN3mxG9x49tGlr60IGrRd2P4xxX4zWarUA2gNdYmL6tYpyykcfd9ce0lOkxB12/x729vZYWlpqy0xNTXF2Lk9BYGZmRp06dVmWnNC6cf0at2/f5tPefTJcJ+z+PYYPGQSpHojDw8OpXLkyiYmJuFeoiJmpGZcuBuDo5IR7hQpUrlIFY2Njgm/e4Pz5c3hUqoStrW2uHltCQgIXzv+LqZkZ4eHhqElJDB+pf40YokjRYk8mFAVj49xoWarv3r1Q/E+doo63t9b5c42anixZtEgr8zj+MY6OZfQSE0UKF9Ga+oFh1198fDxTJk3U2//v6//E2NiYsPthWkIthXP58sTHxxN690lC6FnnyL1CBWp5vc43Uyfz3vudqFe/Pi7OLnpJptxgY2vL52PGUqasfuJx5nfTtL/D7ocRei803XtHi7daZrjtUqVL6007OpbhjL8/j+MfY4UkhIQQQgghRNaZmJhQtGgxoqOjiY9/LLWE8oEkhPKZuZkZRYoUzda61avXwMzcXG+ejY0NZmbmGayRMywsLLMcs4VF7saUFVWrV8fSwkKrleLm5o5LeddnrlO9Zk2MjfXfLqVLl8bY2JiiRYvi6u7GGX9/PL28qFqtOnZ2dlSs6MHZM2e4GBBAz169s5yYyapChQox9suJuJQvz9kz/kz6cjwREWn72CmILgde5vbt26xft47169YZvJ6RkRFmZpm3P059/VlYWLBp2w6D96GgoCYlPXMUvdSsrKwYOfpz/P39mTdnFut+X0M5Z2fm/fCjwfvMDgUoVMgmzXvT1NRUb9rC3CLde8ez7ht5kRQUQgghhBCvHtvChdP8ICvyjiSEXlA1PT2xsrKiabNm2rwD+/dhY2ODuXn+JV9qeNZi1vRp+J8+RZ263gDcCg5m059/0rBx4wzXS4hP4PHj+AyX56QSJUrSf+AgFv+ykGNHj/LVlK+15j/pqd+gAYpiRJWqVbV5O7Ztw9nFRXvYfr9TF76aMJ5Z06czfuIkjI2NadfhHb6ZOpmKHh7PNSpbdlStVp2p337HxPHjaJ3c3M3c3Bw7u+I8ePCAwsk33XuhoXkaV3qCb97kpwX/o2evPrRp105v2ckTfpw940/VatWxsbFl147teNerR/ESJQD459Ah/E+fwqHlW0D2r78UNT09+WPd76iqqtVE2rRxA84u5fGoVMmg4zl75gyBly7Rum1bfl2+ksTERObNmU1sbCwWFhYGn5fcUNPTkw3r1+FRqbJ271BVlb8P7H/mfePkiRN607t2bKd2nToULlwwR0IUQgghhBBCZE5+9n1BOTo58eMP81m/bi337t3j3r17zJ87h1vBwRgZ5e7LeuP6NY4dPaL3z//0aR4/fkxNT09q1PRk3pzZRESEczEggAX/m0/RYsWeuc3w8DA2rFvL6VMntRGvclPZcs5s3LAe9woVKGlv/8yyFT0q8e3Uyezb+xfhYWHcuXObRQt/4sGDJ7VvSpUujUv58pw+dVJLEtnb22NnVxxPT680tTTygpu7O42bNuVaUBCgG9rR0+t1pn09hX8OHWTnju35MvLZ006dPElCfLxewi2Fq5s7J/yOo6oqxsbGRD2MYvq0bwm6epXwsDA2b/pT7/XL7vWXwtHJiSZN32TLpo3aa3340CHadejwzKRhandDQlixbAkbN6wnPCyMbVs288+hg/meDALd8TVq3ETv3rHhj3XMnzvnmfeNkvb2HPz7ABER4QRdvUrUwygaN3lTr/meEEIIIYQQ4sUiNYReUIqi0L1HT5YvW8qSxbp+Vvr060/Lt1rl+kPazh3b2blju948pzJl+HradxQpUpQBgwazaOHPdPugC+bm5vTu25/Y2EcZbE3nnfc68vua1ez9aw+Tv/6GChU9cvMQtBoyderUzbTJkZmZGc1atOT7ObNJTND1zzT2ywl6tX4sLCyo6FGZf8+d0+ZZWVvjUakSbu7uuXAEmTM2NqZZ8xZs2riBAQMHY2xiQu3atZk1fRrTvp5K0aJF6T9wEDu352//70eP+PJa1ao4OjmlWWZra8tRX1/ebtOWEiVK8vmYsfxv3vcMHtAfgOmz5rBm1UqtvIWFRbauvxSKovBh14/4sEsnFv6ka+I1acpUatT0NPh4GjVuTGzsI5YvW8rypUswNzfPcsfeuSXl+Ozs7LR7h5W1Nd179HzmfcPJyYklixYRGnoXgDnz/0c5Z+c8iloIIYQQQgiRG5SC0HGTl5eX6ufnl99hvJBUVSUqKgpVVbUkR0ERERGOtXUhg2vHJCYkEPf4scE1MfJaYkICD6OjURQl1zuIzk2qqvIoJgZLK6sXsoaHqqo8fPgQVVWf+Tpk9fpLLeW1NjIyyvaIfSnbKGRtnesdSmeHqqpERkZmGt+C+fMIDb3L+IlfERUVhaIouTqKoRBCCCGEECLnKIpyQlVVr/SWFbynFJElBTk5kdWOp41NTLAqgA/OKYxNTApc0i07FEV5oYcKNzQhkd3O2iFnXuuCfr0oipKl+AryvUYIIYQQQgiRdQX36VsIIUS+q/Taa5R9WC6/wxBCCCGEEELkMEkICSGEyFCjxk3yOwQhhBBCCCFELpBRxoQQQgghhBBCCCFeMZIQEkIIIYQQQgghhHjFSEJICCGEEEIIIYQQ4hUjCSEhhBBCCCGEEEKIV4wkhIQQQgghhBBCCCFeMZIQEkIIIYQQQgghhHjFSEJICCGEEEIIIYQQ4hUjCSEhhBBCCCGEEEKIV4wkhIQQQgghhBBCCCFeMZIQEkIIIYQQQgghhHjFSEJICCGEEEIIIYQQ4hVjkt8BADx+HMf1a0H5HYYQQgghhBBCCCHEK6FAJITMzMwp5+yS32EIIYQQQgghhBBCvBKkyZgQQgghhBBCCCHEK0YSQkIIIYQQQgghhBCvGEkICSGEEEIIIYQQQrxiJCEkhBBCCCGEEEII8Yp55RNC168FyQhnQgghhBBCCCGEeKW88gkhIYQQQgghhBBCiFeNJISEEEIIIYQQQohXxLQdIQwbOIhDcwYBkfkdTpbdCg7mqK8vEeHh+R3KC08SQkIIIYQQQgghhCjwYqKjuf3fbZKSVC4GBEhS6DlJQkgIIYQQQgghhBAFnpW1NRU8KgJIUigHmOR3AM8ybUcIIVunAjBr3hTANn8Dyq74+2Bql99RCCGEEEIIIYQQL4y4uDiuBQURHhaGqqZdnpSkcuH8BarXqI6VtXXeB2igsasu8ejwfG26UTFoO2kU4JR/QSE1hPLElBGTiDq3Kr/DEEIIIYQQQgjxCngEnDl4PvsbSMr/kbhD797F//Rpwu6nnwxKYWxiXKCTQQCPjhzRmz4aBlzxzZ9gUpGEUB4IS4IJP/myfspwiHyON2UOWrAjhFv75gPxmZZd89WyAhO3EEIIIYQQQohn+2FHCEt+/5HsdBo9c0cIh76fna11c9KVy5dJTEgEQFHAxbU8nl618KjkoZUxNjHGzc1dm44IjyAxMTHPY31RSUIoDx0KiWfC+B8xJAmT2yKBmesvsWnK55mWPRqqizvs8KLcD0wIIYQQQgghRLatCYBbW2cBELjqO7Ly/LnkVDy3tn7H+isQsmNeLkWYuXv37mm1gszMTHFzd8fBwYHHjx9zOTAQeJIMKmZXDNCNPhZw4TzXr13Pr7AzZFm3rt50nRKAi1f+BJNKge5D6GUUlQTDBg7HBPjEx55K7w0Eo/zrG2l/SDz7Bw6iU3Vb6vQYCkbp93UUlQRTVvljv24Qwz9rgolr+xyNY9jAQXrTJkbQtro99T/6GExzv13lpmDYP20Qk95xwqbxKL1le0Kg5NZBVOvxvS7Wxf7MejsE7JvnelxCCCGEEEIIYagl/vGc+WUc8IhigHuXKVlav3tNU45+N5M1owYxbWsI/cJH495lWq7Emh5VVbkSeJnQ0FDMzc2o4OFBoUKFtOU2Nja8XqcOYWFhFCtWTJufkJBAREQEqgohd+5QpmwZTE1N8yzuzEztUgG6fJ/fYaQhNYTyUP2yYF9Xl0hJABYeDGHmiHH5G1SyNf6RzBwxiYTgXc8sFxIPo+bs5eTycZCUs1UIPV3t8KzthWdtLxJM7Vh/KoQ9s77L0X1k5MQxXRvZffuDgeA82acQQgghhBBC5KQzh/3Q9SAEzTwss7WNOqlW++fwI8D/+QMzUExMDBERulHDHEqX1ksGpZY6GQRgYmJC2bJlMTYxBiA8LCx3A31JSEIoD5U0hdEfeoFtdW3erXjY/79xEJ//SYhb8TBm2hYMqVK44lgkS8bnbDKra2Mnun7Uja4fdWP0dxPAqTXb8uC0XEiCqCNHwL0yZ8KAYL/c36kQQgghhBBC5LTzuuSNJVD5dZfn2JBu3TMA/nn3fBQVGUl8fAIAdnZZG6m7kI0NRQoXBiA8PBz1WT1RC0ASQvli0uSeuL89QGsqtikgkikjvsu3kcjc3+mrxZIArBg7HCLTvuktARN7H236TCTc2pG1NqmGsjeCSg0ba9OHQmHC+Plc+P1JEuoCMGH8WkDX4fX68xByYD5rTtxn1NjvGDZwEFHn/sx0X2cCgEenqVT7Y8KAa8ckISSEEEIIIYR4EelaPjhbgo3Hc/RR4/Sa9uetoLyrvBAREaH9bW5unqV1FUXBrkQJFAWiHz4kPj7/++5NEZUE034/z7DBw3XdpcTn/yhuUED6ELp542aaPmSeNmxgxrVRLIGpn3mDR5ccjix32BhBv5YVoOUUEoBRw6YSFh/ChJ98sTHypXf7Cjg27gPkTZvHfo2doPEUwpJg4So/Th5Zxsmxy4BltCprClTWyn43riPQkT1XHrFt8SJmbr0EW4djYwQjunlhU6tbjsQUkgQXDuzTpmOTICriAfEPnzRTiweiIiJJSUhFxsP2rZfoPi6ITlN1/QANGziI7jX3av3/PO1kHBz96UfqWD7i3bqWjPLvxsJ9y5j6To4chhBCCCGEEELkIV1zsUqlAdsK2d6KffXqhARvASDo2n0ccyI0A8RERwO65E522NjYYGZmxuPH8cRER2NmZpaT4WXbhKHLIOlJxYOxw2YzdYgPuHbMx6gKSEJIPGFlDDFYQvwjKAidYFkZ1u7UyhhiEk2xeY5drdgXDGeWAXDS/xLERdIqi/1Jv1HTEmxTZ8LtuftfCBAC2Kcpf8I/HpLOU626JSZApZrVuXAuu0cghBBCCCGEEAXb6Jb20LLgdXAs8l6BSAjZFbeja/e0NUs2nookyl/X5Kdr9y48s8aMQ9baFxYEFyLh1+/XQnyINm/0uG5QLO+HnzsaGs+aOcsgUtfm1N4UOrWpjnPjD9g28DetXFQS/LzpErf++omUmjn1nW1555P2zx33ySv34cp9QHdhdq5rj+f7fbO0Dduiho/YlgBcOOGHCWDvWoGoyEgqucEFKkOSPxhVz2wTQgghhBBCCFHg3A0BuA9kb0TryPAH2t9W5tnrnDo7zMzMiIt7nO3+fxISEkhKSsLExBjTAlI7CHTDzj86/KSGkAw7n4qVlRWetdKejN2hIVpCyLPWa2T3Yi5oho1dpCVeANrWtKfRex2fq0rfc8WTqjmejRFMmj1F61MotUfAhMFPmvZVszel+7iZORbHrE+rQ/WeuphGLWLbEX883zgIzk+GuE9IIt2/s2Puvkg4vwp3J0u2BZpCoO5ac6xixdGfFlGn30zyqtmeEEIIIYQQQjy/6oA/hx7CW4e3YllvQLa28ujwVkDXPUu1enn3nFq8RAmioh4C8Pjx4yw1+VJVleAbN4iPT8DOzg5Ly7xLZGWmoA47XyASQq+Ku3EwbeURvWSQuy006jE2H6N6wt0WunZvnW4yKLXs1t7JCue3O3JtnT+H/txL/SHtk7s/N+XEWfBMLrNvVwi6IRCzl1m95eeHDdCrk7de0ulCEuwe7EedyCNg65PxBoQQQgghhBCiIKlSHc7pnjf/OXiJN+tld0O6To/dLcHkeTqnziIbW1tMTU2Ij0/g3r17lC5d2uB1ox9GE/FAV7OpcJHCGBnJGFqZkTOUhw4FQ8gRXcdcNkbQ1ceFfhMn5HNUugTPOzXt6TdxFDbuzZ9Z1tkWxvdpgueHY8E095rpveVjC4W82X5FN12nBODqzYUkWLH8R0Z9/SMJflNJr18gg93wpVYRwFm/aVglI7gGRJ3wzf62hRBCCCGEECKP1fHxQlevB7Zlc3Cwo4+e/P1GTTswr5xx4RxmZWVFkSJFAfgv+JbWyXSK2NhYrgWlHaErISGB69evkZiQCEDRYsVyP9iXgCSE8kE1e1PGj+yI5/tDczWpYqjPP/Khfo9RYJp5D86DRg/Apkr7TMs9L3cjqNSuva6P/KQgXU2ePj6Y2Ptw8th56iYF0mxQX8DhOfYSQq1adoBLOsssOeEXDESms0wIIYQQQgghCp5GlQGnpk9mRBzM0vohSbDm+12Abuh69yZNyMtuNBRFobRjaUxNTUlIiOf27dskJen6ComNjeXC+fPc/u/JvBRXr1wh8oHu2c3ISCkwo4ulKKjDzivZ7awpJ3l5eal+fn5p5k/bEULI1qkAzJo3hdzoQ+j6Nd0LUc45vaRAzhg2UNfvTn1nW9p2a49JifzvPGrJrhDeMN2Ce8OOmTYRWz99LW27uxSIuIUQQgghhBBCPNvMHSHcSn6WdjSFwcNaY+L07NYgo6b/ScKNvQC8425J/QHDwOg5WmQ8p4SEBExMdL3cxMbGcurESUCX8PGsVYuHDx9y+/ZtIh88QFVBUXQ1g1zd3LT1Cophg/WHnbeEPBt2XlGUE6qqpvswX7DO0kvKsRB80sGHYrXbU1A6Ke7e3B7oaVDZd0bm/kUqhBBCCCGEECJnDG9pz0zGcmvrVG7Fw6hpW2jl8TdvfjYlTdmQOFi/3V9LBgHU7/VxviaDAL1k0IXz57X5SUkqfsf1K1YswCsAACAASURBVJQYGSnY29tTplw5jI2N8zTOF5kkhPLA8C9GgW3mzbGEEEIIIYQQQoicMLylPcO2WqIbLxq2BURycMQg3F2doJADj4DAgDskROp3NtTWwxYs867foGdJTEjgyuXLxD6KzbCMooBHpcoULlI4DyPLmoI67Lz0IZQXJBkkhBBCCCGEECKPdf1yCib23tp0VBycPB/MyWN+XDjmp5cMsjeHQe9Up9FnBWMUbABjExNc3dywtNIfQt7ISMHSyhKHUg5UrV69QCeDAD7vVAF7n75gZIoJ0HZIXzDKvW5rDFWg+xDKC3nRh5AQQgghhBBCCJHfAoMjiQq5RHAkONmCvTXYl6uAiWXO99eb006dPKnVFHJ1c6Wkff42aXtRSB9CQgghhBBCCCHEK87dyRacvPDM70CyoVLlylw4f5642FiK2eX/aN0vA0kICSGEEEIIIYQQokCzsLCgUuXK3Ll9p8CNIvaikj6EhBBCCCGEEEIIUeBZWFjg7OKc32G8NF75tJr0HSSEEEIIIYQQQohXjdQQEkIIIYQQQgghhHjFSEJICCGEEEIIIYQQ4hUjCSEhhBBCCCGEEEKIV4wkhIQQQgghhBBCCCFeMZIQEkIIIYQQQgghhHjFFIhRxh4/juP6taD8DkMIIYQQQgghhBDilVAgEkJmZuYvzPDvr7mXf671v/r6Wxwdnej5cVe9+eMmTOL9Lh9QzcMdgLbtOzBu4lcM7Nebo76+abbTqcuHjB47jqWLFzF31ow0y30aNmLqt98RGxfLsEEDOHfmTIYx/Rt4lT/W/s6XYz7X5llbW/Pr8t9wq1ABzyqVnhl7isHDRtDlw67MnjGdNatWZn4y0vHV19/ybsf3n/s8CyGEoRYtXUGt119n0c8/Mm/O7Cytm3Kvtra21pt/5PDhDO+Vhm7z4cMomtSvp7fsxNnzXL8WxLBBA7B3cOCb6TMpVMiGKRO/ZNOfGwB4572ODB/9OQ8iHuB3/BgHD+xn984d2YolRcq9+enj2rR9J6VKO3I58BKvVanKkAH92btnt97nRcrx3L93D0cnJw4fOsiEcWNY+OtSypZzZtHPP9Kzd19uXL9G27daUMfbm2+mz+SLkcM56uvLpu072b1zB/PmzMbZxYUtO/cQGfmAk35++B7+h5XLlmYY95mAQH5f9RtTJk1Is+zE2fOYmppyJTCQE37H0y0DUMfbm3kLftY7xyn6DRhE7379uRYUBKqqzS/t5IS5uTmLfv6R69euaa9nyjEBNGrSlMlff8v4MZ+zf+9fBr0OuXG9DRwyVO/8p2jxVivGTZiUJr6Rn4/ho+6faN9XDLne+g0YxKd9+mrn+9uvJ6f73SanPeu9dO7SFQ7s28tnfXoBUL1GTWbM+Z5idnbUqlo5W/vzadiIT3r2wqt2bULv3qVpgzeAJ++fDzq+i//pUwD8sHARDRs1Zt9fexjQt7dB76WU12rMqBFs27JZ2+/Zi5cJuHCeju3bGhxrHW9vFi9L+13tyuVAvevAEKPHjOPN5i2wd3DA2NhYm59yXa5cs45qNWqw5reVGb7PnF1c+GPTVn74fi6LFv6U4b42bd+JQ6nS1K5RVZvXtn0HhowYqff+WrR0BXXr1Uvz/Ta16jVq0qf/ZzRo1BhFUQCIj4/XPgs++Kgb3Xt+mun36Pc6dWbEqM8ZOWwIBw/sp1fffrR7510G9OmluzfkoGOnz/IgIoKEhAQcnZxo1siHkDt39O6Thth76DDFi5fQuz++3aYt4yZMQkWlnpenQffup/d38twFHsfFPfNZwN7BgXc7dqLvZwPYuGE9478YnaVzkPLaphYdHZ3uPTozZy9eZu2aVXz15XjWbdxMOWcXZn33LatWrtDK9Ozdlw7vvYeLy5Nnk4zeJymxPe9zTEbPgfN//JnGTd9k+9YtjBgySJvv7OLCuo2bmTX9O35bvoyfFv3KGz4NiImJ4d+zZ5gyaSJXLgfq7cPZxYUZc76ntKOj9nm6Z9dOQu7cea7YDZFTz54ZcXVzZ8bsudy7F8rc2TN5FPOIPzZtIez+fSaMG8PBA/tz8nAAOHjkeIbLjh87muGyFm+1eu59h92//1zrF4iE0KvOp2EjmjZvzoOICIPL9+n/GTu2bU03GQTweu3a2BYuzNGdvs/8EMtIr779qeDhYfC6nbp8SJcPu7Jq5YpsJ4My07BRY/p8NgBLC8s0y27cuM7gz/rRf+BgmjVP/4vM7l07+WHeXAAmTp5K9Ro105SJT0hg/do1nDt3liHDR2JXzC7dbY0f+znnzpwxaH8Z7QtgzaqVrP5tJVWqVWPy1G/TLXM/7D5zZk7HrphdpscPPFdMKce/+reVNGzUmCHDR6a7naye7w2bt6VbJj4hgfc7tKVKtWoZnu+U4z935gy/rf0j3eMH6NBGd0M15Nh+Wbo8w9f2fth9Pv34I4Out2cdW1ZjMuTYcvJ6y+x8G3q95dR7Ka+ut5TznVM2/bkhy19As8vVzR1VTWL92rVcCwrC3sEh3XLr160lJCSEeQt+4t2O7/Nux/e1B4WnvxDmBCsrK6pWq47f8WPs3bM7w3Il7e1ZvXIFX0+elO19XQsKYsumjTRs1JhGTZrSuOmbdHj3PRb+uICd2/Vfd2cXFyLCwzmwf1+62/r6q4l0/fgTXN3dqeDhQas2bdi9cyfffT2F6Ohog+IxMTFGURQqVKyYZll8fPwz1330KAZzCwtsbW0N2hfk7fVmbm5OfEI8jx7F6M2PjX1EUlKSNp3e9RYTE0Pndzto19uC+d9zN+SOdr4XLV1BZOSDLJ/vnJSQkEDAhfPatP/pU9y4fp3Sjo7Z3ubBA/s56Xecpb+tpqJHpTTLU5JBABf+PUe9N+pTtlw5bZ6h76XUySCAR48eYVe8OHW8vQ1OtB319c2xH98+6v4JSUlJ3P7vFjHRMdgWLoxDqVLachtbGxITE3nwIOPvufYODiQmJhIaejdHYspM9Ro1+XbGLMqWK8eVy4EkJiQC4OLqqpWxs7Mj9tGjTL8Lr1uzmrbtO9C4aVMOHtiPT8NG7N29K8eTQSlKOzoSGxvL6pUrnuvhPTY2ljP+p7XprZs30af/Z5Qt56xXztB7d6cuHwJk+iwQcucOP8yby6d9+lLH25sq1apl6Vklu8nv9MTGxlKteg2sra1xdHTiv1u39O6xKT/YA1wKCADA0soqx/afVZaWun3HxOjfl68FBWFiYoqdne671rBBAxgweCitWrfh9Tp12bBlG7eCg1mxbIn2I8q1oCA+69OLP7ds1z5PR48Zx8ihg9N8nuaFrD57Pku7Dh0o7+ZG184dtc+Xw/8cokHDRrRt3yFXEkIvMkkIFQDvvd+JEiVKoigK/wZe1Vu2eNlKQkLuaL96uLq5M2zEKEra2zM1g19ZAOrV9+FRTIzBvzqmVqVaNVq2ehtTU1Nq1qqVJqZ/A6/q/RqZkqCysrbOMEGVEwoXKYKbe4U0v4wCGJvofpFyKFWKCh4e6a5/9uyTG0yZsuXSLRcfH0+JkiWxtramvKsr9vbpP3ClxGDI/jLaF0CJkiW17WVUJiTkDtbW1gYd//PGlHL8oDvfGW0nq+c7ozIpD0zPOt8pxw9kePypGXJsz3ptQ0J0X64MPd8ZHVtWYzLk2HLyesvsfBt6/Dn1Xsqr6y3lHLyIOnbujKWlFV+M/5Ivxn+pt+yb6TMBtC+y/xz8G586XjRs3IQGjRrTpl17Spd25JOPPsjxh++kpCR2bNvKxHFjtHlWVlaE3LmtV27uzBksW7JYmzY1NSM+Pp5bwcFZ2t/nI4YBuhoOb/g0oMenvZkweQrGxsZ6D8rNWrTk3LmzGX7x+2Pt7/yx9nfsHRyo79OA0WPH827H9ylevLhWa8RQtWtUzfC8tm3fId35RYoUJS42lsjIyCztK6/ExcVhamKqPYCksLCwxMhIvwvKp6+3+g0a8s13M/Sut9Tne/zEr6hd1zvb5zsnGBsbU7hwkRzfbnR0NJcCAqhU+TVt3r3Q0EwThJD5e+lWcDDx8fFZfoDOTS3easXDhw+ZM+M7LdmeUiMqRVRkVKbnO+TOHYyNjSlRIm/u0W3atcepTBkuBwbSrtWTHxpOn7+o/X3//n0sLC0NOt8HD+yn3Tvv0rZ9B9zc3Zk5Lf0f+XJCVFQUP3w/V7ufOru4YGpqlqV7aUx0NIULFzHofBty79Z+rN66xeBngcjIB9jaFqZcOed8u56vXwuinLMLbdt3wLpQIU5s26Lds6pUq0aDxo1JSkpiyaJfmDNzOqCrVdmsRct8iTclQW/1VFLK2cWFhIR47ifXFImOjmba11OY9vUUqlSrxoSvplDRoxLDRo7G0tKSX376EdC977y9amqfp42bNE338zS3ZfXZMzPlXd0wMTHR+1wOOH+eem/Ux9nlxWiVlJckIVQApPzSnlpGVQU3bNnGlcBA2rVq+cwv9W7uFVj26yK2bt6U5XjOnTlDy6aN0sxPr9reL0uXU6euN0eP+NKmZbMs7ysrDPll9Msxn2dYNTg1Q24oT1cvz+7+DNmXob/UGfLLcE7FlJPnO7NjO+rra9D5Tl1FPCM59doa+ku8Ia+bITEZcmw5eb0Zeg4yk5fnO6eutxfVt1Mm8+2Uydp0StOqp5uMrf1zEx6VKlO1ohvbtmxm25bNvOHTAKeyZajfoGGO/vK3eeOf9BswiOo1a1KlWjWO+vrS+YMPqd+gISOHDgZ0r23Lt1tT9403+GPtGu2zy6lMGU6dPMH6dWv58qspBu1v3IRJvF6nLsMGDeCory9HfX3xer02VatVx7OWl/YF1qdhIz76+BO6fdAp3e1UqVaNFavXal/y/1j7O9bWhRg8bDjuFSvqPQCG3LmDubk5HpUqp7lG582Zzet16rJi9VpGDB3MlcuB2Ds4MGHyVFBVJn05TitbpEhR6nq/of2403/gYI74Hs7WDzd5Yef2bcyY8z3DRo4iPCwM/9On6Na9B+++34nohw+1culdb81atGTS1K+1623X/r8pWdJeO98D+vamW/ce2vl+2sChw+jyYVdC74bqPaznpIjwcFq3a8+N69dZtmQxnT/4kNeqViUiIjxNWZ+GjRgzfgKFChXCp+7resvOXrxM0NUrjP/ic/xPn8KnYSO869cn9tEjrcyin3+kdp26TJ89l4njxhAdHc2bzVtiZGTE4X8OAYa9l9avW8sbPg0YMfoLRg8fSsgdXQLfysoK338O5UkzvKfZFS9O/OPH3Lt3T5tX36eBXpnfViyjvKsrrdu205onpTTpOHrUl2+nTOZaUBCKovBht4+5dOmiVtNm0pSvKVy4MPPmzmbJol+4cf065V3dtG1bW1vTtsM7WY67SNGiGBkZEZUqITtslH7Tpd+WL2PM+AkMGT6Swf37Eh0dzeBhI/i4R0/uhoTofVde+OMCBgweytRp03kYFaVXG+xpA4cO481mLfh68sQsv2Z+x4/RoGEjvftpvwGDcCpThvXr1uqVdXVz55vpMylTpgxbN2/Sa663euVKhowYwUeffEJQ0FX27tnNkOEjKVvOmTu3dQlIQ+7doP8sMGZ02pq+Kc0xd27fxoxp32jz7eyKc/7cuWw9q+SUubNmMvXb7xg0dDjHjx7lqy/Ha8uKFy+BpYUlCQnx3AsNBXTH0uTN5vkVLsuX/krlKlV4M1VtaXsHByZO+ZpjR4/y2/JlgK5JdGTkA74c8wUHD+ynY/u2zJn/A81atKRGTU9A93na6YMPaf/2W9rn6U//m8+Rk/56n6cpfBo24utp01m+9Fd+XvBDjh5XVp49Uzty0p/Lly4x7otRejXy/E+fol59H3waNtJ+FGrarDnGxsacPeOfo7G/DCQh9AL5bJDuC4GpuRkzZs/VW/Yo9hEfdHwX0N2swsLu88+hg7keU+06dYmPj6dkyZKsWK3/QZTSFEQIIUTeWbdmDUNHjGTvocM8CI9AMTKiaNGi+P5zKMergf+2fBl1vOtR17ses+f9j3uhoTiVKQugVzNn1YrlLFi4iPVbthHzMBqrQtaE3r3Lwh8XZGl/q1auoEvXj1j5+zru3L6NmpSEe8WKhNy5w+5dT/qtady0KUWKFs2wyca5M2dISkqiR6/evPX228REx1DOxQUTExOO+uo3tb4WFISRkRGdP+xK/QYNSExIZNfOHSyY/z2gexD88ZfFWkwl7Utia1uYc2fPUKiQjbYdU1NTevTqTaPGTbAv5UDhwkX0Ho7y2tz/LaBs2XJYWllhbGyMvUMpNmzepte0MvDSRSpU9OCnxUu4c/s2zi4uGBkZsWXjn9p20rveSjs64n/qpHa9bd20iY979NTOd1xcHE5lymrn+2nNmregcOEiWFk9u9bk89j05wY+7PYxQ0aM5L1OnbR4tmzamDaeFi31mnaldvnSJSp4PDlHpR0dsbKy4ojvYa1MdHQ0Sxb/wpz5P1CtRg1iHkbj5u7OpYAA1q5eDRj+Xlr2q+5aW7P+T8Lvh1HUrhh3Q0JY9/uaHDw7htu4/g/GfjmRr77+hs8GDsbYxFiriWlhYQHomiK94dOA1m3bsWn7ThITErEv5YCtbWFGJCe7AE6e8KOudz1mzp3Hf7duaefS959DrF29CoBDfx/Aq3Ydtu3eS1xsLEXtilGsmB337oVmKe69e3ZTr359anh6smHzNqwKWVO6tCOqqmKRqqn03ZAQ6nrXY+vuv4iJjsGpTBni4+PZuiVtEuNuSAilHR05/++/z9x3s+YtKFvOmdp16mY5IbRqxXI8PCrRoGEj1m/Rvb8cHZ24GxKSpmyr1q2pULEipqam1K5bV2/ZimVL8PTy4s3mLfh2xqzk660MCQkJbN+6RW9/md27Uz8LPN1ke/xYXaJ0y6aN9Ozdh2YtWhATHYNVIWuiHz5Mk8TKawcP7OfcubM0bNSYI77/6C3bv/cvzvifpmHjJgwaNpz3OnWmtKMjlpaWBN+8ibOLC9eCgvSavBdPvvZTzkNKNwSGSlnP1MwUc3NzxoyfSFKirjljhzatOOrry4Y/1tG9x6fs3LufmOgYStjbU6RIEfr16qlt59TJE9T1rsecef/j1q1gzC0scHR0IvLBA6056qqVK6jl9bre52nK/ST152mKZi1aUszOjjp16+V4Qig76nh7Y2NjQwUPD6pVr6H3eZ9yP025lwDaPXflsmX5FXKBJQmhF4i9QymMjY31OjVLkbq2UO26dQm4cCFPfikyNjbG2NgYVzf3NMsya/4ihBAi561ZtZLg4JvMW/AT9va6fjnWrlnFjG9zPvkQHR3N4P59GTthEo2bNKW8qxuhoXdZsVS/o+eDB/Zzxv80HpUqY+5kTmJiIv169eSfg39naX9XLgeyeeOfNGzUGDd33efOEd/D/PTD/zh+9Aig++ypVr2GXi2W9EydNEHXp42bG8bGxgQH32TLpo3Mmz0rTdl9f+3hjfo+uLq5k5iYiMlfT/p3OXhgP3/v34+nlxdu7u7cv3eP5Ut+Zf7c2URHR/NalSoA3LsXiv/p07zZrDlJSUn8ezbj5mx5waV8eb3P7kKFClHBw0OvaWW/Xj0ZOmKUdr7/++8Wa377jUU//6itl971FhZ2nyED+mtl5s6aQfDNG9r5Brh9+78Mz/fpU6dwdilPWNjzdZT5LNO//ZqwsDC6fvwx5V3duH/vHps3/pluku70yRM0bdYsTfM5gBFDB/PZoMF413tDe/3/+H0N8+fqd7a7Z9dOzp87h0flyhgbG3Ng/z5mTvtW62fJ0PeS/+lTHPE9jGctL0qUKElcXBx9en6iXf95LTo6Gt/D/+BZy4sKHh7ExcWxdPEiOrz3HtbJ19SlgADGjBrBlcuXGTxsuFYzZ8umjXr9mg3u35fBw0bQqk3bNOcy5Xvu6t9WYm5hwcjPdc3qQkPv8usvC2nTvn2W4t62ZTOWlpb06NUH94oViYqMZOvmTTR5sxkl7e21ckMHfsbAocOoWdOTYsXsuHL5MiuW/sofa39Ps80HDyIoUrQoe9J5mE7t9KlTFLKx4cL5888sl56DB/YzesQwho4YiUelypiYmHD+3Dnmzp6Zpuyxo0do074DDg6luByYtv+48V+M5l5oKK3btcOpTBnu3LnNymVLWbp4kd7+Mrt3G/IsMHfWDJq82QyX8uUxNjYmLi6Ogf36ZPkzIDcc3L+P12vXSXfwha8mjGdETAyNmjSlvKsrVy5fZu+eXbRt/w7lXd24FhSUbpP3lOmUbghSEvDpSd0H4tPbSfmcS23e7Fn8FxzMhMlTMTY2JiYmhoMHDuh9ngzu31frQ6i8qxtxsbH8e/Ysi3/5WUvUX7kcSN9ePfhzy3ZtP9HR0Qzo2zvd+8npkydo3bYdF84/O+GZV476+ib3XfYf169d01uWcj/due9v7dhysy/FF52iphoVI794eXmpfn5++R2GQV6EpgjduvcgOPjmMzskLKhklDEhRF57nlHGRMFT+bUqvPt+J6IiI7U+H/JT6pGuUo+CJIR4uZw4e56gq1f4+IPO+dJRuii4Nm3fmW7CDHjmSHjixVHQRxmzK178hKqqXuktkxpCL6HUHb+9qLLaeZgQQmRHSseFhnT4Kl4M5/89x/kJ5/I7DCHEKyBlRFJnFxeuBAbmSsf94sWX3hD1QhQUmSaEFEVZDLQG7qqqWiV5XjFgDeAMXAPeV1U1XFEUBZgLtAJigO6qqp7MndDFy8jQTmOFECInSG1EIYQQ2VW5SlUqVX4NRVH4YtQISQYJIV44RpkXYQnw9Nh6nwN/qarqDvyVPA3wFuCe/K83kLXeIoUQQgghXiKb/txA7RpVaVK/njQXE+Ils2D+99R8zYMalStK3yRCiBdSpgkhVVX/BsKemt0OSOnlbinQPtX8ZarOEaCIoiilcipYIYQQQgghhBBCCPH8DKkhlB57VVVvAyT/XzJ5viNwM1W54OR5aSiK0ltRFD9FUfxCQ7M2VKQQQgghhBBCCCGEyL7sJoQyoqQzL91hzFRV/VlVVS9VVb1KlCiRw2EIIYQQQgghhBBCiIxkNyEUktIULPn/u8nzg4Eyqco5Af9lPzwhhBBCCCGEEEIIkdOymxDaBHyc/PfHwMZU87spOnWBBylNy4QQQgghhBBCCCFEwZBpQkhRlFWAL1BRUZRgRVF6At8CzRRFCQSaJU8DbAOuApeBhUD/XIn6BVLH25t/A69m+G/T9p1aWXsHB3z9TnHu0hXOBASy75+0o5G4urnzw8JFHPc/x5mAQHbt/5uPe/TMy0PKFYuWrtA7L3sPHaaOt3d+hyUMYG1tzegx4zh36QrnLl3h0LETWFtbZ2tbT18HT18L1WvUZMHCxRz3P8e/gVfZ948vPXv3TbOdnr37ciYgkH8Dr3Lc/xwLFi5OU+ZlfS8JIYQQQgghhCFMMiugqmqXDBY1TaesCnz2vEG9TKKjo7kUEJBmvm3hwjiUKkVwcDCge6ieOm06KiqBFy9ibGKMU5myDB42grmzZmjrzZg9lwoeHjx4EEHY/XuUKu3I4GEjKFKkqF65F83NG9cpVqwYAKZmplhlM6Eg8t7YCZNo3bYdlwN1w62WdnRk7IRJjBk1IsvbsrCwIOjqFeIfx2vz7ofdJzo6GoCJk6fiXrEiERERBN+4QTkXFwYPG46ziwvjvxgNwPhJk3m34/vcvHGDx3Fx2JdyoEGjRri6uWtDwrq6ub+07yUhhBBCCCGEMESmCSHxfM6dOUOHNq305llbW7P0t9XYFi7Mwf37APjgo2541vLik64f4H/6FABdu3WnR+/eHPH9h6O+utpC5d3cCLlzhwnjxnDwwH6mzZxNq9ZtaNay5Qv9EDtx/Fjt77btOzBkxMh8jEYY6u02bWncpCkPIiJo/3ZLAN5s3oIp30zjvU6dWbdmdZa2Z2Nrw88LfmDTnxvSXe5esSKBFy8yYuhgrlwOpP/AwXzapy91vesB4NOwEc2atyA2Npa3m+ty1q5u7sz6fj4fduvGV1+OB6Bdhw4v7XtJCCGEEEIIIQyR06OMCQMs+GUx7hUqsmrFMlatXAFAm3btURRFSwYBbPhjLfb2DrRr/442z8TEhJMn/Dh4YD8A27duISI8nMK2hfP0GFKcu3SFyd9M05vn7OLCll176NW3nzaveo2aHD11RmtW9O2MWdg7OORKTCnNilL25+t3im9nzEpTbsz4CRw6dkKLacz4CWmaOlWvUZNfli7n38CrnAkIZMuuPbzb8X1teR1v7zTN2wYOGcrp8xf1mgO2bd+BY6fP0v6d9/h9/UatOdOoL54kwnr27svOfQe0ZRu2bNfbV4o3fBpw6t8A/g28yql/A/h9/UZtWa++/Th57gI+DRvprdOoSVMOHjmO35lzaba399Bh/g28yrHTZ59xVtP3znvvY2Nry1+7d2nz9uzaiXWhQrzduk2WtlXH25tCNjaEhNzJsEyVCq50aNNKq+lzwu8YERHhFC5SBICq1apz/949dmzdoq1z5XIgh/4+QMWKlbR5pR2dCtx7SQghhBBCCCHykiSE8oG7ewXuhoTw1+7devPj4580k6leoyaLl60EwN6hlDY/NjYWz1pe2gN/85ZvUbhIEW7/lz+DuT1+/JiatWrh7OKizatX3wcHBwd279wB6GpoTJw8lfj4eAIvXuRyYCCt27Zj6rTp2e5rJiMp+/Jp2JD4+HguBwZibGJC67bt9PZlbW3NBx91w8zMjMuBgVwODOSDj7ox94cftXI+DRsxe97/qOtdj0sBAQTfvEnZsuUYO2FStuMbP3ESZZ2duXrlCpcCAvSSH58NGoy9vQM3b9zgUkAA7hUqMPLzMXTq8qFWplOXD5k553vu3g3hUkAAd++GULlKFa3M7p07uH7tGo2b6rfobNCoEUWKFuVaUFCamAIvXiQpKSnb11BCQgL3798DdP1g/fjLYoyMjLArXjxL27G3d6BQIRtq1qzFd7PmMGPO9wwc+3N6BQAAIABJREFUMvSZ6/g0aESxYnbcvRsCwA/z5tKhTSu9GmcALq6u/PffLW064ML5AvdeEkIIIYQQQoi8JE3G8kEhGxsOHfxbrzZQap0/+JB+AwZRNLlPndSOHvGlvk8Dps2cRUR4BE5lyvAgIoLVv63I7bDT9d9/t7C3d8C73htassGzlhcWFpbadKvWrXFxdeXdtq21mh0HjxynarXqNGvxFn+uX5dj8aTs68b16wzs14crlwN5s3kLJnw1RW9fH3zUjdDQu3w55guthsi+f3ypWdOTjp27sGTRL3Tp+hH2Dg4c9fWlRzddwmXI8JF07/kpVapV49yZM1mO78b161pzp9Ssra0xNjZm5bKlfPfNVABmzp1Hi7da0bzlW6xZtRJra2s6du6MdaFC1K1VQ1t32szZvPNeR9asWsm1oCAOHthHk2bNcXZx4VpQEM4uLtSu601SUhKHDx1ME1Ofnp9k+TjS0+TNZowY/QVlypYlMTExy+ubmJigKAoDhw7Tm3/61CntNQKoUq0aQ4aPxK6YHa7u7jyIiGDxzz9luN3OH3yIZy0vvvpynDZv1YrleHq9XqDeS0IIIYQQQgiRl6SGUB6rXqMmD6Oi2L/3r3SXjxk/gZFfjMXcwoJffvoxzfK/du0k9O5dChcuQjlnZwCOHT3Czu3bshTHb2v/YMPmbWn+/Z+9+46rqvwDOP5hCXJF3KDIkiHlAjcqgpqZAxTNcqWlWY4cOBrmypXmzrJylVvTtJw/N4qK5EJERRAxJ4iLcQlk/f6ge+V6L3BBHMX3/Xr5esk5z3me55x77rnnfM8zJk+dXqh8zp4+TenSpWnu1RLIeViv6+5OUmKiOs2iBfNxf72mRhDk4cMHmJqaYmtnW6jyCqIqq0Pb1ury9u/dw8OHDzTK8vL24eiRIxqBhuCjR8nMyqJ6dVu8vH2oXbsOycnJGuPZ/L5lM1ejr2BnZ1+k+v26YZ1WMAhyBh+v95qrOhgEcC3mKhkZGVSuUhnI6XZm7+BIfPxdjW13bPsDhUUZGjRsBMCBffuoVs2Gtu1yxvRp88abVK1aTWertOI0feY3WFetypbNm8jKyir09qVKlSIpKZFpkydSy6UGI4YOJuZqNMNGBGi17qrh5ISrmxsGBgZciYok+soVnXl6efvw0eChmJubs2vHdvVypVJZbN8lIYQQQgghhPg3khZCL5CDoyPTv5nNimVL2Ll9m9Z6hUKBq5sb7/h3VgcNPh6iOWnb5Gkz2LRxvXpw3HruHsxZ8C2Bx0NoVK+23nXp1b3bM+zJExO++Ax7BweatfBi4KDBeDRoSJUqVixf8iSYVc/dg5lz5lGpcmXu3L5FZkamRje44qQqy87enpSUFG5evw5odrtr4ulJdVtbrQGPx302lnGf5QxmPWxkAGUtLbn+1zWNFkzXYmLo6tuxyPW7ejU6z3VhETmf+b34eBIePaK0uTlGRkbq9a/Vqo25uTlhoaEa2wUdDtQIbJ0LPUtyUhL9P/yI27du0a//AJTJyUyZNCHPVmnPwsTEhI+HfMIH7/XmZMgJADr7dy1gK20b1q1lw7q16r/3793D/r17CL14mQEfDeLb+XMBCAkOpnWLnEGkVef/itVraVDndY38lq1cTVPPZkRdvszAD/pprFu1fiMe9RsUy3dJCCGEEEIIIf6NpIXQC9S23VtUq2ajHlvnaVlZWQz96EOtFiRxsXeAnNY3qampGl2VzoWe5eaN65iZmT2/ihdAFYzw8vbB7bXXiL97lyOBger1vp27UN3Wlh7d/PFr3w5/3w7qfSpuqrKuREXRo5s//r4dtMqLi40lLTWNypWr5JnP/fv3ychIx6x0aWrXrftc6ppb7bp1ycrKYsXSJbT2aoa/bwd2bv9Do+tVUmIimZmZlLUsW2B+4eHnsShbFp/WbShfoQLh4ec1gkbF7dTJP9XBIABDQ0Pu37tXLHkbGxtTQUf3SXhy/puammqta9ykKXdu32benG+0vlPWVau+kt8lIYQQQgghhHhRJCD0Anl5+2BiYqJzYN+42JzBhZVKpcby9PR07tzJGeQ2JjoaQ0NDypfXfDguY2FRpDFbikvw8WPcuxePg4MjFSpUJPTsGY2WKI41nDA0NNTZVaq4qco6F3o2z/KuxcQQH38Xhxo1NJYPDxjN1u27GDJsBDeuXydFmYKlZTmcnV3VaRQKBYt++Alvn1bqZebmCipVqvxM9a5Rw4kHD+4TfPxonmlu3rhOWlqa1mDNqpnQcgeuDu7fR3JSEvUbNCQ1NZWgwEPPVL+83Lj+F9nZ2aSkpGgsNzQ05ObNm4XKa9Pv2wi9eFljmUKhID09nbi4nEGjT5+/yICBH2ukKWNhQUZGhsayEaPGcC8+nimTJugMhD1OS3slv0tCCCGEEEII8aIYZGdnv+w60LBhw+xTp0697GropZZLjYIT6bB46XK8fVpxOPAQQwYO0FqvUChYuPhHXq9Vi7g7sRgZG1Hd1o6VK5azcN4cdbqDQcepYmXFvXvxKJOV2FSvjqGhITu2/cG4T8cUeb+eVUdfP8ZP+opbt27Sr1cPjcBWh06+jJ/8FUlJSaQkKzEvo6BaNRuys7NZ/cvPzJ45g8lTp1PP3QMAk1ImVK1mw80bN8jKzGTf3j0sXrRQr3qoyipb1pJbt3ICEtWq2QCw6ucVzJ45A8g53iFnw0hJSeH2rZzZp5xdXAg+dpSRnwxBqVTi5e3DV9NmUMXKiqjLlzE1M6O6rS3p6eka3ZNCL+bM0nXzxnUyMzJxcnHB0NCQq9FX8GvfDsgZ/2f85CkMG/wRIcHBOuseHhmtUR8nZ2cMDAy4cf06gwf251pMDO/27E3AmLEkJCaoj6WNTXWmTprIxvVrNfIbOGgwgz8Zzs/LlrBowfw8j9nBo8exsrJGqVTS2L2OXsc5txnfzKGTX2euRud0h6tmY8P+vXt0no9TZsykW/d3OHH8OAP69dFY5+Tswpz5C7Gqak3cnVj18f5j6xYmfPFZTl3/Of8fPXpEfFwc9o6OGBsbq8//Jp6ezJg1B+uqVYmMiNAqX3Uu5f5sX7XvkhBCCCGEEOLfI+jEyTzXnfwzJM917dp3eOayH9y/X2CaipUqnc7Ozm6oa52MIfQCqAYoTklJybOlhlKpZMSQQew/fBSXmjUBiI+/qxEMAvhszCiGDhtOnbr1qFChIvfv32PL5k0smj/vue9HfnZu38a7vXoTeua0ViunXTu2U7p0aSZNnY6hoSFJiYkEHTlMw0aNqWJlBYCtnT2ubm4a2zm7uABw/nxOt54evXrTtfu7mBhrn7b3H9xnwdzZ6rL6D/wYWzs7lMnJ7Ny+jbKWluqyIOd4r1u9ig6+fupyVv/yM98tnK+uf9DhQAKGDWVYwCg8mzUnMzOT6CtXWLPyZ42y169dTWf/rtRwciYrK4vlS36id1/NMWv0sXvnDnxat8HF1ZXMzEyW/PA93d55F2NjY2o4OXMtJoaN69dy8+YNvvtxCaWqlyItLY2wc6FawSCA27dukZaayvY/fs+33KjLl6lcuUqRp1sf9+kYHty/z/sDPgQgMTGB6V9N0pk29Mxp2rRty6WLF7TWRV+JYkzACDb8thVXNzeys7OJj7+rDgZBzvn/8ZCh1HP3wNXNjVu3brJx3Tr1mFVWVtZYlM3pUvf0+QRPzqWgw4Gv7HdJCCGEEEIIIV4EaSFUSEVtISSe3bCRAQz4aBAmJiZa6+LiYvli7Og8W9+URN//tBQvbx/qurm87KoIIYQQQgghxH+StBAS4gVYtGB+vl2fxBM9evWmQaPGPHr48GVXRQghhBBCCCHEK0gCQkL8xyz9eSW169Rl62+bmDV92suujhBCCCGEEEKIV5AEhIT4jxn4QeHHLxJCCCGEEEIIUbLItPNCCCGEEEIIIYQQJYwEhIQQQgghhBBCCCFKGAkICSGEEEIIIYQQQpQwEhASQgghhBBCCCGEKGEkICSEEEIIIYQQQghRwsgsYy9A6MXLmJiY6Fx34vhxBvTr84JrBBeirvLbpl+ZOO7zZ85r2+49ODm7aC2v5VIjz232BQYBMGbkcM6FnuVC1NU80/74/SIWLZiv/ltX2ugrUfi1b1eYagshhBBCCCGEECWWBIRekAcPHnDv7l2t5Teu//USalN8HBwdMTEpRWpqKjdv3CArM1Ov7SpXqcLxY0c5F3oWgMiICK00hkZGVLe15Wp0tEZ5usq6/i8/jkIIIYQQQgghxIskAaEXJDIi4qW0BHrerKytMTUzJSHhETOmTiYkOFiv7dLT0wkKPKT+29+3g1aagYMGM/iT4ezcvk2jvMKWJYQQQgghhBBCCE0yhpB4JpUqVcbcXEFaahpxsbF6bdPR14+4uFiCjx/LN52Xt4/O8gpTlhBCCCGEEEIIIbRJC6FXxPKVa2ji6UliYgJxd2IxNTOjuq0tO7b9wbhPx6jTHT91hrJlLbl7Nw6AKlWsSExMYOHcuWxcvxaAGd/M4a0OHbl54zqZGZlYVbUm7k4sYwJGEH0lSp2XT6vWnD5/kRvXr1PNxgaFQsGF8+d5p2tnvetdw8kJMzMzEhIe4dvFH1tbOy6En2fzxg0olUqt9A6OjgweNpw5M7/mWkxMnvkuXrocj/oN2LRxvVZ5qrLs7R3Iysriqwlf6ixLCCGEEEIIIYQQukkLoVeIgYEB7/V4F3/fDnRo25rlS37Cu1Ur3ngzZ7BkB0dHLCzKsnvnDnzbtcW3XVt279yBhUVZuvfoAeS0vmnVug3fzJiGX/t2+Pt24L0e7+Lk4qJOo5KRkcHYgBF06fgW4z4dw4P796nh7FTIOuecQnXq1mPQkE/o6OvH2M/HsXrDrzrTt3njTapWrUbgwQP55lu7dh0ePnjAoQOa6QwMDNVlte/YiY6+fqze8KvOQa2FEEIIIYQQQgihmwSEXpCmzZpxIeqqxr/lK9dopcvdgmfZTz9gYVEWj/oNAGjWwovYO3dYs/IXlEolSqWSzb9uID7+LvYOjgD4tG6DRdmybFi3ViNPZXIytrZ2GmUdDTrCwf37ANi/dw/X//qLUqVMC7Vf2dlZKJXJXLwQzoihg5k2eSLx8XdxremmM32Dxo0xMDAoMN/yFSoQHn6eoMOBWuWpyqrlUoNpkyfiWtONUWM/LVS9hRBCCCGEEEKIkky6jL0gumYZK2iGMaVSiZGREVbW1gDUqOHE3bg49cxcACHBwdy5dZtK9SoDUK2aDdnZ2Vp5eTb0KLCOqampGBkZFZgut0UL5mtMCQ9gZGTMyDFj8PL20QjoeHn7ULt2He7di883TydnF1JTUzUGnc6rvPVr1zD6s8+pU7eeVnlCCCGEEEIIIcTL0qhxk5ddhXxJQOgF+a/OMqbLmlW/8E7PntSuU1cjQDNi1GhMTEyYNWNantsqFApmzZ3H+jWrWL9WuwWVLrdv3cLWzl6rPCGEEEIIIYQQQugmXcZeYQqFgszMTPWMWlevRlPFyop67k9a+9Rz96CKlRVpaWkA3L59S2eXrE2/b2Py1OnFXse9gUcIORtGl65vq5c1a96CspaWXP/rmkZaWzt7bt26me/4QX5d/LF3cOTAvn1a62rXrcvewCMaZQGUtbQkLS1NqzwhhBBCCCGEEELoJgGhV0zuwZEHDhrCo4cPORF8HIDjR4OwrlqVPv3eR6FQoFAo6NPvfayrVuWvazkzdgUePEBSYiLv9uytkWdNt9dITUst9vpeiYpCoVDQqXNnFAoFAJ06d6FChYpcCD+vkdbU1JTjR4PynRHMy6cVpUuX1ugWpxIeFsaVqCiNsgAqVKjIvXvxWuUJIYQQQgghhBBCN+ky9oKoBpV+2onjxzW6ks1Z8C1Ozs4YGhqSmJhAW28vdQDlWkwMgz7sz9DhIwgKOQVAxKWLDPqwP8eCjgCwc/s2dm7fxqFjwXw5aTKGhoYkJSbS1sdL3dKoOA0ZOIBGTZoSMGYsQSGnMDY25tbNm8yaMU1jWvmBgwZz/NhR5s3+Js+8Zs9fSEtvH86cPpVvees2/UZQyClMTU3JzMxk1oxprF21slj3SwghhBBCCCGE+C8z0DUA8YvWsGHD7FOn8g4CvEpqudR4LvkuX7mGps2aPbf8X7a+7/fn5s0b6lnNhBBCCCGEEEKIf7ugEyeLtF2FihWfuewH9+8XmKZipUqns7OzG+paJy2ExAux6pcVL7sKQgghhBBCCCGE+IeMISSEEEIIIYQQQghRwkgLoVdESZmSXgghhBBCCCGEEC+ftBASQgghhBBCCCGEKGEkICSEEEIIIYQQQghRwkhASAghhBBCCCGEEKKEkYCQEEIIIYQQQgghRAkjg0qLV8qQYSNo+2Y7AM6FnmXyhC9fco2EEP9lW7fvAiA9I4MtmzayYd3al1wjIYQQQgghXgwJCIlXinXVqri6uQHw4MGDl1wbIcR/nep6k56eTuUqVV5ybYQQQgghhHhxJCD0gjg5u9Cv/wBatW5D5OXL+U4z3/Xt7oz9fBxlLS3p37c3IcHBADg4OrJjz36Sk5OJvXMHO3t7jI2N2bHtD8Z9OibP/IaNDOC1WrUZMnCAxvJGTZrS+72+tG33Fr9t+pWJ4z7PM4+pX8+is39XToaEaNR9176D2Nnb8+jRI+Lj4nBycSEjI4OVK5azcN4cfQ+PllouNYq8rRBC6KuWSw2Wr1xDg0aNXnZVAFAoFKzfvAUHxxrcuxdP6xbN8kx74MgxrKtWRalUMm3yRLb9vrXY6/Pt4h9p2qw5t2/donRpM6pWs2Hh3DksX/oTAOs2/UZps9Ja21WqUoV9e3YzZeKEYq/T08Iiotj864YilTV56nTquXuwb+8eFi9a+Bxq93KoWr6ZlDLB1s6ej/r3U99L/JcEnzpLWUtL4uJi+WLs6ELvo+rerOvb3QkJDta6N5vw1VS6dX+HWzdv8jgtDauq1sTdiWVMwAiir0Sp03l5+/DF+IlYV63Kndu3uXPnNgvmziY8LAx4cp7lpvps7sbFMWbkcM6Fni3iURD/BjO+mUMnv86kpqZy+9YtqtnYYGZmVuA9/NOcnF2YM38hrm5uJCQ8IikxkarVbMjIyKB+7dc00k6ZMZNWrdtQvkIFnef32M/H8d77HxB/9y6JCQnq5ekZGbzj74eDoyMzvplDDSdnYu/cwdDQgOq2dvxv185C1flV1aNXb97t2RuAatWrs2blzyxaML/Q+Xj7tGLk6LFAzm9fhQoV/jPPMarfktxyX7va+njpnZfqO5CRkcHNG9exsamOqZkZJ4KP82G/9zR6ieRm5+DA47Q0pn01iZ3bt2k8eyYmJFDd1lbr2VPfa7fIIQGhF2Dtxs3UqlMHExMTsrKy8k2rUCjo0asPZSwsyM7O1ljX9e3uRF+5wqjhnxB9JYoevXozcsyntGrdho6+fuzcvk1nno2aNGX3ju0ay7bu2I2TszNGRkYF1r9DJ1/atG2LgYGB1jo7e3v+PHGCYYM/QqlUMnL0WN4f8CGd/Do/U0BICCFKmumzvqFN23YoFAqd19unVbGyKvA35Vn07N0Hz+YtmDJxPNv/+B3IuaHz796dA/v3ci0mhl7du2ltV8/dgzkLvuXQgQPPrW4qDo6OPHr4sMhl2drZ4+rmxvnzYcVcs5fL37cDAH5d/Bk/ecpLrs3zoVAodN4r6Sv3vZkuXt4+tH2zHampqXR8sw2Q8zD++87d9O7bVx2AdHB0JGDMWOzs7en9zts6Azu6ur9PnDIVO3sHQs+ekWBQCdCqdRsSHj1i+pTJ/G/XTt54sx2TpkyjdZs3CpVPZ39/ajg7Excby6Tx4wg6HMisufPp0MkXB0dHrsXEUM/dg0+/+BL3+vXJysrK8/fEomxZjIyMOHY0SOdL4a5vd6dW7Tp09e2ofoju0as3I0aNyfe5499iw7q16m7i23bvKXI+hwMPcTjwEADLV66habO8X+T826h+S3LLfe0qjBZeLTUCN6rnWFWwfPGihTpfzJwJv0RUVKT6fCvo2VPfa7d4QgaVfgGcXFyIv3uXFcuWsO33LfmmHTp8JDVfe42Yq9H8/fffGusaNGzMyZAT6ovyhnVruXH9L0qbm+Pk7KIzv46+fri4uBJ8/JjGcjt7e67FXGXe7G8KrH+vPn1RKMpw6cIFjeUOjo6kpaVxLOgISqUSgP379nD3bhzlypcvMF8hhBBP1KlbD4DZX88gJSUl37R9+r5PXGwsD59j19o6dethbm6uDgYBbFy3Fmtra5q1yPutYJu2baliZUXQ4cDnVjeVtu3e4tq1mBdSligeXt4+BJ04yUeDhzxTPkOHj9R5r6Sv3PdmujT1bEa58uW5cf0v9TLV/Zej45O3/13f7o6TswvX//qrUIGdxk09USYnE3jw+QdOxbNzcnYh+NRZxk/6qkjbW5Qty/nzYfxv104A9u/dw7nQs5SxsChUPtVsqmNsbMyZ06fU173dO3fw6OFDarrltBCyd3DAycWlwOeOqlWrkpmZSVJios71DRo2xtDQUKNFxYZ1a/N97hCvnmEBo/hj1x6aeHo+c14Ojo5FvnaZK8yJi43lj62/ATnnUnRUFGZmZgVuqzrX9Xn21PfaLZ6QgNALMHhgf9r6eDF31kwyM/N/m9uufQeUymR279yp9dbr3r14rkRFaix7nPYYQ0NDTE1NdebXoFEjFGXKcC0mRmN5z+5d8WvfjuVLfiyw/q/Xrs3lS5e4fDlCY3kZCwsuR1zSqFNyUhKZGZno8XJbCCFELl9NGE8br2Y8evQw33QOjo507d6dndu3kZGZoTNNc6+WnAm/xIWoq4RFRDEsYFSh62NlXVWrBdK50LOYmZXG1bVmnnVr3fZNHj9+rF62bfce/gw9z5nwS4RHRrP5j+1Mn/UN4ZHR7NizDy9vHyCnxcdn48Zz+HgIF6KucjrsAr+sWUejJk3zrKNHg4Y6g0HNvVqybtNv6v0/dCxY4xhMmTGTC1FX1W9yu3V/hwtRV7kQdZUpM2Zq5NW7bz/CIqIIj4wm+NRZZs6Zp17XxNOTg0ePM3nqdA4fDyE8Mpo9BwPp0at3nnV+lXTr/g5hEVFciLrKyXPh/LB0hcZ6K2trZs6ZR/Cps4RHRhNyNowF3y3Wehh8+nw7dCzvrltt271FhYoVadK06G/Rm3h60q59B533SvrKfW+mi1KpJDoqigvnz2ssz87OplSpJ/dcns1bALB/7/8KVb6trZ3GW++X4YelKwg5G0Z4ZDThkdHMnDMPK2trjTQKhYJxEyap0xz98zTjJkzSSNPcqyW/bvmDsxciuBB1lV+3/EFzr5bq9cNGBhB68TLLV67R2G75yjUa3ze/Lv78GXqeg0eP06Xr2+pzMywiik+/eNLKasBHgzTWbd2xW2vfctfp7IUIjToNHDSYM+GXOBL8p/r6oxJ04iSnwsLp0/d9jeUdOnWirKUljZvmfT0qyN3YWI2///777yKfv7qkpaUBEHIimMED+xf43GFoaERWVhapqbqDqvfuxXPp4kUd2+X93PG8rVq/kfDIaI1lDo6O7Ni7nzPhlxg4aDCQ01I19/n99LX7ZVBdT8MjowmLiGJv4BEGfDRII43qt0t1PX36t0sl97U7LCKKXfsO5llu2zfbYe/gQON8fkv11fXt7sV+7crvO9Czdx9u377Fvj0511d9nj31vXaLJ6TL2Atw9vRpvdL16fs+aWlpfNT/fWrVrq21fsTQwRp/z5o7n7ru7ly6cEFnE7sBAz+ms383nU36IiMitJbpMnfhIg4d2M/oEcO0bpLDw8K0ugtM/GoatnZ2HD6U94WpqFRN3xUKhda66CtR+LVvx5QZM+nW/R2d2+ceJymvJp3p6eksX/Ijf4ac4OvZc7GystZKA6jHdtKnvPyaj/74/SIWLZhPE09PVqzSPbuRamwEKyvrAvcfeKY6qfZ/0YL5+HXx5+vZc3XmU9jjfSHqqs406enpuL9ekyaennke79xjQ/wZel7n/sOTcaf02beDR4/n+dnGxcXSukUzvc63/PatsHXSZ9+K83wr6Hjre74V13fpRZ1vquP9Kjp96mSBaVTdDACOHwvCt0sXrTSTp07n7Xd70MSjrvoNWujFy/h3fZuxASP0Kgfg4cMHGBpqvzcyMDDAMI/uxlNmzMTW1o6VK5ZpLC9VqhTur9dkzoJvad+xE9diYvhp8XcM+GgQwwNGkaJUMnv+QspalmXBnDmsWfULTs4uzPv2O35evZbark5aZY2f9BUtvFpqjY+n2v/QM2fU352Ro8cyYODHHA8K4vSpk0wc97nG90XXOHodOvkyfvJXGBoYUtctJwDi5e3DV9NmMGxkgMZ5VMrUFO9mTQBYtnI14ydPwb9bd97tpv35vApU14AKFSqq983K2ppZc+ezeOly9TH9Y9ce0h8/ZsK4z9m/dw9e3j5MmfE1m//YjketnAHZdZ1vI0eP5WDQcZ3nm+rYF1Wfvu8zcswY7sbd5dbNG0XOp6B7M13dF3r06k1ychJrV69UL7N3cESpTKazfzf1w11mZiZHg45onZsqPXv34caN6890HJ7VsJEB2Ds40Kt7N/Xb86ATJ2nV5g2+njqF37dsZuCgwQz+ZDgJCY/U30HVOXD79i1+Wb4MhULBj8tWsGvHdt7p2hnIuT/9cdkKLoaHF+k7YGVlTb8P+qvPTRWFQsG6TVtwcHTUWDd34SKWr1yjHiNHnzpZWFjQr/+HfPbleI3WB2UtLVm5YhlrVv2iUfaiBfOf6bcjIyOD+/fvAdD6jbYs/P4H7ty5XeiA0KQvv8BcoeDNt9pz/NQZHj18RHVbWxIePVK32IiLjSXuqeDT0xQKBVbWVpiYmDBo6DAGDR0GQGpqKr+sWMai+fO0njsg5zhGXLz40sZcmzjuc777aSk9e/dh/dqcAOMnIwJwcHBk984dLP3xB4aNDGDAR4O4feuW+vxW/XZ26fo2v2/Z/MLrPWPWbHx8ax+4AAAgAElEQVS7+BNzNVr9Xer7fn+GDB9B7J3b6uDKT8t/JvTMGbyaNFR3hxow8GP8u75Na6+ce62lP6/Es3kLjd/FWXPns3XHbvw7tdcqW3W/Vhx69ulb5GvXiqVLeX/Ah2zZvovrf/1FNRsbSpUqxdbfdH8eXt4+DP5kOF9+/qm6YcPTz54KhULr2VPfa7d4QgJCrwjVG98tm38l+kqUzoDQ09q2e4vkpCQ2rFujvgnLrUHjxhgYGBS5KX3Xt7vTrHkLBn3YX+9tGjZuTFxsLKt+WVFw4kLa9vvWAgdN1fdGM79BvVXyG8y1MOXpU1ZIcLBeA9DpM2hscdWpOI93QfsWEhys1/Fu7F6nwDTF9dnqs/+g3wDo+tRJn30rzvNN32NQkBd5vIvrfCtO3j6t+HjoJ1oDK58LPatz3JBn9W7PXliWK8ev69flmaZUqVIYGBho/C6MDRiBiYkJNwvxAB1+PoxWrdvwbs/ebFyfE7D+8ONB+W7j4uLK3bg4Duzbp3O9qitc7i5xpqammJqaYmhkSPrjdB4+zOkGF30lii8/H0uNGtrBIIVCgXv9+iiTk7XWqfY/MelJN4ilPy7mQvj5Qu1/6zfaYmlZTj02BOQ0Wz+4fx9167lrpD2Rq1v21s2bqFWrNtVsbPBp3UbvZvW169Zl6nTt1irXr/+l88HsWbT0bkWlSpWJzNXyNy42lt9/+41RYz/N6dZ1OBAjIyOSUlOJv3sXyNn/z8eM0gjw6jrflv64mMjLEYU63vrq2r07BgaGbNn8a7HnnR8vbx8+GjyUA/v2sSvXuIwmJiYoFAriYmNp7dWMbt3fpe8H/WmeT7dKL59W/HkiWKv1tj50DbyanpHBlk0b1eOh6ENXgOPhwwfY2Ttga2ebU09vH0qVKsXRI0fUaYIOBxJ89CjVq+ek8eviT3z8XY3PY8e2P3CvXx+FRRkaNCz8gP0pKSn8ukH7GqdUKuncQfvh9lrMVdq2e0v9d0F1Ajiwbx/tO/pSrZqNOk2bN97M9/pVHAYM/JgPPx7Els2buHcvngEDPy7U9kqlkgN791CzphvWVatiaVmOzMxM/gw5Uah8HJ2cSEpK4tGjh+zesYP1a9cwYtRofFq3oWfvPiyar92aZsSoMbRt9xbTJk/U+dyRn+K6vl2LieHs6dM092qpDgjVdXcnKTFRfa3VdW7v37uH4QGj1Of2i+Tg6IhHg4akp6drdMFe9csKvFu1xqd1G3VASPXbpTq+qt+u3GOdGRsbawUS582eRd167igUikJ/NoVhZmZW5GvXsaAjNG7alAYNG+Hi6gpAVGQkO7b9oTN9qzY5A6Ln9xz75aSvCnz2zOvaLZ6QgNArok/f93F2cWXZTwV34QLUo+JvWLeWLZs3aa338vahdu063LsXrzV+kD5Ug1ubmpnp3Sd+xKgxpCiVLPlh8X9yNhMhhHiaZblyOLu4arWkevCcxvap37ARV6IiWbPqF62uHSqnTv6Jd+vW7Np3kFMn/yTocKC6uXVhbNqwnhYtvRn92ef07PMepUqVorpt/jfTZSwsOBp0pNCD5B4/dpTLly7R3Kslk6ZOp2q1ahw6cIDwsDD1TE25+XXxx97BkWsx2q3BVPvf0tuHKTNmqve/sMfAydmFrKwsnJydNWZaKW1uzuPHaXludyH8PI8ePaJylcpUs7HJM93TFAoFrm5uWsuNjAue/KGw7B0cMDIy0rqpDz17GnOFObZ2dgD8dS0Gt9de54dlyzlz6hRTJ0/U+n3P63x7Xjfezi6uhAQfZ9lPP+LXxf+5lPE0J2cXRo35lCpWVsycpjlIt4GBAQkJj5gz62viYmNZvGghlSpXovu7PXXm9cab7ahTpy7r16wuUl2sq1bVOk/S09OpXKVKofNavGQZ7vXrU7aspXrg4fT0dCDnIbZy5SqkpaVx7arm92zcZ2PV/69ua8f9e/c0zougw4G0a+Wt/rtZixaFqldSUiJXr0bnuX7AR4P4ZMRISpUqpV6We5wbfep0LvQsJ0NO4JvrHGrQuPFzHeS7qWdzatWpQ1JiIpO+/IJhIwMKnce7PXsTMGYshkZGrFi2hHNnzzL4k2G8+Vb7QgUDwsPC6Pl2V41lX4wdzU/Lf8G9fn2d5fbsnfMCSNdzR0GK8/p24vgxRo4eSz13D86FnqVSpcqEnw/T6MJUz92Dj4cMxb1+fSwty6mXF+W38FnVdHsNy3KWKJOTiYrUHPoj4tJF3D2eHO9Hjx7S0ttHfT2dOO5zrTr/GRJCXXcPtm7fxelTJ9m393+EBAezL/b57tsbb7bj4YMHRZ7EYfLU6Ti5uHDi+DFmTp/GkGHDadP2TWbNmadu/aTi5OxCU8/mpKam5pvnWx065vvsmd+1WzwhAaFXgJe3D23efJOER4/0Tv/xkKH8b9fOPGfyatS4MWUtLQnZE6zzZrogAwcNwdXNTe9tVT8U69euUb9JLm55vYmHJ28Y8pqyENCYVljXFLDw5C1bePh5Ro4eS8UKFXXmNeHLzwkPC9OrvLzKAti4PmeGg7zenADcf3CfBXNnU7FCxQL3H3S/PdS3TrnfMuaeRjOv8vQ93rqmrVSV946/H7Xr1s3zeKv2PzwsLM8pruHJTAj67Nuylavz/GzvP7jPh/3e0+t8y2/fClsnffatOM+3go63vudbcX2XXtT5Vti36AXRtyVZcXBydiE7O4stmzZxLSYmz4DQls2biIuLY9EPP9Gt+zt06/4O+/b8j0UL5hdqulWlUsmXn41l194DuLi6kpaWRkjwcRo1acq9+Hit9F7ePiQnJRV5kNxRwz/hkxEBdOjkS8CYTxkeMJpbN2+yZtUvrF2l2cy7fsNGmJiYEHzsqFY+qv0fOnyEev9TUlI4FnSEkZ/oP5CxkZEhhoaG6pYQueV3HK/FxJCe/hhjYxMqVtR93uuib0vR4lC6tDmA1uDl12JiNOo99OOBBIz5FG+fVvi0bkNLn1ZEXo5g6Y8/sGd3zvdM1/mWkpJCj27+xT69r5e3DwmPHrExnxZyz8MXEybiUrMmUZcv63zgvhcfrxEACz1zhk5+XWji6an1kNKufYcC33rn51m73KnUc/fAu1VrUlJSuBp9hcyMTKpVr64eG8bK2hpTM1MyMzOJj7+bZz4WZcuSmKB7UOLnYfykr3inZy/uxcer75tLm5sXqU67d+2kRUtvOvr6kZiYSO3adfjy80+fS71NTEyo5+FB9JUrTPtqUsEb5KGjnx+KMmXYtHG9evyr+Lt3mbPgW/y6+KtbzRSFUqkk4tJF6nlo/qarnjvMFYo8W3MUpDivbzu3b2Pq17No3LQp50LPYmhoyMlcLaTquXswc8487OztSUlJUQ+TUa169WIpv7BMTU0xNjYhNTWVv//WvOampv6NRdknA4t/OiqAocNH4Pba63Tr/g7tO3biWNARjd/vH777lrtxsUyaOh1XNzd69O5DYmICbb29nmvroHbtOxAefr7I164azs5EXo5g5CdDUCqVjB4xjMVLl9PyqXG8IGc2PZvq1YmKvJxnfqrGEfk9exZ07RY5JCD0CmjzRlsePcgZRFT1IGNSygRTU1PGTZhMfPxd9QPxspWradLUk5ATwYz7dEyeeb7bqw9XoiKZPH5coetTu25datetS0x0NAqFQl2nspaWlDI1Zev2XeruEE7OLsyZvxAnFxd2bPvjuU41n3tax7zkNWXh0/TpyvFhv/cKTKNPefqUFR4WpnNqx6cVtP/FWafiPN4F7Vt4WJhex1vXFNdPK67PVp/9h4L3Td866bNvxXm+6XsMCvIij3dxnW//Vu3ad+DGX9fVD92GRkZYWpbD2NiYjwYPIeHRI+4/uM/I0WMxNzenfu3X1NsGhZxi/eYtTJs8sVABrLjYWBrUraX+e9jIAMpXqKA1IYGDoyOffTmeFcuWFGmgSVWw71zoWfVYPAALvlvMuAmTNAJCw0YG0LbdWxw/dlTnTJnLVq7G3Nycn77/Tn1OtW33FhOnTMOvi7/e+3/9r79wcnbhcOChPMeC0aVL17exsq5KUmIi54vwQuZFiLh0kUZNmvB6rVoayzv6+pGYkEDo2ZwWEj8uXcG+vXvwbJjzgKhQKFj6yyrmffsdtVxqqIPLT59vbdu9VaTzrSBt3mjLvfh4hg4bwdBhI9T3SkZGRoybMJkvPx9bpJdg+Rkxagw1ajgxeOAAnQ9Cd+/GUbWajcaYJmUsLDA0NNAax2XU2E958632RFzSHqj3RRseMFpr7Kxtu/dgZ+8A5DzA37xxg/oNGtKoSVONz3F4wGgyMjJYvGghly6E06JlS43gVz13D0aMGkNWdhYL5s5Wb1ehEAFSXfy6+OPn35V79+I1WhSorgkqBdVJ9ZsUdDiQbxfMY9TYz0hOTqa0uflzm7EwOzubI4cDNa4l9g6OGBsX7lGsQoUKPH78mFs3bqqXnQs9y6NHD6lcxUrvfMZP+op3e/XmzxMnNLp+m5uba4wdp1AoWLxkGSEngvF9q+0r81B971487/X7ABub6uoXHirDA0ZjZ2+v8/x+GcLOhXL/3j2srK2pW89dI0j8Wq3aXP4nYFW7bl2GDh+h87dr/eYtNHavo35ZmZKSojGOVt/3+7N9zz71mJvFTXXtatZQ9wtHfRgbGxN7547GOXT75k2d42j17NOXu3FxTJ00UWtd7mfPp8cZy62ga7d4QmYZewVMnvAl/r4dNP4t+WExaWlpzJg6mQ/7vae+wWncpClXIiP5emr+zd5MTU05fjSoSBdu1cPi03U6djSIyIgI/H07qB8Cv5gwEWdXV/4MOcH0Z3jjIYQQIn+LFy3UuCbPmDqZhIRHpKWlseSHxRwOPER4WBiWlpbqKexzy8hIV89CUxQ9evXmnR69+N+unVq/LW3bvUW1ajZFbo6flpqKnYMD3q1ba83687RGTZqSnp5OUB4BRNX+d/D10y7nqf1XjVeka5yiY0FHSElJoamnZlP2Hr16a80i1rRZc/X//br4U6ZMGWJirr6yU4ofOXyIe/ficXZxVS+zsrame4+eGm+AnV1dade+g9asYqrZ54pyvj3LtPNP3y+p7pUSEh4xY+pkrWDQ9FnfcOLMOdZsKHwXF3jS+vmnxd/n+UBx/tw5SpcuTbsOHYGcB+g32rbDzKy0Vpe8Zv+MK3T8aFCR6lOcHj58QI0aThrdXavb2mmkCTocyOPHjzXGQ/Ly9qFL126kpORcA7b9vpVKlSrT2f/JS413e/WmYePGWJSxIDwsjMjLl0lJUVKpUqUnaXr21mu8zNwSExNJS02lbNmy6mX13D1o/cabGukKqlNumzduwMramhpOTvx1Le9xUZ512vmkxETcPTzw7ZwzyLaXtw8NGjXi77+1Z/jy8vZh9/5DOr8n169fx8zMjOZeLdWfXY9evbG1sy9Ui7yzZ06TnJRErdq11S0tvLx9aNqsORkZT2av/HLSV+rnjlclGARw7uxZLMuVo/UbbTkWpPl9yn1dVx2jUZ9+pnV+vyg54x6dwsTERP35Q04Qp36DhurfifCwsDx/uzIycrpy3n9wn0qVK9OwUWMdaTJ0fkbPOu28QqFQX7vyOwecnF34deu2PL8naWlp1KpTR/0b7+TskucsoqVLl86z+2buZ8+86HPtFk9IC6F/kaHDRwBgYlqKOfM135L/nfq3uoVBPXcPHjy4z7EXcMPR+J8b8ypVqmjdcKm6ggghhHhxNm/cSMCYsRw8epyEh48wMDSkfPnyBB87qu7mUxhbt++iUuXKlK9QgbS0NK2x7hQKBe3ad8DExKRIA00CbFi3Bs8WLWjq2YwFi77n+l9/YV5GgY1NdRITEjTSuri4cvvWrTxbnqj2v6OvH66uNTEwNKSajQ3m5uZa+38i+DgtvFriXr8+W7fvwsjYiL+uXWPY4I9Zv3YNderWo1PnLuw5GEiKMgVTMzOq29ry4/ffaeTTya8zDRo1IiVZiUvNmiQlJrJz28ubTlxXa+OszEwgpwVdSHAwW3/bzPv9P1TvW2UrK8qVK8fgXC0YrkRG4urmxtpfNxN75w7lypejUqXKGi1fdJ1v1WxsOHf2jM7zLfe080t+WPzcjkETT088m7fAwsJC59gl+mz/0eAhWJQtqzMIqOqmuurnFbjWrEnDRo3Zun0X5StWoFKlytyNi9PKs4aT83MftFhfB/fv45t5C9iyYxcpyUrMyygwMTEhOzsbs3+6Cq9bvYomns1o6tmM33fmBHtV36VNG9YDOQ+IKUolfl38Nb4DiYkJ6rFmjh45TMTFSzTx9FR/z6rb2hW6dUzgwQOEnQvFu1VrjfqULl2amzdu4ODoyLWYGK06ATnXklx1yu3x48cYGhrmO0Pus047f+jgATr5dWbCV1MZ8NEgqtnYYGZmprMLVtt2b2Fnbw+g9T1Zv2Y1bm6v0bhpU3buO4AyWYlN9eoYGhoWqnXmzu3baO7Vkk5+nfnsy/H07PMe1WxsUCgUXI64BOQ8d7zVoSO3b9/S+dyRuyXLixZ48AAtvFpSsVIl9u3ZrbHu4P59NGvRAvf69dmyI+capBo83CxXN/gevXqrg2FW1lXp6NuZ1m3aAoUb7Dp3l/dK/4zlpboGq4YhWL50CW6vv45rTTd+3/k/TExMdH5uyuRkOvr60ahJExIePsLG1hZzc3N19+jwsDB2bttGv/4D1Ndu1fdp5/ZtOp+72r7ZDjv7nGnni9J6qNd7fdXXrvx06NQJ15o1MTEx0fk9OXP6FE09mzF34SJu37qFddWqWFhYaM183dHXT2OQ8KflfvZ8eoiACV9+jkKh0OvaLZ4wKOx0h89Dw4YNs0+dOvWyq6GXFzl7TVH1fb8/N2/e4OD+l3/DUViqaaX/DcdZCPHfsHzlGho0avRKT0kvnpg8dTpbf9v83AZ+LQzV9O0L5sx+YWNJCSGKV1hEFJt/3cCUiRNedlXEK8Sviz/jJ0/RmjQCcgKhxd0lVvy7BZ04WaTtnrUrLcCD+/cLTFOxUqXT2dnZDXWtkxZC/0HPY8r3Fy33OEVCCPG8bN2+S/1GT/w7yO+CEKK49OjVm0cPHxZ55iTx3/UiJ40Q4mWSgJB4Jbm6uT23aZuFEEJF1ZVENdWyEEKI/74hw0bwVocOVLe1Y8umX2WcESFEiSUBIfFKKa7pVIUQQh/SPVUIIUoej/r1cXJ2ITEh4ZmmgRdCiH87CQgJIYQQQhRBSHAwrVs0KzihEOKVMvCDfi+7CkII8UqQaeeFEEIIIYQQQgghShgJCAkhhBBCCCGEEEKUMBIQEkIIIYQQQgghhChhJCAkhBBCCCGEEEIIUcJIQEgIIYQQQgghhBCihJGAUAkVevEyF6KuciHqKqEXLzNsZMDLrpLQU7fu7xAWEcWFqKucDrvAspWrnym/P3bt4fT5i4RevKy17oelKzh5LpwLUVfZG3iEAR8N0kqjUCj4bNx4wiOjCY+M5uifp1EoFBpprKytmTlnHsGnzhIeGc2hY8E68xJCCCGEEEII8WJIQKiEcn+9JrVcalDLpcbLrorQk0KhYPMf25k4ZRp13Vyo5VKDlp6NMTM1Y8as2YXOb+rXswiLiGLVz8tpUOd13F+vqbF+9vyFmJqa4tOsCbVcarBr+3aGjQxgX2AQ9dw9AOjo68f+w0fp5NeZ2q5O1HZ1YvL4cRw4coy33+2hzmv3/kPUc/dg0If9qe3qxPKffmLwJ59w8OjxZzsoQgghhBBCCCGKRAJCQrxA02d9w4kz51izYRMOjo6F2tandRuq29pxNy5OvUypVHLowH5q1amr1SonP17ePnj7tOLx4zR+2/Sr1noHR0dq16nLsaAjKJVKAPbv28Pdu3GUK18eJ2dnABo1boJF2bKcPx+m3nb/3j2UsbBQB40ATExMCD5+lHOhZwFYs+oXYq5epUKFioU6BkIIIYQQQgghiocEhP5FZs9fyPKVa7SWr1q/kfOXr6j/rufuQcjZMHUXnuBTZ7Gytn4udVJ1Fwo8foKwiCjOhF9i3abfaO7VUivtoWPBhEVEER4Zzez5C7XqVM/dg2UrV3M67AJhEVHs2Lufbt3fUa9v4unJwaPHaeLpqbFd6MXLbNu9Ryvdn6HnWbZyNWfCL3Eh6irzF32vsd26Tb9xOuwCF6KuEh4ZzWfjxmsFVVR1uhB1VatO9dw92BcYxKixn2rt69qNmwmPjGbuwkUayz2bt8DCwgJXNzfq1nPP79BqqeHkhJmZGcnJyRrLU1KUWJaz1AjAFMTbpxXlK1Tg4oULOteXsbDg1q2bXImKVC9LTkoiMyMTAwMwK10aAFs7ewDuxsZqbJ+dnU316tXVf2dkZBB/N179t0KhQKlUYmJionedhRBCCCGEEEIUHwkI/YsEHjyAi6srXt4+6mU+rdvg6FiDhw8eAODk7MLkqdNJT08n6vJlrkRFYWRszPRZswvVgkRfX076it59+2FRxoJrMVdJTEygbj13ps6YqZFuxjdzsLQsx7WYq0RdvkyHTr78uHQFTs4uQE6LlfmLvqepZzOSkpO4eeMGdnb2fDnpqyLXrVSpUnh41OfmjetERkRw8+YN9Tovbx/q1nMnITGByIgIrkZfoXfffhrl5a5TZESEVp3OhZ7lZMgJmrXw0ji2HX39qOHkRFJiIgf379OoU9Tly2RlZXHn9m3+unatSPv14P599f+HB4wmYOxnmJsrqFS5st55ODk7k52dzflz5/h69lzmLPhWYxyp8LAwPuz3HocDD6mX1apdh3LlypGiTOHG9evq5RkZGdy/fw/IGSvox2UrMDQ0pGKlSuo06enp3Mp1/FesWkvDRo0Lt+NCCCGEEEIIIYqN8cuuQEmwbOVqKuroGnP9+l+MGDpY73x2bt/G9FmzGR4wiqDDgQAMHT4CRZky/PDdtwBEX4nC37eD1rahFy/Tf+BHLFowv2g7kYdxn45h3KdjNJZNmTFTo2XP7PkLad+xE7VdndTLRowaQ49evRk19lOGfjyQ6TO/wdTMjPGff8bvWzYDOd2W5n37HR06+bJrx/ZC183AwIAGdWvpXBd0OFCjPgAHjx7njTfbceL4MR4+fKhXncZ9NpYLUVeZveBbhgwckLNvo8dgambGD999y87t2zTK+HjAB4XeD13+2LWHGk5OhJ8PY+K4z5nyVAAuPw0aNsLewREjIyN69O7DO/6dcaxRg5Gjx9CkaTM+HvC+uptYbl/Pnkv83buM+2wMIcHBWuuDT53F1MyM7X/8Tnp6us6yx02YRLd33mXliuVcvhyhcZ4IIYQQQgghhHhxJCD0AtRwcsLKSrvLlpGxUaHzir97l+rVbeno6wdA9eq23L59i317/qdOU8/dgx+WLadsWUsMDAwA8nxAf1YKhYJPRgTwxpvtsLK2xshIe5+qVbMhOztbY9nCeXNYOG+O+m9zhTkPHjzgypUnXZSuxcTQ1bdjkeuWlpaW7/rV63/F1c0NhUKhPk6qQIitnZ3edUpKTKR27Tp4efsQdDiQSpUqa30mxalChQrYOzjw+5bfmDltCrXr1i3U9sYmxmAAmZmZbN28iegrUURficLU1JQvJ02mRUtv9uzepbHNiFFjSFEqWfLDYp3BoKaezcnIyGDhjGlsWLeWzv5dNdYbGBjQs8971Kpdh1s3b/Lt/LmFCmIJIYQQQgghhCheBQaEDAwMbIFVgDWQBSzJzs5eaGBgUAHYCDgA14B3srOzHxrkPFkvBDoAKcD72dnZZ55P9f8dWrdoVmx5hZ49Q7v2HWjQqBEAijJl2L9vL9diYoCcYNDMOfMwMSnF1egrZGZkAuDo5JRnns/i69lzaf1G23+6Qd0iRZlCWUtLrKtWVaexKGtBZmZmvvkYG5uQ+vffhIeF5ZuuuHTo5ItHgwYkJSZyJSqK7Kws7Bwc1OsrVqyod50iIy/jUb8Brdq0IehwIEZGRhzM9ZkUN1c3N+Z9M4vlS396pnxSU1MJOxeq/lvVAs21Zk2NgNC7PXvTs3cf1q9dw8b1a7XyMTExoZ6HBx+815uTISd0lmVubk6duvU4dfJPpk6a+Ez1FkIIIYQQQgjx7PQZQygDGJ2dnf0a0BQYamBg8DrwOXAgOzvbBTjwz98A7QGXf/59BPxQ7LUuwQIPHkCZnEzjpp40aNgYZXIyJ44fU6/37dyF6ra29Ojmj1/7dvj7dtDZhay4NGzcmOTkZGZMmUy71j74+3bg2NEgjTRJiUk6Ww7llpGRjlnp0oVu7VJUHTr58ueJE7zh3YIuHd/C37cDCQmP1Ovv37+vd52CDgeSnp5O46ae+HXx525cHAf27ct3m2cRH39XIxhUurQ5KSlK7sXH57PVE+FhYaQolRgZGVG5cpV803p5+/DxkKGYKxQaLbqedurknxrBIENDQ+7fu6f+Oysri907dzD0ow+JvhIF5ASJhBBCCCGEEEK8HAUGhLKzs++oWvhkZ2cnAZcAG6AzsPKfZCuBLv/8vzOwKjvHCaCcgYFBVUSx2Ll9G5s2rsfW1g47e3vWrVmlMU5NufLlMTQ0JPbObfWyUZ9+9tzqU6pUKdIfP+bePw//rd9oS4unZhhbt2YVyUlJvNuzt3rZ1u27CIuI4vPxEwD4MySEatVsGDl6rHqA5hGjxnAm/JI6IBMSHMz9e/fw9mmtzsfJ2aVIM1WVL1+Bhw8fqLuIKRQKKlV6MijzutWrNOqk8nSdAJb++ANrVv6Mra0d02fNZsHc2erp1Z/2LNPOr1i6hCtRkRpTtSsUCvq+35+TISEcP3ZUI72Tswu/bt3G+KcG5lYqlWxYu5bs7Cze++DJmEYjR48l9s4dDh08oM578ZJlXL0ajWcD3TOibdn8K4kJCbjWrKle5uXtQ1paGnv/t1u9LD09nXoeHurj1qNXb1q09NYIGgkhhBBCCCGEeHEKNYaQgYGBA+ABhABW2dnZdyAnaGRgYKBqamAD3Mi12c1/lt151sqKHAf27f24gUgAACAASURBVKN9R18AjgQGaqw7uH8fzVq0YMuOXaQkKzEvo1CP4WNmVlqd7tet2zAxzvn4jYyM6OjbmdZt2gIUqkXR1SvR1KpThykzvmbosBHYOzpqBWh2bt9Gc6+WfPbleHr37UtmRiYuNWsSeTmCTRs2ALB+zWrc3F6jqWczdu47QIoyheq2tqSnp2t02ToWFETfD/rTomVLMjMysapqrTU+kT4OHdzP8IDR7Np3kLTUVMpXrICBgQGGhgaUsbDQqtPW7bswNTPTWSd48plUs7HRGkg6t6ennS9MtzKlUsmmDRsIGDNWo96VKlWmc4e3tNJ36NQJ15o1MTMz1Vq3ZtUv1G/YkDfebMfvO/9HqVKlqG5ry/IlP6n37fPxE0lPT6dKlSqs2bBJY/v7D+7zYb/31J9tJ7/O/L4zZ8ykajY27NvzP9avXaNOf+b0KZp6NmP+ou+JuxOL/T/BsPVrVuu9/0IIIYQQQgghio/eASEDA4MywG/AyOzs7ETVILy6kupYpvXEbmBg8BE5Xcqws7PTtxqCnOnOQ8+eUf8/t107tlO6dGkmTZ2OoaEhSYmJ7Ny+jdZvtKWKlZU6nWvNmhqBG9unPgNvn1Z8PPQTSucKIqn8nfo3P33/HYcDDzF9ymSGBYyifoOGuLq5cS70LGdOncL/7bdxdXMjMiICyJmNzLN5cxwca2BoaMiObX8wf843xMXGAjndrgKGDWVYwCg8POpToUJFoq9cYc3KnzXKXjB3Nu/07EkNJ2eysrK4cf06xsaFbyG07Kcf6d23H3b29gDcv3ePSxcuYGdvT9Vq1bTq5NmsOZmZmTrrBHAlKpKEhEeUK18+33KjLl+mcuUqRZ52fuP6taSkKJk+azZGRkakpaVxIvi4uhtWbn+GnMC3iz9XorTXAUz44jPuxcfT672+ZGVlERt7R6NbWLVqNpiamuLk7KK1bVxcrPr/4z4dw4P793l/wIcAJCYmMP2rSRrpRwwZxJeTvqJV6za41KxJfPxd1qxcyfIlPxb6GAghhBBCCCGEeHYG+rSuMDAwMAF2AHuys7Pn/bPsMuDzT+ugqkBgdnZ2TQMDg5/++f/6p9PllX/Dhg2zT506VQy78/zVcqnxsqsgXjFOzi7M+/Y7HBwdWbliGfNmf/OyqySEEEIIIYQQ4gUIOnGySNtVqFix4EQFeHD/foFpKlaqdDo7O7uhrnUFjiH0z6xhy4FLqmDQP7YB/f75fz/gj1zL+xrkaAok5BcMEuLfzNunFas3bMTZxYXLly7x0+LvX3aVhBBCCCGEEEKIAunTZaw58B5w3sDAQDVH9ThgJvCrgYHBAOA60P2fdbvImXL+CjnTzn/Af4iBgUGRxqwR/02v166DubmC40eDmDl9mnqQaiGEEEIIIYQQ4lWmV5ex5+3f1GWsiUddkpOTX3Y1hBBCCCGEEEII8ZL9p7uMCU2/bt1GmTJlXnY1hBBCCCGEEEIIIYpMAkKFZO/gwOSp0192NYQQQgghhBBCCCGKTAJCRdC+ky9e3t6UKVOGnDG3hRBCCCGEEEIIIf499BlUWujw47KfX3YVhBBCCCGEEEII8YrSZ4yfl0laCAkhhBBCCCGEEEKUMBIQEkIIIYQQQgghhChhJCAkhBBCCCGEEEIIUcJIQEgIIYQQQgghhBCihJGAkBBCCCGEEEIIIUQJIwEhIYQQQgghhBBCiBJGAkJCCCGEEEIIIYQQJYwEhIQQQgghhBBCCCFKGAkICSGEEEIIIYQQQpQwEhASQgghhBBCCCGEKGEkICSEEEIIIYQQQghRwkhASAghhBBCCCGEEKKEkYCQEEIIIYQQQgghRAkjASEhhBBCCCGEEEKIEkYCQkIIIYQQQgghhBAljASEhBBCCCGEEEIIIUoYCQgJIYQQQgghhBBClDASEBJCCCGEEEIIIYQoYSQgJIQQQgghhBBCCFHCSEBICCGEEEIIIYQQooSRgJAQQgghhBBCCCFECSMBISGEEEIIIYQQQogSxvhlV+DfatCHH3D29GmUSiXZ2dn5pj1/+Qqn/gzhg/d6q5d1fbs7X0yYxMOHD3jTp6XO7bq+3Z1PRgQwNmAEp0+dBMDJ2YU58xfiUrMmTTzqolQqC6yrg6Mj6zb9xlcTxrNn965C7GXxOncpkpUrlpGens7/2bvvsCiO/4Hjb0AEOYodFAERUFRErICKoMbeYy/RRGONDWtiNNijsWtiieUbjRprjNhiicESERuIoAgqdgXUKHoIIt7vD8LG8yiHYvnFz+t58kR2Z2dm92Zvbz87M1u+ohsD+/RW1u0NOkTx4tb8tGI582bPBGDwMH969+2PR4VyOnllrLt29QqtmjbOdV2+X/IjZmYqBg/oqxzDA0eOYm5uwZQJ32BuYcHwUaO5f/8+w4cMIiI8PNN8Rn05lppeXnRo0yrLsiZNm067Dh3Zsmkj34z9UlkeuHsP+/b8zsJ5c7XSHTt6lN49u+vk07hpMwImT8HQ0AivqpW18re1LaVss3bDZs6fi2TKxIBsj4FKpWLVuvUcPXKYMk7O+NarT6Vyztlu86rOXrjI77t2MmHcWOV4z5q3gKbNW1DRpUyu86vsUYVZ8xawctlSflm7Rq9tws5dIPbyZdq2aJrp+mkzZtL643YcPXKYPp/1zDKfwN17sLN34MfFi1j8/YJc110IIYQQQgjx33H42Il3XYVXJj2EXsHuHds5fPAgjx8/zjEYBJCWloa5hYXWskKFCmNoaMjTlBQGD/Mn7NwFNv0WqJMmKUlN1PlzyrKvxn+Dc9myHAs+qlcwCKBl6zbcunmTI4cO6pX+TXn27BkeVatRw9OLw0F/aq0rWLAQ9+/fI/jokbdSF3sHB0JPn8zyGF6/do0kdRJWVgVxdi6rLFepVCxcvBRfv3oA3Lh+jSJFi+Lp7a2kqexRhZWr17J81c+4ubsry0uUKPFadS5brhxmZiri4+5km+7WrZs4lnHSWtatR082/RbIhMlTlWVqtZqDfx7At14DyleoyN/3779W/XLy6FGi1vEuUaIkqampr5RXg4YNKW5tTeBvW3XWjQuYyNkLF3WWGxsbczchHkgPUI0LmKi13tLKCoCE+PQ0bu7u7A06RJuP2+ukS0lJ4drVK69UdyGEEEIIIYR4H0hAKJeuXrnChPFf52qbJ0lJ2Nk70Llreg8hlUpFbZ+6mJqacu3aNS5fuqSkyZCR5tLFi1o30TU9vbgYHc23kyfpVXbnrt3o2Lkrv+/aqXcA6U25ffsWruUrUM61vM6NfEpyMpaWlri4pPcGquxRhfofNcLY2PiN1OVR4iNKO/7bM0WlUlG0aDHl78MHg4iIOItKpaJF69bK8s/7DaC2T13u3b8HQOBvWylatBit27ZT0nTq2o3qNWtiYW5BRHg4V2NjSU5OxraUnZJm6PCRlLKzz1Wd7927x7NnqRQqVFhZVv+jhtTx0e5hFnTgDyq6udGpS3p7c3J2oX2HTpRzLU9ySrJW2u3bfqOMkxPWNjZERJzNsmwfXz8OHztB3wEDc1XnDKmpqdSp66sEzjp37YaTiwuJDx/qpB3sP5yjJ0+zbdcerUBbBpVKRa06PgCZtunQ06d4/OiRsv8Z9X/27Bnnz0Uqy2p6eVHZo4qyvmKlSiQ/eUJ09AUAIsLDuRgTQ4vWrVGpVMp2hQsX4e7dBCKzOV5CCCGEEEII8b6TIWO51LFtKx4/fpyrbbyrV2Gw/3D6fTGIsd9MIPXpU86Gn+HT7l05EXIMgJ3bAxnsP5zwqBiMjIxISkri5PHjDP1igJLPpGnTMTIyoqyrK4G792iVoVarqelRSfk7IvoSarWa6KgoJn4zjv17tdO/CzOmTmHq9O8A3Rv59m1aMnLMVwwdMZKvxn9D9IULHNi/l0KFC1H/o4Yc2L8PSB+u4+Tsomzn5OxCZMxlUlNTWfHjEooUKarc5L/sTFioEszr1qk9f/4VTET0JQDu3b1L1Llz2Ds44FIuPSg1sE9vKnukf3aRMZdJS0vj0sWLTJ0YoAwhU6vVDOjTm6H+IwiNjCJ//vycCQtlQJ/e/HX4EAArli0lPPwMo8Z8RWTMZZKSkgg68AdnQk/n6vit+3k1kWfPKvVJSUkh6vw5du3YTp26vpR1dSU6Koqd2wOxKVGS/oMG8XXABNSPH3Mw6E/69+lF3B3t3kVXYmNJTU0l4my41hC+lxUrVgyVuTmWlla5qnMGH8/qfB0wkXkLF2FhaUlCQjzLlixmxY9LMimrOObmFiQnJ2NhYamzfvHylbiWr8DunTsyLWvn9kB2bg/k4NEQxk+chIGBAUlJSXzcsjmXLsYA0KZ5U74YMpQly1diYWnJvbt32bVjO9/Pn6vVNgf26c26TVs4HHISExMT0tLSmDFtCmtXr3ql4yCEEEIIIYQQeeXE8ZDX2t5AnyFPb1r16tU1J0+efNfV0ItbWSe9homJd2PFqjV41aqV6bqs5ub5kKlUKg6HnGTx9wtYtmTxu66OEEIIIYQQQvy/8qpzCBUuUuS1y9ZnjuAmzZqf0mg01TNbJz2EckmCQe83CfjkTp/+A7l16yb79vz+rqsihBBCCCGEEOItkoCQEB+gCZOnUtPLm1J2diyYO5srsbHvukpCCCGEEEIIId4iCQgJ8QHK7cToQgghhBBCCCH+W+QtY0IIIYQQQgghhBAfGAkICSGEEEIIIYQQQnxgJCAkhBBCCCGEEEII8YGRgJAQQgghhBBCCCHEB0YCQkIIIYQQQgghhBAfGAkICSGEEEIIIYQQQnxg5LXzb0HYuQs8evSIu/HxOuvOhIW+k1eAR8ZcZsumjXwz9svXymfr9l06y4zzG2Nn70CPLp04Exaqtc7T25tpM2ZhYWnJlAnfEPjb1kzzValUrNv0K8WLF8e7ehUAJkyeSmWPKjppyzg7Ex8Xx8hhQzgTFkrXT3owfNRonjxJ1jrm+/buYdHC+a+zu0IIIYQQQgghxH+CBITekuioKHr37P6uq5Hn2rZsprPsm0mTsXcorRMMAujxaS+KW1vz5MmTbPNt1aYtJW1tOX8uUlmWVeAsPCqGsNDTSnlFihQhXz5joqPC/pPHXAghhBBCCCGEeF0yZEzkqdKOjtT08kb9+LHOuu49PsXT25u4O3dyzMfHrx758+cn7PSpHMtTP35M0IE/lGVFihQlX758/P33/dzvgBBCCCGEEEII8QGQgNB7YsWqNYSdu8DWHbsJj4ohIvoSwSdDUalUWulq+9Rl3aYtnI44z+mI86zbtIXaPnV18vvzr2Aln5DQcKxtbHTSGBgYMO27WUTGXCY8KoaNv26jhqfXa+3Hx+07YGdnT0xMtO66Dh0wMDBk5/bAHPNxcSlLfFwcf+zbl2N5MTHRWnkaGhlhYGBAUlJS7ndACCGEEEIIIYT4AEhA6D1ibGzMyGFDcHd1wa2sE+vXrWVv0CE+atQYSO8Ns2T5Sm7euIGPZ3V8PKtz88YNlixfyeZt2wFo3rIVwSdDWfrD90o+XTu0Y1/QYb4cN16rvNp1fNi/dw8VXcowfMggSpQsyeJly19rH7p078H169d05ib6qFFjihUrzpgR/hz963CO+RQsVIiVy5ZmOuxMKatbd7p076FTlp2dHQDtOnQkMuayEvASQgghhBBCCCFEOgkIvSVetWopwYmM/1asWqOT7tLFfwMXy5cuxsLCkipVqwFQq44Pd27fZs2qn1Cr1ajVajZvXE9CQjwOpR0B8KvfAAtLS9avW6uVp/rxY+zs7LXKOnL4EAf2p/fA2b93D9euXiV/fpPX2k9TU1OOHwvmSmys1vJOXbqyZ/cu9u/dk2MezVu24uqV2CwnnM7g41cPU1NTnbJSU1NJfPiQ33ftpL5PLTat/4Vnz57xeb/+ud8hIYQQQgghhBDiP0gmlX5L7t+/r/OWsevXrma7jVqtxsjISBnuVaaME/FxcVq9ZkKCg7l98xZFKxcDoGRJWzQajU5eGW/qyk5ycjJGRkY5psvKR40a8/f9+/z5xx9ayz/v15+q1WswddIEvfLxq9+AsNOnUavV2ZZVqZI7f9/XnSeob69Ptf6eMP5rLCwtqetXj+VLl+hVByGEEEIIIYQQ4r9MAkJvyeu8ZaxQocIA5DcxITk5WWf9i4EcC0sL0tLSXr2ir6Fx02ZERJzl8MEgZZmTswstW7dFo3mu05MnM6UdHSlfsSIzpk7JsaxChQtz6IWysnMuIoK27dujUqmyDTQJIYQQQgghhBAfAhky9h5TqVSkpaURdf4cAFHnz2Hv4EBlj397+1T2qIK9g4MygfKFqCjy5dON8236LZAJk6e+sboOHzWaRk2aMmrYEK3ljZs243laGtevXmPr9l1s3b6LseMnYGJiQt8BA1m3aQu+fvWU9OMnTKZkSVutoFJWZUWdP6dTXu8+/Th19hzbdu3RmpBbZW5OauozCQYJIYQQQgghhBBIQOi94+Tsovy7T/+BPPj7b44FHwXg6JHD2JQoQfeen6JSqVCpVHTv+Sk2JUpw9Up675ugA3/wKDGRTl26aeVZzrU8ySm6vYvygkqlolYdHwCdgMuihfNp27KZ1n/TJk8gJSWFHxcvomuHdhwM+hMAH18/XMqW5datm9mWl1HW0SOHdco7eeI49+/dw87ens/7DQDS979e/QZczOTNZ0IIIYQQQgghxIdIhoy9JWVdXdm6fZfO8jNhoUwY/7Xy98/rNxB3+w4mpqaUsrNjR+A2pbfMldhYHj1KpGnzFlSrUQOA4sWtSUx8yKb16wHYuT2Q2j51GfP1OLr16EHaszSsS9hwMSZaSZPXun7SgzJOzsTHxb1WPvUaNKBgoUJKgCgrGWVl9kr6M2Gh7AjcRs9evendtx9+9epjXcIGK6uCTJ82+bXqJ4QQQgghhBBCvC9q1PR8re0NMpuA+G2rXr265uTJk++6Gnqp6FLmjeS7YtUavGrVemP5v+8qVHSjXcdOPEpMZN7sme+6OkIIIYQQQgghRI4OHzvxStsVLlLktcu+f+9ejmmKFC16SqPRVM9snfQQEu+Fc5ERnAuIeNfVEEIIIYQQQgghPggyh5AQQgghhBBCCCHEB0YCQkIIIYQQQgghhBAfGBky9p7o3bP7u66CEEIIIYQQQgghPhDSQ0gIIYQQQgghhBDiAyMBISGEEEIIIYQQQogPjASEhBBCCCGEEEIIIT4wMoeQeK8MHDyUho0aA3AmLJQJ479+xzUSQvyXbd2+C4DUZ8/4ddMG1q9b+45rJIQQQgghxNshASHxXrEpUYKyrq4A3L9//x3XRgjxX5fxfZOamkqx4sXfcW2EEEIIIYR4e2TI2DswcPDQHNPMnr+QiOhLeHp7ay2fPmsOwSdDiYi+xMGjIYwZOw6VSpVtXpU9qrAv6DCVPapkur533/50/aRHtnk4Obuwa98BVqxao5P34mUrOXEmgsiYy+wNOkTvvv1z3L+cVHQpI29eE0K8cRVdynDs6NF3XQ0dNTy9GDHmy2zTjP7qa86cj+Z42FlatWn7xuvUvlPnTK9f1jY2yrUpPCqGP/8KzvQ6sG7TFk5HnCcy5jKhkVHU9qn7xuuc4ZtJkwmPinlr5f1/ELh7D5Exl5X/AnfveddVyjOVPaoov0sy2uTrOHg0hIjoS0REX2Lad7O0fne92P5z+m22dcduwqNiiIy5zPJVP2f5u+zT3p9z6uw5ws5dYPAw/9eqe3bGjB2ntW9jxwdkWu+RY75S0gSfDGXad7PeWJ3E+8vJ2YVRX4595e31aW+1feqybtMW5Tqx8ddtmV4rcjqXVCqVVnlHjp9i7PiAV677+ySvvrsHD/Mn7NwFJZ83/X0j3l8SEHoL1m7YrJxwZy9cpFr1GlmmValUbPx1G42aNNVZN3zUaMpXqEj3zh1xK+vE4u8X0LZ9B/YfPELzlq2yzHPEmC9ZuWwpZ8JClWUvfpEOHzUa1/IVsty+WYuW/Lx+A3b29rr7tnEzJiYm+NXypKJLGXZt387gYf7sCzqcZX5CCCF0TZ3xHcdOn+HshYv87+e12X6vA3zy6WcYGr65y3hljyqs3bBZuXZNnDJN5/rVp/8Adu//k8oeVej/eS/cXV1YsXQpAwYN0nqgsWjZCuLj4vDxrE5FlzJUqejK0hX/03nI8CZ06dadlq3bEhZ6+pW29/T2fmtBt7epVdPGVHQpw5IfFpKamvquq5NnZs6dz9qNm+ncri0VXcrg7urCpvXrs30wlhUfXz8OBR/n182bcCvrhFtZJ+p/1JCgoyF06Zb+0Grvn4ey/G2WQaVSsXnbdk6EHMPd1YWKLmUwNTFlzYZNTJsxU0k3+dsZhEfF0LNXb6pVqoBHhXIsnDc3bw7MS5q3bEWLVq35dsokZd+aNm/BH4f+on2nzlr73/WTHkqa+XNmUf+jhsr+i/+2YSNGKQGc33bupkPnLq+Ujz7tbdGyFSxd8T/i4+KU68T169dYuuJ/Sj76nEvNW7Zi/8EjWuVNGDeWps1bKGX9f5ZX390L583Fo0I5KrqU4dJFeWDyIZOA0Fvg5OJCQnw8K5f/SOBvv2ab9oshwyhXvjyxly/x5MkTrXXVqtfkRMgx5aRdv24t169dpYCZGU7OLpnm17xlK1xcyhJ89C+t5fYODlyJvcycmd/lWP+u3XugUplzPjJSa3lpR0dSUlL46/Ah1Go1APv37SE+Po6ChQrlmK8QQoh/VXKvDMDMb6eRlJSUbdruPT4l7s4d/n6DQ2sdSpfGycUl22tXTU8vjI2NCT56RHnosGb1T8RevkxNTy8AqlWvgatrefbt+V25VgAkJj7E0amMTk/YvObjV4/8+fMTdvrUGy1H5J3B/sM5evI023bl/sl3aUdHnj9/rnWDc+rkcawKFsShdOlc5WVnb4+ZyoxtW7coyy7FxGBqaqr87jI0NMzyt1kGv/oNKGVnz87tgcqyP//Yz9OnT6lYyR2VSoWPrx++fvV4+jSFFUuX5nq/c6tGTU/Ong3n9107lWVnwkIxt7BQAmcZ+x93546SZv26tVyKicnyd6d4v7zOuQTp16VjwUeZHPANCQnxr1wPfdqbq2t5HiUmsm/P70qavb/vJjHxoXKd0OdcqlHTEwtLS63y9u/dw5mw0FwHhcW74eTswsatgQSfDM05sXhtEhB6Cwb06UVDPx9mz5hOWtrzbNM2btoMtfoxu3fuRKPRaK27ezeBizHRWsuepjzF0NAQExOTTPOrVqMGKnNzrsTGai3v0uFjWjVtzIofl+RY/wpublw4f54LF6K0lptbWHAh6rxWnR4/ekTaszQMDHLMVgghxAsmjh9HA59aPHjwd7bpSjs68nGHDuzcHsiztGeZpqntU1cZnhUeFcNg/+G5rk/IsWAG9OmV7bXL2saGZ8+ekRCfoCxTqVSo1WpKlCgJgG2pUphbWHDn9m2tbZ8+fYqFhSW2tqUIO3eBdZu2sGPvfiKiLzEuYCI79uwjIvoSp8K1H0a8ODwnIvoS875flOXNaWWPKri4lCU+Lo4/9u3TWteuQ0et3rK79h2gW4+eyvqMbvkrV69FpVLx7czZStf6l3sLzft+kTJEaeuO3bTr0FFZ16pNW46HnWX773uZ9t0sIqIvER4Vw6/bd/K+e3k4VFbDwl9ub5kN0XrxeIdHxegc7xc1bNQYK6vcB3AAOrRphbvrmw1WaDQaUlJSADh/7lyWv80ylHFywtTUVKundlKSmmfPnmFV0IrKHlXw9atHocKFORcZyZrVP2Vb/ovtLTwqRqu96cvO3oH4FwI9AE+ePEGj0VCqVKkct8/Y/3ehd9/+ynkbHhXD1h27ddLU9qnLxl+3ERoZle3Qo4y8IqIvERIajrWNjdb6l3sHZpzPB45oDzVesWoNk7+dwdjxARw9eZrImMvs3v+nMk9dxhQLIaHhytC76bPm6JSnUqk4cvyU8v125PgpZViVSqUiIvoSM+fO19mPWfMWsGb9Jp0hWK9zLgF8v2Aun/f8hCtXLr/S9hn0aW/mFhY8efJE61qR/OQJT58+xdY2PY0+55KdvQNApuXp07bzWpdu3TlxJoIDR47qPAAZPmo0m7dtVz633n37s+fPg0r7fvl68i68+N194kwEi5etzHSIXsbwvMiYy5wKj6TGPw+FMrx8PQkJDc/y+t2sRQvKliuHpZXVG903kU4mlX4LQk/p/1SyWPHibNv6KzdvXNdZN/SLAVp/V/aoQnFra1JSUrhx/ZpO+tKOjtT08ubhgwc666KjonSWZaayRxVSkpNZv24NHlWraa2LCA+na4d2WssqulWiYMGCJKmzf7r9Knz96tHvi0EUMC2gs+7atasM/WKA1lvKXrZv7x4WLUy/iE6YPDXTpwQZbxqKiDjLsBGjKFK4SKZ5jf/6SyLCw/UqL6uyADb8spb169bi5u7O5KnTM01z7/495s2eSZHCRXLcf+C16vTim5Z8/eoxbMSoTPPJ7fHOeJNTZuV1bNsKN3f3LI93xv5HhIezbtOWTPcfoG3LZkDOn+36dWtZvurnLD/be/fv8XnPT/Rqb9ntW27rpM++5WV7y+l469ve8upcelvt7X1+k9ipkyf0Ste9x6c4u5RlxrQptGzTRme9j68fE6dM48GDv3n49wNMTE3p028AxYtbM/6rMXrXJ+7OHa2eAVlJTU1VrlmVPaowdnwAFStVIuRZmla6F/evc9duFCtWnNTUVGz/+YFe0a0St27e5PatW3Tu1p27dxOIvXwJO3sHfHz9OHwwCJVKRYtWrXn8+DEXY9J7ZDRs3AR7BweGDxmk8/CjQcOGFLe2Zt+e37VuIABGfTkWM5WK27dukqROwqVcOUaM/pKCBQvyw4L5xF6+TNqzNAyNjCjt6Mj1a1dJfZrePf/F66qTswsfNWrMgwcPuHHtGk4u6fNs5MtnzIZf/m1rDqUdsbN3IObCBaxL2FDOtbyyX++rxctWULacq3K8Szs6QycLkwAAIABJREFUMnT4CJycnBg7Jv18zay9lbKzY/K3M5T21qlLN/xHjlKONwYG2Ds4aB3vF4WFhlLasQz379/Lk/3wqetHfHwc4WfCcrVd5Nmz3Lt3j9Zt2zF/Tvq8OU7Ozjx+/IjIiLMAdGyrPbTzxd9m2fEfNQaVSoWBgQFFixXDydkZjUbD2TNnaN+pM17etbh6JZaN63/ROg9fbm9mKrNM25s+7t27q/zb2saGxk2bYWhoSJGiRbX2/8WgRacu3XBydmbtz6tyVVZeGTp8JD179eb6tWs8TUnBzFyFS9mydOrSTWv/Z89bgLmFBTdv3gDSH27OnreAubNmKummfTeLFq1acyU2/Vy3LmHDkmUrGek/9JWG0PjVb4CVlRW3b90kPi6eu3cTlN/bEyZPxaVcOR48eEDMhQvY2tnRolVrihYrxuc9PwHSb6rnL1pC/vz5le+3kra2zF+0hKED+6NWpwc+PKpUpbJHFeU7rbJHFSp7VGHj+nVavTDh9c+l3NzD5CSn9gbw+PEj5VrRuWs3vhgyjMJFiijXicy8fC4BPHv2TCnP2saGiVOmUauOD1diXy+w9SoCf9tKu46dcClbjtp1fAgJ/jdgXq16TY4eOax8bl8MGYqhoaHSvl3KlXvl8zsvDPYfzqe9PsfY2Jjbt26iMrfAx9cXZxcXJgWMV65fXwdMxNzcnMuXLirn0vSZsxk7ZqSyv1NnzMTLu5ZyPSlpa6tcvz9u2Vyr3OMhx2jZpi02NiXe9i5/kCQg9J4o7ejInAXfs2DubJYvXaLXXAX/W7OOlORkvp08kV83b9JZP3rsOEqWtGXx9wteqU4ft+/AqC/H0v/zXpwJC9UJCGXm25mzSYiPZ+yYka9UZnYOBv3JwaA/s02zaOF85aYwO/q8zj7jAv265elTVkR4uHLTn52c9j8v65SXxzunfYsID9freL8cgMxMXn22+uw/5Lxv+tZJn33Ly/am7zHIyds83nnV3vJSVoHDM2Gheh2b3Fr2v1V4etdi4y/rskzTtFlzrG1sqOhSRlnWsHETjI2Nsbax0SvI8yrGjg+gVduPWffzai5ciFKe6GZwcnZh/MRJVKteg+vXrnHpYgz2DqWV9VevxNKqaWM8vb35duZsvho1gpDgYAJ378GjShUOHwyicdNm3L51i5HDhig3Q57e3lhb22BuYaFTpw6duhATfYGAr7/SWacyN2fThl+Y9M14IP2moVr1GqSmpqJSqZSgp6e3NwsX/8iPixcR+NtWrTz86jdg8rTpjBnhrwxhcHN3Z86C7/EfNZrHjx8pae/eTVD2CdLnXxo7PoAB167qBLKy8jbbW2lHR0o7lmH+nFksW7JYWb5i1Rpatf1YCQhl1t5UKhW+9eor7a1Js2bZHu+XfTP2S74Zm/2k6jk5cOQo1tbpgYznz5/TvVMHvY9zhjNhoYz2H8aaDZvoO2AgADHR0UydNIETIcd00mcEKzJ+m72shqcX4wImUsbJiVH+Qxk99mvMzdPbrUNpR4yMjOjcrTvbf9vK77t2MmzESPp/MZiaHpVQq9WZtjeAvUGHlPa2c3tgloH6ebNn6nzP9u7Tj8/79cfE1JRlSxfTu08/nf33HzWayJh/b6RjoqPZtWO73scxu4cQuf2+nj9nlhKcyzB4mD/devRQbpi7dOtO0pMkhg4aoJxvPr5+jJswkU8++4wNv6xl5tz5NG3egpDgYK2XmASfDGX4qNF80a9PruoFEHv5Ej26dMp0XWb7Gbh7D9Vr1FT+Xrx8JVWrVcetrJNWuuNhZ/EfOZopEwP4dfNGOnTqwviJk+jZtTNqtZpxEyZiU6KE1nmaIS/OpbyUXXvL4OTswm87d3P92jUCxo1l2Ajde4rszqWXy/ti6DC2b/uNZUsXZ/lQKStZPYjKeMCmD7Vazczp0/h25mzad+qsTNcxLmAi7h4edOvUXklb1a281raDh/nTu29/rfb9NvXpN4DoC1FKWwNo83F7vhr/DVOnf0dd75rUql2HWnXq8M3XX2l9L718L1vZo0qW1++XhQQH08jv7b144kMnAaH3RMYT3+VLcx7CBelPaCB9LHdmwSAfXz/c3Cpx926CzvxB+lCpVHTu2h2Tl7plZmfo8JEkqdX8uHiRVvRbCCH+q6wKFsTZpaxON/37b2hun6rVa3AxJpo1q3/SGWqQ4eSJ4/jWr8+ufQc4eeI4hw8Gac3JkNcMDAzo0v0TKrpVYtiggRzYv49J03R7PH6/5EdsS5Xir8OHCBg3lmX/y30Pg9OnTlKiZEkWL1/B6ZMnCT76F2tXZ52PytycsNOndZ6aQ/qQtfYdO1OlSjVOnTzBlIkBubrJBShfoSLmFhZ83m8An78wlMrKqiAmJiaUcXLi6pUrmW4bEx2Nubk5pR3L6B2oeJvtrZxredSPHxMTrT0cKur8OWp4eip/Z9XeXjyWx0NCcPeoohzv6dMmExIcnOvjnRv169QC0udS7NWnLwGTp2Taiyw7Ts4uTJg8lWNH/2L+3Nk8SXrClsAdzJg1h4BxY3V6d2VMtJzVb7M58xdiYWnJb79u4e+/X/rMDCAtLY2tmzcxZWL625BMTEwYFzCROnV92bN7l17tDdKH52QMVXqRVcGCWn97edemYqVKPEpMZP60KRQrXjzT/XdycaFTuzY8SXrCwMFDaNCwUa56t6lUKso4OWV64/cqevftz6Chw8ifP7+y7MUePaXs7Ll3967Wb9HDB4NoXM9X+btkSVs0Gg2xly9p5e1d/dXnmMmubVX2qEK/gV/gUbUqlpZWGPwzt0JGQLS0oyPFihXPtGdZTY9Kyr/37N6FX/0GOJR2pFWbtiQmJlKqlN0bnU8ur+TU3gAKFDDj+yU/KteJuDt3Mg0IZXsuvVTe9CmTWL9u7Su9QcumRIlMz6XM6p6dkOBgos6fx6duehtUqVR4VK1KcnKyVrreffvTtn17bG1LabXvd8XIyIgrsbFa19Cw0FPcv3ePYsXTe2Md/esIF86fJ2DyVNp37MRfRw7z5x9/6DxAuXolFtfyFVi8fAU/LJjP/r175H7xPSEBofeAj68fDRo1ynRoV1bp+w38gt937dR5SpKhRs2aWFpZEbInmIjw8FzXqU//gZR1ddV7205dutGlW3d+WbvmjUWwZciYDBmTIWMyZOx9GzIW+NtWnR89b4qTswsazXN+3bSJK7GxWQaEft28ibi4OBYuXkq7Dh1p16Ej+/b8zsJ5c9/Im0TMzMyo5F6ZkyeOc2D/vizTFbe2Zv3aNUzLpOeEvq7ExrIjcBu+fvXwq9+Aeg0+om279ixbspg9u7U/99KOjjz4++8se55NmzSB7j0/w8nFhbKurjRr2ZJ9e/bw3bQpmQaQMpMvnxEGBgaULVdOZ11Ob3958iQJE1NTLC0t9SoL3m57MzExIfVZKk+eaA8BT05+wvPn/84plVl7S0pKonO7tkp7W/z9AuLj7ijHe8WqNSQmPsz18X4VO7cHcuP6dVb+vJYGHzVixTL9J2xu3bYtZZyd6d65g1LHo38doa6vH63atNUKiPj4+mGmUrEjcFuWv83y5cvHwrlzWLFsaaaTqScnJ2sNa9u5PZB+A7+gbLly7Nm9S+/29mKPl+xUrlKFSxcvMmViACdCjuncMGfsf/SFKOX34Iihg1m0bIXO/mcnJDhYCdC9rnEBE+nYpSt3ExKU380vTuANYGFpSeLDxGzzsbC0IC0tjYcP9fvt/bqmz5qDvYMDSUlJyrCakqVKKXOAWtvYYGJqQlpaWrb5hAQHc+rECZo2b4GPXz3Ujx9jbmGhNVnz+yqn9gbpQ+SSk5Np+lG9bPPK6VwyNjZWynuda35e9rA6HPQnNWp6olKp8KvfAFvbUty6eVMrzdDhIwCU9l3AzCzb4XJvw8svubgSG0tq6lPy5TNWlg0fMohd+w5Qw9OLml7eDPEfwYxpU7Qe2HzRrw/+I0fj61ePseMDGDN2HNEXojK9fou3SwJC74H2HTtRrFhxDAwMtLrkAqxcvZa4uDtKN3MnZxeGjxxNcWtrpv7zBCkzter48CQpiaADf+S6Pm7u7jRp1hxjY2OqVKumU6fImMscO3pU+cGREaAyU6my/BGUF7J6MgpglM8IyDqSD3D27L/BrayenqWmplKsePEcn2Zl1EGf8rIqC/59wqBSqbJMExd3B5VKpdf+v26dMvYf0o93Vvnk9nhnlSbjB2x2xztj/4Es9/9F+uxbdp9tXFz6cBp9j3dW+5bbOumzb3nZ3nI63vruf16dS2+rveX2qd77pEPnzhQoYMZX47/hq/HfaK37duZsACVY8NfhQ/h4Vse3Xn3q+tWjZes2lCxpy2efdM3zm+/nz5/z+66dTBg3VllmZmZG3B3tSaTnz57F6p9WKn8bG+f/Z/6hG7kq78uR6RNke3p7U9unLr0+T+/9YWRkpNXjpGHjJkREnM3ypnXLpo1s2bQRaxsb6vjUZczX42nXoSNFixbN9XCRjCE9mclqCHjBgoVISU4mMTH7G9d3JSUlBeN8xhQooH2zbWpaQGvCZNBtb3Xq+vLtd7O02tuLx3v8hEnU9PJ+5eOdW2fCQjExMcHO3j5X25VxciZfvnxan23UuXPUql2H0o6OyrKM32YhwUez/W329ZejlaBpgQJmGOczJilJzd2EBJLUaqysClKsWM7fUdm1t9w4eeI4Y0b4K8NIMz7be3fT517J2P+X552MOncOH19fnfzeNDd3d+rWq8fz58+p7/NvgGnwMH8aNm6i/P0oMRHLCtkHWh8lPsLIyAgrq4LZpssrpezsuBgTw/Ahg5RAaeDuPcqw2bg7d0hJTtGrPkEH/qCOT13c3CqRnJLM40ePXun3/tuWU3sDePToEYtemFOstKMjxsb5da4T2Z1LL5eXwdS0gFZZb1vGXEKt2rSlglslVObmnNq1Q1nv5u7O8+fP+WnFcubNngn8O2TsXTJ7KeCa8Zk8e/ZvEFqtVuNbyxM3d3e8a9WmUZOmDB81hgIFCiijX+Lu3FGu38NHj6Fe/Qa4lq9AwOQpEhB6x3IMCBkYGJgChwCTf9Jv1mg0AQYGBo7AeqAwcBr4RKPRPDUwMDABVgPVgHtAJ41Gc+UN1f8/4eXJoiH9B+S4CZMYPKCvVne6rTt2cSkmhtbNmmT7Y8DZpSyr/7dCayynviLCw2nSwE9n+aRp07G1LaX15Gn5qp/x9PIm5FgwLZs0zHVZuaHPk1F9I/n6PD3T52mWPuXpU1ZIcLDW/AtZ0efJcF7VKS+Pd077pu/Twxe7TWclrz5bfZ/E6/O56VMnffYtL9ubvscgJ2/zeOdVe/v/avqUyUyfMln5O2OuHXNzC6ZM+EY5fpt+C8S1fAUqlXNm147t7Nqxndo+dSllb6cMPckr27f9xoBBQ6hcpQpu7u6EBAfTuWs36tT1ZZT/UCD9s23SvAVetWuzZdMG5dpVys6O0NOn+HXzJr6ZNEWv8sYFTKSGpxfDhwwiJDiYkOBgqteoSSX3ylStVl0JCPn4+vFJz8/o0TXz+Tzc3N1Zs36T8sN7y6aNqFTmDB0+Apdy5XBzd1d6RMTduYOJiQmu5SvotNGF8+ZSw9OLNes3KRPRWtvYEDB5Kmg0TPxmnJK2YMFCeHnXVh7uDBw8lGPBR9/bG7k9u3cxa94Cho8azd/373MmLJQen/aiXcdOqB8/VtJl1t4aNm7CxKnTlPa2N+gQxYtbK8d7UP++9Pi0l3K8XzbYfzhdunUnIT6B1s1yN+fHomUr8PWrR32fWsqNZ+eu3Xj48AFHDh/SSe/k7MKa9RvZuT1QGaqV4UxYKLXq+GgNj2rQsBFGRkacDT8D/Dsx8aWYGPp8lvlb01Yu+xHfevXx8fVVbmJ7fNqLQoULs3vnDo7+dYT1a9cybORIPvnsM2JjL3Ng/z6GjRiFvUNp/vynjWTW3jL2OaO96TtH2K+bN/J1wARqenqxfdtvADRr2ZKUlBT2/r5ba/+969TROl4NGjbi1MnjepWTl4oWLUYB0wJaN6KVPapQ/6NGWukWLZzPJ59+xrTvZjF2dPpwo4zP6XxkJJ3atWHdmtWUcXKiRavWyufu5OzC1h27WLdmtfJda2Zmhm+9+gT+thWVSkWrth9jZmamNT+YPgwNDXmUmMid27eA9BviUnb/BiivxMby6+aNDBg0RKu9+fj6MW/hDyycP5efViwH0nuOWVpaMnz0l5hbWLD4+wVZ/t5/nXPpVRw7fYaL0dGM+2q01hA6fdrbyRPHqevrh1ft2srDgwGDhlDKzk4ZgqnPuVSocGEqVKxI2XLllPJ8fP1o1rIlPy764Y0fg6yo1Wrmz5nNd7Pnkj9/fk6EhChzqkF6+372LFUrqFX/o0YYGxtnlt1bERd3h48aNWbkmK+YNeNbrG1smDBlGvYODkrv2959+jFwyFCGDRrI4YNBRISHs2zJYiJjLuNRpaqS19kLF7l86RLDhwxiznczWPrD9yz7aTWV3CvrlOvk7MK3M2djZ2f3WsM4hX706SGUAtTXaDSPDQwMjIEjBgYGu4HhwFyNRrPewMBgCdAbWPzP///WaDTOBgYGnYEZQOa/yESufDEk/ce1sUl+Zr30ysknyU+USWkre1Th/v17/HXk8BuvU01PL1JTUylevDhr1muPl88YCiKEEOLt2bxhA/4jR3HgyFEe/v0AA0NDChUqRPBfR/L8Kdy6n1fj6V0LL+9azF34A3cTEpSbnBd75vyy5mcWL1vBrzt2kfRYjZm5ioT4+EwnQc3OL2vX0KX7J6zduJk7t2+jef4cl3LliLtzh317/50nqV6DBhQsVCjLOT0iwsN5/vw5vfr0pWnz5iSpk3BwdCRfvnyEBGsPtb4SG4uhoSGdu3WnTt26pD1LY++e35UXNixbspgly1cqdSpuXRxLSysizoZrTXJqbGxMrz598atXH+sSNlhZFWTWjG9ztf95af4Pi7G3d6CAmRlGRkZY25Rg6/ZdWkMrY6IvULacK0tX/sSd27cp7eiIoaEhO/65oYPM21tJW1vOhJ5W2tvOwEB69uqtHO+UlBRK2dkrx/tlGa/KNjPLvtdkZn5Z8zOuruXZEriThLg4jPIZUcrOnt937WT/3j066Zu1aIGllRU1vbx01mW079nzFypDO5xdXIiOimLt6tUANGmW/nYcY5P8OsNVMyZxVqvVbFq/nq8DJuDpVYuU5GRcypUj5sIFlvzwPQBrVv9E1erV+ahRY6bPmvPPuWTHs2fPtNrjy+0t4xzIaG9x6BcQ2rk9kG9nzmb8xMlKD4QiRYqyI3Abv6xdo7X/Xt61+G1n+vllU6IEFhYWDB8ySK9y8lLQgT8IPxOGb736Sn1K2tpSoEABbly/TmlHR2W+kyS1mlZt2lKtRg0AbG1LkZj4UAks7NweSG2furRo1ZrA3XuUNyNdjIlm0/r1SpmPHz+mYeMm7Np3ADOVGYULF0Gj0eS67g8fPsCjalXlO9C5bFkArbwya28lbW05feokm9b/opVfRm8TI6N82c4R9zrnUm55entjYWFBWVdX3Ct7aH3/6tPeMs7dur5+bN2+CzNzFba2pYiPi1Py0edcevGzzSivpK0tpqamSlnvyuGDQVhaWZGSksKxYO05XoMO/IGpaQGGDB9B+06dAZS3Dxob/zufkD7f3fro3LUbH3fohHG+fFjblMDIyIjmLVtTv0FDZaj+1i2b+bTX5/T4rBcNGzdGZW5BwYIFuXXzJr+s+RmA9evW4F2nDvMW/sDNmzdIe5aGmbmKxIcPtYaRX4yOpqyrK2s3bub2zZsUKlKYokWLZRrEznjt/LsMhn1IcgwIadK/qTIeBRn/858GqA90/Wf5KmAC6QGh1v/8G2Az8L2BgYGB5lW+PYWWjJPV0VH36feLvYVqenkRdf78W5moy8jICCMjI5ycXXTW5TT8RQghRN7b8Mtabty4zsLFS7G2tiEtLY1NG35h1vS8Dz6o1WqGDuzP1wETqVe/AWWcnElIiGfNKu2Jng8fDCL8TBiu5StgUip9nowBfXrzVyY9NrJz6WIM27f9hq9fPZxd0q87x4KPsnTRD8pbn1QqFe6VPbR6sWRm6sSA9DltnJ0xMjLixo3r7AjcxsK5c3TS/vnHfmrX8cHJ2YW0tDTy/fHvj9zDB4M4FBRE1erVcXZx4d7du/z80//4fv5c1Go1Fd3cgPS3jJ0JC+Ojho14/vw5kWezHs72NjiWKaN17TY3N6esq6vW0MoBfXorcz44u7hw69ZNNqxbx4of/30BRmbt7f79ewwbNFBJM3/OLG5cv6Ycb4Dbt29lebxf51XZhw8GMWbkcBb9uJyyrq6kpaVx585tpafIy46HHKNP/4HKa75flNG+9/x5SGlvL8/HlTEHTGa/zV6cxHnDL2vp3K270t6Cj/7FwrlztOb1Gv/VGO4mJNCidWtK2dlx585tnUnTX25vgFZ7y43V/1tJy9ZtlHzW/bxaa+h/xv6PHjuOdh06Ksv27fn9jcxHpo9JAeMZmZREsxYtSUtL49LFixzYv5dWbT6mjJOzEoQYMWwIQ/3Te6BpNBrCz4Txw4L5Wt85Y0eP5NLFiwwdPkLpwTOgT2+tG9TF3y/g096fY+/gQEJCPP9bvoyWbdrkut6zZ0ynV59+2NnbpwdVA7dhaWWl9ZaxzNrbvbt3GTZooM5nq1ariY+P52L0hWwns37d187nRkhwMM+fP+f2rVuZTqafU3vLOHf9R46iskcV0tLSOBcRwfy5s7Xy0edcGjt6JPfv3VPKS0x8yPq1m9/MjudSUlIScXF3Mg3k7d65A7/6DXApW5a0tDR+XPwD7Tp2Il++f2/Z9fnuzm5+y4y3UhYrXlwn6JIxrDZjqP7CuXO4deOG8t2dkpLC4YMHWfLDQuWlQxntdte+A5RxcsbAwIDkJ08YO2aU1kOo/n16KdcTl3LlUKvVyvX7ZfLa+bfLQJ84jYGBgRFwCnAGfgBmAsc0Go3zP+vtgN0ajcbNwMAgAmii0Whu/LPuEuCp0WjuvpRnX6AvgL29fbWrV6/m3V69Qf/VoQjvi0nTptOuQ0eio6Le2GubhRAiw9btuyhavDgWFhas+HEJC+fNfddVEv8xGUPAHz9+pPXaeSGEeBUqlYqFi3+kppeXzivqhRDvxuFjJ15pu8JFMn/pSm7cv5dzwLdI0aKnNBpN9czW6TWptEajSQM8DAwMCgJbgfKZJfvn/wbZrHsxzx+BHwGqV68uvYeElrKurm/stc1CCJEhYwLqnN4IJYQQQrxry1f9jLOLC0WLFuPmzdxNyC+EEJnJ1VvGNBrNAwMDgyDACyhoYGCQT6PRPANKAbf+SXYDsANuGBgY5AOsALmzF3rJy9c7CiFETqTXpxBCiP8v3Cq5Y2ZmxrmICGa+wznIhBD/Hfq8ZawYkPpPMKgA8BHpE0X/CbQn/U1jPYFt/2wS+M/fwf+sPyDzBwkhhBDiQ6TvGwuFECInXlV138gkhBCvQ58eQiWAVf/MI2QIbNRoNDsMDAzOAesNDAymAKHAin/SrwB+NjAwuEh6z6DOb6DeQgghhBBCCCGEEOIV6fOWsXCgSibLLwM1M1meDHTIk9oJIYQQQgghhBBCiDxn+K4rIIQQQgghhBBCCCHeLgkICSGEEEIIIYQQQnxgJCAkhBBCCCGEEEII8YHJ1Wvnxatp1aYt4yZMQqVS6azbsmljrl6z7uTswogxX1KjpicmJibcuXObtatXsWrlCiVNuw4dCZg8FSMjI54+fUrMhQt0/Li1Vj69+/anU9eu2NiUICUlhZPHj7Pkh4WcCQtV0ljb2OA/cjQtWqVv++DBAxrXq4tarc7tIXjvDB7mT/8vBit/p6am4lGh3DuskdBXuw4d6d7zM5ycnUl9+pTQ0NMsnDtHq+3mJDLmcpbrXm4LmaW9dDGGVk0b69QnLS2NmAsXmD93Nn8dPqS1Tdi5CxgbG+uUteLHJSycN1fvugshhBBCCCFEXpCA0FtgbmGBoaEB9+/f5258vNa6O7dv5yqvWXPnU9bVlYcPH3D/3l1KlLRl6PCRFCxYiPlzZtGpSzf8R47i9q2bJKmTMDNXUcHNjY/bd+DXzZuUfAYP8wfg+rVrFCpciLp+ftjY2DDSfyiXLsagUqmYOmMmXt61uBgTA0BJW1vmfb+IPp/1fM0j8u4lxMcTHRUFQMlSpTAxMXnHNRL6yGjfZioVN65fx0xlhpd3LYoULkLbls30zifjs3+RoZERpezseJqSoiwr7ehIcnIyN65f53lamrL82rWryr9HfTkWM5WK27dugoEBFdzcmDV3PjOnT9M65wwNDblz+zaJDx8qy1KfPSPhpe8EIYQQQgghhHgbJCD0FpQtWw5T0wIEHTjAyGFDXi8vV1d279yh5ONXvwGTp02nU9euRJ0/x9ARI0hNTaVxfT9lm8HD/Pm09+fKzemoL8cSE32BDm1aKWnGBUykU9dujAuYwGefdGPG7Ll416qtVRak93IYPMz//32PhvXr1rJ+3VoAAnfvwd6h9Lut0Adk6ozvcHBwZNxXo7kSG5urbf1HjeZRYiL9en+m9Ajq3acfA4cMRaVS6d17LbPg0epfNmBnb8/K5T8qyyq6VSI+Lo6hX/TPtK6NmzYjNTWVIQP7E3TgDyD9fOvdt7/WOZfh9107mTl9mt77K4QQQgghhBBviswh9BYYGhlhYGBAUlLSGysj7Vkae3bvolb1qvh41cg2rVqtJvLsWa1lKSkpaDQa8udP7ykzqH9fKrqUee0AVl5a/csGIqIv6SzfsXc/pyPO06f/AAAqe1Rh8bKVhISGExF9ieCToUyfNeeN1WvM2HEEHT1GeFQMkTGXWbdpC7V96mqlUalUjB0fQET0JSKiL3Hk+CnGjg/QSlPZowrLV/3MqfBIImMus2Pvftp16Kis9/T25sCRoxw4clRru8HD/AncvUdr2YFaGygSAAAgAElEQVQjRzkedpaun/TgdMR5ImMuExF9ibkLf1DS1Papy7pNW5R1B4+G6AxrrO1Tl42/biM0MorQyCg2/rpN2bc+/QdwOuI8h4KP4+Prp7Xd4WMnOBkeQfcen2ot9/T2xrt2Hcq6uuJe2SPng/sSU1NTHj9+rDU8LClJzbNnz6jsUSXX+WXo0q075StU5Natm+zb87uy3MjIiNTUp1kGrvbs3oWPVw0lGJSd58+fk5z85JXrKIQQQgghhBB5SQJCb4GtbSnS0tLQaDSMmzCJSdOm81Gjxq+UV3JyMlWrVVduwBs1aYpVwYLcvnUr0/TWNjZUq1GTCy8MkVm0cD4Txn+tlc7RyQlDQ0Nu3bqZZdk9Pu1F4sOHhIXqP1dLXjp8MIinT59S2tFRa7mNjY1yI+/k7MKEyVPx8fUlNTWVizExGOXLR4tWrTOdwykvdOvREwtzC67EXiY6Kgr3yh5MnjZd+YxUKhXzFy2h6yc9uBgTw8WYGPLnz0/XT3po1Wnuwh/w8q7Fo8ePiI6Kwt7ega8DJjJ0+MhXqlf+/PkZ4j+CG9evER0VxZXYy9y4cR0AH18/Jk+bjntlD6Kjorh86SIWlpZ8HTBRK4/Z8xZQwc2N+Pg44uPjqODmxux5C+jUpRv79vzO1StXKFS4MPUaNNDarmChQlyJjWXrFu0eMiHBwcRcuMDtW7e4euXKK+3X/Xv3lH8P8R+B/6gxmJubU7RYsVfKD8DHrx6mpqYcPxasFfxxKF2apKQkBvsPZ9a8BXw3Z1627SjjfMuXL5/WOQfw7FkqxvnzM2nadOYu/IEanl6vXF8hhBBCCCGEeF0yZOwtMDQ0wMjIiPYdOynL2nzcjh3bfmPsmFG5yivkWDB1fOoyY/YcHvz9gFJ2djx88ID169Zopdu4NRDjfPkoZm2NlZUVbZo3zTLPzl27UbVadRITH/LnH/t11m/dvov8JibYlirFj4sXcfhgUK7qnFf27fmd1h+3w7tWba2bdlPTApyPjORKbCyDh/nj6OTEtatXGTygH5cuxvBRo8YETJpCw8ZN+e3XzXlaJ09vb54+TWHerFmsWf0TAGvWb8KjalUaNWnK4YNBdP2kB1WrVSchIZ42zZsA6QGZSdO+pUPnLvy0YjmQHkwICQ5m8IC+qNVqho0Yxae9P6dp8xb8sX9vrutmbGzM/Xv3lMmPX9SoSVOKW1sTdvo03Tt3AKB7j0/p1bcvnt7ehAQHo1KpUJmbs3N7IGNGpM85NWP2XJq1aMnH7Tuw4Ze1HD74J2WcnKjp5a3kXdrRkefPn3P0yOFMh3D16/1ZrvclM9Y2Nnzerz8PHzzg+fPnr5WXm1sl/r5/nz//0O7pY2BgSCX3ylRyr6wsc3ZxYZT/MC5djFGWde7ajY87dKKkrS1WVlZEnA1nyQ/fK+tVKhUFCpjRu08/ZVldv3r8tHI5C+e+ud5rQgghhBBCCJEV6SH0Fnz2STcqupTR+m/fnt9p1fZjVv+yIVd5Va1ajeTkZLZs2sicmTOIib5AwUKFaNaylVa6jm1b0b9PL9b9vJqHDx/yae/PM83PydmFcRMmEXE2nIa+PuzasV0nTduWzZg+dTKnT56kXYeOeHp7Z5JT1jKGOkXGXNb67+VhTjm5EhvL4oUL6NWnnzI8qE//AZw+dZJR/kMBWDhvLj27duZK7GV+Xr+ByJjLzP9hMYWLFMHO3i5X5ekjJDiYTevX07NXb2XIWJVq1TAwMKBkSVsAPm7fEY1Gw5qfflK2O3wwiHq1vZVgUNdPenDz5g3mzJqhBFHmzZ6JR4VyNGngR0R4eK7rplarmTd7Zqbrxn81hn69P0Oj0RARfYnImMt8Nf4bzM0tsLa2AWDg4KFEnT+nBIMAxozwp1I5Zzq1awPAnJnfsTNwGw4vzME0euw49v6+mzkzv8t1nfVRuHBhDh87wZ4DB/Gu5sFI/yEkJb36m++cnF0oYGbG4u8X6AQ7F8ydrXPu2js48L+f12oNk1u/bi0d27ZSzrcKFd20zjm1Wq2Tz/nISPoN+IKZc+e/ct2FEEIIIYQQ4lVJD6F35FxEBH71G1C4cOFcbacyN2fThl+YPWM6kP62rFnzFmQ6f0rcnTssWjif1NSndOjcBTd3d63Agkql4qvx3xBz4QLfTp6U7YS8hw8GcfrkCY6dPkPDRk0ICQ7Wu84hwcHUr1MrF3uZtZ3bA5n87QxqenlxJiyUKtWqcyLkmLK+skcVps+ag72DA0lJSVpvEntTPvn0M54/f6682c3SygqbEiWU9SamJqSlpZGQkPXbpIoUKULykyevFPh5Fc1atOSr8QEUKlSIizExaJ4/x9DIiBIlSyppLCwtSXyYmGNeu3ftpE5dX5q3bEViYiJubpX4+svRb6zuZV1dSXz4kIVz5+g9iXR2Wrdty9UrsQT+tlWv9Pfv36dQocIUy2SIWsb5NmDQkBwDp1Hnz1G5ShWKFi36SvUWQgghhBBCiNchPYTekYSEeNLS0jAz039eGzd3d5KTk7WCBmfCQrlx/RqmpqZZbhd59iyWllZavTgAvg6YSE1PL+bM+k5r+EtW1Go1hoaGOJQunWPaN83H1w9Pb29cy5fnUFCQsrxl6zaUsrPjYkwMndu1pW3LZrRt2Yy4O7ffSD0aN23G48ePmTZpAo3r+9G2ZTP+OnJYK01KcgpGRkYUK1Y8y3zu3buHaYECuLm7v5F6vqxZi5YULlyY48eO0aZ5E9q2bMa0yRO00jxKTMTSyjLHvA4fDCIi4ix+9Rvg61ePQoULv9FhhQkJ8Xz95WhWLFsKQIECZhjnM+ZuQkKu81KpVNSq45Pl8LbMJD95Qr58+bC2KZHp+sizZ0lMfIilpVW2+Tx8+IC0tDQKFspdUFgIIYQQQggh8oIEhN6CU2fPsW3XHq3JaEs7liF//vw8fPhQ73xiL13C0NCQQi/dQJpbWJCWlkarNm05HnaWvUGHtNZbWFqSnPyEv+/fV5YNHT6SJs2aczchIdOb9xWr1nD2wkXGvTTJMKT3SnqX7t5NoHRpR5o2a0HhwkW03jjlWCZ9cuwzYaF6BbleV9ly5YiPu6O8wj4zCQnxmJiYULpMGa3lQ/xHMHBw+lC369euYWVVEGfnssp6lUrFwsVLWbdpC75+9ZTluQkiZsXewYHU1FRCT5/MMs2N69coUrSoVk+Xyh5VWLl6LctX/ayV9sD+fVStVp0anl4kJye/dv2ykpycTOrTVO7dvassK2lrSwGzAlrtQF+t2rTFobQjf+zbp7POzd2dvUGHaPNxe63lllZWpKSkcO3qFVq1acveoENagTwLS0vy58+v9UaxcQETWbFKe56vEiVKYmxszN1seo4JIYQQQgghxJtioNFo3nUdqF69uubkyaxvTN8nFV3K5JzoJUOHj6Rnr97ky5ePG9evU7BQQaysCnLn9m3GjhmpNfxq0rTpyqvGjx09Su+e3bXyOnD4KMWtrbl7NwH1YzW2pUphaGjIjsBtjB09UikrIT6OJHUSZuYqbG1LMTngGzb8kh608PT2ZuXqtcTF3eHh3w906tu2ZTOcnF2YNXc+ZV1diYmOBtJvvMPPhDF0YP88Garzqpq3bMW4gIlYWllx/lwk7Vu3VNY1a9GScRMmYmlpxc2bNwCUuXxW/28lM6dPA9InAe7UpVv6+lKlMDMz4+I/+3nt2lWGfjFAr7qoVCr+j737DmvqegM4/g2IIgFRAUFRZIt1b9EiOFEUFEfd2mpdte7VWq1Wq7VV66p1VX+1ddUtrroHVtwDsaK4J25AoyBgfn9grsSEpbjq+3kenofce3LOuTfnJrlvzjhw7ASxsTHcvBGNaQ5TJSh17MgR2rZsrqwyVtW7GmejUoJUhRwdsbCwoEq50sq5TP3a3r97DzcPDxITE/UmHtZNNH350kWSk5KxL+iA5bMVzlJPHr19z14sLa34fuS3RodCfd6tOz2+7I2ZmRnnoqLIZ5MfGxtbEhLi+Xn8Tyz68w8A9h0+hqWVlXIuHR0LExcXy5SJE5X2pHMk4hQmJibMnT2TaZMnGT1fVby9+WH8xHTrlp6WrdvSb+AgLNRqrl65goXaAltbO6JOnyY4MEAvbUbXkq4dAXhXNL5kvY+vHzPmzFVeEwtLNXZ2BZg7eybTp05RjjtHjhzcuH4NVCocHQvz8MEDJk0Yr3eOwiOjSEpK4uqVyzx9qsXD05PTkafo3/vLNJe1F0IIIYQQQrzbQvcdfKnn5bexeeWyU6/AnBYbW9vDWq22orF9MofQGzDl5wnExNynbYeOFC5ShKSkJA7u38f0aVP15r8BOHbkMLXr1iV3bgtO/XvSIK8hA/vTs1dvSpUuQ/78Nty9e4eVy5cpAYMpP0/gzp3bDBk6DFNTUxISEgg/fkzvxlQ3abC9vYPy/4vOnY1iYL8+9Ozdh3r1U1You3vnzlsPBkHKPEIt27SlTNly7H1heNaGdWvJnTs3nbp0o4iTE5qHD1m/NoQ81tYUsLdX0tkVKICnl5fec3WPTXOY4utXk249vyS3eW6jdZg8cTy7du5Ao9EQtvcfyleoiKeXFwkJCcyfN5fg5s1RW1ri6eXFmchI+nzRnT79B9KmfQcg5VyuWPqX3rns16snvfr1p1y58tjZFeDM6dMsmP8/VixbqlduLvNctO+YMm/RlcuX+WvRImrXrZulc/jbrJlotdCuY0c8ihXj7p07LJj/O8HNmuvNIzSgb2/69BuAR7FiaLVawo8fY/rUKfwTutsgz4T4eO7eu8vaNavTLFe37LxDwUIvtez8X4sX8uiRhk5duuHm7k5SUhL7wvYaXakro2vJr1ZtrPLk4cjhtIPRobt2En78GF7FP8LOrgDJycn8OPZ7Fv4xX0kz8adxfNKqDS6uriQlJaV5jv743zwCGzfB1c0dlUrFlk1/M23yJAkGCSGEEEIIId4K6SGURS/TQ0i8f4KaBDNs5Ci9YX6pfT1oQJZ7t/zXhUdGsXzpEkZ9O/xtV0UIIYQQQggh3gjpISTEf0zI6lUS8MmEL3r1oX5AAIWLOLF08SK+/27E266SEEIIIYQQQohMkEmlhRAvrVz58ri5e5AQHy/BICGEEEIIIYR4j0gPISHES+vyWce3XQUhhBBCCCGEEC9BeggJIYQQQgghhBBCfGAkICSEEEIIIYQQQgjxgZGAkBBCCCGEEEIIIcQHRgJCQgghhBBCCCGEEB8YCQgJIYQQQgghhBBCfGBklbE34Ni/pzEzMzO6b9/evXTu2O4N1whORp1nxbKlfDv0q1fOJy1fDxpAyOpVAAQ1CeaH8RMN0qRXh29Hjab5J60o7eUBwNz5C6harZrRtBqNhu9HfkvI6lUENQlm2MhRqNXqTJclhBBCCCGEEEJ8SCQg9Ibcu3ePO7duGWy/cvnSW6hN9jkTGWmwzSynGUWcinLp4kVlm6WVldFzEH3jhtF81Wo1FSpWRvPwobLtyuVL5M+f3yCtq7s7sTExSnmWVlaYmKgMykurLCGEEEIIIYQQ4kMjAaE35Exk5FvpCfS6BQcGGGz7dtRonIo6c/zYUWWbjY1Nls5BUJNgCjk6curfk8q2kcO/MZo2PDKKY0ePKOXZ2NiQI4cZZyKP/SfPuRBCCCGEEEII8apkDqF3xNz5Czj272lWrdtIeGQUEWfOEXboqMGwp+o+NVi0bAVHIk5xJOIUi5atoLpPDYP8dvwTpuSz/2g49g4OBmlUKhVjf5rAyajzhEdGsXTlGipVqfpKx9F/0GCatWjJsaNH9LbbFbDn2rWrmc6nU5duxNy/z8Qfx2VY3rGjRxjUr49eWWZmZlkqTwghhBBCCCGE+JBIQOgdkiNHDuwd7DkXFcXlS5dQW1ryzYjv9NKMnzSZ0mXKEhNzn5iY+5QuU5bxkybTsnVbJc3YnyZgbZ2XixfOE3X6NMnJScycMw83dw+9vHz9auLfIICoM2eIj4+nRKlSDBry9SsdQ7WPfUhOTiZ010697Y6OhdFqtQwbOYpJ06ZTp55/uvkUsLfn+LGjer2MXqRWq6n2sY/RspKTk9FqtUyaNp1RY8dlWJ4QQgghhBBCCPEhkYDQO0SlUtG+VUuCAwMIqFuLubNn4VuzphLMcHZxwcoqDxvXryPQvy6B/nXZuH4dVlZ5aNGqFQANA4OoWas2P439nqAG/gQHBtC+VUvcPDyUNDpJSUkM6teHJg3rM3TwQO7dvYuru9srHUNRZxeuX7/Glk1/6203MVHR/JOWtG7bjnr1G/Dz1F8Y++P4NPN58uQJhw8eSLesoCbBFHV2MVqWqakpzT9pSb36DWjW4hN+nvrLyx+UEEIIIYQQQgjxHyMBoTekarVqnIw6r/c3d/4Cg3TnzkYp//82awZWVnkoV74CkNL7JvrGDRbM/x2NRoNGo2H50iXcvn2Los4uAPjVqo1VnjwsWbRQL0/Nw4cUKeKkV9ae0N1s37oFgK2bN3H50iVy5sz1Ssdpbm7OgX1hXLxwQW97YmIif29YT1ADf2r5VCMpKQn/gIZ83q27QR4NA4O4dPGCskJZWnz8amJubm60rLjYWP7esJ5aPtVYtmQxSUlJRssSQgghhBBCCCE+RDKp9BvyMsvLazQaTE1N8Sr+EQBexT/i8qVLesOo9oeFceHceWU59mJeXiQlJRnk5V2xXIblxcfHY2pqmqU6pjZ+0hRCd+9i1LfDDfZ17fSp3uPpUybzRe8+NGnajMUL/kSj0QApvaB69OrNj2O+V7alVVYNXz92vzBczFhZI4d/w5XLlwlu3lyvLCGEEEIIIYQQ4kMlPYTeA/fv3wPgSUIC5ubmBvvNzc1JTk4G4EHcg1cK6rysMmXLUbZceUJ37shU+tu3b5GcnIyFhVoJeAHU9a9PoUKOBvMCGSvr8ePHWSrvxbKEEEIIIYQQQogPlQSE3mFqtZrk5GRuRkcDcP78OQrY21Om7PPePmXKlqOAvT0JCQkAXL9+DZVKZZDXstUhjBw95rXVtXbduhSwtzc6zKtzl24cPvGv3oppzi6u5MyZk9jYWA4fOqhsr+pdPdNlGRtWpitrzYZNBuW9WJYQQgghhBBCCPGhkoDQOyb1SmBdun9BzP377AvbC8DePaE4FCxIu46folarUavVtOv4KQ4FC3LpYso8Oju3b+NBXJzeqmNu7h4U8ypOfEL8a6mzbrUvwOhwrEMHD3Dv7l0+79ZD2VazVm1y5MjB2agzyjYfXz88PD25fv1auuXpytq7J9SgPF1ZRZyclPLc3D2oWau2XllCCCGEEEIIIcSHTOYQekN0k0q/6MW5hSZMnoqbuzsmJibExcVS19dHCXpcvHCB7p93omfvPoTuPwRA5Kl/6f55J/4J3Q3A+rUhrF8bwo5/wvhmxEhMTEx4EBdHXT8fpadRdpvx2zy8in/ExvXrjO4/fuwodf182LxzN527dsPU1JSD+/fxWfu2HNy/D0gJKvXpPwAzMzNmTJuabnm6sn4e/1OaZXXs1Jm2HTrStccXJCQkEH7sKIP69Xn1gxVCCCGEENniSfyht10F8YHJaV7xbVdBiHeKSqvVvu06ULFiRe2hQ+/HB0IJD9fXku/c+QuoWq3aa8v/XfdRiZI0+6QlD+LimDwx7eXohRBCCCHEf4MEhMSbJgEh8TqE7nu5aUny29i8ctn37t7NMI2Nre1hrVZrtPFLDyHxTvj3ZAT/joh429UQQgghhBBCCCE+CBIQEkIIIYQQQrxV0nNDvA7SC02I9ElA6B2Reh4hIYQQQgghhBBCiNdJVhkTQgghhBBCCCGE+MBIQEgIIYQQQgghhBDiAyMBISGEEEIIIYQQQogPjASEhBBCCCGEEEIIIT4wMqm0eKd80asPdev5A3D82FFGDv/mLddICPFftmrtBgASk5JYuewvlixa+JZrJIQQQgghxJshASHxTnEoWBBPLy8A7t2795ZrI4T4r9O93yQmJmJXoMBbro0QQgghhBBvjgSE3hA3dw86dupMzVq1OXP6dLrLzDdt3oJBXw0lj7U1nTq0ZX9YGADOLi6s27SVhw8fEn3jBk5Fi5IjRw7Whaxh6OCBaebXq28/ipcoyRddOuttr1SlKm3bd6Cuf31WLFvKt0O/SjOP0T/8SOPgphzcv1+v7hu2bMepaFFiYmK4ffMmbh4eJCUlMX/eXKb8PCGzp8dACQ/Xl36uEEJkVgkPV+bOX0CFSpXedlUAUKvVLF6+EmcXV+7cuU2tj6ulmXbb7n9wKFgQjUbD9yO/JWT1qmyvz9RfZ1K1WnWuX7tG7tzmFCzkyJSJE5g7Z5ZS33l/LKREqVLExcUSHx9PgQL23L9/H58qFbO9PsaER0axfOkSRn07PMvPHTl6DGXKlmPL5k38Om3Ka6jd26Hr+WaW04wiTkXp2qmj8l3ifXck4hQmJiZcu3oVtaUaW1s7bkZHM3TIwCwf43djfqBxcFMexMVx5/Ztg3Yw/LvRNGvxCdeuXuVJQgL2BR24eSOagf36cO5sFAA+vn589/1YCtjbE3X6NBaWahwdC3Pr5k1q+ehfv527dqdxcDDOLq50+azDa3tNWrZuS7+Bg7BQqzkXFUU+m/zY2trROKC+Um9IOZc5cuTg6pUrPHnyBA9PT85ERuodn/hvy8q9QFrSam9Rp08THBigpAuPjCI+Pp7r164B4OrmpncPk9lrqWXrtnwzYiRXr1whIT4ej2LFiDp9+j/RbrPrvXvRshXkNs8NgG2BAuzYtvWlXtt3kbOLCzPmzKOAvT2XL17U25e6vYnMkzmE3oCFfy1nRcg6mrX4hLz58qWbVq1W06pNOyytrNBqtXr7mjZvwbmzZ2n7SXOaNKzPT2O/59GjR9SsVZuGgUFp5lmpSlVCd+7Q27Zq3Ubmzv+Tuv71M6x/QKNAateti0qlMtjnVLQoB/btw79mDYIDA5g3ZzYmJiY0CmqcYb5CCCGeG/PjT2wL3YuLqxsmJhl/PBewt+fp06evrT6t27bDu/rHjB4xnCYN6+Nfy491IWsIbtECZxcXALp90ROvjz7i+vVr9OvVk0D/uuwL20u+DD7rsouziwsx9++zY9u2l3p+EaeieHp54VCwYDbX7O0KDgwgODCA2TN+JSEh4W1XJ9v4+PoB8L/f5tCwXm1aNm3CmdORFLC3x79B1m4EevbuQ2DjJsTHx+NTtRLBgQF6wSAfXz/q1vMnPj6ehvVqExwYQPtWLXH39KRthw5Kutbt2lPA3p6tmzcRHBiAf01fNm3cQAF7e73yxvz4E/0HDc709f2y1Go1LVq1IreFBUsWLiA4MICGdWtz7MgROnfpqqRzdnFRflQMqFuLJg3rc+vWTVzd3WkcHPza6ifeDfUDGmbpXiA9abU3d09PJU3DwCBiY2L4duhXNGlYnyYN6xMbE0Ot2nVo3rIVkLlrSde+lyxcQEDdWgQHBihlpW7f76vseu9u06KZkteZyMhsrOHbZ+/gQC7zXMTGxijHqPsTL0cCQm+Am4cHt2/dYt5vswlZvTLdtD1796VY8eJcOH+Ox48f6+2rULEyB/fvU6LfSxYt5MrlS+S2sMDN3cNofg0Dg/Dw8CRs7z96252KFuXihfP8PP6nDOvfpl0H1GpLTp08qbfd2cWFhIQE/gndjUajAWDrlk3cunUzw8CXEEIIfaVKlwFg/A9jefToUbpp23X4lJvR0dx/jUNrS5Uug4WFBWvXrFa2/bVoIQ4ODlT72EdJY2Jiwu4dO9gfFoZGo2HV8mU8iIvDr1bt11Y3nbr+9bl48QKhu3a+9rJE9vDx9SN030G69vgiy8/19CzGzeho1qxaAcDN6GgiTpzAxMSEokVdspRXg0aBmJqasnLZUqP7q3pXI2++fFy5fEnZpvv+5eLyvBezl1dxHsTFsWXT38q2zX9vJC4ulire3sq2UqXLsGXT35m6vl+FX63aFC7ixK2bN1m/NgQAjUbDjm1bKVGqNGq1GgBnF1diY2LY/PdG5bmHDx4kR44cuLq5v7b6iezj5u5B2KGjDBvxXZaf6+HpmaV7gfSk1d6ePHmitLdKlatw4kQ4f29Yrzzv+LGjWFpZUaZsOSBz15KufevKApSyUrdv8W7bd+Q4C5YsU35cygpbWzssLNQkxP93fux422TI2BtQtXwZ5f9RY8elm7Zdx09Zs2olB/fvY9jIUXr72rZsrve4TNly5M2bj8TERG7fummQl7OLCz169SYpKYmLFy7o7atQ6iPl//6DBqdZnzJly+Hm7s53w7+hbPkKODoWVvZdvHBBLx+AokWdsbK04vFr+LLj61eTbj2/VLpApnb58iX69OyhNyn1i1J3BdcNE3iRbmLZiIgT9B0wCJv8NkbzGv7NV0SEh2eqvLTKAvhr8UKWLFpIydKlGT3GeNu4e+8ukyeOxya/TYbHD7xSnVJPrOvrV5O+AwYZzSer51vXBdZYeZ8EB1GydOk0z7fu+CPCw/W6wL5I98tAZo7tt/l/pvna3r13l887ts9Ue0vv2LJap8wcW3a2t4zOd2bbW3ZdS2+qvb3LE0cHNUg5tqAm6f86P2zEd3zSug1dPuvAD+MnGuzXdbtHBbH3Y8hlbk7hIkVYs2olw78ekun62DsUNOiBdPzYUXLntsCreMp7v42tLU+ePOHG9etKmvVrQ2ja/BNKlS7Nzu3bCNm4CVc3d85GRVG4SGFy57YgISGBB3Fx2NjasnH9Ogb168PYnybQKKgxiU+ecPnSJewLOmBtnZfoGzeoXaO60eNs3/EzOrRpafT4dcMOdMcPUNor5ccTXVuyfTZvVPWPfZR2k7ot9ek/kI6dOnPj+nUSExNxKFgQS0tLqpQrjUajoYq3Nz+Mn4itrR03blzn0UMNHsWK8fDBAyZNGM9fi9/NtgbQq19/Pu30OXdu3+KR5hF29vbkzZuXHl06KwG23+b/SVXvaspQ9UKOjqjVak5HnqJpYEMg6+3Nzs4OtTCSm94AACAASURBVKUlefJYZ7nOc+fMUoYrQsp3lLLlypGUlMTJiPAs5eXi4srG9es4cvgQEyZP5fatWyxf+pcS9Bk/bizjx401eJ5KpeLOnTvKY0srK6JvXFduUFu1aUvP3n2xts5L5SpVleEembm+U18D165dxdGxMGY5cyrtNjNc3dwwNzfn5IkTHD92FIDe/QbQruOnANSuW4+Q1as4uH8f1nnzUq9+A3ZuT+lhV75CReLj4zl29Eimy8tO20P3UsDenlu3bhJ7P4aiqXoxvTisyMTUhPt37yntbe7sWXpTFew7fAxLKyuuXbsKgKNjYb3rslfffnTu2p3DBw/qTYVwMuq83tCpUWPH0azFJ8TE3CchIYHY+zEAxCfE07p5U+D563bxwnmSk5LJZ5Ofp8lPGTFsqHIt6dIkJSVx9cplbO3ssLbOy5nISIIDA5T3krx581G+ZHG983Iy6jy7du4wmPrB1s4WtaUl+W2Mf76mZ9rkSUybPEl5nN69QEbSam/m5uZKeyviVJRjRw8rz7F3cKCGX01UKhVlypYFMnct6dq3riyAbj2/xNzcnIKFCinlvUlduvegcdNmfNmti3K/1aZ9B/oPGkx0dDSN6tUx+FwyzWFK4SJO5MiRI0vXd3Zyc/dg/KTJFHIsTPSNG5iZmeFYuDAb1oYwdEjK97HUwwFvXL8GKhWOjoV58uQJc2fPZPrUlM/KsT9NwL9+A65du0pyUjL2BR14/OhxmkN51Wo1VnnyYG2dN8v11rWB2NgYJkyeytOnTzkZcYLlfy1ROiiIrJGA0DukXYdPuXrlCr/P/Y0SJUtmmL5N+w44FCzIqZMnjb751a5Tj4IFC3Ey4sRL16nDZ53Y+88eVi5fRtnyFTJM36xFS6zy5GHXju0vXWZarPPmxd3D02j03zSHKaA/KfWLTpx4/mVRN0zgRbqJZdVqNa5ubtjbOxjNS1eHzJSXVlmAMomtWq1OM83Nm9Go1epMHf+r1in1xLrWefOmmU9Wz3daaRITEwHSPd+64wfSPP7UMnNs6b22N29GA5lrb5D2sWW1Tpk5tuxsbxmd78wef3ZdS2+qvb3vE0fXqeePf4MAYmNi0kxTu05dCtjbK0ELgGP/nqb6xz5UqFiJw4cOZqqs+/fvZTi05UHcA3LmzIljkec/FqS0H/2b/aSkJJo0rM+EyVNp0LAR27du4dLFC3Tu2h1nFxcqVKxEVe9qPHmSwOQJE1jwx++4uXvw89RfcHM33lvB168mefPlM/jBQ3f8x44coV2rFgD0HTCITzt/rhz/r9Om8Ou0Kcydv4Cq1arxz55Qg/kVAhoF0rJNG54kJNCwXkpvJ92X+k5duurdTKW+YdUFUZo2b/HOBoSqeHsT3Kw5pqam+NfyA1Ju0H6cOInW7dorN7ElS5Xm/r17fPftMLZu3oSPrx+jxv6Ai6ubkpex9tZ3wCCCmgQbbW8rly9j5fJlr1R/XUCvoKMjFhYW7Andzaxfp2cpj6dPn/JRiZLUq98AU9OU95hGQY2ZNOGnNOvXqk1b4uJi2bFtq9H93435gcDGTUiIj+f+/ftZqs/n3brj3yCAO7dvK4EE3c1Y85atWP7XkizlpzPzt3lU+9iH2JiUYJ2ORqMhNiaGRkGNKVuuPAnx8dja2bEndDeLF/z5UmW9Kt11263zp2g0Gtp1+JS+AwdStVo1qnh7sz8sjNbt2mPv4EDlsqX02luDho3YtnUzEeHhqNVq1JaWrF8bwpAB/QD4ceIkAhoFvvR1aWWVh+qVDG/cq3h7U7VaynuXLuinVqvZfzScevUbKNdSzVq1efToEZMn/MSSRQufv795pOS5PyyMwwcP0qBhI738fXz9ePTokcHUD7rnvK1gQlpSt7e0hjTXqlOXgUO+Jjr6Bg4OxofrZuZasndw4Lvvx5IQH8/Tp09f61DM9GzZ9Ddde/TEu1p15fOofIWKmJvnVkZX1KvfwOBzSde+dW37TevcpSsensVo17KFEmDr8GknenzZi4aBQaxfG0KdevWwtLJi08YNDOjTC3h+LdWpV5/pU6con9+TJoxnwR+/AynBpjUb/qZBQCOjx1aq2Mv3QlSpUl7nUqXLKD2rAxoF0ji4qfJDhcgaCQi9I5xdXGjaogUrly/l3NmoTAWE6vrX5+GDByxZtMBoRLRC5cqoVKqX7krftHkLqlX/mO6fd8r0cypWrszN6Gj++H3eS5WZnpDVqzKM+n879KtMTZqW3qTeOulN5pqV8jJT1v6wsExNpJ2ZXz2yq07Zeb4zOrb9YWGZOt+Vy5bKME12vbaZOX7I3ATomalTZo4tO9tbZs9BRt7k+c6u9pad0upJdvzYUUYO/ybby2vZug3WefOydPGiNNPkzJkTlUql97kwqF8fzMzMuHr1SqbLijgRTs1atWnZuq1yA/V5t+56aU6cOE6pMmXwq1mbHb7bCN21k36DhuDu4Wn0hwHdUJnUQ2Zy5cpFrly5MDE1IfFJIvfvpwyDO3c2im++GoRrquCDjlqtpmz58mgePkzz+OMexCnb5sz8lZMRJ7J0/LXq1MXaOi+7Ut2Ihe7ayfatWyhdpqxe2n2phmWvWr6MEiVKUsjREb9atZXeFxlJq6do6h552aWGb01sbe04c/r53BI3o6NZvWIF/QcNThnWtWsnpqamPIiP5/atW0DK8X81sL9egNdYe5sz81fOnI7M0vnOitTB4djYGA4fPJjlX4ZNTEwoXKQIO7dv48/5v9On3wDKli/P5916GA0I+fj60bVHT7Zt2cKGdWsN9ru5e9C0eQuuXL7MhB9/oO+AtBf7MKZmrTrkzJmTnTu2Kd/b/lq8EB9fX0qWLKUEhIz1lEyr92OlKlWp7lODiBPhzJ8316CX+qGDB6hdtx5FnZ0BiL5xg43r1mb5XBrrlfk4/jGzpv+id/1kpKSn/rUeFXWauLg4LC2tsLd3wMfXj5IlS/Hw4UO9Oq5euZwmzZrh5FSUiPBwgpoEc/v2LVYufz4ccF3IGsqWL4/ayjJLx6ZzOdXQwdSMfZ5qNBpUKhWFCjkq26zy5GF/WJjyGp07G8X2rVto1aatkmbn9m187FNDuRmHlM+YSxcvvPFeLy/jxfY2eKjhZ2DnLt34vFt3cpmb87+5c+jcpZtBmsxcS5WqVGXYiO9wdXNjUL8+DB76DZaWVlmqb1o9k7P6+X3xwgVy585NdZ8aLF64AIDSZcvyIC5Oef8f/vUQgx6Tuvad1g9mr5OziwvlKlQkMTFRr7fVH7/PY/DQb/CrVZv1a0PIkSMHWq2W2NjnP0L9PP5Hdu3Yrvyoq/v81n12Q0r7Hjp4oPIja3bSap+i0Tzk+rVrtGgSROu27ej6RU88i2X8A60wTgJC74h2HT7F3cOT32bNzFT6lq1TPkCWLFqY5heXkiVLcefObYP5gzJDN7l1rhe6ZaanT/+BPNJomD3j1//MaiZCCJGetHpS3XtNc/uUr1iJs1FnWPDH79g7GP8SeejgAXxr1WLDlu0cOniA0F079eZkyKxlSxbzcQ1fBgz5itbt2pMzZ05l6NXzNEuoUsUbTy8vfpz4Mw8ePNC7Ccqsvf/s4fSpU1T3qcGI0WMoWKgQO7ZtIyI8nIhww6FAQU2CKerswsUL5w326Y6/hq8fo8aOU44/q+fAzd2Dp0+f4uburnfDm9vCgidP0p674GTECWJiYrArYEchx8yfi7R6iqbukZddijo7Y2pqatC76tjRw1ioLSji5ATApYsX8Cr+ETN+m8uRQ4cYPfJbg8/3tNqbsaBJdtEFh4d/N5rAxk3o2bsP5ua5lOELmaHVagndtZPeX6QEOcePG8uEyVONXldu7h70HziYAvb2jPt+lMH+3Lkt+GXmbP4J3c2IYUO5GR2d5YBQwUKF0Gq1VK7qrdfe8lhb671OxnpKGuv9mD9/fn6eMo3VK1cw7vtRlCxdWm+/Wq2mTj1/Tp08ybQpkzhzOpK1f2/hm29HYmGhzlIvGmPtVqPRYJ03a8NBqvvUoPsXX+Lp5YVarVYWM9EFf4o4OWGhtjB4f7144QJ+1aoqjwsXceLunTt6bTV01078a/pmqT6p3byR9o2tWq3myz79aNuho9Lb7EVarZYL58/pbZvy8wS9YW664ba6m3Fd4HvvntB3fihMRu0NoKp3dUqUKsWDuDimjP3eaI/dzF5LP0+ZhlWePKxeuUIvEJEVafVMfpnP7wdxcRTzKk6ZsuU4fuwotrZ2RJwI15vrSNe+y1WoYHShnjepmFdxrPNaG/1R5enTp8rn+IH9+yldthzNP2lFuXIVGDd2NPvDwvTe33Wf3yNGj6H5Jy35Z08oO7Zte21BzBeHOy5euABT0xz0HThQ+TFDZI0EhN4BU6bPoHbdeqhUKk5G6X+5nffHQm7ejObrQQPYHxaGm7sHEyZNwaNYMb3u2S/q038AZmZm/Dj2e6NfptNTsnRpJkyaqnwhfLFOJ6POs2/vXqVngK4Lva2d3WvtuhrUJJhhI0cZHcJy7mwUQQ38lbHexqQeE64bJvCixMRE5s6eyYH9+/hh/MQ0o/adOrRlf1hYpspLqyyAmdOnMW3yJKp4ezPvD+NfvnSvv729Q4bHD7xSnXTHP23yJIKaBBudnyR1eZk93y+2odTllf2omDJ2Pq0hTLr2f+DYiTSHVel6hWTm2Lbv2ZvukLFaH1fLVHtL79iyWqfMHFt2treMzndm21t2XUtvqr3pznd2yWxPsuzw1bDhmJubU8yrOOs36/c60Z27kNWrlCE5arUa35q1qF23HpN/+ZWTJ07wWfs2mb6x0Gg0fN6xvd42ZxcXFi1bwT+hu4GU1+bFlT38atVm9Nhx7Mhkzxidbp0/U/7v0r0H4yZMpJhXcRITE/XmqytTthydunQj5v59vh85wiCf1EOSUlbJrMeI0WPIly+f3jCTjJiampCcnMz6kBCmTjLeNo2xtLJ6qSBOZnuKvknNGwcq/1fx9ua33//AxdWNuLhYqlUsD2C0vY0YPYbrV69mqb29jNEjhnPyRDhfDx9BsxYtsxwQSj331fFjR7l86ZLRIN6qdRu4GR1Njy6djR6PVZ48/Dp1itI72tnFBTOznFy7ejXzB6OChIQE5s2ele6Qusz2lMxnk58Rw4ayfesWIOVG+9EjDXdu3wag38DBHDqwn8/aP++hMu77UXw9fASdu3XLUkAou9rt7Hm/P+tx4603R1dWe368Sf4NAhgx+ntMTEz1vgOn990gPb2/6MbeQ0e5cf0arm7uFPMqrncdvquMtTezHGZKewNITErEv5YvN6NTgmuDvhqKiYkJd1PNyZXZayl1WX61amOWw0yvfWdGdn5+z/ttNj2+7M2AIV8xfepk7t65w8Qfn/fI+2XmbGrWrsP+sDClJ5yufb/LZvwylRm/TAVShugNHzmKaTNmY2Fhwa4d2+nZrQvw/PO7ZOnSeFerzrgJE3F1c2fGL1Mz3dnhVSz443c+ad2akqVKS0DoJUhA6B1w/949ok6f1ttmltOMIk5FuXjhArdv31K+gHw9/FvcPT3ZF7Y33S9ZRZyKcuXypUx3VX/R1atXDCaGzmNtTc5cubhz65ay6obuVzNbOzvWhax5qbIyS4aMyZAxGTImQ8betSFjb1JcbJze8rEmpqYULlKEHDlycOXyJWJjYpQJwy0sLGjTohkb1q1lw7q1VPepgbOr6ytPuBnYuAnXr11jz+5daaap51+fvPnyZenHCN0wGF1X/TkzZzBn5gwm//KrwZLINfz8sCtQgL3/7DHag/W3+X9iYWHBrOm/KMdf178+3476PkvHf/nSJdzcPfD66KOME6fi7u6JtXVeHj96zPVr17L03Dfl0sWLJCcnG6zwUqJkKR5pHnHl8mUgZSiQbpLt/WFhtGoWzJzf/1Amkk+rvdX1r8+YH8dn+wSvvfr2o4ZfTVo0CVK25cuXHxMTEx49ynrg6cXeAebm5iQnJ+tt69N/IHdu32bUiOFGbzQePnhA7ty5uXv3+U2ts4srlpaWyoTGmXH3zh3s7ApQsnTpV5pj6fy5c8THx5P4JFHvRruQoyOxMbHKNRMXF4ellX6gRXcun7zCctevIjExkaNHDqX5/fbK5cs80jwymIhWrVYz5/c/lCFqV69cpkGjRnpzs5QpW44+/QfyVPtUL9D9MhMyp+ZZrBgWFmouX7qYbjqVSqU39xZA2w4dadK0mV571mg0aLVafGvWxtLS8rWuJJld0mpvuS1yK+3tyuVLFHBwUIJBkBJgMDEx4eqzYE9mriVd+zZW1rWr1zI9qiG7he39hxatWuPs7EKDgEYcO3pEry5ORYsq7ftdcDryFLExsUZ7RJqYmHD9espn16JlK1Lmvnq2uMuX3bvS4dNO9Ok/gOIlSgDPP7+DAwOUXr1zZs7gZNR5vKt9nO0Boc07d2NtnZcfRo9i9crlAFSr/jF5rK0zvA6FcR9kQMijyKu8ub7ahfxVf8N8hhpfWAeIA2ye/aVEyju0BIgBiqdbl3LFnwAFgbS/tOuknI86Sn5HDujKSUsBwB/w51QENKwLEAv4pVunzPiqP3zV/94r5yPEf1FO84pvuwriLdJNhKyT+tfz2TN+VebqsLa2VlYBSy0pKZGEV7jRa9WmLZ+0asP8/81N84atd78B1PGvz4NU8/dkRkJ8PE7OzuTNnw+frel3+a5UpSqJiYlGJ1mF58cfEBhkMH/Ji8evG2pgbJ6if0J3U8W7GlW99QOaujk/Us/XUrVadSXwEdQkGEtLSw5HnnrpH2Vet927dtCgUSPcPTyVbfYODrRo1ZqIiBPK+Xf39MTE1JRNGzcoq28BymSxEeHhWW5vPr5+jP1xPH/O/x+zZ/yapXqfP3eOth0+pcOnnfjj93mo1Wqq+9QgV65cnDt71iD9mB9/onZdf86eOaNM5KoTc/8+nsW8lLxatWmLm4eHXttt2botrdu2Y9KE8Wm2ycjIU9Tw9aNhUGNleEiDho3Imy9flobPh/2zB89iXgQ0DOT0qUilh07fAYMIP35M6Q2RkZ3bt/HZ513wLOZFk2bNOH7saMrwsLr+nDkdqVy7585G0aZ9B1q1aau05eo+NTA3N+fys4Dgm5YjRw6cXZ4H9Js2/wRbWzvi4+OBlGFfEREnqOHrh1qtVo7l8249yJ/fhrv37gIpPzB8PXwEjYObKa9ByzZtqVi5sjLJ75nTp3n0SIOtra1Snm46hqy4e/cuSUmJ5MuXX9lWq05dg3QP4uIoUbKkMiebm7sHzVu0VCaVTu3q1Su4urmhUqnYnc57oZu7BwuWLGX92hC+/86wt2R26tWvP63btuP2rds0DtCfw+rqlctG25u5eW7lNTp4YD/fjBhJYOMmrF2zGoAKlSrx+PFj/n22+E1mriVd+9aVBShlpW7fb1pEeDjHjx6lXv0G1KpTl5/H/6S3/0HcA732rVarlfb9Nly8cIGjhw8R2CRYGeYGKZNKp577yNbOjgIF7KlT19/gR56kpCTg+ee3seFajx8bX3V635HjnD1zhmFfDzYYupyRs1FR1PD1o1HjxkpAqFHjJuTPb2OwkJKPrx9Dh4/A0tISn6qVslTOh+SDDAgJIYQQ/1XL//qLfgMHsX3PXmLvx6AyMSFfvnyE/bOHTRsNJ3/NyKq1G7C1syNf/vwkJCQY/bVvyvQZFHV2wd3DgwdxccydNctITmlbsmgB3h9/TFXvakyeNp3Lly5hYanG0bEwcbGxemk9PDy5fu1amj1PdMffMDAIT89iqExMKPRsNaoXj39f2F4+9qlB2fLlWbV2A6Y5TLl08SK9enRj8cIFlCpdhkaNm7Bp+04eaR4pS1zPnP6LXj6NghpToVIlZdn5B3FxrA8J4W3RzUFjltOMXLlyMXT4SJ4+6/kSHBjA/rAwVq1YzqedPleOLfWy8zpnz5zB08uLhUuXE33jBnnz5cXW1k7vV35j7a2QoyPHjx4x2t7q+tcnv40NVapWy3JAaP3aEH4YP5H+g4fQsk1b1JZqbG3tuHXzJsuX/qWXtoq3N97VP8bKysroPCEhq1fRtkNH+g4cRPOWLZUloHW9nat4e9O1xxdY5clDqzZt9Sb/BZSeU4sX/ImXV3Fq+Pqxau0Gpd3eunkzS8c269fpFP+oBN7VP+br4d/Sqk1bJa/M9IzU0Wg0LFuyhH4DB9GsRUvKli1PPpv82Nra0TjgeW873bkcPHQYbdp3IDkpGY9ixbgZHf3WVhlLSEigrn99NmzZTkJ8PO6eKQFLExOV0ptJd77Xb9mmt+z8nFkzlBtWjUbDI40mZaW7Sik3gY6OhYmLi1V6X+3ZvYvIf09RxdtbufYLF3HKcp3XrFxBk+BmlChVyiAf81Sruu3Yvo1GQY0Z8s0w2nbogK2dHXnyWOtN7K5zYF8YRYs68/DBg3QDgQGNGpHH2prKVaummSa71K3nj7V1XiwsDIeRp9XeUo9+0LW34d+NpnPXlHm7bGxsWReyRpmIOTPXkq59fzNiJFWqViMhPh6PYsWIOn3a4H35TdNNCm5ja8uWTRv19u3YvhWvjz6irn99Vq3dQD6b/NjYpAQjU/fUy+i9O7MWLVuhLHZhW6AAzi4uSt53793l847tmTtnNl4ffcSseb8bLDuvC8itDwmhY6fOdOrSlQYNG5KQkKC8V+qCdLrP78nTpivLzltYqomLjTXafqt4eyvvy6XLlM1yQEjXTqp6V9NrJ0+ePDHIq65/fZyKFs1S/h8ilVarfdt1oGLFitpDh95cj5BX6yEkhBBvh/QQej3mzl9AhUqVsn1+IfF6jBw9hlUrlr+1oQGp6XppTZ4w/r1YBUiId82T+Off/+UzLsXSlWtw9/Rkxi9TmTNzxtuuznsvdRuD97ud9erbj85du2NmZmawL/Wcm+LNC9138KWe96rDVgHu3b2bYRobW9vDWq3WaOOXHkJCCCE+WKvWbsDWyEon4t2VleWAhRDifePp5cXVq1deanVI8d/24gpbQmQHCQgBUVfyZ5zomf/qZKXvitSrCKVeyUyID9GLv2qJ7KcbSpKYmPiWayKEEOJDNnL0GCpX9SY5OZnVK5ZneSiNEEK8DBkyhgSEhBDvpv9SN2chhBDiRTJkTLxu8l1KvAnv85Axk1eugRBCCCGEEEIIIYR4r0hASAghhBBCCCGEEOIDIwEhIYQQQgghhBBCiA+MBISEEEIIIYQQQgghPjASEBJCCCGEEEIIIYT4wEhASAghhBBCCCGEEOIDk+NtV+BD0bFTZ9p26IiDQ0ESEhI4eGA/E38cx7mzUUqazl2706f/AExNTYl//JijR4/wecf2aeY56Kuh7N61g/1hYQb7Nu/cjYNDQUxNTXn06BGtmgXrlfW+Ctm4CTd3D+XxubNRBDXwf4s1Eplh7+BAv4GD8fWriVWePNy+fYsF8+czd/bMLOVzMup8mvu+HjSAkNWrAOjVtx/de/bS25+YmMjc2TOZNnmSQX1iYmLYsDaEKT9PQKPRZFimtDshhBBCCCHE+056CL0B6zZvZcDgrzh04AClvTzo3/tLin/0ESvXrqf/oMH4+PqxO+wAPXv3obSXByU8XAmoV5v8+fPz7ajRenmF7jtIxJlzRJw5R4tWrbG3d9Db7+ziwrrNW5WySni40r/3l0pZ77ugBv6U8HBl5vRpJCYmvu3qiEzauHUHZcqWo/vnnSjp6cbcWbPo8eWXbN+zN0v5lPBwNfj7a/FCkpOTlWAQgF0Be1YsW6qXruxHxZg2eRIAm3fspnKVqgwe0I+Snm6MHzeWwMZN2LprDw0Dg5R8qnh7c/NmNJ06tNXLS4JBQgghhBBCiPddpnsIqVQqU+AQcE2r1TZSqVQuwBIgP3AEaK/Vap+oVKpcwB9ABeAu0FKr1V7M9pq/R6zzWBMbE8PmvzcCELprJ4cPHqRBw0a4urkTGxOL2tKSm9HRynNuRkcTceIERYu66OVlYWHBvrC9bPn7bwYM+cqgLGcXV6zzWCtl6crLkSMHrm7ur+kIxX+dj68fY38cz5/z/8fsGb9m+flmZmaE7d3D8WNHAVjwx+80btoUD89ir1QvZxcXKlf1RvPwod72ggULcub06TSflyNHDo4cPkTorp0ArFm5gs5duuJU1BlXNzclna2tHQnxCXrXphBCCCGy35P4Q2+7CkII8cHJSg+hPsCpVI9/BCZptVoP4D7Q+dn2zsB9rVbrDkx6lk6kIT4+nrlzZlGh1Ec0qFMzw/QB9Wrzecf2XLyY9tCZ9Mp603S9n/YfDdfb3rptOw4ej2DLzlDKlC0HpAyZ27RjF+GRUYRHRrFq3UaatfjktdVt0bIVHA4/ycmo80ScOceQocNQq9V6acqULcdv8//kZNR5wiOjWLd5q0GdqvvUYOnKNRw9GcnJqPMsXbmG6j41lP2jxo7jZNR5Ro0dp/e8kI2b6NW3n0G6ufMX0LlrdyLOnONk1HlC9x+iVp26AKjVaoYMHUZ4ZBQno85zJOIUi5atMDi2zl27s+OfMMIjo9h/NJzxk6Zg75DSm+zbUaM5cfos6zZvxdlFP+B4KDyC0H0HqVNPvwdMXf/65LexoUrVapk9vXqSkpK4feu28litVqPRaDAzM3up/HSaNm9BkSJOREWd0dtuYmJKfPzjNJ9XwsOVgX17Z5i/qakpiYlPuHjhwivVUwghhBBCCCHeNZkKCKlUqsJAQ+C3Z49VQC1g+bMk84Emz/5v/Owxz/bXfpb+gzVpwk8ATP11Jhu2bGfvoSPUq9+AXTt3MOKbr/XSrlq7gdXr/2bfkeM0adqM3l9009ufUU+Fndu3MWnCT0pZq9ZuYO+hI0bLehNCd+1kxi9TMTEx0Qs+dPgsJX44b84sjh87ypoNm+jdrz/bNm+mtJcHpb08OH/uLN+N+eG11Gv0Dz/yNPkpNbwrU8LDlZKebrRo1Yq1m7ZQxdsbgF/nzGXRshWYqEwo4eFK05h31AAAIABJREFUaS8Ptm7axPDvRlOydGkgJbAx87d5XLhwnnIlvCjh4cqFC+eZ+ds8/lqx+qXqVqlKFdzc3Snp6UYJD1d8qlRk+9aUeq3dtIUWrVopwwF9qlTkafJTRv/wPO7aMDCILt17MGv6L5T28qBNi2Z4FvNiy85Qvho2nFHfDufvDetxdnbRC1L5+Pqh1cKv06awdfMmvTp9O/QrSni40rlju5c6psTERK5dvQJAqzZtCTt8jIKFCr1UXqm1bteBK1cu8+3Q573l1Go19g72dO/Zi5NR55W/Xv36Gzx/yvQZrFq7gWP/nsahYCFmz/hVGVYGKT3u3Nw99PJZuXa93jxWQgghhBBCCPE+ymwPocnAYODps8c2QIxWq0169vgq4Pjsf0fgCsCz/bHP0n+w/tkTyqGDBwAo6uyMtXVebt+6xcZ1aw0msPX08sLD0xMrKysePnxgsD+z5enK8vTywto6r9GyMvJFrz6sWrtB72/pqhBatWmbpXxCVq/i+rVr1K5TT9lWwN6eSxcvKPO+NA7wp0xxT376YYyS5uKF8yQlJRnklx2Gfz2Edq1a6J2TuLg4LC2tsLd3wMfXj5IlS/Hw4UO9uWlWr1zO+XNncXIqCkBQk2Bu377FyuVLlTTrQtZw/fo11FaWVKhYKct1u3z5ErNnTDfYvj8sjFofV6Ni6ZLKNo1Gw/nz5yhUyFHZ5lerNidPnGDJooVAygTI27duQfPwIUWKOAEpgcMHcXF4eHgqz/P1q6n3mrwOQ4ePYNDX3/DbrJlGJ0PPKnNzcw7sC9PrwePi5saDBw9YvOBPavlUI6iBP9u2bKZ123YENArUe76LqyueXl6YmZlx7coVZUibjlb7lH9PRtCnZw9KeLjy/chv8Szm9Z+Yj0sIIYQQQgjxYctwDiGVStUIuKXVag+rVCo/3WYjSbWZ2Jc6365AVwAnJ6dMVfZ9NebH8VSp6s2pkyeZNmUSvn41CQpuyjffjsTCQs1fixcqaUt4pPRIaNO+A4GNm9Czdx+mT52S6bLUajVjfhxP5L//Mm3KJM6cjqRLtx5Gy8qIQ8GCeHp56W1LTEzErkCBTOcBKUGLw4cOUKFyZebOmQWkzOGyd0+oXkCmc9fuBDdvjqNjYXLmzJmlMl7Gn4uX4unlhVqtRteJTVefIk5OWKgtuHfvHmfPPh+OdPHCBZoGNlQeFy7ixN07d/SCG6G7duJf01d53LhpsyzV6+aN6DSHKKnVar7s04+2HTpiamqqbN+39/nkzIUKOXLq35N6z5vy8wSm/DxBebx+bQhNm3+i9IZSq9WULV/e4DXJLiqVitbt2lOiZCmuXb3K1EkTDYbQZVWdev7cv3ePHdu26W2PCA+ndfOmyuObRPP1oAHsPxpOzdp12LBurbJPNzn0vD8WUrFyZUaN+YGhQwYqr+e0yZP0egwtXriAAUO+olTpMvj4+ilzEAkhhBAi63KaV3zbVRBCiA9aZnoIVQeCVCrVRVImka5FSo+hvCqVShdQKgxcf/b/VaAIwLP91sC9FzPVarWztVptRa1WW9HOzu6VDuJdV6ZsOaJv3GDMqJGE7trJ99+NYF3IatSWljQMCjJIf+5sFKNHDGddyGr8atXOUllBTYIpU7acUtbN6Gi+/25EmmWlRzdMKK2VmrJix7ZteHh4UqZsOcqULcfdu3eUnkwAw0Z8R5/+A3ByKsr9e/c4ExnJlcuXefr0aTq5vryARoGUq1CBp8nJnI2K4kxkpN4cSzY2NuTIYUb848dEhIenmY9VnjzExca9ljoa88P4ibT/9DNuXL/GmchIzkRGEn3jxgt1siI2NibDvLZu/pvHjx9Tpmw5gpoEU9TZhZXLl72WeltYWFCqdBmOHD7El927Zkue/g0CiIg4kamgjEajQavVYmtra3R/rx5dOXM6kgL29tStVz/dvO7du0duCwv+6+9bQgghhBBCiP+2DANCWq32a61WW1ir1ToDrYDtWq22LbADaP4sWUdgzbP/Q5495tn+7Vqt1qCH0IfEzMyMmJj7esNRrl25ypMnT8ifPz+9+vbj2L+nWbY6RO95165cJXfu3AYTHafHroA9ZmZmBkNfdGVlJa/sFLprJ/ltbBgw5CsGDPmKOTN+1etVExTclDt3btPlsw7U8qlGcGAA69euITk5+bXUp3vPL5n16y94VyxHk4b1CQ4M0AuiHDt6lLjYWOwdCtKkaXNlu1qtZtqMWfj6pUwAfupkBC5urkpPG0gJAM77YyG/zf9TmWsIUla+ehW9+vajhl9Nzp87i38tP4IDAwgODNALrAGcjoykbLkKetvadujIstUhjBz9fEje4oUL+ON/cxk1dhyfd+vB40ePXtvkyU+fPmXpkkV82rY1585GAeDm5k5iYuJL5dd/0GDq1W/AICMTQw8b8R0nTp812G5iYqIMQTxx+izDRnyn7NNoNETfuIGJiQkWFhYAlCxdms07d+u9/gA5c+YkOTmZhISEl6q7EEIIIYQQQrwLsrLK2IuGAP1VKtVZUuYImvts+1zA5tn2/oDh2ugfmMePHlHEqagy945araa6Tw3Mzc25fPky58+dU9Lo6NKcO3s2S0N4zp2N4vGjR3rz/KjVaqWs1zEcKLNu3LiOV/GPKOZV3GCemoT4ePLkyYOHR8oy5GXKlqNWnXqvvApVWh7EPcDZxVV5rFarsbV93uMjdNdOIiJOoFaradS4sbL98249qO5Tg7v37gIp8yPZ2trROPj5sLCWbdpSsXJlrCytiAgP59KFC8THx+NYuIiSpk//gRQukrWhknfv3iUpKZF8+fIr22rVqcvHqVY0g5T5gUqULEnL1iltwM3dg+YtWlLMqzjxCforza1dsxpXNzfsHRyIiDiRZtk+vn6E7jtI1x5fZKnOOomJiXxcw1cJnLVq0xY3Dw/iYmMN0vbq15+9h46wZsMmvUCbjlqtptrHPgBG2/PRI4d5+OCBcvy6+iclJekNpatctaqywp2Prx8lSpUi/vFjzpxJWa4+Ijycs1FRNGrcWC+Qmj+/DXfu3OZkOudLCCGEEEIIId51Gc4hlJpWq90J7Hz2/3mgspE08UCLbKjbf8aO7dtoFNSYwUOH0aZ9B/Lb2JI3b15uRkezeMGfhO7aSXWfGjQKasz6zdt4kpBAPpv82Nra0aNL5yyVtX5tCNV9aihlJSclY2dvr5T1Nh3YF0aLlq0Bwxv58OPH8K1Zi979B9C8ZSsKOTqSO3dutFotzi4uSs+VKdNn4ORUlNwWFpiammLvUJBVazeQmJTEymV/KRMpZ2TH9q307jeADVu2kxAfTz6b/KhUKkxMVFhaWQGweMGfeHkVp6p3NVat3UAuc3MKFylCYmKiMoxMo9HwSKMhqEkwFSpV4tFDDR7FihEXF6sMv9q5YztBwU1xc3dX8nEsXJisLr63ZuUKmgQ3o0SpUqxauwHTHKYULuKEmZkZN65fV9KtXxvCD+MnMuSbYbTt0AFbOzvy5LHmzOlIli1Zopen7rw+fPCA7Vu3pFl26mXnZ8/4NUv1Bjhy+BBVvasxadp0bt6IpuizFeeMtcm69fyxts6LhYWaylWqGkw+3aZ9B1zd3Ll186bRsnTXgO74nz7VUsjRkXNno5TX5OyZM3h6eTFr3u9E37hBIUdHLCws2Be2l2VLFit5LV7wJzPmzGX9lm3cv3sPC0s1SUlJbFy3VpaiF0IIIYQQQrzXXqWHkMikoYMHMmfWDGJjY3B2cSW3uTmHDuxnyMD+yvwnujRFnJyeTXRsSeiuXS81ae3QwQOVsjy9vMhtbq5X1tuyY9s27t+7x/17BlNKMWrEcDauX4dKpcLD05NrV68ye8Z07ty5jaubu5JOtypUEScnTExMsLS0xNPLC89ixbArUICRo8cYrIym+0s9XOq3WTO5e/cOTkWL4lGsGCpUnDp5kqTEJGU59NBdO+nXqyf7wvbi6eVF4SJFOHf2LGNHjdSr+4C+vfk3IoICBezx9PIi/PgxBvXrq0zgfe5sFN9/N4J/IyLw9PLCrkABNv+9kcPPVp7LLI1Gw5hRIwnb+8+z+jgReepf5s+bi/rZedCZ8vNEpQ2oULEuZA09unRWhmullpiYyJkzp1n+1xKDfTrHjhwmISHBYLLqzOrzRXdCVq9ChQqPYsWIjY1h+tQpehNdK2UdPUpycjL37t3l1L//Guz38fUjZ86cHDt6JM3yhg4eyIO4OFzd3PHw9ESlUjGoX18liDOwXx82bdyA9ulT3D08ePzoEX/+/j/6fNFdL1gZumsn4cePkSePNZ5eXhQsWIiJP43L0kTvQgghhBBCCPEuUr0L0/tUrFhRe+jQoTdWnkcR/YBE1JX8aaQ0VMLDNeNEQrwn+g4YRB1/f77s1kV6vLyDnsTrvy/KaixCCCGEEEK8W0L3HXyp5+W3sXnlsu/dvZthGhtb28NardbojUSWhowJIf4bRo4eQ+Wq3hQuUoSpkyZKMEgIIYQQQgghPjAyZEyID1CZcuVxKlqU+/fu8dusmW+7OkIIIYQQQggh3jDpIYThELL0vbmhbUK8Lg3rANwHTJE2LYQQQgghhBAfHukhJIQQQgghhBBCCPGBkYCQEEIIIYQQQgghxAdGAkJCCCGEEEIIIYQQH5gPcg6hrCwz/yJZdv7tGzl6DGXKllMeBwcGvMXaCCGEEEIIIYQQ758PMiD0trm5e9D8k5Y8efKESRN+etvVee8UcSqKp5fX266GEEIIIYQQQgjx3pIhY2/BnP/Np8NnnahZu7bBvlFjx3Hs39P06tvP6HNPRp1X/rbv2UsVb+/XXd1MqeLtzfY9e99YeYmJicycPs1oj62gJsEcOHZC71ydjDrPqLHj9NJt3rmb8MgoTkad5+DxCL1eR1nRsVNnvbx+nTMXN3cPo2nXbNjE4RP/GrzGo8aOM6iv7s9Ye/h56i/8c/Aw4ZFRhGzcZFDOkKHD2LV3PxFnzrHnwGGGDh+BWq1O9/zo/ubOX2C07gOGfEXJ0qXTPRfNW7bii1590tz/aefPWbNhU7pt/LsxP7Bz7z7CI6OMtqlmLT5h/eZthEdGsffQEb1j07F3cODg8QhORp0nPDKKpSvXGOwfN+Fnwg4dJeLMOXbt3c+QocMM8ilTtpxePpt37k73+F9FmbLlmDFnHgePRxAeGcWOf8Lo3LV7us8Z/PU3HDh2gqAmwVkqK6vtDaBSlaqZagMTp0wz+t5U3acGS1euUa638ZOmYO/gkGY+lapUTbesv1as5uDxCCLOnDO4tjt37a53Tc6YM8/g+tad79Sv7YvnW61WM3T4CPYeOkLEmXOs37yNZi0+MahL567d9d5LZsyZl2a9Af7f3n2HRXH0cQD/DkWRE0VUQAGlqomVqLFHo1GsqDFGIxFjNzEW7L1GY+wtzUQTe429RmOJiSV2xAp2EBQb6qEouO8fd7vecoUDKW/C9/M8eSJ7e7ezuzO/mZ2dnd2+e6/Zsla1WnWcjLigpKtFy1ZGabJUvmULFy8zOrdZVWcYHm95/y3FUz//AAweNiLD25s2a46qXNaq857qc2vLt6EPGgWZrCcMmYpvWVmWzly4nGb5njFnHiIuX1Gd17TSJNfX5tYxrFP69A/D6fOXTP6OTD7eEZevqPJkatbEbo1Go+Rt+bdMnTc3d3eMn/S1Uk9kR1tIo9FYjMlu7u6quisjaTIsSyfCz+HnxUtNliV5/+XtmdpWm7YfY8PWHTgXeVX5rdTMtQVSl4NBQ4cr5+Tw8VOYPHW60bbkcxtx+YrZczti9FhEXL6C8IuRZuPpoKHDlTwgb8tc2ZVjiaX9N3Us09MOkusuOb5ZU3cNHDrM7OeZ7eCRY2nGLmt07dELHTqGGi13c3dXYqkcT82dj4qVAk22TdN7vFes/Q3nIq/i1LmLWLN+k1GMT902S10urY1vgHX5Lb31qbm2mZu7O6bNmqNqm6beN0N+/gFmt9Gm7cdKeZPjqSWm6hNrY3dmSW9bwRzDsm0ujwC6+svaejm34wihHFDU1RV79+zG7Bm6oKPVaq3+7uWLFwEA9nns4WihcZub5Xdygo2NwIMHD3Dv7l1leVxsrPLv0eMnwtXVDbdu3sSLpCS4FXPHuImTMCisH65ERaZre/0GDIKdnR2ib93CixcvULfe+yjmXkz1WxqNBtNmz4WPry/uxcfj3r17iE+VNvncymxsbeHp5YUXSUm4euWKsvzDj9oiqElTJCYm4vq1q7h29apRmkJCO+H58+eIioxEcQ8PtA/5FPmdnDBiyCAkPHqEqMjLyOeQz+h7/qVK4caNa8rfGo0G7UM6omXr1sjv5IS//jxg8hj4+Qfg12UrUMjFBUcPHzb6XN7/2vqAffHCBdX+y+rUrYePPm6HpKQk3Lp5E7Gxt1Wfd+3eE5/36YM8efIi+tYtuLm5oUPHULgXK4a+X/RStjX32x/w8uULXL54EwULOePtcuXQtHkLbN+6BQAwa963qFCxEp4+fYqoyEiUKFkSIaGdUNDZGSOGDAKg6+QcO2ESkpKSEH3zJvI6OMDTywv9BgzCnJnqxnBmGDdxEgJKl8ajR48QezsGnl4l0G/AQHj7+GD08KFG61erUQNBTTL2uGR68pucB/oNGIh79+LN5gFAlzdr1qqNZ8+fqZY3bd4Cw0ePRaFChRB5+TLcixVDk2bNUaJESbRr87rDwzC/efv4ontn48YpoOs0LFu+PB4+eIDomzdVZRuAUtnL5fu9evXg7u6ulEn53JYoWRKXL15Uzm3vvv2wcMEPyu98PW0G6n/QEImJiYiKjISvnx+Gjx4DZ+dCWPjTjwB0saRN249VseS9evXg5x9gNpZ4enmZXF6nbj2M/2oyHj9OwMP7D1CwkDNGjhkHR0cNVq9cDgAYOXY8mge3NFu+ZQ4ODrh29QpevnipLLv/4H666htryPsP6I53IZdCyvFO/TjvR+3ao+3H7VH6rbfw4sULTJsyOd3b02g0aNKsOR4/TlDK95TpM/H1xPEmy/fVK1Hw9CphVL5Ta/dJB7PbtBTfsqosVatRA3Z2dkhKSjK7jlzehBDpSpNWq8XVK1eQ8PCR0W8WKFgQ0dHRyt8ODvlgY2ODuNhYPE5IUJa/TE5W9mnSN9NQvUZNREXq8ntxDw906BiKyRPHK+tbE7sBXf7O5+io+q2RY8erzluduvUwfNQYlPT2xvVr1xAbezvT83Vqs+d/h+o1a8HJyUkVI1KnqUCBgkrdld409RswCJ26dIWNjQ1u3bwJTX4NqteoCR8fXzR4r5Zq3V+WrkBJb2+lrky9rXafhCBs0GA4ajS4fPEiChV2QfUaNY3ikqm2EqBuL02eOh3Ng1vi6pUoAICHhyeCW7XGpPFjle0OHz1GObfJyS9Nnls5nkZFRiJfPgd4lSiB4aPH4Le1a4y2lZycjKtXopRtubq5od8XvVT7ufq3jUosuXTxgtHxHDxsBBw1GkTfugVHjSOq16iJwi6FMSisX7raQVOmz0ShQoUQExONggWdlbqrS2iIKj0ajQYr162Ht48v7t2Lx4xv3qyDxlqFXDI+DQag68AK6RiKhkGNVecCALx9fDB56nQIGxtERUbCxkbA06uEUZkEdPEobNAQ5MmT16htau54F3F1hbOzs3K85TrQ1c0Nly9ehGN+Dd4uVw4TJ09B/To1AZhum/Xu2w/58+dX2mbWxjcACO3cJc385uDgoFsnKkr5nrn61FzbTG6bli1fHpcvXlTaplOmz0SdalVU6xrWlWNGGHcuyvFULm8eHp5GMTd1mlLXJ9bG7sxk2DYzbCuk99pLLtuxt2MAIfB2uXKYPmsOpk2ZjPXr1irrOTjkM1l3mbr+yO3YIZTNunbviWtXr6DP5z3NrpOSkoJEbaLJz+QGdnCr1ug/aHCWpDEr9QkbgE9CPkVYn94mOw4yQ6lSpeHgkA/79+7FoP59jT4fPGwE2rZrjwpl1KN4zl6Kwqix49C5Y4jV26pXvwG0T59i9Ihh2L/3DwDA9Nlz0aRZc4QNGowve/VAnbr1MGnKVDhqHI22Kftu3hx8N2+OatmSlavhVaIEFv28ANu2bAaguzPepFlzvFupvNnG5riJk/DXwT/xRfeuyrL5PyxAcKvWSElJwejhQ3Fg/z6j7w0YPASxcbGYMGa0suyPg4dw5NDfWLd6NT7r1s3oO8GtWmPEmHF48vgx/jywD60+/Mhkmnbs2QdHjSOmTp6EZUt+NbnOgMFD0KlLNwQ3CTJbMfT6sg8ePHiAAX2/RER4OADgu58WokHDRqp9fbtcOZQv7a8sa9YiGMNHjUFiYiL27/0D5StUxKrly/DV+Nd3QtZu3IymzVsgLjYWmzeux7iJukZJ5fJvK+uUq1ABvyxdgVs3b6gqnczgFxCAxYsWKhfJ1WrUwNfTZiCoSVOjDqFPQz9D/0GD8MhEg8ca1ua3zTt2wdXNHUcO/Y3nz59b/M0hw0ciJLQTbly/hvxOTsrymrVqY8iIkbC1tcHAfn2wa8d2AMCYCRPRtt0nqt8wzG9fmriD4+3jg/k//gQvrxIoV8rPZDoGDxuByMuX0LZVsLJs1NjxaNchRCnf8rmdNW2q0rFTrkIFzJw7Hx9+1FY5tw0aNsK+P/bgy149AOjuSk6fPRef9+mDZ8+fwcPDE23btcelixeMtmcqlmg0Gvy48FdcPH8eZcuXN0p7ry++RJGiRVVx4nh4BLr27Ilz584iIjwcwa1a488D+02Wb7mBXrlKVRQrXhwTxo5W4lJWMbf/7Tqo933h4mWQIGH9urXo2bs38ud3Sv1TVpn/wwKjsnv4+Cmry3fqixg//wDM+/5HuLq5Gm3LmviWFWVJLt/3792DQz7jC1bZ2ImTcOP6NdjZ26crTRHh4ejWqaPR730S8ikGDBmGwQb1ZqnSpQEAO7dvM9mB982MWahRsxZ2bNuqqm8379iFPv3DMG/2LACWY/f02XMxqH9fjJs4CcGtWhuV7YjLV5S6C9DlgZjoaIt1RWbp0z8Mn3Xtju/mzUWBAgVN3q2X666Y6Gi8U+6tDG8rtHMX3ImLw+fdu+D6Nd0F8oTJU4xG0QwYPAQAzO5/sxbBCBs8BE8eP0bPrp1x5vQpALr258y589Gh7YcGF7rm20oA0KFjKBo1bqwcb9ny1esQNmgIvho/Fh9+1DbNczv5m2lm42mHjqFYsXQJACjbMjwGy1evQ7XqNZTtLVy8DJUCAzH168lmY0mzFsF48uSJ0f5/0bcfZs6dj5ZNg6xqB9WsVdts3bVg0WKEtNPFBLl8fzd3jsm6KyvIsetKVBT8A0y3LS2R83ZM9C1s2rABDYMaG60zaco0lK9QUdWeAoDT5y8hLjYWc2fNAPC6bXr65El82r6t0e8c2L/P5PE+c+EyDv55QDne47+ajHz58mHowDAldgY1aYqxE79CtRo1cCcuzmTb7Pf9f+LTTp8pbTNr41uHjqFp5je5Pn2ckJDmnKWW2mZy29SwbmrWIhijxo5HvfoNlLr6RPg5nDp1UqkrU/vwo7ZKPG3asL6y/LufFirlzVSaUtcn1sbuzGJN28waQU2a4uXLl+j7RS/lmPXpH4auPXrhs67dVG3zUqVLm627SI2PjGUzR41jmuukpKQgPj7jvZeph1ybGt6Zekj90VPhmD3/O6NHnWrVeU/1+EKfsAEZThcANGwUhIIFnfFutepv9DuW2NjaQgiBxETTnWparRZXIo0bUZIkIU+evJmWDrnRX7fe+yjk4oLz586l6/tvvV0Wt2/HYPeunQB0F6wVAwPx7Nkzi3cevUqUxN24ONWyZ8+eQZIkeHp6mvyOt48P6jdshIOpKuwGdWqi/5df4NGjhya/d/TIYXzevQsa1quDlJRXZtMk77+5ziAAqFGrNgBYbODb29sj4dEjpdEJABfOReDly9ejIAoXKYIXL16ovrdty2bkd3LCW2+X1X3n/HlERV5WrfMi6QVsbGyQN29e5HdyQkxMtNFdx6dPnkAIWLxIy6gKZQKsrrQ+bNsWQtgoDabMkDq/AcD40aOUPGCJfEdMq32KHdu2qT4rUrQoHB01ePr0KWJiXt+ZO33yJJ49e4bKVaoqy9LKbw0+aIRixYrj1q2bZtOi1Wpx7uxZ1bKkpCRV+ZbPrWEeePrkCVKSU1Tn9tWrV6o75WdOn8LNGzfg4JAPpUqVVmKJqe2ZiiWfde2Gt8uVw6VLF40+syQlOQVPnzxR/jZXvmV29naAAJ49Mx0DM5O5/TdMDwDMnzsL3Tp1xPXrxiMa06NwkSKIva0eORgff9fq8p3aZ127wdPLy+QNCmvjW2pvUpa8fXyU8p2ckmx2vWo1aijlLfWxtjZNqTVp3gJ58uRR1S82NrZ49eoVnqca9Sf7slcPlA3wNduhILMUu+V2h1eJkia/a6ru2vP7zizvDAKA7Vu34pO2H2Lhgh/MHme57trzu/lja43K5d9Gkw/eVy5MzalRq7bF/ff184ODgwOePn2qdIYAQGKiFgWdC6oe0bDUVkqLPNrAw9MzzXPr5l7MbDwtVap0mtuSJEnZ3vy5s9C0UQOLscTXz8/k/icnJ6Ogc0GT3zHVDipStKjZuqtY8eLKMrl8m6u7soIcu+7Fx2fo+3LeDm4SZHLUGwDcuxePC+fPGy2X20oyuW26bcsmo3XN8fbxwYsXL1THO7+TE549e6bKJ8+fPcOLFy/g4eFptm2WkpySZtvMVHwzxzC/yfXpy+SXFr9jGLtNtc3ktqlh/bVty2bEx99V6i4AaNqogcW60sPTU4mnhi6cizC6hrNUn1gbuzOLNW0za+zasR11qle16maXjY2t2bqL1DhC6P/MmBHDTA4PtJZGo8Hs+d8hf/78quG9s+d/h/5ffqEEQnmYoPzITHEPDzQMaowSJUviwxbNALweuvno0UMkPHyEvA4O6N7zc7i6uhmNWDh6+DDq166ZZvpOnzoFbx/UhfQFAAAgAElEQVRfkxVMZvHw8ERKSgokScKsed/iyZMn+HP/Puz5XffcsKm7p4Cugrt9OyZd2zp29AgKOjujUeMmSnB6p3IVPH/+HBcv6PbRz98fkiTh7Jkz+HraDNjb2+PG9WtYs2ol7qS6sDPk4OCAf44cVhqHpUqVRqFCLrgXH4+hI0bBvVgxo32T3b9/T/n3Dz8vQs3adWBjY4PCRYqY3FbDoMYoXtwDmzduUC1Pq+K8ExdncR9k8v5/1K49Klepitsx0ar9r1mrNoq6uuJxQgL69A9DSW8fvHz5EieOH8O61ass/vaN69eNOoBMEULAzs4WAPBx62DVZxUrBcLVzU03BPnWTbN30MuWK49EbSJu3TTfIZFZ6rxXDy4uhREdfcvoM/+AUjh6+BAO/X0Qn3xqnM6MSJ3fAODE8WNWfTf0sy4o6uqKTRvWI8ZEek25cycOT58+0TW29NLKb37+/nBwcMDF8+cx6ZupyJvXAfF372LdmtXKxZGpsu3j56cq3+bOrbOzc5rnNiYmGkII2Njamo0lPn5+RrGkYqVABLf+EEnPn5u9sLxw/hzKV6yoPNah0WiQN68Drl6JUs5LcnKyUr7d3N0x/qvJSvmWubm5I39+JwQGVkabtu1gY2ODaVMmW1VW08vUHVP5eBs6deJEpm9bFn83HiVKeltVvg01bd4CDRo2RPzdu1jy6yLUq6+e18/a+Jbam5SlT0M/U8q3n4W7/qGfdcEfu3dbXd5MpclQsxbBCAgohbt37qiWF3UtiuTkl7DPkwcTJk/BixcvsGvHdhw7esRi2lzd3HH61Cmz6wCvY7et7eu8kpz8+qJFzt+GdVfNWrXxOCEBZ8PD0ad/GIp7eFpVT2SUNZ1Oct11Njwc02fPtbruSkvFSoGoFBioOiZyXSnvv1xXDh880Oj7D+7fV/7dN2wgPu30GQBdJ4dMbiuNGjcBhQsXxrYtm1XtiXNnz+L+/fuqOXPafRICP39/LF+62GzaTZ1bU2JiomFja6v8LW+rZes2mDNzurKtp0+f4FyE7mJSjiXePj4Wf9vU/ms0GqNHLGXm2kGpyXUXDH7G2vKdWQxjV0ZZk7f79f7caFnFSoFKW0kmt00vXrxgst1tSsOgxrhx/ZrR8X769IlyPNt3CEHvvv3hUrgwPDw9zY7+cXZ2ttg2Mxffzp09m2Z+k+vTB/fvY+rM2bCxscGN69eMRtAYxu70tM3i78YrdReADNfTN65fNypv1tYnMmtjd0ZY0zbLCDd3d1Su+i7s7OxwKdWj0kVdiyp1l5OTE1YsW2qx7srNOEIoG2k0GgS+U8Uow2amBYsWo3rNWqhcoSyCmwQhuEkQKlcoixq1auP7n19PdFqxUiBib99Gzy6foVWzxni3Unl0CQ3Brwt/VtZp0rQZ3NzdUb92TbRu0RRNG9bHwH59cPjvvyxOqGfJmBHDUKFMgMVK4k2lpCRD+/Qp8ufPjymTJuJVSgqmzpyNbj1NTwZZp2497D14CBt+W4fBYeYnRDZFq9Xij92/o3lwS2XCMkmSMG7UCPz8o+6OS0lvH9ja2qJ9yKf4+ccfsHP7NgQ1aYq9Bw+ZnZhvzISJqmG0AFChYiXky5cPXiVKIOJsuGrfPg39zOg3unbvicPHT+HOnTv46cfvVaNoDDVrEYwu3Xrg/r17WTIPQ+UqVZX9L1euPH7+8Qdl/5et0g3trBj4DgoWdIZL4cJ4+OAhpk2ZjFs3b2LQ0OEYMnyk8ltxsbEoXeYtZULLqtWqo8cXvVXH8dDff8He3h516tZTlo0aN8FiGn9ZtgL58+fH1xPHY+Vy05NqT546HV9Pm4HBA/rh4IH9GTwalhlO9tqpS1ecOnEcI4eoHw396ZfFWLNyBbp37pRp2zWV36zl7eOD6jVrYe6sGSbnOtq8cQOuXbmC4sU90H+gbl/8/APQp98AuLmlL468VbYcAKBR4yZYuXwZVq9cgfIVKmLT9p1mJ3KvU7ceatd5z2L5ls9tYmKi6ty+SEpC8+CWaPeJbihzt5690LhpM7Ppk2NJ7TrvGW3rB3387dWti9nvfzV+LObNnonNO3bhXORV/HP6LFYuW4rePbsbrdu1e0/s2LPPZPl2dHTE06dPkJDwCEMG9MfO7duw9+AhrFm/yeLEyplB3v8Nv63Lkt8/9PdfCAntpCrflatWNbt+vwGDVOXb0FdTpiL65i0EN2mUaY8wv0lZAoCPP+lgsXx7+/hg/ZZtqF6zlsnylpE0VawUqJTN2TOmqT7TarVISkqCYz5HfDt3NlxdXbFw8VIcOn7S6HfkOnDoyFGIjYlRlSVrYrdMrrvk/G2YtysGvgOXwoUxfNQYPHzwUKknzly4rKorspNcdw0fNUZVd525cDntL5sgTwq+Yu1vKFK0KObOmql8JteV8v7L2zO3/1WrVcfZS1GoUauWyZuNcltp5bKlmDJpIqbOnI0TZ88r7aUzp09hSFh/hJ85rZzfMRMmIi4uTpmza9/eP1TnFoDRuT34534lnspMxVN5W9169lJtq1/vL5TtpUfVatWxafsuZf/v3DF9sW2uHbR54wal7tJoNBmuuzJT7779VLErO8nxNHVbSW6bjv9qsqptejw8wuTvyMd74tgxJtudfv4BOHspCqGdu2LsqBEWO68mT52OxMREi20zc/HtzOlTaeY3uT599eqVUp8GNWlqVJ+mFbvltmlI6OvPR42bYLH+MmXf3j+UeCqT46mhn35ZnGaaZJZid1YxbCuk99oLeP3Cg70HD6Fc+fJYuWwpxo16/bKKchUqQKvVKnXX/DmzlbqrafMWmbkr/wnsEMoG5SpUwM+Ll2Lb7j9QtVo1/PDt/CzbTlE3VyQlGc9P8Pz5MxQt6qrcUblx/RqKFS+O739eiJDQTnBzd8fRw4dVPfXHj/2DR48eYvvuvZgweQoaBjXG7l07sX3rliy505xZenT5DDWqBGJgvz66541Hj8S+P/bgvXrvG63r5x+AAYOGwNXNDVO+stxpYIpGo8EHjYJw8fx59OrWBfXr1IRTgQIYOWacchEJobsLt2HdWlyJisSe33fh+/nz8DghAbXfq2syTdVr1DJ6fEt+FC4mJhrbtmxW9u3I4UOoXlM9Oqt6jVroEzYAycnJGDtyOGDhkYL6HzREficnnD5l3MDPDHb2dsr+jxs9EleiIpX99/XXzRVhZ6fbt4SER1i25FfciYvDd/PmYPvWzaharZryWzu2bUVycjKaB7fE9t178e2PP6FkSW/ViIu1q1bhSmQkvpkxExu2bMeufQcsThgrn6dVK5abnReo34BBaNy0GRK12iyb+wp4PdJuyID+uHzpIqpWq46xE79SPu/WsxfeqVLV4qN36WUuv1nr09DPcPL4MaUD1JT169bi6ZMnqF6jJjZs2Y6lq1ajYmCgVY+6GLK1tYEkSTh4YD8iwsNx7OgRTJsyGbG3b6Pe+/WN1pfL9z9Hj5gt34bndsH336k+O3XqJPI7OWHg0GHYuG0n+oYNRD4zQ9INY8k/qe5AaTQa5HVwwOYN61WPMJhKy+df9sW0KZPxbqXy+GrcGLQP+dTojT5y+Z46+SuT5XvViuWoX7um0mDf8/suPLh/Hz5+ful+I116+PkHKPufkXhqjbWrVsHN3V1Vvm0NRhik9knIpwCMy3fFSoFIev4cq1Ysy7SO8DctS9169kJU5GWL5Vu+43vSyhEJ1qQpqElTuLq5ITLystGjDp989CFqVa2Mr8aPxZ24OAwfPBDhp0+jQAHjx27KBviiV7cuOHr4MPwC1G/HsSZ2y+S6S8nfBuzsbJGQ8AjTv/kay5b8qtQTNjY2qroiO8l11/RvvlbVXaYeUbTGmBHDUDbAF6tWLIednT169319sSTXlfL+y9sztf8uLi6YOWceNq7/Dd06dcTDhw+MtiW3la5EReJOXBz2/bEHefPmVdpLfv4BGDdxEt6pXAXt2rRCcJMg7Ny+Dd4+PkqnbER4uOrcbty200S9vFKJpxu37cT23XtNxlN5W0cO/Y12bVop2/pm+kxVJ7A15P0v6e1tdv9lltpBct21fmvG667MJI80zczYZY12n4Qo8TR1W0lum44ePkzVNs2Xz/Q0GfLxNlUf5svniPk/LMDfB/9E544dsHfPbrNpkuvvBd9/Z7FtZi6++fkHpJnf5PpUnq9nz++7MHvGdFV9ak3sltumbu7uqrappfrLlIjwcCWeyuVNjqeG3qlSNc00ySzF7qyQGW2FebNnodLbpfHt3Dl4/jwJ7TqEYNioMcrnEeHh+OSjD5W660pUpFJ3vd/gg8zalf8MdghlA41GA18/PxQt6opHDx9m2XPvGo0GdnZ2ePXKuKJ69UpCXoe8ysie3j27Y+vmTRAQGDF6LHbvP4h1m7aoZsZfv24thgwIg3uxYmjT9mPMnv+dyXmG/g3OR0TAxcVF1Zuv0WgwfPQYBJQujchLlzJUsQa3ao242FhMmjAOBw/sx524OGzdvBGa/PnRLPj1YwvPnz9H+JnTyt/yc8PyhJ2GWrZuDQ9PT7PDlqNvqh8RuHAuAiVKqudeqBgYiBvXr2NAvz5p7kPVd6vh6ZMnWTr5rLn9d3RU3x1O/Sz86ZMnUbhIEaVymjNzOhYu+AEJjx6hRMmSiL97F+vXrVUNqb8SFYlxo0fC0VGDUmXKwMWlMLZv3YKXL18iJtXbJerUrYeeX/TGzu3bzL45zLARZG70UGbbtmUzJowZjdjbt1HSW9eJ6+cfgBYtW0OSXqU5x0R6pJXfLKlTtx4aNGqE1StXWFxv9crlmD1jGmJiohFQujSSkpKwfOliJCYmquadsEZycrLyOCbweh6KkqkeHzAs319PnGC2fBueW/lNXrKRQwdj757dsLWxQUAp3bDrE8eP4eXLl0Z51TCWfD1R3cDp3usLnI+IUI3ATM3bxwcNG+sm9fx14c/QarVYuXwZtE+fonad95TGqb29vVK+V61Ybvb3UouLi4W9vT2KurpZ/Z30kI+3vP9ZdaFyJSoSJ44fU5Xv69eumi3fjhqNyfLdoWMoDv39V6ZODv8mZUku3+vXrjVbvuXylvDoUZplLj1peu/995GSkmLVXWGtVouLF86bvSA+eGA/+nzeA5cvXUTDRq8nqbUUu+/fe/2Is+6Ral3dZS5/34uPV40Wked0MfdIdHYwl6Y3ubCaOHY0tm7eCPtUk4YDMBotI++/4fZKlSkDOzs7jB4+1OryeD4iAklJSXDRv7mqZevW8PX3x+VLFxERHo4rUZEY2K8PDv39l6pz2fDc+vr5GZ1brVarxNOAUqXgXqyYEk8NY6m8rf5ffoGI8HBlW65ubunuzJb3f96smWnuv6V2kFx3eXh4ququxGzsjDFUrFjxTI9daZHbSnI8NSX65i1VB0/q+R0Nf0s+3qYU9/CAq5sbenXrYvHms2HbLHXdbcjbx8dsfGvZunWG8tue33cp9ak1sRt43TY9cfyYqm16/dpVo7orLXI8lcubHE/l8ubnHwBJepVmmgyZi92ZLbPbCt/Nm4PFi35GcnJymvFWrruK5GBd8f+KHULZ4Ojhw2gR1BDzZs2EsLEx++jSm9JqdRPm2dgYPx9tYyOQ9DxJCa534uIwbNAA1KgSiIU//Ygb16+hzFtvq0YjAMDfB/9EnWpVMDisH7Zs2oiGQY3x9dTpWf7YQWaTOx/KvPX6rQQjx47Hu9WqI/b2bcycPjVDv+vnH4DoWzdVlWBEeDieP38O92LFUK5CBSRqtbC1tUXRosZvsUlNo9GgZu06AIznU7kXH4+XL1+iQMECaf7O8WP/oEeXTsqzsvKrgw0b3rJCLi4m75xklojw8DT3PyY6Gi9fvrRqsuZv585BnepVUa6UH5o1agCNRmN0h+XM6VOo9HZplA3wRdWK5bBj21Y8fPgAly9fUtYxHNExyeBtRKkpjaBtW7PkdfPmnDl9CtG3bioTN7Zt3x4+vr7Il89RGd67aMlyaDQafD1tBv45fTbdDWZL+c0aH33cDkWLumLOt98rafp62gy4ublj0ZLl2PvXIWXdVSuWI+j9uihXyg/v16qhm8DZwSFd271/7x5sbW1RsKBzmusalm9znfBKh4GZc3snLg59v+iFyhXKomyALyZNGAc3N3ckJmqNJrU0jCWG2ytXoQIaN22GwMqV8c/pszgXeVV5a1D1mjVxLlI3cWTpMm/BxcUFjx8nqH73/v170OTPr5p4VS7fMrl8W/I44THs7OyUC73MJh/v1PufFUI/aacq387OhcyW76OHDxmV7w8aBaH2e++hcdNmSr6Vz0Obth/jXORVTJicvldGv2lZksv38NFjlPS4ubmryrdc3lwKF1bK3NfTZkCj0SjlzbBBbG2aihf3SHPCaUMJCY+QkpJi9nOtVovLFy+ipLe3arm52H3F4FXOAFR1FwBV3RUTHQ2HfPlQrkIFq9KaHeS6KyvSJLcn5Mn35brSmm3Fx9/FyGFDlL/z5XNEYqLW4iTE8fF3kZKSotys8fXzh52dHS6nmurg4vnzqjl8DM9thTIBJs+tHE/LBvjinXJvKfHUMJbK2zLMrxfPn0dycnKacwaZ23/5TZL58jnC3s4eiYnGZcFSO8hc3ZWZN2as9UGjINja2qpilzxCvE3bj9Mdt6wlt5VMxVMAVrdNAeD9Bg2U423KkydPMGfG6/rY28cH9vZ5jDpNDNtmljQMamw2vvn6+Wc4v8n1qanYnbptJjtz+hRCP2mnaps6OxdS1V3W+nbuHKW8yfFULm9t27dHvnyOFusTU8zF7syUFW2Fc2fP4vHjBJMjVw3JdZdzoaxpB/2bsUMom2i1Wvzw3XysXrEsy4bsR4SHY8vGDbCzs0f9Dxoqy+t/0BA2NrZYv26NUoGdvRSFTdt3wc8/ADOnfoP2bVoj/MxpODm9DuhrN27G2UtR0Gq12L51C4YNGoAHDx7As4SXyUedrNEnbAAOHT+ZZcMRK1YKxO79B3Ey4gL6DdC9XtjPPwCdu3bHiePHlInq2n0SgvoNPsCk8ePQsF6dNO+MHjp+Epu27zJK96mTJ/BW2XJob/CK5ebBreDo6IioyEhEhIdj1fLlkKRX6Ni5s7JO/4GDUaKkN/aluhs1bfZclHnrbfy+c4dRGhYu+AER4eEo89bbSoecn38APmjUGIf+/ktZb/26NShVurTyJrc6deuhaYsWSEpKMvrd7r0+x40b199oIvO0aLVaZf/lfCnvv/wmifXr1uLP/ftQvLiHMopNo9Gg1Ycf4eyZM2aHAq/+bSOCmjTFnxYeh+gbNhBTZ87G7OnTlNEok6dOx4atulfItmza2OTFkkajwc+Ll+Lq1SuoUbkSRgwdbLROZjoXeRW/Ll+p7H/7DiF4q2w5JCTo3iQx5auJqFAmAGUDfJX/uoSGQKvVYvjggXi3UnmjUQB16tbDwSPH0ONz0282spTfrNGv9+coV8pPlabhgwfizp04dAkNMTnRvJu7O374eRE6ftYZF9M5ufzypUvw6OFDNG/ZSlnWvkMIypYvr8oDR06cVpXv1ORz+92Cn9N1bpeuXANXNzcs+mmB8gridp+E4MiJ02ZjSUR4OBo3qKc6Rr+tXaNL56FDKBvgCwD4688DuB0Tg8KF1XeuPL1K4MH9+zh+7B8AwOOEBLPlWzZmwkScvRSlGs0ZWLkyHty/jz8sDL/PCHn/5eOdGXMP+PkH4PDxUxg1dnya6/YNGwhbO1uz5bt7505G5XvP77tQq2pl1TmRz8Nva9egbIBvumPim5YlU+X7zp04Vfk2V960Wq1S3gxjpTVpatYiGE+fPME3k74yeYF7+PgpHDlxWnm0tk7demjdpq0yMa+c1zbveD0vYJ269VCjdm38/ddBs9s1jN3yK5jXr1uDxwkJqreQ1qlbT1V3rV+3FsWLe2DQ0OFwc3dX6glHR0ecPXPGmkOd6eS6a9BQ3eNthmkyVXdN+mYqjpw8Y3TBefj4KRw5eQahn3VRfqd5cCvky5dPabvIdaW8//J68v4fPXwYi35agKjIy3BxKYw6desq64R+1gXHjh5V2gsVKwWq2koA0Llrdzg6OirbO3P6FJKSklCjdm1lHT//ADRo2Ahnw00f74qVAo3ObWrtO4Qo8VSOpQCUbckjIuVt2dramt2eKYt+WmBy/wu5uODY0aOqddPTDjKsu4YOzJ7Xyxva8/suo7h15JDuxstva9cY7YOffwDWbNhsdTw1RX5kuWXTxibjKQClbTpt1hxlux80amx0o6J7r8/R6sOPzB7v48f+Qf78+VG9Vi1l2edf9oWnl5cyIio9bTN5riJz8e3M6VNp5jc5xm3Y8jqf9hswSKlPrWmbmSK3TW3tbNM9UtpQxUqBSjyVy9uUryYa5ZPU9UlGY/ebSE9bQb5e3LRdPedsxUqBOH3+khJvAaDlh21QuHAR3DA4x81aBOPw8VOvp+8AlLrr4IGMPdb9X8a3jP3LyAHJPo89ChZ0xojR4/BKf6eudYumWPTTAlSoWAnTZs5WXpPp4eGJE8f+wYqlS5Tfibp8GaXKlMHyNesQGxODQoVdUKRIUdXwzHWrVyNs0GDs/esQEh4+grCxQaFChXD4779UFXh6GL52PivmYjlz+hS2bt6ETl26omuPnqj3fn24FXNHwYLOmDJ5IgDdxL09Pv8Cic8S0b5DiKozBwB2/77LaDb8ggWd4eioMUr3ti2b8fW0GRgyYhQ6dAxFSnIKAkqXxp24OKxcthQAsGzJr3inShV80CgIG7ftRJ48eeDp5YXk5GRVJdCsRTACA9/Bk8ePTQ5b1mq1+HXRzxg9bgLWb92OxKdaZd8G9P3SKE2jx09E1x69UNzDAw4ODti6eZPqkSeNRoOgJk0tvnkms/y5fz8+aBSEKdNn4nZMDHz9/JCcnIwdBnd2lvyyCKVKl8bq9Rvx8P4DJU9+3r2r6rfadwjBh23boWDBAvDw9MLxY/9g5jTjEV4bt+2Em5sbnAoUwJPHj1VD7OWJLO3z5sH0Wepz/ez5M3Ro2wbDRo3Bu9Wq4/q1q8rk17L7D+5j9oxpqvO3eccunD51KsOda3fi4lCl6rv4bfM2PLh/D55eJWBnZ4etm61/jWtqDYMaw6VwYVSrXtNofhwAFvNbZqtb73307P0lyrz1NvLkyYMrUVGY9s3X6fqNPb/vQuA7lRES2klpxMjHSZ6ovlqNGnAqUAB37sSZLd/FihfHu9Wq4+XLl2me23ETJ6FipUAUKVoUjhoNfluzWpkvSY4lTgUKWB1LzNFqtVi7ahXCBg3Grn0HkPhUC0eNI169eoWtmzcpoxD37f0DzYNbmizfsuVLliAwsDKWrlqNO7FxyOvgADs7OxzYv8+oETZh8hS0afux0iGSXvL+mzre6dl/Q02bN0eBggXxbvXqJj+f8+33KOnto5TvWdOmmi3fho14QPf4x4EMzvFjSXaWJWukVZ8o6wW3RETEWbONczm/DR05CiGhoXAvVhwajUYZ1SHntVJlymDjNt0d+OIeHnB0dMTaVStVv5VW7N62ZTNq1XlPydvyb+3etVNVd127egVVqr6L1es3AgCKFCmKu3fuYN2a1artyXn7SlQkgpsEWXnk0k+uu6pUfRcbtmxX6q7UbzQCdDGjRq3acHJyQoWKlVR1r3ysBwwZinYdQqDJrzH5O0t+WYTla9ap6krD/TeMJW3atkOlSu8oaWrZ9PWjIHJM6dqjJ4KaNEXS8+coVaYM4mJjsW6N7g1pK5YuQbUaNVG9Rk3l/LoXKwYnJydVu8Pw3BYr7oETx4+ZrJfHTZyEBg0boZCLC5KSkvBbqnN28sRxVK9REzPmzMPtmBhlW5cvXsTyJUuMfs8crVaLRK0Wbdq2Q7XqNeGocUSRIkUReemSah5Pa9pBct1VsKAzSnp7K3VXRh912bxDdzM2ozE3PZo2b45SpUvD3t7ebDy1pHfffmjctBlu344x2Vb68dv5OLB/n9I2bdKsOfz9A5S2aeqRZUFNmsLe3t7s8V65bCnKlHkL79Wthw1btsMxvwYeHp6qMpCetlmz4JYoULCg2fi2YukS9BswyGJ+M4xxG7ZsR14HB3h6eWHThvUZugEy59vv8W616krbdOGPP6b7NwBdmevcrTuKFfeAjY2NctPIWumJ3cCbtxXSapulfmOpfL2YemqJM6dP4dWrVwjt3AUNg4IAIeDh4YmnT56oHqOU6xO57nr1SoKbu25EYnY+bvlvwQ6hf5lSZcqo/vZP9RpBrVaL/l9+ge2798LXzx+A7jELw1fOA0Cv7l0QNmgI6tZ7HwGlS0Or1eLI4UP48btvlXVWr1yO6OhbmPf9j3Bzc0dKSgrWrl6J6VPSdxFnKDteOz9n5nQ8evQQIaGdUKpMGSQlJeHY0SNKR46bmzucChSARqMx+aaIs2eNe+pTUlLw4MF9k+n+6cfv8eFHbeHt4wtbW1scO3oE386bqxryPnr4UNyLj0eHjqF49eoV4uJisXyJ+pWt9eo3gFOBAjh54rjZx7f2/L4Lz549w/c/LYStrS0SExNxYP8+o2GXS35ZhBYtW8E/IACPHydg1fJ1Ro/EBLdqjZLePpgzc4aZI5l55P1v3rIl/AMCcPt2DJYvWYzFixYq65w5fQqjhg3FL8tWoGhRVyQlJSH8zGmjCreoqytKlS4NSZIwf/YsLF38i8mGmbzvfx7Yj19+/kn1mfwYlo+PccUm/1bx4h6wtbU1OWfWnTtxmf7Y5NBBA9Dzi96oWCkQ3j6+iIuLxeoVK7BwgfnJmtNy+uQJNA9uiQvnz5n8PK38lpkKOjvDP6AU7t+7h31/7MHCn37M0OT006ZMxoMHDxA2SHdX8P69e9iyaaPyuVym3dzczZZv+dza2toaxdTU59arREklRg4dGKZ6Q6IcSwDj2CxvKz1Wr1yOR48eYsr0mcjjmQcpKSn4ZvJXqlgxYojuzqSl8n0lKgOF7/MAAA6USURBVBKDwvph1W8bUKpMGUiShB+//xbzDN5WlFnk/Td1vNO7/7J/jh5B915fICrS9HByH19f+Pr5K+VbfiREZql8F3RO+3HDjMjOsmQNa+qTOnXroVy58vh+/lyzv2OY33z9/PHs2TPs3rVTeeWynNd69+2HRo2bANCVyd/WrDaKy9bE7hFDBqFW7TpK2+bx4wSjR1RGDRuKPmED8E7lKgCA8DOnMWv6tBx7nbBcd/UJG4AaNWspddes6dOM1j16+DAiL11C0aKuuHH9uuqzEUMGITb2Nlq0bAWvEiWQlJSEgwcO4Idv5xlt78jhQ3inchWlruzZtbNq/1evXI7ERC26dO+ptIOOHD5k1FaYM3M6QkI7wdPLy2T7RavVot8XvTBkxCjlUVetVovdu3aqfsvw3N68eQO9e3QzWS97lSiJPHnz4tSJE1j8y0KjN87K22rQsBH8AwKUbc2bPSvdj5dMmjAOXbr3hJ+/P5KTk3Hk8CHMmzVT9TvWtIPkusvBwQErli7JcN2VE/45egQtWrWGu3sxs/HUEjf3YsibN6/ZtpIcT+W2ab+wgShbvrzSNp3xjfoRtpLePnj44AH2/WG6k/rggf0YOmgAwgYNRsVKgUhJScH5iAjMmfX6/FjbNpPjW2Jiotn902q1+G3tGov5TY5xA4cOQ91670OSJMTH37X6LY+p+fj6QoKktE0zGreKurrC1c0dN2/ewNaNG7F08S/p+n56YndmSKttlpp8vfjgwX2jz2ZMnYKP23eAj68vkpOTEX7mNL6dOwd/H/xTtZ5cn/j6+UMIoZzbnHjc8/+dyMmZ8mVVqlSRjh8/ntPJsMqb9uh/+FFbfNkvDIPD+ilDcil9Fi5ehspVq2Lhgh+URilRTtJoNFi9fiN+37kTc2dlfQcbUWaZ/M00tPywTbbcrSbKTnLe/ufIYXTuGJL2F4iymNxW8PQqgUpvG79UhOj/FdsKaTt4JGPX9S6FC7/xth/cN+44S61wkSInJEmqYuozziGUzdavWwvnQoXw069L8NMvi+HpVSKnk/SvZG9vj169+ygTgRLlpJFjxqFESW92BtG/ykft2qP+Bw0RHX0r7ZWJ/mXkvD17Rva9DIDIErmtkHokA9H/M7YV/vv4yFgOWL1yOVq1boMiRV0RfetmTifnX6drp09zOglEKqtXrsDjJ49zOhlE6bJj6xYUK14c16+yY53+e5YvW4LrV6+q3gJKlJPktsLaVatyOilEVmNb4b+Pj4wREREREREREf3L8JExIiIiIiIiIiJKF3YIERERERERERHlMuwQIiIiIiIiIiLKZdghRERERERERESUy7BDiIiIiIiIiIgol2GHEBERERERERFRLsMOISIiIiIiIiKiXIYdQkREREREREREuQw7hIiIiIiIiIiIchl2CBERERERERER5TLsECIiIiIiIiIiymXYIURERERERERElMuwQ4iIiIiIiIiIKJdhhxARERERERERUS7DDiEiIiIiIiIiolyGHUJERERERERERLkMO4SIiIiIiIiIiHIZdggREREREREREeUy7BAiIiIiIiIiIspl2CFERERERERERJTLsEOIiIiIiIiIiCiXYYcQEREREREREVEuww4hIiIiIiIiIqJchh1CRERERERERES5DDuEiIiIiIiIiIhyGXYIERERERERERHlMuwQIiIiIiIiIiLKZdghRERERERERESUy7BDiIiIiIiIiIgol2GHEBERERERERFRLsMOISIiIiIiIiKiXIYdQkREREREREREuQw7hIiIiIiIiIiIchl2CBERERERERER5TLsECIiIiIiIiIiymXYIURERERERERElMuwQ4iIiIiIiIiIKJdhhxARERERERERUS7DDiEiIiIiIiIiolyGHUJERERERERERLkMO4SIiIiIiIiIiHIZdggREREREREREeUy7BAiIiIiIiIiIspl2CFERERERERERJTLWNUhJIS4LoQ4K4Q4LYQ4rl/mIoTYLYSI1P+/kH65EELMFUJECSHChRDvZOUOEBERERERERFR+qRnhND7kiRVkiSpiv7vYQD+kCQpAMAf+r8BoAmAAP1/PQB8n1mJJSIiIiIiIiKiN/cmj4y1BLBY/+/FAFoZLF8i6RwB4CyEKPYG2yEiIiIiIiIiokxkbYeQBOB3IcQJIUQP/TI3SZJiAUD/f1f9cg8Atwy+G61fpiKE6CGEOC6EOB4fH5+x1BMRERERERERUbrZWbleLUmSbgshXAHsFkJctLCuMLFMMlogSQsALACAKlWqGH1ORERERERERERZw6oRQpIk3db//y6ADQDeBXBHfhRM//+7+tWjAXgZfN0TwO3MSjAREREREREREb2ZNDuEhBAaIYST/G8AjQBEANgMoJN+tU4ANun/vRlAqP5tY9UBJMiPlhERERERERERUc6z5pExNwAbhBDy+iskSdophDgGYI0QoiuAmwDa6tffDqApgCgAiQA6Z3qqiYiIiIiIiIgow9LsEJIk6SqAiiaW3wfQwMRyCUDvTEkdERERERERERFlujd57TwREREREREREf0LsUOIiIiIiIiIiCiXYYcQEREREREREVEuww4hIiIiIiIiIqJcRujmgM7hRAjxBMClnE4H5SpFANzL6URQrsI8R9mNeY6yG/McZTfmOcpuzHOUnTIrv5WUJKmoqQ+see18drgkSVKVnE4E5R5CiOPMc5SdmOcouzHPUXZjnqPsxjxH2Y15jrJTduQ3PjJGRERERERERJTLsEOIiIiIiIiIiCiX+X/pEFqQ0wmgXId5jrIb8xxlN+Y5ym7Mc5TdmOcouzHPUXbK8vz2fzGpNBERERERERERZZ//lxFCRERERERERESUTdghRERERERERESUy+R4h5AQorEQ4pIQIkoIMSyn00P/DUIILyHEPiHEBSHEOSFEP/1yFyHEbiFEpP7/hfTLhRBirj4fhgsh3snZPaB/IyGErRDilBBiq/5vHyHEUX1+Wy2EyKNfnlf/d5T+c++cTDf9OwkhnIUQ64QQF/WxrgZjHGUlIUSYvk6NEEKsFEI4MM5RZhJCLBJC3BVCRBgsS3dcE0J00q8fKYTolBP7Qv8OZvLcNH3dGi6E2CCEcDb4bLg+z10SQgQZLOc1LVnFVJ4z+GyQEEISQhTR/53lcS5HO4SEELYAvgXQBMDbAD4RQrydk2mi/4xkAAMlSXoLQHUAvfV5axiAPyRJCgDwh/5vQJcHA/T/9QDwffYnmf4D+gG4YPD3NwBm6fPbQwBd9cu7AngoSZI/gFn69YjSaw6AnZIklQFQEbq8xxhHWUII4QGgL4AqkiSVA2ALoD0Y5yhz/Qqgcapl6YprQggXAGMBVAPwLoCxcicSkQm/wjjP7QZQTpKkCgAuAxgOAPprifYAyuq/853+ZiCvaSk9foVxnoMQwgtAQwA3DRZneZzL6RFC7wKIkiTpqiRJLwCsAtAyh9NE/wGSJMVKknRS/+8n0F0oeUCXvxbrV1sMoJX+3y0BLJF0jgBwFkIUy+Zk07+YEMITQDMAP+v/FgDqA1inXyV1fpPz4ToADfTrE1lFCFEAwHsAFgKAJEkvJEl6BMY4ylp2APIJIewAOAKIBeMcZSJJkv4E8CDV4vTGtSAAuyVJeiBJ0kPoLu6NLr6IANN5TpKk3yVJStb/eQSAp/7fLQGskiQpSZKkawCioLue5TUtWc1MnAN0N0+GADB861eWx7mc7hDyAHDL4O9o/TKiTKMfph4I4CgAN0mSYgFdpxEAV/1qzIv0pmZDF8Rf6f8uDOCRQYPCME8p+U3/eYJ+fSJr+QKIB/CL/jHFn4UQGjDGURaRJCkGwHTo7lzGQhe3ToBxjrJeeuMa4x1lpi4Aduj/zTxHWUIIEQwgRpKkM6k+yvI8l9MdQqbuFEkmlhFliBAiP4DfAPSXJOmxpVVNLGNeJKsIIZoDuCtJ0gnDxSZWlaz4jMgadgDeAfC9JEmBALR4/RiFKcxz9Eb0Q9FbAvABUByABrqh7KkxzlF2MZfHmPcoUwghRkI3DcVyeZGJ1Zjn6I0IIRwBjAQwxtTHJpZlap7L6Q6haABeBn97AridQ2mh/xghhD10nUHLJUlar198R35MQv//u/rlzIv0JmoBCBZCXIdumHB96EYMOesfrQDUeUrJb/rPC8L00FEic6IBREuSdFT/9zroOogY4yirfADgmiRJ8ZIkvQSwHkBNMM5R1ktvXGO8ozemn6S3OYAQSZLkC23mOcoKftDdbDmjv5bwBHBSCOGObMhzOd0hdAxAgP4NFXmgm6Rrcw6nif4D9PMULARwQZKkmQYfbQYgz8LeCcAmg+Wh+pncqwNIkIcnE6VFkqThkiR5SpLkDV0c2ytJUgiAfQA+0q+WOr/J+fAj/fq8k0RWkyQpDsAtIURp/aIGAM6DMY6yzk0A1YUQjvo6Vs5zjHOU1dIb13YBaCSEKKQf2dZIv4zIKkKIxgCGAgiWJCnR4KPNANoL3VsUfaCb6Pcf8JqW3oAkSWclSXKVJMlbfy0RDeAdfVsvy+OcXdqrZB1JkpKFEF9Cl3hbAIskSTqXk2mi/4xaADoCOCuEOK1fNgLAFABrhBBdoWvcttV/th1AU+gmh0sE0Dl7k0v/UUMBrBJCfAXgFPQTAOv/v1QIEQXdHfP2OZQ++nfrA2C5vvF5Fbq4ZQPGOMoCkiQdFUKsA3ASukcoTgFYAGAbGOcokwghVgKoB6CIECIaurfopKvtJknSAyHEROgu0gFggiRJHJ1GJpnJc8MB5AWwWz8X/hFJknpJknROCLEGus7wZAC9JUlK0f8Or2nJKqbynCRJC82snuVxTvBmDRERERERERFR7pLTj4wREREREREREVE2Y4cQEREREREREVEuww4hIiIiIiIiIqJchh1CRERERERERES5DDuEiIiIiIiIiIhyGXYIERERERERERHlMuwQIiIiIiIiIiLKZf4HMkQIOl0ZGMEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "img = cv2.imread('kaggle-training.png')\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.imshow(img)\n",
    "plt.title('AccVal_acc')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(r.history)\n",
    "\n",
    "# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "#plot the loss\n",
    "# plt.plot(r.history['loss'], label='train_loss')\n",
    "# plt.plot(r.history['val_loss'] , label='val_loss')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "# plt.savefig('LossVal_loss')\n",
    "img = cv2.imread('LossVal_loss.png')\n",
    "plt.imshow(img)\n",
    "plt.title('LossVal_loss')\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#plot the accuracy\n",
    "# plt.plot(r.history['accuracy'], label='train acc')\n",
    "# plt.plot(r.history['val_accuracy'], label='val acc')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "# plt.savefig('AccVal_acc')\n",
    "img = cv2.imread('AccVal_acc.png')\n",
    "plt.imshow(img)\n",
    "plt.title('AccVal_acc')\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5 Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# the requirement is to print the classification report using Sklearn API, so we are re-predicting out test data set to get the y_true  and y_pred classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Keras doesnt output the y_true and y_pred classes without shuffling hence this extract step is implemented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first we are loading the saved model\n",
    "# Keras  supports saving a single HDF5 file containing the model's architecture, weights values, and compile() information\n",
    "#model.save('model_resnet50.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "from keras.preprocessing import  image\n",
    "from keras.models import model_from_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "model1=load_model(\"face_model_resnet50(complete-model).h5\")\n",
    "# #loading the model\n",
    "# json_file=open('face_emotions-model.json','r')\n",
    "# model_json=json_file.read()\n",
    "# json_file.close()\n",
    "\n",
    "# loaded_model=model_from_json(model_json)\n",
    "\n",
    "# #load weights into model\n",
    "# loaded_model.load_weights('face_emotions_weights.h5')\n",
    "print('Loaded model from disk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is an additional utility function to label the classes between 0 to 6 , required to generate y_true\n",
    "import os\n",
    "\n",
    "def folderEncoder(folderName):\n",
    "    if folderName=='AN':\n",
    "        return 0\n",
    "    elif folderName==\"DI\":\n",
    "        return 1\n",
    "    elif folderName=='FE':\n",
    "        return 2\n",
    "    elif folderName==\"HA\":\n",
    "        return 3\n",
    "    elif folderName=='NE':\n",
    "        return 4\n",
    "    elif folderName==\"SA\":\n",
    "        return 5\n",
    "    elif folderName=='SU':\n",
    "        return 6\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "result=[]\n",
    "for folder1 in os.listdir(test_folder):\n",
    "    y_true=folderEncoder(folder1)\n",
    "    for filename in os.listdir(os.path.join(test_folder,folder1)):\n",
    "#         print(filename)\n",
    "        img = image.load_img(os.path.join(os.path.join(test_folder,folder1),filename), target_size=(224, 224))\n",
    "        x = image.img_to_array(img)\n",
    "#         x = x / 255\n",
    "        x = np.expand_dims(x, axis=0)\n",
    "        img_data = preprocess_input(x)\n",
    "        y_pred=model1.predict(img_data)\n",
    "#         y_pred=loaded_model.predict(img_data)\n",
    "        y_pred=np.argmax(y_pred, axis=1)\n",
    "        result.append([y_true,y_pred[0]])           \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6] [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0, 4, 0, 5, 0, 0, 3, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 5, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 5, 0, 0, 3, 5, 4, 5, 0, 0, 3, 0, 1, 5, 0, 0, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0, 0, 0, 5, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 5, 5, 5, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 4, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 0, 5, 1, 1, 1, 3, 1, 0, 5, 3, 5, 2, 1, 1, 1, 5, 1, 1, 5, 1, 1, 2, 5, 1, 1, 1, 0, 0, 0, 2, 5, 5, 1, 1, 0, 1, 1, 0, 1, 4, 1, 5, 1, 1, 1, 1, 1, 0, 1, 0, 2, 1, 1, 1, 1, 1, 1, 2, 1, 5, 1, 5, 1, 1, 1, 1, 0, 1, 1, 5, 5, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 0, 2, 2, 2, 2, 6, 2, 5, 2, 4, 2, 2, 2, 6, 0, 1, 2, 3, 2, 2, 2, 6, 2, 2, 2, 3, 4, 2, 3, 5, 2, 2, 5, 4, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 6, 4, 0, 1, 4, 6, 2, 2, 2, 2, 2, 5, 4, 2, 2, 2, 2, 4, 2, 2, 2, 6, 2, 2, 6, 2, 2, 2, 6, 2, 2, 2, 0, 2, 2, 2, 6, 2, 2, 4, 2, 2, 2, 2, 1, 3, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 6, 2, 5, 2, 2, 0, 6, 2, 5, 1, 6, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 5, 3, 1, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 5, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 6, 3, 3, 4, 3, 3, 3, 2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 3, 5, 3, 2, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 0, 4, 5, 4, 4, 6, 2, 4, 2, 4, 4, 4, 5, 4, 4, 4, 6, 5, 4, 4, 4, 4, 4, 4, 2, 2, 4, 6, 4, 4, 2, 4, 0, 0, 4, 5, 4, 4, 5, 4, 4, 2, 5, 4, 4, 4, 4, 0, 2, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 4, 5, 4, 4, 4, 3, 4, 4, 4, 4, 2, 4, 4, 1, 4, 0, 0, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 2, 1, 5, 5, 5, 5, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 0, 5, 5, 3, 5, 2, 5, 2, 6, 5, 5, 0, 4, 1, 4, 2, 5, 5, 5, 0, 0, 5, 0, 5, 5, 5, 5, 5, 5, 5, 3, 4, 1, 5, 0, 5, 5, 5, 5, 5, 5, 6, 5, 5, 5, 5, 0, 5, 5, 1, 5, 1, 5, 5, 5, 5, 5, 4, 5, 5, 4, 5, 5, 0, 5, 5, 5, 5, 5, 5, 0, 2, 5, 5, 5, 5, 5, 0, 1, 5, 5, 5, 2, 5, 4, 0, 5, 0, 0, 5, 5, 0, 0, 3, 0, 5, 5, 5, 5, 5, 5, 2, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 4, 4, 5, 6, 6, 6, 6, 2, 4, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 6, 4, 6, 6, 6, 6, 5, 6, 6, 6, 6, 6, 2, 6, 6, 6, 6, 6, 6, 6, 6, 2, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 2, 2, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 6, 6, 6, 6, 2, 6, 2, 6, 6, 5, 6, 6, 6, 4, 6, 6, 6, 6, 6, 6]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_pred=y_true=[] #empty list for placeholder\n",
    "\n",
    "y_true=[y[0] for y in result] # separating true classes\n",
    "y_pred=[y[1] for y in result] # separating predicted classes\n",
    "print(y_true,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "importing SKLEARN and printing the classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.classification import classification_report\n",
    "target_classes = ['Angry','Disgust','Fear','Happy','Neutral','Sad','Surprise'] # creating variable to hold target classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Angry       0.69      0.81      0.75       132\n",
      "     Disgust       0.81      0.66      0.73       131\n",
      "        Fear       0.73      0.69      0.71       142\n",
      "       Happy       0.89      0.84      0.86       132\n",
      "     Neutral       0.75      0.75      0.75       131\n",
      "         Sad       0.61      0.67      0.64       129\n",
      "    Surprise       0.84      0.86      0.85       132\n",
      "\n",
      "    accuracy                           0.75       929\n",
      "   macro avg       0.76      0.75      0.75       929\n",
      "weighted avg       0.76      0.75      0.75       929\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true, y_pred, target_names=target_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "#for final verification\n",
    "img=image.load_img('emotion_SU_0_1349.tiff', target_size=(224,224))\n",
    "x=image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0) # just increasing the rank \n",
    "x = preprocess_input(x)\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6]\n"
     ]
    }
   ],
   "source": [
    "a=np.argmax(model1.predict(x), axis=1)\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Predicted as Surprise as per the label -emotion_SU_0_1349\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}