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rnett/audio-denoiser-using-lip-reading.ipynb

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Audio Denoiser using Lip Reading.ipynb
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audio-denoiser-using-lip-reading.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Audio Denoiser using Lip Reading.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"language": "python",
"display_name": "Python 3"
},
"accelerator": "TPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/rnett/fb8b2646ae3dcd5e37933262e3c0c813/audio-denoiser-using-lip-reading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "4_jxmQj2MIOS",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"# Downloading and preprocessing data#\n"
]
},
{
"metadata": {
"pycharm": {
"metadata": false
},
"id": "pDq23_xo7CUU",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Files##"
]
},
{
"metadata": {
"id": "r83v0t7msyEp",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Raw video files get stores in `files`.\n",
"\n",
"Video is then loaded, turned into frames, then the numpy arrays are saved in `data`.\n"
]
},
{
"metadata": {
"id": "MUVnBSuVY0D-",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"from typing import List, Dict, Iterable\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"mpl.rc('image', cmap='gray')\n",
"from matplotlib import pyplot as plt\n",
"\n",
"#!pip install git+https://github.com/avivga/face-detection.git\n",
"# !pip3 install git+https://github.com/avivga/mediaio.git\n",
"# !pip3 install imageio\n",
"# !pip3 install imageio-ffmpeg\n",
" \n",
"# import sys\n",
" \n",
"# print(\"Version:\", sys.version)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "yFbgnqff_Jgj",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"File constants"
]
},
{
"metadata": {
"pycharm": {
"metadata": false,
"name": "#%%\n"
},
"id": "Ym3XZ_39Wq67",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"\n",
"raw_dir = 'files'\n",
"video_dir = 'videos'\n",
"audio_dir = 'audio'\n",
"test_dir = 'test'\n",
"train_dir = 'train'"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "h9iwenmr_ML7",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Download videos"
]
},
{
"metadata": {
"id": "ZR7wsmkMyDlA",
"colab_type": "code",
"outputId": "7f47c1d0-e755-427c-d2db-db9af9a81753",
"pycharm": {},
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"import requests, zipfile, io\n",
"\n",
"import shutil\n",
"import os\n",
"\n",
"\n",
"#@title Force file refresh\n",
"force = False #@param {type:\"boolean\"}\n",
"\n",
"if not os.path.isdir(raw_dir) or force: \n",
"\n",
" try:\n",
" shutil.rmtree(raw_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
"\n",
" os.mkdir(raw_dir)\n",
"\n",
" for i in range(3):\n",
" url = \"http://spandh.dcs.shef.ac.uk/gridcorpus/s{}/video/s{}.mpg_vcd.zip\".format(i+1, i+1)\n",
" r = requests.get(url)\n",
" z = zipfile.ZipFile(io.BytesIO(r.content))\n",
" z.extractall(\"tmp\")\n",
"\n",
" files = os.listdir(\"tmp\")\n",
"\n",
" for f in files:\n",
" files1 = os.listdir(\"tmp/\"+f)\n",
" for f1 in files1:\n",
" if f1 != 'Thumbs.db':\n",
" shutil.move(\"tmp/\"+f + \"/\" + f1, raw_dir + '/' + f1.replace('.mpg', f\"_s{i+1}.mpg\"))\n",
"\n",
" shutil.rmtree('tmp')\n",
" \n",
"print(\"Have {0} data files\".format(len(os.listdir(raw_dir))))\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Have 3000 data files\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "nhx0aGVd-TR6",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"test_s3_limit = 25\n",
"test_others_limit = 25\n",
"train_limit = 220\n",
"\n",
"test_s3_count = 0\n",
"test_other_count = 0\n",
"train1_count = 0\n",
"train2_count = 0\n",
"\n",
"if not (os.path.isdir(test_dir) and os.path.isdir(train_dir)) or force: \n",
"\n",
" try:\n",
" shutil.rmtree(test_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
" try:\n",
" shutil.rmtree(train_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
" \n",
" os.mkdir(test_dir)\n",
" os.mkdir(train_dir)\n",
"\n",
" for file in os.listdir(raw_dir):\n",
" if '_s3' in file:\n",
" if test_s3_count < test_s3_limit:\n",
" shutil.copy(raw_dir + '/' + file, test_dir)\n",
" test_s3_count += 1\n",
" elif '_s1' in file:\n",
" if train1_count < train_limit / 2:\n",
" shutil.copy(raw_dir + '/' + file, train_dir)\n",
" train1_count += 1\n",
" if test_other_count < test_others_limit:\n",
" shutil.copy(raw_dir + '/' + file, test_dir)\n",
" test_other_count += 1\n",
" elif '_s2' in file:\n",
" if train2_count < train_limit / 2:\n",
" shutil.copy(raw_dir + '/' + file, train_dir)\n",
" train2_count += 1\n",
" if test_other_count < test_others_limit:\n",
" shutil.copy(raw_dir + '/' + file, test_dir)\n",
" test_other_count += 1\n",
" "
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "uIiTuv51nwBX",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"import cv2, os, gc\n",
"import h5py, imageio\n",
"from PIL import Image\n",
"from mediaio.video_io import VideoFileReader\n",
"from imutils import face_utils\n",
"import numpy as np\n",
"import argparse\n",
"import imutils\n",
"import dlib\n",
"import cv2\n",
"\n",
"def rgb2gray(rgb):\n",
" return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
"\n",
"lip_size = (30, 75)\n",
" \n",
"class VideoLoader:\n",
" def __init__(self, file):\n",
" self.file = file\n",
" self.training = train_dir in file\n",
" self.name = self.file.split(\"/\",1)[1].replace(\".mpg\", \"\")\n",
" self.loaded = False\n",
" \n",
" def load_and_save(self):\n",
" \n",
" try:\n",
" with imageio.get_reader(self.file) as reader:\n",
"\n",
" size = reader.get_meta_data()[\"size\"]\n",
" video_shape = (75, size[1], size[0])\n",
" gray_frames = np.ndarray(shape=video_shape, dtype=np.uint8)\n",
"\n",
" data = np.zeros(shape=(len(gray_frames),lip_size[0],lip_size[1]), dtype=np.float32)\n",
"\n",
" # initialize dlib's face detector (HOG-based) and then create\n",
" # the facial landmark predictor\n",
" detector = dlib.get_frontal_face_detector()\n",
" predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')\n",
"\n",
" for i in range(75):\n",
" gray = cv2.cvtColor(reader.get_next_data(), cv2.COLOR_BGR2GRAY)\n",
" gray_frames[i, ] = gray\n",
"\n",
"\n",
" # detect faces in the grayscale image\n",
" rects = detector(gray, 1)\n",
"\n",
" isset = False\n",
"\n",
" # loop over the face detections\n",
" for (k, rect) in enumerate(rects):\n",
" # determine the facial landmarks for the face region, then\n",
" # convert the landmark (x, y)-coordinates to a NumPy array\n",
" shape = predictor(gray_frames[i, ], rect)\n",
" shape = face_utils.shape_to_np(shape)\n",
"\n",
" # loop over the face parts individually\n",
" for (name, (l, m)) in face_utils.FACIAL_LANDMARKS_IDXS.items():\n",
" # clone the original image so we can draw on it, then\n",
" # display the name of the face part on the image\n",
" if name == 'mouth':\n",
" # clone = gray_frames[i, ].copy()\n",
" # cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,\n",
" # 0.7, (0, 0, 255), 2)\n",
"\n",
" # # loop over the subset of facial landmarks, drawing the\n",
" # # specific face part\n",
" # for (x, y) in shape[l:m]:\n",
" # cv2.circle(clone, (x, y), 1, (0, 0, 255), -1)\n",
"\n",
" # extract the ROI of the face region as a separate image\n",
"\n",
" (x, y, w, h) = cv2.boundingRect(np.array([shape[l:m]]))\n",
" roi = gray_frames[i, ][y:y + h, x:x + w]\n",
" roi = imutils.resize(roi, width=250, inter=cv2.INTER_CUBIC)\n",
" #roi = np.resize(roi,(100,250))\n",
"\n",
" roi = np.array(Image.fromarray(roi).resize((lip_size[1], lip_size[0]), Image.ANTIALIAS))\n",
" isset = True\n",
"\n",
" if not isset:\n",
" print(\"\\nCould not find mouth for video\", self.file)\n",
"\n",
" del data\n",
" del gray_frames\n",
" gc.collect()\n",
" return False\n",
"\n",
" data[i] = roi\n",
"\n",
" if self.training:\n",
" h5f = h5py.File(train_dir + '/' + video_dir + '/' + self.name + '.hdf5', 'w')\n",
" else:\n",
" h5f = h5py.File(test_dir + '/' + video_dir + '/' + self.name + '.hdf5', 'w')\n",
" h5f.create_dataset('video', data=data, compression=\"gzip\")\n",
" h5f.close()\n",
"\n",
" del data\n",
" del gray_frames\n",
" gc.collect()\n",
" return True\n",
" except:\n",
" return False"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "9vCf-apD_VW4",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"## Video##\n",
"\n",
"Here we extract video frames from the file, and save them."
]
},
{
"metadata": {
"id": "zy1RgbHyRGPT",
"colab_type": "code",
"pycharm": {},
"outputId": "f6cc5582-c9b8-4653-d68d-b35207154838",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"cell_type": "code",
"source": [
"import sys\n",
"\n",
"#@title Force video refresh\n",
"video_force = False #@param {type:\"boolean\"}\n",
"limit = 200 #@param {type:\"slider\", min:10, max:1000, step:10}\n",
"\n",
"print(\"Training videos\")\n",
"\n",
"videos = (VideoLoader(train_dir + '/' + f) for f in os.listdir(train_dir))\n",
"\n",
"if not os.path.isdir(train_dir + '/' + video_dir) or video_force: \n",
"\n",
" try:\n",
" shutil.rmtree(train_dir + '/' + video_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
"\n",
" os.mkdir(train_dir + '/' + video_dir)\n",
" \n",
" done = 0\n",
" \n",
" while done <= limit:\n",
" try:\n",
" video = next(videos)\n",
" except StopIteration:\n",
" print(f\"\\nFinished with {done} training videos\")\n",
" break\n",
" #print(\"Video:\", video.file)\n",
" \n",
" if video.load_and_save():\n",
" done += 1\n",
" \n",
" sys.stdout.write('\\r{}/{} ({} %)'.format(done, limit, int(100 * done / limit)))\n",
" sys.stdout.flush()\n",
" \n",
" \n",
"print(\"\\nTest videos:\")\n",
" \n",
"test_limit = 40 #@param {type:\"slider\", min:10, max:1000, step:10}\n",
"\n",
"videos = (VideoLoader(test_dir + '/' + f) for f in os.listdir(test_dir))\n",
"\n",
"if not os.path.isdir(test_dir + '/' + video_dir) or video_force: \n",
"\n",
" try:\n",
" shutil.rmtree(test_dir + '/' + video_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
"\n",
" os.mkdir(test_dir + '/' + video_dir)\n",
" \n",
" done = 0\n",
" \n",
" while done <= test_limit:\n",
" try:\n",
" video = next(videos)\n",
" except StopIteration:\n",
" print(f\"\\nFinished with {done} test videos\")\n",
" break\n",
" #print(\"Video:\", video.file)\n",
" \n",
"# if os.isfile(video.file):\n",
"# done += 1\n",
"# continue\n",
" \n",
" if video.load_and_save():\n",
" done += 1\n",
" \n",
" sys.stdout.write('\\r{}/{} ({} %)'.format(done, test_limit, int(100 * done / test_limit)))\n",
" sys.stdout.flush()\n",
" "
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Training videos\n",
"\n",
"Test videos:\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "MVlBYV8L_dys",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"This provides methods for loading videos."
]
},
{
"metadata": {
"id": "Cy6-_EHrpJG4",
"colab_type": "code",
"pycharm": {},
"outputId": "0d054b54-0184-406e-8baa-b73207ad3d78",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"cell_type": "code",
"source": [
"import os\n",
"\n",
"class Video:\n",
" def __init__(self, name, training):\n",
" self.name = name.replace('.hdf5', '')\n",
" \n",
" if training:\n",
" self.file = train_dir + '/' + video_dir + '/' + name\n",
" else:\n",
" self.file = test_dir + '/' + video_dir + '/' + name\n",
" \n",
" #h5f = h5py.File(self.file,'r')\n",
" #self.data = h5f['video'][:]\n",
" #h5f.close()\n",
" \n",
" #self.data = np.load(self.file)\n",
" \n",
" def data(self):\n",
" h5f = h5py.File(self.file,'r')\n",
" data = h5f['video'][:]\n",
" h5f.close()\n",
" return data\n",
"\n",
"def get_videos(limit=10, training=True):\n",
" if training:\n",
" files = [f for f in os.listdir(train_dir + '/' + video_dir)][:limit]\n",
" else:\n",
" files = [f for f in os.listdir(test_dir + '/' + video_dir)][:limit]\n",
" \n",
" return [Video(f, training) for f in files]\n",
"\n",
"print(len(os.listdir(train_dir + '/' + video_dir)), \" Training Videos\")\n",
"print(len(os.listdir(test_dir + '/' + video_dir)), \"Test Videos\")\n",
"\n",
"videos = get_videos(5)\n",
"\n",
"video_shape = np.shape(videos[0].data())\n",
"\n",
"print(\"Shape: \", video_shape)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"201 Training Videos\n",
"41 Test Videos\n",
"Shape: (75, 30, 75)\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "bIyKCgla_hZ3",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"An example video.\n",
"\n",
"I'm not sure whats up with the colors, but it shouldn't matter."
]
},
{
"metadata": {
"id": "gxVNq9DjulY3",
"colab_type": "code",
"pycharm": {},
"outputId": "4ea02bff-909e-409e-b529-0f8aad7dcad6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 874
}
},
"cell_type": "code",
"source": [
"\n",
"video_shape = np.shape(videos[0].data())\n",
"print('Video shape:', video_shape, videos[0].data().dtype)\n",
"for i in range(0, 75, 15):\n",
" plt.imshow(videos[0].data()[i])\n",
" plt.show()\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Video shape: (75, 30, 75) float32\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "AADkvcGUZQoi",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Sucesfully loads visual data, now for audio"
]
},
{
"metadata": {
"id": "-fEODXR936sk",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"## Audio##\n",
"\n",
"\n",
"Here we extract audio from the video files and apply **mfcc** to it.\n",
"\n",
"We then save it for later use."
]
},
{
"metadata": {
"id": "9K6M6YZu3OmX",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"video_frame_rate = 25\n",
"framerate = 22050\n",
"n_fft = int(float(framerate) / video_frame_rate)\n",
"frame_step = int(n_fft / 4)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "5e57VlcA3c6t",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"def reconstruct_audio(magnitude, phase):\n",
" \n",
" magnitude = librosa.db_to_amplitude(magnitude)\n",
"\n",
" mel_filterbank = librosa.filters.mel(\n",
" sr=framerate,\n",
" n_fft=n_fft,\n",
" n_mels=80,\n",
" fmin=0,\n",
" fmax=8000\n",
" )\n",
" \n",
" magnitude = np.dot(np.linalg.pinv(mel_filterbank), magnitude)\n",
" \n",
" mag_phase = magnitude * phase\n",
" \n",
" used_mp2 = magnitude * phase\n",
" wave = librosa.istft(magnitude * phase, hop_length=frame_step)\n",
" \n",
" pad = 65664 - len(wave)\n",
" \n",
" return np.pad(wave, (0, pad), 'constant')"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "IMRtSoUuduPY",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"def signal_to_spectrogram(signal):\n",
" D = librosa.core.stft(signal.astype(np.float64), n_fft=n_fft, hop_length=frame_step)\n",
" magnitude, phase = librosa.core.magphase(D)\n",
"\n",
" mel_filterbank = librosa.filters.mel(\n",
" sr=framerate,\n",
" n_fft=n_fft,\n",
" n_mels=80,\n",
" fmin=0,\n",
" fmax=8000\n",
" )\n",
"\n",
" magnitude = np.dot(mel_filterbank, magnitude)\n",
"\n",
" magnitude = librosa.amplitude_to_db(magnitude)\n",
"\n",
" return magnitude, phase"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "7rq-5o2UtpJH",
"colab_type": "code",
"pycharm": {},
"outputId": "3354fc5f-1f3f-4861-e160-8d2e8082ec3f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"cell_type": "code",
"source": [
"audio_force = False #@param {type:\"boolean\"}\n",
"#limit = 100 #@param {type:\"slider\", min:10, max:1000, step:10}\n",
"\n",
"import librosa, scipy\n",
"import shutil\n",
"import tempfile\n",
"import urllib.request\n",
"import cv2, os, gc\n",
"import h5py\n",
"import sys\n",
"\n",
"print(\"Training audio\")\n",
"\n",
"#only load files we have video for\n",
"files = [train_dir + '/' + '/' + f.replace('.hdf5', '.mpg') \n",
" for f in os.listdir(train_dir + '/' + video_dir)]\n",
"\n",
"if not os.path.isdir(train_dir + '/' + audio_dir) or audio_force: \n",
"\n",
" try:\n",
" shutil.rmtree(train_dir + '/' + audio_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
"\n",
" os.mkdir(train_dir + '/' + audio_dir)\n",
" \n",
" total = len(files)\n",
" done = 0\n",
"\n",
" for f in files:\n",
" #audio = mp.VideoFileClip(f).audio\n",
"\n",
" #arr = audio.to_soundarray()\n",
"\n",
" wave, _ = librosa.load(f, mono=True, sr=framerate)\n",
" \n",
" noise = np.random.normal(0,0.05,len(wave))\n",
" \n",
" mel_spectrogram, phase = signal_to_spectrogram(wave)\n",
" \n",
" noisy = wave + noise\n",
" \n",
" noisy_spectrogram, _ = signal_to_spectrogram(noisy)\n",
" \n",
" #mag_phase = get_mag_phase(mel_spectrogram, phase)\n",
" \n",
" # this gets something, not entirely sure what\n",
" \n",
" # add any preprocessing here!\n",
"\n",
" name = f.split(\"/\", 1)[1].replace(\".mpg\", \"\")\n",
"\n",
" h5f = h5py.File(train_dir + '/' + audio_dir + '/' + name + '.hdf5', 'w')\n",
" h5f.create_dataset('spectrogram', data=mel_spectrogram.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('audio', data=wave.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('noisy_spectrogram', data=noisy_spectrogram.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('noisy_audio', data=noisy.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('phase', data=phase, compression=\"gzip\")\n",
" h5f.close()\n",
" \n",
" done += 1\n",
" \n",
" sys.stdout.write('\\r{}/{} ({} %)'.format(done, total, int(100 * done / total)))\n",
" sys.stdout.flush()\n",
"\n",
" \n",
"print(\"\\nTesting audio\")\n",
" \n",
" \n",
"#only load files we have video for\n",
"files = [test_dir + '/' + '/' + f.replace('.hdf5', '.mpg') \n",
" for f in os.listdir(test_dir + '/' + video_dir)]\n",
"\n",
"if not os.path.isdir(test_dir + '/' + audio_dir) or audio_force: \n",
"\n",
" try:\n",
" shutil.rmtree(test_dir + '/' + audio_dir)\n",
" except FileNotFoundError:\n",
" pass \n",
"\n",
" os.mkdir(test_dir + '/' + audio_dir)\n",
" \n",
" total = len(files)\n",
" done = 0\n",
"\n",
" for f in files:\n",
" #audio = mp.VideoFileClip(f).audio\n",
"\n",
" #arr = audio.to_soundarray()\n",
"\n",
" wave, _ = librosa.load(f, mono=True, sr=framerate)\n",
" \n",
" noise = np.random.normal(0,0.05,len(wave))\n",
" \n",
" mel_spectrogram, phase = signal_to_spectrogram(wave)\n",
" \n",
" noisy = wave + noise\n",
" \n",
" noisy_spectrogram, _ = signal_to_spectrogram(noisy)\n",
" \n",
" #mag_phase = get_mag_phase(mel_spectrogram, phase)\n",
" \n",
" # this gets something, not entirely sure what\n",
" \n",
" # add any preprocessing here!\n",
"\n",
" name = f.split(\"/\", 1)[1].replace(\".mpg\", \"\")\n",
"\n",
" h5f = h5py.File(test_dir + '/' + audio_dir + '/' + name + '.hdf5', 'w')\n",
" h5f.create_dataset('spectrogram', data=mel_spectrogram.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('audio', data=wave.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('noisy_spectrogram', data=noisy_spectrogram.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('noisy_audio', data=noisy.astype('float32'), compression=\"gzip\")\n",
" h5f.create_dataset('phase', data=phase, compression=\"gzip\")\n",
" h5f.close()\n",
" \n",
" done += 1\n",
" \n",
" sys.stdout.write('\\r{}/{} ({} %)'.format(done, total, int(100 * done / total)))\n",
" sys.stdout.flush()\n",
" \n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Training audio\n",
"\n",
"Testing audio\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "AEIxgWb2ByMC",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"This is code for loading audio files.\n",
"\n",
"We show a sample audio file, that has been transformed."
]
},
{
"metadata": {
"id": "ltj7OJqGpJRC",
"colab_type": "code",
"pycharm": {},
"outputId": "bcf71d90-85d0-451d-b948-7fbcad72a876",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 492
}
},
"cell_type": "code",
"source": [
"#@title Default title text { run: \"auto\" }\n",
"noise_level = 0.5 #@param {type:\"slider\", min:0, max:1, step:0.05}\n",
"import librosa.display\n",
"np.random.seed(0)\n",
"\n",
"n_fft = int(float(framerate) / video_frame_rate)\n",
"hop_length = int(n_fft / 4)\n",
"\n",
"class Audio:\n",
" def __init__(self, name, training):\n",
" self.name = name.replace('.hdf5', '')\n",
" \n",
" if training:\n",
" self.file = train_dir + '/' + audio_dir + '/' + name\n",
" else:\n",
" self.file = test_dir + '/' + audio_dir + '/' + name\n",
" \n",
"# h5f = h5py.File(self.file,'r')\n",
"# self.data = h5f['mfcc'][:]\n",
" \n",
"# self.audio = h5f['audio'][:]\n",
" \n",
"# self.mfcc_formatted = self.data.reshape(self.data.shape[0], self.data.shape[1])\n",
" \n",
"# h5f.close()\n",
" \n",
" def get_data(self, keys):\n",
" \n",
" if isinstance(keys, str):\n",
" keys = [keys]\n",
" \n",
" h5f = h5py.File(self.file,'r')\n",
" data = {}\n",
" \n",
" for k in keys:\n",
" if k in h5f:\n",
" if k =='noisy_audio':\n",
" data_audio = h5f[k][:]\n",
" audio_shape= np.shape(data_audio)\n",
" data_noise = np.random.normal(0,noise_level,audio_shape)\n",
" data[k] = data_noise + data_audio\n",
" else:\n",
" data[k] = h5f[k][:]\n",
" \n",
" \n",
" if len(data) == 1:\n",
" return data[next(iter(data))]\n",
" \n",
" return data\n",
" \n",
" def __getitem__(self, item):\n",
" return self.get_data(item)\n",
" \n",
" def audio(self):\n",
" return self['audio']\n",
" \n",
" def spectrogram(self):\n",
" return self['spectrogram']\n",
" \n",
" def phase(self):\n",
" return self['phase']\n",
" \n",
" def noisy_audio(self):\n",
" return self['noisy_audio']\n",
" \n",
" def noisy_spectrogram(self):\n",
" return self['noisy_spectrogram']\n",
" \n",
" def noise_spectrogram(self):\n",
" return self['noise_spectrogram']\n",
" \n",
" def reconstruct_audio(self, spectrogram):\n",
" return reconstruct_audio(spectrogram, self['phase'])\n",
" \n",
" \n",
"def get_audios(limit=10, training=True) -> List[Audio]:\n",
" \n",
" if training:\n",
" files = [f for f in os.listdir(train_dir + '/' + audio_dir)][:limit]\n",
" else:\n",
" files = [f for f in os.listdir(test_dir + '/' + audio_dir)][:limit]\n",
" \n",
" return [Audio(f, training) for f in files]\n",
"\n",
"audios = get_audios(5)\n",
"\n",
"spectrogram_shape = np.shape(audios[0].spectrogram())\n",
"\n",
"phase_shape = audios[0].phase().shape\n",
"\n",
"audio_shape = np.shape(audios[0].audio())\n",
"\n",
"print(\"Sound Shape: \", audio_shape, audios[0].audio().dtype)\n",
"print(\"Spectrogram Shape: \", spectrogram_shape, audios[0].spectrogram().dtype)\n",
"print(\"Phase Shape: \", audios[0].phase().shape, audios[0].phase().dtype)\n",
"\n",
"print(\"Waveform\")\n",
"\n",
"librosa.display.waveplot(audios[0].audio())\n",
"plt.show()\n",
"\n",
"print(\"Spectrogram\")\n",
"plt.imshow(audios[0].spectrogram())\n",
"plt.show()\n",
"\n",
"# plt.imshow(audios[0].mfcc_formatted, aspect='auto')\n",
"# plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Sound Shape: (65664,) float32\n",
"Spectrogram Shape: (80, 299) float32\n",
"Phase Shape: (442, 299) complex64\n",
"Waveform\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Spectrogram\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "KlLsduShHZo6",
"colab_type": "code",
"pycharm": {},
"outputId": "f72ded93-228f-40bd-8569-4344f3b7ca25",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 392
}
},
"cell_type": "code",
"source": [
"import time\n",
"\n",
"print(\"Reconstructed Waveform\")\n",
"\n",
"start = time.time()\n",
"recon = audios[0].reconstruct_audio(audios[0].spectrogram())\n",
"end = time.time()\n",
"\n",
"print(\"Time: \", end - start)\n",
"\n",
"print(\"Recon Shape:\", recon.shape)\n",
"\n",
"librosa.display.waveplot(recon)\n",
"plt.show()\n",
"\n",
"import IPython.display as ipy_display\n",
"\n",
"ipy_display.Audio(recon, rate=framerate)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Reconstructed Waveform\n",
"Time: 0.05281829833984375\n",
"Recon Shape: (65664,)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "execute_result",
"data": {
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ASMBIgEAAegA6gDZALMAuADLAKwAkQCPAHoAbwBxAFkARQA6ACEAEwAHAOr/2//O/7T/sP+l/3j/aP94/2v/Tv86/yz/Lf8l/wj/+P7z/uL+2/7k/t3+vv6o/q7+qP6R/pT+lv59/nP+cP5j/nb+f/5V/kv+aP5s/m/+df5p/mf+bP5u/nz+gv53/nP+d/5//o3+mP6f/p7+of6y/rn+v/7T/sz+vf7T/uP+4/7y/gL/Ef8R//X+9/4c/yb/IP8h/yf/Nv80/zH/R/9E/z3/WP9X/z3/Pf88/0b/Yv9e/1j/Yv9a/1z/W/9A/0n/Vv8v/yX/Qv87/y3/J/8Q/w7/F//8/uT+6f7m/uH+3v7P/sj+zP7B/rD+p/6f/pj+jf5+/n/+gP55/nj+cf5i/l7+Uv5F/k7+Sf46/kH+PP4o/iT+JP4n/h7+AP7+/Qr+/f3//Qb+8v30/fz95/3p/fL93f3a/d390P3Y/eD9zf3E/cH9vv3H/cT9uf29/cv90/3J/cD9z/3K/bX91v31/dr91P3r/eL94v0B/gr+9/3x/Qn+Hv4M/vf9Bv4d/iH+F/4g/kT+Rv4k/jP+Z/50/nP+fv5z/mf+dP6D/oH+e/6E/pP+i/5+/of+jP6Q/qb+qf6R/or+lP6b/qP+ov6f/qb+ov6X/pz+mf6F/o3+mP6A/nb+hP58/n3+mf6K/mz+hP6c/pf+k/56/m/+kP6I/mz+ff53/mX+e/53/m3+kf6X/or+kf5+/nP+hv5//oD+j/6B/oD+kv6R/pL+iP56/pH+qv6v/qf+lv6g/q7+oP6v/sn+wf7I/s3+wP7a/un+zP7V/vf+9P7y/gL/B/8E/xD/Hf8F//L+F/8u/yL/K/8t/xz/HP8m/zH/N/86/1T/Xv9B/0L/Uv9E/0L/UP9c/2//bf9a/1v/Zf9t/3v/g/+L/5j/lv+R/5v/oP+S/5D/o/+q/6v/wP/M/8j/zP/I/8H/y//N/8v/1P/c//f/EAAFAAIAHQAhABoAIwAiAB4AJgAnACEAJwAwACMADAATACwAKgAVAAwAEQAQAAUABAAJAAMA/f/+/wAACgALAOz/1//e/9r/1P/e/+H/4f/q/93/zf/c/+H/xP/A/9j/0v/D/9L/1//R/+f/9f/d/9X/6//4//b/8v/z////CwARABEAEwAnADsAQQBQAF0AVwBpAJIAnACTAJMAoQC1ALcAygADAQ8B7wD7AA0BCQEfATABMAFGAVIBVAFvAXQBZwF7AYoBfQF3AYUBmwGoAaMBpAGmAasBwwHEAbIBwwHQAbsBwgHgAeEB1wHYAeAB6AHjAc4BygHbAdsBzAHSAdcBwQG+AcgBswGqAbABlwGQAaQBkAGDAZQBfgFtAYsBhgFlAWoBZAFDAUEBSQE2ATYBSAEyARIBFgEQAfsAAgECAeEAyQDBAL8AvwCnAIwAigB4AGEAZABYADsAOAA0AB8AEAD8/+f/4f/I/6r/s/+x/4//jP+J/17/Tv9U/z3/N/9D/zD/G/8W/wr/Bf/7/uz+8P7p/tj+5P7i/s/+2v7f/tr+5v7c/s3+2f7O/r7+y/7I/sj+6/74/vD+8v7n/ub+Bf8M//X+8/7//gb/C/8A//H+/P4V/xv/Df8F/wz/Ev8N/wz/G/8m/xj/B/8M/xH/Cf8M/xb/Cf/t/uT++f4L//r+6f7v/u7+6/73/vP+3P7P/tD+1P7K/qj+k/6a/qP+pv6h/pL+jP6L/nn+af5n/l3+Q/46/kv+R/4o/h7+If4X/hX+Ef4B/v39/P3t/ef94P3Q/c790/3S/dL91v3d/dj9x/3C/cP9x/3S/b79oP20/cT9tv2+/b79r/3B/cL9sf3G/cv9sv2y/a39qP29/cT9yv3b/cv9xv3b/db93P3q/dL9zv3j/eL98v0M/gj+Af4D/hP+Hv4H/hP+P/4v/if+Tv47/iP+Ov42/jz+YP5c/k/+T/5J/ln+Yv5k/oT+g/5r/ob+jP5z/oj+m/6Q/pT+lP6M/pv+rf61/q/+o/6o/rb+t/6z/rD+sv61/rb+uP6v/q3+wv68/qX+s/66/qL+of6v/qr+pf6w/rf+nv6M/qf+q/6F/n3+kv6Y/oj+av5p/oH+c/5m/nf+dP5t/n7+hf55/mz+c/6W/qH+hv6C/pP+mP6Q/oX+h/6S/o7+jP6Y/p3+m/6d/p/+pf6y/sz+2/7P/s3+5v75/vr+/f4M/x3/GP8U/yb/Jf8R/yT/Tf9W/1T/Y/92/4P/h/+Q/6f/vv/Q/9L/xf/S//b/9P/m/wkAKgAtADcAOQA7AFwAcQB6AJYAnQCRAJoArwDIANYAzgDVAPEABwEXARABEAE7AUUBMAFFAV0BZgF9AXsBfAGfAaEBkgGeAacBuwHRAb4BvgHjAe0B8QH8AfoBAQIEAvwBGAI6AicCDgIbAigCJgIwAkgCRwI4AkkCWQI9AjQCVwJhAlUCYQJoAm0CiAKFAmACXQJsAmMCYQJwAn0CiAKMApICogKjApwCpwKoApQClwKtArQCtALCAs0CzQLJAswC1QLWAtUC4gLoAt8C7gL+AuoC6gL6AuIC2wL4AvgC9gIGA/0C+gINAwgDBgMYAxUDFwMwAywDEQMUAzMDPAMpAywDRQM6AyYDOQM/AyYDLANHA0YDOgM9A0ADOAMxAzEDMgMyAy4DNQNJA0kDNgM2Az8DRANSA1EDPgNCA1EDUANVA18DVwNRA1sDaQNzA3MDawNtA28DawNqA2kDagNyA28DYgNoA3EDYgNYA2IDZQNVA0MDNQMiAxQDHwMnAwkD9QL/AvIC3wLkAs4CqwKpApUCewKIAocCbQJdAkMCMQI0AiICGAInAhYC6QHUAdsB2QG1AZcBnAGZAZMBlQFyAU0BSQFAAT8BSAE1ASUBIAEHAfYA6gDcAO0A6gC9ALQAzgDGAKwAqgCrAJAAfQCKAIUAfgCJAHIAaQCSAIQAXAB7AIQAaAB8AIYAbABvAHcAdAB+AHoAcAB4AH8AhACNAI4AiwCLAJYAqQCsAKAAngCwAMwA2QDRAMkAwgDIAOUA7ADZAN0A7gDzAPoA/wAEARUBHwEgAR8BFwEdASsBKAEuAT0BMAEnAT0BPgEkAR8BKQEiARMBDgEVASQBNwE1ASgBLQE8AUABUQFkAWEBXQFZAUUBOwE8ATIBJwEgARYBEgEQAQwBCgH3ANYAyADHALoAsACwAKUAkwCFAIUAjgCMAIIAhgCQAIkAdABlAF0AVABMAEUAKwAMAAIA+P/o//H/AQD9//T/6f/K/6r/p/+z/7n/vf+2/5T/ef92/2v/Zf95/4L/b/9e/1X/U/9Q/0n/Vv9b/z3/M/8//zH/L/88/y3/Lf9C/zH/Jf81/yL/BP8E//f+4/7m/vL+/v7//vv+C/8J/+n+6/4B/wD/A/8D//L+8P7v/vD+BP/+/vL+DP8N//b+/f4E/wT/CP/4/vj+B/8B/wT/Dv8O/x//H/8N/x7/Mv84/z7/J/8p/1r/Yf9e/4P/g/9v/3z/i/+Y/6f/pP+j/53/iP9//3j/a/9x/4f/lP+V/6L/qv+S/5v/1f/i/9b/6v8AABkAHwD2////IAD+/+//AADn/+z//P/Q/8f/2f+8/8D/2//N/93/+P/o//j/IgAfABMAHwAlACMAIQAYAAsACgAUAAcA7v/1/wEA8P/v/wUA/v/v/wEACwAFAB8APQA3ADYAQAAxACQANgBBACwAFwAXACIAOABDADkAUAB7AGoASABkAH4AdQB8AHsAYQBYAGMAcwCAAHMAYwBrAHEAcgCAAIQAfQB9AJEArgCtAJ8ApwCsALEA0ADSALYAvgDDALMAugCyAJ4AtQDBALsA1QDVALsAxgDLAMkA5gDqAN8A7ADZAMcA3wDfANUA3QDFALYA0gDfAOAA6wDnAOcA8wDxAO4A8gD0AOwA1QDVAO8A4QDFANQA2wDAAKsAqACvALEArwC2ALEAowCyAMEAuwDHAM4AxgDVANwAyQDIANIAyQC+ALQArQCqAJgAjACcAKQAngChAKMAnwCiAKYApQCqALYAswCsAL0AxACrAKcAvQC5AKUAngCdAKsAwgDAAKUAqQDRAN0AygDRAOAA7AAHAQYB7AD1APcA5QDwAPYA+QAXARYB/gAQARkBBgELARMBDAETASgBNgE3AUQBYgFhAVEBZgF8AXkBdQF0AYABjgGKAYwBkgGTAaQBpQGTAZkBlQGFAaIBwgG1AakBswG8AbcBsgHCAckBugHDAdEBxAG5AbABsAHFAb8BqgG4AbsBowGfAZ4BkgGfAbABowGOAZYBsAGtAZEBiwGWAZ4BoAGaAZcBlQGQAZYBlwGCAYQBlQGMAY0BnQGXAZYBoAGNAYQBnwGiAY8BmAGlAZ0BnQGmAaQBpgGsAaQBmwGYAY8BjgGbAaQBqgGeAX4BgQGZAYkBegGKAYUBewGGAYEBdAF6AXIBYQFgAVsBUQFXAVoBUAFNAUsBRAFNAVcBSwFFAU0BTQFKAUIBMgE3AU4BVwFcAWMBYwFnAWcBYAFsAXkBeQGEAY8BjgGXAaMBpAGnAakBqgG1Ab8BwgHCAb0BxgHdAdMBwAHbAfQB5AHZAeAB5AHsAfkBAwIOAhECFQIdAhcCFgIcAhcCHQIwAjECLgImAhUCIgIwAiUCMAI/Ai8CNQJGAjoCMQIxAjUCQwI5AiYCKgIgAhMCIgItAi8CLAIgAigCMQIgAiUCNwIpAiYCPQJDAkICSAJAAjYCQwJNAjoCJgI4AlICTQI+AkYCVwJVAkUCQgJJAkMCQwJOAkcCQAJJAj8CNgJFAkECOwJaAmACQwJAAkYCQQJEAjwCOgJKAj0CKgI0AjACJQIgAhYCJAIrAgsCFAIrAg0CBQIbAhYCEgIKAvwBCQIIAvoBCAIGAvEB6QHhAfMBBwL0AQUCFgLYAcEB7wH3Ae4B+gH8Ae8B1wHdAQkCDQL0AfoB/AEBAg4CBwIUAi0CFwIKAiQCMAIzAjkCNgI9AkQCNwItAjcCTAJJAjMCOQJAAjYCTAJdAk4CVwJpAmUCaAJpAm4ChgJ4AlkCbwJ/AmkCbQJ3AnECdwJsAkkCSwJeAk0CPgJFAkMCQwJHAj4CMgIvAicCJwImAgwC/wEEAvkB7wHwAd4BzQHNAcUBvAHCAcgBwgGuAaMBrgGqAY4BigGWAYcBZwFZAVsBUQE2ASoBLgEnARQBCAH3AOoA7gDoAM4AyQDMALIAowCrAJ4AhwCAAHcAbQBiAFMATwBEAC4ANAA1ABoAJwA/ACYAHQAsABcADAAbAAkA9f8FAAgA8v/x/wAABAAFAAMA9//5/w0AEAAFAAsACwAFAA0AEgAWACUAGQAEABcAIgAUACUAOwA5AD8ASgBRAFwAYABpAHIAbwCEAJkAiACQAKQAiACIAK0AqQCrAMYAuwCuAL4AwgC+AMEAvwDEANkA7gD9AAIB9gDbAMoAyQC/AL4AzgDCAKoAsgCzAKMApQCqAJ8AlgCOAH4AcQBrAFsASgBXAFwAOAAnAC0AFwD+//n/8v/l/9X/0v/c/8X/qv+w/6T/iP+N/4b/aP9k/2v/X/9J/z//RP81/xf/Ev8W/xP/Ff8N//7++f7x/uf+5v7f/tL+x/67/rT+qv6a/qD+pf6T/pT+m/6E/n/+j/58/mT+ZP5a/k3+VP5X/lL+Q/4u/jT+Qf44/kD+Sf4q/hL+F/4c/jb+VP5G/iH+FP4r/lD+Zf50/nX+UP47/k/+Y/5z/nT+Wv5S/mn+hf6c/pH+e/57/mb+V/6I/qn+mf6i/rT+tP66/r/+yv7G/pT+GP9fAWAE8wWJBRAEVQLkADkAMAAiAM//QP9f/ob9dP37/Vj+pf4u/5r/3v8kAB4Awv9e/+n+Yv7//b/9t/0I/oH+2f4A/wD/7P7i/hP/bv+U/4H/if+a/5j/t//R/4r/8v5a/vr96P0x/sr+Y/+0/9T/zP+E/yz/6f6p/oX+kP6d/p/+qP6k/qL+xP7r/gX/MP9f/3n/jP+X/4//gP9b/zT/M/8x/yH/QP9r/3f/kP+u/6X/hv9f/y3/+/7d/uv+CP8U/y7/Tv9K/zL/AP+5/pL+f/54/qL+yf7W/vn+E/8U/yD/JP8o/0n/YP9o/33/i/+J/4v/k/+V/5v/uP/Q/9L/5/////X/6v/l/+L/AAAiAC0ALQARAPn//f/5//j/6f+l/3L/Yf9P/2v/jv+C/4z/l/9+/4j/lP9x/1b/Pv8i/yL/If8f/yr/Iv8e/zD/OP89/0D/Nf8x/yz/KP9B/0T/HP8D//L+zP61/rD+ov6V/pf+k/6D/on+mP6O/o7+nP6O/oT+jv6C/n7+i/5//nz+hv5v/mn+g/6E/o/+s/6//tL+7/7p/u7+D/8L//P++P74/uH+1P7i/vT+7/7q/vX++P4F/yf/NP8t/y//Kv8U/wL/Af8I/wv/Fv8s/zj/Rf9a/2D/Z/92/3j/ev+I/53/uP+7/6r/sv+6/7f/1v/4//n/AwAcAC4ARQBYAF8AZQBmAG0AewCCAI4AogChAJ8AtgC8AK8AvwDXANgA7AALAQ8BGQEsASABFAEkASsBKQEyAUABWQFwAW0BbQGJAY8BfAGTAbMBnQGYAbwBrwGUAaMBnQGNAZsBkgGBAZIBlQGMAY8BfAFuAXgBbwFcAVkBWAFWAU4BPwFAAT0BLQEyAUQBPAEpATQBTAFBASsBLwE0ATcBQQEzASEBLwE2ATMBOwEsASABRAFWAUgBVQFjAVsBXAFhAWsBeAFtAW0BfQFxAWsBbAFOAU4BbAFbAVYBdwFuAW4BiwF+AXkBlgGZAZQBiwF2AYQBjQF2AYABjwGGAZUBnQGWAacBrAGqAbwBvgHDAdYByAG4AbsBtwHCAcgBsgG/AeQB5QHfAekB8gH5AfsB/wEHAhMCJQIjAhgCKQIvAiACNwJUAk0CTAJZAmUCcQJvAmwCeQKAAoYCiwKEAoMChAJ/AoECewJzAokCmAKLAo4ClAKGAoECgQJ+ApECogKPAn8ChwKFAnkCfQKBAoECjQKTApACkgKKAncCbQJnAmICXgJVAl8CZwJFAisCMQIqAiQCLAIjAh4CKgIjAhgCGQIUAg0C/wH5AQYCAgL4AQcC/QHhAegB6gHQAcwB0gHHAcoB2gHPAbIBpwGuAasBngGaAaIBqAGiAZQBkQGRAYYBiQGfAZ4BhQGEAY0BjAGcAaQBjgGJAYoBbwFtAXoBZwFiAWkBVQFQAVQBSwFaAVwBPAFKAWMBRAEwAT0BRQFOAUkBOAE8AUUBQAE8ATgBNwE3ATQBPQFGAT4BPgFHAUMBRAFOAUgBNAEuAUEBTQE1ARoBKgFFATsBKAEoASABHQE2AT8BKgElATEBMQErASUBEwH/AA4BNQElAfQA+wAXAQYB9AD2APMA5gDMALgAwwDOAMIAqgCRAIsAjACEAIQAhAB/AIgAhgBvAG4AdgBmAFoAUwA9ADEARQBNAC4AHgAvABwA/P8OABoAAwD///L/4f/9/wwA7v/i/9//zf/K/8b/uv+//7v/rf+5/8j/uv+p/7f/1P/R/77/wP+8/7j/yf+7/57/ov+m/6r/uv+s/6D/sf+k/5r/r/+p/6D/tP+4/7j/xv/D/7v/yP/d/9v/xv/P/9j/r/+q/9f/4P/W/9H/y//h/9//uf/B/8b/rf/F/9b/x//T/8b/sv/R/8j/tf/o/+7/w//U/+v/7P/6//j/9//7/+r/+f8bAAwA+P/8//7/AQAEAP3//f8AAAQAAADp/+L/8P/3////9//Z/9v/7f/n//L/DAD7/+b///8NAPX/+P8YAA8A9f8BABQADQAHABIAGwAVABAAEgADAPb/BQAKAAEAEwAcAAIA+f/+//j/+f////z//P8AAP//AwAUABIABgAPABwAKgA+ADgAKwBAAE8ARgBPAGEAZABmAGcAZgBdAFUAVgBZAGcAgACAAH8AkgCMAH0AhAB9AG8AfACFAH0AdwB5AH4AfwCGAJgAnACaAJEAdQB0AI0AhwB/AJMAlQCMAIoAggCNAKMApACqAL0AuwC9ANUA2wDUAN4A3QDQANkA4wDoAAEBCwEEARQBGwEPARMBDQEMATABOQEuAUkBUQFHAVcBUgFPAW4BbQFkAXgBcgFxAZIBnAGeAaoBogGfAaMBpgG9AcQBsgHCAdIBvgHAAc4BxgHEAcwBzAHHAcUByQHEAbIBsAG5AcIBxgGuAaEBuAGoAYoBogGuAZcBjQF/AYgBsQGeAXABfwGYAYoBewGBAY0BhwF4AXsBdQFmAW4BdgFvAWsBZgFkAWIBVQFUAVcBSQFFAT8BKAElATgBPwErAQ0BEgEkARAB+QD3AOgA3gDbAMwAzADRALsArACsAKgAowCZAJMAmACWAIwAewBqAHUAhQB3AGAAUwBPAEwAQQA/ADgAFQASACwAFgD7/wsACQDy/+3/6//n/+f/5P/X/7n/ov+r/6//oP+X/47/hP98/2f/Wf9U/0D/QP9M/zb/JP8m/w//A/8S/wj/+/4I/wP/6v7a/tz+5f7X/rv+uf6y/pj+lP6R/nf+cv5w/lT+SP5a/lz+S/48/jX+MP4y/jb+Kf4X/hn+Dv73/QX+Ef7y/d393f3c/eT92P25/bv9vP2q/bH9q/2F/YD9jf2Q/ZH9ff1o/Wj9Wf1Q/V39Tv04/UP9Sf1E/UP9QP1K/Vb9T/1L/UX9N/07/TT9Hf0u/U79Pv0s/UD9Rf02/Uf9V/1E/Uv9ZP1b/WD9ff15/W/9cv1k/W79jf2F/YD9of2g/X/9jv2z/an9mf2w/cP9wv3K/dP9zv3N/dj96/3t/dz94f32/fD97v0J/hX+Bv7+/QP+Ff4z/j7+L/4u/jv+Qv5b/nX+Y/5T/mH+av5v/m7+Vf5Q/mP+a/5w/m3+aP5+/or+ef5y/nX+ev6E/nb+Yv5n/m7+dP6B/oH+dP5j/l/+bv50/nr+i/6I/n3+gv6C/pH+n/6M/o3+pv6Y/or+l/6U/ob+hv6R/qH+ov6m/rj+qP6Q/p/+nf6P/qz+t/6j/q/+wv7E/s3+zv7U/ur+2v7A/s7+wP6o/tD+5v7Q/uT+9v7g/uX+6P7a/vP+/P7k/vr+C//x/vn+FP8R/xX/Gv8Z/yv/Kf8S/yL/Rf9D/y//Mv9K/1X/Tf9U/2L/Z/91/4P/hv+S/5j/jv+j/8f/v/+v/8T/2P/a/+P/6P/h/9//5P/u//T/8f/x//f/AgATABAABgAdADQAJgAhAC4AOQBIAFEASgBKAFcAbQB7AG0AZAB7AIoAdABhAHEAhgCEAIIAhwCEAIUAiQCBAIsAmgCJAHgAegB8AIoAlwCJAIMAiwCMAI4AiQCAAJQArACsAK8AtQC2AMcA1ADHAMQA0wDRANAA5QDlAMgAxwDRAMUAzgDvAOsA2gDlAN0AywDdAOYA2ADpAPgA4QDkAP0A/QD7AAAB+ADzAPcAAgESARABDgEVAQYB+gAEAfoA7AD1AO8A1wDIAL4AwwDUANgA2QDYAM0AygDTAM4AxgDMANAAwgCyALYAwAC6ALsA1ADjANsA0gDHALUArwCqAKEArQC4AKcAngCkAKAAoACXAHwAhQCkAKAAmQCqAK8AnQCKAIoAkwCKAIAAigCLAIcAjACIAHsAbgBjAGgAaABVAEUAOAAsACQADQAAABMAEAD2/+b/0//P/9r/zf/A/7n/of+Y/5L/fP+K/5P/dP90/4H/b/9y/3b/ZP9r/2T/Tf9T/z3/Hf85/0r/Of9A/z3/Jf8e/xv/Hf8W/wH/E/8w/yL/G/8e/wf/D/8v/yP/CP8D/wj/Av/0/u/+7f7e/t3+2v7C/sj+3v7K/q3+rv6z/q/+qP6e/pL+hv6N/pf+h/5z/nD+cP5o/lv+Xf5h/k3+T/5k/k3+QP5Z/kj+MP4//jH+Hf4r/ib+EP4V/hn+B/71/fz9Df4O/hT+Gf4E/gT+Ff4B/gf+L/4d/gH+H/4u/hj+Gf4j/hv+Fv4b/hj+C/4A/v39/v36/ef95P34/fP95P3u/fH92f3K/df94/3L/a79vP3N/bj9pf2x/cT9tv2W/Zn9q/2m/aL9nP2N/Yb9bP1W/XD9d/1Z/VL9U/1Q/UX9I/0a/Sv9Gf0P/RX9+Pz0/Ab96vzc/O784fzd/PD86fzg/OD80vzX/Ov84PzM/Mb8xPzN/Nv82PzU/Nr80vzC/Mn81fzV/N385vzZ/ND81PzU/Nj80/y9/MD8zPyz/Kn8x/zM/L38xPzO/NL81PzQ/Nr84PzR/NX83fzF/L38zPzS/Nn81PzH/Mj8v/zD/OD8yPys/NP82vy8/Nb88vzk/Nj81fzi/PP87vzt/PH87Pz6/AH97fzl/Oj87Pz9/AP99/zy/Pb8A/0A/fj8Ff0k/Qb9+/z//Pz8Bv35/Oz8GP0q/Qj9CP0Y/Rb9Hv0b/fr85vz2/Aj99Pza/OP87Pzs/Pr8+vzw/Or84/zx/Pv83/zf/P789/zq/PP86vzZ/Nf82vzd/Nj82vz3/A39Bv37/AD9E/0Q/f38Dv0l/RX9EP0n/Tf9SP1N/UL9TP1Z/Vv9a/1p/VT9Y/14/XP9e/2J/YP9hf2S/Zr9qP22/bj9sv2r/bD9x/3R/cj9y/3a/dX90f3p/f39+v39/f79+/0H/gv+Av4P/hj+Ef4d/hr+/P3v/ev97P38/e392v3x/fX93P3r/f799f3x/er97v0K/g3++P36/QP+/v0A/gf+BP4I/h3+Kf4m/iv+Lf4v/kL+Rf45/kP+R/43/kT+Vf5K/k3+YP5g/mP+cv52/nb+dv53/nz+gP6I/pf+m/6e/qX+of6j/qv+pf6t/sP+x/7W/u/+6v7l/vH+8f7u/vf+BP8R/w7/CP8H//v+B/8o/xf/B/8d/xX/Cv8h/yD/Hf8y/yP/Fv8r/yr/K/87/yv/K/9E/zv/P/9Y/0P/Mf9J/1L/V/9o/2j/a/9//4r/jf+H/3//jf+Q/4T/j/+a/53/pv+f/53/r/+u/7P/zP/J/8n/3v/W/9H/4f/V/8T/zP/R/8z/v/+8/8L/uf+9/8r/s/+1/9n/yP+z/8n/xP+3/9P/4v/P/7n/sv++/8T/vf++/7r/rv+u/6r/l/+T/5//nP+R/5T/iP90/4j/m/+J/33/fP+D/5P/fP9q/5H/lf91/33/ev9w/5H/mv+G/5b/rP+t/67/tf/B/8P/wv/D/7P/sv/N/8P/rv+9/7//s/+z/6v/pP+j/5r/mf+U/4X/kP+i/57/mv+Q/3v/ev+F/3b/Yv9x/4L/cf9v/3v/af9s/4v/eP9k/3v/eP9x/4r/i/+C/5v/qv+Y/4r/kv+Y/4v/j/+k/5X/f/+G/4v/h/+B/3b/gf+Q/4v/lf+g/4v/jP+i/5D/f/+Z/6T/h/91/3//kf+V/3//cf+L/5f/e/92/4X/d/9u/3r/f/9//3P/av+B/4f/c/92/3n/c/91/2L/Wf9x/2b/Sv9Z/2H/Uv9U/1X/UP9K/zz/Mf8w/0P/Yf9T/zf/Rv9K/zn/Sf9X/1P/Yf9r/2T/Xv9e/2D/Uv9N/3P/iP92/3b/e/95/4T/g/+B/4z/eP9z/5//nv9//5D/nv+L/4v/nv+s/6z/qf+y/7T/sf++/7v/r//E/93/7P/0/+P/4f/z/+//AQArABsAAAARAB0AHwAhABcAHQAjABEADQAUABcALAA2ADAANQAxAC8AQgA9ACcAJAAhACgAKQD4/6z/cf94//D/fwClAIYAbwBvAIwAsQDKAMsAowB3AGkAVgBFAEIAKwAmAEsATAA2AEYASAA0AEMATAAuAB0AHQAcAD0AeACPAHcAVAA8AD8AWgBgAD4AGAARAC8AYgCLAJUAggBmAFAAOQAyAEYAOwAPABoARwBIADkAMQAYAA0AEwAMAAgACAD8//f/AAAKAA8AAADv//z/EQAbABQA+//s//7/CgABAPr/+v8BAB4AQgBTAFcAXABbAFAASwBNAEkARgBHAEQARQBMAFEASwAuABkAIQAiABYAHQAYAAAABgAPAAAABQAUAAYA/f/+//z//v/0/+j/8P/r/93/8/8CAPD/8/8RAA0A7v/w////5v/e/wwAHAABAP7/DgAiAC4AEAD9/xgAEQDt//7/EQD7//n/AwD+/w8AIAAIAAMAGgASAAYADAAAAPL/+P/9/wgAEQACAAEADgAFAP3/AAACAA8AEAAAAAQAEQAHAAYADgD8/+z/7P/m/9//0/+8/7z/z//Y/97/1/+5/7r/1v/N/7X/sv+x/7T/uv+u/6r/uv++/67/pv+z/8P/xv/F/7z/sv++/8D/rP+r/7H/s/+5/6T/jv+d/5T/gP+M/4j/f/+S/5P/jf+H/3L/g/+Y/3X/a/99/2//c/9//2z/b/+C/3r/cv9y/4D/lf9+/2r/iP+K/3b/hf+G/3X/if+Z/4D/bv9+/4P/af9i/3T/dP9x/3b/aP9k/3n/gP95/3f/ef96/2//bP+C/4f/hv+e/5r/hf+X/6L/mv+k/53/h/+S/6j/s/+x/53/l/+f/6L/rf+w/5z/mP+d/5v/nf+X/4//mf+d/53/n/+X/57/qf+h/57/mf+L/5P/o/+r/7T/ov+T/7j/zP+t/6P/sf+k/5n/rP+x/5j/k/+h/6X/rv+v/57/qP+5/67/rP+0/7b/xP/O/8n/v/+7/8z/x/+m/77/5//c/+f/8//R/97/AADu//f/CwD3//L/9P/w/wAA+v/v/wwACQDp/+7/+P/9/wsADAAQABsAFAAVACAAIgAyAD4ANwBBAE4ASQBTAF8AVgBQAFIATgBTAGIAaQBuAHUAcQBnAGgAbgBvAGgAYQBjAGcAZwBtAHkAcwBiAG4AkgCVAIcAnQCrAJUAlQCpAKsAqQCiAKEArwCjAJwAtwCvAJEAnQCeAIsAkgCVAI8AkACIAIUAewBeAF4AcABhAFQAYABxAHYAYABPAFMATABKAE4AOgA1AD4AJQAhADYAIQAYADAAHgAIABYACgDz//f/7//g/+j/3//K/8z/yP+7/8r/1v/I/7//u/+z/7b/tv+m/5j/kv+W/6H/of+e/6D/nf+U/5r/rv+x/5v/kv+f/53/jv+I/4f/if+R/5P/hP9z/3L/c/9r/23/eP93/3r/h/+K/4P/f/+I/4f/cP9q/3D/aP9u/3X/V/9U/2//bP9r/3L/Zv9w/4z/kP+E/2j/V/9x/33/a/9l/2H/Yf9l/1L/TP9c/1z/Yf9r/2j/dv90/1b/Yv9//3X/cP94/3z/jP+L/3b/cf9t/2r/ev+B/3z/fv90/2n/bP9y/4v/nf93/1z/gv+P/23/a/+I/47/gf9//4f/hv+B/5D/of+W/4f/jP+Q/5D/mv+e/5r/o/+w/6r/rf/D/8b/tP+t/7j/wf+5/67/t//I/8j/v//D/9z/7P/g/9n/5//s/+j/5//m//b/EgAPAP3/DQAcAP3/7/8WADIAMABAAFEARwA9AFAAZABZAEsAUQBPAFIAbQBwAGgAgwCJAHgAiQCOAH4AkQChAJ4ArwDEAM8AzwDFANcA7ADTAMoA2gDSAM4AxwCtAL4A3ADQAM8A0gC8AMAA0ADHANMA5gDSAMwA2gDFALEAxADIALAAogCcAJoAoQCkAKgAsgCrAJgAlQCUAJQAoQCvALEAqQCTAIYAlACTAHcAhAClAH0AUgB1AIkAagBlAF0ATwBpAHMAWwBdAFkAXQCBAHEASABiAGYAPABFAFQAPQA0ADEAKwA1ADgANAAyACYAJgAtACgAOgBBACIAJQA7AC0AMABCADUAOABMAEMAPABJAEwARQA+AD4AQQBCAFAAXABNAEMATABVAGQAYQBAAD0AVwBdAF4AbgByAGsAawBxAHkAggCDAIIAlQClAI4AigCsAK4AnwClAJ4AmQCoAKQAqQC6AKUAoADIAMUAqAC4AM0AwACsAKsAvADKAMYAtwCuALcAwwDEAMgAywDSAOkA5wDUAOUA9ADiAOQA+gDyAOAA7AD/APcA8wAKAQwB/QAHAQQB8wAHARkBEgEVARUBFQEnAR0BCAETAREBAwEWASQBHQEfARwBGAEgASIBJwEwASIBGgEsASgBDwEHAQQB+QDvAOoA7gD3APYA6wDcANgA3QDTAMsA1QDUAM4A2ADhAOwA9ADqAO0A+QDvAO4AAQENARwBJgEbARMBEAEVASUBJQEjATIBNAEwATcBMQEmASgBKQEoASYBLgE6ASIBCwEiAS4BHwEuAUwBTAE5ATMBQQFMAVgBagFgAVABXAFZAVgBawFdAU8BawFtAVoBWQFKATgBOwFAAUgBTwFCATEBKgEsASkBHwEiASYBHQEqATgBKAEpAToBNQE0AT4BOgE1ATUBKQEaAScBSgFUAUwBXwFkAU4BWAFqAVgBRwFHAU0BUgFHAT4BQwE9ATcBPwE/AUIBRQE3ATcBPQEnARoBIgEUAQoBIQEyAS4BIwEVAQwBAwH5AAsBHgENAQcBGQEVAQYBCAEQAR0BJAEaAREB/wDuAPoA/wD6AAMB+QDyAA4BDgH+AA4BBgHxAAMBDQEFAQgB/ADwAPsAAwEBAfQA4wDuAAcBFAERAQQBFAE7AT4BMQE2ATsBRwFLATkBOQFCAUIBVAFVAUYBWgFYAUMBYgFyAVYBYAFpAUsBSgFdAU8BMwEwATwBLgEdASABFgETASQBFgEGARwBIgEWARQBFAEgARkBAgEUARQB7ADxAPYA3wDxAPYA2QDfAN4AywDIALUAsgDVAMsAsgC2AK8AqwCtAKUAswC3AJ0ApACzAKcAoQCaAJYAoACaAJQAlgCHAJMApgCKAIsApACIAHoAkwCCAGsAeAB1AGcAYABLAEgAbgCBAGwAXABZAE8ANgAnADcAQwAzADAAPgA1AC8APwA+ACkAFgAJAA8AHQAUAAIA9P/u//z/+f/V/8z/4v/g/9L/1f/T/8r/x//J/83/w/+q/6j/uf+0/6f/uv/L/8L/vP+//7P/rv/A/8f/tf+i/5z/qf+t/5L/lP+7/7j/of+x/7r/tf/H/8j/sv+3/8D/t//B/9z/5f/b/9b/2//b/9n/3f/Z/9P/3f/i/9f/1P/e/9//z//L/9X/0v/F/8f/0P/X/9P/xf/P/+j/8f/p/8//v//Q/9//5v/y/9v/xv/f/9X/uf/d//f/2v/I/87/3P/j/87/yf/a/8z/wP/Q/8P/q/++/9n/0f+7/7L/sP+s/7L/tv+r/7f/zP+0/5v/s//A/6v/of+m/7P/wP+1/6T/oP+S/4b/jf+J/4f/lP+C/1v/X/90/2z/Yf9l/2P/YP9r/2//W/9T/2D/X/9j/3T/cP9x/3v/Zf9i/4f/fv9c/3D/gP9s/3D/iv+G/3D/cf9+/3f/bP9t/2f/Zf9t/2b/W/9c/1b/Uv9X/1b/T/9L/0//Uf9M/1f/Z/9X/03/Xf9U/07/Z/9h/1H/YP9G/yT/RP9K/zD/S/9P/zD/Rv9V/0D/U/9l/1j/Yv9j/1P/Xv9i/1j/Yv9i/1r/aP9o/13/af9v/2b/Zf9r/3//j/+F/4n/rf+6/7L/vf/E/73/zP/m/+r/3P/N/87/2v/m//j/9//j/+7/AADy/+n/7//0/wcABQD6/w4ACADw/wcAEAD0//H/+v8OACYACgD1/w8AEQAKABAA9//l//b/+v/6/wIAAAD4//L/7//k/9X/3P/T/7b/xv/U/7P/s//B/6z/qv+x/6//wf+8/6r/v//L/8f/yP+u/6D/s/+m/5H/nf+n/67/tv+x/7D/q/+m/7z/xf+3/77/x/+9/7//zf/N/7z/sP+0/7L/qP+m/6X/of+c/5b/of+y/6z/pf+o/6j/pf+b/5f/q/+t/5r/pv+9/7H/m/+e/7H/s/+e/6H/t/+y/6r/uv+9/7P/vv/O/9z/6f/h/83/0f/c/9H/yP/W/+L/1//J/9P/6f/w/+v/6v/x//7/DAAOAAwAFwAfAB8AJAAiABIACgADAPT/5f/f/+v/9v/k/9D/2P/k/+b/5P/a/97/7f/k/9b/3P/W/8r/1v/O/7T/u//M/8T/xv/P/77/s//H/9j/zf+8/7r/xv/J/8P/xv/W/9v/yP+v/7L/y//S/7j/ov+l/6v/sf+//8H/t//C/9H/xv+//8n/yf/O/+n/9//z//r/BQASACYANgA4ADQANgA8AD4ATQBuAHYAbAB/AJUAkQCKAIYAigCaAI8AfwCMAIkAiQCpAKAAigCtALkAnwC0AMwAxwDOAMgAwADVANYA0ADrAPAA5wD9AA0BCgELAQgBEAEdAREBAAEFARcBHwEXASUBQAE1ATUBVQFRAUcBYgFvAXkBiQF2AWQBcwGEAZgBpwGhAaQBuQHNAdkB0QHKAdoB7AH5AQQCCQIMAgMC+wEMAhQCCwIfAjYCJAIPAhgCJAIgAh8CIAIYAiUCPgIxAiUCNQIyAisCMQIlAioCPQIqAisCSAJDAkECSgI3Aj0CWgJKAjYCRAJTAlkCTQJCAlQCTwI5AlMCZwJSAmECdwJcAlECZwJrAnMCggJ4AnECfAJ/AoECkgKoAqACfgJ+ApYCiwKEApECjgKVAqACjgKOAqACkwKJAo0CjwKXApACgQKKAoACaAJzAoACegJ5AnMCbQJzAngCcQJlAmICZgJtAoMCiwJ4AoQCjQJsAmsCdQJbAmoChAJqAl8CZgJeAmkCagJgAm4CYAI6AkYCWQJGAjACLQI5AjYCKAI6AjcCEgIgAjICEwIbAjcCKAI3AlsCRwIqAi4COQI/AjwCOAI2AisCLwI/AjECIwIsAigCHgIeAh4CIAIYAgYCCQIRAgwCEAIXAg8C+AHmAfUB/QHfAdgB5AHOAcUB0gHJAcMBwgG4AbUBqgGnAbMBlQF4AZoBqAGUAaEBmAFuAW4BdwFoAWgBYgFKAVEBXQFKAUgBXwFdAUgBRQFLAUIBJwETAR4BJgEUAREBEQHzAOkA+wD8APoA+gDvAO8A8QDiAOEA7gDvAOcA3QDRAM4AywC8AKQAnwCwAKIAewCAAJYAigB6AG8AZQBrAGcAWgBlAGgAVwBNAEcAQwBMAEgALwAYABwANwA2AB0AJgA0ACIAIAApACUALAAqABgAIgAuABsADwAEAPT/+v/1/8n/sf/I/9v/yP+4/7//wf+//8X/t/+o/7X/rf+P/5L/mf+L/5T/qP+n/6j/qf+X/4v/lP+Y/5D/j/+J/3v/g/+Z/5D/c/9n/17/Wf9o/2r/U/9I/0r/Q/9B/0z/Vv9R/zb/Gf8R/xj/Hf8Z/xb/If8j/wr/+/4G/wb/+P70/vP+8P7v/vX++/75/vP+6/7o/vj+A//6/vv+BP/0/tL+uv6//tT+1f7M/s7+zP7N/tb+2P7d/tf+xf7S/t/+1f7g/uL+0/71/gT/2v7d/vj+5/7i/vj+/v76/vj+C/8a///+A/88/z7/If8x/zX/KP88/1D/Q/8t/yP/Pf9a/0j/Nv9J/1b/U/9J/zn/Rv9f/1T/U/9x/3T/Yv9n/2j/WP9Z/3X/gP9y/3b/e/9t/3b/fv9r/3z/jv9w/3f/jP9w/3f/kP+C/47/l/96/37/g/9o/3X/f/9k/2X/bv9n/3D/d/92/3H/af9x/3D/Zf9y/3n/Z/9l/2D/Vf9d/2r/fv+M/4X/mP+s/5X/mv+x/57/of+7/7X/t//A/6n/o/+4/7L/n/+e/6T/nv+f/6f/oP+c/6P/nP+b/6H/jP+L/6T/lP9//4X/h/+W/6b/lv+Q/5L/h/+f/8P/v/+u/6j/r//C/8T/v//J/9T/6P/7//L/7f/8/wMABAACAAYAGAAeABMAFAAcABsAJgA9ADoAJQA5AFYAPQA1AF0AZABYAGIAZAB3AJsAjAB8AJsApACXAKUAuAC7ALYAuwDTAN8A4QDqAOsA+AAbASUBIAEpAS4BMgE4ATMBNgE1ASIBLQFBAToBPgFLAUYBTQFhAWoBbwFvAXcBkgGTAX8BggGKAY0BngGsAbEBswGvAb4B1AHFAbsB2QHpAeoB/gEEAv0BCgIUAhYCGQIjAjsCPQIYAhQCLgItAjECOwI0Aj4COgIWAh8COgIlAi4CWAJRAj8CSwJRAlMCTgJBAk4CYAJXAlQCZAJsAnACbAJgAlwCUQJBAk8CZQJiAmcCcgJuAnICeQJsAmoCcwJpAmcCcQJsAmYCXwJNAkkCTgJHAkQCTAJOAj0CLwI9AkMCJwIXAh0CHQIYAhECAwL7AQcCHQITAvgBBgIMAuUB6QEEAuoB1QHjAeQB4AHaAdQB1AHKAcYB0QG5AZsBpAGiAY4BjQGMAXwBagFoAXMBaQFWAVsBUwE7AT8BRgE/AUMBRAE4ATQBOwFFAVABVAFOATgBIwEoATIBLQEmARgBCwEXASUBJgExATEBGgEVARAB9QD2AAcB+gDzAPwA6wDiAPYA+QDhANIA1ADRAMAAtAC8AMMArgCRAKAAwwC5AKQAsACyAKYAnwCPAIgAjAB/AH0AfwBlAF0AawBaAEcARQA/AD8APgAuACoAMgAkAA8AFAAXAAYACgAdABcAEwAcABcAHwAoABAADgAkAB4AHAAdAAAA9/8CAAIACQAHAPj//P/+/+//6v/r//P/8v/d/+D/8f/d/8f/wP+4/8H/u/+a/5L/iv9x/3v/i/+N/6T/pf9//4H/pP+i/5D/i/+J/4z/nP+n/5X/f/+J/4r/cv9+/5//lf9//4n/mv+W/4X/d/95/4z/of+l/5n/k/+U/43/kf+h/5z/lP+c/4z/a/9t/3X/Y/9l/3H/Yf9m/4f/h/97/4b/k/+h/57/fv+C/6T/lf99/4T/gf+E/5b/jf97/3X/cf+E/5z/kv+L/4//h/+L/5f/jP+D/4z/iv9+/3X/b/93/4T/hf+E/3v/c/+B/4H/bf98/5T/jf+T/6X/mP+H/4P/gf+G/4//n/+q/5j/lv+r/6P/mP+k/6j/s/+5/6//x//R/7X/y//g/73/u//L/7z/z//n/+f//P/7/+P/6v/v//L/CwAPAAoAFAALAAYADQAPACgANwAjACMAMAA2AEMAQQA8AEIAPQBBAEIAJgArAEIANAArACMADwAjADAAIAA2AEsAQwBXAGYAWwBfAGwAdgB4AG4AgwCbAIYAiQCjAJQAlAC6ALwArgC/AMkAuQCzALsAtACtALcAtwC0AMkA0ADBANMA8ADkANwA+wANAfUA6gD8AAEB/gAVASMBGQELAQIBFAEyASEBAwEaATEBJwEtATsBNwE7AToBJwExAUEBKQEiATYBJgEaAS8BNAE1ATcBHgEbATQBJwEQARMBFgEZARsBFAEYAR0BIQE3ATABEQEeATUBKAEjAS4BLAElARoBEwEcASIBIwEhAQ4BCgEgARsB+wD3AAkBDQEEAf0A+QD3APgAAgEOAQQB8gDzAPMA7QD6APsA6QD5ABMBBQH7AAMBBwEQARQBBAH+AAAB8ADrAPwAAQH3AOgA2gDcAPIA9QDcANYA2QDJAM8A6gDcAMEAyQDVANcA0ADCAMcA2ADYANYA0wDSAOMA8ADmANYA2QD3AAAB7wD1APYA6QD6APsA6QD8AP0A7gAHAQAB3ADzAAcB9AD4AAIBCQEnATEBIQEeASMBHwEdASoBMwEjASABNwE2AS0BMgEnASABMwE3ATABNQFEAVIBUQFMAUsBQgFCAUQBKwEaASMBHgEUARkBGQESAQYB+AD4APoA7ADcANMA1gDhAN4A1ADWANYA1gDbANYAzQDUANoAzAC5ALgAvwC6ALwAygDHALsAwAC7ALEAvgDDALYAuADCAMYAuwCZAIkAmACaAJoAnACIAH0AdwBiAGwAgQBuAFoAWQBXAFYARAA0ADcAJgAhADsAMQApAD8AKAAJAB4AHQD+//H/4//l/wgADADq/+f/AwABAOb/8P8HAPj/5P/j/9n/2P/m/+f/6P/t/+n/5v/h/+L/9P/u/9b/6f8AAOb/1//z//T/0P/Q//D/8v/V/9L/6//w/+P/2f/Q/9T/2/+7/5//tP/A/63/q/+0/7D/p/+h/6D/nP+W/5X/jf9//3n/bP9f/1r/S/9L/2X/YP9H/0z/U/9P/1H/Sv9M/2H/Yf9R/1n/Zv9i/2P/bf9l/1X/Vv9U/z//Ov9P/07/OP85/0P/Ov85/0L/N/8v/0j/Xv9T/07/aP96/23/Zf9q/2L/a/+C/3P/YP9t/3T/df97/3T/e/+G/3b/gP+a/5D/hP+G/4b/kf+I/33/oP+s/5H/o/+x/5v/pP+5/7v/wv+5/7D/yP/S/8L/xv/L/7r/sv/B/87/wP+1/8X/v/+f/6P/vP/C/8n/z//H/8D/u/+9/9X/4f/Y/9//3v/M/9r/9P/4/wAA/f/p//H/5v/M/+j/9P/T/97/7//n/wIADwD5/wcADQD+/xQAJQAgACMACADx/wkADgAAAPf/1v/G/9//5//o//D/3v/N/83/yP/J/9b/2//T/8z/0f/L/77/w/+5/6f/uP/D/7T/sv+2/77/zP/D/7//u/+k/7j/0f+s/5z/rv+i/63/uv+O/4r/sf+n/5n/pf+g/5j/lf+L/4H/dv9x/3b/dv9y/2r/aP9y/2n/Zf+K/5j/iv+Y/5n/gP+A/4H/c/9u/2X/Yv9z/3D/Wv9Q/1P/V/9S/0b/SP9Z/2P/Vf8+/03/Z/9Z/1L/av9t/2f/av9V/0T/Wf9s/3D/bf9q/23/ZP9U/2X/e/9r/1P/Uv9n/3b/cP90/4b/hf+G/4f/eP+H/6L/kf+M/6T/ov+W/5z/pf+m/5v/pf/B/7b/qf/C/8v/zP/R/7r/tP/J/8P/z//l/8L/sP/Q/9D/zf/j/9f/zv/x//z/4//e/+f/6//3//f/5v/r//D/1f/R/+X/3v/e/+//4P/U/+z/+v/3/wAABgDw/9L/2v/p/93/6P///+7/5f/7/wQAAQD3//n/DwAJAPn/CgARAAMAAADx/+3/AwAAAPf/CgAFAPf/CAASAAMABQAQAAYABQAcACYAGQAWAB8AHwAVAAYACgARAPf/6P8FAAcA3//Q/93/3v/b/+L/3//V/9n/3P/R/8//0//P/8j/tv+k/6X/nf+S/5z/oP+g/6T/oP+g/6X/qf+4/6//lP+x/8j/qP+o/7L/lf+Z/7P/pv+f/6X/nP+c/6X/o/+b/5H/nP+y/6v/qP+//8H/vf/X/+X/zf/A/9L/1//F/73/uv+y/7b/vv/F/8//xf+v/7L/uf+x/6//tP++/8j/vP+0/7z/rP+c/7P/u/+m/6X/q/+m/6j/r/+8/9P/1//E/7n/vv/P/93/3P/a/97/2v/T/8v/yP/S/9L/zv/n//T/4P/f/+3/7f/y/+7/7v8SAB0ADAAgACkAGgArAC4AHgAxAD0AOwA/AC0ALgBRAFUAUQBVAEQATgBgAEkASABWAEwAXABmAFUAbwCBAGgAbQB1AF4AYABvAG0AXgBKAFAAZgBZAEwATQBCAE8AagBoAF4AVgBKAFcAaQBgAE4ATwBZAE8ASABhAF4APQBTAHUAYABUAFcASABMAFUASwBRAFcAVABhAGYAXwByAHMAUwBSAFwASgBDAFAAVABZAF4AUABKAFMASQBCAFcAWAA8ADcAOwAuACUAHQAeADoAQwAoACIAKQAgACEAKwAiABwAKQAnAAkABgAkABsA+/8DABUAGQAvADYAIwAeABkADgARAAoA/P/9//f/5v/T/8P/zv/V/7n/rv+1/6z/tP+2/5j/mf+s/6b/sv/D/7X/u//Q/87/zP/F/8H/y/+5/6r/z//d/8v/3v/q/9H/1f/y//P/3f/R/9r/3v/K/7b/uP/C/7X/kf+A/4b/gv99/3//fP98/3v/cv98/4b/dv9s/2n/Wf9d/3D/Y/9O/1P/YP9m/2j/Yv9c/1r/XP9a/1b/Xv9n/2D/av+C/2z/T/9h/2r/Wv9a/1f/Uf9S/0L/RP9b/0z/Of9D/z//Tf9s/1L/N/9Q/07/MP80/0//Yf9j/2j/e/90/1z/bf+D/33/gf+K/3//fP+Q/5b/ef9r/4X/kP+H/5H/j/9+/4b/g/9m/2f/fv9//3L/av9i/1z/av94/2r/Yv9q/1r/Tf9i/2T/WP9m/1//Rv9F/zb/JP9I/1z/Of8p/zH/Lf8j/xj/FP8V/w3/EP8e/x7/Gv8T/wP/AP8I/wv/B//y/ur+/P4E/wT/Bf/3/vT++P7o/uX+6f7f/uD+zv61/tL+5f7R/tL+zP7K/u3+6P7X/vr+Av/r/vP+8f7o/vr++f7m/vP+Bf/9/vL+8/7x/uf+5v7a/sv+3P7d/rz+v/7a/tn+0P7L/sr+zP7E/sn+2f7Q/sD+vP7A/tH+0P7G/tb+1f7F/tP+2/7Z/t/+zf7V/gT///7h/un+8f7v/vb+8/7z/vL+5f7o/u3+4f7e/uT+6/7w/vD+9v70/tr+yv7Q/tj+5P7l/tT+y/7K/tD+1/7I/rn+vf6//sX+xv6+/s7+1v7A/sP+yf60/rv+yv68/sL+yP60/rr+xf6r/qD+q/6q/q7+vf69/rH+uv7S/tL+xv7G/rz+uP7C/rH+rP7Y/uL+y/7X/un+7v78/gX/Cv8L/wf/HP80/zD/PP9S/0T/M/89/0r/V/9l/2b/Xf9Q/1L/a/9//3r/d/+G/4j/ef+C/5P/f/9u/4z/oP+O/4j/lf+Z/5v/mf+Q/5P/mP+Y/6v/w//C/8L/zP/Q/9D/zP/O/+P/5v/d/+j/6v/j//H/9v/7/wwA+v/j//j////p/9v/2f/v/wcA///3/wAADQAoADQAKAAzAEEAQABEADkANABOAFkAXgBuAFcARwBrAHYAbAB+AIAAeACBAIAAiACYAIcAiQChAIsAeACSAJMAewB0AHQAcwBoAGAAfgCNAG4AaQB5AGwAagB0AGUAXwB4AIMAcwBhAGEAZABYAFMAWwBcAFsAVgBNAEwASAA4AC0AMwBIAFoAWwBeAGAAXQBqAHIAaABsAGsAYwBvAGkAXQBzAHgAcgCMAIAAXgB1AIYAcQByAHwAegB9AHMAaABwAHIAbABsAG0AdwCHAIMAcABvAIwAmACEAIUAmQCWAJMAnACbAJQAiwCRAK0AtAChAJgAmACYAJwAnwCoAK8AoACQAJoAsACxAKcAsAC9ALsAwgDEAL0AygDRAMwA3QDeANMA7AD2AOAA6AD4APIA+AACAfoA9QADAREBBQEBARwBGAECARIBEwEDASEBOQEkASIBOwE+ATABMgE8ATMBJQExATkBNgFFAUgBNwFAAU8BQQE9AUoBOQEbASABNAE3ASkBGgEaASEBGAERAQsB/QAEARABBgEOAQkB5ADxABYB/gDiAOgA7QD2APEA8AANAQMB5wD/APsA2gDsAPEA3gD1AAMB+QACAfcA4ADsAPMA5QDpAOoA2gDRANIA0gDRANEA2QDlAOMA2ADcAOIA2ADVAOUA5wDXANQA4gDnANoA1gDgANcAyADVAOAA2QDXAMwAswCvALwAvgDFANQAzwC5AKoAoQCXAJgAogCtAKgAjgCHAKAApwCSAJgAsACyALIAwACwAJ0AvgDWALcAqwC+ALoAugDHAMAAvQC+AKgAnACtALsAxADMAM0AzADLAMoAzwDUANUA1ADWANoA1QDSANwA0gC+AM4A2ADCAL8AzgDVAN0A0AC6AMcAzQC3ALcAuwCuALUAvgCuAKwAvwC+AKgAngClAK4ArwCsAKYApgCnAKUAtgDHALIAqAC4AKwApQCzAJYAdgCKAJsAkQCGAIwAnwCXAIYAmwCmAJkAmQCDAHsApwC1AKYAuQC9ALAAtwCxALYA1QDKAKgAqwC1AK8AowCWAJIAkQCLAIkAjgCNAH0AcgB3AHIAawByAGkAXABhAF4AZwByADsAEwA7ADwABQD6//n/7f/+/wUA9f///w0AEAAlACcAEAAaADIAMwAzADgARABVAEoANgA2ACsAHQAnACgAKQA2ACQADAAYACcAJwAjACAAJAAcABQAKwAyACMAOAA8ACEAMgBEAC4ALQAvACEAJgApABsAFgAIAPj//P8CAAoADgAAAP7//v/j/9j/4P/W/9H/y/+u/6b/rf+k/6z/vP+v/6X/p/+h/6L/pP+V/47/k/+C/3H/hP+f/5b/e/96/4j/h/+K/5n/mv+b/6v/qv+f/6T/rv+4/7//sv+k/6z/sf+z/7X/pP+X/6T/rv+x/7X/s/+1/7L/pf+o/6b/lP+g/7D/mv+Y/63/pf+n/8X/xP+2/7v/v//M/9n/yv/O/+z/6P/k//j/7P/e//f/BgD9//H/7P/q/+b/5P/j/9H/w//O/9j/1f/M/8j/1//e/87/xf/E/77/s/+i/6X/sv+f/5T/qf+n/5L/kf+c/5//lf+j/7z/rf+w/83/n/+K/9j/6/+//8H/xP+5/7H/mv+n/8b/sv+o/7L/rv+1/5n/Zf9//6D/hf99/4L/dv+A/4z/f/92/3j/ef9v/2P/Z/9m/2b/c/9r/1r/Xf9U/07/WP9X/1z/Yf9U/1z/Y/9M/0//Xv9S/1P/XP9b/2f/bf9n/2X/YP9v/4T/df9m/2n/av96/3z/Zv90/3j/Vf9f/3v/cP90/33/av9m/2r/YP9l/23/Z/9b/1P/VP9K/z3/UP9Z/z//Nv83/zD/M/80/zT/MP8Z/yn/Vv9D/x3/Kf8q/x//Lf8v/yD/Fv8P/xv/I/8H//L+/v4U/yH/Hf8Y/yT/Lf8g/xP/Hf8w/zD/LP87/0L/Nf81/0H/Pv8p/yT/Pv9J/zn/Q/9a/1D/Ov83/z7/UP9Y/0n/RP9N/1P/Yv9p/1b/Wv92/27/Wf9b/0//Rv9f/2P/Tf9K/0n/Qv89/zP/Pv9S/zv/JP80/0L/S/9N/0D/TP9j/1n/Wv9w/23/Zf9v/3P/aP9g/2f/Yf9M/07/Xv9e/2P/a/9f/1D/Rv9B/0P/Ov9B/17/Yf9X/2H/Yf9W/1P/W/9z/3P/Sv9B/1f/U/9T/13/S/9C/1P/S/8//1b/XP9L/17/cP9V/07/Xf9I/zj/Rv9A/zz/Uv9S/zz/Of83/yv/Jf8d/wz/Cf8Y/xn/E/8e/x7/Bf/3/vT+8P76/gv/D/8M/wD/9f4D/xT/Gf8j/xz/+f73/hP/Bv/0/g7/D//y/v7+Fv8Q/wf///7u/uP+4/7p/uP+1/7k/vD+6/74/v3+6P7l/uv+6f7w/u7+3/7Y/tD+y/7P/tT+4f7m/tP+xP7A/sL+yf7A/rj+zf7W/sr+zv7T/s3+1f7V/sP+w/7P/sX+xP7d/uP+1P7c/uX+1/7Z/uT+2/7a/uX+6f7q/uv+5P7h/ub+6P7o/uj+9v4B//X++/4W/w3/+v4F/wf/Cf8j/yv/Lf9H/1P/V/9n/1//Tf9U/1v/Yv9z/3P/cP+A/4r/iv+J/4j/iv+J/5X/pf+b/5n/s/+7/8H/0v/K/8D/zf/W/+n/9f/n//D//P/q//H/+//o/+//9//l//j/FAARABcAFwAEAA0AFQAGAA4AGwAXABoAGgAWACEANABEADoAHwAiAB4ABgAaADcAJgAqAEIAPgA7AD0APQBXAGIAUwBXAFAARgBjAGsAVABdAF8AUgBiAG4AZQBlAFwAVwBoAGgAYQBsAGgAXABcAFIASgBXAFkATwBOAEwATQBXAFYARgA5ADoATABWAE4ASgBKAE0AVABHADUAPQBBADkAPgBDAEgASwA5ADcATABHAD0ASABIAD8AOwA4AD0ANwAlAC8ANwAjABcADAABABkAMwAoABkAHAAeABcAFAAbABMACgAVAA8A9f/x//D/4f/a/9j/3f/t/+v/3v/b/9v/4f/d/87/1P/b/83/yP/K/8H/wv/E/7T/pv+2/87/xv+2/7z/tv+2/8f/uv+s/8P/xf+4/7//tP+v/8L/uP+t/7L/q/+x/7f/rP+0/7f/qv+7/73/pf+y/7z/sf/B/8//xv/N/8z/wf/T/+X/1f/G/9D/3P/W/8b/yP/e/+v/4v/k//r/+v/r/+//9P/w/+7/5f/n//3/AAD6/wQACAAKABsAGwAMAA0ADwAOABQAEgAPABIADgAPABsAGAAYAC0ANAAtADcAQAA1AC4AMAArACMAKAAyADEANwBBADMALABDAEUANAA7AEcAQAA2ADsATwBUAEUARgBOAEcASwBYAFMATABPAFAATgBQAGAAaQBVAEUASwBRAFYAYQBlAGAAUwBGAEMASQBRAF4AYQBQAEQARgBDAEMASgA7ADQASgBDACkAPQBMADMAOABPAEkAPwBCAEcASgA6ACgALAAyADYAPgA/AEYATQBDAEAARAAzACcALAAyAD0AQwA7ADIAIAAVACAAIgAeACwANAAuACQAFQAbACEACgAKACUAHAASACYAMAAkABUAHQA2ADAAKgBAAD8ALQAsACkALwA3AC4ANAAyACIANgBDADwAWABVADAAWQDEACAB2QDv/+D/tABeAGz/6f+fABEApf88AMEAhwAYABYAVQA3AOr//f8fAO//3v8pAE0AKQAsAEkAHwDx/yQAQQDt/83/IAA4AAYAAAAFAPn/CgAtADwAMAAYAB8AFQDg//H/OAAkAOb/+P8mAB4A/f/4//7/+f8NACkADQDr//X//f/4/w0AJgAVAPD/9P8OAP3/2f/f//j/AQD2/+n/9v/5/9//1v/n//P/8//2//z/4f+//9P/6v/R/8X/zv/Q/9z/4P/a/+D/1v/F/8z/y//A/7r/sf+9/8j/vv/R/+f/1P/Q/9L/uP+v/67/oP+l/6z/o/+e/5r/mf+a/5X/mv+f/5n/ov+u/67/s/+w/5X/fP98/4j/d/9S/1z/ef+A/5b/tP+5/73/sv+c/5n/j/+D/47/jP+B/4v/kf+a/6n/n/+X/5z/lP+S/5P/iP+H/4f/f/+E/4n/hv+E/4r/nP+e/4v/h/+A/3D/ef95/2v/iP+b/4L/gv+T/5T/lf+U/5n/of+M/4b/n/+a/43/m/+e/6H/rv+t/63/tv+8/8X/xf/D/8n/v/+y/7b/vf/F/8n/xP/T/97/0f/Z/97/x//G/8//z//l/+z/1P/V/+L/7f8AAP//+P///+v/3P/u//D/6v/w/+T/3f/l/+P/6v/2/+//5v/c/9b/4P/c/9f/6//x/+//AgAGAPz/+v/v/+///P/8/wQABwD1/wIAFgACAAYAGAAAAPf/CQD8//H/BgAMAPb/7P/z/+//6P/v/+X/2//3//7/4f/e/+v/6P/n/9//z//R/9P/0v/R/77/t//B/7P/rv/A/7z/t//D/7r/rP+t/6n/pf+f/5P/kP+M/4T/jv+O/3v/ef+B/37/fP9z/23/fP+I/3//cP9i/2X/fP98/2X/Zf9s/2P/ZP9z/3L/a/9q/1//UP9b/27/af9l/2r/Xf9W/2D/Xv9Z/1z/UP9L/1r/W/9j/3j/c/9p/27/Zf9i/27/ZP9V/1n/Wf9W/1X/Tv9R/1j/T/9H/0r/Rv8//0n/Vf9E/yv/Lf82/zT/Nf87/0P/RP8z/yj/Kv8e/xL/FP8X/yD/IP8O/w//Dv/4/vv+D/8Q/xr/LP8r/yX/IP8Z/xT/Dv8I/wH/+f4C/w3/Cv8T/x//Gf8a/yX/JP8s/z7/P/8+/0z/Qv8l/y3/R/9B/z//TP85/yX/Nv9G/0j/Rv87/0H/VP9U/1T/X/9e/1j/Zf9v/2H/Sv9G/13/c/9w/2H/XP9m/2//d/96/3j/eP95/3z/gf97/4H/o/+y/6n/qf+s/7D/u//H/9f/3P/U/9n/1//B/8r/6P/m/+P/+P/7/+D/0//i/+//9v/9//D/7v8BAPz/+/8XAAkA9v8ZAB8ADAAnAC8AEQAUABwAFgAgACcAIAAgABwAHgAyADsALwApADIAPQBBAEEAQgBIAFAAVABRAEgAQwBOAF0AYwBeAE8ATgBeAFwAUQBhAGoAXABeAG8AagBLAEQAbQCGAHUAfwCQAIEAjACbAIQAhgCWAI8AqQC5AKIAsgC3AKUA0gDQAIkAtgD5AL8AoQDLANsA5wDhAMkA3ADsAOAA3gDPANIA9QDuAOAA7gDcAM8A2ADDALsAzgDOAMgAugCnALEAuACsAKgAngCgALIApgCbAKQAngCaAKQAqQCuAJ4AiwCaAJsAiQCcAKwApgCrALAAsACzALAAvgDHALEAvwDiANQAzADiAN8A2wDkAOUA8gD/AO8A7gAGAQUB+QAHARgBHQEkASQBFAERASkBMAEYAQ8BHQEnAS8BNwE3ATsBPAEqASgBRwFPATEBIwEqAS4BOgFCAUEBSgFIAT4BTwFWAUcBWAFmAVQBWQFpAWQBaQFxAW8BegF+AXIBbAFmAWoBegF7AXkBfgF7AX8BiQGGAYQBiwGNAYsBiQGTAaYBogGRAZoBqgGiAZ8BsgG4AbQByAHSAbkBuQHUAdEBygHeAdsBxQHVAfYB8AHgAeUB4wHjAfkB/AHyAQQCBALwAfsBCAL+AQACDgIYAhYCBAIDAg8CDQIQAhIC9wHqAfYB8QHxAQwCDgLwAfEBBQL/AQACEQIDAvUBCgIYAhYCEQIHAhMCJwIbAhACGQIXAgoCBQIRAiQCHgITAhoCFQICAv0BAQIEAgMCAQL8AeQB1AHgAd0BzgHQAdIB1QHhAdYBwwG+AbgBrAGfAZcBmgGVAZMBoQGdAYkBfwFwAWMBYwFbAVIBTwFGAT0BOgE3ATMBMQE8AUQBLwEkAToBQwE1AS8BNgFEAUwBSgFRAVQBTgFWAVsBWgFpAWsBVgFSAWEBawFjAVUBYwFwAWIBbgGHAXYBbAGBAX0BdAGAAXgBcgF9AXQBawGAAZEBkgGTAZIBlwGcAY4BhAGZAaIBiQGGAaUBrQGeAZ4BpwGtAa8BqAGpAbsBxwHFAboBqQGjAa4BsAGvAcIB0wHLAcYBzQHKAcQBuwGsAbEBtAGpAbEBsAGdAbYBzgG9AckB0wG2Ab8B2AHCAa4BrQGwAbcBqgGfAbMBuAGlAZoBmwGqAawBpAGwAZwBdwGJAY8BcAFuAW4BaAFyAVgBPgFYAVkBQQE+ATQBLQElAQ4BGgEnAQ4BFQEoAQoB9QDvANgA2ADlAN4A2QDYANQA0QDEALIAsgC8ALoApwCXAJ0ApwCnAKkArQCtAKIAmQCgAJ8AiwB/AHMAawB7AIEAZwBZAGEAYABPAEEARQBCADIAOQBIADQAIgArACkAFgAHAAAA/v8FABcAIAAOAP7/CgAVAAAA6P/m/+X/3P/e/+H/3v/r/+7/0v/b/wIA9//c/+n/5//T/9v/3P/G/8X/0f/U/9z/6//q/+H/3v/c/9P/yv/O/83/uv+1/8z/3//f/9r/4v/v/+X/1v/l//r///8DAAAA6//j//D//P8EAP7/7P/w/wEAAQAAAAEA+v/+/wcA8//i//T//f8CAA0A9P/j/woAEwD1/+z/3//V/+n/7f/u/wAA9P/r//f/5f/W/9//y/+2/7b/tP/A/8D/oP+e/7H/rP+r/7f/t/+v/6X/oP+g/5P/hv+H/4X/gf+D/4X/hf+E/4f/jv+T/5b/lf+R/5L/iP99/4b/jf9+/3j/gP+H/4z/iv+H/4D/bP93/5f/ff9a/3D/Z/87/0P/Sv82/0z/WP85/z3/Sv85/z7/P/8q/yr/JP8M/wv/Bf/v/uz++P4J/wb/5v7T/tP+3v72/uT+wv7U/tL+uP7K/sr+u/7a/t7+0v7u/uH+v/7S/uD+1v7U/sb+w/7O/sv+2/7l/tX+4v7n/tX+5v7o/s/+5v7z/tj+6v7+/uP+5P76/vH+9v4d/yz/G/8S/yD/I/8T/w//H/8v/zr/Ov8x/zf/Vf9l/1P/RP9O/13/Zf9r/3n/jf+N/3P/bP+H/5v/lf+X/6P/nv+a/6r/tP+u/6f/o/+1/9T/1v/N/9X/0v/D/9H/7f/q/9X/3v/r/9z/3f/3//b///8rADkAKgAvADsAOwA4AEQAVwBIAC8AOwBLAE0AVQBaAGIAbgBoAHAAhQB2AGIAbQB/AIQAgACCAJUAowCnAKYAlQCIAI8AkgCOAIwAgQB2AH0AkwCgAJwArgDEAKEAggCdAKkAkwCFAHcAcgB/AHsAdQCAAIYAkQCcAIwAeQB2AHMAdACBAIoAigCEAIMAhgB/AHcAegB7AHUAbQBqAHMAdgBwAHAAYQBUAGsAdQBWAEoATwA7ADIAQgBAADEANQA+ADEAGQAHAP3/+f/6//b/8v/x/+r/5//n/+H/7P/w/9b/zP/Q/7P/o/+0/7f/uP/D/8H/tP+x/7z/yf/C/7L/r/+q/6L/pf+o/6n/uf/J/8n/xf/E/8H/x//J/7P/q/+7/7r/v//Z/9H/wP/U/9//3f/v//T/5//i/+D/4v/n/+v/8v/x//f/BgD6//T/BwD0/9b/6P/4//L/4//a//j/AwDn/wYAIwDr/+X/FwAEAOf/8P/p/+f/5//W/+P/8f/b/9r/8v/0/+f/2v/V/+H/5//X/8r/1v/m/93/2P/l/+X/5P/v/+T/3P/n/9z/yv/S/9j/3f/x//j/9//8//X/7v/5/wAA/P/1/+//7f/q/+P/6/8AAAkABQAOAB0AIgAlACMAHAAjACgAHgAfACYALwA+ADoAPgBeAGAAUQBgAGMAWgBgAFkAWwByAHgAgwCPAHkAfACcAIsAdQB9AG8AXgBoAGoAZgBqAG8AbABmAGwAegBzAGAAWQBSAEsASABDAD4APAA8ADYALAAzADsANQBEAFkATgBEAEMAMAAwAEEANAAxAEsAVABVAGUAaABeAF4AYQBeAGAAbgB6AHsAhQCSAJAAmQCsAKcAnQCZAJAAlwCoAKwAtgC9AK4ArQC4ALEAqgCvAK4AqACkAKkArQCfAJcAnwCZAIwAjQCQAIsAfwCAAJYAnACOAIwAfgBgAGEAcQBwAHYAeQBnAFEASgBZAHMAgQCJAIoAhACBAG8ATwA/ADsATgB8AJAAggBtAEkAMQA3ADgASABxAH8AdABiAEMAKgAZABIAMABSAEoAMwAjABUAEgAWAB8AGwAEAAYAHAAjACYAGQD3/+j/3//P/97/9P/t/+z/5/+//53/kv9//3P/hf+e/7X/zf/M/6//ov+t/7r/vf+z/6r/rP+l/5r/r//Q/97/8/8MAAUA6//c/9T/yf/J/93/AwApACUABwAMACsAPABHAEUAHgD4//j/BQAWADQAOwAmACcAPABPAGQAZgBTAD4AHwATADQARwA9AC4AIQA8AFoAPQAwADUACAD2/xgANABRAFMANwAsABsAFgAfAPD/0P/S/7P/6/9LAEwAWAAeAKT/3/+r/sH5bfSd8nr0fPhB/LX+wQBYAe3+VPs2+X75Wfsz/Qj/IwH2ARMB4v/j/mH+yv6c/8IAfALFA/wDxAMkA84BkABQAPIAAQICA2wDLQONArgBxgDK/xf/Hf+Y/wAAgwAqAWEB3ADf/9b+HP7U/R7+6v64/1AAygDLAPr/v/7N/WP9dv0A/tb+j//4/xQA5P93/wj/1P7w/i3/X/+O/6P/f/9O/zb/Jf8d/zf/Wv9O/xj/9f7r/uj+9/4R/yL/N/9I/0v/SP8t/wr/B/8U/yP/M/8s/xD/8/7m/vX++P7I/pb+hP5//pX+r/6o/p/+k/5w/lf+Sf5B/mD+k/67/tP+v/6G/lP+JP4O/g7+z/1S/ef8wPwD/Yz9AP5I/mz+av5d/jb+5f2i/YT9k/3X/Rn+P/5Z/kL+F/4x/nT+rf7n/gT/4v64/q/+p/6g/sP+Bv8z/0T/UP9Y/13/Wv9C/yj/I/87/2v/iP+A/3v/j/+s/8H/vf+p/6T/u//o/x4ARwBHAB0A6v/B/53/hf+G/5T/sP/c/+v/zf+o/37/UP9A/zf/Kf88/1D/Ov8p/yf/Ff8o/2L/bv9X/0v/NP8b/xv/Gf/3/s3+uv7J/uj+9/7s/ub++P7+/uz+z/6R/jX+C/46/oL+nf6L/nP+af5v/mb+Ov4g/jj+V/5w/oX+hP6B/nz+cv6I/pz+kP6i/rb+pP68/uf+2v7W/vH+8v7c/sP+sv66/r/+wf7T/tj+0f7U/tH+zf7E/qL+jP6B/mD+UP5R/jr+Pv5m/mP+Tf5g/nL+Y/4//hv+Cv7//QH+Iv4s/hr+Kf48/j3+WP5Q/gj+6f0B/hD+GP4N/vP9A/4s/k/+bf5o/kn+Sf5i/m3+Zv5M/jL+Pv5u/ov+f/5y/nH+ev6g/sL+wv68/rf+vf7X/s3+qv6m/pb+gP6Q/ov+X/5B/jL+S/55/nT+Vf41/gP++f0N/vn99f0M/hD+Kf5H/iH+3P2k/Xv9k/3Z/e39yf2v/br91P3f/dr91f3l/Rn+U/5d/jD+Bf4L/j/+g/6h/oP+YP5q/pD+sv68/rn+yv7l/v3+Jv85/x//JP9g/5D/kf9q/zf/JP84/0r/M/8S/wz/C/8S/yD/7f6i/qL+u/68/rT+if5P/jP+Kf42/j7+G/4H/gr+CP4R/v79wv2t/bf9vP3G/bj9qv3D/e79Kv5T/jf+Lf5M/lT+b/6X/ob+gf6u/sT+xP7E/qv+m/7H/gf/IP8d/xr/A//u/vj+9f7d/tP+0/7p/gr//P7g/tn+tv6D/lH+Cv7o/f79Cf4R/g7+5f3i/Qn+BP7z/fL94f3s/Rz+J/4M/vL95/0D/jL+Vv5//qT+tv7a/g//Fv/8/hH/Tv9n/23/ff9//4z/tf+0/5z/qv+u/6b/vP+4/5b/jP93/1z/aP9t/1z/Tf8u/yr/Y/99/3L/gf9r/yb/F/8i/w3/+/7s/vr+Rv9p/0f/QP9T/3H/sP/a/9v/0v+1/57/q//J/+L/2//e/xkAQwBiAJsAggBKAHQAkACEAJ8AmAB4AHkAawCEAMcAygDAAL8AnQCdAKcAjQCNAHsARwA+ADEAGgA5AFoAeACiAJUAZwBKACkAHgAZAPf/7v8PADgAUwBUAFMAUQA7AEIAZgB2AHQAUwAuADUARgBMAEIAJwBAAHcAcwBqAGMAOgBAAFgARAA9AB8A6f/8/xsAFAAaABEAEQA+AEEAIgAMAN3/xf/T/8b/sP+e/5j/vv/R/9L/BQAgABwAQQBJACMACQDq/9z/7v/t//H/7f/J/+b/JAAoAC0AJADt/9//0f+J/3H/kf+4/+z/+P/m/+j/1f/V/wIABgD7//P/uv+g/83/6v/9/xsAQwCIALIAuwDeAOcAyACmAIIArQAFAf0A3QDZALIAywAhATUBPAFGAR8B9gDAAIcAdwBBABQAUwB/AGkAbQBmADUAEwAjAFkAVQDy/4//bf+l/wsAMgAtAEEAVwCFAKkAiQCAAJQAcwBvAJAAaQArAB4ASgCyAAwBGAEBAfAA+gD0AKsAZgBtAIwAlgCRAJUApQCBAD8AWwC0AK8AQADN/4r/W/8U/+D+4f7o/u3+EP83/zX/5/5//ov+6v7x/r3+k/5N/jT+b/6v/gP/N//t/sL+Av82/0v/K//u/iv/j/+F/2r/Q/8X/2z/9f81AEEA4/9s/43/8v9AAGgAIADq/z4AjwCdAH0ADwDE/+z/LwBbAC4Ar/+I/+v/bgC9AI8ALAAfAFgAnwCxADEAlP+I/+L/NABHAAQAu//W/1AApABlAP//8P8KACsAKwDE/2D/eP+7/+7/GAD+/5n/Yv+z/yMAAQCA/z//QP9a/3L/Nv/f/uj+Gv87/2n/Xv8D/9j++v4V/xb/6f6P/m3+i/6d/sP+AP8J/xv/a/+P/3X/cP9//4D/ff+V/77/sP9n/1H/pP8DAAoA4f/S/7j/jP98/2X/X/+W/5D/OP8b/yX/LP9X/2f/Wv98/5X/fP9g/1j/fv+L/yL/x/7V/uD+wP5+/hT+6f0r/n7+vP7U/pn+Of73/eP99P3q/ar9hv2n/dD97/0X/in+Hv43/m/+dP5I/hH+uv1o/Wv9of3T/Q3+Rf5y/pD+h/5r/lL+M/5Q/q3+wf6P/nr+ev6W/rP+n/7Z/m7/c//s/pz+n/6q/nf+Sv6z/hX/3/7P/uH+xP4Z/3f/Kv/8/jj/ev/V/+D/fv9s/6b/4f8dABUA5//S/+//cgB7AKP/dv8GAEIAaQAAAE3//P9l/zj7dPjs+QP8u/2p//z/Y/9G/+D9CfwK/ev/AwLLAgsCyADoAOIBnAJCA4gDrwMxBPMDAQPVAnADbARqBVIFhQTqAzED1AL3AmoCnwGRAX0B/QBuALf/AP/n/nv/3P9u/wH/8/5v/pz9Pv1c/dT9Mv4w/lX+av7p/X/9zP1s/qv+W/4q/mn+mP61/v3+cf8dAGIA5v+1/0QAxwDuANkAnQBdABcABABgAMAAwQBhAKb/DP84/9b/MQBFAAsAQf99/mz+fP53/t7+Bf9g/u79AP7Z/Zz92f1N/i3+dv1A/bf90v1+/S79yfyg/Lr8hvxz/Hf81fq29831N/bh99r5kvtL/I37EfpP+X75Dfp8+4T9Kf4Z/SP8P/wp/Yf+BAAOAfAA3v8K/1D/fwCIAbcBygHuATgBVACIAF8BCwI0AtIBXAGmAKL/k/+sAIoBcAG8AGgA1QAoAToBdQFZAdUAigCCAK4AOQEWAucCMwPWAuIBHwEUAkMEnwXdBUUFCgSjA40ExgXYBjAHtgZDBg4GdgZwB4wHDgcjBzkHGwe7Bs0FuwWjBvoGJwcSB/cFbAXUBe0FDAa+BXUE5QM9BFQEcgQ9BGEDGQNjA3sDkwNUA5ACFAL7AfQB4gGGATUBRgFeAX0BkwEfAXAANAB6AAIBFAFVAIb/Ff/q/jr/m/9A/13+hf0K/Q79N/0O/aL8S/wi/NT7V/sP+9/6jvqE+qL6L/pR+bj4kPic+GH4zPdn90P3Cvfx9vf2nPYD9o71UfWJ9cT1PfXC9P/0D/XJ9Lb0nvSB9Kz0IvW99e/1j/Vw9dv1dPYG92r3nPeR92330Pd8+NT4L/mu+UL6//o2+xP7pfu1/Pn9G/8v//b+eP8uADABfQJkAx8ElgTQBKEFawb9BlAIfQkjCi4L6wuWDDEOhQ9BECARrhGREiYUAhViFSgW7xbAF1gYRhg1GH0YYhnuGpMbExsHG3Ub+RvGHDodMx0THagchBzdHKIcAByjGx4brhqLGt8Z4xhqGBkYoBfvFpcVDxQbE1QSNRHKDxUOLAxBCqQIrge7BuoEuwL/AM3/5v5//XH7x/lo+KP2DvW/80HyH/FX8GXvpO7q7fjsiexn7OTrU+u56gbqyunY6bXpeek56UXpsunm6c/pt+mB6VrpTOkT6dXomOgl6KnnQ+fz5t7m8Ob55vfm1uZ+5gTmnuVM5ezkr+SU5Dvk9+MX5CTkHuRQ5KfkS+UK5lDmYeal5gbnpeeF6Frp+ulr6jzr5OzJ7lPwfvFK8izzkvQA9j73l/gU+tr76v2g/8MABALrAwsGkQeACIQJ+QrXDIEOcg+IEE0SsxOJFLYVLBeVGC4aJxwIHuseER+cH8YgnyKEJCMl7iVMKO4pMirZKsgr9Sw5Lq4uhi+fMFYwgDDNMX0yFzMNM/kxDzKhMXYw3DN4OVE6VDcqMyEv9ywCKvIkwCGyHysbShXrD4cLUwg0BSEBgvu+9D7vc+vR58DkGeKU3gDbPtgH1ozUk9O/0kvS9NFt0QPRJdFC0gvUBNaf2N3bEd8i4h3lEOgY68Pt8O8e8kD0JvYO+PH5efuG/Er93v0Y/iX+Hv6O/Z78uPtt+sv4ZPfr9Vj0FPOW8XfvS+1f66XpHui45n3lhOT74wnkB+Q540DiW+IB5HrmVOjQ6LDoaunC65nuHPA+8Drw/vDh8uP0ffUp9Yr1zPbl9+33ZfYO9Ojyl/N59FD0a/OI8sryjfQI9q71WfSz81r0jPUT9nD1CPRD82D0ufYn+UT7sfwD/icASwKzA0YFmgekCjkOxBHqFKUXIhrUHJMfoCFsIjEirCL2JHknFSlaKmMrxSxmLwYyDTO+MgcyAjJGM0w0ajOcMa4wKjEPMzo1oTVJNNYyGTJKMkEyOjDBLSctCy27K5kpPidoJUAk9iIbIfcdLhkuFIkPUApABD79wfWe707rnefa45vf5toV103V6dSF1GfTYtHOzuPMUcy5zFjOPNFv1LTX99o53eve9OGw5rvrre8K8p7zj/X/9676xv34AGUD1wRxBUUF8QS/BCoE1wKdAOz9wvu/+Uf3JPVY80jxgu8q7r7sRuug6dHnKuaE5D/jMuM/5IHlGOZC5jvnYOl17OPvcvKT9D73KvkN+mP7S/3w/0wDnwXVBrQH4Aa6BIYD6gLwAQgBjP/C/Av6vPgP+Mv2JfV28ynxLe4J6ybou+a75/jp0uuy7JfsfOyK7W/vi/HY89X1kPcW+uz8Y/6m/qj/+AIHCF4MiQ4zD5IPKRGMFA0Y1xr+HXYhgSTLJgontiQvIkAiKiXHKMgq4CqhKsgrOi5eL48tbSt4LEUwcjNaM54w1S4jMBMzxzVBNqYypy7+L/o0BTnPOrk4fzMZMBIvZy27K6EquCg8JgAjSB6ZGRIWlRLiDewG7fwY8cbmtuBK3prcttnB1frRkM8oz4TQbtIk1FjVO9WB1A3V6daC2kLhiOnp8Cr3q/uY/rwB2wQ0B4cJNgulCw8MgAxWDGoMAQ08DYUMMQrfBVwA3/oV9gLyVO4g6/voAehW5yHmeuT54hXi0uHf4QviPuJh4vvi3uQy6GfsV/Al82j1FPgb+9b9sv/kAEICngNVBJ0ErwTGBGMFCQasBdEDYwA0/En4k/Qa8Tnuzev86Qzpxejl6IPosuYH5Dnhy94P3oPfOeIR5eLmNOhQ6y3wafQL96z4HPoi/Lb+/AC/AgQFMwjuCiwMSwwHDLUM6w6eEBMQDQ7zC94L9A8bF+odLyJfIwMirh/xHT8dNR3UHdofiCNqJ3cpayqILXQzDzkyO8o4bDTuMnE1XDnGPBo+Pj3RPTZC+0fESqdHTUCzOU411DDgKw0oMSaYJZUkJyLnHfQWZA2CAo72BukE27XP3cnjyJLJdclCyBzH68ayxxfJ6MnnyFTHwsc3y1LRI9nx4ZLrAPWm/BgClQWOB8EIhwkkCqQK2wraC4MOhxGCE8MTdxEXDX4HegCv+E/xyurf5RbjseED4Zjgy9+k3kHdJ9t52ALWMdSH05fUH9fj2vzfheVw6n7uFfEk8u3y9/P19DP2/PeU+jz+QAKKBX0H3AeFBmYDOv8r+2X37PMN8a7uZ+2U7bnt+uxs62boQORI4Lvct9nC11rXUNld3bLhKeW857PpiOs07QLuc+5M8NPztPdc+7f+tAH9BJ0IqgsADsoPhxDkDyEP4Q8fEdIRJxQXGCgbRh4UIgclmCe/KcoqBywcLRAtiCy1K/UsCDKFN9o7zz7/PVE8FD30PRlAEkSzRNBCmkJuQyhFYkY8RDtBJj9lPA06QjdMMUoqniQ5IGEdARpKEwEKo/+v9Cvq/uDK2UDV9tII0dzNoMlfxZDC4MKaxTzItsnWyZLJwctr0WfZmuK/6lrw5vTk+PP7Tv8VA4oGPwrrDWQQhRFeEZEQMRDMD4wOJwwaCMICJP25917zdfAE7qLrMOnW5a/hrN1K2vnXzNZc1nPWq9bF1i7XXdgE27HfNOWX6SzsWO0x7gLwPvOP9xb8sP9XAsAEfAZJByEIwwiaB4gELAFA/+z+SP7C+/b3ffRq8sXwce3m6GLlXuM44kvhOeAJ4EXhe+L24qDjYeVf6Hzrg+3q7g7xrvRr+TX+aQIBBjwJlgxLEBcUPRfOGUQdOSHvI+gmgCvSL14zNDZQN8Y49TsFPnw+TD+bQMdCUkWVRvZHB0p9SY9Gi0ViRyNKxUzXTSVMy0kVSfZIkUggSPFFEEETOzw1lTC4LYgr/yhoJGcbZw9PA7n3+O0X573hx90N253Xq9Mw0ILMeMkHyMnGRcVKxOrD0cQByCvNntPM2qDhteZ26bfq4usf7h7yQ/cy/HAA4ANsBrMItQqeC1ELlAkGBpkBW/2/+Z73yPbg9Vf0+/GI7iXqs+Sj3qXZodY81bzUP9QJ1A7V4daE2EDZiNhA1xDXiNiL24/f8eOA6H/sfe898gX11PeE+of7qvoK+lP6Lvu9/G/+vf/A/xj96fgi9R3yt/D68Ffxg/F58MfslOga5vzkJuX95bPmCejJ6V/raO2M73PxevNh9fj3SfuU/UIAbAU9CyQQsBO/FTMZ3x/wJsErKy73L5UzHznGPwRHg03SU5lZ1VkkVGdPbU8mVP9blGEHYlphF2J9Yvlh/mChXoNaKVVUThVHZ0K4QLo/lj04OIguiSLjFWUIBvoa6/bd/tVW09fT3dTZ0nHMDMNtuPevtazJrTOxIrVgt2a4/bpAwFjH1M7n1BrYzdiQ2dbcweNY7vH5rAJuB9wIJgiJBxIISgn3CmUM0ww4DDYK7QZ1A4EA+/29+rH1mu+W6SzkR+BG3lzdIdxZ2Y7VNtLIz17Ouc1fzb3NCM/S0PvSNtWA1zXaGt0r4H/j4OaF6uvtyfBE9NL3RvpZ/KX9yv2W/gAA3AA1AToA8v32+0b6D/lU+a757PcH9NHvW+0B7b7tPe6T7evshO177pLv9PD+8eDz9Pdf/cMC6AVeBosIww4WFpgcTSKTJ6gtATTOOH888UBmSM5Si1tTYf9nu20vbrxrLGkPaFxsW3S/eb97VXtBeQZ4PnWQbWpkiVyfVlVUJFLfSz5D/TiMLOMeTg8K/5Tx3+aM3vjY2NNLzhPJocJEuk+x26jlolWhiKOop1KscbBis6O1FrgMu0y/V8UCzN3SVNrA4SLp4fAn+LX+bARsCOIKSAwxDRIP7BG2FNIW7Ra0FOEQ1wrTA9r+5/ve+Sr4qfTH7o7oyOKb3VnZItUV0V/OE8wOyVbG9MTtxBrGxcf1yHDJqMm9ytfNyNJ72PfdJ+Lz5N3nZeuF7kTxQfRv9+v6c/4NAUoCOgLeAa0C7QM0BJ0DVgJAAagAav5J+yH60/mt+R76tfgf9j31K/WH9hr69fz5/mgAff+R/3MDZAi0DR0UNRuNIhIoPCzNMRk4Mz/7RvhNKFisZF9qyWltaClodm52eS1//38Jf2R8w3x5fqh7F3f0cbNrrWcuYpNX10ySQos3Qi4YJNoWcQmv+8vtoOKt2TnScszjxeG8oLIbqRKij567nsWg+aEDoj2hJKBfoceluKupsjS5E77jwoTIHM+N163g/OgS8Pf0ifjy/DICwgfrDEQQARLUEpkSfxFOD0EM8Qn4CHUIywZ4AkH85fVH8DPs5OiF5Evf2Nmo1G/R9s/XzXPKfsahwg7Ab7+jwIHDLscDyi3LdcsbzAfO1dHx1qjbTd9k4kLlSugT7Ffw9vNg9tH3Y/iq+Bf6Nf33AN4DGwX8BG4DTQCB/Tb90f8MBLwGBQaDAwMBhgBVA5kGlgi/CiYMfwxEDu8RcxeLHgElPirDLRAvizI3O6RG8FJZXPtdR1u1W31hqmrRcw96vHzTfN97+nmLd6h3Znrye1R6bXNXZh9ZjFGYTpJMHEYUNwkjBRGTBBX+7fo89vjsT990zxXBe7cds+6xu7DTrDSmdp88m/+aVZ56ov+kzaXTpfSmcKv4spm7LMRQyzbQNNR+2KDdq+Qg7Tv1QvxlAQMEUAWiBoMIwQoNDDwMgQzIDJQMngsXCUYFPQFF/Wz5QfWY8IfsdOn25jTkaN/62HPTsc+WzU/MG8qfx7nGUMeGyDTJucj5x7LH68hMzEfQAtT41vzXbdlr3bLhmuXr6KDpHOqM7THycvaF+Xz6gvs5/vwAWQKvAU0AYgECBY8IbQpACr8JOQv/DSoQWhFZEqUUnxheHHQe1x9kIlYnkS5WNsI84EGrRs9KZU0CTwxSX1jZYL5o3m0Tb/ttwG3NbiRwmnFbckByhHFObkho0GHhW5RWLFEaSUo+QjMAKQIgNRjcD5EGLf2p8wTq0N/01G3Ld8R2vwi827hGtOyuwqkNpT6iWaJkpBSnhaksq5es6K6TsjW3Lbz5wK/FGst70djXXN0S4kDma+re7g7zYfah+A76n/v9/e0AuAMBBQAEmQHk/tP8APxD+7354Pc/9fvx5O7Z6s/lzOHr3uHcDtz/2uXYiNbH0wnRI8/UzaLNuM6Cz5bP8M8K0fbSN9Xi1oHXw9eq2fndjuIZ5bzlzeao6Sft/vBA9bb3I/ge+Tr7JP7jAfQE3QYFCWcLIQ2sDisR+RSJGYMdER/GHq0fuSLEJ7cuEzTvNeI3kDsNQBFGzkxiUSdSnVFQVd1c6WLiZghpZmgPaAVpmmn6auVrxWqeaaRmw2AwXJFYmFM7TqBGFDxRMkAq2SKaG4UTmgscBL/6pO9X5L7Z/9GFzezJRcWjvvm2jLAErEGpG6h9p7CmiKVQpH+k0aaSqsWuDbI+tO62q7o+v93E5Mqg0DbWGtup3mvh+ePt5vXq6+8w9d/5f/y8/OL7RvsS/E3+GwBmAGj/Lv3P+pj5xPgg94X0YvFy7uXrruny5y3mkuSL43Ph5N3W2rLYl9dB2DTZ4Nia1ybWuNWt1unX1Ng/2f3ZS9yH3iTfjN+s4IjjMOmZ7jfwFu+x7uDx6Pfd/Oz+s//UAUQGUgrnC7wM9g7gE4saoh6+HoUe7CByJu0sBjEWM641djmRPS5B40XYTM9RsFGWTztPwlOZXT9mK2i4ZM5fPV6PYRhlLWa1Zf9i/l67W/9Xq1NAUDVMuUVMPQY0+itjJmchFBv4EhsJ1v4e9nHv9una47/b3dJuy1rGV8OOwOC70bVSsIysLquqq3+sPK2yrWmtJ63yrQ2ww7N5uMK8ZMDZw1HHFcv4zp/SitYG21ffkeJ85HDmQeqz76f05fbZ9RX0pvQ39+f5rfu++5b61Plk+QT4q/Vd8wzyvPFi8cjvyezg6XjoHeho5ynlkOHD3urdbN7337bg7N4I3aHcINwx3JTdp94D4HfiLeTU5F/lBOaz54bqkO3n8LDzwvSd9ar3dvqG/fv/HQFMAjYFYwn7DFMPPRExE4MVPBj9GUIbqR6fIzIoPSyCLkovqzEtNXY49zzPQKBBD0KMQ/RF4EqHUGVTklOvUSFP6k6mUJlSAFVuVhdVyVF+TaNJtkdZRv5DxkDBO8c0Ji7wKKwkQCElHUYWNg3hA8n7bfaD84bw2+tA5Qrd7tSpzp7Ki8j5xivEd8D2vAq6DLjVtt+1ObXTtNG0FbabuDO7L721vivA4sH0w47G/ck6ztfSONe42u3cK95b33rhJuXN6cTt2+8d8LDvaPDh8p314/ZN9r/0kPN480H07fTO9Fn0bfNk8UjvH+6e7RfuFu/i7nLtrOsE6nTp/umo6k/rqOt161TrW+ud61vs9eyG7azux++38PLxW/Md9bb2gfeR+EX6F/x2/s4AYwLMA9IESwZwCasL6Qu8DKUNKw7XELoUQRd5GB4YcxewGdIdhCCwIQcj+iRsJiYnIimiLIkv/zBDMWUx6TJ8NHo08zQJN804nznnOWs5FznIOT46fDkhOMk2Czb/NXI1QjQ0Mxwxei0qKoon+iTZIqAgCh6xG0kYShOyDgYLwwfoBJ0BGP4Y+wL3jPEv7W7qD+hZ5aPhbt3e2fTWytR10xXSWtBkzgfM2MmYyIjIksnhyqHLuctvy97Lc83rzinQ9tG/02rVT9fr2JPakNzt3dveRuAw4qPkXOe46ajrMe1p7oDvc/DL8djztPXG9jb3XvfP94X4Avkt+TX5n/mB+g37+fp8+rv5Zvmt+df5vvm5+cn58fln+vz6wPrt+RD6vvrV+l77hPwZ/an9Yv5U/qb+/v9cAJYALwOrBekFlgatB+IH6wiJCRoJ6AoBDZcN3A8zEW8P7Q6PD14QFxL/ERsTmRcfGFkVzROEESsTwRjxGCkY1xqjGmEaARv+F0cY/BtaG5wbPx7JHT8dOhw4GYAZDxt8G9Ad3B1CG9YbDBwrGiAaQhk5GOQa/BscGWUXChdQFsQVkBRjE7ATdBOAEZQPhA7nDesM4ArWCIEHwAXYA3wChgBV/sv8f/oT+Of2iPS88HTuCO1o63jq/+h95ozknOI74Kneut0t3UrdCN0U3ALbpdm12PjYmNl42tPbndzi3Fvdsd1o3krgbuI/5OzlQ+fA6MzqwOx37iLwnPE58xz13fZ7+ND5ifo1+0/8iP3W/hgAjABnALgALgFIAboB/wFEAfsAmgF6AYYAnf8R/4j/IAAs/zP+Of+BABIATP+//yMAmP8AAG8BmAGFAf0CfgPSAegA/gG4A4oElAMWAwAFeAXYAlECjARNBZgFfAZ7BZEEngV1BbEEiQWRBeAEFgWaBIUEmAXnBFcENAZaB58HdwdFBSkFkwh6CS8IhghuCYwKXgpWBxYHPQswDWkMfgzjDCMN3gxkC1sL3w2+D6IPvw6lDVgNpg6wDzgPFQ9yD/wOSg+bED8QqA77DbsOkxDkEJUOaw3HDkQQjxAADzINkQ3DDbkLlgmECMEIuQmpCM8F+wPvAoQB1v8L/sL8xfvm+XH3V/XJ86ryXvF271Ttjut/6tDp0OhW59PlLuVh5f/kNeR05BrlUOV95UvlKOV/5nTorenD6unr8exe7sDvlPCs8UXz6/Su9mT44PlL+x38JPxk/Dv9Sv6R/5QAuwDeAKgBHQKfAZcAtf8MAHgBGgKqAUUBuwDc///+FP6c/fb94f5FANsAIgAJAKv/M/2e+xz8SvwY/bT+5f5c/5cAmf+a/Zn8vPv6+zf9CP5l/xMAS/+c/7X+afsf+7T8iPyA/Qn/wf/dAcsCigFAABD9ZPtg/k3/TP7N/ywBzgLGAqb90vuX/pT+xP9fAQz/KgDvAQj+UP3X/mD8v/xk//v//wGcAj8B8gKQA5sBSAFhAaYC9gTvBC0GOAnKCAwHrAawBqgHEAhLCLsKRQzTC1kMTg21DdoMmQpkCncM1w3NDlsPYw8oEFoQRg9BDm0Nng2CDhIOdA2YDRwNYgyWC00KeAmjCCYHzwVtBC4DxwIqAr8AJ/99/Rn8/fpf+W33+PUT9UD0PfNR8oHxbfBN72bufe2q7D/sDOzx6zvss+zI7LbsyOzx7IbtV+697jPvKfAF8crxtvJj8wX0//S/9fb1RPbI9lP3Mvgg+aT5Hvpp+ln6kPp7+qT5W/nI+TL6ivpm+jj6Zfrl+av5cfpR+vz5pfqc+lT6evp/+df4s/mk+cr42vj8+OH4QvmN+T35OfkV+pr6Qvr2+Wn5z/hS+b35n/lZ+qT6wPmb+ST6L/r3+Sb68vpC+8r60/oK+y77bvuV+in6o/sg/Kr7zfsU+zX7zfws/C77SfzD/Ar9bv0K/MP7Uf1r/Vz97/2b/Sn+wv+OAEsBzAF/AY0BqgFNAfAApQBWAfcCuAMXBIEFPAcwCAMIJwfABkcHLghxCEcIWQl3C/UMrQ32DV0OcA+7D54OGA59Dn4OVw7FDqAPhhBfEUYS0xKgEgQSRxGIEL8PjQ5rDTQNiw3QDeQNqA0/DcAMlgtwCfEG4wRvA08CSwFtAAwA/P9U//j9jPwm+5/51/eu9Z7zEfL08FTw2O887yXvku+g7yHvTO5N7Xzs3Os+68jq0OqU667sk+1u7lXvCPCL8KrwafBd8JXwBvHe8ZjyUfPX9HP2Sfe698r3mved90X3pvaj9t32H/fb95H4M/kf+qr6ofpl+rf5zvgY+KL3sff39zX4Dfkt+tb6Xvuf+yv7bPqp+fj4n/is+Cz5wvkO+nv6I/ux+xD8yvsK+7v6e/rd+Y/5nvn6+e76uvvd+yn83PxY/UT9xvxk/Er84ftC+0H7tvtQ/JP91P4k/4L/HADQ/xX/df4A/rD+w//V/ycARgE8AigDaQPNAi0DUQSYBE4E9gMKBPgEmgV6BbIFdQZTB9sH4Qf4By8IcggYCU4JBAmZCW4KfQqhCvAKAwtfC9EL+As5DMUMeg3oDc8N1Q1ODhUP5w/bD1sPARD7ENIQUBAeEDEQzxBRESgRKxGjEfARwhEvEcgQ8xAMEZoQ9Q9LD4MOnQ1+DCAL+AlFCYUIjgfIBhEGFwX3A30CrAAU/4j9rPve+T742/Yf9p/1vPTu833z4PLs8c/wlO+A7qztpuyD6x3rl+s57Lzsc+1q7m3vafA38Y/xlPGq8brxnfGj8Q7y+PJI9HH1U/Z99+74Lfon++n7afyb/Ez8h/vV+of6bPpY+oT6FPvT+5D8Jv17/ZD9Qv2p/Ab8KPsi+n/5Gvm8+Kb4zPg/+fr5UPo/+iD6ofnz+G342veg9+v3Dvj/9+730Pf690v4fPjg+G/5EPq3+sX6ffqf+on6//nd+SH6dvrz+nj7/vuG/Nv8Df1U/ar9xf2i/cn9Pv5z/nv+c/5c/qr+NP+c/0UAzACPAD8AHwDm/yQACgEeAk4DPgR6BG4EZgQ2BEkE9QSeBS0GPgd9CK8JXwvPDBMNGw1XDSANjQy7CwILiQv/DDwObQ/gEHkSBhS9FIoUSxQFFJETRhMWE/sSCxMkE2gT3hNZFBMV4hVYFsUWVheoF6MXSRedFtwVHRVJFHETyBJREt0RbxEOEXQQxQ82D2QOTw04DNkKPgl2ByQFpwJ6AGv+svx2+3f66PmJ+b34yPfG9mT1zvMW8k3w0O6h7eLsw+zb7B3tyO1t7ufulO9g8BPxsPEg8jXyGfIt8mnyyvK08/z0R/bx98f59fqI++L7E/w3/CT80fvR+zD8cPy0/DT9j/2u/dz93f2A/UT9I/2Z/Nv7Q/ux+h/6nvky+Rr5cvkB+nD6c/oO+tz5SPqU+i766fkB+sz5rfnp+cX5Sfmw+Or3a/dA9zT3lvct+Kf4Cvkf+Tb5TvnI+GD4lPiS+HH4HvhG9yX32fcU+CT40vgg+nf78vsy/Kv8Pvzr+9v8P/3Y/Kn8Rfye/MP9hf0r/TD+N/6w/dH+HgDXAIwBXgFMARcCBwIOAvICegJhAeMAYQDgAXMEZgTLBE8HuwdwB8cIFwn/CJEJTwnRCeMKMArWCYsKvAq4CwENWg1fDkQP2g7VDkUPGhDdEa8SehIIE1cTyhJVErcRJhE3EZgRfxLME6gUSxUnFh8XAhhbGA0YfBevFqwVpBSTE44SlxFxEEYPfw7vDQENoAs9Cu4IcQfQBQIE4AGp/4z9gfu6+Sn4cfbL9HbzJvLI8G3vJ+5Y7ejsY+wH7MzrQuut6kvqIepk6trqbetz7J3tyO4n8D7xFfIS8+zz4vQi9hX32fex+Jb50vr0+7f8p/1F/nX+Fv9l/+v+yv73/vP+8P7k/gD/Af9h/r79PP2O/C/88fvh+178NPxW+8j62vnn+K34JvjA99b3c/eS9x34rPed9yP40vdU9772Rvbw9jL3t/au9v/1VPV19an0mvQ/9qH2jvZS9xP3d/Yg9oL1pPWx9T/1ovWv9fb1tvfn97z29PYk9x73wvcA+LX4yvm4+aP5z/lK+gr85/yH/A39Fv1z/LL8HP2b/qkAXwDB/wAAyf4h/nr/ygB3AuEDXgPJAhsDkAMgBBoEGwQgBWgFqgS8BPoFCAikCcAJ9AnJCtIKjAqnCqoK9ApVCysLiQvoDDIOnA9uEVQSRRKvEr0TEBVLFpMWQBYnFu0VyhVkFnIXzBj/GX0aGhsGHHEcxBxMHZ8dwx1LHS4cXBu+Gt0Z2xhtF24V/BI5EN8N8QvMCd0HGAbXA7MBw/9D/bT6cvjb9UPzNfFC7z3tfusJ6qroXedW5qrlW+V25c7lO+bW5n/nC+i06I7pneoH7Hbtve5g8EPy8vOy9Y/3O/nc+ob8Gf6j//AAzwF6AgoDUQNNA14DtgMKBHkENQWwBeMF8gUJBVEDswH5/47+AP4s/Qf8pPuP+137Lfui+hD60/mC+Un5M/nS+Gj46vcX93n2UvZ+9g734fft+Ab6nPqo+m369fk6+UH4wfcT+A74cvf49nj2c/YP9832bfZu9zH4PPjg+PL43/cQ9zr2dfUY9gT37vb39mz30ve6+Pn56fq8+y78JfxL/E/8JvzH/G/9A/12/FT8ePx//fX+5P/PAKYB3QFyAk0DsQKpAXsCwgPkA3sE1wV0BuMGewc6Bz8HvQhZCjMLJgzuDcYPORAbELcQohDCD9sPOxB9EMcRGRO/E+8UWhaAFxkZ7BpdHIQdcB72Hs4ecR6aHuUeZx+7IBEi9yLnI3gk+CQNJoomASaCJeAkwCOlIkMhQR/sHCwaJhd0FNcR0Q6bC4EImQX1AloAdP0K+hL2I/Lu7kLs0emg52nlOuOW4UrgEd9M3vrd6t1t3n7fteDz4SXjSeSd5U3nSull66TtTPA/8x722/hB+yf9Bf/8AMYChgQYBhkH0wdiCIgIkQieCG4IIAjLB0oHrAbwBewEhgPrAVYAmf6I/H36dfhh9rv0f/NP8lTxYvA+74zuhO6z7ifv3e9U8H/wkfBw8DHw+u/C797vnvDJ8UHz/PR29nD3BPgb+M73MPcm9kH18fSg9Df0IPT786vzqPOu83LzNPPI8k/yTvJT8hDyD/LL8bDwjO/T7pPuCO+U7yXwffHR8m7zPvQx9cT1bfbu9g73pvfC+Lb5ePoT+9b7YP0v/1IAOAF5Ap8DWQSHBCcEHQRWBPcDHAQyBScG8AdOCjgL4gurDPYLpwtmDEYMrgy7DcYNow4sEMQQTRIbFI4UIRbmGHIb4B7qITAjICSsJNEkaiXZJbImayiHKUsqdSu0LIIuQzAvMRAyjTKwMmozlDMHM7kydjE3L/Eszik0Jo0ich2qF3oSJA0ICHcDaf4q+TP0LO+n6t3mRePg36fck9np1qDUyNJx0VnQwM/6z97QfdLo1OzXfNtb3yLj1ea06rru0PL49g37vf4XAj0FEgjPCm8NYA/yEJQSxxOmFEkVpBSsEkYQqw1pC9YJEwj1BdQDawEA/9b8VPpw93j0a/G87szsW+sF6ijoh+UC43vhOeES4krjZOTi5Sbon+qw7C7uHe8F8J3xyfMN9gX4Nvnq+dH6yPu9/KX95P3k/Yr+Uf+M//r+Ff0f+v32JPTb8ejvre1M60Ppwueu5ojl7+MW4rLgW+Cw4CPhkOGs4dDhmeKG43fk/OXA59jprexz7+/xd/Q09gP32ve8+LD5Lvu3/N/9K/+TAKYBsgK4Ay8EhwRKBQYGiQa5BjIGgQVqBb8FDQblBWgFZwX4Bc8GwwdCCGIIvwh4CcYKjgyyDpsRZRTVFpAapx6iIcYkwScHKv4sMy9qLxMwizFAM1I2hjnmO6U+iEDKQDlBhUH9QIZAhz/SPb88ujvMOZs32jSiMBMrBCRLGwASzgh4/4X2hO4O5yngDdpe1ILPEMyayfHH5MamxWXEnMPWwm3COMP+xM7HHsy00ZTYmeCk6Bvw6/bE/NkBdwaWCr8ODxPmFjMayBxeHmQfuh8VHyYe4RzQGkYY2RRJEIILVgZ7APf64/Ui8WLtR+pu5yzl8uKH4E3eB9w82qHZqdli2gzcA95v4Injt+Y46lXuufJa96/7O/9kAvoE1QZyCJIJagqyC9IMYg2iDVYN2wxIDL8Kbgi5BUkCff5r+tr1XvHu7I3oP+UH41/hFOBZ3jLcP9pP2M7WB9Zv1abV19Yq2FLaVd0v4LPjC+hA7OnwpvUx+S78sv5RACIC5QP3BJgGqAglCqMLiAynC+EJegcDBLYAEf6C+7v5ofgN96r1AvUv9LHzNfTA9FT1xvaf+Ir6o/xc/qf/TgHdA3QH4QuSEIUUahd0GsEdWiBXI2InsSuKMcE4Ej4EQTNCK0EjQGFAmkAGQvJEJkc0SbtLtU0aT19O3ko5RzlDAD6fOWA1gzCVLcArLynnJh8jIxzaE8QJ8/yJ79Dh79PWyF3B87xivHO+ScE3xU3J/svezZjOg82yzHjNXs9T03bZdeC66F/y7Ps4BRgOJhUhGi4dER55HegbhBlcFxEWUhUAFcMUDxSEEoMPzArTBML9zPW57Q7mOt/22XDWZtTg04/U6dXr10rag9zN3jPhY+OY5ennWOpz7TzxbvVk+t//QwWjCrcP8xNCFzsZvhmPGdYYQxcnFaoSpw9qDEwJSwY1A7T/o/tR9/rysO6P6oHmY+KA3knb+th/12vWaNW21BfVH9co2vXcR99+4d3jgObJ6GLqPewS76LyVPZs+fH7Kv5n/1j/iP56/RT9fv3d/UX+3/4W//7+Jf7E+874CfYm873wJe+s7UvsTeuK6u/pgul36aPqy+1R8qj2Zfr6/VcBjgScB/4JJgwSD/gSkRcrHDwgxSOIJuEogSuKLU4uSy7iLZ8uFDLPNsU60z0JQPFAtUB4QPVAtEEYQjFBAj8vPjs/Ez8bPWk6BjdrND0zCDIBMQ4wwy2dKvkmMSL2G3gS3gS29fTmwNmjz0zIeMNIwjjEyMfvyxPPC9AQz/fM4srgyc3KEs5y0wjbwORE72j5ggK6CSAPJRM2FRwVkBNIETIPTQ6+DgMQkxHyEpsT4xLcD7kJxQBs9gjspuJa24rWB9TR01rVldce2kvcBd2F3KLb8dqR29bd7OAT5cjqQ/ER+OH+pQQcCbUMTw+rENQQJRAgD/gNygzUCzcLHwuSCw0MpwtzCSwFGP/H947wdOpg5ZHhIt9q3aDcutxr3M3bk9sa24baaNoh2vnZ6dqx3GnfrePB6KPtC/I69Zf2QPdV+C75b/nV+Wr6ePtk/Yv+Gv5C/QD85for+1v76frS+r/54vd29o3zke/Q7L/qi+rs7KDuPO9w7xrucO337r3xGffc/SwDXQgpDoMUUBuYHmYdZxsqGr4bfyAsJSIqqC5LMD0yrDOSMLstJC77Lz41ljsXPko+QT0OOzo52jdFOVU96T6GP79BAULLP5g61TE3LbMvbzKSMgQwJCvEJ4AlViCuFx8LGPrC5srUD8gnwdK92r2FwfrHtc+t1CzU5s9LykvGPsY9yabOrdZS4NPrNPngBa4PzBWyGEEapRrUGG0VXRHXDQAN8Q7WEcIU9hYgF3EUdw64BLf3c+mV3FrT1s6DzjrQtNG70mHUztZ12QXbu9mf1u7UH9aP2hrit+rv8uv6XwL8CDUOqBBlENkOLQ2WDC8N6A2uDs8PAxEFEvkRiQ8xCoUCyvnp8KzoweIt4Izg+OJf5fDlCuXL4hLfmts42UvXytah2BXcr+DF5SnqFu5X8nD2fvlu+3f8QPw5+976tvrs+Xr6vvwP//0BBwQiAqT+Mvsg9u/xTPD17kvvKvI29ev4WPxT/L75l/WQ8MvtAO2J7KrtFPCg8135NP/bAwYI3gogDcQQnBWNGqMe0iJIKBwtvDAsM0kyZzDwMGkyQDVDOYU6TTp+O3k8nzzBO+E5OTm2OdY60zwsPGY4FDbSNeQ1mDWiM48wny1SKlEm3iEcHhwbFBfUEhYOAQV++GrrNt5x1GPPlcsJygzMYM8x1BnZMdqw11LTIM+7zrnSbdkH4v7qePOz+8sCDQh2Cw4Nog3SDYcNrQxnC5oK4wq+C7AMmwwYCrgFYgDk+Zzy8+rW4q3bj9fx1vnYLdxA3rzdcNud2ArW9dTY1T7YjdzJ4rrpoPC29vj6q/11/3sAUAFiAqcDagXdB4sK1AxSDpUO9AyaCeEFgQIx/+T7ivgg9UryLfBq7gbtaevr6FrmmeS343Hj1uLd4fnhO+Pu5E3ndemV6t7rhO0K7yTx5PIf83bz2fT+9ob6+f1U/6j/U//n/fT8qfxq/Cv9LP7M/v//LQB1/hT8aPgK9cr0u/Xt9rP4Yfij9on1v/OY8gvzH/N89Gn43v2VBTYNQRIVF1kc7CGHKEIu9jLcNSo0TTFaMJ0wkzTpOY08QkApRC1FhkbIRVBBgj4hPKs5wzqsO7k7Xz1kPLM5mDf7MY4rCieEILgaIRgCFSARmQuoAvz4Pu+D5dLdcteV0obPL8w1ykjLq83p0BzTrtJi0hDT89Qt2kDhYujL7+71DPunAM0FWQoADooPIRBgEN4P1A8YELkPIQ/FDRILbAfDAon9Hfjd8cDqfOOL3dTaYNuF3TPftN4a3JTYdNUx1OvU6db92cDdM+K053ntzPJr97z6Ff3h/r//TQAsAd4BmAIXBHkGngmpDO0NgAwKCe8EFwHp/Ub7j/jw9Rn01vKj8fnvl+296xrrG+qa5yfkZuFZ4fXjGuc+6fLpF+oA67brL+sF63jsw+/99Fv6Wf6LARIDswEl/4D9pP2O/wUCaAR+BiUHTgaTBDMC1gDjAF0AAP+c/av8Gf0A/tf96/xB+3r50vgy+cD7dwCcBE8JdRA8GLUg2yg5LaEvujGzMRYyxDQdN3s5gz00Q/hKVVNTWaxa1FZgUThNH0mBRZtDF0OLRC9GYkTKPlM2+iw0JSwe0BebEhwLQgAJ9KLmCdum1KjRotBw0H3NuMf3wQW+E75bwhjIZc1g0XzU4thT3xnnau/A9s38zgLFCAoOYRJkFbEXcxpfHbQfzCDCH7McVhj/Et0NuwnCBbcBF/1H9ibu++Z44TDeu9z92lzYSNUS0iLQDdAW0VTTk9Y+2n7e0uJ55sDpUu3o8YL3y/xyAMcBSgEPAeYCLQeuDBAR7xI4EqAP4gxTCuUGiANLAYj/Rv4V/WP6c/Yu8s/tVOrL5+vkMOIY4f7hO+Ts5brlauWd5vbnfujI6C/p8ur37p7zCviq/EgANQIpA54DBQWdB24JDQtIDVcO2Q6tD3MPsQ8AEa8QHBBlELYOlAwlDFALdQpgCcoFtAIbAg0CUwShCJAL3A5WEhgTkxRMGXkfEyeyLi01uTpjOzE4yzipPRNFE0+YVbFWZVesVpVTnlG7UGBRvFNdU8pPtUrcQhs7ZDaaMtYuJilyHswQvQKC9YzrXuPQ2sPTxc1jx0LCb76Iu/i6MLzwvdW/u8AfwVvDYcjbz+rY/+EE6hfxuPec/YsCVwdCDB0RORb+Gi8eVx+OHv8c5BvQGj4ZsxZLEoUMNAZt/wv5pvMa75/rROjH46HeZ9mn1HrR78+8z+vQktLe0+nU6NUN11DYztlE3NjfSOSJ6b7uGfPP9tf5WPzy/lsBWQOGBaYH6giDCdEJswlWCbII/QZEBFoBHv6K+o/3U/VS82fxG+8n7BHpkuYh5e7jG+IM4YbhLeIc48vkU+YM6NDp++kz6iPtTPG19G33aPmP+zP/UwOiBmQJgwugDHsNXg6mDh8P7xDkEpMTaBP6EskSJhN0E2gT5BLCES8RGhGVEFMSuReWHdciVCc5KdMqFS7hLg0sIyq4LMU0NT46QoRBQEJuRk9LpEw2Sc9F0UZySkNM80nCRMw/5jxWO8M3xC/mJTMdxhR3Cx0B8fVB7JvlVeAI2wHVYc2Fxda/+Lw7vL+747nKt5e39blhvnnDEch/zH/RENe63OvhuuY27Evzlvt9A6AJ5w3lEDwTYBUpF1cYFxlTGcgYihejFQAT6g+UDMYInQSTAFL8MPfN8Tjtruk/51rlz+Kx36Tcitmc1ibUNtLW0cHTDteF2gXdDt633hzgheIy5qXq7u6v8of1sfdf+sT9OgFYBDAGbgZZBnwGbwaTBg4HfgcJCC8ItQbSA6QA1/0h/G77cPqC+Of1P/PF8cbxE/IK8oXxQ/Ah79nuxe7I7pLvR/Hn81f36frk/Zr/4f/y/zsBHAQjCBIMcA4kD3oPtRB5EzsXPhpsG5gbORzVHbAfriC1IL0gtyEcJIsn1ipPLfIuYC9qLkwseinfJ6gpzS2iMSQ0yjSzMxgz9zPqNG81JDUIM1Uwsy6ELY8sKiyGK3cqHyltJvch/xu4FLUNdgjJBBQCTf9n+wb36/Le7gTrWOen45PgLt6L22/Ya9V504vTN9UY12XY5din2EvYh9iS2XPbLt574QHlteh27LfvMvJU9JT25/gA+7r87f2e/nr/9wCWAtoDlASiBE0EvwOSAsgA6v5r/cH85fwa/eb8TvxN+xb6CPkf+C33ZPY19s324ffV+Df5Wfnp+Q/7Xfxm/RL+i/4Q/83/tACPAW0CcANgBOcE4QR9BAcEcwPXArgCEwOeAy0EtwPDAfr/hv+k/6v/e/8m/yP/Wv8m/6L+GP6C/Uj91/3E/nn/yf/C/8r/ZgCTAdICqAO0Aw8DXwIYAigCXQKQArMC6QJOA64DqwNBA9ACkwJjAhACqgFnAVQBVQFOARwBvQB+AHMAagBiAE4AIAAlAHAAxgAvAaEB4QH+AQ0CBQL2AfIBEAJ4AhwD6QPVBJgFFgaRBvcGBgfqBuMGDgeWB2YIIwmqCQcKSwqECrAK1grWCoUKKwoTChEKLQqCCs8KKQurC+ULwgt2C9AKBwqeCWcJNgk6CUMJJgkMCdgIUgiMB5sGhwVsBHUDxAIsAlYBZgC3/yj/c/6M/Xb8T/s0+g353/fY9v/1RvWw9Cj0jfPX8hHyRvGB8M3vNu/S7qDude467gju9e0O7knufO6l7uLuOe+972/wLPED8u3ysvNq9DP19fW19o33iPid+a/60vsJ/Rf+Ff8KAJ0AAgGDAdEBCgJ3AtoCQwPWA0sEpQT3BAkF/wQPBQcF5wTSBLQEcQQWBMYDfQMZA6UCMQLGAYYBVwETAc8AagDP/0z/7P6D/iL+w/1b/RH91PyN/FX8I/zq+8v7w/vC+8X7x/vo+zL8fvy8/Ob8/Pwr/Yn9/P2E/h//r/8uAKcAEwF5AfgBpwJiAwgEuQRiBd4FYAb5BooHMgjiCGAJ0wleCt8KXAvqC3EM7QxkDc4NGg5WDqUOCA9oD8sPKBBxELcQ5xD9EBsRQxFkEYURlRGREX8RXxFNEUkRMhEYEfYQrhBlEBkQrA9UDw0PjA72DWsNuwz+C00LfwqYCbEI2wceB08GYgV3BG8DVwJ2AZUAfP9t/nn9fPyA+5P6xPkD+SH4Rvec9vP1S/W69B/0e/Pd8kTyzvFz8SPx+PDQ8JvwhPBk8C/wJvAo8CTwV/Ce8NHwEvFP8YXx4fFN8q3yD/N88/Xza/Tn9Ir1N/bX9o/3P/jE+E353/lu+hX7wvtf/PH8cP3w/YX+A/9s/+j/XgC6ABoBfAHNARkCaQKqAuACHgNQA10DaQOGA5YDoQO+A9gD5wP7Aw0EEwQOBAUEBQQQBAIE1QO/A8IDrwORA4ADXQMxAx8DEgP1AtsCxwKvAo8CYgI+AicCEgIBAukB1AHPAbMBigGCAXQBWQFlAXMBXwFdAXQBhQGeAcAB2AHwARQCNwJMAl4CdAKlAvUCJwMzA10DlAPCAwMELQQ+BIEEwgTeBBsFWwV2BZgFsQXBBeUF/QUZBkgGUQZDBkUGRgZFBjUGHgYmBiYGCgYFBvoF3wXeBdcFwgWzBZcFfgVwBU8FMAUZBfwE+AT6BOYEygSVBFsERQQtBP0DvwNyAzAD9QKpAmQCHQLCAW0BFgG1AFEA3f93/zb/4v6J/kz+Af6h/Uv9CP3X/Jz8Xfwx/AL84PvZ+7f7lPuc+5v7jfuT+5X7lfug+6T7tvva+/X7C/wq/FT8gPyk/Nf8EP01/Wv9qP3K/fv9P/53/rj+6/4I/0X/jP+4/+r/LQB4AL0A6AAdAVMBXQF5AcQB9AEZAkcCXAJ+AqoCsgLKAuwC6QL8AhkDAgP2AvsC4QLcAt8CugKsAqwChgJyAmoCNgIHAt8BnQFyAVYBDAG8AJAAZQAmAOT/sf+R/2r/Mf/1/r3+gv5I/iD++v2//ZX9h/1i/Tz9OP0g/fv8/Pzu/Mv82Pzv/Of8/Pwt/UL9Sv1a/WT9X/1n/Y39s/3G/ez9GP4h/iz+W/6A/pD+r/7b/gH/Jf9C/0z/Tf9d/3//mP+u/+D/FgArAEEAaACMALMA2QDtABABQgFXAV4BcAF5AXgBegF4AXsBiQGRAZMBlwGYAY8BgAF6AYEBgAFkAT8BNAEyARUB/AD8AOcAwgDEAMkAtgC3ALsApACTAIAAWwBDABcA0f+2/7f/pv+e/5b/gf9s/0L/F/8I/+v+wv6o/on+e/5r/ib+Bv45/mX+cv5X/gb+0P3T/eD9B/41/i/+BP7K/aL9t/3t/SD+WP5+/oX+kP64/v/+Sv+D/7//7P/f/8j/1P/k/+b/+P8YAB0ACQAHACkAXgCGAIgAdABgADsAGgAhADUAPAA+ADIAJAA1AFwAfgCOAJAAlwCuAMAAxgDJANAA5ADuAN0A1wDbAMsAzgD1AAgBDQESAfoA3wDVAM0A0gDfAOAA3ADBAKEApAClAJoAogCoAKsAugCzAKcArACQAG8AewCIAIoAngC7ANMA4gD9ACUBNQFAAWEBcQFrAWIBRgEtARsB+QDtAPEA3wDhAPIA2QC8AL8AsACTAI0AiQByAFUAQQAuABMAAAALACAAKwA/AFQAVgBVAGYAdgB4AH0AjwCbAIQAXABQAF8AYwBVAFMAXQBaAFYAcgCRAI4AjgCmALYAugC9ALcAugDVAOsA7QDuAPYACgE1AV4BdwGcAdEB8wH/ARYCVQKiAucCSQOsA70DsQPYAxoEhgSCBNoCcgCC/9D/CgBfAOkAJgFJAWsBYwGGAY0ByQB7/wD+nfwQ/Dz8OPzk+z/7J/ps+cv5zPrW+7z8d/0D/k7+e/7e/mn/2P8XABUA1v+T/4n/p/+g/37/cf9I/xT/K/9u/73/GAA8ACQAFAAaAE0AjwCWAJYAtwDJAPEAMAFAATcBIwH5APwAJgE0AUABVwFgAWABVwFVAV0BRgEoASgBJAEZAQUB1QCnAHkAMwASAA0A5//L/9n/2//Q/9P/4f/o/+X/7//0/8j/n/+Y/4v/if+X/5T/pf+y/4b/Yv9L/wj/0P7A/rT+vP7J/s/++/4u/1L/i/+7/9b///8iAEUAbQBsAFYAQgAZAPX/2P+k/4P/mP/a/0IAqAD4AEIBcgF3AWEBOwEWAegAqgCLAIkAfgB2AH4AlwC9AOQAGwFbAYEBmAGiAZYBkgGBAVgBRwFBAS0BLwE8AUgBUgEqAeUAvQCnAJMAcgBPAEUAKgAHAB8AKQD9//H/9//s//P/8f/s//v/8f/a/8H/gP9Z/3D/gv+W/8X//v83AFEAUwBaAEkANAA7ABwA6//l/+D/1P/4/ykASABrAKEA2QD8ABwBXAGPAYMBbQFwAXMBbQFnAWcBhQGyAb8BwwHiAe4B4QHeAcUBhgFMARgB5QDBAJoAeABoAEcAIwAcABkACgD//wAABwD4/9D/sP+P/3b/dP9s/13/Z/9w/2n/av9x/3T/ev+A/37/ef95/3H/Uv8w/yD/Fv8C//H+7/7w/ur+//4r/zP/N/9p/4P/fP+h/8j/uv+x/7T/p/+t/87/5f/y/wUAGQAcAAoA9//r/9b/u/+f/3X/TP9B/0H/Kf8U/yD/Kv8e/yL/Kf8T//7+9v7f/rv+k/5w/lb+O/49/mf+fP52/on+ov6b/nv+WP41/gz+8/34/ef90v30/Q7+Cf4w/mL+hf7G/vb+H/94/6T/jv+c/6//qf/F/+f/+v8aADIASABlAGUAWgBOAC8AJgAvABUABgAKAOT/u/+q/3n/TP9V/1//V/9W/0n/Ov9H/0//Qf8+/0b/Qv8//0P/Qv9A/zr/Jv8U/wj/6v7R/tP+yv6n/pD+g/5t/lz+SP4t/hf+Bf79/fz94/3H/b79pf2V/a/9vf2w/aX9kP16/XX9cf2A/Zr9nf2i/bP9vP3b/Qz+If4p/jT+N/47/jv+Jf4W/iD+J/4e/hz+JP4q/j/+WP5U/k3+Xv5g/k7+T/5R/jT+F/4Z/iT+Iv4j/iz+Mv45/jv+N/5G/lL+Pv45/kz+R/4i/vj96/34/ej9zv3S/c79x/3e/ev96f3t/d79xv25/az9p/2c/Yv9iv17/WT9dP19/XX9kP2o/bH92v36/fz9GP5I/mb+d/6V/sn++P4c/1H/gv+Y/7L/0//n//X/DQAnADUAOAA8AEIARgAuAAEA/P8CANn/tf+y/6P/nf+i/5n/of+h/3z/af9V/x3/9/7b/rL+qv6t/qn+uv7A/r7+0f7U/sz+1/7c/uT+9v7s/un+Cv8j/0L/ev+c/7f/7P8bAC8ANwBJAGkAaQBBADIAPQA/AFkAfgB/AHcAcwBUADYAMgAmABEAFAAjACQAIQAwAEYAUQBKADEAFQAEAPb/4//P/6z/hv93/27/Yf9n/3P/Yv9G/z3/N/8v/zD/Kf8Y/xj/Gf8L///+9v77/hr/Lv8w/z3/V/+C/6//x//t/yIARABeAHIAYwBDAAwAwP+E/1H/F//p/sj+x/7f/uT+9v4v/13/d/+F/4b/iv9m/xf/4v6y/mb+Mv4U/u/91f3G/cT90P3f/QD+Jf5E/nj+mv6Y/rf+x/6U/nr+hf58/n3+ev5X/lX+bf5v/nf+kP6k/rb+xP7I/tD+zv6r/m3+OP4S/uf9w/2f/XT9d/2c/Zn9lP23/cz90f3T/b79sP2l/Yb9gf14/T/9JP0q/R79G/0Q/f38Gf00/SH9Ff0e/TT9Uf1e/XX9jv2G/Zr9xv3G/c799/0A/vb9A/4Z/ij+J/4p/jD+Hf4F/gX+/v3j/cj9t/2x/Zr9ev1l/Tr9Cf0Q/Sz9L/0q/R/9HP0n/Rj9BP0T/Rn9Cf0g/UD9M/02/VP9RP03/Vv9Zf1W/Wz9g/15/XX9gf2L/YD9b/14/Xn9cP2B/Xr9UP1C/UH9Pv1H/UL9Rv1a/U79Sv1o/Wv9bP1x/Vb9Tv1Z/UX9QP1M/Ur9Vv1g/WH9dv2B/Y39vP3X/dr96v3s/fb9Fv4O/vb9/v3+/f/9Dv75/db94/33/eb92P3e/cv9qf2W/X79b/1w/VD9Kv04/UL9J/0W/Qr97vzb/NP8wvyw/K38sPyf/IT8ePxy/HP8cPxb/Fv8cPxv/G78gvyN/J78ufzD/Nb8+PwJ/Rv9Pf1W/W39if2Y/an9wv3H/cv95f3r/d795v3l/c79z/3S/bb9rP2s/ZL9hv2Z/ar9tf27/b39wv2z/Y79g/12/Tr9DP0P/Qr99fz2/Pf88fz4/Pf8/fwc/SX9FP0k/UD9P/0+/VX9dP2G/Yz9lf2r/c798P3+/Rf+QP5I/kv+b/54/mn+df57/nL+cv5l/lb+S/4q/iX+Uv5g/lP+Xv5y/oz+pf6j/qD+rP61/rf+of6K/pf+l/6I/pX+k/6J/qD+oP6W/rD+uf63/s3+0v7e/gX/FP8f/zT/L/85/1L/X/95/3j/Zf+B/47/e/+X/6//pf+n/6T/pP+s/4j/bP9+/3H/Vf9J/zj/Pv9K/zf/Ov9a/2//f/+E/4f/lf+R/3//dv9z/4f/j/9u/2X/dv91/3f/gP+N/6T/pv+o/9H/6P/v/yAAQAA3AEoAawB6AIwAnQChAKkAtwDPAOkA8wACARgBGAETARYBCQEDAfsA0gDIAOMAyACnAMAAuwCfAMEA0gCnAJwAnwB8AG8AfwCAAI0ArgCyAKgArwCrAI8AgACCAG8ASQBAAFoAYgBUAGAAcgByAIIAjgB7AIEAjwBwAFkAYgBVAD0AOAAzACQAHAAcAB8AIgAjACYALgAvACoANwBKAEoAVQB0AHIAYQBsAHkAdQBuAFgAPgA6AEAAPwA7ADEAJwA7AFcASwA/AFUAYQBgAGsAZwBiAGwAZQBcAFoASgBbAIUAhwCLAKsAqQCZAKQAoQCDAGYAXABbAD8AFwAJAPn/3P/Y/9D/qf+N/5T/nf+V/5P/k/+E/3H/Yv9R/0X/Pf80/y3/IP8H//D+6/4D/yD/FP/5/gv/MP87/zv/Sv9a/1T/V/92/4n/jf+Z/53/oP+1/8L/v//E/8L/t/+2/7z/vP+u/6v/uf+t/5H/if97/2j/a/9c/zz/N/8u/xr/G/8f/xz/GP8K/wf//f7Q/sD+0P67/pf+if52/mH+Tf4//kz+Tf4t/ib+Mf4i/h7+Mf4p/hj+G/4V/hP+KP4g/vn9/f0b/hn+Cf4F/hL+Hv4R/gn+JP4q/hP+D/4C/u798/3r/c/9yf3N/c/92f3b/c39wf23/af9mP2T/ZH9jv2Q/Yz9gv2B/XT9Zv1z/W/9Tf1B/Uf9QP0+/Tv9K/0c/Rf9Gv0T/f38/vwU/Q798vzy/Ar9Cv34/AP9HP0f/S79QP0w/TD9Sf06/R79If0m/S/9Qv1J/VP9Zv1s/XX9jf2e/aj9sf25/df9B/4W/gz+Hv41/jP+Rf5u/nr+b/50/oL+g/5o/kr+SP5W/mX+df5y/mv+a/5U/kD+Pf4l/iH+Nf4a/gX+Iv4l/h/+Lv4g/hP+G/4P/g/+Gf79/f79KP4y/ib+Jv4t/jj+Pf5C/k3+Rf5A/lL+VP5O/ln+Y/5s/n/+j/6c/rH+w/6y/pL+n/66/rf+x/7f/t3+9v4T//b+3f7v/vn+9f7//hj/H/8S/x7/JP8L/xf/Kv8Q/xv/PP8x/0X/Y/9B/zX/Sf8w/yD/Nv83/yv/Jv8n/zL/JP/+/vD+9/76/vT+6v7t/vL+8v76/vL+2v7c/uT+5f78/gP/+f4N/xH///4U/yf/Iv89/1v/Vv9O/0T/PP84/yH/Hf8t/xz/E/8k/x7/Hv8r/yH/I/80/yn/Jf8q/yL/JP8m/xv/G/8W/wf/Ff8s/y7/M/9R/23/Zf9P/1L/Vf9Z/2//c/9s/3T/av9n/47/m/+P/6L/rP+n/7T/tP+r/7//z//K/8b/vP+3/8L/zf/X/97/0f/D/8n/yv+6/7r/yP/H/8D/yP/O/9L/4//w/+P/1//l//P/7f/0/wUACgAPABIAEwAkACkAGQAlADoAMgA1AEgARgA8AD4ASwBZAFkAWgBgAF4AZgB0AHEAcAB+AJAAmgCTAJYAqQCuAK8AugDEANQA1wDEANEA5ADZAOUA9gDoAPUADgEFAQoBHQEaARoBHAEbASEBHgEiATQBKgEaAS4BPwE4ATIBKwEpATEBOAE7AT8BOwEvASUBKQE0ATIBLwExASQBHgE0AT8BMAEtATQBKAEVARMBIAEjARUBFQEjASABIwE6AUABNgE4ATsBPAFAAT4BQQFFAToBKgEiASgBMgEsARwBGAElATUBLAEVARYBHgEZARsBGwELAQEBBAEBAfYA+AAHAQAB6wDzAPwA9wANASEBGAEbAScBIwEjASYBMAE+AS0BHwEpARwBDQEbARYB/wD2APUABQEQAfsA9wARASABIgEYAREBHgEZAQkBFAEYARUBJgEcAQsBJAEwASYBNQFAAUABVQFlAWEBWwFbAW0BfAF2AXkBfwF7AY8BoQGVAZMBmwGZAZsBoAGkAbQBugGyAbIBvwHVAdEBswG4AdABzQHLAcsBvgHDAcoBuwHCAdIBzgHLAcsBygHPAc0B0gHnAegB1wHIAbcBxgHqAe4B6AHvAeABxQHBAdAB2gHKAbMBrgGvAbABsgGqAagBrAGpAa0BrgGiAasBwwHIAcMBwgHQAeQB3AHUAegB8gH9ARIC+gHgAfsBBAL5ARACGgISAiQCHALvAeMB5wHdAegB9gHnAdgB5gH9AfsB3wHaAfAB7AHYAeIB+QH2AeQB3QHeAdIBywHgAesB4gHkAeoB5wHoAdwB1wHwAeQBsAGnAa0BmwGYAaEBpgGqAZsBlAGfAZsBowGtAY0BgQGOAXkBUwEdAQABOQFHAQUBDQE3ASwBJAEKAfkAKQEtAQEBFAFCAVABNgEcATQBEQGsALEA7gACASYBSQGBAcIBlwFxAZwBmwGpAYkB1wCdAGUA+P6m/Xz8hvlV9iP2Hfhw+PL1U/Tc9YH3kvft98H4bfkQ+pz5QfiX99j2rfXK9Zj24vZz9w74xPjn+rP8xvx4/Vr+pP2i/N75evTd8W/yRfGk8EfzxfUd9wv4/PeM+Nf5Mvr7+uj7HvuS+gj7Bft++z38E/yE/HX9f/25/UX+Y/7a/iD/nv6t/gz/1P7A/r3+N/5N/er7tvpg+kr6VfpR+pH5Nfnv+Wf6vPqo+2z8+/yQ/cv9Cf5f/pX+TP8wAHsAzQBbAbsBJQJlAmECqAITA2sDFgThBI8FKwZwBm4GXgYfBuMFrgU7BdIEdATKAxkDjAL6AZ4BewFbAWABiAG0AfIBFQIEAvsBFgIwAg8CtgFoASIB1QCrAGUA4v+f/6D/oP/6/5YA8wA7AaQBAwJpAtoCHwM5AzAD/AKhAv8BGQEzAE3/Pf47/XX8svvZ+iD6hfnh+IT4wvhd+Sf6H/sQ/Pb83/2N/v3+Of/r/jX+jv3S/Ob7EPtI+qH5WPk++Uf5j/ng+U/6HfsJ/M78TP1V/Tv9bP2k/Z/9av38/HH89vuC+yT79frh+t76Bvt8+0P8Ff26/Tv+rP4p/7v/KgBMABEAcf/E/nP+Pv7Z/VX9vvxJ/GT8B/33/QH/pv+//9z/MQCSAPkAKAH6AMwAzADsAFEB5QF3AisDFAQMBQEG0AZKB5oHCQhkCDsIlwfQBikG0wXTBRMGoQZUB/kHsAh+CWEKmgsCDWYO6g8uEd4RdhIaE4cTlhPcEqcR9BDNELsQdxCrD6IO2A1XDYANQw76DuAPBxEPEmATmRTfFL0UghQfFB4U7BPuEvsRHREbEIoP/g4+DgUOEw5IDhsP5w8pEAwQjA9RD5IPdg8lDwIPwQ6uDrUOSw7KDTYNTQzKC8ALjQsYCzMK7gjEB30G8QRUA2sBXv+J/b77FPqR+Nn2KPXD85Py6/Gs8VbxIPEb8QjxJPFw8dPxhfJE87vzA/QU9PLzwvNo8+vydPL48Xjx4vAu8Irv5+4/7trtlu1J7R3t3Oxo7Bjs3euX64rrtev361XssuwO7ZPtRO4k7y7wKvH18ZLyH/PA81n02/RV9aT17PVe9s32UvcE+Iv4CPmu+Sr6m/oX+0T7UvtM+9v6TvrO+TT5vPiK+PT4Tfrm+4r91P+ZAooFYQiTCj0MQA1TDUQNLg1UDP4KkQlXCKMH2wYUBgcGVgYZB5MILgo9DLQOnBB1El0UmRWuFlQXExdWFzwY2BjnGaQbXR0KH6MgRCIaJMYlFCfxJ5MoJin0KC8opyfNJuklsCU2JeYkrCU1JiwmACbOJCYj3SHrH1sdtxpJFzMTDQ+nCg4GMgHU+2L2RPGi7OroLOb54z/if+ES4uXjv+YY6nPt6vBt9MH3LfuX/n4B4QPABf4GsgfpB9AHkQcfB6sGIQYJBXgDqAGf/5X9evsa+aP2IPSd8VDvKu0866bpWOi/51Hoyent65fuYPEh9NL2ZPnx+3r+8AAsA9AE3AV2BnAG8QVVBX8EgwOcAqUBoQCx/5n+X/04/Bn7EfpB+Y749fdn96r2wfX89LH05fQ+9ZT1Dfaj9l/3VfhG+QD6l/oc+7b7cPwR/Xn9p/3a/X3+g/+eAKYBFAJwAQ8Ah/4y/Tf8oPtu+1j7DvvO+rv6rPqy+pP63/mm+OL2lvSW8pDxcPEF8hDzcvQz9gD4kfkN+2v8bP3t/c/9N/14/ID7Sfo0+YX4Ovg1+Df4Efic97/2w/Xv9B70XfMF8y/ztvNY9P70CPat97H55ftC/vkAGQT3BvUI0Qk6Ca0HMAYXBaQE3AT1BMQEuwTsBL4FPweTCJgJxwpuDAQPJxK4FHgWpReKGIUZJRrhGRYZNxjHFx8YmBi3GOAYShnsGbwajRs5HEUcOBtXGQAXDxSCEMoMdAmbBooDpv8b+zX2SPEn7VfqqOjU557n++dM6XPrse0B8Mzy4PUT+Vf8MP8/AagCpwOQBJ4FygbuB8cIPQmBCYQJHAluCHsHHQaCBOMCIQEA/3n8ufn99ov0j/Ir8YjwrPB38czyb/RD9nf4+vpy/bX/ogEPAyIEEwXiBX8G5gYlB1gHeQd+B3kHVgfQBs8FbgTRAiQBk/8P/o38NPsF+uP4B/if92T3L/cl9yv3Kvc+90T3MPdB93j30fde+OP4Qvmi+e35HPpH+kH67flf+b/4dPia+Ov4Tvl7+T75Bvny+Kb4FfhS94z2KfY39pH2LPfE9zj4yPiQ+Xf6XfsP/Gb8Yfwc/Lv7OfuK+rn5q/g59571ZPT181P0FfWk9cr14/US9g722fWt9bf1Hfar9vD2z/Zt9hL2+vUE9kn2Evfo90T4SPgy+Dv4WvhF+Fj4G/lr+vT7IP1c/f38evzx+9T7g/wD/ioAUAL9A34F2AbmB9YIngkOCjcKbAobC18Msw0oDqkNvg1PD0URdhKCEvsR/hG6EUUQfA9FEM8RnBRRGNYbFx9IIL4emB1PHT8c/xoHGfgVzBNvEf4MbwgbBUoDSAQ2BwcKiAvXCegEbP+t+rP2wvOW8ULwFvCC8CnxFvI+8xD17ver++r/rgPdBUsGbwXrA20CTgH4APgBIwSlBq4IoAllCX8Idwd9BmYF1QOWAeP+OPwO+nj4LPdG9mX2sfej+av7Tv0u/ln+LP75/fT9Jf6F/ib/GgBmAfYCjAT2BS4HHAirCOEImAioBzEGQwT1Aab/sf1h/Mr7qfvQ+xj8APxo+5j6uvks+R35Ofll+ZT5gfli+XT5ovkT+uz6Rfwz/hYAXAEsAlgCuwHzAFQA2f+W/2b/S/9h/27/i/8CAKAARQHZAR8CKwLpAfkAgP/L/f37f/qM+fX4ufj3+KP5jPp3+zn8s/zz/D39aP0O/Wb83Ptg+8D6z/mk+Of34/cU+B74/fe694X3UPcH9wr3bvcA+Mz4f/nR+SD6WvpK+i36wvnt+Eb44PfA9wL4DPjN98L3y/cb+Ov4u/l6+ij7hfv0+6P8Y/1p/on/tQBqArQErge6CjYMIwyaC8kKCwp3CcQIwgi4CS8LaQ22DxoRJRIEE6UTmBRfFZsVCxaPFt0WgxfBGFAamxtKHJ4cSBy+GnUYMRbDFGcUFRQ5Ew8SsxDGD6oPhg93Do4LIQZc/wD5pvNY7yXsXepR6rTr8O1A8Kbx0/Fx8VzxEPJr8wj1yPa5+PX6cP0DAIwCsQRuBjEIugl1CkIKHQldB7AFPQQUAz8CVAFYAK7/Bv/g/RX8zfl191T1qvPc8r7y/fK98/H0Z/YZ+L/5NPul/Br+qf9KAZoCeAMCBFAEsgRFBdoFaga5BosGEwY0BbkDAQI/AGz+zPyH+5f6+Plb+a74Jvio9zr3E/cE9+D2rvaE9sb2g/dX+En5cPqf+8v8ov3H/XH93fxU/Hf8Uf2C/t//DQHrAbwCSAM/A8sC/gHuAPf/KP9O/mD9pPyE/PH8WP1X/eP8IvxV+4f61fme+d75Qfq5+jT7fvuZ+8X7JvyF/KH8fvwp/Ln7aftM+1j7l/vu+0H8gPx5/C/86vuT+8b6Zfm09w/2uPQj9IL0DfXu9Cf07fK+8W3x/PHC8k3zTPPg8qLyyPJo88P0i/Y/+M75Afux+w385vsg+2H6Nvq/+vr7T/0u/tP+gf9AAEABbQKCA08EoQSnBP0E9wVKByQICAi5BzUItAnuC+UNWA4oDQoL4QgLCFUJ/gvYDjYRxRKlE1AU5RQfFeIUHhQjE/QSDRTsFfMXMhm3GL8WGBRmEVcP+Q3CDCYLTggGBKD/OvwB+s34JPid9wL3x/XZ8w7yCPHT8CHxk/Fu8jD0o/Yy+Xv7V/3H/gUAWwHSAjsEhgWxBtAHBAkSCo0KYgrDCf4IOgg1B88FPASOAuMAbf8X/u38Bvwi+1D60PlM+W34f/fR9nf2rfaz9zL5kPq2+8r8sf13/jf/2/+HADYBuAFUAhEDmgMgBIMEWgQoBC4EAgSyAwIDhgHA/yT+7/yb/MT8svx7/Br8dvvD+s35bvge94n2Offb+Bf6Fvox+Zj4gPnK+yL+ZP9F/7X+I/+2AFcCCwOuAhQCUgJtA2AEYQSDA28C0QGaAdQA+f64/Av7nvpg+w38tvv/+rX65Pop+7X6Qvmk99b2bPcy+Qr7IvyV/Nj8cf1g/vH+9P4Z/+n/NwFPAooC5QG9AFH/4f27/AD8ifsi+976wfpj+qj5y/ia9yj2L/XB9Hr0pvR19Zz2zfdb+NL3vfax9ev01vSz9XL3gvnT+iD7N/ur+3L8J/2I/dP9W/5V/8oADgKcAvkCrAPjBHsGrQciCCgI0AeQB8oHzAeiB0gIxgmiC2YNjA5iDzwQoxCeEFcQow8+D8oP2BBbEhwUIRVvFXgV6hQeFOQTChRGFGEUyBOUEjgRTg93DOQI8gRmAeL+Gf2t+4P6UPnN9w72XfT+8v/xafFb8Z7x5PEz8pHyEvMN9M71N/ja+i390v7a/7EAyQE1A84EWgajB64IlglNCrwKwwpICogJwAjxBy0HQwbWBD0DBQINASYAPv8G/p38h/uu+tP5M/nx+P34a/kG+kT6Ffrd+d/5TfpA+4H8uv3I/rL/kgB8AWQCOgMRBO4EqwVHBssG/wbEBksGrgUbBbYEFATxAr0BvgD+/5j/TP+9/uH9nfwP+8D51Pgp+Nv3/PdY+J74o/il+A/5BvoQ+1/76fqG+uD6Gvyz/bP+Cv+S/5QAtAFhAigCdQFBAcMBeALVApQC/gGeAbYBGgI/AscB4QDo/yv/2v68/pb+j/6R/iL+Qf1s/NT7b/tq+6T7nvs2+5j6BPrr+Ub6bvpA+h36Evog+mz61PpC+577Z/uy+mT6wPo7+1r7+vqH+qf6RPvP+wD84/ux+2v7sPpm+QX4QffX9+P5T/yU/Tv9Mfxt+x37Efsd+4P7sPxO/tn/WQGrAqADVgSqBOkEvQXfBs0HZwimCD4JxAqaDFIO3Q++EKIQYA9RDdsL6wtdDcMPEhISE20SrhAuD70OwQ7zDqAPWRCZEBAQgw6YDA8LjAmRB0QFAwMJAWn/Gf74/An8evvj+p75u/eA9VzzIvIV8t/yIPQ09a31sPVz9V310/Wt9sD3DPlu+sj7+/wI/gX/yv+eAPABNAPMA9UDcwMRAzEDmQP9A0oEQATZAyUDEQLXANf/R/87/03/9/40/jT9N/yQ+0n7J/vt+nD6uPkG+a/46PiB+S/6zPo0+4H74ftV/Mb89/zG/Kn8J/1U/uP/JwGlAaEBfAFeAXgBwwHvAb0BSwHrAK8AjgCtAPsAHAHIAOT/pP6J/f38K/2w/dj9j/0S/Yv8ffz//Ez9Bf0v/O76Hfpa+jL7E/xy/Bb8tvvM+/77J/w//C/8Nvyd/KD9Af+k/zf/r/55/mD+L/6u/Uv9lf0T/lT+b/42/qX9A/1V/H37cPqJ+Xb5PPoy+3z7aPqU+H33i/cn+L344Piv+K74VvnR+j38Y/yB+7v68vp1/E3+Jv8p/1v/SwDCAc8CEQMcAx4DNwNIBI0GCQmnCtkKFgqMCeAJBguRDPkNLA/uD5wPkw7KDXsN4A0lD5sQoRHoEXgRVBHNEeQRmxGdEbwRChLHEksTxhIYERgPJg5xDpsOIw1vCZgE/QAyANABuQN9A3AA8vvr94b1q/R/9FP08/N88+nyBvID8Tfw1O8i8OjwU/FF8WzxXvIv9Eb22Pe9+Fr5KPpL+3n8ev1Y/kX/twCgAhUEmARxBAIE1ANBBPgEkwXaBYcFuQTkAz8DzAJ8AgkCQAFFAFP/fv7I/R39cfy6+/r6YfoX+vP5vfld+dj4WPgm+Hn4PPn8+VD6PPrt+bn5G/oP+xD8yPwO/cj8W/xm/BD9BP7C/vL+uf6F/p/+7v79/qP+Tv5I/mP+bP5R/iv+H/7v/Vz9qPwi/OL74Pu8+zf7kPoa+kz6ePva/EL9fPxV++f6qvvY/JT90v3O/ev9iv5d/7z/eP/n/sD+Xv8AAOD/QP++/sf+ef8bAOP/B/8y/gD+w/5o/2T+J/xh+ur5v/o7/BL9dvze+m75/vhk+Y759fiL+En5nPp3+7n7ufvV+wj83/uW+/n7IP21/iwAsQBaABoAdgCDAc0CfwOnAxgERwU7Bz8J8QkRCQAIGgi5CSkM5w0eDtoNZw5uD9wPQw8wDgIOqg++ErQVaRZFFO8RVhJUFTgYVRjlFRQUFBUTGJcaOhrUFh8TwBHLEkkUERRzEWANkQmGB0MHSAdKBjwEtwET/0P8kfme96z2Ufac9cvzJPHq7knuGO/y78Xvie7+7HjstO3d76zxmvLn8mTznPQx9pv3wPjY+Tj7/Py//gUAvQA9AeIBzQLYA7AEFwUZBQIFEwVQBXMFDgUNBOYCCgJ1AecAHwAI/9L9tfy8+9f62Pmg+GX3d/bs9Z71XvUA9Xj0FfQW9DT0DfSQ8yPzh/Pn9HD2Rvci95P2sfbW92f5q/oP+736kfr1+vb7i/3n/k7/3P7o/S/9bv1F/hz/xP/O/w//8v2i/Cf7C/oE+iH7cvy9/JD7h/nM9z/3t/cy+N339PZY9pX2ffdB+Ev4CfgP+Cz4P/hM+ND3ova39RP2rPdr+UH6Bvpl+QT5t/gb+KX35vfP+BT66/qC+mr53Pha+Xv6D/tw+nv5D/lo+Yz6y/s//AP8xPvK++L79vtX/Cz9X/6M/+z/m/96/6P/PQC7AVEDagSQBZcGJgeAB4EHlQe+CNMKGg0LDwIQORDJEFkSdRSoFSoVExTJExQV0xdzGrYb0xsXG0gaahoCG1EbdxtNG80aZRr3GXIZ5xiyF3UVtxLyD4gN9gtVC/4KugncBgIDMv8U/A/6H/mq+LT3lfW18hfwOO4q7Qztfu2K7Zjs/eqw6ajpCesW7f/uOvCU8IvwAPFs8rb0bPfx+bP7hPzf/KP9Vf/bAYMESwbPBqUGiAbcBsIH8QjsCV8KEAocCQkITQcJBxMH0wbPBUIErgJcAV4Akf/K/vr9/Py7+236Uvl7+OP3fvcb91T2GvUR9N/znfTO9X72MfZi9cL01/TI9fX2tvca+Iz4XPlu+k/74vtO/L38aP0v/qP+lf5J/nv+gf+RAPUAnQCW/23+2v3L/e39BP7v/er94v1n/Yf8ffuY+lL6iPqv+nj6y/kl+RH5RvlK+b74k/eh9q72l/fi+LL5Tvkp+FP3e/d1+HD56vnr+dD5MPr2+oX7wPvE++n7h/wB/eL8sfzY/Lr9XP+AAHUAuv+z/j7+Qf8rAfECywNqA74C3QLSAxIFzAUKBvUGbwg1Cd8IHQjnCGQMVhAAEtIQpg2hC/IN6hJrFncWDxQmE1cWmRoaHOca3xgpGPMZ4RwfH/4fhh+3Hkgebx3UG6saXBu+HX4fSh7zGRAUbw+GDrIQoRJbEXsMlgahAvIADQC4/l/8UPls9vTzifFB767tOO1j7Zzs4+ll5jfkaeSS5troh+nE6MPnhueq6KvqgOz97WXv3fCU8lX06vWt97D5m/s+/Yn+u/80AfcC1ASKBpMHuQeLB8UHkAiPCV8KxAp7CnoJRgh+BzsHPQcWBycGOwT6ATQAY/9a/yr/EP4n/Pr5OPhB9732SfaU9VP0FPO08gLzOfPb8t7xwvAb8CDwvPCc8X3yRvO486rzPfPu8pnzcPVV9w74jvfY9gf3WPgu+on7o/vi+nD68fpP/MP9Wf4W/qX9VP0u/Rr9Gf2K/UL+gv4P/ij9GPwz+4H6DfoS+kH6Avpj+b74MPi/92L3Bfe19nn2MvYM9l323fYX9yT3Ffe89lX2f/as94b53frq+hH6ZvkE+uT7uf2t/hH/ZP/2/xkB3wK0BPsFsgbqBr4GrwZOB4oJXg1MEKUQVA9ODYgMMA9hEwsWsxagFXMUSRUeF4QYthlvGuQakRv3G2Mc/xxOHQ0e5B6DHu0d9R0cHrgeCh/tHaUcHhy+Gw4b+hgNFToRaw+PD0wQ0Q/jDOQHzQKE/xD+J/2L+6v4H/UR8hXwCO9B7hbtRuuy6Mjlc+NL4sHiieTq5bblVuTg4qXiLOR45mLocenQ6X7qTOzt7pjxoPO79Gf1dPY1+JX6Rf2f/yMBCgLGAoQDXARSBVkGWAciCKUI0AilCHQIPAi2BxkHgwa8Bf0EYAS0AwcDMAL/ALb/Qv6l/Gf7hfqc+ZL4Ufcv9q71iPUs9Sv0WPJd8D3vWO9y8LDx+vEB8X/voO4L7z/wSPGY8UfxE/GV8eLy8/Tz9nT3mPap9Tr1fvWs9nP4dPoe/I381fsR+xP78Pv5/Gj9UP0d/SP9e/2C/f78z/wu/Vb9y/xq+9b5Xvk7+lv7gfsb+vL3fvZu9lP3G/gZ+JX3+far9gL3i/er94r3j/fs94z4RvlS+rr7/vzL/Tf+iv4x/5MAswKkBJMFLwaGB54JmgtCDDIMog1qEHsSORMZEz8T4BQ2Fx4ZfhrVGrAaxhuNHVYeRx6dHv4fryGWIrYiiiK4ItgjDyWFJVElEiSpIuQiqiMqI4khqh5MG5kZPxmGGBkXSxT3DwcMfQlTB7QEhAEI/pj6OvfT88fwmu7r7JXqUue+42LgId6j3fbduN1u3IbaMNlD2VvaudvI3EvdvN243pHgGeOO5aTnpOl060Xtqe9v8kH1D/hy+l78Jf7c/8sBCgQZBs4HMAk2CiYLDwy4DCANMw0KDQsNKQ0UDaEMtAupCvUJbAm5CLMHPAaHBNsCXAE1AEf/OP7u/EX7XvnO97/29/VW9V30xvJh8b7wfPBJ8Nrv+O4u7t7tpe1/7cLtbu5f7w3wye8D79vu3u+u8SnzgfNs8xf0t/WB94X4ufjh+Jv54vog/P78yP1Q/jT+JP7M/sH/cgCDAAQAuv/S/9f/2f/n/97/4v/F/yv/Vf7O/Rn+9v4S/9z9bvwU/Ev9JP/H/9H+fP3n/Lr9q/+EAZYCyAJ7AtUCWwSBBrsIJgpeCmYK9ApLDLgOGxFZEj0TWBRcFTUWtRZ9F6gZXBwGHoEehR7zHuMf5SCSIsckyiVtJdAkWiSWJM0laifVKMsovSbYJJUkgySVI/UhpB8IHesapRn0GM0XJRUFET4M2gelBIQCqQBA/vP6Pve/853wz+0o62LobOVu4tHfM9673bLdGN232xLam9jn13TYztlk27nc7Nwz3RXg7eM35TLlnOZl6HXpyetE8Jb0A/c7+A/56/mJ+9f9KwCuAkAF2AY0B2AHMwhlCW8KSguqCzkLqwrXCqkLVQz0C5gKCQlsByYGygWIBY8ECwNFARoA5P9N/6r9Mfv193D1APW89TL2KPU78ofvr+647qruEe5e7EzqSeni6YXrxey/7L/reero6aDqEux67UPuhO5U7+HwGfKV8qTy1/LV83D14vbE9zv47PgO+hj7x/sP/CD8rfzP/d3+W/8b/7D+9f6t/wgAvf8Z/93+S//3/4oAxwCpAH0AXwBiAIsAxwBWATACAwOuA8MDbwPuA5wFpQe9CCUIcwd1CKcKBA2gDuAO0g5SD4IQuxLYFL8VHhYsFtsWgxlaHG8dCR28G4sb0B18IBEieyKwIWchkSLSI5IkdiR0I/QixCK+IT4ghR4hHegcwhyqG4YZ+hVIEskPeQ3fCmIIngXXAl8Amf2b+pH3EPR98HntCesF6f7m1+Tj4urg1N5L3WTce9td2lvZ39gS2bTZf9pF28rbFdx93HrdW9+64ebjtuVK58Xoeup87JjuuPDP8uP0EvdY+af7x/1i/3UAMwHoARgD5ATYBlcI7AjkCPkIYwkJCoIKMQpkCd8IvAjECI8I+geIBxkHEgaUBNkCTgG7AMAAWwA+/2z9kPuE+gb6ifnV+J33HPbP9NHzWvNk82nzOvPQ8vPx0/AP8DbwAfFm8Q/xsvDh8IjxK/Jo8k7yEfLt8UPyTfPv9IT2M/cM99L2//bV9xf5D/qg+jj7CfwR/fb9YP5+/qL+PP9lADcBXQGyAXgCaQNRBKAEXgRABI0EjQXkBkMHtwaYBlMHWAi0CBkIoQcrCMEJ0AukDM4LQAssDCwONRDYEGIQwRCOEhkVLRdrFxMWIRWFFuUZ9xtYG5Ma1hqTG0gdHB8SH4wdgxxPHWgfaSAwHwwdkxswGwMbZRrGGd0YKxdoFZgTIxGRDnkM3ApJCboGQgNiAOX+Kv7I/ML5u/UG8n/vge427ivt6+oQ6K3le+QP5JLjrOJ+4YrgGOAT4IfgQeGx4bDhjuGp4VfiouNi5Urny+i26YPqgeu57Cvuo+/88EbyiPPh9F32vffl+Oz50vqq+5D8j/3V/lUAlgFAAkcC6AG5AQgCrQJsAwgEbgSaBCUEFwNTAj4CZQJ0AiUCSAE/AJT/Yf8+/5D+Pv2x+036dfks+e74kfgG+PD2o/We9Jrz5fIl86jzj/PR8pvxlvB68A/xxvHt8fTwsO+N76DwxvEy8ifyKfJS8sPycfP08270afXF9s33IPgY+A/4bfj++Sf8Gv3U/KD8Rv37/qUAKAElAQYB0ABDAUsCYQPEBCcGoQbjBRIFhQUMBlsGLgmdDJALXgioCOoLVA48DkIN+A0kECMS4hK6EQkRshP+Fk0X9xUZFRcVeRb3GMcachpmGDYXwRhbG4McCRyHGuAY3hhYGj0bLxtAGsMXEhVuFAAWAxjMF6UU6xAoDmEM7QvnC70KUwgDBYUBP/+u/hb/tP7I+/n2iPIC8PbvSfGK8VXvaOuc53vlZeWb5m7neeYb5MXh4+Dq4ajjwOTi5C3ke+Pg41XlXedT6UvqNurC6brp6Oof7WHvGfEI8m/yE/NQ9Aj21fck+d/5Rfq++tH7V/3e/joAAAEcARABEAFtAXICrQO5BD0F2QQiBM8D5gNaBKwEXQTdA3sDJQMOA/wCjALHAagAZf+G/kX+Zv5H/or9j/xv+yv6T/n7+N340/hv+Fv39PX/9DD19/Xj9eX03/Ms8zTz2vMH9IrzYvPt80X01PPI8/H09PUa9l/2/Paa9w74H/iK+N75CPtG+xT7fPsX/Yf+pP54/nL+lP7W/z4BZQGPAXICLAOiA/YDawSSBTIHDwjDBikFXgcPC44KmwjrCsQOOA68CjELzQ97EdIPFRB/EQ8SBROeEw8UExaDF70WXBWtFC8WBxmDGSQYuxfmF2kY2BkUGxcb4xl0GFkYfBmEGnsaLxnYF0UX1Ra2Fu4WUBYOFboTmBEvD8ANjgxeCmEHiwRqAnIA+v2S+7P5HvcU81vvfO1e7LLqmOga5kDjF+FK4B/gyN8N3/vdSN3I3R/fkOD64Rvj0eOs5AHmv+fU6cjrUu2y7g3wofGY83X1C/el+Bv6Tftu/KP9H/+fAHIBnwGiAboBaAK6A50EcgTNA1YDbwMTBH8E+wPMAr8BZgHBAQgCfQGOAAAAwf95//j+Gv5S/Uf9t/3n/WX9I/zS+kr6XPpY+gL6bPnc+LT4yfhn+GP3UPah9bX1qfZQ92b2lvRg82zzUPSl9LfzpfJ48ijzJ/Rx9NzzivMG9On0qvXX9cr1X/aq9wH5g/nb+DX48Pjj+uf8Hv6A/qH+C/8TAJEBygKAA0gEuQWHB78ISglACiYMTw7eD7IQjhH7EusUZRe3GckaGRuhG54cbR5cIDIhWCIyJYQnmydGJ1gooyr4LCcuai74Lr4v/i8aMJkwDjFJMbExLDI3Mr4x7TAWMMQvYy+VLbcqHyjiJdsjBCJoH8ob2hdqE8cOQQujCOIFzAIN/476L/Zo8jzvs+wr6iHnG+SD4ULfZt0N3BDbHNoU2UnYBdhn2JDZK9uN3HXdEN7A3hrgU+L/5IDncOkG68jsz+7v8BDz/PSV9hz41/mx+2n93P4HAP0A4AGoAi0DjwMcBNcEiQXbBZEF0wTeA/wClQKGAjoCngEWAeQAuwDp/2b+DP1t/KX8V/18/aT8T/vZ+dH4w/gC+d34bfiq9/T25fYS9w330vYv9sf1Bva/9bH0//Pv82X07fRX9NnytfE78Y/xffLM8hryUfEq8dDxnPJr8lbxxfBg8XTya/NE9Lv0sPSd9O30t/W99q73wfgg+lf7LPzi/Mb90/4BAIcBBgMNBGwFnAcVCpcMOg5lDlgO/Q7nEKUUeRiVGjob6BoEG5Ycgh6NIKYi8yOXJbEn1CgDKhgrASveK/ktWy9uMAQxBTEzMn4zXTPhMhQyJzFCMekxtTITM4Mxuy4xLP4paCjrJqck0SFVHo4aThcOFGIQLgwaBx8C9f1L+oX3GvXU8fztrekJ5TbhXt463O/atdkK2DrWWtT90prSudIg047TudNV1PHVK9h/2mPckN1X3mnfeeF65Knngeqk7AHuLu+o8HfyhfSR9lv42vkk+3z87v0i/wsArwDQAL0A8AA1AZ0BWwKjAg8CNAEnACX/rP4t/ov9kf32/SX+0f1I/Bb6t/iZ+H35avoP+qT4APfn9Sf23fbJ9in2xfQk8yTzK/S89Pn0VvTS8qzxvfA88Obwg/GR8Tjxd++V7YHtPu5S70Pw+u6K7LrrdOw67krwnvBO7/LtN+3D7WjvFvFx8gHzxfK98uvyVvPa9OX2dviU+QP6Kfox+xr9af99ATQCWQILBAYHzAnqCxYNlw3MDhUR3BPSFvAYjRkPGk0bzRwJH8Mh8SNKJj4o4yeeJ8wprSvRLHcvgzHdMTQzEDSdMj0yUjNsM4Q0+zZrN0Y25jQGMkAvDC/bL/ovLy9lLJQn3iKxH3IdPBvAGPwUYA9xCTEEjf9//KH6ovcq8/jtTui34z7hxt9k3pDcqdlB1mnTpdE90eHRp9Lt0rjSvNKZ00rVjtfO2Wjbx9x+3n3gAOMP5v3ohuua7fDu/++y8T70QPcS+h78T/0S/vv+IgBjAd4COQTrBEIFZgVTBZQF9gXnBcsFjAXSBDYEsQP1AqsC5QL4AowCVQGW/z/+2/1k/jT/Xv+U/iD9uPvw+pT6m/rs+sn6JPo0+Q/41fey+Pz4Ovjo9kH1XPTK9Ff1D/UQ9PDyCfIn8WjwNfCD8CrxqPFO8Xbw9u8h8OLwlvGW8TzxfPGG8o7zSPTg9Cb1rPXR9nr39vd3+TH74vzJ/oX/Xv/m/9sAaALVBOsGkAgsCnML6wx4DpMPHBEkE+0UjhYGGMYZCBzgHYIfsiBCIDwgoiLgJe0oSiv6K+oryiulK+MsTC+gMaMz8TPPMuUykjOwM440NjV6NLoz+jIgMoIyJzNXMmgwuS2hKkMoAycyJr8kASJPHlsaphZQE8sP4Qv/B/0D3v9z/Nz5Rfdb9Anx0uwl6Ijkb+JT4eTgLOBL3vvbzNn313XXINgE2WDa29ul3HrdxN75307h3+Jc5ATmAehA6pfsnO5g8Orx9PID9Gr11faS+HP65/t+/QH/i//H/xEAEABzAE0BAgK0AgkDwQKbAnAC7wGEAd4AFgD1/xUAJwAxAI3/mf4X/ob99vys/Bn8kPuC+437wPuv+7b6hPlm+FL3Evdj95L3BPg6+Fz35/Vp9FzzZvM89N70ZvTs8qTx3/BK8DrwRfAI8B7wAfA77+/ube858E3xEPL/8WTxyvAB8RDyXvMJ9ZH2Gfcm9xL3Evc4+EL6B/yO/cP+Z//t/1kAqABlAacCXASCBn4I6QnaCkoLsAudDNQNZQ+0Ed8TFBXoFX0WnRYbF0kY6BkEHM4dyB7BH5QgBCHbIQAjVSQOJikntyfKKNUpxCoELIQsRiw6LEEsDi2tLh8vUy6rLRctvSy5LPIrkiq1KRopLSjiJtAk1SHDHh4chRmsFpMTPhDqDJIJ0QXwAXT+aPvN+Ev2ffOI8FTt7ukB55jkfeLs4IffDd7v3P3bJdvz2ijbWdvR25jcm93l3gvg9eD/4SjjdeQH5qTnKemz6k/sC+6+7zfxovID9E31y/Z2+PH5Tvuf/M/95/6y/xkAfgDzAF8B9AGaAvgC/wK1AiUCiAE3AUIBLgHMAIIAXAA5ADEA0//e/gf+pv11/VX9Hf2r/B78evvC+gz6XPnO+GH44Pdb9+r2XPaf9dD05fPm8gLyRfG38FXww+/l7jXuqu3m7EPs5+uK65TrDewb7MnrmOtt643rLOyd7LvsKu3/7dzupO9C8LfwdPHF8iP08/R79TX2EPcN+Dz5Ovrp+sX70Pyh/WP+cv/EAD4CswO7BB4FXQUiBj0HTgh4CYwKYQtUDEgNRg6xD8EQPhFZEgAUmhU4FzwY3Rg0GtcbSR2ZHjwf0x9FIeoiXyRnJWclQyX/JTonTCi4KPko1CmpKiIrfCs3K8kq0yqkKj4quimyKOgnaidOJvokdSMgIW0emRvIGF4W0RMGETAOrAr1BtkDtQCV/d/6Gfhk9cjyv++t7NDp9uaT5LniJuEO4Frf9N7X3qDead5k3j7eRd7T3ozfceCW4ZnijuOo5NHlH+eM6OXpSuvL7FnuBfCv8SrzifTn9VT3xPgT+lP7lfyw/ar+lf9XABEB0wFdAsACKQNmA3cDggN1A2ADVgM7AxkD+wLDAo8CbwI4AusBlgErAagADABc/7j+M/67/Tn9m/ze+xD7VfrJ+Tb5cfi39xD3Tvaf9RP1bfS18+Hy0vHB8L3vvO4L7qDtLu3S7J3sieys7MHsrey/7OLs+Owv7WPtku0B7oruIO8L8CHxFPIO8yn0IfXV9W/2Avd49+T3T/i/+Gr5Sfoo+y38V/1l/oH/wADCAYACRQMIBK4EWgUbBuYGyAfPCPMJGQssDDsNWA5sD18QNhEZEjgTgxSzFboWqBd4GEoZZRrRG0gdsh47IL4h3iKxI2ok7CRZJfolwiZ+Jx8ovShZKcQp/ilBKoQqiSpOKgIqjynNKPonNCczJgsl2CNIIn8gwh65HGUaIRi4FfcSFBARDdgJjgZfAz8AIf0/+qX3B/Vn8trvS+3W6pLoh+b35ODj+uJC4q7hJOGw4FbgL+BM4Ijg6+CR4UXi7uK747PkreWp5svnCOlX6sbrJ+1w7uvvhfEC84n0HPaQ9/H4QfqF+8v8+f0M/xUA/ACuATECnQIZA5kD+gNDBGgEZQRfBEUEBQTEA48DZANQAzID5AJ4AvkBbQHwAHQA4f9L/6f+0P3v/Bv8Nfta+qf5+vhN+KD34/Ye9lL1ZvRm83ryrvHS8NTv+u467mzt0+xv7A/s5OvC63frX+tW6xjr8ert6gPrROtj64nr/uti7ODsuO2D7m/vh/Ay8bfxTvKW8ujyfvMn9BX1FPbn9tb3q/hQ+SL69/rR+9X8sv2H/mr/EQC6AI0BVwItA/gDtQSOBVoGNwc+CCoJNQpXCzcMDw3HDUwOJA8fEC0RqRIGFEYV1hYdGCAZMBoVG1gc8h0iHysg/SB5IT0i1yI8IzAkCiW9JeEmhyeoJ8knYScRJz4nECfrJtwmfCaEJmsmjiXYJOkjgSJLIXYfBx3aGjsYmRWeEyoRhw4tDC4JJQZqAyoAE/1f+n/3BPWa8q/vCe2m6l7o3Oa95aLk1OPt4uzhHeE34Ivfct+N3+/ff+DT4EXh6eFp4hLj9uPf5A7mb+fA6CDqhOv27KjuUPDS8WTz3/RG9rL39/hE+rb7Cv1v/tL/ugBlAQ0CiwIeA74DIQSBBOEECgUaBQ0F7ATnBNUEvAS6BIAEAQRtA8MCJAKYAfsAagDc/xz/VP6W/bb8u/vG+uT5BPkm+Gn3tfbe9fL0CfQz81bySPEg8O7usO2e7MXrD+uW6kbqE+oS6u/pdenm6HToRuhi6IPooOjk6EDplen36aDqlOuV7JHth+5S79XvFPA/8J/wRvEt8jvzNPQc9Qf21faZ91v4Gvn3+b36Vfsc/Ov8lP1s/l3/PwBGAS4C2wKoA20EFgXzBdUGiQdQCBEJqAlaCi4LAAwDDT4OaQ9sEFoRYhKVE8UUBhZhF4AYixmKGv4aVRsHHK0cpx0SHw8g4yDTIVAiyiJbI4gj5yNGJCgkLCToIzgjPyN5I5AjbSQbJewksSQBJOIiAyLIIEMfzR2zG10ZMRe1FHkSpBCqDuUM9go/CFEFLALQ/vD7Y/kI9xL1BPPj8NzukuyB6hDp2efr5izmPuVh5Jrj7eKY4l7iX+Lg4m3jDuTp5JDlPuY750Lodem26sXrBO1c7pDvA/Ge8hL0jvX29jT4cfmc+tP7Jv1V/nn/lABdAecBQgJpApUC2wI3A68DAAQPBAQE3wOuA44DewOLA68DpgNdA9oCEwIgAUgAtv9D/8D+N/6q/fP89/vi+vX5IvlY+L/3Ifcw9ij1KPQQ8xPyPPFn8KPv6u4y7ozt4ew47KvrLOvP6qLqdepL6j7qNuo/6oXq5uon62rr6utv7OrsqO2R7mfvTfAx8fbxqPIn85LzO/QV9Q32Effp95/4QfnU+X36GPun+338Xv0P/sP+S/+Z/wYAiAAEAYkBAwJ+AuoCTAPoA4QEBAXABWkG4gaNBwUIPgjICFEJuQlQCrkKGgvcC6IMdw2BDnYPmxDkEesSBhQUFdIVzRbIF2QYQhk/GvYayRuXHCwdsh3tHTQe5x5rH90fhiC1IIIgYiDuH3cfZh80HwUfGR/SHjQegR17HHEbiRpUGQgYsRbdFNIS4BDaDu4MKQtZCYsHjAU9A/gAtP5o/HP6qvi99t70EfM48XTv4u2p7NfrLuuH6u/paenk6G/oTOho6Hrot+g66Znp2+lI6trqneuX7Kzt4O4U8DTxWfJ085L0zvX39iP4c/mT+nn7X/w4/Rr+Hf8AAMYAfAHzATECVgJeAm8CkQKKAl8CNgLuAYUBLAHRAIMAdwBpACIAx/8y/2H+mP3G/Pr7X/u4+gn6bvmj+N33UPev9h32wfVH9br0HfRK84jy0/ED8VrwvO8W77jucu417kDuPe4G7u7t5O3P7bftme2l7dDt+e1K7rTuFu+T7xXwlvAu8cvxhPJA88/zdPQj9Zr1LPbY9l73CfjX+In5Qvr3+pf7K/yY/Pr8Wf2b/Rv+xP4a/17/y/82AKMA3gD2AGsB5QH2ASICcwKHApEC0AI8A68D6wMTBGIErwTeBA4FagUZBuUGmgdKCNMIKwmhCSwKugp5C18MbA2NDk0P3Q+wEHkRWRK0E+EUrBXEFuIXghgDGYsZ8Bl/GlIb9htGHIYctxzfHD4dpx3iHS0ecB5tHkMe+R2uHWcd5hyHHBQcyxqWGbQYGhfKFSAVlhMgElcRdA8rDXIL/wh4BuUEOwNUAYv/gv1R+x35Gfda9YHzIvJS8Qvwx+7N7W/siesz64Tqberk6q7qjOrl6hDreesk7Lrsgu1Y7ivvCPCl8ITxyvLE88v0E/YO9wr4Ivny+ej6I/w//U3+MP+9/y8AkwDkADsBkgH/AWcChQJ/AmAC4gFbAScB6wB/ACYAwP8m/5H+7f0f/XX87/tO+5364/kO+Sn4S/eM9tr1HPWJ9AL0K/Ms8ijxKfCA7wDvZe4Y7gHup+1F7ebsZuw27FvsfuzG7P/syuy/7Dztpu3a7Tfut+5G7/Hvm/Ah8YrxN/Jb8yH0PvTx9Er2w/af9jj3JPiv+E35JPrE+hP7dvtV/Cj9av3s/eX+Vv9Z/33/rv8WAEwA9/8zAPkAFgEHAQMBeQBMAIMAJwALAF8AHADO//b/HgBVAGQASQDRAIIBggFUAVwBlQEQApUCPQMrBMUE8gRUBfMFcAbIBiUHyAfKCN8JxgqcC28MQQ0xDgoPsQ+GEF4R8xGhEjATiRNsFGQVvhVKFvYWDBdDF8UX8BcuGKIYvhiyGJ8YOhi6F44XwxfqF4AX5xbKFswWURalFUYVMxUsFfQUgxQSFI4TvhLUEcQQVw/vDaoMFAtzCToI+gZQBaEDVwIEASX/Nv2g+wL6cfgt9+31z/T18/Xy1/H18GLw/u997xfvNe9f717voO8c8MzwoPEu8szyufN89EP1IPbF9qD3pvhm+UX6U/tW/Dz9nv0j/lL/u/9b/6f/BgDd/9f/1/+y/4r/M//b/q3+if4d/gb9M/w4/JL7L/py+ff4Pvhg9xj2I/Xl9DD09PI58unxQfFK8KHv/e5G7k3uLu5D7Trtq+0g7eDsK+0F7dfsjOxf7PXsJu3h7G/tWe717ifvJO/u7xHxZPFd8ZrxiPJw80TzCPTm9a/1GfXH9hD44vfM91346vnC+sv5Dvo2/CH9Qfw9/GH+Yv9+/WX9zv/a/6b+X/9NAHgAfQAeAJ//ov9kAJYAg//K/wEBHQBx/3EANQB4//P/SgDX/97+r/6l/zH/Bf5m/lP+Rf3q/Gj9yv6X/gf8Wvzx/mr+3Pzl/DD9fP0N/fH8Qf4u/df65ftQ/Yf9ff7f/bf9yQAIAU7+7/5fAH7/Iv+P/2sAFwJnAscBqAIBBG0ENgQHBPsEIwbtBcoFTQY1BqIGpQf+BnEGCAgxCCAGiwbHCDgIiQYCB5EHuwa0BtcHKgiVB1EHeAfjB2MI7Ae3Bo4GXgf3B18InAjYCFoJYQlKCasJRQn4CEoK/Ap5CskKBAusCoELqQxQDJwLYgw7DecLhgoiC2ELHQtQC2oKBwrrCjoK+wmmCqcIygdICZUHFQaNB40G7AS6BVUFXgRuBE8EhwRzA8gBUAOwA78AqQDVAWgA0/8VAJn/BgDg/6n+2v5I/4L+Xf1Y/Gv8/vyF/Mr7Fvu/+uH7XvxW+8X6kflE+Nb58PsD/Kb6q/i2+cz7WPjr9nP8I/xL95f4/vdk9cf6nf2T+eH6Kvzj+M37fP+6+5n6Nv40/ob7xPtp/rz8Afmb/nQEGPx59U/8HAFA/kr6sfju/RABsfff8hP8iQCj+zz4avX18on2Mfvu977yDPex+8r0YPFc+Nz40vVh96nzVfLu90DyLu5s+sP6m+/B87j37fGU9a75TfPi8Xv1GvWh9D3zB/IA94L5p/SX8/31MvVV9n33pfNl85H1LPOk9W35s/Pk9Dr+GPqn85z3qPap9BL6cvmk9TD4ivfB9I/4tPpg91r4Uvz7+R/3Ifun+4z2j/gh/SD7k/rM/IL7EPrW+nP7VPsX+yv7/fny+k0A0f6Z+Yf/sQGC9+z4hAI0/if2K/bI+Yv/Xv9v92r4TwCo/m/6qP3W/9788fnp+pMAugJd/cX89AFCApcCkQWOA3QAlQCAAxAKSAmi/vj8rQZZDL0H/ADIBrEO7gMr/EsIEwxjAQz/FAZJCj0GcwEHBUAJVwrcCZME5Ae2EIMF3vuKCzgVmAv6AdEDfhI7FBkBfwK5E9MN9gNBCZgLKwoeCXYFQgdQDqsP5AiQAxEIgQtqBz8IdQo0B8wHcgj8BTwJyAvLCcUJ7gm8DD8O1gRgAcsLGQ8wCoAJGQxmDV4IHAVsDfoP/wdQB/cHMgTdBmAKDAoICY4EPwd4D8YHMwDrCNYH7P/yBYYGJAA0B44JiP74/T8EMgQzBKUBT/3EANwBL/xc/LX/eALRBDb8x/WsAnQHy/pZ++kCz/14+lb7zPjb/GsAu/ci9X3/FQCN9O70Rf9d+QTuQPgXAgX4E/XD/M/5n/X/95n3Ifj3+Nz0efWr+XT3NPa0+TL6afj29c/ymPVr+0j5YvOB8hr0Dfb29xf2b/IA89X3mvl88k7uNvix/PbwuO2I99j4q/J57z7wKfYq96HuFPFN/fL46etB7Wn0D/V784XzAvYS9pjxivR8+qbzZO/U95v3Je9177T1PvpQ9R/tWfZs/r7waO7l+m72OvS2/sL1dOks9/QAJvZr8ff2d/mG+vH4nfQT+Fb9Ivkc9y38Svtr9Gzz5fwiA+n4gPMW/7sAY/qM/mr92Pjg/lwAWP0V/R36pP9RA0f3vP7kDs/4MOh8Ay0SBP0I8H39hgzEA5LyTvqdDQsKF/ma+jEJZASS9En8sQwMCioBVf2xAP8KLgil/dkCWQZbAToGlAcoAe4BYAOzCBYMD/zZ+/gRFgzr/lsH2QVoBgwOR/xZ+YMUnBDl/0cKpQyyAj8Hhg96DK0BIgHKCw8JVAMsCSIH3gchEq0Jdf94CjwPLwV7AAMG3gm4BeAIHQ7LAq0C/hL8Ctb5lAKAEpgN4PjR+PMN4AvAAXUH/wNVCssVaP0p+tQUnv/S77ERGRJ29+L6tAgrENwFH/RVAr8QQgSd/fT3TP3tFRsEteVMBo8fgP9+7XAARw22/rjyOAhIDhvyx/gjDx3/7/mDCIYBp/74BT7/Wvkt+HT94Afc9lfqKgsdE+7ta+miBesRSAKW5PPtAReCB/Lf+/fIEpH6m+9wABoD0vnT8fPzawPtACPsJPPhBbb5rfDa/7X7c+w5+fIDDPZf8Zr3efSS9fX5tfIJ9XcCv/fv5p31aARd9wHrU/F7AFUCRO1F6FX+iABR9fL2YfLy9YoE6PK040H9EQTx7aLtEPqQ/Fr4XO957sH5YP038Zjrf/yX/+HnR+3UAzD4dvMm/zPxG+8F/9fzj/Dz/lb1T/GSAE78bvCx9bIAKf7Z7iH0iwWf95Hr7/s4/Bn0oPvV93n0fwLdAL7x/fGl/FoCM/ng7YH4IgI88zHucP8HA273dPgdAkkA2fQg8y8A7wKE8xD0iAXHAY/1yf4zBWn8lf07An351PiJBsUEHfg9/C8C6/59A0IC3/jAAHUDTPigAUMInvp4/SUCp/hwAREMwgFN/E7/hwQSDAYEyvfi/+sIwwIw/1oGdQYK+x/8UwkmC4IB5vPy9hMRVgxZ8uoE3RH3+JP/Ogs1+YADdA4Y+ugBPQ3Q+acAtxFvA4L9mwQ9BIcJvAe9/ikHOAugAcIDHgX4/0MGlAVz/YEHLQoH/V0DPwZr994FLBZT+9vziBDvCnn0fwIpEyEICvtZ/SUJYAyZ/kf5qgeZDCgB7PqQ/v4IwAxw+xX5nhPHCuXl9/S3GP0K/PDu9xoLTg7p/j/5zgnvCrP0X/MYC4QOyvR97SQKlhfi/cDtfQICEqr9lO1l/x0KjP+l/pv9DPuECCsEqPN0BBMMCfT49aMG2QFq/Sv7rveu/Q7+cv7jBaD89vXJ/yb5S/YACd0DdPF1+IQBv/8v/Vv4lfxwAOPypPIfBsgI7frB77Hy8QWPB2Xz//bOA135bvfxAEP8ofzY/0P2sfmT/qHxdfoxCer0IfDbBP79j/Xx/iD7WP00Awb1qvqSB37zePLMBkr+8vgE/rL2SP/EAQnvNfyACLbwCfTWBYb4Y/Wo/3j64/k+/8T+7PzO9Sf2qf+H+MrwFABgC+v9Ve/5+ykMZPoq7bcDzAga9HP25QNyAVf9ZP7a/8D/HvoN9kv8rP999APzPwfYBwjzW/miBBT1R/d8DC8JI/0J+n77dAj3BoHuQfTzETQK4u1/8eAHawrD+UXyIf81Cd7/zvY6+9cAsQRKAj33wf/tD4v7We0GB6wInfUwAIIEs/2gCaMA3e/VB+QQivZf9/gHiwU2AWT+TPy8ACYCOQLO/ob2zgEtEPX+tvF2BA8T2QaD8bfzvw1/Dzf33vScAzoL4wsf/tj0JwfQD0/8YvRrA44MBwHx9mEE2Qyo/hD+FAmzAEH40f7nBdYJcALl96oCVAw/AqYAZAeTBjQGpv+M+BEFXAkT+0QDbBLFAZTzTQAyCH4ENQOu/mD84wWiBsH21vY6DN8PV/5Q+acCUgcdASH2H/rnCkIKjf1X+l76mP9MBzIBU/7YBYkCl/7q//v7ngFFBsb5EPz2CGMCKv7RAu3+2/9SBcoBhvu992L+fAh4AWL9EgYk/xn4NQbKDA4CbPeE9fv95QKM/kL99/1qAY8HTwOC/fv+0v+UBdMF2/iL+vMAVPhi/JgCpfeQ//8NP/4l82v8iPwt+Aj6Ofrj+Gz3lPhc/fb9GvoX9Zbz9vwZAwf4Yu6v9DoBiAl0CIkBUgHrCT8L8/1/9r39C/8W+24APgT1BHEKwQb6/MH+EABQ+Gv1MPpb/3X/cPzTAMQF1f0L+NH+FQC19+T0l/mq/Bz5ovVi/mgHx/un75H7SQdWAIr7MwCCAFX6+PbY/FoCPP1q+9cDNwaRACr+IwEpBIz+sfe7/rMEz/x5+pMCcQbnBH0BVv+UAfMCsAJcA5L/Jvon+1f+ngB/BcsJrwgJBEgA3QCIA24AFfo6/RwFbAJc/FD/vgRpB8EIhAiQCLEEn/tL+kMACATzB94E8Pr8/7EJAAPqAZkMVQpDAzcD4PwV+OUAlQZkAzwGZQ1ACCf5HvmuCdQKk/zT/h0J2gMh/hAB3AGXCGcR5Ab2/MwIjwlu9yT7Tg67C4UBigWxB4sCkwFtAuEGABDsCqP8eQA5CD8Adv+BCNUHYwlZDSgDE/4hBnAGtgXZCWEFcQCrAWQCjwZiCKME7AlDEIULtAfRBfsDjgiCCOQDEAqwDIIAGf1QCasNNwMH/3AKhw5X/2P1t/6iCb4IggFc/1gGIArrADP62QPHDUYIsf5s/z4H5QgyAasAxQtzDcwEPwR0BwkGLgUXBLgDOwbqAxMBZAaYCPEDlARkCMgHsAWSAjIApgSsBjz+dv3eCHwJ4wL1BosJbAXMBegCUf7oBCAHzP+aA5IJGgLt/gQGpgYfAscDNgdIA2n93AAjB6cDEf/eA3AIUAXQAOoAJwWIBjkCjgFbBeQDUAFoA2oCXf9VAu4FRATRArwFUQh8BIT/lgKeBmICy/4hAh4EdAJDAlwCPQLGBR8IJQNC/zQC0gKq/sL+PATaBbcA+P93BskF/v+jAh8EXv+YAXoEQ//u/usEwgV6As8BAgXtBYUCZgOUBHf+Hf6tBLgCq/4DAjIE6QEo/xX/uwIkA87+/v1OANkAkv8b/sf/JwIuANb+zgAUAgADhwH6/RQBkwbUAsD8L//PBKUEBwF4AT4EtgMuAlIBYQCWA0AINAUI/8D/1wIcAXEAJQVVBgACnQJKBloDtP8DA0oGUQOb/xYBRwN/AG/+4QHXBPADKgLoASoDiAFR/UT/kAUmBnUCAgF2A6EGwwNF/gsBPgc7BUj+IfzG/2oBy/4yAOoEkgSPAioC/ACWAmkELgHSAPMDDALE/+sAVwH5AtgE9AJkAQQC3ALQA84CAAK1BOoEMQFPAKABMwFRAEkBAwRpBbQD7ADB/1gCCgW+AnsBAQSoAqwAoAMTBQgFvAerCH8HpwfEBjYEyQJNBD8HfQbmAvQBZwLMAjsEjwMLAaYBOwTaAykAG/5MADUCDQI6AzoEjwM6BCYFdQQ9BAEEfwIQAsQDMQVHBMcCJQQUB2UH7gWoBTEFSwNxAs8CLAJeAaIBnAJKBAAFQQRwBNcDSAFoAYMDzALfACIA3QAnA4QDhQFLAvwEPwXvA64CdgGMAHEAwgHqAuwB0wAbAfMA+P9q//P/0gB7AA0AFwFVAc7/lv8TAm4ETAM8ALv/vwD2/5D/YAGwAv8B/P9T/k7/HAEFAIL+bgDiAk0CjADL//L/6wAAAmsCeQKZAfv/zv/mAHcBwgHSAVkBIAHOAEcAfwCOAEgAMQEcAqwBAgF3AIEA2gHIAnkCTwL6AUsBeQHLAUoBDgE0ATUBRwLlAz0DVAG8AT4DGwPUAl4DBANJAokCAQNXA9oDLwSTBEMFJAW7A4YCLgM2BWcGQQYsBiAGzQWrBn0IFgmYCCkIqgdKB/cGZga6BjMIuwk/C+oLvgq1CRoKWwoRCs4JEglRCOgIbwpNC1oLewu8C/wLEgzuCrEILQcaBwMI2ghxCHkHIgdKBwYIdwhUBwUGjwUdBSUFYQW1BEwEkgR2BIkE0wSNBD4EtAOOAscBYgG+AN//o/7n/W/+/f4k/3H/Cv///V/9h/xb++T6m/pI+u36zPvi+/j7D/x/++b6nPpo+oj6jPr0+Wr5QPkr+S/5GvnB+Ev4nffg9oP2a/ao9kb3e/fb9hD2vvUV9qD2lPYT9vL1TfbC9jD3iPcb+GL5+PoM/Fr8+Ptu+4/7efy4/bH+Fv+w/+oA2gGBAm8DDARqBFcFlAZxB1MHgAZYBuEGlQcZCX0KbwpZChIL2gvzDG0Osg+sED4RsRFZEs4SPRMOFIAVLxiSGq4anhqmHFofNiEdIjgi5SGGIa0hFiJpITIg/x+9ICAifSOdI/8iXCJfISAgLh54G2wZBxjmFsgWaxaPFKgSEBFVD2YOmw18C5cIjgViAsn/Uv6t/Vj9S/2S/VP94fvy+TP42fZS9mf2Qfah9fP0+/Qp9tz3Ovks+rL6oPov+sv5gPlW+YX5Avq7+s/7pfyC/Nv7QfuX+gv6mvm7+Jj3n/a79f/0SfQ8817yMPIf8qTxk/AL77Xtxezd6zvrEevc6nXqJ+rx6anpe+ms6ebpsOlF6dzon+ju6GbpvOmo6ufro+w07Zvtgu1f7Srt9+y/7TTvevDi8TzzIvQX9eH1NfbW9sj3sfjy+Tr7T/z1/ff/ygGFA2EEigSDBZ0GLQeECAwK6wpJDKkNwA7kEPESSxSrFiYZuhpoHJMdmh54IF4hpyECI90jeiRFJuYnnCmiKyUsiSzbLcktuiwiLNErBC2TLzgxqDG9MC8udSvqKAAmWiNtIXgg4B+SHWsZKRU/EbcNiAqUBhkBcfqh8xnugeqI6Jjn5Obr5XTkEeIt3x7dSdwp3HrcEt3U3RPfL+E55C7ok+xh8Bvz/PQt9vz2D/jB+T/8hP/eApoFoQe9CLUICQhHB1cGAAUlA/IA+v6D/YD81Pv7+m/5O/dp9Bnxyu3S6oPoLud+5hXmDuZf5tjmN+c85y3nTueA5+Xn1Ohe6mns3u648bX0S/c6+ZH6ovvr/DP+vf5r/ub9eP70AG8EQwc+COYGigTvAh4CiQH0AA4AT/80/x//mv6N/Qr84/o2+jH5svfA9djzmPNi9c73nvnv+ZD4afbN9A71EPcb+Yz6GPxW/j0BawMiBNYEGQZSB40IOgk8CcQJFAspDdcPlhH/EdERNhHeEH8QMQ+RDjcPUA9ID1YQRhLQFKcWExcoF34WoRQWE4MS+BKQFJYW3xjsGs4bfxxeHWYd/RwlHK0aLRrOGrsbpB20H24geSDAH1Ad1Rk0FvASZBD6DYgLyAlDCPwFCwOS/zn7F/aQ8E/rZOcQ5cXjT+Mn42biPeEY4N/e4t0k3XfclNyr3UnfzOEt5aToFexc79zxcvN09Ff1nPaO+CT7Af7TAF0DQAVsBgcH7AY4BkAF8gNlAgEBDQC1/9P/4P9P/+L93vuK+e32VPQM8krwc+9Y73Xv1O858DzwHfDw757vZ+9O743vvvDG8k31N/jt+vj8Tf7O/u3+hP+xAFQCQwTyBUcHpwjkCcgKTgvsCpUJRwhsB8kGZQbbBdQEGAT2A2sDHgJ4AET+3ftO+iv5uPfA9p729/YB+J/4dPf09WX1CvX79Fv1ovWB9hz4dfnH+jj89vwP/fX87vx5/ar+MgCCASACigIMA2oD+QNMBIIDNQIwARgBygIsBVEGSgb5BTcGkAfpCEAJNgliCQcKggsnDfUNpw5yEKsSLBRMFTgWdxZtFoQWtRZsF4UYgBnTGm8cPh0wHQYdkRzQGqoXURS6ERIQBQ/EDQcM7QnrBj8Dsf+w+xL37PKy77Lt8OwY7JzqSulY6JLnv+Zt5eTj++Is44rkq+bU6KnqcOyc7gjx/vJE9E/1zPYc+cb7F/7h/0EBlwI3BLwFfgZdBtQFhQWBBVgFwQTDA70CCQJ7AbQAcP+r/eL7Z/oZ+dr3ivZJ9Wf0oPPc8mryCfKp8ZbxgfFX8UzxdfGn8vr0+/b99wz4gfcX+If6VP11/5sA5gBpAccCyATHBp0HdAdZByMH1AagBgIGsgVtBj8HnQf0BmQEYAGj/9X+mf6U/gz+U/0U/U39W/1m/KL6Afl1+Mn52PuL/B38QfsB+rP54Pp3/FP+DADGAP4A6QBRADgACAExAr8DSQXHBeMEMwMvAuECpQRJBq4GjwVuBAoEhANyA7MEMwaSBzAJ+wl6CZkIxgejB84ITwptC8IMBw54DrsOkg/kEJwSGRRsFFAU4hTJFfoWsRgeGiAbAxzBGxAa5xetFQwUjRMXE+oRWBA0DnELZwgjBeUB9v5j/I76f/l8+Oj2ZPQi8SnuLuwa67rqueqG6gjqwenj6TPqveqK633sxO1E74fwqvH48pD0ufYS+dX6GfwO/bH9h/69/+sABALiAlMDrgMOBBsEtgMBA1kCDgLtAagBDQH9/87+uv2S/HX7j/qs+cf4DviP9xH3P/Zx9QX1mPRA9Gn04fTM9Rz36PcX+EL4h/gv+Tf6Fvvk+xP9o/4IALUA/gAoAT4B9AH3Al4D7QOTBEcEsgMCAyYCqAJABFgF0QUDBfQCaQGTADsAwAAfAQkBDwHBAEgAof83/jv9ZP30/d/+Xv/K/kb+3v1K/V/9tP31/Yv+ef7F/cT9Cv49/r3+5f7m/nv/wf8l/2/+Kv6H/l//CABSAKQABAEUAckAQACu/6n/cgBxAS0CkQKpAt0CMwMnA+UCEQPZA+0E5wWSBt0G8gYoB2gHigfZBy0IUAjZCNoJngriCsMKlwq0CukK8grXCrMKrwqzCpkKaArYCc0ItAeyBscF+QT7A9kC5wEVAUgARf/O/UT83/qS+Yj4c/cg9jX1uvQs9KXz/PLW8aXw6++p78Lv+u8S8A/wM/Ck8Bvxb/HU8TryifIB87Pzl/S/9ej20/eQ+Bf5ZPmy+TT66/q9+5D8Uf3T/Rr+YP6b/qP+mv63/iH/uf/8/8j/qf/K/8T/kf9s/z3/9P7I/rz+tf7e/jf/aP82/5j+rf3v/ML8Ff3H/YP+wf6Z/m7+I/64/Xb9Sv0i/TL9o/2f/uf/mQA8AE//ov6N/sr+K/+5/zcAqQA+AYwBbwExAZIAwP9p/0n/Sv8CAOgAQQFaASABSgBf/4/+y/2s/Wb+Wv8EADsA8P9Y/67+2f2+/Of7Evwp/Xr+WP9H/37+n/3k/Hn8mfzy/Fr9Iv4c/+j/dgBpALn/Ov9u/yAA+ACsAU4CBQOXA9wD2wOUA2cDoAMPBJ0EHgVWBaIFQwa1Bn8GtAXZBJIE5wRLBVwFNQU2BWoFYAXhBPoDyQK0AQsB1AAGAVkBdgFCAXYAB/9a/bj7mvpv+rv66vru+or6svnK+NX3wPbU9TH16/QJ9WH15PU99vH1LvVM9HnzFPMy87fzp/R89bf1uPW69bb1yPXE9cn1XvY+9/H3kvgp+a75Jvpi+nD6hPq7+kX7Ffzt/KP99P3l/eX9//0p/n/+t/7E/vb+J/9T/5X/gv8m///+3v6e/nr+Tf4q/l7+mP6O/kf+xf1l/Wr9cf1d/Xf9rf3m/Tz+j/6m/nf+Hf63/Yj96P2c/hz/W/9c/zL/W/+3/7X/nf/H//X/NQC3ACAB/QCGAGcAjABTABUATgCQALgA6wD7AOwAvQBKANn/lf+K/8L/6//x/wwAJQA5ABIAWv/E/v/+i//j/+v/u/+Z/4P/jf+9/5D/QP9s/6T/oP++/9n/yv+V/zr/Jf9V/2D/bf91/0v/P/8+/wr/2v6O/jj+Zf65/pT+UP5B/iP+4f26/bL9jf1j/Y39yv2t/WT9OP0q/Tn9P/0h/S79f/2u/aX9mf2G/Yz9uv3G/dH9E/5C/mD+jP5//mD+X/5G/k7+iP6K/o/+xP7N/rn+kv5I/j/+ZP5r/n3+fP5Z/l/+Xv4n/vj92v3g/Q3+FP4d/kT+N/4V/g3+/P0T/lb+a/5t/oD+g/6H/pD+gP50/pH+u/7P/tP+5f7//hL/Hf8d/y//av+d/6X/s//Z/+v/8P8RAEEAVQB1ALcA4gDwAAUBEgEZASsBQwFlAYwBqwHcAQ8CDgLqAdEB0QHzASACPwJhAoUCiQJgAjICMgJCAi8CIgJCAmcCcQJlAkYCFwLjAbsBpQGdAZsBlQGMAYABVQEVAeQAuACOAHgAcAB5AHUASAAzACIA0P+b/5r/e/97/4//ef+B/2r/Ff8M/wP/3P4H/77+QP7m/l//iv4e/mb+U/47/h3+6P08/oL+KP7m/dH9sv25/Z39cP2W/an9iP2A/Wz9Vv1b/Tb9Ev0q/TD9JP03/S79Av30/Or8wPyp/LX8wvzJ/NH80/zN/L38qfyl/KL8ofyy/MD8x/zf/PD88Pzz/PP88fwJ/SX9Mf0//Vn9df2H/Yf9hP2J/ZL9pv27/cX94P3+/fn98v0A/gX+Ef4n/iT+I/4//kr+Pf49/kz+Vv5b/lz+Vv5O/l7+eP5z/mz+ff6B/nj+fv6P/qD+nv6Z/rT+0v7N/sX+zf7X/uL+5v7t/vb+7/7t/vf+8P7s/vv+AP/2/vT++f7//gL/Av/5/vH+8/7r/tD+xv7M/sv+0v7P/rP+pf6u/qf+lP6N/ov+f/50/nH+bP5v/nH+U/44/kT+Qf4t/jf+PP4i/g3+BP4H/hP+Ev4I/v797P3c/db93v3k/cD9lv2g/a39qf3F/dv9xP21/bT9rv2v/bH9s/28/cP9yv3N/cj90/3g/dr94v34/fv9B/4l/jP+Nv5C/lH+bf6P/pT+iv6W/qP+nf6n/sj+2v7z/iL/P/9I/1n/Z/93/4z/jv+N/6b/xf/Z/+3/BAAaACYAKQA/AFwAYwBsAIEAgQCGAKMAsACyAMgA1ADbAO4A7wDtAAYBDQEFARYBKAEqAS8BOgFBATcBKQE4AUkBQQFHAVoBWAFZAWEBWgFUAVwBVgFHAUIBPwEzATIBPgFFAUABLQEUARIBFQECAfYA7gDXANMA1wC9AKYAmQB8AG8AdQBwAGAAVQBNADgAGwAWABAA8v/q/+P/u/+v/7H/lf+R/5T/c/9t/3H/Qv8h/yn/I/8M//L+4P7d/tb+zP7A/qn+q/67/qb+iv6A/nb+eP5y/lr+V/5X/kD+QP5S/kz+QP43/ib+Hv4i/in+Nf41/i3+M/41/i7+NP44/i3+KP4q/i3+Mf4z/jn+P/41/jf+Rv5E/k3+Wf5F/kj+af5c/kT+VP5c/ln+Zv5t/mv+a/5w/nb+c/56/o7+hP5x/nn+hf6P/pb+lP6h/qz+nP6W/qP+p/6r/qz+rf66/rL+mv6d/q3+q/6k/qT+qP6l/qT+qf6a/on+if6E/oX+i/5t/lj+av5u/mj+b/5c/kr+Wf5c/kr+Pf4z/jT+O/4x/iv+Lv4e/hH+Jf4j/vn97f0G/vz95v3p/ej95f3t/eP93f3v/ez93/3p/e795/3m/eX96v3v/d390f3g/eX93v3r/fD93v3e/fD96f3R/cz92P3i/en96P3m/fb9B/4D/gP+Ev4c/hz+Ev4V/i7+MP4m/jv+S/5H/lT+YP5i/nD+c/5v/nf+h/6k/q/+o/61/s7+zv7X/tv+2/71/v/+Af8O/+z+4P4d/y//Iv81/zL/Ov9g/1v/Yf+D/4D/h/+W/4b/of++/5v/mP+1/7b/xv/O/7r/y//j/9v/6f8BAPf/4v/p/wAA/v/r//T//v/x//D/8f/i/9//6//u/+T/1f/a/+//8P/d/9n/6P/n/9D/0v/e/8L/q/+6/6n/kP+n/63/j/+P/57/l/+J/3z/cP9s/2r/X/9U/1j/aP9t/2L/Vv9P/0z/S/9O/07/Pf8t/zb/Qf9M/1b/Qf8y/zz/Kf8a/zb/N/8l/zP/MP8X/xb/Gv8Z/xj/Gf8k/yP/Ff8W/wr/+v4H/wX/AP8X/w7///4g/yz/FP8a/yf/Jv8l/x3/If81/zT/M/9L/1L/P/86/0b/T/9k/3f/Y/9T/2j/a/9h/3X/fP9z/3//iP+I/5T/lP+N/5v/tP/E/8D/v//P/8z/w//X/+D/1//Z/9n/1P/W/9X/2v/g/+b/+P/7/+7/+P/9//H/9f/7//z/DQAaABsAGgAOABsAPAA0ACYAOQA+ADUAPwA/ADUAQABKAEIARwBaAF4AUgBKAEgARwBLAFUAWQBLAEEASwBVAFIATQBIAEQAQgA+AEMASgBBADYAOAA3ADEAPgBKADkAKwA3ADQALAA8ADcAJgA1ADgAJQAnACYAJAAvABgACgAoACIAAAAJABUAEgAKAO//6P/7//D/4v/o/+L/4f/m/97/4P/j/9z/3v/Z/8r/z//a/9//3v/P/8n/1f/N/8L/1f/l/9j/yf/K/9L/z//N/9z/4v/Z/+H/9f/3//D/7P/v//r/+P/s//P/AAACAAgAEAATABwAIAAYABwAKQAoAB8AGwAmADQAMwA8AFIATgBCAFEAVQBDAEIATwBXAFUAUgBYAGIAYQBXAFUAWgBdAF8AYQBeAFgAXwBlAGAAWQBVAFoAWQBKAE8AXQBIADkAUwBgAEwAPwBEAEoAQQA6AD8AOgAzADcAMQAsADUANQAtADMAPwBLAEwARwBMAEwARABKAFEARwBDAEoASwBHAEUARwBQAF4AYwBbAFkAWABQAEwATABNAFkAXABQAFAATABGAFMAUwBHAEcAQAA/AEsAPgA3AEoAQgA3AEUAPwAxADMAJwAeACcALAArACcAHgAeAB4AGwAeACAAIgApACwANwA6ACAAFgAfAAsA+f8FAAwACAAPABQADQAFAAkADgACAPr/8P/V/8n/0//N/8P/zP/V/9L/x//L/+L/2P+1/7n/z//F/7T/pv+c/6j/sf+l/6z/uv+q/57/rv+w/5//lf+M/4j/mf+c/4L/e/+O/5//qv+q/6n/sf+n/5T/k/+N/3j/cf92/37/kv+f/47/gP+f/8f/w/+v/6b/oP+l/57/ff+C/6r/qf+a/7P/z//g/+z/1v+2/73/z//P/9D/2P/o//7/AwABAAoAEQAlAEsAUQBGAFcAUQA3AEoAYwBsAIgAjQB9AJUAowCTALMA0QCZAHIAmwCqAI8AowDHALwAtwDHAMAAxADKAKoArwDQALEAlwCuAKUAnQC2AKsAoQCyAJkAkQCyAJsAhACqAK8ArwDVAMoAtgDRALsAhgCJAIQAbwCFAJoAmACoAK4AlQB+AHwAhwCNAJAArgDeAOwAzgC6AMEAtACKAFoAKgAsAFIASgBTAJAAgwBaAGsAZABpAJMAcABJAFQAMQA1AGEAMgAfAEMAKwA9AGcATgBiAGAABgDw/9T/iv+J/3j/bf9q/6z+PP6v/or+Qv5J/pr90/1u/gT7gfVw89zzGvTX9Nb2lPqn/+wDEQdSCgoN3w3SDIEKjgdKBLAAQ/3j+qX5cvmI+lD8Wf4VAZgD3wTcBS8GrwSfArwAef6N/CX72vmO+Tz6+fru+wr95P2g/jn/o//C/2b/4f5R/pf9G/3p/NP8I/3h/b3+if86ANwACAG1AH8ABAAE/2z+Ev55/Vb9fP2f/ZX+uv82ACQBOAIAAn0BcwGpAGj/l/6V/cT84Pzm/ND8gP1D/tv+xP9QAHUA4AAQAaoACgA8/3n+D/69/XL9j/3o/ej9Ev7o/mH/Uf9//0D/u/7C/k3+s/39/bL9Jf3x/Y7+Xv6r/gz/M/9G//r+5f7q/ov+hv6J/jn+c/59/tj9yf0q/i3+F/40/r7+fP/m/wwAMgCgANkADgB4/4D/o/71/VD+2f01/a39IP64/oz/uP89APIAsgCxALIAiv+7/oj+//0J/kX+o/1w/S7+yP79/un+AP+s/wgA0//p/wAAmP91/9j/FQDt/7r/0v/f/5P/iv/R/6H/Gv/L/rX+sv6M/qL+Tv9y/xX/zv+zAD4Apf+x/7D/l/9m//b+cP4d/pD+WP9Y/zn/m//l/0kAggD1/8X/7v+J/9X/WgAm/2X+yv9lAH3/RP9p/xT/7/7v/sb+iP43/jv+cf52/un+g/91/2P/bf9//wAAlv91/vP+sP8g/9j+Qf5L/WD+4/+N/zr/mv+E//n+mv70/uz+8f1Z/qD/Xv9S/xcA+v9RABcBhwBhAOMA5P/8/i//WP5Y/c39DP4H/iD/0v9z/9f/egDx/0f/Tv8z/1z+s/08/o/+7f1O/jn/IP8I/9L+zf4LAJz/2/0S//j/Jv64/pkAmf/w/mMAfgDH/jL+ov94AHH/Wf4X/vv+CQC//or9FP+j/9b+QP8Q/wH/jABzAEf/sf/3/4X/sP6M/VX+Uf+Q/QP9u/5h/sr9b/+n/9H+JACxAK//3ABgAdz+u/5TAA7/bf4I/4P9L/7wABP/xvwI/8H/D/7Y/n7/JP6a/ikAw/+Y/vb9Fv4q/x3/Yv1G/SD/Wf82/Rv8j/2V/jz+wP7m/mX+fP+C//T9Of7r/SD9k/6J/ZX60/pv+m/4n/da9t/1XfR87+7uYfEV8Kzxj/Yo+bX8Cf+r/2gEcAaiAuEBDAQbBUQDOv9A/7kATwBYA0sENQO4CeMNcgwrDZ4Gpf8bBFsB8/mO/sf/9fx5A0IG+wHjACsB3AKkAeT5kvUO8jXsT+3U647k8uml9EX2Y/kW/9cBnQizDHEEOAD7B/cGoPrH+bABBP/f+lsCtgiTBs8GXAxJD0sMiwhMCo4LTwJE+wsDhAWb/Df/zgTp/k8BQAifASb9ev8Q/z8Akfrk8eD65gOf/4X/uP8NAB8IJgNY90sAMwmI/0n5lP4qA+YBrP8pAoIEegLWACYB1wIDBaEE/gCf/iAHrxDtBFj3Uv+4BgAGlgMm+PT4gglDBhT7Pv23/zgH+Qm8+tL5jwVxAyMCYgKoASUHvPzH928QUgwo8kP/aA0cAhn+aPuN/s8KGwOK/NMEFgGxAs4ILPwp/PMHHwMnAjYDPv3HAwUE+/nrAnQJ4//X/TIAnwIiBF/9f/6cBZYB4/8iAR//sATGBUAAKwRtAwP+0gPnBBr+VP+SBaoIJAEr+LX/ogSN+7/7pARyC9oMhgCZ+jIGhwQ5+v7/5wV/A/0A0vt5/V4ITAgo/8H+7wJ6AokA//xK+UcArQlDA4j7wgGaB80GowKx/N/+LgM5/3b+m/8E/9YHZAo3/1YAmwYtA9cFuQRL91n6ygezAQ35DQMFCmf93vWnA/MJCP24+Mn9yv4eAy4AbPS//cQMBgCi8uL8BgfIAGT30vqfAV//sAGaAuX3LQD2Dk39C/KFAE4AOPyAATP8pvwoBm0EXgIkAYz9lgA1/if81gAg+Pb4/Adv/Oj2LQrfAp34RQf8AxD56fve98v4KQDZ/YYA1QFK/WgEqgOS+cb+1AE//9sBc/mJ9qUEAQML+bT9dAVUCh8DxvVqATUNovoC840C8wXiAGIAV/6Q/TP+RP6J/8H8CP29BjAFwfgC/IIEH/0L9wn9MwAIACsCov9++1/+6v+b/bYFyw7MAGzxAgADDSb7XPCNAaMKwACC/4IHJQI0973+2QpNAp73IAG3CncAH/QY/CYKCgW8+vAA9AgaBQL+WPqd+8T+EwFkA0sAw/zBAFcA/QDZCg8F1ft6C7ULEfCi8UgMSQvG9oX3+QqIDLb6yfq/C/YK9vlD9uIE0gcU9YjyaQeLDCsA2PtTAn4IrALN/CgH9Qmn/F/5u/wR/voE4QMC+Ez50gcsDE0BNP0FBlwEefvr/X3+9PqAAOwCef3T/fUEKAk3ArX7egNbCEECa/2i+Z79WAby/vz5vAelCdX70fkhBd8Jof7Z+RIH6gfx+v75rf0yAKsDrv7P/FgEsgaWBr0BD/g8AsIRkAKZ7t35TQofAR33aQIhCOIB0QO7AEn7tAaPBSb03fosC2YHPfwC9+b9fQcSAXn6nQAIBAsEYgMtAU4FxQSp+Iv3wwOhBZv6A/qqCBUIefiS/VcJbgEi/YUE7gfUBSH8LvkrBskGd/4HApv+wvqvBWcFTQAXBXv/SwBQDQAC7/aWA2oBx/nLAVP+P/2JDSYJ9PnHAxoNnwGl+5cGhwzh/831SQDkCMX9avciBi0NM/+s/WQJuwMIANEJkgSRAIIGLPec8W0KBAm08lH4FghZDcwJVvs8+SEHiQTw+Er8QgKM/537jfzYAacFbwRqA5IIlgvlAiD/zgcDBKP4DgDWB+L9CvurB+AJP/1P/m4Nuwd0+JAByQVO9BL4VAiH/Cby0gWME0AEPPbqBaUWBQEy7akGFhMV9PPvBwybBwz0Mv6KDCYFrPwZBjMPFQJ6+JwERQQS+VgA5gFW9+8AAw7cBW/65/twCdQNtvxO9mYBNwLdAP3/IPce/8kLJ/8+/EoMKwmbAMIDjwJ2BREHavWP8YAFPwfX/GcB7gQ0BPMJEAgGAW0EsAXb+7/zJPjQBeEG2PkT/6gNVwQx+jMErwhMArX/wgBKA+oBov13AYEEgv8qAZcIZQlpBJ//NwF/A6H+QP6i/0z6TAPwDjn8ePDOB+sQUQCL/QwFKgTBAW7/bP0BAswFOASOBdoDEvug/qoKIgj9BDsH7fo6+YIMjQUH9ckE7Qqo+5gCEAy4AK/9AwVVBFr/Q/+PBQkGzgGPB7EDdvSGATQWOAiK9nn8hwcdCgkAW/n8B6IPjAAT/IQGOwYVAGMAFAMuCFIIPPsI+vQMuAog9Tv6MQ1nDcgHwgP1/ucD8QmUAm33uvqSDj8UK/529wkL3w28/6P8FQNnCZ4Ei/fm/LoKlAJM/BsNQBPHAzX97gPSBJH/8QAvB14EMP8QB2oKQv+/AKMNgA1tBpgAvP5QBvQG4f4JAfAF0Ai2CaEAIgSED+EBkP1tDG4Dzf4rCrv/Q/7vCooBOAI0C07/tQKiC1wBtQUdBwT7SgYUCRj7lAWnCNP+oAomC/4APgqbCJ3/VgRNAIX/jgiJAeAAZA0LB8L+zAVACpAI/QAv/aMIOApM/b39WwNnA04InQaS/fcCVgx0BV38ywAKCBQGPAK/BO0HiAYFA8kDzApSCpr9FfvOCJANJQBO98IDjg/pAvz38QXpDykGTf3K/pEF2gXa/DT+KAbQArwEUwr0/XP6wQtbCRr4YP9vD6QFOvMj/DwRdAtY+Vr9lArXCuoCefz6/RUFBwZNAM39egJxCOkGZP+2/I4EiQwkBXz65AAMB0n/Dv7sBMUEggGCAJMDswkgB2v9LvsMAhEJ4AV1/bT/iwTsAZUCiwTZA8MGEASs/UUDgAe5/+D7df8SA+YF8AYQBSEA2/4tBW8Fvv8QAm4CgP2qAm8HBQDJ/JkCigVbBSwHvAV+/U38MgU+BJ7+8wQqBnP+ZQGkBT4C3wJGA3sBaQRKAhz98wKmB0wAU/yYAqAGXAN0AToDAAK3APwDdgQKAcgAEwIBAnICkgNABLQCcgCjARsDHwJgAoMBX/4dAEcEbAPmAWgCkAG9AusE8f+e+9gCugf///r8WAVBB4//AP5GBEIGHAKm/6YA/AF8At0AHf/4ATUEpf/h/XoEgwZ3/yD9JwOiBBL/Uv5TA58DKgD1ACgDMwI1AqUDqgH+/ikA7AFqAZv/3P4CA7kGyQCO+6QCKQhAAjH+0AFJA/z/g/9QA3ADx/9LAaEEqgEIAHgDbgHF/LgAHAUPAfb+bAIjAvT/+QCDAW0C5wNwALv9MQEfAof/eQClAt8CfQFh/4UAcAIRAIj/FQN8A0IAR/1m/pQECAUa/bP8+APjAtb87P67BLwDAv/e/40DSgHB/bAArQOBASMAxgCJ//v+MQFxAj0CewKJAZX/fP8CAVcCuAHS/lz+TALGAzoAs/7KASkD+v9v/nwB7gJXALn+EgAwAuABvf4Z/6sDwgKa/WP/PwSwAUX9bf9LApgAvf89AS8B9ABVAev/OP8CAeUB2wDe/yYAhwCL/yEAOgLAAA//EQF1AcUAzwEKAE3+QgEaAlT/W/9FAbAB5wAXAJcAMwGVAJgAFAHRAO0ADAFiAPX/OAAsAdYBuACX/68AuwE5APv+twCcAZz/dv/yAJL/I/6Y/9QAtQAJAFn/AAB3ALT/UwD4AHf/Zv8fAeAAwf+6/wwA4wAOAWf/X/9oAdIAv/5s//gAKwHEAK7/Rf9/AIQAKv87/yYAKQAZ/z7+Q/9MAGD/8v7H/ycAMQCa//j+l/9z/9f+BAD6/wH+av4zACAAQP8p//j/OQAh/9z+h/9c/1L/Vf/f/rL/ewCA/yr/1//F/0L/z/4D/5z/Dv+2/pz/bv+A/oz+o/7M/jv/5/69/kL/Lv8i/4D/0v45/nL/JADO/kD+PP9o/9L+n/7W/l//J//E/u7/WQCA/zUAcwB4/xIAvf9l/pv/1v90/mz/vf/3/rz/Jf8e/z4B5/+L/skA7P9l/mIAt/8Z/jr/Lv7L/MD9Kf39/Br+Qv2G/b/+gv0w/b792fxb/eT9Nf1a/sT+Ff7I/10Avv8aAcoAUQBNAjoBsf9lAXEAVv8eAREAAADWAlsBkQBMA1cBPf8pAbX/4v2l/5/+uvxf/pH+3/wQ/sn/g/4S/hMAef8m/oAAjQCE/jMBHQID/28AnAGq/r7/ygH+/9UAEgOSAXoAqQGSAT4AfAByAWsAUwBJAugAgf+VAlsChf/mAfIC9P+3ALwBXv+1/7EAXf7Y/VP/if71/Sr/Iv+m/q//LgCe/+T/DwBa/5j/RgDO/5j/TAB3AGgAwQCDAPj/YQD1ALoAyQCRAcwBZwF9AbYBOAG3AJ4AGABb/+P+Fv6W/Tj+vP7Z/nD/7f80AH0A6f85/6r/HQDZ/7H/+f9aAIAAeQBdACsAVACcAGQAXwCSAEcASgC5ALwA0wA0AWQBpAHXAZgBSQE1AfAAOwAQAJkAewBzAGoBiwEiAdIBPAIYAkwCuAHKAMAAcAC3/4b/W/8P/1H/w/9i/8H+K/+L/xT/Of95/zv/0v8dANv/IwEUAnwBDQLqAnACTwJ0AkkCpALoArwCwgLbAvkC4wJ7AiAClQFCAWkBCQGYAJAALwA+AKUAUwBwAJsA3v8KAKYAIQAlAM8A5QAhAZIB7AE0AtMBmAHYAYUBUQFDAXwAQwBAAB//rv5g/4//2v+UAJkABACO/0v/1P5b/on+mP5B/o7+pv4j/j7+BP57/WL+AP9j/rD+Of/z/iL/Jf+G/q3+5P5N/jT+lf5U/v/9U/6j/oH+ef6j/tP+M/8k/4f+pP4K/5D+Uv64/oj+HP4q/i/+Jv5r/sH++/42/13/Y/+e/8j/T/8i/7b/0/+w////9//J/+L/1P8PADUAuP/z/3cALwBsAGMAt/5F/Vz81PtZ/e3+RP7X/XT+RP6E/fn8o/w7/JT7jPvj+2f7JPuv++77SPwf/Yj9k/3N/TH+gv57/lb+Bf5X/dX8ffxV/BX97P0I/j/+Xf4S/gL+l/3H/KL8rPyb/Pz8Gv3V/Pj8+fxn/O771/v9+1X8vvwX/Vz9nf3N/dv90f2i/Yb9vv3j/dD93P3W/Zz9gP2M/bf98P0M/jX+Z/5A/t/9nv1n/Qr9rPyF/H38d/yB/IT8dfx//JX8o/zD/Oj8AP0E/f/8EP0S/fz8E/0v/Sv9P/1X/VH9O/0L/e78+vwO/Tr9UP08/WD9bP0c/fn86/y1/M38Df0e/Tz9X/19/bT96P0u/nT+g/6k/tT+1f76/j7/YP9+/4j/kf+3/4n/Nf9K/2b/Wf9j/1L/LP8G/6v+a/59/n3+ff65/uP+y/6e/m3+Ev6h/Wz9Uv0s/VP9tv3p/QH+BP7z/Rb+Qf4u/iX+Nf4//k3+Tf5K/k7+Kf4a/kn+TP4w/k/+eP6O/rv+7P75/vT+/f4R/xb/J/9f/5b/vP/Y/+f/+/8IAAIAHQBfAJwA3QAmAWABlAGzAcAB0AG/AZABegFvAWUBfAGYAaUBwAHWAc4BswGQAW8BXQFkAYIBiAF5AYoBgQEwAfkA+wD4APoADgEgATgBNgEDAdcA0ADcAPAAAAEXATEBKQEHAeIAyADjABgBKQE7AV0BZgFtAXUBYwFcAVwBQwE2AS4BFAESASsBTwGMAbcBsQGgAYsBbAFGARQBEgFaAYYBaQFCARkB2gCYAGoAWQBjAHIAYwAnANz/mf9e/0L/Sv9M/0L/OP8X/+D+mP4z/u39/P0b/h/+K/43/iT+Cf7k/af9gv19/Vj9C/2+/Gv8I/wL/Aj8AfwH/AT84vus+1z7APvD+pz6gvqN+qz6v/rH+rL6evpV+mL6dfpk+kb6NPoc+v354PnF+cn59fkZ+jv6YfpZ+kP6Rfo7+jP6QvpM+m36o/q8+tb68/r1+hH7T/t0+3z7bPty+7b74fvb+wH8ZPzP/Ar9+fzW/Mn8vvzK/Nj82fwC/UP9gv3N/e/99v04/pr+6P4c/zf/d//L/9r/3/8RAE8AoQDOALIAygARASEBNwFzAawBFgKvAjwDtQMDBCgESQRSBEAEJAQABAYEOARCBC4EMQRKBIwE/QRnBZ4FigVUBVQFegVbBQkFAgVTBZ0FrAV8BSsFCgUfBQ8FvQRWBP8D2APKA6ADWwMlAzQDiAO/A6oDcwMgA8UCgwJAAuYBiwEpAb4AYgArAAIAtP9e/0z/ff/I/+3/s/9P//H+i/41/uL9Xv3n/MP81vzm/Ln8Xvw1/Eb8SPwA/Hf7D/v1+tv6oPpZ+gb6xvmc+Vr5AvmX+Az4i/cz99P2SfbQ9Yz1TfUA9bf0TvTN82zzLPMG89rycPIF8ujx8vEC8g3y/fED8kXyfvJv8jjyNPJz8p/yqvLQ8hPzXvOw8//zTvSm9Av1lvVL9vX2aPe99yj4n/jR+Kz4t/hE+cr55/kJ+nz6LPvz+3L8kfy1/Nf82vz2/A79If2d/YL+mf/fADgCeANvBAEFaAWyBdMF7AXUBbMFHQbdBocHIAh2CMkInwlqCp4KkgqKCp8KqgpcCkcKBgsADJ8MEg2UDTkOww7oDuEO7g4WD2sPxA/dD8MPpw/eD4wQEBHpEG8QCRD4DzQQQxBREPoQ4RGFEvoSOxN2E94TERQNFAgUpxMgEwETHhNeE+QTkBRMFagVKBUyFEMTcBLCEf8QBxAQDygOXQ3ADCYMiAvYCvQJzggAB5IEVwKEAPv+6f0N/S38SfsL+rf4yPfH9pT1p/QM9Lzzc/PQ8jTy7vHI8cbxv/F+8S/xvfA/8DTwWPBG8FfwmPDK8Pjw/vCu8D/w0O9X7+vujO4i7rntgO2L7bzt/O1Y7szuFO/57qPuQO7s7dDt1+3w7Vvu9u5h77zv9O+973PvSu/s7nbuE+6q7XPtfu2o7SLu4O6R7yDwevDf8JLxE/JD8sLyd/Mi9Or0gfXh9YH2VfdF+DP5kfl8+W/5SflH+cj5Y/oD+wX8SP16/i3/I/8C/1n/tP/E/+f/ZQBPAacCJgSYBT8H5wjUCSUKPgr7CfEJkwoGC14LXAyVDdgOURCUEbUScRMbE4USCRIQEXwQSBCPD6gPlBCiECcRvxI9E1ITdBQRFdoU9xS6FM8T6BKgER8Q1g+9EI0RGRKQEoMSFhKeEcMQxg9YD3wP4w/kDxsPhQ7JDmUPFRBfEMUPBw+5DpMOsw4eDz0P+A7UDgMPSg+qDzcQshAOERYRdxDKD1MPhw6rDWwMjgmqBdQBOP68+3z6Wvla+ID3Zfaq9WP16/Rb9NnznvPv8zP0JPQy9Iv0TPUz9tz2l/dB+GD4WPhn+Gj4tvhk+TD6Cfu++wz81PtV+/j6l/od+sj5MPlI+Lz3d/dp9+73j/gB+Xr5hvkZ+aD4+/eF97T3N/gU+Sj6w/oJ+z77Q/t4+8r7w/ua+0373vrQ+hL7ovuq/IX9Kv7J/qv+Jv6O/Zf7pPhU9ov0OPNx8kvxCfBS71ru5Oxs6wjq8ugv6Krnouf+59zocOrw6wrtC+5o7g/uk+3V7BHs5eso7M3s9O1E76Hw3PGT8i/z4PM79Ir0+PQf9WX1EfbN9gr43fmQ+yD9N/4R/kz9efxc+0H6Wvnw+NX5svuo/Wz/iwAaAWkBXQFtAYwBMgE9AdYBagK9A3kF0AaWCP4JvgnuCAMIHQdSBzkIWgkYC6cM/g3xD84R3xLcEnYR8g9dDzgOEgtEB3QFTAZvCDEKLAqVCbgK1QsIC+UJgwgBB38HzQhSCaIKSAwWDQ8Okw7CDe0MoQzDDHANEw5kDrcOcA/WEIYSchQmFvMVShQFEwUSbxGnEYcRbBHYEXAQLgy2BmcBz/25/I78yvtj+rf4Xvfg9lr3b/iK+Z76cPsl++35q/ig93H3mfgP+gj70ftp/NH8cf1F/h7/8v+KAK0ASwBx/3X+7v03/gv/qP+L/7z+gf0s/AX7Z/p/+s36//pC+0371foq+nD5Cfma+ab6afvl+wv89PsI/Dz8f/zc/CD9Sf1M/QT9vvyp/Nv8iP1P/uv+ef+F/xD/1/7Q/t3+JP8Y/3v+r/3H/Kj7TvoR+Vr4VvcA9YHxlO2X6nfpCOl26I/odemt6u3rm+yw7NDsUO0u7uPu/+7+7kXv/e9s8d7ykfPy8yn0DvQE9Bb0GvSA9Ib1w/bJ92z4sPjn+Kv54fq6+yv8fvxg/OL7avv2+un6fPsA/EP8k/zM/P/8cf0f/g7/+v9wAMYAfwF8AoQDiQRSBaMFeAUrBTEFgwX1BYkG8AYsB8kHLQiiBzwHdgeTB/4H1Ah9CZoK2ws8DNYMVw3CC1YJoQcbBm0FlQVABUkFVgZsB58IkAlWCb0ImAgvCXMKAgupCs0KeguPDCIOPA/HD1EQsxBdEQUSbRFxEIYQbhFsEo8SqhEZEVURxhFkEhsTghMoEwYSIRHXECAQsQ7IDEkKhQeaBHkBAv+d/b/8N/zZ+0L7lvoo+hf6RfpN+u75QfnA+OX4W/mw+Tb67/po+737EfxT/Ln8R/3P/Xb+Hv9y/4//fv8j/63+L/6r/Vr9K/3w/ND8vfyw/Mn8sPxP/BT8z/tP+//61PrQ+kr71fss/MD8Zv3U/U3+t/7z/jT/WP9E/xX/yf51/jH+DP4y/ob+3P4f/xf/8P7r/sn+qP6W/hv+dP3p/Av8BPsW+vr44vev9uX06vLf8Lfubu0d7ebs/+yF7Qvu3u7O7xvwIfBX8Irw5PCE8f/xU/Ld8rnz4PT+9YL2l/bj9kf3Y/dh9zb32/bh9mH33fdh+Bj51fmx+sj7svw0/X79dP0n/SD9Of0T/UH98v2q/n3/GgD3/8//CQA1AI0A4wDiAEsBHQLhAvQD1AQmBckFSAYBBqwFLAWhBAQFhgVlBacFmAa8B30ITAj2B/QHswe8B9MHJQciBxAIsQgZCgcMhwydDG0MawqICAQI/AbaBd4FugYrCeIMrw9TEWsSkxIXEkkR4g9VDk0NBw2aDZgOdQ9BECYRJBIDE9gSTxHlD1kPpQ7pDbUNRA3cDD0NuA2MDgoQqBBaEDIQyw9HD/QOHw4zDVAMZwrdBzwFUALP/9n9A/zL+vD5LPl1+W76A/tl+2/77vp9+u353Pj095b3ivfx99H40fnZ+gb8Ff3O/WD+qf6G/mr+bf5B/hX+CP4K/lv+5P46/17/W/8i//L+1P6F/iH+0v2O/Wz9Xv09/Tf9Zf2+/Wb+N//W/zYAVQAzAPj/m/8t/8/+bP4g/ur9mP13/Y/9cP1n/Z/9if0o/bT8Dfxo+/z6p/oz+mH5T/gm98/1oPSm84nyy/Fg8ULw3u4J7nztdu0O7onuJe8P8IjwvvAX8UfxsfGb8mnzEfSm9ML02PRk9er1WPbU9uT2ifY19tH1dPVk9Xr1jvWp9e71kvZd9wn4tfhZ+fD5Vfr/+Vn5RvmB+ff5Gvt4/Mr9Uf+aAIgBXAKUAhYChAHjABcAcf8B/6D+b/7h/g0AYQFtAvgC4gLPAhkDLQNQAwoEqgTnBEwF3AWYBpkHWQjlCMcJlwrgCuAKVQoLCd0HNAexBscGsQdtCOwIDgqNC/AMBA7gDacMrQvrCgUKmQmzCUMKrwt6DfUODxB9EC0QrQ87D60Ozg3IDBIMqAudC0wMPg30DZwO/Q78DrkOuQ0wDBELegpdCr8KOQvWC7EMWQ2uDYMNuQyxCy8K4Qc1BT0Cd/8A/pv9oP0W/nL+av5l/hr+Zv2a/KX78voC+1772Pux/KL9mf63/4sAtwBeAMv/Kv/O/vX+U/+d/xwA6ADJAY8C4gK9AnAC5wEsAW8Ahv+b/hH+6f0//tn+M/+G//H/EADu/3//tf4H/pr9bf3D/Uv+rf4D/zL/XP+h/4P/7P4t/mr9Df0Z/Qz9GP11/eT9gP4b/yr/v/4E/hL9U/zA+wf7Bvqu+ID3uPZD9aXyyu+V7bPsAO1T7aLtlO6G7xHwkvC/8KDwsPDX8EnxUPJV8xn04PR/9eT1GfYS9gn2APbX9dj1+fXh9bL1oPXW9V72vvbR9gz3jvdP+Ef5B/pK+jL6+/kZ+pD6wvrM+kn7OPxo/Xb+3/4e/6z/5P+S/y//u/5s/ob+qv6+/vr+cf8/AP8AXAH/ATQDoQTVBT0GFQbIBc0EoQNPA5IDRAQWBXkFRgZAB94GywWeBAcDXgK6AsECCAPbA9wEtQa3CNMJcQqHCn0KIAtgC7gK2gmrCLgH2welCOoJRgvgCzcMvAz6DBkNMg07DWkNKg11DGAMvAwdDSsOOg8gD4EOsw17DN4LzwsmC/QKCAwJDe8N9A4FD9wOKg+7DgQOdw2DC3oIaAUNAn7/Pf4e/bv8Zf2s/dT9Pf7j/Tj9zfz3+zz7Svt++8X7d/xK/T7+Wv8aAGYAUwCV/53+Kf7f/aX9EP64/j3/EQDLAP4AQQFVAdcAiQBnAOH/S//Q/mf+hP4G/3//AQBiAGoAVwAaAJz/Sf88/yj/L/+N/wcAbgDAAMwAfAAXALr/U//v/oj+NP5E/qz+Gv9p/2L/4/4d/in9D/wO+yP6G/kv+J33LPex9if2RvUQ9AfzLfJR8cvwm/By8JvwNvHb8W3y1/L18hjzd/Pp82b03fQ19Xf1qPXS9fT1G/Z39tX25vbW9qX2P/b29cz1rvX69YL26PZ39zf43Phv+cP5r/mD+WT5Yfm4+Vn6G/v2+5381fzK/KX8e/xg/Fr8hvz4/LP9sf61/2MApQCLABQARv9u/gv+I/6A/mj/8gCmAlQE3wW0BlIGxATGAi4B3P+h/iL+y/6VAAQD2QS7BXkGXwd5CJUJ0AnsCGUHxwUJBV8FwQWkBRQFngQ8BaIG2QfFCB0J3QjxCIwJZwoyCxILZQrGCjUMZw2KDYgMJgs4CrkJYgkgCQMJNAnFCfkKzAxVDg4PNA/pDp4OnQ5YDs0Njg2aDQMO1A55D70P7A87EOAQdhE+ET4QnA48DI0J6gZdBFgCBAExAPL//f/q/wYAegDIAJAAwP+k/sP9Zf2z/Z3+q/+tAKoBaQLvAlYDWgMTA+ACrQJsAkAC5QFEAegA/wBPAd4BXQJYAiIC/gGdARYBfwCu//T+g/4g/vb9Fv5Q/sT+Rv+L/8X/3f+q/4b/dv84//n+7f4Q/0D/Tv89/yT/KP9m/5b/jv9a/7T+1P04/YP8i/uW+lH56/cV93b2x/VY9d/0N/TL84jzTvNL82nzkfPC89Tzm/P28gLyLfGl8GPwYvBq8Hfw0vCA8V3yMPOZ85bzi/Oj89bzIfR89On0T/WB9ab1/vVu9r32+/Y992D3aveF9573tPfi9/T39PdC+Jf4uPgA+T/5TPmf+eb54vlI+hD72fv6/DL+9P5F//j+Nf56/dH8Qvzi+9T7hvyt/ef+NwDsAC0BrQFrAYwADwCw/p38fvuu+oT65/tV/eH+QgH1AiYEiwUBBrYFtARUAsP/q/1E/Lf8f/7eAL0DxwVVB6MJggt1DLgMuAtECsUI7gbeBZAFgwVWBl0Hdgg+CoELXww+DVMMRwqbCKQGeQXJBSsGHgeJCCsJ5wmvCrIK6wopC9IKiQoFCn4JxAmACuwLuw2RDvEOVQ88D2oPvA9LD9IOcg7dDegNNA4QDgkOvw2QDO4KyQgoBv0DgQJeAZcAEADL/xwA7QDRAY4CzQJwAvgBrgF6AWUBTgFBAZ8BMQKsAiwDcANvA4wDtgPoAwgEqQMwAwoDxQKDAnACGgLJAcABrQG7AdMBqwGNAVQB1wB/AAUALf9i/qP9Af2l/Dn88PsY/GD86fyz/UD+vv5D/03/0f7o/Z/8Rfvv+Yb4Dvem9c70uPQp9Qr2+vbF96P4+/iB+AP4bfdo9pn1//Q79M7z1fPW8+DzUvT19Or0BfQd8yzy3vAj8EDwLfAb8OzwCvK18mHzR/Tc9Bz1WvVw9Tr16PSY9HH0o/Qi9cb1ffY498v3H/hh+Jn4p/iz+Mr4yfjN+Or4I/mm+U76rPrL+gX7PPsi++n6u/pS+rP5FPmP+Ij4Bvmo+Zj62Pv//Oz9Tv70/Uf9jvwZ/On7jfts++T7e/xZ/Xb+Rf8LANEAkgHMAqsDmgNBA18CPQHGAF4AAwBRAN4AFALuA1AFbwYYB7MGPAZ1BdcDqwLmAWIBGwITA58DfQQWBbYFGQfwBzIIgggZCKkHzgeGBzAHEweiBsUGmwdGCEcJWQq4CgYLGQtQCjcJGAg7B0EHzwd0CD4J+gnICpsL8wsDDOMLUwvjCtEK1QpIC/MLRQycDO0M+Qw9DXENMA0nDUAN4wxNDGELxAn8B4UGaAWoBFQEcQS8BCYF+AW/BvAG1waWBkEGMwYcBsQFjAVBBagEPwQcBOgDogNjAzwDRQN3A58DegMZA7gCZwImAucBkwFYAUoBOAEFAaUAFgBx/8b+PP7n/aX9hP2j/b79q/2F/UL9wvzx++D6xfmo+Iz3yvaj9uP2QPea9+/3S/ia+M/4E/lk+XD5LPnW+ID4Mfj598X3sffl9yz4VviK+OL4K/k1+Rr57fic+D/44fd69zf3JPcg90P3ffeZ97D3z/ft9yP4aPiq+Pj4Ufmh+c355/kN+hz6DPr2+cr5lvlJ+cr4lvjW+DD5vvl7+i/7+vuI/NL8ZP3G/av9qf2a/YH9v/3b/fX9hf4G/5X/cwDmAN4AggCF/3L+pP34/OX8UP3g/cr+kf/0/30A6ADbAJAAJgD+/xgA8f+4/63/yv82AFoAAQARAE0AGgAMAGsAFwHOAe4BDQLmAn4DaQM2A8ECPAIRAgcCXgIxA/kD8QQ0BhkHmQe0Bx4HIwa9BKACbACg/iP9hfwv/YT+LABpAtAEvgZFCF0JzQnLCSwJ/QdlB4wHqwcdCMUIEQnjCSULrQv/CyEMQQt0Cj8KrgkuCQAJgAhKCKwIAgl+CRgKQQpKCnIKlQr8Co8L3gsxDNkMmw09DpUOfA4kDuMN3g0oDpEOvg6zDooONA7ADSgNSQwfC6cJPQgpBwAG9AScBI0EswR5BVgGMAcpCIQITggLCDMH3wWwBHkDdQLwAYQBUgF3AYkBswEFAvQBiQEIAVUAkP/z/mj+1P1I/bb8Cfxr+wL7vPqy+uv6O/um+x38ffze/Cv9JP3S/Dz8e/u++vD5Kfmu+HT4ivjz+GT55fmC+u/6K/s4++T6WfrU+U354Piy+Mn4IPmm+U76+vqR+yD8h/yM/Er83vs6+4H61Pkw+bb4avgy+CH4Pvhr+LX4H/mD+eP5Lvo/+jX6FPq9+Wf5MfkE+RX5Vvlr+Y759/lu+vf6kPv0+xj8DfzZ+437MfvU+nf6D/rd+f35Nfpu+q76CPuO+/j7QPyo/PL8Af0M/Q39NP2W/cP9zv37/Sr+d/6r/n/+ov4j/0X/Tf+b/9//JwB/AJUAfQBBALP/+f5r/iv+DP7b/b79Dv7N/pX/LgC/ADIBewHgASQCFgIXAhcCNwLYAkgDUgO0AwwEGgRmBKgE5wRZBToF5AQTBf8EmgRYBLYDKgM6AyEDAwMlA+sCywItA5YDPwTzBBgFVQXLBQAGggYeByYHQQeTB6QH/gd+CH8IkQjRCMwIEwnECUcKyQpcC9ELgwxNDa0NvA2tDa4N0g3TDd8NIQ4VDtgNyw2kDYUNhA0EDVMMGAz2C5wLJwubChYKnglDCTYJMgkWCS4JdgnfCTwKJArmCcIJSgnLCKcIYwj5B54HGwelBkIGkwX1BJUEDgSWA1wDEQOuAjYClAHyAGcAAACv/07/Cv/w/q3+T/7q/VT9s/wm/IP7//rF+qn6pvrN+g37R/ta+2v7pfvc+/r7Gvw//F/8Yfwy/PD7uPuI+1f7Fvvo+uz67frI+o76Pvrq+Zv5L/nH+Jn4dPg4+Cz4YPiD+IP4avgq+O331fem9133KPfq9pj2TvYT9vX13PW49cT1APZK9sX2TPej9+/3Kfg4+ET4M/js96H3Uff99tL2v/ai9p/20vYg94D3Dfir+B35f/nr+Sj6KvoP+tv5n/la+Qv52/jM+MT43Pgq+Zz5Fvp++vb6oPtd/BH9qf0a/nT+qv6u/qP+pP7T/hj/+f57/gn+iP3m/GL8/vvK+9P7wvu3+yD8rPwG/U79av1+/fj9oP43/w4ABQG5AUcCuwLjAp8C2AHWAAoAeP8E/7n+iv6a/gj/l/9QAHIBrwKmA3QELAV6BToFtwQdBIADGQMDA00D8QOsBIcFnAagB5sIqAlPCoIKjApaChoK0gkuCYgIGQh/B/4G6QYLB34HTQhCCWMKkQuWDGIN1A0BDgMOvg1RDcwMJAyXCy4L9go4C50L9AujDGwN+w2CDtMO7w4WD9IOCQ5QDcQMYwwvDMoLTgtFC5sL6Qv7C+wLDgxpDL4MAQ01DT4NIw0WDRoN8QyBDPILTAt8Cp0JvgjUB/wGYwb/BcAFvwUFBlMGagZbBlYGcgaIBlQG2wVUBbcEAQRcA8ECIQKqAV0BKQEoAVgBnwHgAewBzwGxAW0BAAF8AMT/B/+I/hD+eP3n/HD8Evy/+4X7fvuC+337ePtG+wv7Bvv++uP6z/q1+sb6BPsj+zz7Q/v6+r76q/px+h36uPlG+RD59fi++IP4M/jT95f3e/d+95n3tPfe9wb4LPh/+L/4xPjA+Jn4cfiE+Hb4Lvji93L3Jfcg9/j27fY392H3i/fr9zj4iPjS+Nv45fjy+NL4t/iW+Gb4Z/hi+Ej4dfi++Pn4ZPkA+sD6k/sk/GP8b/xM/Pr7YfuV+ur5TPmP+AD40/fR9933Nvgc+V/6uvsm/WP+Nv/b/1kATwCr/7j+tP20/L778fpg+gv69Pkg+qj6kPt8/DD94P2f/lT/9/9UAGYAeABsAAsAuf+Q/yP/q/6I/mj+Rv6C/uz+XP8SAAsBHAIgA8IDEwRbBIYEeAQmBJYDEQOgAhgCpwFuAVoBfgHZAW4CXAN4BIUFagYjB8cHMwgqCOsHuQeUB5YHpAevB+8HJwgkCFwI6Qh7CfYJMwpqCgELsgsmDFkMOwwRDAQMtQtAC/cKqgpTCg8K1gnKCb8JdglHCVsJjAn3CW4KogqfCmIK8Ql5CekIVgjzB6EHZQduB6wH8wc3CJcIDgljCcAJSQqFClsKDQqQCfsIUQhcB2gGtwUxBfcEAQU0BbgFSAaTBsgG8wYcBz0HDQe+BqEGdAYNBmsFpQT4AzQDVgLEAVcB/AD5AA8BMwGYAeUBFAJgApICrQKfAjECswEdATEATP9j/l79rvwq/J77Z/tr+5P7DPx3/Lb8BP0z/TP9Df20/Gr8Nfzd+4j7Kvum+j365vmK+V35R/kz+VH5i/nB+Qb6T/qR+rT6hfoc+q75KPmI+PX3Wfe+9nr2ePZP9hT2FPZD9p/2Lvev9w/4dPjK+O345vjL+Kz4jvhe+Br4+vcA+NT3gfdL9xb38fYE9wL39fYx92L3Vvdp93f3S/c790f3KfcA9+/29PYg92f3p/fT9/D3BfgT+Af45/fn9xL4Rvh/+Mj4DvlU+a/5BPo4+mf6j/qX+qL6uPqp+rT6Gvt6+7/7Rvzj/GT93f0b/hn+Bv6i/Qz9ufx//En8YPyZ/Lj8Av2f/Uj+0/50/zIAvQAgAZkBAAIwAksCWgJaAjYCzgFFAc4AbgAfAAEAPwCXAMYAGwGgASIC4gLGA30ERwUCBmQGxgYgBzgHaQeMB1QHGQfUBk8G3QWOBUkFLwUyBVAFrAUqBroGWQfkB2IIzgj+CPQIpwgTCIMHHwfOBpUGgAaSBtcGNweYBwIIdAjiCDYJaQmcCcIJrgl4CSQJjAjxB5EHRAf/BrgGWgYUBucFpgWKBaoF6gViBu8GXAfbB2gItwjLCLMIawjzB0sHlgbaBfcEIwSSAx4DzQKhAoQCqAIUA50DYAQ2BccFJAZBBvMFiwUjBYcE6wN2A/sCigI6At4BcgElAe8AtwCIAGIAPQATANv/qv+u/8//2v/f//n/EAD8/6z/Of+6/jH+sP1F/fb8zvzN/PP8Q/2d/fP9UP5//mf+N/7p/XP98fxM/Iz78PqK+kj6H/oM+hX6JPo3+lv6dPp6+nP6QfoZ+ib6HPoH+h/6PPpm+qr6u/qe+n36L/qt+SL5tfht+Cj49vf89w/4PPiZ+NX4+Pgv+UP5PflP+UD5Cvno+NH4tfir+MP47fgP+UD5gfmY+Zb5ufng+ev58/nu+eD56vn4+QH6Jfpa+oT6nfqf+pL6f/pn+nH6o/q1+rr6APtZ+5n76fs1/Gr8uPwP/Sr9AP26/IH8S/wW/AL84fuW+2b7Tfsi+xj7NftQ+5f7Ffx//M/8Jv1w/Z/9zP3x/QX+I/5A/ij+CP4d/j3+XP6E/of+hv7D/hD/SP9t/3j/m//w/zsAhQDWAPEA6wDtAOEAygCuAIAARAD4/7X/uf/W//H/RQC0AAwBdQHIAcoBpAFyAVUBagF6AXIBfwGgAdkBIgJuAugCbwOkA80DLQR7BKsE2wT4BB0FUwVuBXcFcgVKBSAF+gSlBDsE+wPQA4sDWANnA4sDpAPeA0gEqwThBPME/gQPBRIF9QS8BHgEOQT5A7gDkAOLA5MDmQO0A+8DIQQuBCsEJgQkBCYEEQTgA6EDWAMwAz4DQgMoAwkDzAKEAmkCWAI6AjACJQIdAj4CSQIgAgQCBwIOAhMC/gHPAZoBWAEoASwBNAEjAQ4B+gDpAPYAHQE2ATEBHQHzALUAjQB3AGIAcgCUAJEAjQCSAHgAUAAjAOH/qP9m/w7/4/7T/qT+lP63/r3+sP6v/pD+Yf5O/jX+Cf7x/df9rf2K/Vn9Ev3f/Lv8pPyi/I78cfx//I/8ffxs/Gv8e/yN/If8h/yf/J/8kfye/K/8sPye/HL8Rvw3/CH82fuF+1/7UPss+wH73/rN+sX6t/qp+qj6qPqo+p/6hfp1+nz6mPrJ+u76/fod+0b7X/tr+2f7Zvt7+3z7Z/tm+3X7eftq+1T7SvtV+3L7mfu3+8b7yPvK+9v70/ub+3P7b/te+0H7Nvsz+yv7HPsb+zD7Wvuh+/H7Pfya/Pr8UP2y/QP+JP5E/mP+Zv5i/lf+Rv5L/mP+i/7O/gr/L/9T/4n/2f8gAD0ATABGACEAHgA4ACoAFwAoADkARABXAH8A3ABJAYQBqgHXAekB/wE4Al8CYgJ8AroC9QIcAyEDBQPmAs8CywLYAswCrwK5AtMC2ALiAvACAgM2A3gDpQO1A7MDuAOeA0kDAQPOApQCeAJZAiECIwJFAksCcQKqAsMC5QIEAxsDWQOFA3gDeQOTA6UDqQObA4wDkAObA6MDlQOMA68D0QPIA8gD3APxAxkEQQRBBCEEBATwA9ADvQPTA9YDqwOLA3EDNwPxAqwCbgJJAj8CRAI5AhMCBwIbAiMCLQI8AjMCNwJWAlACJQIVAh8CHQIQAgwCBwIQAjECJgLvAewB9AHAAZUBbQEVAeYA5gDIAMUA9gAeAUkBYQFVAW8BgwFbAVIBaQFiAWEBVgEjAfEAvgCQAIMAcABjAG0AXABYAGwASAAkADwATwBnAI8AiwB4AGYAMwASAAwA8f/Q/6j/af83/xH/4v6u/n3+Zv5U/hr+x/1t/Sb9GP0M/fn8Jf1G/Sz9Rv2e/fn9Vv6j/t/+Ef8M//3+Ff8E/7z+ev41/vH9u/1//UX9G/0F/Sj9V/1P/Ur9b/1//Xf9hP2W/ZT9kv2d/a79yP3c/dT9xf2n/WT9J/0O/QH9/PwF/Rz9Sf16/Zz9vP3q/SH+Uf5q/nb+af46/hP++f3T/b39s/2f/bz99v37/Q3+b/7G/s7+wv7U/uP+2v7w/ij/OP8u/zr/NP8V/wD/5f7j/iD/Uf9W/17/Zf93/6//xf+o/63/yP/H/9f/AAANAAQABQD1/8f/rv/O/wAAIAA4AFcAhQCuAMAAyQDFAKkAmQCcAIcAZQBVAGUAnQDYAP8AMwFdAWoBlAHNAdYB1QHqAeEBxwHVAfUBBQIfAjkCNAIyAjoCFALSAaABfAFvAWcBSgFAAU4BSgFgAaEB0QHyARoCLwIpAh8CGgIAAs4BpgGRAYABgAGfAcsB1QGjAXMBZwFFAS0BSgFMAS4BPgFSAUoBWgFvAWwBbAFfATIB/gC/AHoATQAiAPD/1//I/7P/wP/j//j/DgAWAP3/6//u//7/GQAjACEAKAAYAOv/vf+P/2v/Sv8e/wz/B//x/u/+7v7L/rP+nv5w/mL+c/5s/lX+S/5P/kT+G/7v/cD9iv14/Yv9jP13/W/9iP2u/dH9C/5C/ln+hP6y/rD+sv6w/nr+Tf48/iL+Gv4p/j3+Uf5M/jv+Jf72/eH97P3C/Zf9sv3d/QL+Jf4u/jX+KP7t/cH9oP1i/Ub9Tv05/Sr9OP0y/S79R/1m/aD96/0A/vf9DP4F/tv91v3f/cn9vv3H/cv90P3J/cT91v3W/b/9xP3c/dr9x/3G/dX9zv3D/e39Kv5E/lz+jv64/sD+s/6l/pL+hf6A/mP+P/4//kD+Rf50/qX+0P4O/yX/F/8f/zH/Q/9M/zH/HP8j/xT/Bv8h/zz/SP9a/2v/cv9t/1v/Uv9l/4T/mv+0/9P/7P8LACkAOgBmAJsAmwByADMA8v/g/8z/iP90/5v/rP++/+L/5//h//z/HQAlACEALQA1ABYA8P/s//j/BQAnAFQAYwBdAGoAbwBRADgAJwATABkAJQAfACcANwBTAJQAuACtAMgA4wC9AKYAxADlAPUA8wD5ACUBRgFPAWUBcwF6AZEBigFkATwBBwHaAMgAvgC+ALYAwQD7AAwB+wAkATYBIQFIAUoBAgH8ABcBCwETATIBSgFRAUUBTgFdAUcBQwFYAVQBRAEtASEBIQH+APMABgHSAKwA3wDdALUAzADRALIArACeAHUAWgB1AKAAjAB9ALEArQBtAF8AXABYAHIAXwA1ADgAMwAkACkALgBIAGAAVABfAGgATABUAGEARwBTAGQAVABdAGEASwBRAFAANwAzACoAJwAxABIA/P8MABQAKgBAACsALAA5ACEAGwAlABkACQAAABoAQQAZAOr/BQANAPb/+P/3//X/7//g/+7/6//G/9b/8v/d/9//BAALANv/r//c/xoAAADh/+7/2P++/9L/3//X/83/vP+z/6//nv+J/3b/Xv9F/z7/Sv9X/1r/R/8z/zz/O/8y/z3/Gf/s/hv/PP8e/zL/UP9A/0L/Nv8N//7+9P7t/vT+4P7R/tP+xP7O/u3+3/7U/t/+zv7G/t3+3P64/qv+yv7S/qH+ef56/of+m/6x/sf+0f62/q3+0v7O/qT+s/7l/vj+Cv8z/zf/CP/5/hH/C//5/gD/Df8l/yj/Df8Q/wz/6P73/gf/6P77/iT/GP8b/yn/Ff8I//v+7P7x/uv+6f75/uD+uP6y/q3+uv7Y/uH+9/4c/yP/Ov9k/2n/XP9P/zz/RP9Z/1D/O/83/0v/Zv9s/2v/bP9j/2v/jv+j/5n/g/+C/5j/nv+a/6T/pP+r/9H/5f/o/wkAJgAwAEgAWQBbAGQAXwBYAHMAhABuAFYANAAHAPz//P/l/+H/8P/h/8n/yv/K/77/rf+d/6b/yf/j/+v/CQA9AEcALgA0ADQA/v/e/+v/6P/d/9H/qv+T/5D/dP9x/6D/sv+j/6v/tP+v/73/xv+v/6b/uP/P/+//AAD1//z/DAACAOj/uv+E/3v/jf+L/4z/mv+c/5//s/+9/7H/pP+q/7n/uP+j/6L/xP/K/6P/lf+p/8L/1v/P/8n/5//u/9L/wf+Z/1//Yf+B/23/Nf8l/0L/M/8N/yT/NP8c/0T/hP+C/23/av9y/3v/cP93/5//tv/C/9b/4P/n/+L/2//t/+P/v//P//z/CgAQABgAIAArAEEAaAB9AHEAaQBiAEoAOQAfAP7/7v/j/9X/wv+f/4z/j/9v/0z/Yf9y/1P/Xf+h/7L/mv+t/77/nf+L/6H/tv+o/5H/p/+z/33/eP+i/4L/bv+l/7r/wv/n/+//+f8XABcAHwAwACAAHAAoAB4AIQAmABgAFgAaAA4A+v/q//3/FAAXADAANwAEAPb/EQAQAAAA7P/Y/93/2P/R/+z/7//a/+n/9f/1/w4AHwAxAGoAmwC4ANoA6ADmAOkA7gABARIBAwHvAOsA3wDXAOAA3gDCALIAwgC6AIUAbwCEAIIAdQB0AFoASwBuAJYAtgDYAOcA9wAFAQABGAE6ATABOQFXATsBFQEZARkBGgEqASoBGgEIAfkA8QDuAPQA8wDkAPgAHgEkAS4BOAEnASsBOAEsATIBPwEyAS0BNQFAAVEBTwFEATgBIwEuAUEBIwEQARsBAgHlAOwA7wDnANsA2wD4AAgBAwERARcBAQEIATUBVAFJASwBIQExAUcBQAEeAQ0BEgEGAeUAxACyAKgAkABvAFEALQAfACMAAQDj/+X/0//C/8b/rv+1//T/+f/Q/9D/2v/N/7b/nP+d/6T/of+z/73/pv+W/4L/a/98/4//m//A/8b/vf/p//7/1//N/9z/1//W/9z/3f/W/8P/sf+l/4j/aP9g/1//Uf9L/1b/WP9E/yj/Cv/y/vH+9P7u/ub+4P7q/vj+6/7Q/sH+sP6d/p3+r/66/rL+uf7L/rP+ov7L/t7+y/7T/tf+zP7V/sj+p/6h/qL+o/6h/oz+g/6A/mn+Y/5o/l3+WP5S/lX+ef6G/nn+kv6x/rb+sv6k/qb+tf6o/pf+m/6l/rP+tv6y/r7+t/6a/qP+wv7Q/s7+wP7L/u3++P77/vj+4f7w/hP/+P7a/vT++v7Y/sb+vf6k/pf+q/6z/pX+hv6i/rz+rf6R/pT+o/6W/o/+pv6p/p7+sv7B/rf+yP7Y/rj+oP6u/q7+qv60/q3+nf6l/qv+lv5m/jP+IP4c/gb+8f3Y/bP9rP24/az9p/2+/cT9wP3b/fT96v3k/fr9+/3d/dX9+/0e/hX+AP4H/v/92/3F/bH9of2w/aP9f/2Y/a/9jf2L/Z39mf22/dz95/33/fP98/0f/iv+Gf4u/i3+FP4d/hn+Bv4I/vv98f36/ej97P0b/iD+F/46/kn+P/5J/kL+H/4b/jr+Ov4d/hL+Bf7y/Qv+LP4n/hf+8v3M/dL9zP2s/bv9zv27/bX9rv2d/bP90f3N/cn92P3h/dr94v36/fT94f3l/eL92P3k/ef92/3q/Qr+Gv4K/uv96f37/fP99f0T/hv+Ef4S/gj+/v0U/ij+K/4z/ij+Gv4z/jD+Cf4e/j3+DP7g/e399P3r/eb94f3W/cf9yP3U/df95f33/fv9Gf5F/kn+RP45/hb+IP5R/k/+Mv4v/jX+Nf4r/h3+If4e/gb+Bv4Y/hX+Cf4R/ir+K/4R/hr+RP5J/jz+TP5Y/lf+Yf5u/nz+iv6U/qv+yf7b/vL+AP/8/gv/Gv8S/xr/J/8k/zb/T/9N/0D/Nf88/0n/MP8U/xz/Hv8b/yr/KP8f/zP/RP9B/0H/PP9D/2P/dP9w/3v/gf9x/27/e/99/4f/kv+M/6T/0v/O/7j/vf+3/7X/yf/B/7v/0//J/6z/tf+7/73/2v/w//n/DAAaAB8AJAAmADAAQABQAGkAeAB6AIgAlgCXAKcAwADQAN0A4gDmAO0A3QDAALoAwAC8AKYAgwBjAFYAVwBXAEEAMwBJAFoAWABdAGUAaQBxAG0AaABvAG8AbQBzAHgAfgCDAIMAhAB1AGIAaABlAFUAXwBoAGgAggCUAJMAmACHAHMAiACNAHYAgACRAI4AngCuAKgAoQCaAJkAswDGANAA4wDiAN0A9AD1AN4A4QDaAMIAvACmAIEAegCDAIYAgQB0AHUAhACfALsApQCGAJYAlgCZAL0AqQCGAJwAnQChALYAnQCjAKMATQBYAK8AnACxAPcAvQCYAMsAwQDOACgBRwEvATgBawGWAYkBgQGdAY4BZwFkAXABdAFwAX0BlgF9AVkBcgGBAVsBOQEvASEB5ACiAKcAnABXAFgAlQCjAJsAngC1AN8A6ADwABMBEgEEARsBLQEyATEBHQEOAfAAvAC0ALYAlgCQAJcAfABrAGIATABOAFgAWQBnAG4AYwBnAHIAcABjAFMAUQBSADsALgAxACAAEwAlADkATgBhAFwASABCAFoAewB5AGkAbgBsAGEAZQBkAFgATgBFAEAAQAA4AC8AMAA0AB0A8v/w/wsA+P/X/9P/yP+//7P/jP+A/43/d/9r/4X/f/9l/27/iP+W/5f/i/+I/6D/pP+F/4z/tv+z/6v/xP+6/6v/yP/O/8r/8f8CAOz/1P/C/8//5//Y/8v/0//M/9L/3v/N/8P/z//T/8//vP+f/6L/tf+z/6T/m/+Z/5P/iv+R/5z/lv+P/5f/n/+b/4j/fP+O/6T/nP+F/3//hf9//3r/fv91/2j/ev+T/5L/hP+B/4v/nv+q/5j/f/+B/4j/ev9y/3f/b/9p/3n/hP+F/4v/k/+T/5T/pv/B/87/zf/J/8j/1v/g/9L/zf/d/9r/x//B/8H/tP+w/8b/zP+w/6v/s/+X/4X/kP+L/4X/g/90/3T/dv9g/1n/Xf9Z/13/WP9M/1T/T/9F/0n/L/8k/0//V/86/0H/TP9W/2z/Xf8//zn/M/8+/1j/VP9P/17/Y/93/5n/qf+8/87/zP/W/+L/3//s//j/3//I/8L/sP+e/53/mf+N/4f/iv+T/5j/lP+H/3j/e/+O/4z/gv+V/57/kP+f/67/lf+G/4//kv+N/4b/kP+h/5z/p/+5/5z/hf+Y/5L/d/9s/2r/bf9n/2L/af9R/zH/Nf8t/yL/Mf8e/wb/Gf8c/yP/Pf8s/yD/Mf8O/+/+Cf8I//3+Fv8X/w3/K/82/xj/F/8x/zb/MP84/0H/Rf9O/1H/Uf9W/1X/S/9Q/2P/a/9l/1z/Uv9E/zP/J/8n/yj/IP8U/xL/Ev8W/zL/Sf8q/wv/Ov9l/0n/Pv9W/1D/Vv9z/2r/YP91/37/g/+X/5//n/+e/5n/nv+k/7b/1v/f/+j/CgAjADAAPAAwACEAIgAmAC0AKwAhABkABgABABoAGwATACYAKwAiABsAEgAwAF8AXgBFADYANwBZAGsAZAB4AIgAhACZAKcAmgCWAJwAsgDLAMIAtACrAJEAjgCbAJAAkQCeAI0AfACIAJEAgQBiAFsAdACEAH8AagBTAGQAggB5AHAAegByAG4AdgBmAF0AcgBvAGEAfwChAKAAoQCxALIAsgC5AL8AywDJAL4A3AABAesAzwDdAOgA7QD0AOoA7QD7AOwA2wDcANIA1ADjANkA0QDdAOMA7gD0AOcA+QAeARMBCQEjAR4BBAEDAQQBCgEcARIB/gAJARABCQEVAR0BBwHxAPEA+gD3AOAAzgDYAN4AyACxAK4ArgCgAJoAqgCwAKEAogCiAIkAfwCCAHQAYwBZAGQAfwBzAFkAYQBZAFEAbwBlAEsAbQB9AGAAWQBbAF0AaQBgAF4AcQBgADkAMgAzAC0ANQBHAEgAPwA+ADYAGAAFAPv/5f/i/+7/5P/J/7r/x//c/9L/xP/M/8//0f/k//T//v8DAPv/9f/9/wcAHwA8ADsAKQAiACIAKgA0AC4AIQAtAE0AXQBRAEIASwBYAFUAYgBwAFoATwBVAEkASQBKACwAMQBQAEIAQQBWADgAHQAvACwAHgAmACsAKQAsADEALAApADkAOwAbABYAKgAiABkAFwD//+f/4f/j/+b/2f/M/9n/5f/o/+n/2//W/9//1//K/8v/3P/3//7/+f/4/+r/+P8bAAEA6P8aADQAJgBAAFwAUgBMAE0APwAoABoAGQAbACkALwAWAAQA/v/i/9j/3P/M/9L/4f/h//L/6//K/9T/0f+r/7b/yv/G/9T/yP+t/7P/s/+x/7H/j/+G/6b/ov+a/6n/of+M/3v/df+K/5j/nv/C/9n/1//m//P/6f/X/8j/y//h/+r/5f/j//P/AQDn/8L/wf/H/73/rv+f/5v/pP+s/6//pv+V/4r/g/+K/57/mv9//3H/dP+B/5L/jf92/3X/g/99/3T/fv99/2v/aP9p/2H/av9+/4D/hf+j/7P/pv+m/8P/0f/K/9P/4f/n/wAAHAARAAMAGAAlACEALQA7AD0ARwBQAEwAUwBuAH8AfACBAIoAewBxAIgAkwCKAI4AhgByAHEAaABaAGMAbABwAHoAfgB+AHcAdgCRAKkAtwDHALQAqgDUANgAyADtAO8A0gDoAOoA1wDxAPsA7QD4APcA8AACAQUB/QAIAREBFAETARQBHQEnATkBRwE+AUkBYQFPAUcBYwFkAVgBZwFzAXsBhQGDAYkBkgGUAawBugGcAZEBoAGRAYYBkQGKAYkBjgFxAWMBgwGLAXoBfQF9AXsBfgFpAVsBfAGfAZkBigGRAZwBjAFxAXUBiwGLAX8BjgGhAYwBdgGGAYMBbAF2AXwBaAFqAW8BYwFsAXYBbAFpAWcBYAFrAXwBggGCAYEBjAGaAZMBmAHCAdYBxgHMAd4BzwHEAd4B6QHfAe4B9QHWAcUBygHPAdUBywHDAdoB4QHHAcEBvwGzAbkBtwGlAaoBvQHHAcIBqAGdAagBpQGgAZkBhgGMAZkBiQGLAZgBkQGdAbEBrAGwAbUBrAGxAbABoQGmAaYBmAGdAZ0BkAGXAaQBmAGKAZoBrQGXAYUBowG5Aa0BnwGUAZgBoAGXAakBzQG/Aa4BvwHEAcIByQHPAdMBxwG0AboBwQHCAckBvQG2AcYBuQGaAYwBigGbAaMBiAF3AX8BhgGOAYgBcwFrAW8BbAFUAUEBUgFWAT0BQAFEASMBFQEfASIBIgEUAQcBDQEMAR0BOwExASkBQgFMAVABTAE8AU0BVgE/AVwBfAFgAWwBhAFRAUEBbgFqAV8BfAF/AWwBYwFXAVgBWgFEATMBLAEkASIBHQEUAQcB7QDzABMBAgHcAN4A5gDrAOoA0wDRAOMA2ADFALAAmwCmAKcAnQCrAJ4AgACMAI0AeQBwAFYAWQCAAHIAWgBYAD4ARABuAHYAfQB5AFQAYwB8AFcATwBnAE0APwBTAE4AQgA9ACwAJAAmACQAKQArABYACAAWACcAKAAlACEAGQAdACgAIwATAAEA8f/1//3/3/+5/8P/zf+j/4X/mP+W/33/f/98/27/gP+O/3//ev95/3D/d/+I/43/hv99/4r/mP+E/3X/iP+U/5P/kv+J/4T/gf91/2//bv9p/2X/W/9R/1H/Tv9E/zT/Iv8e/yP/GP8Q/xf/Fv8I/wL/Bv/8/uP+1f7K/r/+xP7C/rz+zP7O/r/+wf6+/sD+1/7R/sv+6v75/gT/E////vj+B//n/sz+4v7l/tj+1v7N/r/+rv6Z/p7+q/6l/qb+uf7I/sD+tf7C/tD+xP64/r3+yv7a/ub+6f7t/vH+3/7E/sz+3f7H/sb+9f4C//b+BP8C/+T+3f7j/t3+4P76/gf/8P7o/gX/Af/h/uT+8P7j/s/+yv7a/uT+1P7T/uP+3P7L/sP+vP7E/tb+0/66/qj+pP6q/rP+q/6b/qP+r/6q/rH+v/7P/uz+4f66/s3+6f7V/uH+DP8U/xr/I/8d/yn/OP8r/xz/E/8P/xn/GP8J/wj/C/8L/wv//v71/gX/Ff8a/yf/Kf8W/xP/Gf8V/xn/Gv8T/yj/Tf9Z/1H/T/9Y/1f/QP8v/yv/Jv8t/z7/Qf89/zr/OP84/y//Lf82/y3/Jv8s/yD/Gf8k/xz/Ef8Y/xn/GP8N//n+Cf8n/yj/Iv8Z/w3/Fv8i/yP/If8Y/yP/O/84/z7/W/9Z/0j/UP9d/2f/b/90/4b/iP91/37/h/9q/2n/gf92/3P/k/+Z/5X/s//D/8H/0//c/9H/3v/0//D/5P/i/+n/5//d/+T/6P/X/+H/+v/5/wcAGwAJAAkAIgAcABIAHAAhACwAPQBGAE8ARQAtADkATABGAE0AaABtAGIAXABWAEkATwBvAHEAVQBVAGwAdgBvAGMAYwBiAFgAZQB6AH4AjACKAHAAfwCQAHMAfQCeAIcAegCWAJgAkQCaAJMAjwCiAK0AsgDFAM0AwQDKAOUA5QDdAOEA2wDqAAgB/AD6AB8BHQEHARQBFgEKARMBGgEfATYBSAFHAUgBTgE+ASoBNwFGAUYBWwFoAV8BbAFtAVsBewGXAYABfAGMAZcBrAGuAaABnQGPAY8BqgGoAZ0BtgHSAdQBvwG0AccBywHCAdIB1wHaAfMB7QHjAfsB9AHUAdcB7gH9AfUB6wEAAvwB5QECAgkC4gHsAe8BwgHCAdkBzwHJAbsBpgGwAbABoQGvAbcBqQGmAa8BrwGWAYcBqwHFAbYBvAHBAboB0gHZAbcBtAHEAcsB0wHGAccB7gHwAdoB6gHxAfEBBAL9Ae8B/AH+AQECEAL6AeEB6QHuAecB5gHuAfsB9AHiAeUB4gHSAdIB1gHhAfEB5gHbAe4B+AHsAfABBAIIAgUCGgIvAioCMQJDAj8CPAI8AjMCNgIwAiICNgJPAksCQwI8AjcCNwIkAhECEwITAgoC/AH6AQsCCgL2AfAB7AH1AQoC+wHqAfwB/gH2AQAC/wH4Af8BAwIFAvsB6wH1AQIC9wH4Af0B6gHgAfMBAAL0AeMB4QHmAegB4wHdAdIBxAHFAdQB1AHMAdIB2AHcAeEB2gHiAfkB+AHpAekB6gHmAdwB3AHrAe8B6wHrAd0B2gHuAeoB3wHsAfIB5gHVAc0B5QH2Ae4B5gHQAbsByAHGAcMB4AHWAbwB0gHKAaYBrAGiAZABpwGgAYMBmwGlAYIBeQF7AW0BbAFvAWYBYAFjAWgBZQFbAVkBWgFQAVYBawFiAU4BUQFHATUBTwFjAUkBSQFgAU8BPwFDAS8BGQEYASIBPgFJATcBOwE/ATYBRwFOAT0BSQFQAT4BRAFKAUMBSwFFATQBMwEjAQ8BGAEaAQsB/QDtAOwA+AD2AO0A5QDjAOYA2wDPANcA4gDcAM4AzgDcANcAuwCzALsAvAC+AMgA0ADPAMwAzwDIALkAswCoAKIAqQCYAIwAogCUAHQAjQCfAIQAcwB0AH4AjwCLAIAAjQCiAKsApACWAJQAngCyALgApACnAMAAugCxALEAmACIAJQAnwClAJwAigCHAIEAeAB/AHQAXgBeAFUASwBXAFoATgBHAEgATQA8ACEAMwBaAGQAZgB0AIQAigB4AGkAdAB+AIIAjACNAJMApgCrAK4AugCqAJwAsQC6AK8AswCyALAAvwDDALcAqQCmALMAqgCVAKMAqACZAKYAoACTALoAsAB1AH8AkgCHAKUAsgCXAJEAkgCbAKQAigCQALIAlgB9AJkAmwCLAIkAgAB3AG4AaAB5AH8AawBhAGYAbwBhAEQASQBWAEwAVgBkAEcAPABYAE0AKgA1AEUANQA7AFkAWABNAFwAaQBnAFsATgBVAF8AWQBkAHgAawBUAE4ATQBDAEEAUwBaAFAAUgBQAEYATwBNADoASQBbAFIAVQBrAH0AfgBuAHMAhACFAJAAjwB7AJAAoQCMAKAAuACbAKMAwQCnAJ4AugCuAJwAsQC8ALAArQCxALYAvAC8ALIAsQC5ALMAqgClAJMAkAChAJ0ApADDALwAtwDVAMgArwDFAMQAtwDkAPsA2ADIAM8A0gDVANUA2QDZALwAqwC6AL8AvwDGALwAtwDDAL4ArgCtALsAyQC/AKgAowChAI4AhQCGAHYAZQB1AIsAgwB5AHwAbgBlAHcAdQBhAGEAVABEAFgAYwBXAFUAVgBTAEoAOQBBAFAAQQBJAGIAUQBIAFUAPQAnADUAOwAyACQAGAAgACcAIAAgACMAJwAjAAUA9/8LABQABQD7//3/CwASAAAA9v8CAPz/4f/m//n/4P/K/+j/6//K/8z/1P/O/+H/5v/R/9//7v/j/+b/4//P/87/y//G/9T/0P+9/8n/1v/J/8f/w/+n/5f/of+e/5H/mP+h/5X/iv+P/5L/j/+P/4r/g/+D/4b/k/+h/4n/Yf9x/5P/h/94/3j/dv9//4D/ef+F/37/af+D/57/kP+J/4j/d/9w/3P/f/+R/4b/cv93/3f/bf95/4P/bf9b/2v/ef9w/2b/W/9M/03/U/9M/0b/Qf87/0b/Uv9G/zn/Qv9a/2X/Vf9N/1f/XP9l/23/Zf9t/4L/ef9v/33/gf9z/2T/Yf93/33/Yv9g/3L/Z/9P/0j/Uf9g/2T/Y/9n/2z/cv9s/2D/aP9n/1b/af+C/4H/if+G/3T/d/96/4P/nP+U/4r/nf+e/5z/rP+s/7T/u/+k/53/qP+y/8T/s/+Y/6//sP+d/7n/w/+s/7b/wf+y/6T/qf/K/9P/sP+u/7v/rv+4/8H/sf+5/73/rP+y/7H/nP+k/7L/pf+j/7L/rP+U/5P/oP+V/47/mv+I/3D/if+i/53/ov+u/67/oP+P/4j/fv92/5L/pP+B/3j/ov+q/5T/nP+l/5j/iv98/3n/kP+c/5L/k/+d/53/oP+m/6v/tv+//8T/y//R/93/5//b/87/2//s/+//8P/2/wAABAABAA0AHQAgADIAQAAyADMASABKAEYARwBCAEQARgA9AEUAVQBLAD4ASgBRAEAANQBKAF0AUwBCADsAOgA7ADQALAAwADMALQAlAB8AJgAtABwADwAQAAcAEQAoABgAAAAAAPv///8JAP3//P8VABcACQAMAAkA/f///wcAAgD8/wcAFgAUABsAJQAcABMAFAASABQAHwAqAC0AHwAfAD0ATQBAADoARwBMADsAOQBRAFUAVABgAEwAOgBTAFoAUwBgAGEAYwBnAFsAbwCHAHEAbQB3AG4AhACPAH0AkgCUAHAAeQCIAIQAjQCBAHkAlACUAIYAhwB4AH8AkgCCAIcAjwByAH4AnACAAHMAhAByAG0AiQCJAIAAkACdALEAxwCwAJkAtQC4AJcAowC2AKcArAC4AKcAmgCbAKIAsACvAKUAoACbAKMAqgCdAJcAlACSAKUApACMAJYAogCOAIwAkwCFAH0AgQCEAIoAhwB4AHAAewCSAI4AeACBAIoAcQBkAHAAbgBmAGkAcgCDAIkAdQBlAGkAbgBwAG8AaABjAGoAaABdAF0AYgBfAGEAXwBOAEUASgBEAD0AOAArADAANwAaAA4AJwAdAAUAEQAXABIAJAArABoADQARACEAIAAUACAAJQAIAPH/8f8FABcABwD+/wgA+f/s//b/6P/T/9H/1P/b/8v/rf+6/8j/vv/G/8L/rv+0/6f/lP+0/8L/sP/E/8//sP+0/9j/1//F/8P/vP+4/9D/4v/Z/9r/4f/T/9H/5f/s/+X/5//7/wMA5//Y////CgDv/+//7//f//j/DAD2//f/AADe/9X/7//o/+D/9/8AAPv//f/6//r/9//g/9T/7v8FAAUAAgD7/+v/8f8BAPX/5f/r/+X/4P/r/93/vP+4/87/0f+6/7f/wf+h/4b/mf+O/3P/ff90/2j/gf9v/0n/Vf9V/0f/Q/8i/xf/Nf8h/wX/Gf8T//7+Cf8K/wD//P71/vf+/f78/gL//f7t/vT++f7p/t7+3f7n/u/+2/7S/uH+1f7F/sf+vf60/q/+nP6W/p/+of6q/qr+mv6X/pr+ov6m/ov+jP6s/pL+b/6B/of+g/6J/nn+eP6C/m3+Z/5p/lD+Vf5p/lD+L/4m/iv+Lf4h/hz+Gf4F/g/+If4L/v79Cf4J/gz+D/4M/hX+Bf7m/fD9+f3p/ev96v3Z/eb9Af7//fb9+/0E/v/97/3n/fP9//3+/QX+Ef4L/gH+9/3o/ff9Fv4T/gb+B/4F/gn+EP4P/hD+Ff4g/i3+Kv4l/i7+M/43/jb+Hf4P/iL+MP4i/g/+Gv4w/iD+EP4h/h/+C/4P/iP+O/5B/iX+Gf4l/ij+Mf5F/kj+RP5J/lb+YP5e/mj+f/5x/lH+ZP6F/nP+XP5u/nr+df6M/p/+k/6U/pP+gP6O/rL+uf62/r7+z/7a/tL+0v7c/t3+8v7//uz+/f4b/wL/9v4L/xP/Hv8c/w3/Jf8y/xn/Gv8g/xn/H/8S/xL/Mf8c//7+GP8Y/wP/Gv8n/xP/C/8N/w//EP8V/x7/Hv8Z/xr/Fv8R/xL/EP8U/xL/BP8G/wT/7v75/hb/E/8K/xD/Fv8e/x7/E/8O/xL/H/8r/yT/GP8V/xD/Df8S/xb/FP8Q/xP/Hf8i/yf/Mf8x/yf/HP8g/zr/Sv89/zr/Q/89/zz/RP9C/0v/Xv9Q/zr/QP9E/0r/XP9V/0z/Yv9j/1D/W/9j/1v/bv9y/1j/Xv91/3r/hv+T/4//iv+I/5H/l/+E/4b/ov+f/5H/nP+o/7r/yv/E/8b/1f/V/93/5//g/+r/AAABAPz/AAANAB4AKgA9AEYAOQBHAGkAZABXAF0AXgBlAHQAdgB6AIIAiACHAH8AjACfAJEAjgCdAJQAmgCsAJkAlwCpAI0AfgCgAK4AqQCxAK0AmACIAJYAsgCrAJUAmgCuAMIAugCXAJgArQCfAJkApQCiAKAApwCyAMMAuwClAK4AugC1ALgAwADFAMcAyQDYAOMA2QDRANEA0wDgAOUA3QDiAOgA6gD8ABMBFAEDAfkAAgEDAe4A3QDqAP0A/gD8AAcBDAEKAQUB/wAFAQIB8ADyAPMA3QDYANQAvwDKAM0ArgCxALYAnACiAJ4AcAB4AJQAbQBRAGIAaQBtAG8AWABJAEwAQQA3ADwANQAsADsASgBIAD8ALwAdABYAHgApADIANwAxACAAHAAkACMAIAAhABcAFgAfABoAGQArACsAGQASABMAGQAeAA4ABQAXABcAAAACAA4A+//z/wMA9//i//D/9v/m/+L/3f/U/9r/3//b/9L/xP/E/87/yv/O/9j/wv+q/7n/wv+1/8H/x/+s/6//x/+u/5f/sf+7/7n/xf+7/7L/wP+5/6P/nP+Z/5z/o/+s/7n/tf+u/7f/p/+f/9L/5v/C/8H/yP/B/9L/zv+7/9n/5P/I/9r/9v/y//P/7//n//D/6//f/9//2P/b/+f/3v/V/97/7f/z/9b/wP/d/97/vv/M/9n/xv/O/9j/wv+0/7b/vf/D/8X/1P/V/7T/t//c/9f/vv+7/7T/rv+3/7z/u/+8/73/v/+8/73/zf/M/7D/qv/B/8T/t/++/8P/uP+7/8v/zf/J/8L/t/+3/7v/v//N/8n/uf/I/9X/yv/P/8v/sv/B/9r/zv/R/9z/xv/F/9f/yf+2/7j/x//L/6b/i/+e/5f/iP+c/4n/Zv97/4T/av9r/2r/WP9k/3n/a/9W/1r/Y/9X/0v/Rv85/0H/WP9T/0H/LP8Z/yD/KP8h/yv/Mf8q/yf/FP8K/yD/Hv8X/zL/N/8g/x//Mf86/yz/JP9B/1H/N/8n/y//RP9S/0v/R/9F/zr/Tv9m/1j/V/9l/13/Xv9h/1r/cP+I/4D/dv90/3z/if9+/27/df+D/4b/ef93/43/kP+M/6X/tf+5/8f/w//C/9f/3f/o/+//3f/u/wYA7f/z/xQAAgADACUAFwARAC8AJgAdAEQATgA2AEUAXgBLADgAQABCAEIARwBJAFgAXQBEAEEAVQBPAEkATgBJAFEAZABsAG0AYgBaAHMAiwCDAHoAggCMAIwAjwCgAJ8AjgCaAKcAkQCIAJoAmwCKAIcAnACkAJYAqADIALYAqADDAMMAtADAAMIAxQDSALwAqgDAAL0AqgC7ANMA4QDnANcAygDXAOcA7QDjAN4A7ADkANgA9AAEAfsACAEKAf8ACgEMARMBQQFVAUIBQAFXAW8BcAFkAXMBgwF6AXwBigGNAYsBfgF4AYgBhQF0AXwBjQGNAYkBkQGaAY8BgwGZAaoBlwGPAZYBkwGcAasBnAGEAYUBkgGZAZ0BlgGKAY0BlQGTAZUBnAGfAaYBpAGbAaUBpQGQAZEBmgGTAZYBmAGTAZ0BnQGVAaMBowGNAZUBsAG+AccBwwG9AcUBzgHRAcUBuAHKAdUBzAHdAe8B5AHqAfYB9gH/AfsB7QHvAe4B9gEQAhcCDgIHAgcCGQIcAgsCDwIOAgACBQL+AfoBCgL7AfUBDgL5AeAB9AHuAekBBgL/AegB8QH2AfUB9QHmAe0BCgIEAucB6gH5AesB3QH0AfkB2gHaAecB0AHBAdEB1QHbAeYB0wG2AbkBwQG0AaUBogG0AbsBnQGPAaIBmAGCAYoBiwGHAZoBpAGZAYQBeAGKAZEBhgGYAaMBjgGNAZEBjQGXAZIBkQGsAa4BpAGqAZ4BmwGqAZYBgAF/AXoBgAGPAY0BhQF8AYIBjgGCAXcBdwFkAWABaAFhAWoBbgFgAXQBhAFzAXYBewF2AYYBhwF7AYYBhwGAAY4BlwGaAZwBkAGRAZkBlQGcAaIBnAGaAZUBjAGCAXABfAGPAXABXQF1AWcBRwFIAUIBOAFEAT8BJgEaAQ4B+wDwAOwA7ADiAMwAxADBAL8A0gDTAK0ArQDNALsApgC5ALAAnACpAJwAfACAAIQAewByAFIARABfAFUAMgA1ADMAHgAdAB8ADQD5//H/8//k/9j/6v/n/83/2v/l/8v/wP++/7n/xf+//6b/ov+c/5n/pf+V/4H/i/+B/2n/c/9+/3X/Yv9O/1P/VP81/yv/Qv9E/zH/GP8J/xH/D//1/vH+9/7h/tb+6f7m/sn+yf7e/tz+1P7U/tT+2/7e/sj+tv65/sb+y/6+/rP+v/6+/p/+lP6k/qn+of6i/qj+pf6n/qn+k/6L/q7+uv6j/pv+l/6O/pr+ov6R/oD+gv6C/mz+YP56/oz+gP6C/pb+ov6b/pL+l/6P/n3+jv6n/qP+nf6I/nX+i/6O/nX+gP6D/mf+a/50/mv+c/51/mL+WP5X/mj+ff50/mv+ef54/mD+Wv5w/nT+X/5e/mv+fP6Y/pL+dv6H/pX+iP6b/q3+ov6d/o3+if6j/qf+m/6R/nf+dv6H/nP+b/6B/m7+Yf5l/lr+Xv5j/lf+W/5N/i7+QP5F/g7+CP4k/gT+5/30/en93f3r/eD9w/3A/cP9vf2+/bv9q/2k/an9qv2u/bX9l/13/Y79nP1//YH9kP1+/Xv9ef1i/V79V/1K/Vv9YP1Z/Wf9X/1U/WX9VP0+/Vb9YP1d/Wf9Xv1a/Wf9Zv1s/W/9WP1U/WP9Xv1U/Vf9Yv1m/Vz9W/1o/Wn9ZP1b/U79Vv1m/WH9W/1k/WT9V/1f/Xb9aP1L/Vv9dv1y/W79cv1y/Xv9gv1+/Xr9dP1z/YT9k/2R/Yz9kP2f/aT9oP2v/b39rv2f/aL9rv2+/cj9w/3C/cL9tv28/dX90P29/c796v3j/c79zv3a/eD94f3j/d391P3a/eT95v3s/fb98P3o/fT9DP4U/gn+Bf4V/h3+Cv4J/ir+O/44/kH+SP5L/lH+Rf5L/nH+b/5g/nn+bv5J/mL+ev5v/nz+h/52/nX+ev58/oX+hP6F/o/+l/6v/r/+rP6v/sr+yv7O/ub+6f7v/gb/B//2/vf+Bf8H/wX/Ev8i/y7/QP9A/yf/K/9A/zT/LP8z/xz/Df8r/zv/LP8n/zH/OP83/zr/Rf9B/zn/UP9d/0D/MP86/zj/QP9P/0P/PP9G/zj/OP9W/0//Pf9Q/1T/T/9m/2f/Uv9V/1j/VP9X/1D/UP9a/1X/Vv9T/0T/Vv9p/1r/Wf9g/1n/ZP9w/3r/hP9r/2H/e/9r/13/e/90/3P/lf96/1z/hP+Z/5T/nP+U/47/m/+d/5j/mP+Y/53/oP+p/7f/vv/I/77/rP/I/9n/xP/Z/+r/zf/d//v/7//4/wAA8v8HABYA/P/7/xgAMAAvACAAOABYAEgASABlAF4AUgBeAGsAfAB/AHoAiQCKAIUAnwCpAJkAnQCjAJoAlQCOAIMAfwCXALoAsQCXAJwAngCYAKEAoACdAKoAqQCcAJgAoQCtAJkAhACUAJ8AlgCaAJoAmgCeAI8AkwC0ALQAowChAJ0AngCkAJ4AkQB+AHkAiwCSAI8AlwCaAJ8ArQClAJMAmgCfAJcAnAClAKYAqwCrAKMAtQDQAMwAxgDJAMAAwgDVAOEA7wDrANQA3QDqANsA5AABAQEB9gDxAPgACwELAQwBHgEZAQ8BIAEeARABFwEfASkBOAEtARgBHwElARsBIAExATYBOgE4ASEBGQEpASoBJQEuASkBIgEqASwBMAE0ASQBKQFRAVYBMQEkATwBRQEwASQBLwE1ATUBOQE6AS4BIQEiASIBHAElAS0BGgEOARgBIwEkAQ8B+QANARoBAgEFAQ4B+AD6AAUB8wD0AAMB9QDzAA0BFAEJAQEBCAEZARcBBwECAQsBEQEAAewA9wD6AOcA+AAVAQcB+wAJAQ8BAwH5AA8BKAEJAf4AKQEgAf8AFwEdAQ4BKQEwASIBOQFDATQBNwE6AToBRAFDATwBNQE2AU4BVQE3ATEBTAFYAVEBRQFBAUwBUQFMAVABVgFVAVoBVwFCAT4BXQF0AXEBaQFqAW4BbAFsAXgBewFyAXcBdgFiAV8BYgFVAVYBVwFRAWEBZgFOAUwBTgE6AUABXAFUAT4BSAFYAVQBUQFNATMBMgFOAUgBOQFEAUUBQgFLAUwBTgFOAUQBSwFNAT8BRwFPAU4BWwFRAToBSgFcAWIBaQFYAVIBYAFPAUUBUAFBAT4BUgFKAUgBUgFJAVoBbwFQATsBRQFBAUUBTQFDAUcBTgFJAUwBSwFHAVABSwE5ATIBNAFAATwBHwEiATIBJgEjAS4BNgFFAUIBPgFRAUQBMQFAATABGgEsAScBJwFKAUABJwE0ASQBBwEYAS4BMAEfAQQBCgEYAQQB+gAHAf0A5wDoAO4A5wDkAOgA5gDuAPgA3AC/AM8A3ADFALIAtwC9AL4AxQC6AKEApgCvAJUAlAClAIIAagCEAIMAcwB1AFoAQQBMAEcAPABUAGcAWwBSAFkAZwBxAG8AYwBZAFkAWQBUAFgAZQBmAF0AWwBaAGQAdQBmAFAAYwBvAFoAVwBVAEMAUgBiAEoAPQBBADMAKwA3AEMAQwAvACUANAA4ADMANAAkABYAFwAOAAsADAD8//r//f/n/+n/8//Z/93/+//m/83/2f/i/9v/y//F/9v/4f/G/8P/0v/N/8L/u/+0/7v/x/+4/6b/sv+1/5r/mv+m/47/iP+W/4j/iP+b/5P/lv+r/6H/j/+H/3b/df+A/4H/hv+M/4H/df9+/4f/e/+D/6X/lv91/4X/gP9W/1n/bP9v/27/Wv9V/2j/U/89/0v/PP8f/yT/J/8d/yD/If8N//H+7P74/uz+1/7c/uH+z/6//sb+y/68/rP+sv6g/pz+rf6p/qX+pP6D/nT+i/6E/nH+fP56/mT+W/5f/mr+aP5V/lj+bP5x/mv+XP5M/lT+V/4//jr+TP5K/j3+M/4j/hv+Jf4s/h/+E/4q/j7+Gf75/RD+F/4K/hX+Ev4H/hX+Dv7+/QX+9P3f/f79Gf4H/vP97/30/fz9Av7//fT99f0D/gP+//0I/gT++f34/fv9CP4N/v79Bv4h/hz+Cf4E/gX+Cv4M/g7+F/4U/hD+Gv4b/hz+M/48/jX+Of4y/iL+LP49/jb+Lf47/lH+Wv5R/kn+Sf5J/kj+Rf45/j3+Vv5Z/kn+Vf5o/l/+Vv5Z/l7+av5x/mv+eP6E/nL+aP50/nP+d/6B/nH+bf50/mD+aP6H/m3+Yf6E/nn+aP6C/oH+fP6K/nT+dP6V/nz+Xv58/o/+hf58/m7+af5z/nz+iP6O/pT+oP6b/ov+iP6Q/pH+hP6I/qH+nf6W/qr+of6W/q7+qP6V/qX+pv6d/qL+nv6e/pn+i/6n/rL+ff5t/oL+fv6A/nz+av52/n3+dP55/m3+Wv5l/m3+bP5r/l/+ZP5m/kv+Q/5L/kz+Yf5o/k/+Qv49/kP+Xv5Z/jj+Nf5D/kv+Uf5M/lD+Xf5R/kX+W/5w/nD+ef6J/ov+k/6O/mj+Zv6N/oj+ef6Q/o7+ff6T/pr+hf6T/q/+sP6n/qH+mv6d/q/+sf6k/q7+uf6k/p/+uf7E/sf+1v7i/tz+1v7x/gb/6f7g/gX/Df8D/xf/Lf8y/yf/Iv9G/1//VP9m/3//dP9//5r/lf+X/6f/q/+z/8L/0v/m/+r/7v8HABUAGQAkABoAGQA9AEcAOwBLAFQAVwBxAIIAhQCMAJEAoQCpAKAAtgDHALUAyADnANsA3gDsAOYA9gAEAQQBFwEYAQUBCgEfATgBOAEgATIBRgE0AU4BbwFOAUQBWQFWAV0BXQFKAV0BdAFtAWcBZwFyAYMBhAGMAZQBjgGlAb0BrgGnAasBtwHbAeUB0gHaAeIB1QHXAecB9QH3AfAB/AENAhMCHwIZAgoCIgI4AjcCSwJVAkQCTAJaAloCYQJiAmYCfgKBAnUCggKJAnkCgwKXAokCdwJ7AnoCdQJ3AnUCfQKKAm0CSQJeAnkCbQJkAmICYAJjAmICXwJgAl8CYgJaAksCXAJsAk0COQJEAkICQAJEAkgCUwJOAj0CPgI4Ai4CQAI9AiYCNAI8AigCNAJFAi8CGQIKAvsB+gHuAeIB+wEOAvoB8wH6AecB2AHYAcgBrwGsAcABzwHFAbwBtgGbAYsBlwGRAX8BfAF9AXgBbAFlAXEBawFSAV4BawFZAVMBTwFFAVwBZgFEAT8BUgFLAUUBTgFUAVcBSgFFAVgBVAFEAUgBOwEvAUIBQwE4AUQBQQEsASUBKQE3ATsBLQE1AUQBPgFMAWEBUwFIAUsBTAFXAV4BVgFVAVMBUQFcAV4BUwFPAUoBQwFKAVIBRAE5AUkBWQFbAWUBZAFNAT4BOQE7AUgBRAFAAVMBVgFFATwBOAFCAVMBSAE2ATABLAEvATEBKAEfARcBEgEYAR4BIQEbARIBEgEDAfgAEAELAeoA/gAPAfYAAgEYAfsA6QD/AAAB6wDqAPYA7gDhAOQA7gD4APwA8gDyAPgA6ADaAOEA5wDoAOkA6QDhANUA2wDgAMMAswDKANIAwwC4ALEAsACpAJwAoACfAJAAmACuAKUAigCLAJ0AjABpAGYAagBqAHQAYQBCAE4AWABCADsAQgA/ADYALwAvACsAIwAyADoAKwArACsAGwAaABgAEQAiACMAAgD2/wEABAD7//P//f8BAPP/6P/o//X/AgD4//n/CADz/+r/AgDq/9f///8AAPL/AQDp/9b/+P/t/9L/6f/o/8//1P/c/+D/3v/I/8H/yP+//8H/yf+6/7j/zv/H/6T/l/+x/8n/v/+u/7H/sf+k/6X/rf+l/6f/vP+7/7T/uf+q/5f/ov+m/53/l/+M/53/uP+i/4z/of+y/7r/uP+i/5v/p/+p/6f/l/+Q/6j/pf+I/5D/of+d/5//pP+o/6L/hf+B/5b/l/+Y/5X/hf+I/4X/a/97/5j/i/+B/4T/fP90/2z/av9z/3L/bP9d/0f/VP9e/0L/Rv9X/z3/Of9O/0T/Tf9l/1D/P/9L/0r/RP9L/1L/Uv9B/zz/VP9a/0//Wf9U/0H/Rv9L/1b/d/97/2P/ZP9v/3H/dv9+/4f/hv+D/5P/mP+E/4T/k/+d/6P/k/+C/5v/rf+e/5b/l/+N/4b/j/+X/4j/hP+f/6T/l/+e/5D/fP+H/4f/hf+b/5z/kv+L/3D/d/+Y/4z/gP+F/3D/cf+E/2//Yf9t/2//dP9u/13/Y/9m/1b/SP8//0n/W/9X/1z/Zv9V/0r/UP9M/0//UP85/zD/PP87/yz/Iv8k/y7/MP8s/yz/H/8R/xT/F/8U/yP/LP8X/wj/Dv8G/wT/Dv8D/wb/Iv8V//z+Df8P//z+Cf8X/xX/JP8x/yz/M/84/zP/O/82/x7/NP9T/zr/Of9r/2X/Sv9l/23/Wf9n/27/Wf9a/2n/b/97/4X/hf98/3X/hv+c/5j/kf+a/6P/n/+Y/6H/qv+X/5T/sP+u/5//rf+3/7j/y//M/8L/0f/T/8n/3//1/+T/z//d//z/BQD+//j/8P/r//b//v/7//v/9f/x//3/BQD9//b/+P/3//P/+v8FAAAA9f/w/+z/8/8EAAAA7//x//v////7//3/CwARAAwADgAOABIAHwATAAAADAAcAB8AHAAbACAAJAArAD0AOwAwADsAOwAvADUAOwBAAEIANQA/AFUATQBNAF0ATAA4AEcAUgBIAD8ATgBcAEsAQwBdAF0ANgAhADkAVABBACgAMgAxACwAOAAmABkAOAAhAPT/BQAKAPb/AAD7/+z/9v/k/9r/8//l/9X/7v/l/9T/5f/c/8X/0f/Z/8T/qf+i/6b/mf+Q/5P/jv+b/67/m/+N/5H/jf+c/5r/ff+M/5r/dP9n/3T/e/+N/4v/hf+Y/43/ev+J/3z/af+D/5D/iv+H/3L/dP+U/5L/iv+R/3z/dv+b/6X/jv+S/6X/p/+k/6r/sP+j/5L/ov/A/77/tP+9/7z/sv+7/7//w//S/8X/r//G/93/0P/W/+7/2P+//9v/7f/d/+j//v/2//T//f/0/+b/6v/9//7/4v/g//3/CgAPAAkA9P8CABkACQAKACMAHwAbACYAIwAWAA4AGAAuADQALgApACYAKAApACgALQAqADEAOwAtADEARAAtACYARAA5ACEALAAwACgAKgArACkAGQAVADAANgAqAC4ALwA4AEkANwAnAC0AJQArADkAIwAfADkAMgAlAC0AKAAkADIAOAA1ADEALwA7AEoARgA8AEgAVQBJAEEATwBXAEsAQABIAFoAXwBZAFoAYgBkAFoAUgBbAF8AUgBUAF4AaQB8AHoAcwCRAJoAfQBxAGgAZAB6AHsAaQBuAGkAaAB5AGwAYgB+AIIAaABlAHUAdwBpAGQAYQBMAE4AXwBIAD4ATwA0ACEANQAvACwANgAXAAgAHQAWAA0AFgAFAP3/FAAcAAEA7f/8/wUA5//Y/+D/0f/U/+f/0f/C/87/x//H/9T/zf/O/9z/3P/g/+f/6v/4//D/1P/h//7//v/5//r/+P/7/wAACwAhACUAGAAcACQAJwA0ADoANQBFAFcAVQBhAG0AWgBWAG0AcQB1AI0AkQCVALEAuwCsAKYAqQCzAMYAzwDPANoA6ADhAMoA0ADtAOcA2wD0AP8A9QD2AOwA8AAUAREB+QAHAQ0B/gAKASMBLQElARcBGQEdARoBLAE1ASMBKgE9ATgBOwFAATcBPAFIATkBLQFCAU0BNwEtATYBLAEiATcBRgFAAT8BQQE3ASUBKAFHAUwBOAE0ATQBLwEvAR8BDwEXARsBFwEdARMBAAEIARcBGgEXAQ0BAgELARgBEQEEAQIBAgEIARUBGQEVARUBGwEeARkBIgE1ASUBHwFFAUkBLQExATkBOAFBAT4BRAFdAWIBZQFtAWgBcQF6AXkBiwGUAYoBlAGYAZYBoQGiAbMBzQG+AbgBzgHGAbkBxAHLAc4BwwG7AdMB3AHTAd0B2wHeAfoB+QHkAeAB2QHWAdwB3AHoAfEB4wHeAeEB5gHyAd4BxQHZAdsBygHbAeUB4AHnAc0BsAG7AbwBwgHcAcYBtAHSAcoBrgG1AbUBsQG4Aa8BrAGrAZgBogG2AaUBlwGgAa0BvAG2AaIBnwGhAaYBnwGKAZkBqAGJAZABvwG3AZkBoAGuAa8BoAGeAbsBwgGyAbgBwAG8AcEBvwGxAbABuAG1AbIBtwGvAaMBrAGpAZsBqgGuAZwBqAGqAZsBsgG7AZ8BogGyAbEBqgGRAZUBsgGWAYUBugHGAZgBiwGUAZUBlgGUAZABhAF3AXoBfwGEAYgBggGCAYQBegGBAYsBewFyAWsBVwFcAWMBVAFNATwBJAE3AU8BPAEnASMBGAEJAQAB/AD2AOgA4gDuAO0AzgCyALcAzADXANMAxAC3AKEAgwCFAJsAkwCCAIEAdABoAG4AbwBnAF4AWQBVAEIAPQBbAE4AJQA7AEwALAA2AEoAJgAWACIAFQAWACAADQAKABEABgAYAC0AFgALABoAEQAKAAsA/v8KACMAEgD7//j/+v8FAAwAAAD2//n/AQACAOb/yv/S/9f/xv/A/8H/tv+u/7H/qv+b/6L/rv+h/5T/jv98/4L/lf+C/23/cf9u/3D/ef9r/1//Yv9j/17/S/9B/1z/Z/9O/0D/QP8+/zn/KP8j/y3/K/8j/yH/Iv8z/z//Lf8f/yb/Lv8n/xT/E/8o/yX/E/8Z/yX/KP8x/yj/Dv8X/zX/L/8b/yn/NP8g/x//MP8w/yf/If8g/yn/Kv8l/yr/Lv8p/x7/Hv8z/zL/HP8s/0L/PP8+/zf/K/9D/0v/MP80/0T/Rf9S/1P/Pf89/1T/Vf9E/1H/Z/9X/1b/cf9n/2L/c/9o/3D/hP9r/2z/ef9U/1r/fv9y/3r/l/+P/5L/m/+Q/4z/h/+N/6H/l/+Y/6r/kf+B/5L/h/93/3L/cv+K/4r/dP+H/4n/df+U/53/cf9w/3//bP9j/2v/c/95/2n/Yf9z/23/U/9P/1j/Z/96/3b/YP9Q/0//W/9k/13/U/9T/1X/VP9N/0z/Uf8//zT/Rv9C/y7/Mv86/0T/Uv85/x3/Lv80/zT/Tf9I/yn/L/8//0D/Sf9M/0b/RP80/yn/Ov9D/zj/MP8v/yr/Jv8u/zz/Nf8u/z7/Ov8k/zH/QP8u/zP/TP9A/y//NP8s/yf/Ov85/yT/If8g/xL/E/8p/zL/L/85/0L/N/81/zr/KP8g/zH/MP8n/zP/MP8i/y3/Qf9O/1P/Rv9E/1f/V/9O/1r/X/9o/33/dP9r/37/cP9e/3n/ff9r/3z/e/9n/3X/ff9x/3n/hP+B/3r/cP98/4j/c/91/4f/bP9g/4X/jP9x/2f/Yf9i/3f/ff9o/2D/b/9x/13/Wf9q/2v/Xv9l/3P/cP9y/4D/ff9v/3P/gf+P/6H/lP97/4v/mv+E/4b/n/+g/57/pv+l/6b/pP+Z/6b/vv+9/7v/2P/v/9n/yP/k/+3/0//U/+f/6f/u//D/6v/x/+z/4v/4/wAA6v/y/wAA9v8AAAsA+v/y//7//f/4//7/BAAAAPn//P////L/4//f/+X/8f/3//b/8f/l/+n/+v/v/9X/4v8HAA4A9P/q/wIACQD1/+r/4P/d//H/7//e//D/AAD6//L/6f/o/+v/2P/T/97/y//C/9X/0v/B/73/xf/Z/9b/wP+4/63/r//N/8r/tP+4/7n/tv+2/6b/q/+3/5X/jP+r/57/i/+d/5z/kf+U/5T/of+w/6L/mP+l/6z/rf+w/6v/mv+G/4T/kf+Q/4z/n/+x/63/ov+f/6z/xf/N/8H/vf/N/9z/0P/H/93/5//d/+///f/y//b/9//2/xEAHQAWACIAHgAVACQAHgAeAEAAQAA6AFoAYQBWAGEAYgBlAHMAbABlAHAAfwCTAJgAigCMAJMAjQCXAKYAmACFAJgArQCXAIEAjgCXAJEAmQCfAJQAiwCWAKQApQCmAKsApgCoALAAqQCrALEAlwCLAKEAlQCBAJsAqQCWAJEAlwCdAKsApACKAJQAqgCdAJIAmwCHAHgAmACpAJcAmwCtAJ8AiACMAJ8AnACDAIMAlgCTAIcAiQCLAJMAngCDAG8AiQCHAHEAiwCPAGoAdACEAGkAaAB1AGoAYwBZAE8AUgBIAEIARwA1ADMARAA7ADAALQAgACEAIQAaABoABwD5/wYAAQAAAA8A+f/f/+z/9P/8/wEA7f/i/+P/2v/U/9X/4v/o/8v/wv/c/9z/0//b/+H/6P/b/8D/0P/d/9H/6f/7/+L/2//e/9b/2//k/97/2f/j//P/7//n/+b/2P/W/+D/y//M//L/9P/p/+v/2v/h//b/5v/m/+n/zP/h/wUA8f/q//X/5P/v/wQA6v/V/9r/3P/d/9n/2P/W/8b/zv/m/9f/xf/J/7//yP/l/9v/2P/x//H/8f8AAOv/1f/n/+n/4v/6/wEA8P/2/wwAFgAJAOv/6/8LABEADQAgACQAHQAmACwAKQAnADAAQgA0ABoALQBAADgAQQBGAD8AUQBLACkANgBVAFUASgA5ADoAVABPADsAQABBAEAATgBMAEcATgBFADwARABHAEgATwBIAEAATgBfAGUAYgBWAEkATABfAGkAYwBlAGkAZQBuAG0AUwBXAG8AaQBmAHEAawBqAG0AYQBbAGEAbwB4AFsAUAB9AIMAYgBqAHMAcgB+AHUAbQB0AFsAUABsAHEAdAB+AGQASgBMAE0AUwBiAHAAcABUAEkAXABbAFoAZwBbAE8AVQBLAEYASgBFAEcARAA0ADcAOwA0ADwAOgAtADsAQQAsACEAHwAcACUAJQANAP//BwAPAA8ADQAHAAgAFAAWABoAKgAlABQAGgATAP3/AwAOAAUADgAVAAcAFwApABEAAAAJAA0AFgAoACQAFwATABIAEwAXABsAHgAeACsANAAfABQAMQA9ADMAQQBJADMAOgBVAEwANwA4ADwARABLAEAAPgBQAEwAQQBLAEwASABWAFsATABKAFkAWQBMAFwAbwBZAFQAdQB0AFkAYABsAGsAaQBlAG4AewB2AG0AXABLAF4AaQBaAGsAdABKAEYAcgBuAEkAUQBkAE0AOQBJAEkAMgApACQAKQA8ADgALQAnABYAIQAvAA0A//8ZABUAAwAFAAgADwAHAPf/AAAFAPX/9f/v/9v/4P/v/+j/4P/0/wsA/v/l/+X/6P/q/9//xf/V/+f/uv+t/83/tP+h/8P/wP+r/7b/sf+c/4z/dv9v/3f/cf9n/2r/f/+J/3T/Z/96/4H/dv9x/2r/ZP9s/2v/WP9S/0z/PP9J/2L/VP8+/z3/Pv9B/z7/Nv9I/0r/J/8r/0f/Nf8e/xf/A//8/gL/+f74/gH/+P7n/uf+7f7r/uP+3f7e/ub+5/7e/tb+zf7L/s3+vv65/tH+z/65/r3+u/6v/rD+qf6x/sf+s/6e/rH+tP6u/rb+s/6z/q3+kf6Q/qf+qv6k/qD+oP6j/pP+kf6m/pf+kP6p/pn+if6h/qD+ov6w/pj+nv67/qX+pf7D/q/+qv67/qb+pP66/rb+t/6z/qj+uf6//r7+0P7F/rn+yv65/qr+w/7G/sX+1P7N/sn+2P7d/uH+3v7V/uD+5/7p/vL+6f7l/gH/Ev8N/wv/Cv8N/w//Bv///gf/HP8l/yL/MP8//zP/MP8+/0f/Tv9W/17/af9m/2X/cf9w/3T/hv+G/4P/jP+S/5X/mv+o/8H/vv+l/5r/n/+z/8X/wP/I/93/3P/Z/9v/0f/P/9j/3//l/9b/1f/x/+n/1P/k/9v/yf/r/+7/yf/W/+T/0f/T/9L/xP/U/+D/1v/j//L/4P/N/8n/zf/V/8//xP/G/9H/2v/Y/8z/wv++/8H/0P/X/8v/yP/Z/9//z//S//P/+P/Y/9P/2//S/+D/9v/x//T/AAD7/wQADwD6/+z/9v8AAAYA///1//3/AAABAAMA9v/y/wUAAgDs/+D/5P/4//3/8P/1//3///8KAAMA9/8KAAkA8//3//P/4f/l/+H/2f/r/+z/1//M/83/5P/1/9L/vf/o//r/1P/Q//L/5v+//8v/4v/S/8f/1f/b/9P/1f/k/+7/8v/+//X/2v/q////5v/p/woA+f/h/+3/8//8/w8AEgAYACQAIAAbABYADQAcACwAKwAzADIAMQBQAGAAVABcAF0AXACJAJQAbAB1AI0AgACQAK0AqACpALMAuADHAMkAwwDQANEAyQDhAPsA9wDuAP4ADgH+APUAFgEmASQBOAE5ATMBUQFdAVABYQFsAWUBcQFxAWMBfAGgAZ8BlQGgAawBqQGrAbcBxAHQAdoB5QHpAdwB2QHsAfEB7wEAAvkB3wHrAf4B8wH6ARACAwLpAd0B1wHmAfoB6AHaAfEB8QHgAewB8gHkAeEB4gHqAfUB4wHSAd8B5QHoAfMB6gHbAeEB6wHsAecB5gHmAdUB0AHqAfQB7gHsAeAB3wH3AfsB3wHOAeYBAALyAeAB4wHeAd8B6QHeAeMB9AHgAd0B8wHkAd4B6wHYAdMB6wHgAdgB6wHoAeQB9gHsAdEB2AHnAeMB4wHhAdEBzgHiAe0B4wHbAd8B4wHZAdcB4QHcAdMB3gHcAcgBxgHRAdQBxQG2AcMByQGzAbwB3wHXAb0BugGzAbABuwG8AboBwgG+Aa4BqAGpAaQBowGoAZ4BkAGUAZgBmQGbAYMBcwGCAXoBWgFWAVwBWAFPAUYBRgFGAT0BPgEzARMBCwERAQ8BEgEHAfcACQEVAQYBAwH7AOwA9QD3AOIA2QDfAOsA6ADSANMA6QDkAMIArwC+AMwAvQC3ALsAqwCjAKMAmwCzAL8AnQCiALsAnwCMAJQAjwCWAJ0AlACeAKUAnQCpAKoAlgCRAJEAlQCZAIoAjQCaAIAAdACJAIAAaQBpAGwAbwBzAG4AZwBTAD8AQAA/ADQAMAAvAEAAQgAUAAQAGQALAAYAFQD8//H/CAD4/+D/6v/k/9r/5v/0//j/4v/J/9X/1v/J/8z/uf+r/8b/xP+0/8f/v/+g/53/mf+P/4r/if+f/5z/dP98/5H/ef9+/43/ev+E/5z/k/+U/5n/h/+A/4P/i/+Y/33/Wf9t/3//av9j/2b/Wf9Y/2H/WP9Q/1b/XP9a/1L/SP9J/1D/P/8q/yz/Lf8m/yz/Lf8k/y3/Ov8s/yD/Mv8+/y//LP84/x3/BP8q/0P/Jv8e/yP/Hf8s/zv/N/87/zL/Kv86/0D/RP9W/1D/RP9K/0f/Uv9k/2D/af9t/1b/Xf9y/2n/a/+A/43/kP+J/4L/ev9u/3z/lP+Y/57/n/+Y/6X/pv+U/5//rv+o/6j/s/+9/7v/rP+n/6n/qf+y/7T/r/+q/6P/rv/F/73/uv/J/8D/u//Q/9f/zv/J/8v/0f/N/8P/yP/O/8z/y//S/+D/3f/M/9X/4P/P/83/7P/3/97/1f/p/+7/5f/0/wAA8//s/+X/3v/1/xMACwDs/+//DAAMAPf//P8JABIAIAAbABcAJQAaAAoAHAAoACAAHQAXABAAEgAfACwAJAARABgAIgAeABUACwANABAACQASAB8AFgARABUACQD9////CwASAAoAEQAbAAkAAwAUAA0ABwAVAA4A9P/t//z/AADs/+L/9P8AAAAABQAKABAAGAAeACUAJAAhACMAGwAYACsAMQAnACEAKQA2ACMACAAeACYABgAUACgAEgARAAYA5/8AABUA9v/9/woA7P/w/wkA///6//n/4v/X/9v/2v/c/97/1f/T/+z/9f/Y/+P/DwAAAN7/5P/o//z/FADz/+j/CQDw/9//DwAUAAYAFgACAO//AAD+//7/CQD9/wMAGQAVAB0AJgAaACkANgAhABsAHQATABoAGQANAB0ALQAtADUAOAAyADYALwAZABcAIQAbABcAIgAyAD0ANgAkABwAGAAgADkAOgAbAAsADgAUACIAHgAPABUAFAAGAAAA8v/r//7/AwADABAACQD6/wAABwD9//H/9P8LABoAAADg/+T/+v/6/+f/4f/n//T/BAACAPL/9v8FAP7/7f/0/wIA//8DABQABgD3/wQABgAAAA8ACwD1//P/7//n//f//v/2/wMADgD7/+n/8P/z/+r/7f/z/+7/8v/9//H/3//p//j/5P/X/+3/7f/U/97/7P/a/9z/6P/X/9f/6P/Z/8b/yv/V/9//1//I/8//yP+7/9n/7f/Y/9P/1P/Q/97/2P/G/9r/3//C/8r/2//A/6r/u//P/8T/sP+w/6z/n/+t/7f/pf+l/7T/sf+m/5n/kv+a/5r/lP+S/4z/jv+R/4T/gv+G/3v/f/+O/4f/d/93/4b/h/9v/2n/c/9g/17/gv+A/2z/dv9r/1D/Zv90/13/W/9h/1//dv+G/3j/df9u/2P/ef+E/37/mf+g/33/gv+k/6H/i/+L/5z/pP+h/6D/ov+u/8P/wf+4/7//t/+0/8f/w/+//8v/sv+m/8j/y/+//8//1v/O/8j/wf+//8D/xf/H/7v/vv/Q/83/0P/W/8X/vP+6/63/sP+4/7v/wP+z/67/uv+q/6T/t/+x/6//sv+c/6D/rv+h/7D/tP+M/5f/sv+X/4z/o/+q/6b/nP+f/7D/tf+7/7//sf+6/8P/qP+v/8v/uv+u/7r/sv+2/9H/1f/J/8P/xv/T/9f/xv+5/7n/w//N/8z/x//K/8j/xf/H/7z/t//P/8n/pP+w/8T/sP+4/8j/tv++/8n/tP/F/+L/zf/Q/+n/2P/E/8r/0f/R/7//u//N/8b/uP/E/8P/u/+6/7L/s/++/8T/yP+3/6b/u//C/6//r/+m/53/sP+l/4v/nv+l/4n/fv+K/6f/s/+T/4T/k/+D/3P/gf9//2z/Yv9h/3D/f/90/2j/cP9+/3z/bv9x/3f/Zf9j/3j/c/9d/2H/e/+O/3//cv+E/4T/ef+C/37/gv+o/6r/lP+f/53/kv+i/6L/oP+0/7n/uf+//7f/vf/E/7f/vv/H/8H/2v/m/8r/0//z//L/6P/q//T//P/v/+7/DgAVAAUADQAVAAEA/P8XACsAKgAsADAANgBHAEcANwBCAFQAWQBrAHAAXwBmAHMAbQB+AJoAnQCbAJoAlwCjAK0ArACrAKMAoQCyALoAsQCuALAAvwDLALkAsQDDAMcAxwDWANUAzwDaAN0AzgDBAMUA1ADeAOcA5ADPANAA4gDhAOgA7ADPANMA8ADhANkA6QDaAMkAzQDLAM4A3gDiANcAyADMANYAyADBAM0AygDCAL8AsQCnAK0AsgCvAKgArgCuAIcAdgCaAJ0AfACAAJAAgQB1AHgAdQBwAHEAcwB0AHEAYABUAFkAUQBEAEwAUwBLAE0ATgBEAEUARQAzACMAKgA8ADsAHgANACEAMQAjABQAEwARABsAJgAWAAAAAgADAAAACwAJAOb/3f/y/+7/5f/1/+r/x//J/9X/0//a/93/1f/T/8b/wP/J/7//u//U/9j/zP/G/7L/sP/F/77/s/+6/6P/if+X/5n/g/+L/5v/i/+B/4n/gv98/4L/cf9X/17/dP92/2X/Yf9k/1L/S/9m/2f/S/9D/0f/TP9O/z7/OP85/yP/Iv83/zL/L/88/zv/Mf8i/xD/HP8w/yP/Ef8c/y7/I/8N/xr/MP8k/xn/K/8v/xr/GP8z/0L/Pv9D/0f/Rf9N/1H/Q/86/07/cv9w/2L/hv+U/3b/gv+O/27/c/9//3H/j/+m/4v/kv+d/4f/lf+i/4n/jf+k/5v/lf+p/7//xv/E/8b/w/+5/7j/u//C/8P/rP+j/7L/sf+p/6H/k/+P/43/i/+S/3//cP+G/3b/Xf95/33/av9+/23/QP9V/2n/Tf9G/1j/Xv9S/0P/T/9a/0r/Qf9E/0f/T/9F/z//Tv9D/zT/R/9I/0L/Vf9O/0f/Yf9Z/z//SP9T/1j/XP9G/z//WP9e/1X/U/9T/2L/af9X/07/Vv9d/2L/Tv81/0L/Wv9a/07/OP8v/0f/Tv87/zP/Mf8r/yD/FP8k/zT/Hv8f/0P/Pv8k/yz/Lf8o/zv/PP8n/yD/Jf8x/z3/Mf8q/zj/O/83/y7/F/8X/yr/Jf8p/z3/M/8m/yv/Lv8v/x3/A/8H/w3/CP8d/x7/9/7v/vj+7/7z/vv++f4B//r+4f7c/tv+zP7G/s7+0f7F/rL+rP6m/p7+qv6w/qH+nv6n/qn+ov6V/pT+ov6j/pb+kP6L/oz+lP6W/pj+jv53/nr+iP55/m/+ff6B/oH+hf6B/oX+jP6Q/pn+iv5z/n/+hP56/oj+e/5Z/nP+k/6E/oL+iP6C/oj+hP6A/pX+j/5w/nL+fP56/oD+hf6D/ov+j/5w/lP+Zf51/mr+b/5p/kX+R/5J/iz+OP5B/iP+Mv49/hz+Jf45/iH+Gv4Z/gT+Av4O/hj+Ff79/fz9Dv7//e798/39/Qb+Bf4N/iH+Ev75/Qb+D/4J/gX+/f0D/hz+Lv4v/iP+JP45/jn+NP49/kb+WP5W/j3+Uf5o/lP+XP5n/kr+Vf51/nn+fv50/mj+gf6E/mz+eP6Q/pL+hf5+/oz+jf6C/o/+g/5l/oj+rf6U/oz+n/6h/p7+qP64/r/+uf68/sT+zP7l/uz+2v7i/vL+5/7s/gP/B/8P/yL/JP8f/yv/O/82/zX/SP9P/0P/Tv9n/3H/d/99/4H/h/+P/5//sv+y/6n/rP+z/8L/yf+5/7v/1P/X/9///v/3/9j/4f/5//v/9v/u/+r//f8GAO7/4/8AAAIA4//w/wkA8//t/wUA+//l/+j/6//z//X/5v/z/wkAAwD9//X/6v8AABIAAwAGABMABwAAAAkADgALAAEAEQAtACIAFwAxAD0AMgAzADAALgBEAFkATAA0AEAAYwBgAEwAWwBrAGMAYQBwAIMAhwB3AHcAggB+AIcAlQCHAIAAhABxAGUAcgB5AHQAZgBaAF0AWABTAFwAVwBNAE0AMgAaADMARQAyACoAMAAsACYAHAAPABIAGgARAAAA9f/z//b/9P/m/93/3//g/9b/zv/N/8j/x//X/9r/yP/H/8f/vv/S/9z/x//U/+L/vv/A/+D/yf+0/8D/s/+x/9b/3f++/7H/u/+6/6r/rP+8/7T/pv+m/6P/nv+k/6X/lf+I/4f/gf94/3r/cv9p/3T/dv9s/27/Xv9G/0X/Rf9Q/2b/V/9J/1n/T/83/zz/RP8+/zj/Rf9V/0f/PP9N/0z/Rf9R/07/SP9X/1r/Xf9s/2b/Wf9k/2//bf9y/4D/fv90/3z/jP+P/4b/dP98/5T/gf92/4r/f/95/5X/hP9z/5D/k/+L/5b/f/9p/3H/Yv9U/2D/YP9b/1f/SP9S/2X/VP9G/1T/Zv9z/27/X/9e/2L/Y/9t/3X/dv97/3n/cP9w/3r/k/+e/4z/i/+g/5v/mf+s/7f/w//M/8v/2f/l/+H/5//m/+r/BQD8/+X/CQAbAAMAHQA2ABwAHgAuACkARABbAEgATQBbAFAAWABhAE8AVABbAE4AWABdAEIAPQBKAF0AeABxAFsAaABiAEUATABZAEkANQA3AFIAUwA0ADEANgAoACkAKAAcACAAIQAfACEADAAJACEAGAAOAB0AJgAiAAgA9v8IAAAA6f8RAC4AGAAgADQANAAzACoANQBHADgANgA+ACUAMABSAD0AIgAmACcALQAkAAsACQAHAAAADQAOAP//+P/r/+v/+//0/+r/5f/J/7//zP/D/73/vP+s/8D/4P/P/7z/yP/J/8T/yf/L/9D/2P/T/8f/v/+7/7X/uf/Q/9n/yv/S/+n/4f/N/8r/zf/G/7f/vP/U/93/2v/X/9X/5f/v/9//4P/u/+r/5v/h/9b/4//x/+7/9f/4/+3/9P/9/+z/0//S/+X/4//O/8v/xf+6/8f/xP+v/7j/s/+Z/6D/qv+Y/4f/h/+b/6b/kv+M/4z/cv97/5H/cf9g/3X/Z/9N/0z/Uf9T/07/Vv9q/2H/Tv9Z/2H/Yf9h/1z/Zv9t/1L/TP9i/2b/av93/33/hv+C/3D/fP+P/4n/hP+M/5j/nv+P/4H/hP+K/4j/if+M/4j/dP9m/3D/ev9//4z/jv99/3X/dv9v/27/cP9k/2D/ZP9T/1j/g/+D/2n/dP95/2z/b/9o/2//kP+E/3P/kv+P/3b/j/+V/33/g/+J/3z/hP+W/5P/gv9+/4r/i/+E/5H/of+m/7L/vv/B/8X/yf/D/7j/tv/E/9b/3f/Z/9n/4P/l/+v/4f/A/8T/6f/f/8n/2f/Z/9b/7f/r/9T/yf++/8n/1//C/7n/xf/I/8z/wP+o/7n/1P/M/8X/yP/O/97/4//b/9z/5P/l/9z/1//c/9j/2f/l/+P/4v/r/9v/zf/m//3///8AAAsAFQACAPv/IAAqABEAGwAkABYANABYAEoAPQBNAFQAWQBoAGkAXgBjAGgAZAB6AIsAcgByAIoAeQBwAIYAfgB7AJMAjQCPAKcAjQBvAI8ApgCbAKIAtgC5ALAAswDFAMYAvQDKANMAxwDCAMoA2ADjAOEA6AABAQsBBwELAREBFQERAQ4BEAELARMBLwEmAQgBFAEpASgBMQEyASsBNgE7AS8BKQEiASgBOwE7AToBRAE9ATMBNQExASsBKAEmAS0BJwEeASgBJAEbASABEwEQASUBEQH9AA0B+ADaAOAA1ADOAOMA0QC+ANAAwwCuAMMAzAC2AKAAmACiAKAAigCGAH4AZQBnAGwAYQBvAHsAXAA1AB4AHgA7AEcANwAyADEAKAAqADAAKQAjACMAIwAaABIAHAAfAA4AAgACAAcA/v/h/+//FgAAAOL/8f/q/9//7v/g/9n/7//e/8//2f/G/8T/2f++/7T/1v/Q/8P/2v/Q/6//u//R/8f/wP/T/9v/2f/q//H/2//W//H//f/w//H/AAD3/+f/6//t/+v/8f/w//D/+//4/+v/7f/4//f/5v/i//j//P/r/+f/5P/e/+f/9v/0/+b/6P/6//n/+P8CAPv/+f8QABUAEwAWAAIA/P8PABMADgACAAAAHAAcAPT/9f8GAAQABAAAAP//AQDw/+7/BwAKAAcACgD+//n/AAD//wIABQAQABsAAADs/wQACAD9/w4AFwAMABAAGAAZACkAOgA3ADkAUQBWAEkAUQBaAFkAZQB1AI4AlQB2AIMAtACeAIUAnACTAJUAsACOAH0ArACwAJsAngCUAJkAqACaAKIAsQCkAK0AvgC4AMIAzADCAMcAywDLAN8A6ADeAN0A7AAHAQ8BCAEcASUBFQEgASQBDwEcATABKQEyATsBKgEfASEBHgEWARIBFQEEAe0A/wASAQMB9ADiANQA5QDqAN8A4wDkAOMA6wDrAOUA1gDHAN0A7ADlAAYBHgEAAfgACgEGAQABAAECAQ0BEwELAQABBwElAS0BHAEgASYBKAE7AUEBQgFVAUcBMQFKAVQBOgEvATcBQwFFAToBOwE7AS0BKwEgARQBKQE5ATgBOAEcAQsBKQEkAQEBEwEmARQBHAE2ATUBNgFEAUEBMQExAT4BQAE6AT0BQAFCAT0BJwEbASUBIwEdAS0BKQEMAQsBDwH+APwAAQH0APIA+QDvAOgA4wDZAOUA9QDmAOcACwEbARIBHgEuATIBNwEwASQBPAFjAWwBbgF2AXMBfAGYAaIBmwGZAZQBiQGFAYoBkQGWAZgBjgF7AW0BZQFbAVABQQE7AUUBQQExAS4BJAEPAQ8BFQEVARIBBQEIARoBDQH/AA4BBAHxAAYBCwHyAPcACgEMAQoB/ADvAPQA7QDfAPIABAH8APsADAEaARcBCAENARsBDgEJARsBDwEEARcBDgH+AAgB8QDVAOQA7gD0APcA0gC4ALYAoQClAL0AqgCcAJwAhwCIAJsAmQCgAKcApgCzAK8ApAC2AMEAxADOAMAAtgDHAMsAxQDEAMkA2wDYAMYA0gDSALoAvADEAMIAyQC8AKsAtwCzAJsAngCtAKcAnQCeAKoAvgDLAMMAugDJANgA2gDtAAcBDgEYATgBSwFKAU0BVwFlAYUBnAGCAWABaAGAAYABZgFZAXABcwFWAVwBcQFbAT0BPwFEATgBLQE0AS0BDwETASYBCgHuAPQAAgEMAfkA1gDcAPgA+wDoANEA2wDvAM8AwgD1APwA3QDqAPUA4wDZANgA5ADuANwA3QD9AAIB+wAPASEBFgEFAQQBCwERAR0BIQEQAQcBDgEIAfoA+gD1AOMA3wDqAOUA2gDaAMoAvADXAOQAzgDKANMAzADPANkA0QDOAOYA/QD6APkAAgH7AP8AFgENAQIBIwE1ATQBPQEuASYBPwE9AToBTwFFATgBRgFLAVABUQE7ATkBSgFLAU0BUAFOAVkBYwFeAV8BdAGMAYkBiAGvAb4BswHQAecB4AH4AQsC/gEFAg4CAAIAAhICJgIoAhYCHgI3AjACIgIcAgcC9wH3AfgB+gHyAeQB4wHWAbwBtgG9AboBqwGUAY4BkAF/AXABbAFgAWUBfAF/AWwBWgFXAV4BUgFAAVQBYgE+AScBOwFAATABPAFEAScBIgE+AT4BOwFDASABBQEdAR4BAgHwAN8A5gDqAMcAywDtANsAygDCAJkAkgCeAIIAfgCKAHMAbwB2AGgAXgBGADkAUgBPADsASQBEADcASgBJADcAPwBOAE8AQAA2AEMARwBIAFoAVwBNAFYARAA0AEwATQBCAE0APgAvAEEAPQArADMAOgBFAE4AQQBHAFQAQQBJAGIAVgBlAJAAhAB5AJMAkQCFAJcAowCjAKcArwDIANcAywDLANYAzgDGAMcAuQCoALAAwQDEAMQAwwCxAJ8AnACUAIwAjwCGAHUAcgBwAHIAcQBYAEUATgBQAEIAPwBHAEQAPQBBAEEANgA3ADwANAA2AEoATQA/ADwAQgA/ADQAKgAkACsAQwBIADcANgAzAB0AFgASAPv/5//o/+z/4v/N/8z/2v/L/7L/r/+o/5z/n/+b/4f/h/+S/5D/jP+A/2r/fP+h/5n/kf+a/4D/c/+Q/5H/hf+Y/6z/uv++/7L/uP+8/6j/rv/E/8D/tv+z/7j/yf/N/8f/v/+y/7v/1f/b/9n/2//m////AgDv//P/BgANABAAIAA4AEEAOgA+AEcAUQBqAHwAfACBAJAAogCgAJgAoACgAKEAswCnAJsAtACxAKUAswCZAHwAiQB0AF4AaABGADEAVgBGABcAJgAuAAgA/P8EAPD/2//m/+j/0//K/7n/n/+n/6//oP+n/7L/qv+i/5n/kf+T/5L/kf+M/4X/kf+T/4T/j/+O/3P/fP+V/43/hv+C/2v/Wf9U/1H/Rv80/zX/Tv9P/zT/Kv8w/x7/Bv8O/yT/J/8k/yf/Kv8u/zD/Lv8r/yX/Jv8y/0D/UP9c/1r/WP9Z/1z/cf+K/4P/cP9x/4v/qP+e/4L/jv+d/5D/nv/B/9D/1v/R/8j/1P/a/87/2v/s/+n/8P8DAAMAAgAeADEAHgAeAEcAVABFAFMAZwBuAIAAhQCHAKIAsQCzAMsA3gDlAPAA6ADeAOwA9wD2AO8A6QD9AA0B7QDdAPMA5gDQANsAyQCiAKQArgClAJwAgwBpAGMAXQBNAC8AGQAoAB0A7/8BAB8A+v/w//r/zf/B/+L/1P+7/7j/sv+2/7D/nf+t/7L/jP+E/5n/jf92/33/jf+J/3f/aP9d/1f/XP9W/0j/S/9B/yX/KP82/yj/Jv8z/zT/Ov8x/xn/LP89/xb/CP8i/yX/Jf8t/yz/Pf9O/0X/Qf9I/1D/XP9j/3D/c/9k/2r/eP9v/3f/if+O/5j/lP+P/6f/q/+b/6n/uv/E/8b/u//Q/+r/3f/j//T/6v/u/+n/1//6/xkACQAQABgAEQAhACQAHAAsACkAJwBHAFoAZgB1AGkAbgCIAIMAiwCxALcArgCvAKwAtwC8AKEAkgCjALIAugDBALcAogCcAJ4AjgCDAJEAkAB2AGwAYwBMAEYATgBFADAAGQAEAPn/7f/k/+b/4f/T/83/xv+7/7z/xv/C/7f/rP+k/6b/oP+G/3z/i/+F/3f/h/+K/2r/X/9x/3X/ZP9b/2P/bP9g/1X/XP9P/zf/Sf9e/0j/Nf9C/07/Tf9Q/1j/VP9L/1T/Xf9a/2n/b/9U/1n/ff+F/4f/hv92/4b/ov+e/5b/lf+h/7//vP+u/8j/1P/P/+P/4P/K/8//1//g//j/BQALAAwACAAQABwAKwBBAD4ANQBEAEMANgA9AE4AZABnAFAATwBRAEgAaQCMAHsAeQCTAJ8ArwC9AL4AzADbANgA1ADdAPgADAEKARABFwEKAQgBHgEqASMBGQEVARgBGwETAfsA8QD+APUA5ADkAM4AuQDBAKsAlgCpAJoAeQB9AG4AWQBlAF0ARQA9ADEAKwAuACsAMgAsAB0AJwAiABYAKgAhAAcAIQAwAB0AGgAPAAcAGAAPAAIAHwA5AD4AOAAoAC4APAAvACkAOwBBAEYAWQBoAF4AVABhAGYAaACDAIoAfQCKAIoAggCgAKQAkACqALcApQCzAL8AtgDDANcA3gDjAOcA9AACAfwA/QAIAfoA+gAlATcBJgE3AVoBWgFfAXcBdwFyAYYBiwGDAY4BmwGjAbkBwAGuAa8B0AHeAdIB3AH4AQ0CHQIhAiMCOwJKAkcCUgJaAmUChwKJAn8CnAKeAoACiwKVAoICjwKeAosCiAKUAocCdgJ3AnYCZwJQAjwCOgI8AiIC/wH/AQkC5AGxAbcBxwGlAY0BmAF/AVABRQE+ASYBHQEdARkBFAEEAfQA7QDvAPkA+wDnANoA4QDoAOUA4QDaAM8A0QDnAOoAzgDJAOkA9wDsAPAA/gD4APwABwHxAOwACwECAfQAGQEgAf0A9AD9ABIBKwEXAf0ABwESARoBEwH1APQAAAHwAOgA9AD8AAgBAQHrAPkADgH/AP0AEgELAQUBIQErAQ8BCAEZASkBOAExASYBQQFTAUYBTAFcAWQBbAFnAW0BggF+AYABkwGOAZIBsAGzAacBpAGaAZ0BrgGwAawBtQHCAcMByQHcAdoB0QHeAd0B0wHlAfMB8wH3Ad4BvgG6AbkBvgG/AaMBngGvAaABiQFyAVUBSgE7ASkBMAEWAewA6QDXALUAwADAAJ0AhQB1AGcAXABQAEwAOwAnADQANQAfABoADQD8//n/5P/f//z/9//2/xcAFQAOAB0AEAAPACwAKwAwAEEAMgA7AF8AVgBDAFIAXgBcAFcAXABkAE0ANgA/AD4AOQBBADgANwA+ACMAEgAVAP///P8KAPD/5v/z/9z/4f8FAPf/2f/U/8b/vv/C/8f/1P/L/67/pP+b/5j/q/+x/6z/rf+q/6v/pv+T/5n/ov+X/6X/rf+P/4v/k/96/2z/dP9r/2b/dv96/23/ZP9l/2X/ZP9i/1b/Vf9g/1D/SP9h/1T/M/9G/1j/Qv84/zr/N/84/yj/D/8L/wb/9P7d/sb+yP7O/rX+nv6c/ov+cv5n/lz+Rv4z/jD+Jv4L/gL+AP7i/c/92/3T/bv9vv3Q/cv9tP2z/cD9r/2r/cv9wP2j/bn9zf3M/dv92v3P/eD96/3t/Qr+Iv4c/hn+JP4k/hz+Mv5Y/l7+Vf5f/mf+Y/5l/mX+Z/5p/mD+Y/50/mf+R/5O/mr+af5e/mf+af5a/lj+ZP5i/lL+R/5G/kj+S/5O/kf+Pf5B/kn+Q/40/ir+Iv4h/iL+Hf4Y/hL+Cf4K/hv+J/4f/gD+6P32/Qb+9P3X/cT9v/3J/c39x/3J/cL9uv3C/bf9n/2h/aL9mf2d/Z/9lf2A/W39fP2M/Xj9aP1u/XT9dP1l/VX9Wf1X/VH9Tf05/SH9FP0Y/Sj9Gf0A/QD94/zG/OD81fyo/LT8tfya/Kb8o/yM/Jn8m/yK/Ij8gfyJ/Jb8ffxu/Hz8dvx8/Jj8lPyN/Jv8l/yS/KT8r/zF/O/88vzr/Av9Ff0I/SX9MP0b/TX9Vf1Q/V39Z/1d/Xb9j/2I/ZP9qf2t/a/9u/3L/cb9rv21/dn93P3D/b79z/3Z/df91/3f/e797f3R/c396/3q/c/9yP3T/eX97/3o/d/92f3V/d/95/3n/eb91v3E/cH9vv3F/dX92v3e/dD9vP3R/dr9v/3I/cz9r/21/bv9pv2u/bz9uv26/Z/9i/2Y/ZX9kf2j/Zn9iP2O/ZH9mP2U/XT9bP14/XD9bf1y/XP9ff2A/Xf9fP2B/Yb9if12/W/9i/2Z/ZP9i/19/Yf9n/2R/Yz9rv2t/ZX9qv3A/bz9yv3R/b39w/3d/eH97v0L/hr+If4s/j7+T/5R/l7+fv6F/oT+of7D/tv+5v7g/vr+K/82/zn/S/9e/4T/oP+a/6D/r/+0/8n/1P/S/+3/CQAVAB4AGgAeADIAOgBAAD4AOgBdAHEAVgBaAHIAbABsAHIAdAB+AHAAZAB/AIYAggCeAKwAogCgAKIAtgDJAL0AuADIAMUAuwDBAM4A1ADXAOoA7gDMAM8A8gDnAOAA+gDsANgA6QDrAOIA5gDmAOgA4wDSANgA0QCtALoA4gDQAK4AtgC8AK0ApwCjAI4AigCZAJUAgAB6AHwAbQBNAEQAWgBbAEYASgBLADUAMgA3ADYAQQA+AC0AKwAiABYAGQAPAAQAEAAVAAoADAAcACEAEQASACsAJwAYACcANQBEAFgATABAAFEATgBLAGUAbQBmAHgAjwCUAJkArgC+ALgAuQDNANoA5ADtAO4A+gAFAQQBEQEhASIBLAE3ATgBPwFFAVEBYgFUAUIBXwFzAV0BZQGJAYcBdwGKAaMBoAGUAZ4BuAHGAcABtgG5AcoB1wHSAdkB9gH5AeoB/QEJAgACFAIiAhUCLQJGAjYCQQJTAjICJQJHAlkCXgJfAlkCYwJxAmoCXAJTAlACTAI/AkICTwJGAkMCSgIsAgcCDAIWAgYC+QH+AfcB3AHRAdoB1QHJAckBwAGpAZ0BkgF/AXkBfwGEAYQBdAFlAWwBcwFqAWYBZAFfAWUBbQFjAVIBTQFPAUsBTgFdAWIBYgFsAWcBXgFsAW8BZQFsAW0BbAGAAYIBfQGXAawBoAGfAbYBwgHEAc4BxQGzAc8B5AHHAc4B6wHfAegB/QHlAdwB6wHrAeYB0wHKAeEB2gHPAegB3AHGAdgB1wHSAdIBsAGkAbMBoAGcAaYBlAGPAZUBjQGMAXYBYwF9AXoBWgFrAYkBiQGKAYkBfQFzAXIBdAF3AXsBegFrAWQBZAFoAXwBdAFOAVkBeAFxAWoBZgFYAWEBXwFPAWcBcQFNAUIBRgE8AT4BNgEdARcBGQEMAf4A+AD2AOoA2QDPAMQAuQC4AKoAngCoAKUAkwCQAJAAlQCXAIMAgACNAHwAfQCUAHQATQBhAHMAZgBfAGMAaABoAGYAaQBqAHIAdABVAFQAigCcAIAAiwCdAIsAkgC4ALwArAC7ANQA1gDPANQA4wDqAOYA8gAQARQBBQEIAQ0BCQEYASQBGwEiATEBIgEaAS0BLQEgASgBJgEWARYBFQEMAQcB/QAFASABGQH9APIA5ADoAAMB/QDkAN4A1QDcAPAA1wC6ANAA3QDJALoAsAC1AMEAtQC2AMwAwQC1AL4AqACdALgAqQCOAKUAqgCXAJ8AmwCNAJMAjwCEAIcAhAB+AH8AhQCCAGgAXQBkAEgANgBEACoAEwAjAAQA4P/w/+z/4//w/97/zv/R/7D/m/+n/5v/lP+W/3X/Wf9i/2X/Wv9M/z3/MP8k/yX/L/8m/xP/DP8O/xD/Bf/4/gr/Fv8E/wH/DP8E/wz/HP8Q/wX/CP8E/xT/LP8o/yD/KP8x/zj/Of8//1r/a/9s/3T/dP9z/3n/cf9p/3X/hf+U/5n/mf+w/8P/vP+5/7b/t//I/8z/0v/o/9n/uP+2/7z/yf/U/8f/wP/I/8f/xv++/7z/2v/a/7T/uP/Q/8P/tP+9/8r/x//B/8f/yv/L/+T//v/7//H/6v/i//D/BQABAPn/CAATAAkACQAWABQABgD0/+z/AAAHAPL/+f8ZABwACgABAPn/9v8BAAUA/v/1/+3/8f/8//f/5v/V/8D/wv/L/63/lf+h/6X/rf+9/6b/jf+K/3H/Yf9u/2v/bv92/13/Tv9X/1D/Rf9C/0D/Pv8u/yD/I/8Z/xb/IP8P/wb/G/8T/wD/D/8Q/wH/CP8R/wv/C/8N/wX/Df8x/0L/L/8p/0D/SP8+/0P/XP9o/1r/WP91/43/if+J/5//qP+b/6X/u/+4/73/xf+4/8f/6v/k/9n/3//P/8z/5v/t//D/9//x//L/+f/x//H/+v/7////DgAgACAAEgAYACgAIgAcAC8AOQArACkANQArACIALwAtAC8ASgBBACkAOAA1ACQAPABEAC8ALAAuACwAKwAnADoAQAAeAB4AOwA0ACUAKAAwADAAGAAQACAADQD5//7/5//e//r/7P/N/8r/xv/I/8b/rP+o/6//nv+R/4z/j/+T/3n/ZP9v/2T/Uf9e/1z/P/8z/z7/Q/8z/y//Pf83/zX/Pf8q/x//Iv8F/wT/LP8k/wn/Ev8W/xz/Nf86/yr/Jv9B/2j/af9X/2D/Z/9o/3z/gv93/4L/mv+r/6//pf+t/8n/0f/C/7//2f/r/+f/9f8IAAcAGgAxACMAHgArACkAKgAuACsAOQBAADwASgBLAEYAWwBdAFUAYABSAEEAVQBfAFwAVwBCAEcAXQBQAEYAVABVAE4ARgA8ADoAOgBDAE0APQApACsAMAApAB8AJQAkAAsADwAsAB4ACQAPAAUA//8AAOj/3f/h/93/5v/V/67/xP/e/8j/zP/f/9j/0P/D/73/xv+0/6H/r/+t/6H/l/99/3r/g/92/4H/kP9q/07/Xv9k/13/W/9T/1D/Wv9X/z3/Lv8//0z/QP8+/03/VP9W/1f/Uv9L/0b/UP9m/2T/X/+A/5f/k/+n/7X/p/+4/9T/0f/n/wEA9v///xwAHgAsAEUAQwBUAHsAhgCNAJgAmACqAL0AsACrAMMAyQC9AM8A8ADpAN0A8wD9APUA/AD1APMAGwEsARoBJgExASQBKwE6AUYBUwFGATwBVgFYAUkBbAGMAXIBZAGAAYgBcgFxAYoBjAF/AYgBmAGjAa4BpgGdAagBrgGwAcMBzQHNAdcB0gHGAc4B3gHgAdkB2AHeAd8B4QHjAdkB2gHsAesB5QHtAeQB0AHPAc4ByAHJAcIBuwHNAd4BzgG0AbUBuwGtAakBswG0Aa0BoAGNAYEBhwGRAYcBdwF8AXgBaAF0AXEBTgFXAXsBdAFcAUUBNwFGAUcBMQE7AVQBZgFvAVQBOQFQAVoBTAFbAWYBUwFLAUYBPgFSAV4BVAFgAWcBRQE2AU4BVgFJAT8BPAE7AUABQwE0AR8BJwE3ASQBEwEnAScB/gDxAAIB9ADrAAgBAgHiAPYABAHkAOAA5ADVAOAA4QDJANMA1QC5ALwAyADCAL0AsQCzAMoAxwC+AMkAxwC9ALIAqgC/AMYAuADLAM8ArgC2AMEAqACqALMAowCmAKMAjgCaAJ8AhwCMAJIAeQB5AIsAhwB/AHoAfgCJAHoAawB/AIQAggCXAJAAbwBrAGoAYwBvAIEAhgB1AGMAewCVAIwAgwB7AHAAewCIAIYAhwB+AHMAewB5AHUAiACUAI4AhwCHAIwAiACAAIUAhAB9AHwAdwByAHUAeQB3AG8AbwCAAIgAiwCTAIwAjQCVAIUAgwCSAIIAfgCQAIIAewCJAHwAbQBzAHIAZwBgAGsAdQBlAGQAcgBlAGMAcwBlAFsAXQBOAFoAaQBHADsAVwBTAEIARgBKAEsAPwAsADUARAAyACgAOQA2ACUALgAtABEACwAQAPz/7//2/+n/2//c/9f/1//j/9//yf+9/8v/1f/D/7j/v/+z/67/xP+7/6P/sv+//67/qP+v/7L/tf+0/7f/xv/G/7T/vv/Q/8z/0//w//P/4f/Y/9f/5P/x/+D/0f/j/+7/6//x/+7/2//M/83/5P/x/9D/tf/I/9H/xv/J/7z/l/+Z/6v/n/+Z/5z/iv+F/4n/dP9v/3j/dv+N/5n/d/9u/37/dv96/4X/c/9w/4P/i/+I/4H/i/+c/4//gv+O/4//lv+k/6D/qv+w/5P/lP+u/6f/ov+g/5D/m/+l/5P/mv+e/4X/kf+m/4v/d/9+/4T/hv+D/4n/j/93/27/dP9P/0z/fv9z/1H/Zf9p/1//aP9b/1f/dv95/2n/bf96/4X/f/90/3v/e/94/4L/gv+T/7L/q/+t/8f/wv/D/9z/4P/t//z/9v8BAAsABwAWABYAEQAxAC4ADAAcADEAJAAaAB4AOwBPADcALgA8AC4AGwARABAAJAApABUACAAEAA8AGAD8/+v/+v/0/+///f/p/9L/5v/s/9P/2v/5//D/2f/d/9n/2f/2//f/5P/6/wgA9v/1/wUAFgAbABMAGgAqACgALQAuACMAKwA0ADIAPAA+ADYAOwA9AEcAYwBhAEcAPABDAEcAOQApADMAOwAxACsAIgATABMAEQD+//X/BgASAAUAAwAKAPv/8///////DwAtACQAHAAyAD8ARgBSAFQAVgBaAGAAdQCJAJMApQCxAKgAowCuAMcA5AD1APwAAQEKARABDgEUASEBJAEoASUBFwEjAUQBQgErASUBJQEjASQBLgE9ASwBDwEkATsBEQHzAAMB/gDzAAEB+ADkAOUA2QDGAMMAsgCdAKQApwCiAKwArACYAIUAbgBjAHQAgwCAAHkAcgB2AIAAbwBYAFkAVwBPAFcAXgBgAGQAYQBfAFYAPAA8AFMATgBCAEMAPABAAFEASQAvACIAIAAfABYABAABAPz/2//H/9P/z/+9/7z/qv+T/6f/rv+H/3r/df9R/0f/X/9X/0H/TP9j/1z/Sv9M/0n/Qf9H/z7/Ov9e/2n/Xv9w/3v/gf+G/2//fv+l/5X/nv/P/77/r//L/8v/0f/U/7L/uf/S/77/xP/f/9f/0f/I/77/y//D/63/vv/G/7P/p/+e/5z/nv+N/4X/iv+F/4L/ff9x/2n/VP88/z//P/81/zz/Ov8k/xj/Ff8X/xr/C/8M/x//Ef/2/u/+5v7n/vT+6v7l/vH+6f7n/un+zv7E/tL+wv6w/rj+sf6h/qz+wP67/qH+l/6R/nz+ev6J/nf+Xv5e/mD+V/5O/lT+Wf5C/jL+N/4l/hf+Iv4Y/gP++v3y/fv99v3Y/eT9//3y/fT9Av74/fr9A/4E/g7+Df4N/in+Lf4X/iH+Rf5Z/lL+TP5i/nP+eP6Q/pr+h/6M/qb+sv61/rD+rf7F/uT+4P7S/tb+0v7H/uD+8P7Z/tX+6v71/v7+7f7L/tb+4f7Q/ub++P7S/sX+2v7M/rr+yP7U/tf+2v7T/sn+yv7T/t7+6/71/vb+9f74/vv+Av8V/yX/J/8l/yf/H/8b/yz/Nf8x/zz/Tv9R/1z/bP9d/0v/Y/+A/3T/cP+J/4T/a/94/4L/b/9w/3P/XP9a/2v/aP9n/2//Wv9E/1D/Vf9E/0H/Pv8s/zH/Of8p/zH/S/87/yD/Fv8P/yD/LP8T/xT/Nv84/yn/J/8q/zX/Mv8d/yb/Qf9C/zv/QP9K/1L/Wv9l/2n/af95/4j/if+W/6X/mf+C/3//lv+l/5z/oP+n/5T/lf+n/5n/jP+Y/5z/nf+a/4z/iP97/2T/Z/9r/2D/Wv9I/zn/S/9O/zH/I/8n/yj/JP8d/x3/If8c/xf/Ff8N/wP/+/76/gD///4A/wn/Cf8A//z+/v4D/wr/Bf/5/vz+B/8B/wn/If8T//v+D/8o/yv/LP8n/yv/Of81/yf/G/8U/yn/Nf8t/z7/Pv8g/yr/N/8j/yr/Kv8X/yv/Lv8X/yj/Lf8j/zf/Lf8g/0H/P/8w/0v/RP81/1j/Wf8//17/fP9w/2//hf+b/6X/nv+W/5n/q/+7/8H/1P/h/8z/zv/7/xMADwATAB0AJAAvAEEAUQBoAIoAjQBqAG4AjQCFAIEAngClAJcAmwCqALAAsgDDANMAwgC8AN4A6ADQAMwA3gDrAOkA3wDjAOkA5ADiAN4A1wDiAO4A7ADoAOkA9QDxANIAzgDgANsA2QDrAOsA4gDqAPoA+gDmANoA4QDlAOcA4QDSANwA5ADWAOAA5AC/AL8A2ADHAMAA1wDPAMEAygDKAMMAuwCvAKsAqACcAKQAsgCfAI8AoQCmAJIAkgCbAJUAlgCdAJkAngCVAHgAdAB7AGsAZwB8AI4AlgCHAHQAfwCEAGwAXQBiAG8AeQBuAFoAVwBoAHkAdQBvAHQAZQBgAIUAjABxAH0AggBnAGcAbABhAGwAeAB6AIMAdwBsAHcAbgBjAHQAbgBkAHYAdABiAGoAbABYAFYAbQBwAFoAYwB7AFoAQQBuAHwAWQBhAHcAaABcAFgAWABmAGoAZgBsAF4ATgBNAD0AKgAqAC4AOAAzABUAEAAYAAwABgD6/+b/8//z/9j/3f/h/87/w/+0/7H/vv+0/7j/xP+g/4//pv+f/5r/pf+e/6X/q/+h/7L/t/+h/5//of+w/83/xv/A/9P/1f/m//f/4f/s////5v/2/xoAEQAiADgAHAAgAD0ALwAvAEgAQAA5AFgAcABvAG4AegB/AHUAbwBxAHIAfgCKAHsAagB2AIQAfgB1AHMAdgB2AGoAYgBkAGcAaABfAFEAWgBlAFoAVQBXAFEAWgBeAEQANAA/AEgATQBRAEUAMgAuADAALQA0ADoAMQAtACgAFgATABoAGQAgAB8ADgASABgACwALAAwABAABAPj/8P/5//z//P/t/8//0v/Z/7L/lf+k/7P/p/+E/3b/hP94/2L/Zv9e/0P/Mv8y/z7/Kv8C/wz/HP///vj+Cf8G/wf/B//0/vH+/v75/uv+7P7r/uT++P4P//v++P4W/wz/C/8z/zH/If8w/y3/K/83/yT/Mf9d/03/Nf9C/0j/U/9c/1b/Z/9y/3D/hP97/1j/Y/9t/2D/bf9u/1T/S/9O/1//cv9Y/0H/U/9R/0L/WP9j/0j/O/8+/zb/O/9D/yL/B/8Z/x3/E/8e/xD/9f77/gL/Av8H//v++P4I/wH/9v4C/w//DP8A/wP/Fv8H//H+Bv8P/wT/GP8c//n++P4S/wT/6v70/gL/9/70/vH+5f71/v/+1P68/s3+yf67/rL+n/6e/qP+mf6h/qf+iP5w/nL+df5x/mX+XP5i/mD+RP4n/ij+Ov46/jT+Qv5C/jH+QP5a/lT+Q/5C/kX+Qf5C/lf+Z/5b/k/+XP5p/mv+c/51/m/+ef6D/nX+eP6U/pn+kP6Y/pz+of6x/rH+sf66/qz+pP62/sD+zP7W/sX+xv7g/uX+5P7g/tD+3P72/v3+BP/+/vT+A//6/ub+/v4K/wn/If8T//X+CP8R/wn/Iv8x/yD/D/8E/xP/Lv86/0j/T/9E/z//Of8y/z3/Qf8//0//Uf9F/0P/QP87/zz/Qf9O/1r/U/9P/1H/RP84/zX/M/87/0L/OP8//03/M/8T/xT/GP8d/yD/EP8O/xH//P79/g//+/7q/vT+8/7w/uf+1/7l/vb+4P7N/s7+2P7k/tb+wv7T/u7+7/7q/ur+7P7w/vX+8v70/gf/DP/+/gv/H/8W/xP/GP8f/zP/MP8e/zP/R/9B/0b/Rv9E/1n/Zf9n/2//df+K/6D/of+x/8b/vf/A/9j/5P/3/wMA/P8EABsAHgAeACMAKwA5AEMASQBUAGAAZgBgAFQAYwB4AG4AZwB5AHcAbAB3AHwAewCRAKcAogCVAJkAnACQAJ8AswCLAGoAjgCZAIIAkgCdAIsAjwCTAIcAewBsAHUAgQBtAHMAhwBrAF0AdwB8AHoAcQBKAEMAWABLAD4ATABSAE0AQwBAAEcAPgAyAEMASgA4ADYARABEAD0AQQBJAFEAVQBGADgAUABfAEoAUgBsAGEAZAB+AHsAdgCEAIYAewByAG4AewCGAIUAiQCOAIoAjgCpALwAqACkAMUAxwC8ANUA2gDIAMQAxADiAAgB/QAHASkBDQEEATUBNAEnAUgBUgFOAV8BYwFfAWcBdQF4AWYBZwGHAY0BgAGFAY8BlwGLAXoBhwGIAXkBmAGzAaMBmwGTAYUBjAGNAYIBgAF8AX0BggF+AYIBdQFfAXEBfQFjAVwBZAFlAXABbAFXAVoBZwFyAXoBaAFdAWkBXgFFAUgBUwFSAVIBVwFZAVEBTQFhAXEBXgFKAU4BUQFSAVYBTgFEAUcBQgE2ATQBNAE2AT0BNQEtAUQBVwFOAUUBSQFOAVQBXwFcAUkBUgF2AX4BdgGJAZMBhQGFAYgBhAGXAawBrAGyAbcBrgGuAbIBtAHMAd0BzQHGAdwB7gHlAdkB6gEEAgUC/gEHAh0CKwIbAhMCLgI0AiUCNgI+AjYCVQJhAkcCTQJYAlkCbgJjAk8CawJxAlYCYAJpAloCVgJKAkMCTQJNAlECUQI6AjQCNAIeAiACJgIPAgoCDwIAAvsB+gH5Af8B5wHNAeUB6QHAAbcBzAHMAboBpQGbAZ8BqgGoAYkBbAF4AYkBgQF9AXQBWAFUAWUBWAE/AT8BPwEtASgBLgEdAQkBEQELAesA6ADxANoA0QDaAL4AogCvALgArQCtAKkAjAB4AIMAigCBAIAAeQBoAG8AdwBoAHEAggB+AIoAkgCBAHoAeAB7AJAAlgCYAKgArQC5AMIAsQDEANYAuADKAOoAxQDSAAUB3wDGAOkA5ADcAOgA1ADUAOwA3wDeAPMA3QDTAPwACwHyAOkA9QATAS0BIgEhAUABPAEhAS4BNwEcARsBNAEwASABLQEuARMBGQE0ATYBQAFGAS0BMwFMAUQBSQFUAUYBSAFMAUMBTgFKAToBTgFdAV0BagFhAVcBXwFYAVgBZgFfAVoBUQFJAVkBUwFAAU0BRQEuAS4BHwEgATQBFAH3AAUB+wDjANgAzgDWANMAuACxAKgAjAB+AHYAdwCAAGYATABPAEQAMQAxAC0AIgAdACMAJQAaABUAGAAXAB0AGAALABkAIgAfADQALQAPACMAOgA0ADoAQQBKAF0AWABWAG0AcwB9AJQAkACNAJcAmQCnAKsAlgCZAJkAhwCWAJ0AiQCYAKUAiACGAJ4AlwCIAIYAfABrAGcAbABlAFoAWABTAF8AgQCDAGIAXABoAGQAYQBrAHEAawBkAGcAcAB3AHwAdwB5AIcAkACSAJgAlwCZAKEAmwCdAL0AygC4ALwAywDEAMAAywDMAMoAzADIAMwAzAC/AMYAzACpAJoAsQC3ALAAqQCfAKUAqQCbAJ8AmwB/AHcAcwBdAFgAUwA5ADAALwAcAAwAAwDx/+L/3f/a/9b/z//E/8P/v/+k/4n/jv+m/7H/pf+W/57/p/+h/6j/s/+2/8T/yv+7/7//0f/X/+D/8v8IAB0AFwAOACQAPgBHAEUAOwA2AEYAXgBlAFkAVABaAFsAXgBmAGgAagBqAGkAcgB1AHEAbwBgAFwAbABfAE4AZQBrAFEASABBADgAOAAzAD0AVABZAFUAVwBXAFgAVgBeAGwAWgBGAGIAfAB2AG8AbgB0AHoAcQB1AIAAfQCCAIkAfwCIAJYAhABxAHEAcwBvAGsAbQBpAFIAUQBgAEoALwAuACUAJgA/ADIABAD1/wkAFgABAOv/7P/m/8//wf+4/7P/tv+s/53/l/+Q/4z/hv95/3H/ZP9U/1n/Vf89/zz/Qf8n/xH/Ff8c/xL/Av8C/wz/DP8I//3++f4E//T+4P72/un+v/7f/gP/7f79/iH/Ev8F/wj/AP8L/xX/EP8l/zb/LP8y/z7/Pf9H/07/Pv9A/1b/S/8y/0D/Uf9C/0L/ZP9v/1n/UP9Z/1b/Uv9c/1P/QP9T/2n/WP9W/3T/ef9m/2X/dv+M/5n/kv+R/6T/tv/C/9D/0f/N/+3/EQAFAPf/CQASAA0AHgA0ADIAHwAVACIALwAjABwAKQArACcAJwAVAAwAJwAeAPf/CAAWAOf/4P/u/8n/y//u/9D/uf/L/73/qP+l/5r/pP+0/57/e/9m/2//iv99/2L/av9c/z3/Rv9L/zz/P/89/zj/QP85/zD/MP8g/xv/G//5/vb+Hv8e///+9f78/gz/Bf/v/gP/Dv/n/uH+Af8H/wr/DP8F/wT/9v7r/v/+Bv8P/yH/DP/8/gj/+f70/gn/Af/7/gL/+P7//gH/7v75/gH/8P71/vL+7P4E//3+5/77/vj+5P72/vr+8f76/gH/CP8M///+Bv8R/w//Hf8f/xb/I/8b/w7/Kf8z/yz/QP9J/03/Xf9V/1b/ev+C/3D/cP95/33/e/95/37/d/90/4T/f/90/4f/iv9x/2f/b/92/3j/b/9k/2z/eP9r/1X/Wv9g/1j/Z/9y/1n/R/9Q/1n/WP9J/0T/U/9T/0n/S/9B/y7/Kf8r/zD/LP8U/wT/CP8Q/xf/Ff8G//z+Bv8K//v+//4O//j+5/78/v/+9/4B//j+7v75/vT+9/4O/wn/A/8U/xb/HP8//0f/Lv82/2X/dP9k/2v/ff+G/5b/pP+r/7X/sf+w/8f/1//d/+r/8P/6/wIA9f/s//r/CQAYABoAFAAaACMAKQAhABMALQBOAEgATgBgAFUAUQBXAFEAVQBRAFAAeACJAG8AbQB4AHUAfAB6AHEAhwCfAJUAiwCTAJkAngClAJgAjACjAL4AvwCwAJoAmQCuAKUAkgCXAJIAkgClAJkAkQCrAKAAfQCEAJgAjwB4AGgAaQBuAG8AcQBkAFUAWwBfAE8APAA1AEAARwA3AD0ATwA5ACgAOQA4AC0ANAA7AEIAOwAcABUAGgAGAAYAFgAIAP//BwAKABIAFwAUAB8AIAATABYAGQAVAB8AJwArACkAFAAKABQADwAOACAAIQAeACsANgA6ADoAOAA9AEgAOQAbACAAOgA2AC8AOgAyAC8ANAAmAC4AQgAqACEALgAWABoAMQAMAPz/FQAEAP7/GQAMAPX/9v/j/9D/3v/k/9r/2f/e/9v/2//o/+7/5f/Z/9P/1f/X/9j/2f/K/7T/tP+3/7L/u//B/7b/qv+b/5X/mf+P/43/nv+X/4b/hv9+/3X/e/+A/3H/Tv88/1D/VP9B/0v/Tf8w/y3/Nf8f/w//EP8G//n+7/7l/uD+2/7O/sH+x/7S/rr+ov67/sL+pP6z/tX+zf7O/tn+x/68/sj+yP6+/sL+0v7S/sL+0f7r/t3+1P7q/uj+2P7m/vr+/v4A/wT//P7v/vb+Af/x/tn+2P7i/uX+3P7R/sb+v/7J/tX+xv6x/rb+yP7O/r7+q/6n/qb+rf66/rH+of6c/pX+lf6a/pb+of6w/pn+gv6U/pr+fP5v/oT+lP6R/n/+af54/pH+a/5M/nP+cP5H/mb+kv6H/oH+hv55/nL+eP53/nT+ef58/nj+gv6P/nb+Yv59/pb+nf6g/pX+l/6i/on+c/5+/n/+hv6d/pP+f/6J/of+c/5w/nH+a/5q/mr+af5k/mH+bf5p/lL+Xf5u/lr+YP59/nb+cP55/nb+h/6a/n/+bv57/oX+m/6p/qP+s/7B/rL+tP7L/t/+5/7j/tn+1P7p/gv//P7j/gD/Cv/2/gv/GP8R/yX/I/8Z/yr/E/8I/z7/QP8c/yv/Mf8s/zz/Mf8g/yb/Gf8V/yn/Mf84/zf/Lv83/zb/Kv87/0T/O/9J/1f/Vv9p/3v/bv9n/3P/c/9s/37/jv+E/3n/ef96/3//f/+E/5X/l/+f/7b/sv+s/73/uv/A/+D/6P/n/+v/1v/a//z//P/6/xIAGwAZAB8AMgBPAFAAQQBJAE8AUwBlAGQAYABnAFUAWACBAIkAfgCJAIIAdAB6AHoAcwB4AIIAiQCCAHkAewByAGkAdwB2AGgAdgCAAGsAYgByAHgAZgBdAGQAWQBKAFwAbwBqAF4ASAA+AFEATQA/AE4ASgA0ADYALgAjADIAIQAAABAAIwAlADAAKAAdACwAJAARACAAMgAyAC4ALQAvACYAIAAwACwAGwApADMANQBEADYAGQAlADwAOQApACQAMgAxADAASgBAABwANABMADMAMQA+ADsAPwBDAEQAQAApACgANAApADAAOwAhABoAMwA3ACgAFAAFAAcABwALABcACQD4/wUAFAAOAAEA+v8BAA4ACgAAAAIADAAMABIAIAAhADAATAA9ACQALQAwADwAUwBFADEAOQBEAEsASwBJAGIAbwBkAGsAcAB2AJYApQCYAJEAiwCSAJ0AogC4AMkAtQCcAJMAowC2AKQAngC2AKsAmQCgAJQAgwB9AGsAagB4AG8AbwB8AHgAZwBfAG0AbwBPAEcAWwBMAEIAWgBXAEMARQBKAEEAKgAiADoAPgAoAC0AQABLAE0APwA9AEsAQwBGAF0AVwBXAF8AQwA7AE4APgA7AEUAMQA8AE4AKAAjAEEAMgAwAD8AKAAiACsAHAAoADQAIgAyAEYAKgAfAC0AGwAEAA4AGAALAP//BQAFAP3//P/r/9n/7v/x/8j/uf/F/7//tv+3/7j/w//J/7L/oP+p/6j/kv+P/6H/pv+g/6D/oP+k/6j/lv+L/6b/t/+Y/4H/kf+c/5T/m/+h/5P/m/+2/67/mf+d/5z/lf+b/5v/lP+a/5j/j/+G/3H/eP+V/4D/Vf9c/3P/dP9s/17/WP9V/0T/Rf9U/0//Q/8//zv/Of8v/yb/M/80/y//Q/9B/yH/Jf85/zX/Mv86/0P/RP8s/x7/L/8z/yv/Nf83/zn/Rv9A/zb/QP9O/17/aP9b/1z/af9l/2//gv93/3b/j/+M/3//lv+u/6r/qP+u/6//tf/C/8T/1//5//b/5//9/wwA/v/8/wQAAwAKABcAHgAgACMAKAAiABwALQA3AB8AEwAjACEAFwAnAC8AGwAbADYAOgApAC4AQgBEADgANwA2AB4AEAAmAC8AIQAjAB8AFgAlACYAHAAtAC4AGQAYABsAHgAqACEAEgAbACMAIwAgACYAQQBEACcALABDADsAMAAtACoALgA1ADsAOQArACcALQAsAC4AIwAMABUAHgABAPb/AQD5//D/8P/l/97/4f/a/9L/3P/r/+b/2//f/93/0v/k//r/8f/n/+7/7f/u/+3/4v/n//L/6f/s////+v/0//v/+f/6/wIABAAJAA4ADgAdAB8ACgAOABwAHQApADYANwA/AD8AMAAzAEAAOwAsADkAVwBTAEoAYQBoAGgAgwCAAGEAZgBuAG8AiwCSAHAAZgB3AHUAcwCTAKIAiwCLAJoAjACNAJIAewCPALYAqACoALkAoQCpAMgAugC8AMkAuQDTAPIA0ADLAOoA5gDnAPAA4gDoAPgA8gDvAOAA0gD5ABMB9QDpAP0A/QD7ABQBLQEsASgBOAFHAUgBQQE9AUIBSQFSAWIBVwFDAVcBXwFIAVMBWQE5AUABUwE/AT4BSwE+ATEBJAEiATkBNgElATEBMQEoATsBSgFCAUIBTgFUAUcBRwFVAUwBTQFpAWQBVgFkAWQBZwF+AXQBYgFyAYkBkwGIAXcBewGFAYsBlwGbAakBwAGtAZQBrQHDAcIBvQGoAaIBvgHHAbsBwQHJAcoBywHCAb4BwwG/AcEByAG6AcEB3wHdAc8B1AHQAdcB7QHkAdYB2QHMAdEB5gHQAcQB1gHPAc0B2AHMAdQB4QHFAcQB3AHPAdEB6gHjAdUB2QHlAfMB7QHpAfIB3wHWAeoB5AHaAd8B1AHdAfEB4AHdAecBywHBAdkB2wHVAc8ByAHTAdcBzAHTAdIBwAHAAcsB0AHaAeEB2wHaAeIB2wHLAcsBygHFAdMB1AG4AbABugG2AbkBzwHcAcgBpgGpAbgBrQGtAbsBvQHKAdUBywHMAc4BvwHCAc4BzAHNAcgBugG8AbwBqQGeAaQBogGZAaQBrQGfAZ0BnQGMAZABlgF3AXEBggFyAW8BhAF+AXIBdAFrAV0BSgFDAV0BZwFFATcBTQFRATsBNwFQAVYBQAFHAV4BVAFQAWoBdgFtAWgBYwFkAXEBcQFjAVgBVwFaAVIBTQFgAWMBTQFLAU0BOgEvASgBLQFHAT4BIAEtATkBLgE0ATIBJAEyAT4BLwEhASABKAEoARUBAwH7APYA9ADrAO8A+wDmANMA5gDrAOIA8AD5AOwA5ADrAOkA1wDQANgA2ADdAO4A7gDeANcA4ADqANkAyQDNAM4A2wDrANQAyADSALcArgDTAMkAowClALIAtwC8ALoAvADDAMwA0QC/ALkA0wDRALYAtAC8ALQAowCfAKUAkQByAHQAeQBzAHAAVwBAAE0AUABCADoAIwAYACQAGAAFAAEA8f/o//D/4f+9/6L/qv++/6f/jP+Y/4b/Z/9y/3L/bv9+/2L/SP9n/1z/Of9U/17/O/8u/yv/Kf87/zj/KP8w/zf/NP8r/xb/Cv8N/wX/+v7+/gL/+f7r/uL+4f7l/ub+4P7j/u7+4P7K/tn++v79/uv+5v7n/tj+zv7c/uP+1/7R/tn+1v7C/sT+0P7A/r3+4P7u/ub+5f7X/tr+9v73/vn+G/8q/yL/Iv8j/yf/KP8n/y//MP89/1n/UP84/0f/Wv9e/1z/Vv9j/27/Wf9E/0T/V/9w/2j/ZP99/3j/cv+S/5r/pv/O/7n/jf+Z/6b/qv+9/7X/n/+p/7j/vv/D/77/sv+s/6z/o/+Z/5//oP+W/5v/nv+X/5z/kf91/3z/iv97/3//j/94/1//af9r/1T/T/9k/2H/S/9F/z3/Kv8v/zr/KP8j/zT/Jf8O/yT/Nf8h/xP/HP8r/yb/Dv8V/y7/Jf8g/yr/Gf8P/xz/If8o/yn/E/8M/xL/FP8a/xD/DP8p/yv/IP87/0f/Of84/zv/Sv9b/1T/V/9V/zn/QP9c/1L/QP87/z7/RP8x/zL/Zv9z/2P/ev+C/2//eP+I/4f/if+R/6X/rP+i/6v/tv+z/8b/1//L/9P/7//6/wIADgAQAAwADwAlADQAJwAoAEEARgBDAFAAVwBdAG0AcAB4AJIAoACgAKgArwCoAKUArgCwAK0AtwCxAJsApgC3AKsArgC5AKkAqQC5ALUAsgC3ALMAugDGALkAqgC4AMkAvwDCANwA0gC7AMYAxAC1AL4AuQCsAKsAnwCeAJkAcQB3AJ4AgwBlAHAAbABuAHAAWQBcAFsAQwBQAFkARwBSAFsAXgBxAF4AQwBOAEMAOwBTAD8AJwBMAFkARQBRAFkASQBIAE8AQAAxAD0ATQBOAFgAXgBVAGQAcQBiAGAAZwBjAG0AcABaAFsAcABuAFkATABRAFcARQA4AE0AYABOADkAPQA1ACQALgAmAAYAEgARAOT/5v/6/+b/5//n/8T/x//R/77/yP/R/8P/0P/Q/7T/qf+q/8b/8f/g/7//zf/b/9v/3P/T/9H/0P/H/9X/5v/L/6z/t//F/7D/mv+f/6f/pv+m/6P/p/+v/6D/kv+S/4b/h/+U/3f/V/9n/3z/dP9q/2T/WP9U/2H/X/9H/z7/Pf8m/yH/Mf8k/wr/DP8H/+3+3v7X/tD+1/7f/sr+tP6s/qf+pv6n/qP+pv6f/o7+mf6t/qr+qv6s/qX+rP6q/qL+tv6+/qr+rv67/rH+rf6w/r7+0/7C/q/+w/7G/rn+yv7O/r/+yv7e/uT+4v7h/vP+/f7t/u7+/P78/hX/N/80/yj/J/8j/yv/PP9C/z3/M/9E/2v/Zf9T/2n/Z/9W/3X/jP+F/5v/qv+W/6H/xP/I/73/xP/c//f/AAAAAAoAFgAUABEAJQBHAFgAaQB7AG4AcgCYAJoAlAC5AMUAtgDCANEA0QDKALsAxgDYAMYAxQDVAMsAxgDAALkA1ADbAMUA3QDpAL8AtgDIAMgAyADAAK8AogCUAJMAlQCHAIYAigB+AIUAiwBwAGAAYgBPADoAQABNADwAKQAxACUABwAMAAgA8v8HABgAAQD9//j/6v/3//b/7v8FAAMA8/8DAPn/4//5//r/7P8GAAUA6//4/wAA7v/s//D/8v/w/93/1//l/+L/1//c/+r/7//Y/8L/zP/N/8z/4f/o/97/3P/a/+f/9P/m/+b/9f/q/+f/+/8HAAkABAAGABcAGwAVABkAFgAIAAQAEgAiABsADQAaACUAHAATABIAIQA7AEQAQgA9ADsASABEADQAQwBSAEUAQABIAE0AVABiAGYAVgBRAGAAUwBEAFwAXAA9AC0AIQAqAEAAIAD5/wUACwD///T/5P/m/+r/yP+y/8D/yf/H/7n/pf+i/6P/mv+Y/5H/g/+E/4X/df9p/23/av9Y/0v/Rf9B/0T/Qf84/zr/Qv9B/zX/Jv8q/yf/D/8U/yX/DP8B/xL/CP8G/xX/CP8L/xz/Cf/9/gX/Av8L/w7/9f7z/v/+9v7r/ur+8f70/uf+7f79/uz+4P7o/tf+zf7Z/tL+xP7H/tT+4/7c/sn+0f7e/tz+1f7F/sL+3/72/vn+7v7Y/tP+5v70/vj++P7u/uX+3v7f/vL+/f76/gT/Bv/1/gH/Iv8o/yL/KP8p/yP/Kv8//0r/Qv9A/1b/av9o/2j/fv+J/3P/ZP93/4T/f/+E/4X/if+f/6f/ov+l/5T/iP+h/6n/nP+m/6X/k/+W/5v/i/92/3v/mf+P/2f/av91/1r/S/9c/2//aP8+/y3/S/9U/z3/J/8Y/yX/M/8c/xL/KP8k/xH/Gf8g/xT/C/8L/xD/Gv8V/wr/Fv8R//j+C/8a/wL/GP8u/wb/Df8w/xn/EP8W//z+AP8Q///+DP8f/wn/EP8w/yj/Ev8a/yf/Hf8e/zf/Kv8U/zT/Nv8E/wb/IP8U/xf/Jv8i/yD/If8e/x7/Iv8v/0H/Qf8z/y//PP88/yP/Hv80/zb/Of9B/y7/IP8n/yD/Hv8t/zP/Kf8b/yD/Ov9G/0f/Sf9D/1T/b/9m/23/iv94/3L/oP+k/47/of+1/7n/w//D/8r/2f/L/8L/3v/w/+n/7/8FABEABgD//wYABAD3//X//f8CAAoADgAMAAUA+//x//D/+//5/+D/1v/l/9z/zP/a/+X/3f/Y/8//wf+6/7T/sP+n/5f/nf+n/5P/hf+H/33/hv+g/5D/dv+D/4//h/+A/3X/a/95/4j/gP91/3r/i/+c/5v/jv+F/3z/hf+c/47/d/+K/5f/if+L/4b/dv91/2v/X/9k/2L/Zf9x/2n/Y/9k/1r/ZP9i/0L/Sf9Z/0L/R/9b/0z/Tf9X/03/Xf9r/1P/Tv9d/1//Zv9o/1n/X/9y/3P/b/9t/2b/VP9C/0f/Vv9W/1r/Y/9P/0P/Wv9W/0z/cf+C/2f/Zv93/3//jP+W/6T/sv+r/7b/zv/E/8z/8f/v/+3/BwAFAPT///8UAB0AGwAUAA4ACgAcACwAHQAVABsAFQAYABwACQAOACkAMQAtACMAIwA8AD8AMgA4ACUAFwBFAFMALwA9AE8AMQAvAEYARABIAFUASwBCAEUARgBHAEYAPwA5ADkAPwA9ADIANABAAD8AMgAiACUAPwBBADEAOAA9AD8AVgBXAEYASwBHAEAASABEAE4AZwBQAEEAZQBmAFMAXQBWAFEAYABYAFAAXQBbAF0AXABAAD4AVABVAF0AawBoAGMAVwBPAGAAawBoAGAASwBLAGMAYQBTAFkAZgBsAGwAdgCGAIAAdgBxAGUAbAB4AGMAYABqAFkAWgBuAGgAYwBeAFMAXQBdAFcAYQBPAD8AVgBZAFAAXABhAGIAWgBGAFUAagBdAFwAbgBwAGkAYgBuAHMAUgBIAGAAZQBtAHAAUwBPAFsATABMAFoAUgBCADAANQBMADsAJwBHAFIANwA4AD4ANwA8AEMATgBgAFYASwBcAFEAQQBpAIAAXwBgAHEAXQBmAIMAeABwAHkAdgCFAJMAegBrAHkAfQB9AIEAgQB7AHcAgACGAHYAcQCAAI4ApQC2AKwArQC+AL0AtwDHAOAA6wDdAM8A3QD0AP4A+gD0AAsBJAEQAf0AEAESAfoABgEqASwBEgEGAQgBAgEDAQsBCwEQARkBHAEkASsBJgEwAUABPQE/AUoBVAFjAWYBYwFwAW8BYwFwAXsBcQFwAXcBewGAAYABbQFcAW8BfwFsAXcBmQGMAX8BhwF8AYYBmgGDAYMBmwGGAXwBkQGQAZABnwGmAacBpgGzAboBogGmAbkBlwGGAacBoQGNAZ0BnQGQAY0BiwGOAX8BXwFkAXcBZwFOAU0BUAFCAUQBUQE3ASUBRgFMATwBUgFZAUgBTwFIATYBSwFWAUQBPwFJAVkBYQFTAU4BWQFRAToBLAEtATIBMgE5ATkBJQEdASABGQEkAS4BDwH7AAQBAgH6APIA6AD6AAkB+QDzAP0A+gD3APoA+AD1APEA7wDwAO4A6gDjANcA0ADPAM4AzAC6AK0AvADCALkAuQCtAKMAtQC6AK0ArgCnAKIAqQCjAKUArQCZAJ0AxADEAKsApQCrAMIAywC4ALwAxQCzALkAygC+ALUAuwDAAMAAsACjAL0A2ADHAKQAnQCtALAArgCsAJYAjwChAJIAfQCUAKgAoACZAJMAjgCKAIUAgwCHAJMAngCaAJwAqQCfAI4AjgCOAJgApwCcAJAAkACJAJcAugC0AJkAnwCqAKIAoQCpAKoAnwCaAKIAngCWAJ0AlACDAJgApACLAIsAowCmAJ4AkwCWAKEAlACTAKcAoQCbAJgAegCDAJYAeACDAJwAbQBqAI0AZABXAIMAZQA0AEEASQBFAEgAOwAwADQAOQBBAEYANgAdABcAGgAHAPf/BgAWABIAAQDs/+3/9f/d/9j/5v/U/9L/6f/S/7X/vf+9/7X/sf+x/8P/xf+x/7b/uP+p/7D/qf+V/6z/vP+y/8b/z/+v/5r/mP+Y/5z/mP+T/4v/fP97/4P/hv+S/4n/bf93/4v/e/9s/2z/Z/9q/3H/b/9m/13/Yv9p/2D/Xv9h/1H/Sv9a/17/U/9Z/2z/av9T/1P/bf9w/13/W/9s/3T/dv97/3b/df98/3b/dP9+/3H/bf+M/5j/kv+U/4j/gv+G/3f/ff+Q/3v/bP94/3T/cv92/2v/aP9l/1v/XP9W/0//W/9Y/03/Vf9W/0j/Qv9H/1P/Vf9B/zb/Qf9N/0b/Mf81/0L/NP8y/0j/Qf8z/0D/QP8s/yj/Mf86/z3/N/8n/x//L/89/zT/Nf9F/0T/Pv9E/0X/Q/9P/1z/U/83/zj/V/9V/zv/Q/9R/0//VP9O/z//Sv9Y/1b/V/9X/1r/ZP9U/0T/W/9c/zr/Qf9Y/03/Sv9S/0D/N/9J/0n/Ov88/z7/Lf8j/y//MP8l/zH/OP8g/x3/MP8q/y7/Q/83/y3/Of8s/xv/G/8d/yv/MP8S/wv/I/8p/yX/I/8c/xr/HP8q/zr/LP8a/x3/FP8W/yz/Kv8i/y3/Nf83/zH/J/8w/z3/QP9D/zP/KP87/zr/Lv9B/0T/Nf9G/1j/R/82/zn/R/9D/zP/OP8+/yv/KP88/0T/Qv85/zD/Mv8n/x3/Lv8v/yH/M/8+/zD/Lf8j/xv/Mv87/zb/O/8q/xr/HP8T/x3/Kf8T/yT/Rf8l/xL/Jf8d/xr/Hv8S/xn/GP8J/yH/NP8o/yX/GP8T/yv/J/8c/zf/Qv82/zf/Mv8j/x7/Jf8y/zD/Ff///hT/Lv8Z/wz/K/8p/xb/Kv8j/wD/A/8U/y7/Qf8c/w7/Lv8h/xD/Jv8h/xP/Jv89/0X/Q/9L/1b/PP8z/1X/Xv9a/2H/UP9W/3T/Zv9g/3b/c/9//5P/ev90/5j/nP+N/6P/yP/I/63/tv/Y/97/2f/Z/83/0//h/9b/1v/m/9//2v/o//L/9f/y/+j/4P/d/+L/7f/n/+P/7f/w//r/EAAKAPr/CAARAAAABwAYAA4ACAATAAkA/f8AAAQADQAIAOr/3//s//b/DAANAOj/8v8eABAA+v8JAAcA/f8DAP7//v8YABsADAAVACUAIwAeACMAKQAcAAoAHAA6ADoALQAsADEAPgBLAEIAOwBJAFUAUQBNAE8AWQBkAGgAagBtAHcAhAB6AGMAbACFAIsAjwCgAKEAjAB+AHwAfgCIAJQAjgCDAH4AewCMAKUAmgCBAIcAmQCeAJQAiQCYAKIAjQCKAJ8AngCTAIsAfgCGAJcAkQCLAJoAqQCiAJYAngCmAKQAuQDJAL0AwwDFAK4AugDNAMQA0QDWAMMA1gDsAOQA8QAAAfEA6QDvAPsABAEAAQkBEQEGARsBOwErARcBIwEtASYBGgEkATMBHAEaATwBNAEdAS8BOAE2AT4BOgE4ATkBMwFAAUsBSAFTAVUBUQFfAU0BMAFMAWQBVgFgAW8BaAFxAX4BeAF7AYcBhwGMAZEBggF/AZoBmwGQAaUBrQGZAakBxAHDAc4B1AG5Ab4B3wHXAcYB1gHhAd4B3gHVAc8B3AHaAcsB0wHdAdkB3gHdAcoByAHOAcgBzwHRAb8BxgHUAcMBuQHCAcIBwAG2AaABmAGVAZABjwGGAYIBkAGQAYgBhQF5AXEBagFiAXMBeQFaAVQBZgFlAWQBXQFQAVoBXQFRAVwBYQFOAUsBUAFKAVABVQFGAVABdgF3AWcBeAF2AVkBYQFrAVsBYAFoAVoBTAFFAUkBTgE7ATkBSwE7ASoBOwFFATwBLwEuATgBKQESARgBGgEUASMBLAEkARkBEQEeARwBBQEWAS8BHwEZARsBCAENARwBAwHzAAgBCAHzAP0AGAEOAfsACQEWAQQB9QABARUBGAEEAe4A9AATASEBEwEXAR8BAwH0AAYBCQEUAS4BHwEDARYBKwEcARQBHAEYASIBPAE2ASIBJwEnASQBKQEbAR4BOwEoAQwBHwElARgBFwEVARwBGAEBARUBOQExASQBFgEAARQBJwEUAR4BNQEnASQBMwEkAR0BNwFCATcBNwE7ATgBNAE1AUEBSAEzATABTQFOAT8BUQFYAUMBPQFJAVkBaAFgAVQBXQFoAWYBZQFuAWgBVQFaAWgBXgFaAWQBbQGAAX4BXgFYAV4BXwFlAVUBRwFXAVMBTAFYAUUBPQFPAT0BNAFHATcBMgE1ARIBEAEjAQ0BCgEOAfkABgEKAegA8QACAesA6QDsANQAzADaAOgA6wDhAN4A0wCwAKkAwADJAMcAvACvAKsAngCbAKkAoQCTAJkAkgCLAJ8AowCGAHAAeQCDAHAAYABlAGIAYQBmAF4AXABbAE4AXABqAFAASwBaAE4ATgBWAEcASwBSAEcASAA0ABAAHwApAA4AGQAqABgAGAAcABcAIwAZAAMAEAAZABgAGgD+/+//DAAUAAcA///p/+j/+P/3//f/9P/s//z/+P/a/9z/3P/J/9L/1P/I/9//6//L/7z/yv/O/8X/zf/k/9j/vf/G/8n/sf+3/8X/sP+o/77/vf+m/6b/tP+v/6z/tP+u/57/mP+d/6n/o/+S/5n/m/+J/4n/jP+D/4z/lv+Q/4n/eP9u/3z/g/+G/4L/a/94/5f/e/9Y/2j/df91/3b/Z/9e/2H/V/9P/1b/S/8y/zb/Vf9P/yn/MP9K/zr/M/8+/zP/OP9I/zf/Lv8y/yH/J/88/zL/Lv8+/zL/FP8N/x7/J/8a/xv/JP8b/yH/Mf8j/xv/Jv8d/xH/Ef8N/wz/Av/s/uP+5P7n/uz+4/7e/uj+2v7E/tD+2f7O/sn+yv7L/sv+y/7Q/tD+xf7O/uf+3v7L/tX+zf68/tv+5v7A/sX+3f7W/t3+2f65/sj+3P68/rP+wf6s/qH+t/7G/sP+wf7J/sv+0v7q/tv+rf6w/r7+sf62/r3+uf7A/qr+jv6r/rv+pv6m/p/+k/6b/pH+jP6i/pf+gv6S/pn+hv5+/oT+i/6R/pn+k/5//oT+kv6I/pD+mf50/mj+h/6L/o3+l/6F/nj+fv59/oX+if6E/ob+b/5W/mv+dP5f/mf+b/5j/nD+hP56/mv+av53/nj+Z/5r/oH+cf5T/lH+Vf5X/mD+Y/5l/mr+Yf5c/mX+av5u/mf+VP5g/nb+Yf5S/mT+Zf5a/l3+X/5l/mb+W/5c/l3+Vf5V/lH+RP5B/kP+U/5k/lX+UP5o/mb+U/5M/kX+Sf5P/kX+P/5G/lL+Wf5J/jn+Pv4+/kD+RP48/kj+Vv5E/j7+VP5X/kf+OP42/jz+M/4r/jD+N/5I/kv+K/4m/kD+Pv49/lP+VP4+/jL+O/5F/j7+PP5A/jT+Kf4t/jT+PP42/ib+Mf48/jH+M/43/ij+Jf45/kf+Pf4u/kD+Uv48/jP+Qf45/j3+S/42/jT+Tf5A/jL+RP5F/kf+UP48/kX+Z/5O/jr+U/5H/jn+V/5k/mD+Xf5I/k3+YP5K/kL+Vv5W/l7+b/5l/mT+a/5Y/k/+V/5O/kn+W/5o/m/+fP57/nH+dv51/m7+df5y/m3+gf6L/on+j/57/nT+o/6t/oX+hP6V/pb+nP6Y/pL+pP6s/qP+q/68/sH+vv7B/sb+wf7G/tj+0f7C/s/+5P7q/ur+9f4G//3+8P4N/yL/EP8Y/zD/Jv8a/x7/IP8y/0b/P/86/0b/S/9R/2D/Zv9p/3D/dP99/4D/dv99/5H/lf+l/8L/u/+p/67/sv+5/9b/5f/W/8r/z//X/+D/8v8CAAAA9//5/wgAFAAPAA8AGwAgAB8AIQAeACsASQBEACkALAA0ACkALwBCAEQARgBWAF0AXwBjAFcATwBfAGQAWQBqAHcAZgBmAHAAYgBhAH8AhgB5AIMAmACWAI0AlACdAKkAuQC7ALkAwAC2ALAAvAC1ALAAwADEAMwA2QDRANoA5wDHALgA0gDqAPwA6ADEAOMAAAHjAOoAAAHjANkAAAENAe4A5QATASkBCgEVATMBFQH/ABIBEQEMARIBCgENASEBHwEPARUBJAEkASQBMgE2ASkBFwEBAf8AGAErAS4BKgEiASMBMQE7ATEBJgE6AUkBQAFIAU0BNQFDAWEBUAFQAWsBZgFmAXgBagFeAXYBhgF6AXcBggGCAYYBlwGRAZABpwGaAYsBqwGsAZUBngGUAZEBtAGhAXkBlAGsAasBsgGdAY8BrAG4Aa0BqQGnAbgBvQGkAakBugGiAZYBpgGkAZ8BngGeAagBmwGIAZgBmwGGAZABpAGnAasBpAGbAaUBrAGrAaUBngGpAbABpQGpAbEBswHGAcwBtAGzAcIByAHMAdAB1wHWAcwB5gEHAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=\" type=\"audio/wav\" />\n",
" Your browser does not support the audio element.\n",
" </audio>\n",
" "
]
},
"metadata": {
"tags": []
},
"execution_count": 96
}
]
},
{
"metadata": {
"id": "gSPUXfj6XQ3k",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Remove old files"
]
},
{
"metadata": {
"id": "dTnSG4lOXXHN",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"#shutil.rmtree('files')"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "-KLR9J3phXNL",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Combined"
]
},
{
"metadata": {
"id": "_p2x5TXTMCmE",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"class AudioVideo:\n",
" def __init__(self, name, training=True):\n",
" self.video = Video(name, training)\n",
" self.audio = Audio(name, training)\n",
" \n",
" def get_data(self, keys):\n",
" \n",
" if isinstance(keys, str):\n",
" keys = [keys]\n",
" \n",
" if 'video' in keys:\n",
" data = {'video': self.video.data()}\n",
" keys = {k for k in keys if k != 'video'}\n",
" else:\n",
" data = {}\n",
" \n",
" d = self.audio[keys]\n",
" \n",
" if isinstance(d, dict):\n",
" for k, v in d.items():\n",
" data[k] = v\n",
" else:\n",
" data[keys[0]] = d\n",
" \n",
" \n",
" if len(data) == 1:\n",
" return data[next(iter(data))]\n",
" \n",
" return data\n",
" \n",
" def __getitem__(self, item: Iterable[str]):\n",
" return self.get_data(item)\n",
" \n",
" def audio_data(self):\n",
" return self['audio']\n",
" \n",
" def video_data(self):\n",
" return self['video']\n",
" \n",
" def spectrogram(self):\n",
" return self['spectrogram']\n",
" \n",
" def phase(self):\n",
" return self['phase']\n",
" \n",
" def video_data(self):\n",
" return self['video']\n",
" \n",
" def noisy_audio(self):\n",
" return self['noisy_audio']\n",
" \n",
" def noisy_spectrogram(self):\n",
" return self['noisy_spectrogram']\n",
" \n",
" \n",
" \n",
"def get_audio_and_video(limit=100, training=True):\n",
" \n",
" if training:\n",
" files = [f for f in os.listdir(train_dir + '/' + video_dir)][:limit]\n",
" else:\n",
" files = [f for f in os.listdir(test_dir + '/' + video_dir)][:limit]\n",
" \n",
" while True:\n",
" for f in files:\n",
" yield AudioVideo(f, training)\n",
"\n",
" \n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "DkFTUmqMhCoR",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Training Utilities"
]
},
{
"metadata": {
"id": "vldFdtwVhPAb",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Data generators\n",
"\n",
"Our data is quite large, and Keras supprots using generators for training and prediction, so we take advantage of this."
]
},
{
"metadata": {
"id": "_sG4xKle4ZSr",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"def get_training_data(limit=100, batch=10, use_video=False):\n",
" i = 0\n",
" \n",
" av_getter = get_audio_and_video(limit)\n",
" \n",
" while True:\n",
" \n",
" if use_video:\n",
" video_input = np.empty((batch,) + video_shape, dtype='uint8')\n",
" \n",
" spectrogram_input = np.empty((batch,) + spectrogram_shape, dtype='float32')\n",
" \n",
" spectrogram_output = np.empty((batch,) + spectrogram_shape, dtype='float32')\n",
" \n",
" for i in range(batch):\n",
" av = next(av_getter)\n",
" \n",
" keys = ['spectrogram', 'noisy_spectrogram']\n",
" \n",
" if use_video:\n",
" keys.append('video')\n",
" \n",
" data = av[keys]\n",
" \n",
" spectrogram_input[i] = data['noisy_spectrogram']\n",
" spectrogram_output[i] = data['spectrogram']\n",
" \n",
" if use_video:\n",
" video_input[i] = data['video']\n",
" \n",
" data = {}\n",
" \n",
" if use_video:\n",
" data['video'] = video_input\n",
" \n",
" data['noisy_spectrogram'] = spectrogram_input\n",
" \n",
" data['spectrogram'] = spectrogram_output\n",
" \n",
" yield data\n",
" \n",
"def get_testing_data(limit=100, use_video=False):\n",
" av_getter = get_audio_and_video(limit, training=False)\n",
" \n",
" noisy_spectrogram = np.empty((limit,) + spectrogram_shape, dtype='float32')\n",
" audio = np.empty((limit,) + audio_shape, dtype='float32')\n",
" noisy_audio = np.empty((limit,) + audio_shape, dtype='float32')\n",
" \n",
" if use_video:\n",
" video = np.empty((limit,) + video_shape, dtype='uint8')\n",
" \n",
" phase = np.empty((limit,) + phase_shape, dtype='complex64')\n",
" \n",
" for i in range(limit):\n",
" \n",
" av = next(av_getter)\n",
"\n",
" keys = ['noisy_spectrogram', 'audio', 'noisy_audio', 'video', 'phase']\n",
"\n",
" if use_video:\n",
" keys.append('video')\n",
" \n",
" data = av[keys]\n",
" noisy_spectrogram[i] = data['noisy_spectrogram']\n",
" audio[i] = data['audio']\n",
" noisy_audio[i] = data['noisy_audio']\n",
" \n",
" if use_video:\n",
" video[i] = data['video']\n",
" \n",
" phase[i] = data['phase']\n",
" \n",
" result = {\n",
" 'spectrogram_input': noisy_spectrogram,\n",
" 'audio': audio,\n",
" 'noisy_audio': noisy_audio,\n",
" 'phase_input': phase\n",
" }\n",
" \n",
" if use_video:\n",
" result['video_input'] = video\n",
" \n",
" return result"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "0bDj-2J57Q9j",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"## Tensorflow Inverse STFT\n",
"\n",
"Tensorflow's istft doesn't play nice with librosa's stft, so we had to implement our own. We use this for turning the spectrogram back into audio in the model, which isn't really nessecary now, but we implemented this so we could try a residual network (it didn't work)."
]
},
{
"metadata": {
"id": "RAIBdh6pH9mP",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"import tensorflow as tf\n",
"import tensorflow.signal\n",
"\n",
"from tensorflow import convert_to_tensor, map_fn\n",
"import tensorflow.keras.backend as K\n",
"from tensorflow import float32 as tf_float32\n",
"import tensorflow_probability as tfp\n",
"\n",
"def log10(x):\n",
" num = tf.log(x)\n",
" den = tf.log(tf.constant(10, dtype=num.dtype))\n",
" return num / den\n",
"\n",
"def tf_db_to_amp(signal):\n",
" return 10.0**(0.05 * signal)\n",
"\n",
"def tf_amp_to_db(signal):\n",
" amin = 1e-10\n",
" topdb = 80.0\n",
" signal = signal ** 2\n",
" \n",
" log_spec = 10.0 * log10(tf.math.maximum(amin, magnitude))\n",
" log_spec -= 10.0 * log10(tf.math.maximum(amin, 1.0))\n",
" log_spec = tf.math.maximum(log_spec, log_spec.max() - top_db)\n",
" \n",
" return log_spec\n",
"\n",
"tf_mel = tf.constant(\n",
" librosa.filters.mel(\n",
" sr=framerate,\n",
" n_fft=n_fft,\n",
" n_mels=80,\n",
" fmin=0,\n",
" fmax=8000\n",
" )\n",
")\n",
" \n",
"ifft_window = librosa.filters.get_window('hann', n_fft, fftbins=True)\n",
"ifft_window = librosa.util.pad_center(ifft_window, n_fft)\n",
"ifft_window = tf.constant(ifft_window, dtype='float32')\n",
"\n",
"ifft_window_sum = tf.constant(librosa.filters.window_sumsquare('hann',\n",
" 299,\n",
" win_length=n_fft,\n",
" n_fft=n_fft,\n",
" hop_length=frame_step))\n",
" \n",
"approx_nonzero_indices = ifft_window_sum > librosa.util.tiny(ifft_window_sum)\n",
" \n",
"divisor = tf.where(approx_nonzero_indices, ifft_window_sum, tf.ones(ifft_window_sum.shape[0]))\n",
" \n",
"def tf_istft(stft_matrix, hop_length, window='hann', center=True):\n",
" win_length = n_fft\n",
" \n",
" n_frames = int(stft_matrix.shape[1])\n",
" \n",
" expected_signal_len = n_fft + hop_length * (n_frames - 1)\n",
" \n",
" y = tf.zeros((expected_signal_len,), 'float32')\n",
" \n",
" i = tf.constant(0, dtype='int32')\n",
" \n",
" _, y = tf.while_loop(\n",
" lambda i, y: i < tf.constant(n_frames), \n",
" lambda i, y: tf_loop_body(y, i, hop_length, stft_matrix, ifft_window, expected_signal_len, n_fft), \n",
" loop_vars = [i,y])\n",
" \n",
" #y = tf.math.add_n(y)\n",
" \n",
" y /= divisor\n",
" \n",
" y = y[int(n_fft // 2):-int(n_fft // 2)]\n",
" \n",
" return y\n",
"\n",
"def tf_loop_body(y, i, hop_length, stft_matrix, ifft_window, expected_signal_len, n_fft):\n",
" \n",
" sample = i * hop_length\n",
" spec = tf.squeeze(stft_matrix[:, i])\n",
" spec = tf.concat((spec, tf.math.conj(spec[-2:0:-1])), 0)\n",
" ytmp = ifft_window * tf.math.real(tf.signal.ifft(spec))\n",
"\n",
" #tf.assign_add(y[sample:(sample + n_fft)], ytmp)\n",
"\n",
" ytmp = tf.pad(ytmp, [[sample, expected_signal_len - (sample + n_fft)]], mode='CONSTANT')\n",
" \n",
" return [tf.add(i, 1), tf.add(y, ytmp)]\n",
"\n",
"import functools\n",
"\n",
"def tf_reconstruct_audio(mp):\n",
" \n",
" magnitude = mp[0]\n",
" phase = mp[1]\n",
" \n",
" print(phase.dtype)\n",
" \n",
" magnitude = tf_db_to_amp(magnitude)\n",
"\n",
" mel_filterbank = tf_mel\n",
"\n",
" magnitude = tfp.math.pinv(mel_filterbank) @ tf.cast(magnitude, 'float64')\n",
" \n",
" \n",
" mag_phase = tf.cast(magnitude, 'complex64') * phase\n",
" \n",
" wave = tf_istft(mag_phase, hop_length=frame_step)\n",
" \n",
" pad = 65664 - int(wave.shape[0])\n",
" \n",
" if pad > 0:\n",
" wave = tf.pad(wave, [[0, pad]], 'constant')\n",
" \n",
" return wave\n",
"\n",
"def tensor_reconstruct_audio(ip):\n",
" tensor = tf.map_fn(\n",
" tf_reconstruct_audio, \n",
" ip, \n",
" dtype=tf_float32, infer_shape=False)\n",
" tensor.set_shape((None, 65664,))\n",
" return tensor\n",
" \n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "F5vpLYMCctGg",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"# with tf.Session() as sess:\n",
"# m = tf.constant(audios[0].spectrogram())\n",
"# p = tf.constant(audios[0].phase())\n",
"# recon = tf_reconstruct_audio([m, p])\n",
"# start = time.time()\n",
"# recon = sess.run(recon)\n",
"# end = time.time()\n",
"\n",
"# print(\"Time: \", end - start)\n",
"\n",
"# print(\"Recon Shape:\", recon.shape)\n",
"\n",
"# librosa.display.waveplot(recon)\n",
"# plt.show()\n",
"\n",
"# import IPython.display as ipy_display\n",
"\n",
"# ipy_display.Audio(recon, rate=framerate)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "uJJURBxOq94A",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"# Denoising Autoencoder#\n",
"\n",
"Audio encoder and decoder from [here](https://github.com/avivga/audio-visual-speech-enhancement).\n",
"\n",
"Video encoder from [here](https://github.com/rizkiarm/LipNet)."
]
},
{
"metadata": {
"id": "7JD6KOuZuXC-",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"from tensorflow.keras.layers import Input, Flatten, Dense, Reshape, Concatenate, Dropout\n",
"from tensorflow.keras.models import Model\n",
"from tensorflow.keras.optimizers import SGD, Adadelta, Adam\n",
"from tensorflow.keras.layers import Conv3D, ZeroPadding3D, UpSampling1D, ZeroPadding1D\n",
"from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Cropping2D, Cropping1D\n",
"from tensorflow.keras.layers import MaxPooling3D, MaxPooling2D, UpSampling2D\n",
"from tensorflow.keras.layers import Dense, Activation, SpatialDropout3D, Flatten\n",
"from tensorflow.keras.layers import Bidirectional, TimeDistributed, Subtract\n",
"from tensorflow.keras.layers import GRU, LSTM, Lambda, LeakyReLU, ZeroPadding2D\n",
"from tensorflow.keras.layers import BatchNormalization, MaxPooling1D, Conv1D\n",
"from tensorflow.keras.layers import SpatialDropout2D, Conv1D, Permute\n",
"\n",
"import tensorflow.keras.backend as K\n",
"\n",
"from progressbar import progressbar as tqdm\n",
"\n",
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \n",
"tf.logging.set_verbosity(tf.logging.ERROR)\n",
"\n",
"class Autoencoder:\n",
" def __init__(self, use_video=False):\n",
" \n",
" self.use_video = use_video\n",
" \n",
" self.reconstruct_layer = Lambda(tensor_reconstruct_audio, name='reconstruct_audio', output_shape=(65664,))\n",
" \n",
" self.video = [\n",
" ZeroPadding3D(padding=(1, 3, 3), name='zero1'),\n",
" Conv3D(32, (3, 5, 5), strides=(1, 2, 2), kernel_initializer='he_normal', name='conv1'),\n",
" BatchNormalization(name='batc1'),\n",
" Activation('relu', name='actv1'),\n",
" SpatialDropout3D(0.3),\n",
" MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max1'),\n",
" \n",
" ZeroPadding3D(padding=(1, 2, 2), name='zero2'),\n",
" Conv3D(64, (3, 5, 5), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv2'),\n",
" BatchNormalization(name='batc2'),\n",
" Activation('relu', name='actv2'),\n",
" SpatialDropout3D(0.3),\n",
" MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max2'),\n",
" \n",
" ZeroPadding3D(padding=(1, 1, 1), name='zero3'),\n",
" Conv3D(92, (3, 3, 3), strides=(1, 1, 1), kernel_initializer='he_normal', name='conv3'),\n",
" BatchNormalization(name='batc3'),\n",
" Activation('relu', name='actv3'),\n",
" SpatialDropout3D(0.3),\n",
" MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2), name='max3'),\n",
" \n",
" TimeDistributed(Flatten()),\n",
" \n",
" Bidirectional(GRU(256, return_sequences=True, kernel_initializer='Orthogonal', name='gru1'), merge_mode='concat'),\n",
" \n",
" Dropout(0.3)\n",
" ]\n",
" \n",
" self.spectrogram_encoder = [\n",
" Reshape((spectrogram_shape) + (1,)),\n",
" \n",
" Conv2D(64, kernel_size=(5, 5), strides=(2, 2), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2D(64, kernel_size=(4, 4), strides=(1, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2D(128, kernel_size=(4, 4), strides=(2, 2), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2D(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2D(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2D(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Permute((2, 1, 3)),\n",
" TimeDistributed(Flatten())\n",
" ]\n",
" \n",
" self.spectrogram_latent = [\n",
" \n",
"# GRU(384, return_sequences=True, kernel_initializer='Orthogonal', name='latent_rnn1'),\n",
"# TimeDistributed(BatchNormalization()),\n",
"# TimeDistributed(LeakyReLU()),\n",
" \n",
"# LSTM(384, return_sequences=True, kernel_initializer='Orthogonal', name='latent_rnn2'),\n",
" \n",
" TimeDistributed(Dense(384)),\n",
" TimeDistributed(BatchNormalization()),\n",
" TimeDistributed(LeakyReLU()),\n",
" \n",
" \n",
" TimeDistributed(Reshape((3, 128))),\n",
" Permute((2, 1, 3)),\n",
" ]\n",
" \n",
" self.spectrogram_decoder = [\n",
" \n",
" Conv2DTranspose(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2DTranspose(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
"\n",
" Conv2DTranspose(128, kernel_size=(2, 2), strides=(2, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
"\n",
" Conv2DTranspose(128, kernel_size=(4, 4), strides=(2, 2), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
"\n",
" Conv2DTranspose(64, kernel_size=(4, 4), strides=(1, 1), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
"\n",
" Conv2DTranspose(64, kernel_size=(5, 5), strides=(2, 2), padding='same'),\n",
" BatchNormalization(),\n",
" LeakyReLU(),\n",
" \n",
" Conv2DTranspose(1, kernel_size=(1, 1), strides=(1, 1), padding='same'),\n",
" \n",
" Cropping2D(((8, 8), (0, 1))),\n",
" \n",
" Reshape((80, 299), name='spectrogram_output'),\n",
" ]\n",
" \n",
" self.discriminator = [\n",
" Reshape((80, 299, 1)),\n",
" \n",
" Conv2D(32,4,strides=2,activation=None,padding='same'),\n",
" LeakyReLU(alpha=0.1),\n",
"\n",
" Conv2D(64,4,strides=2,activation=None,padding='same'),\n",
" LeakyReLU(alpha=0.1),\n",
"\n",
" Flatten(),\n",
" \n",
" Dense(1,activation='sigmoid')\n",
" ]\n",
" \n",
" def get_autoencoder(self):\n",
" \"\"\" Builds the full autoencoder model with encoder and decoder. \"\"\"\n",
" \n",
" \n",
" # Spectrogram Encoder\n",
" \n",
" \n",
" spectrogram_input = Input(shape=spectrogram_shape,name='spectrogram_input')\n",
"\n",
" s = spectrogram_input\n",
" \n",
" for l in self.spectrogram_encoder:\n",
" s = l(s)\n",
" encoding_layer = l\n",
"\n",
"\n",
" spectrogram_encoder_model = Model(inputs=spectrogram_input, outputs=s)\n",
"\n",
" print(\"Spectrogram encoder output:\", spectrogram_encoder_model.output_shape[1:])\n",
"\n",
" print(\"Spectrogram encoder:\")\n",
" print(spectrogram_encoder_model.summary())\n",
"\n",
" print(\"\\n\\n\\n\")\n",
" \n",
" inputs = [spectrogram_input]\n",
" \n",
" # Video Encoder\n",
" \n",
" if self.use_video:\n",
" \n",
" video_input = Input(shape=video_shape,name='video_input')\n",
"\n",
" v = video_input\n",
"\n",
" v = Reshape(video_shape + (1,))(v)\n",
"\n",
" for l in self.video:\n",
" v = l(v)\n",
"\n",
"\n",
" self.video_encoder_model = Model(inputs=video_input, outputs=v)\n",
"\n",
" print(\"Video encoder output:\", self.video_encoder_model.output_shape[1:])\n",
"\n",
" print(\"Video encoder:\")\n",
" print(self.video_encoder_model.summary())\n",
"\n",
" print(\"\\n\\n\\n\")\n",
" \n",
" inputs.append(video_input)\n",
" \n",
" encoding_layer = Concatenate(axis=2)\n",
" encoding = encoding_layer([s, v])\n",
" else:\n",
" encoding = s\n",
" \n",
" embedding_shape = encoding_layer.output_shape\n",
" \n",
" # Spectrogram Latent\n",
" \n",
" print(\"Embedding Shape:\", embedding_shape)\n",
" \n",
" se_in = Input(embedding_shape[1:])\n",
" \n",
" se = se_in\n",
" \n",
" s = encoding\n",
" \n",
" for l in self.spectrogram_latent:\n",
" s = l(s)\n",
" se = l(se)\n",
" \n",
" print(\"\\n\\n\\n\")\n",
"\n",
" print(\"Spectrogram Latent:\")\n",
" print(Model(inputs=se_in, outputs=se).summary())\n",
" print(\"\\n\\n\\n\")\n",
" \n",
" # Spectrogram Decoder\n",
" \n",
" sd_in = Input((3, 75, 128))\n",
" \n",
" sd = sd_in\n",
" \n",
" for l in self.spectrogram_decoder:\n",
" sd = l(sd)\n",
" s = l(s)\n",
" \n",
" spectrogram_decoder_model = Model(inputs=sd_in, outputs=sd)\n",
" \n",
" print(\"Spectrogram decoder output:\", spectrogram_decoder_model.output_shape[1:])\n",
" \n",
" print(\"Spectrogram decoder:\")\n",
" print(spectrogram_decoder_model.summary())\n",
" \n",
" print(\"\\n\\n\\n\")\n",
" \n",
" \n",
" self.spectrogram_model = Model(inputs=inputs, outputs=s)\n",
" \n",
" \n",
" discrim_in = Input((80, 299), name='discriminator_input')\n",
" \n",
" discrim = discrim_in\n",
" \n",
" discrim_on_decoded = s\n",
" \n",
" for l in self.discriminator:\n",
" discrim = l(discrim)\n",
" discrim_on_decoded = l(discrim_on_decoded)\n",
" \n",
" self.discriminator_model = Model(inputs=discrim_in, outputs=discrim)\n",
" self.discriminator_on_model = Model(inputs=inputs, outputs=discrim_on_decoded)\n",
" \n",
" print(\"Discriminator:\")\n",
" print(self.discriminator_model.summary())\n",
" print(\"\\n\\n\\n\")\n",
" \n",
" print(\"Discriminator on model:\")\n",
" print(self.discriminator_on_model.summary())\n",
" print(\"\\n\\n\\n\")\n",
" \n",
" phase_input = Input((442, 299), name='phase_input', dtype='complex64')\n",
" \n",
" reconstructed = self.reconstruct_layer([s, phase_input])\n",
" \n",
" reconstructed_model = Model(inputs=inputs + [phase_input,], outputs=reconstructed)\n",
" \n",
" #print(\"Reconstructed decoder output:\", reconstructed_model.output_shape[1:])\n",
" \n",
" self.model = Model(inputs=inputs + [phase_input,], outputs=[reconstructed])\n",
" \n",
" \n",
" print(\"Full Model:\")\n",
" print(self.model.summary())\n",
" \n",
" print(\"\\n\\n\\n\")\n",
" \n",
" \n",
" \n",
" return self.model, self.spectrogram_model, self.discriminator_model, self.discriminator_on_model\n",
" \n",
" def compile(self, ops, losses):\n",
" return self.spectrogram_model.compile(ops[0],loss=losses[0]), \\\n",
" self.discriminator_model.compile(ops[1], loss=losses[1]), \\\n",
" self.discriminator_on_model.compile(ops[2], loss=losses[2])\n",
" \n",
" def train(self, data_limit=100, data_batch=10, epochs=10, steps_per_epoch=10, autoencoder_reps=3, discriminator_reps=2, d_on_ae_reps=1):\n",
" data_gen = self.get_data(data_limit, data_batch)\n",
" \n",
" self.discriminator_model.trainable = False\n",
" \n",
" ae_loss_history = []\n",
" discrim_loss_history = []\n",
" d_on_ae_loss_history = []\n",
" \n",
" for i in tqdm(range(epochs)):\n",
" for j in tqdm(range(steps_per_epoch)):\n",
" data = next(data_gen)\n",
" \n",
" x_real = data['spectrogram']\n",
" y_real = [1]*data_batch\n",
" \n",
" if self.use_video:\n",
" x_gen = [data['noisy_spectrogram'], data['video']]\n",
" else:\n",
" x_gen = [data['noisy_spectrogram']]\n",
" \n",
" y_gen = [1]*data_batch\n",
" \n",
" # train autoencoder\n",
" \n",
" ae_loss = 0\n",
" for k in range(autoencoder_reps):\n",
" ae_loss += self.spectrogram_model.train_on_batch(x_gen, x_real)\n",
" \n",
" if autoencoder_reps > 0:\n",
" ae_loss /= autoencoder_reps\n",
" \n",
" \n",
" x_fake = self.spectrogram_model.predict(x_gen)\n",
" y_fake = [0]*data_batch\n",
" \n",
" # train discriminator\n",
" \n",
" self.discriminator_model.trainable = True\n",
" \n",
" real_loss = 0\n",
" fake_loss = 0\n",
" for k in range(discriminator_reps):\n",
" \n",
" real_loss += self.discriminator_model.train_on_batch(x_real, y_real)\n",
" fake_loss += self.discriminator_model.train_on_batch(x_fake, y_fake)\n",
" \n",
" self.discriminator_model.trainable = False\n",
" \n",
" discrim_loss = 0.5*(real_loss + fake_loss)\n",
" \n",
" if discriminator_reps > 0:\n",
" discrim_loss /= discriminator_reps\n",
" \n",
" # train discriminator on autoencoder\n",
" \n",
" d_on_ae_loss = 0\n",
" for k in range(d_on_ae_reps):\n",
" d_on_ae_loss += self.discriminator_on_model.train_on_batch(x_gen, y_gen)\n",
" \n",
" if d_on_ae_reps > 0:\n",
" d_on_ae_loss /= d_on_ae_reps\n",
" \n",
" ae_loss_history.append(ae_loss)\n",
" discrim_loss_history.append(discrim_loss)\n",
" d_on_ae_loss_history.append(d_on_ae_loss)\n",
" \n",
" return ae_loss_history, discrim_loss_history, d_on_ae_loss_history\n",
" \n",
" def get_data(self, limit=100, batch=10):\n",
" return get_training_data(limit, batch, self.use_video)\n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ZL2XP9L9h8zN",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Compile the model.\n",
"\n",
"Using SGD with MAE for the audoencoder, and Adam with Binary Crossentropy for the discriminator and combined."
]
},
{
"metadata": {
"id": "wcO7ZQdFCjUB",
"colab_type": "code",
"pycharm": {},
"outputId": "61845869-f919-4a32-9d00-fb392a33f060",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 10557
}
},
"cell_type": "code",
"source": [
"from tensorflow.keras.optimizers import Adam\n",
"\n",
"#@title Autoencoder Type\n",
"use_video = True #@param {type:\"boolean\"}\n",
"#use_audio = True #@param {type:\"boolean\"}\n",
"#use_spectrogram = False #@param {type:\"boolean\"}\n",
"\n",
"autoencoder = Autoencoder(\n",
" use_video=use_video)\n",
"\n",
"model, spec, discrim, d_on_g = autoencoder.get_autoencoder()\n",
"\n",
"#TODO try other optimizers (Adam, Adagrad)\n",
"#SGD(0.02,momentum=0.9)\n",
"autoencoder.compile(ops = [\n",
" Adam(lr=5e-3),\n",
" Adam(lr=0.0002,beta_1=0.5,beta_2=0.999,epsilon=1e-3),\n",
" Adam(lr=0.0002,beta_1=0.5,beta_2=0.999,epsilon=1e-3)\n",
"], losses = [\n",
" 'mean_squared_error', #mean_squared_error\n",
" 'binary_crossentropy', \n",
" 'binary_crossentropy'\n",
"])"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Spectrogram encoder output: (75, 384)\n",
"Spectrogram encoder:\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"spectrogram_input (InputLaye (None, 80, 299) 0 \n",
"_________________________________________________________________\n",
"reshape_8 (Reshape) (None, 80, 299, 1) 0 \n",
"_________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, 40, 150, 64) 1664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_26 (B (None, 40, 150, 64) 256 \n",
"_________________________________________________________________\n",
"leaky_re_lu_30 (LeakyReLU) (None, 40, 150, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_17 (Conv2D) (None, 40, 150, 64) 65600 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_27 (B (None, 40, 150, 64) 256 \n",
"_________________________________________________________________\n",
"leaky_re_lu_31 (LeakyReLU) (None, 40, 150, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 20, 75, 128) 131200 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_28 (B (None, 20, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_32 (LeakyReLU) (None, 20, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, 10, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_29 (B (None, 10, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_33 (LeakyReLU) (None, 10, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_20 (Conv2D) (None, 5, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_30 (B (None, 5, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_34 (LeakyReLU) (None, 5, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_21 (Conv2D) (None, 3, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_31 (B (None, 3, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_35 (LeakyReLU) (None, 3, 75, 128) 0 \n",
"_________________________________________________________________\n",
"permute_4 (Permute) (None, 75, 3, 128) 0 \n",
"_________________________________________________________________\n",
"time_distributed_13 (TimeDis (None, 75, 384) 0 \n",
"=================================================================\n",
"Total params: 398,016\n",
"Trainable params: 396,736\n",
"Non-trainable params: 1,280\n",
"_________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n",
"Video encoder output: (75, 512)\n",
"Video encoder:\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"video_input (InputLayer) (None, 75, 30, 75) 0 \n",
"_________________________________________________________________\n",
"reshape_11 (Reshape) (None, 75, 30, 75, 1) 0 \n",
"_________________________________________________________________\n",
"zero1 (ZeroPadding3D) (None, 77, 36, 81, 1) 0 \n",
"_________________________________________________________________\n",
"conv1 (Conv3D) (None, 75, 16, 39, 32) 2432 \n",
"_________________________________________________________________\n",
"batc1 (BatchNormalizationV1) (None, 75, 16, 39, 32) 128 \n",
"_________________________________________________________________\n",
"actv1 (Activation) (None, 75, 16, 39, 32) 0 \n",
"_________________________________________________________________\n",
"spatial_dropout3d_6 (Spatial (None, 75, 16, 39, 32) 0 \n",
"_________________________________________________________________\n",
"max1 (MaxPooling3D) (None, 75, 8, 19, 32) 0 \n",
"_________________________________________________________________\n",
"zero2 (ZeroPadding3D) (None, 77, 12, 23, 32) 0 \n",
"_________________________________________________________________\n",
"conv2 (Conv3D) (None, 75, 8, 19, 64) 153664 \n",
"_________________________________________________________________\n",
"batc2 (BatchNormalizationV1) (None, 75, 8, 19, 64) 256 \n",
"_________________________________________________________________\n",
"actv2 (Activation) (None, 75, 8, 19, 64) 0 \n",
"_________________________________________________________________\n",
"spatial_dropout3d_7 (Spatial (None, 75, 8, 19, 64) 0 \n",
"_________________________________________________________________\n",
"max2 (MaxPooling3D) (None, 75, 4, 9, 64) 0 \n",
"_________________________________________________________________\n",
"zero3 (ZeroPadding3D) (None, 77, 6, 11, 64) 0 \n",
"_________________________________________________________________\n",
"conv3 (Conv3D) (None, 75, 4, 9, 92) 159068 \n",
"_________________________________________________________________\n",
"batc3 (BatchNormalizationV1) (None, 75, 4, 9, 92) 368 \n",
"_________________________________________________________________\n",
"actv3 (Activation) (None, 75, 4, 9, 92) 0 \n",
"_________________________________________________________________\n",
"spatial_dropout3d_8 (Spatial (None, 75, 4, 9, 92) 0 \n",
"_________________________________________________________________\n",
"max3 (MaxPooling3D) (None, 75, 2, 4, 92) 0 \n",
"_________________________________________________________________\n",
"time_distributed_12 (TimeDis (None, 75, 736) 0 \n",
"_________________________________________________________________\n",
"bidirectional_2 (Bidirection (None, 75, 512) 1525248 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 75, 512) 0 \n",
"=================================================================\n",
"Total params: 1,841,164\n",
"Trainable params: 1,840,788\n",
"Non-trainable params: 376\n",
"_________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n",
"Embedding Shape: (None, 75, 896)\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
"\n",
"Spectrogram Latent:\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_5 (InputLayer) (None, 75, 896) 0 \n",
"_________________________________________________________________\n",
"time_distributed_14 (TimeDis (None, 75, 384) 344448 \n",
"_________________________________________________________________\n",
"time_distributed_15 (TimeDis (None, 75, 384) 1536 \n",
"_________________________________________________________________\n",
"time_distributed_16 (TimeDis (None, 75, 384) 0 \n",
"_________________________________________________________________\n",
"time_distributed_17 (TimeDis (None, 75, 3, 128) 0 \n",
"_________________________________________________________________\n",
"permute_5 (Permute) (None, 3, 75, 128) 0 \n",
"=================================================================\n",
"Total params: 345,984\n",
"Trainable params: 345,216\n",
"Non-trainable params: 768\n",
"_________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Spectrogram decoder output: (80, 299)\n",
"Spectrogram decoder:\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_6 (InputLayer) (None, 3, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_14 (Conv2DT (None, 6, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_33 (B (None, 6, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_37 (LeakyReLU) (None, 6, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_15 (Conv2DT (None, 12, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_34 (B (None, 12, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_38 (LeakyReLU) (None, 12, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_16 (Conv2DT (None, 24, 75, 128) 65664 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_35 (B (None, 24, 75, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_39 (LeakyReLU) (None, 24, 75, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_17 (Conv2DT (None, 48, 150, 128) 262272 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_36 (B (None, 48, 150, 128) 512 \n",
"_________________________________________________________________\n",
"leaky_re_lu_40 (LeakyReLU) (None, 48, 150, 128) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_18 (Conv2DT (None, 48, 150, 64) 131136 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_37 (B (None, 48, 150, 64) 256 \n",
"_________________________________________________________________\n",
"leaky_re_lu_41 (LeakyReLU) (None, 48, 150, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_19 (Conv2DT (None, 96, 300, 64) 102464 \n",
"_________________________________________________________________\n",
"batch_normalization_v1_38 (B (None, 96, 300, 64) 256 \n",
"_________________________________________________________________\n",
"leaky_re_lu_42 (LeakyReLU) (None, 96, 300, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_transpose_20 (Conv2DT (None, 96, 300, 1) 65 \n",
"_________________________________________________________________\n",
"cropping2d_2 (Cropping2D) (None, 80, 299, 1) 0 \n",
"_________________________________________________________________\n",
"spectrogram_output (Reshape) (None, 80, 299) 0 \n",
"=================================================================\n",
"Total params: 695,489\n",
"Trainable params: 694,209\n",
"Non-trainable params: 1,280\n",
"_________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n",
"Discriminator:\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"discriminator_input (InputLa (None, 80, 299) 0 \n",
"_________________________________________________________________\n",
"reshape_10 (Reshape) (None, 80, 299, 1) 0 \n",
"_________________________________________________________________\n",
"conv2d_22 (Conv2D) (None, 40, 150, 32) 544 \n",
"_________________________________________________________________\n",
"leaky_re_lu_43 (LeakyReLU) (None, 40, 150, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_23 (Conv2D) (None, 20, 75, 64) 32832 \n",
"_________________________________________________________________\n",
"leaky_re_lu_44 (LeakyReLU) (None, 20, 75, 64) 0 \n",
"_________________________________________________________________\n",
"flatten_8 (Flatten) (None, 96000) 0 \n",
"_________________________________________________________________\n",
"dense_5 (Dense) (None, 1) 96001 \n",
"=================================================================\n",
"Total params: 129,377\n",
"Trainable params: 129,377\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n",
"Discriminator on model:\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"video_input (InputLayer) (None, 75, 30, 75) 0 \n",
"__________________________________________________________________________________________________\n",
"spectrogram_input (InputLayer) (None, 80, 299) 0 \n",
"__________________________________________________________________________________________________\n",
"reshape_11 (Reshape) (None, 75, 30, 75, 1 0 video_input[0][0] \n",
"__________________________________________________________________________________________________\n",
"reshape_8 (Reshape) (None, 80, 299, 1) 0 spectrogram_input[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero1 (ZeroPadding3D) (None, 77, 36, 81, 1 0 reshape_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, 40, 150, 64) 1664 reshape_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1 (Conv3D) (None, 75, 16, 39, 3 2432 zero1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_26 (Batc (None, 40, 150, 64) 256 conv2d_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc1 (BatchNormalizationV1) (None, 75, 16, 39, 3 128 conv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_30 (LeakyReLU) (None, 40, 150, 64) 0 batch_normalization_v1_26[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv1 (Activation) (None, 75, 16, 39, 3 0 batc1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_17 (Conv2D) (None, 40, 150, 64) 65600 leaky_re_lu_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_6 (SpatialDro (None, 75, 16, 39, 3 0 actv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_27 (Batc (None, 40, 150, 64) 256 conv2d_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"max1 (MaxPooling3D) (None, 75, 8, 19, 32 0 spatial_dropout3d_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_31 (LeakyReLU) (None, 40, 150, 64) 0 batch_normalization_v1_27[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero2 (ZeroPadding3D) (None, 77, 12, 23, 3 0 max1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 20, 75, 128) 131200 leaky_re_lu_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2 (Conv3D) (None, 75, 8, 19, 64 153664 zero2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_28 (Batc (None, 20, 75, 128) 512 conv2d_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc2 (BatchNormalizationV1) (None, 75, 8, 19, 64 256 conv2[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_32 (LeakyReLU) (None, 20, 75, 128) 0 batch_normalization_v1_28[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv2 (Activation) (None, 75, 8, 19, 64 0 batc2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, 10, 75, 128) 65664 leaky_re_lu_32[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_7 (SpatialDro (None, 75, 8, 19, 64 0 actv2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_29 (Batc (None, 10, 75, 128) 512 conv2d_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"max2 (MaxPooling3D) (None, 75, 4, 9, 64) 0 spatial_dropout3d_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_33 (LeakyReLU) (None, 10, 75, 128) 0 batch_normalization_v1_29[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero3 (ZeroPadding3D) (None, 77, 6, 11, 64 0 max2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_20 (Conv2D) (None, 5, 75, 128) 65664 leaky_re_lu_33[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv3 (Conv3D) (None, 75, 4, 9, 92) 159068 zero3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_30 (Batc (None, 5, 75, 128) 512 conv2d_20[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc3 (BatchNormalizationV1) (None, 75, 4, 9, 92) 368 conv3[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_34 (LeakyReLU) (None, 5, 75, 128) 0 batch_normalization_v1_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv3 (Activation) (None, 75, 4, 9, 92) 0 batc3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_21 (Conv2D) (None, 3, 75, 128) 65664 leaky_re_lu_34[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_8 (SpatialDro (None, 75, 4, 9, 92) 0 actv3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_31 (Batc (None, 3, 75, 128) 512 conv2d_21[0][0] \n",
"__________________________________________________________________________________________________\n",
"max3 (MaxPooling3D) (None, 75, 2, 4, 92) 0 spatial_dropout3d_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_35 (LeakyReLU) (None, 3, 75, 128) 0 batch_normalization_v1_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_12 (TimeDistri (None, 75, 736) 0 max3[0][0] \n",
"__________________________________________________________________________________________________\n",
"permute_4 (Permute) (None, 75, 3, 128) 0 leaky_re_lu_35[0][0] \n",
"__________________________________________________________________________________________________\n",
"bidirectional_2 (Bidirectional) (None, 75, 512) 1525248 time_distributed_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_13 (TimeDistri (None, 75, 384) 0 permute_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_2 (Dropout) (None, 75, 512) 0 bidirectional_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_2 (Concatenate) (None, 75, 896) 0 time_distributed_13[0][0] \n",
" dropout_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_14 (TimeDistri (None, 75, 384) 344448 concatenate_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_15 (TimeDistri (None, 75, 384) 1536 time_distributed_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_16 (TimeDistri (None, 75, 384) 0 time_distributed_15[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_17 (TimeDistri (None, 75, 3, 128) 0 time_distributed_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"permute_5 (Permute) (None, 3, 75, 128) 0 time_distributed_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_14 (Conv2DTran (None, 6, 75, 128) 65664 permute_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_33 (Batc (None, 6, 75, 128) 512 conv2d_transpose_14[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_37 (LeakyReLU) (None, 6, 75, 128) 0 batch_normalization_v1_33[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_15 (Conv2DTran (None, 12, 75, 128) 65664 leaky_re_lu_37[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_34 (Batc (None, 12, 75, 128) 512 conv2d_transpose_15[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_38 (LeakyReLU) (None, 12, 75, 128) 0 batch_normalization_v1_34[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_16 (Conv2DTran (None, 24, 75, 128) 65664 leaky_re_lu_38[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_35 (Batc (None, 24, 75, 128) 512 conv2d_transpose_16[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_39 (LeakyReLU) (None, 24, 75, 128) 0 batch_normalization_v1_35[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_17 (Conv2DTran (None, 48, 150, 128) 262272 leaky_re_lu_39[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_36 (Batc (None, 48, 150, 128) 512 conv2d_transpose_17[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_40 (LeakyReLU) (None, 48, 150, 128) 0 batch_normalization_v1_36[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_18 (Conv2DTran (None, 48, 150, 64) 131136 leaky_re_lu_40[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_37 (Batc (None, 48, 150, 64) 256 conv2d_transpose_18[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_41 (LeakyReLU) (None, 48, 150, 64) 0 batch_normalization_v1_37[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_19 (Conv2DTran (None, 96, 300, 64) 102464 leaky_re_lu_41[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_38 (Batc (None, 96, 300, 64) 256 conv2d_transpose_19[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_42 (LeakyReLU) (None, 96, 300, 64) 0 batch_normalization_v1_38[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_20 (Conv2DTran (None, 96, 300, 1) 65 leaky_re_lu_42[1][0] \n",
"__________________________________________________________________________________________________\n",
"cropping2d_2 (Cropping2D) (None, 80, 299, 1) 0 conv2d_transpose_20[1][0] \n",
"__________________________________________________________________________________________________\n",
"spectrogram_output (Reshape) (None, 80, 299) 0 cropping2d_2[1][0] \n",
"__________________________________________________________________________________________________\n",
"reshape_10 (Reshape) (None, 80, 299, 1) 0 spectrogram_output[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_22 (Conv2D) (None, 40, 150, 32) 544 reshape_10[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_43 (LeakyReLU) (None, 40, 150, 32) 0 conv2d_22[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_23 (Conv2D) (None, 20, 75, 64) 32832 leaky_re_lu_43[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_44 (LeakyReLU) (None, 20, 75, 64) 0 conv2d_23[1][0] \n",
"__________________________________________________________________________________________________\n",
"flatten_8 (Flatten) (None, 96000) 0 leaky_re_lu_44[1][0] \n",
"__________________________________________________________________________________________________\n",
"dense_5 (Dense) (None, 1) 96001 flatten_8[1][0] \n",
"==================================================================================================\n",
"Total params: 3,410,030\n",
"Trainable params: 3,406,326\n",
"Non-trainable params: 3,704\n",
"__________________________________________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n",
"/usr/local/lib/python3.6/site-packages/numpy/lib/type_check.py:546: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead\n",
" 'a.item() instead', DeprecationWarning, stacklevel=1)\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"<dtype: 'complex64'>\n",
"Full Model:\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"video_input (InputLayer) (None, 75, 30, 75) 0 \n",
"__________________________________________________________________________________________________\n",
"spectrogram_input (InputLayer) (None, 80, 299) 0 \n",
"__________________________________________________________________________________________________\n",
"reshape_11 (Reshape) (None, 75, 30, 75, 1 0 video_input[0][0] \n",
"__________________________________________________________________________________________________\n",
"reshape_8 (Reshape) (None, 80, 299, 1) 0 spectrogram_input[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero1 (ZeroPadding3D) (None, 77, 36, 81, 1 0 reshape_11[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, 40, 150, 64) 1664 reshape_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1 (Conv3D) (None, 75, 16, 39, 3 2432 zero1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_26 (Batc (None, 40, 150, 64) 256 conv2d_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc1 (BatchNormalizationV1) (None, 75, 16, 39, 3 128 conv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_30 (LeakyReLU) (None, 40, 150, 64) 0 batch_normalization_v1_26[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv1 (Activation) (None, 75, 16, 39, 3 0 batc1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_17 (Conv2D) (None, 40, 150, 64) 65600 leaky_re_lu_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_6 (SpatialDro (None, 75, 16, 39, 3 0 actv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_27 (Batc (None, 40, 150, 64) 256 conv2d_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"max1 (MaxPooling3D) (None, 75, 8, 19, 32 0 spatial_dropout3d_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_31 (LeakyReLU) (None, 40, 150, 64) 0 batch_normalization_v1_27[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero2 (ZeroPadding3D) (None, 77, 12, 23, 3 0 max1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 20, 75, 128) 131200 leaky_re_lu_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2 (Conv3D) (None, 75, 8, 19, 64 153664 zero2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_28 (Batc (None, 20, 75, 128) 512 conv2d_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc2 (BatchNormalizationV1) (None, 75, 8, 19, 64 256 conv2[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_32 (LeakyReLU) (None, 20, 75, 128) 0 batch_normalization_v1_28[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv2 (Activation) (None, 75, 8, 19, 64 0 batc2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, 10, 75, 128) 65664 leaky_re_lu_32[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_7 (SpatialDro (None, 75, 8, 19, 64 0 actv2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_29 (Batc (None, 10, 75, 128) 512 conv2d_19[0][0] \n",
"__________________________________________________________________________________________________\n",
"max2 (MaxPooling3D) (None, 75, 4, 9, 64) 0 spatial_dropout3d_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_33 (LeakyReLU) (None, 10, 75, 128) 0 batch_normalization_v1_29[0][0] \n",
"__________________________________________________________________________________________________\n",
"zero3 (ZeroPadding3D) (None, 77, 6, 11, 64 0 max2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_20 (Conv2D) (None, 5, 75, 128) 65664 leaky_re_lu_33[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv3 (Conv3D) (None, 75, 4, 9, 92) 159068 zero3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_30 (Batc (None, 5, 75, 128) 512 conv2d_20[0][0] \n",
"__________________________________________________________________________________________________\n",
"batc3 (BatchNormalizationV1) (None, 75, 4, 9, 92) 368 conv3[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_34 (LeakyReLU) (None, 5, 75, 128) 0 batch_normalization_v1_30[0][0] \n",
"__________________________________________________________________________________________________\n",
"actv3 (Activation) (None, 75, 4, 9, 92) 0 batc3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_21 (Conv2D) (None, 3, 75, 128) 65664 leaky_re_lu_34[0][0] \n",
"__________________________________________________________________________________________________\n",
"spatial_dropout3d_8 (SpatialDro (None, 75, 4, 9, 92) 0 actv3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_31 (Batc (None, 3, 75, 128) 512 conv2d_21[0][0] \n",
"__________________________________________________________________________________________________\n",
"max3 (MaxPooling3D) (None, 75, 2, 4, 92) 0 spatial_dropout3d_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_35 (LeakyReLU) (None, 3, 75, 128) 0 batch_normalization_v1_31[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_12 (TimeDistri (None, 75, 736) 0 max3[0][0] \n",
"__________________________________________________________________________________________________\n",
"permute_4 (Permute) (None, 75, 3, 128) 0 leaky_re_lu_35[0][0] \n",
"__________________________________________________________________________________________________\n",
"bidirectional_2 (Bidirectional) (None, 75, 512) 1525248 time_distributed_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_13 (TimeDistri (None, 75, 384) 0 permute_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_2 (Dropout) (None, 75, 512) 0 bidirectional_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_2 (Concatenate) (None, 75, 896) 0 time_distributed_13[0][0] \n",
" dropout_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_14 (TimeDistri (None, 75, 384) 344448 concatenate_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_15 (TimeDistri (None, 75, 384) 1536 time_distributed_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_16 (TimeDistri (None, 75, 384) 0 time_distributed_15[0][0] \n",
"__________________________________________________________________________________________________\n",
"time_distributed_17 (TimeDistri (None, 75, 3, 128) 0 time_distributed_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"permute_5 (Permute) (None, 3, 75, 128) 0 time_distributed_17[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_14 (Conv2DTran (None, 6, 75, 128) 65664 permute_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_33 (Batc (None, 6, 75, 128) 512 conv2d_transpose_14[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_37 (LeakyReLU) (None, 6, 75, 128) 0 batch_normalization_v1_33[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_15 (Conv2DTran (None, 12, 75, 128) 65664 leaky_re_lu_37[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_34 (Batc (None, 12, 75, 128) 512 conv2d_transpose_15[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_38 (LeakyReLU) (None, 12, 75, 128) 0 batch_normalization_v1_34[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_16 (Conv2DTran (None, 24, 75, 128) 65664 leaky_re_lu_38[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_35 (Batc (None, 24, 75, 128) 512 conv2d_transpose_16[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_39 (LeakyReLU) (None, 24, 75, 128) 0 batch_normalization_v1_35[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_17 (Conv2DTran (None, 48, 150, 128) 262272 leaky_re_lu_39[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_36 (Batc (None, 48, 150, 128) 512 conv2d_transpose_17[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_40 (LeakyReLU) (None, 48, 150, 128) 0 batch_normalization_v1_36[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_18 (Conv2DTran (None, 48, 150, 64) 131136 leaky_re_lu_40[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_37 (Batc (None, 48, 150, 64) 256 conv2d_transpose_18[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_41 (LeakyReLU) (None, 48, 150, 64) 0 batch_normalization_v1_37[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_19 (Conv2DTran (None, 96, 300, 64) 102464 leaky_re_lu_41[1][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_v1_38 (Batc (None, 96, 300, 64) 256 conv2d_transpose_19[1][0] \n",
"__________________________________________________________________________________________________\n",
"leaky_re_lu_42 (LeakyReLU) (None, 96, 300, 64) 0 batch_normalization_v1_38[1][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_20 (Conv2DTran (None, 96, 300, 1) 65 leaky_re_lu_42[1][0] \n",
"__________________________________________________________________________________________________\n",
"cropping2d_2 (Cropping2D) (None, 80, 299, 1) 0 conv2d_transpose_20[1][0] \n",
"__________________________________________________________________________________________________\n",
"spectrogram_output (Reshape) (None, 80, 299) 0 cropping2d_2[1][0] \n",
"__________________________________________________________________________________________________\n",
"phase_input (InputLayer) (None, 442, 299) 0 \n",
"__________________________________________________________________________________________________\n",
"reconstruct_audio (Lambda) (None, 65664) 0 spectrogram_output[1][0] \n",
" phase_input[0][0] \n",
"==================================================================================================\n",
"Total params: 3,280,653\n",
"Trainable params: 3,276,949\n",
"Non-trainable params: 3,704\n",
"__________________________________________________________________________________________________\n",
"None\n",
"\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(None, None, None)"
]
},
"metadata": {
"tags": []
},
"execution_count": 103
}
]
},
{
"metadata": {
"id": "hLL_ydwVICNN",
"colab_type": "code",
"pycharm": {},
"outputId": "73185117-05e2-4346-bb3a-634b8fc41fff",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"data = next(autoencoder.get_data())\n",
"\n",
"size = 0\n",
"\n",
"for v in data.values():\n",
" size += v.nbytes\n",
"\n",
"print(\"Size: \", size / 10**6, \"MB\")"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Size: 3.6011 MB\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "3cg5_fYA3B5Y",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Fit the model using a generator."
]
},
{
"metadata": {
"id": "cHSZQRC8QEup",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Should train for testing with `autoencoder.train(data_limit=200, data_batch=20, steps_per_epoch=10, epochs=10)`."
]
},
{
"metadata": {
"id": "rKwZkytZrlpK",
"colab_type": "code",
"pycharm": {},
"outputId": "2836df38-ff59-4404-e444-556958abb17b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 884
}
},
"cell_type": "code",
"source": [
"#history = ae_model.fit_generator(get_data(1000, 10), steps_per_epoch = 5, epochs=20, verbose=2)\n",
"\n",
"ae_hist, discrim_hist, d_on_ae_hist = autoencoder.train(data_limit=200, data_batch=20, steps_per_epoch=10, epochs=50, autoencoder_reps=3, discriminator_reps=5, d_on_ae_reps=1)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"100% (10 of 10) |########################| Elapsed Time: 0:01:31 Time: 0:01:31\n",
"100% (10 of 10) |########################| Elapsed Time: 0:00:47 Time: 0:00:47\n",
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"100% (10 of 10) |########################| Elapsed Time: 0:00:47 Time: 0:00:47\n",
"100% (10 of 10) |########################| Elapsed Time: 0:00:47 Time: 0:00:47\n",
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"100% (10 of 10) |########################| Elapsed Time: 0:00:47 Time: 0:00:47\n",
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"100% (50 of 50) |########################| Elapsed Time: 0:40:19 Time: 0:40:19\n"
],
"name": "stderr"
}
]
},
{
"metadata": {
"id": "bXwWO-eReua-",
"colab_type": "code",
"pycharm": {},
"outputId": "7c40c8d3-4c6e-41e0-86f4-2bd4b6724f64",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
}
},
"cell_type": "code",
"source": [
"plt.plot(ae_hist,label='Autoencoder')\n",
"plt.plot(discrim_hist, label='Discriminator')\n",
"plt.plot(d_on_ae_hist, label='Together')\n",
"#plt.plot(history.history['val_loss'], label='val_loss')\n",
"plt.legend()\n",
"plt.xlabel('Step')\n",
"plt.ylabel('Loss')\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "YC4yK7FK34qg",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Losses after the 100th step"
]
},
{
"metadata": {
"id": "Uiokrr8J36ks",
"colab_type": "code",
"outputId": "3b02e3eb-f52e-4e4d-f930-6eaa199bf586",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 283
}
},
"cell_type": "code",
"source": [
"mean = np.mean(ae_hist) + np.mean(discrim_hist) + np.mean(d_on_ae_hist)\n",
"mean /= 3\n",
"\n",
"limit = mean * 2\n",
"\n",
"start = 50\n",
"for i in range(len(ae_hist)):\n",
" if ae_hist[i] <= limit and discrim_hist[i] <= limit and d_on_ae_hist[i] <= limit:\n",
" start = i\n",
"\n",
"start = 40\n",
" \n",
"plt.plot(ae_hist[start:],label='Autoencoder')\n",
"plt.plot(discrim_hist[start:], label='Discriminator')\n",
"plt.plot(d_on_ae_hist[start:], label='Together')\n",
"#plt.plot(history.history['val_loss'], label='val_loss')\n",
"plt.legend()\n",
"plt.xlabel('Step')\n",
"plt.ylabel('Loss')\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "fWLje2N33GX7",
"colab_type": "text",
"pycharm": {}
},
"cell_type": "markdown",
"source": [
"Here is a sample output:"
]
},
{
"metadata": {
"id": "OkMYsWtpmrPl",
"colab_type": "code",
"pycharm": {},
"colab": {}
},
"cell_type": "code",
"source": [
"def clean(av, use_video):\n",
" if use_video:\n",
" return reconstruct_audio(spec.predict([[av.noisy_spectrogram()], [av.video_data()]])[0], av.phase())\n",
" else:\n",
" return model.predict([[av.noisy_spectrogram()], [av.phase()]])[0]"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ytszGuuDezYw",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Testing"
]
},
{
"metadata": {
"id": "Ak2KW608fCBM",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Test on seen data"
]
},
{
"metadata": {
"id": "zNUkuYGKfu5q",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Graphs"
]
},
{
"metadata": {
"id": "JvwlI25B7AxD",
"colab_type": "code",
"pycharm": {},
"cellView": "form",
"outputId": "87b41ebc-6dd2-4d4f-e0d6-464812a32128",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1007
}
},
"cell_type": "code",
"source": [
"#@title\n",
"import librosa.display, librosa.output\n",
"\n",
"\n",
"\n",
"gen = get_audio_and_video()\n",
"#next(gen)\n",
"test = next(gen)\n",
"\n",
"print(\"Known\")\n",
"audio = test.audio_data()\n",
"librosa.display.waveplot(audio)\n",
"plt.show()\n",
"\n",
"print(\"Known Spectrogram\")\n",
"plt.imshow(test.spectrogram())\n",
"plt.show()\n",
"\n",
"print(\"Noisy\")\n",
"noisy = test.noisy_audio()\n",
"\n",
"librosa.display.waveplot(noisy)\n",
"plt.show()\n",
"\n",
"# if autoencoder.use_video:\n",
"# p = spec.predict([[test.noisy_spectrogram()], [test.video()]])[0]\n",
"# else:\n",
"# p = spec.predict([[test.noisy_spectrogram()]])[0]\n",
"\n",
"p = clean(test, autoencoder.use_video)\n",
"\n",
"# print(\"Spectrogram\")\n",
"# plt.imshow(p)\n",
"# plt.show()\n",
"\n",
"# p = reconstruct_audio(p, test.phase())\n",
"\n",
"print(\"Predicted\")\n",
"librosa.display.waveplot(p)\n",
"plt.show()\n",
"\n",
"\n",
"# Use facial point recognition instead of video\n",
"# Jane Zang doing similar research"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Known\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Known Spectrogram\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Noisy\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"Predicted\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "SO9tgJ9wfq6p",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Audio"
]
},
{
"metadata": {
"id": "VfWjzdU4vD_A",
"colab_type": "code",
"pycharm": {},
"cellView": "form",
"outputId": "8b348aeb-c4f1-43ca-d613-27bcd124a8b2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
}
},
"cell_type": "code",
"source": [
"#@title\n",
"\n",
"all_validation_audio = np.concatenate((test.audio_data(), test.noisy_audio(), p))\n",
"ipy_display.display(ipy_display.Audio(all_validation_audio, rate=framerate))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.lib.display.Audio object>"
],
"text/html": [
"\n",
" <audio controls=\"controls\" >\n",
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