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@fgolemo
Created March 1, 2022 22:28
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Omniglot toy example for Autobots: Latent Variable Sequential Set Transformers
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "autobot_omniglot.ipynb",
"provenance": [],
"collapsed_sections": [
"vHBx9L924APp",
"jl8rVC3x4q0M",
"qihb0PM-4kte",
"5yRdFh2R48_P",
"6Qf-IiT8646l",
"8m8q_EM_7DuV"
]
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "dX5V-W5036Cq"
},
"source": [
"# Autobot for Character Generation"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MIVW1za6R_42"
},
"source": [
"This Jupyter notebook contains a standalone demonstration of the AutoBot method/architecture applied to the Omniglot stroke-completion task (\"task 1\" in the paper). This means, we download the Omniglot stroke dataset, and train to complete character strokes by showing the model the first half of a stroke and training it to predict the second half."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XOyNUeBDWQu4"
},
"source": [
"This notebook has the following sections:\n",
"\n",
"\n",
"\n",
"1. [Downloading the Omniglot dataset and preparing the data](#scrollTo=vHBx9L924APp)\n",
"2. [Creating a dataloader for Pytorch to prepare the data for training/testing](#scrollTo=jl8rVC3x4q0M)\n",
"3. [Setting up the Autobot model](#scrollTo=qihb0PM-4kte)\n",
"4. [Setting up the loss functions](#scrollTo=5yRdFh2R48_P)\n",
"5. [Training the model (est 45-60min)](#scrollTo=6Qf-IiT8646l)\n",
"6. [Plot/test functions to generate qualitative samples from the trained model on the test dataset](#scrollTo=8m8q_EM_7DuV)\n",
"\n",
"This notebook should contain a trained model and if you scroll all the way to the end of the test section, there should be sample images. If that's not the case, please rerun the entire notebook.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vHBx9L924APp"
},
"source": [
"## Setting Up the Omniglot dataset\n",
"In the section, we download and process the Omniglot dataset to generate and training and testing partitions."
]
},
{
"cell_type": "code",
"metadata": {
"id": "gGs7GVjbqmy1"
},
"source": [
"!git clone https://github.com/brendenlake/omniglot.git\n",
"!unzip \"omniglot/python/images_background.zip\"\n",
"!unzip \"omniglot/python/images_evaluation.zip\"\n",
"!unzip \"omniglot/python/strokes_background.zip\"\n",
"!unzip \"omniglot/python/strokes_evaluation.zip\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "LZBPUhfLsOh0",
"outputId": "697c4fc4-3375-43f3-c384-366b4b055aa6"
},
"source": [
"import numpy as np\n",
"import os\n",
"import random\n",
"from sys import platform as sys_pf\n",
"\n",
"\n",
"# Color map for the stroke of index k\n",
"def get_color(k):\n",
" scol = ['r', 'g', 'b', 'm', 'c']\n",
" ncol = len(scol)\n",
" if k < ncol:\n",
" out = scol[k]\n",
" else:\n",
" out = scol[-1]\n",
" return out\n",
"\n",
"\n",
"# convert to str and add leading zero to single digit numbers\n",
"def num2str(idx):\n",
" if idx < 10:\n",
" return '0' + str(idx)\n",
" return str(idx)\n",
"\n",
"\n",
"# Load binary image for a character\n",
"#\n",
"# fn : filename\n",
"def load_img(fn):\n",
" I = plt.imread(fn)\n",
" I = np.array(I, dtype=bool)\n",
" return I\n",
"\n",
"\n",
"# Load stroke data for a character from text file\n",
"#\n",
"# Input\n",
"# fn : filename\n",
"#\n",
"# Output\n",
"# motor : list of strokes (each is a [n x 3] numpy array)\n",
"# first two columns are coordinates\n",
"#\t the last column is the timing data (in milliseconds)\n",
"def load_motor(fn):\n",
" motor = []\n",
" with open(fn, 'r') as fid:\n",
" lines = fid.readlines()\n",
" lines = [l.strip() for l in lines]\n",
" for myline in lines:\n",
" if myline == 'START': # beginning of character\n",
" stk = []\n",
" elif myline == 'BREAK': # break between strokes\n",
" stk = np.array(stk)\n",
" motor.append(stk) # add to list of strokes\n",
" stk = []\n",
" else:\n",
" arr = np.fromstring(myline, dtype=float, sep=',')\n",
" stk.append(arr)\n",
" return motor\n",
"\n",
"\n",
"def space_motor_to_img(pt):\n",
" pt[:, 1] = -pt[:, 1]\n",
" return pt\n",
"\n",
"\n",
"def space_img_to_motor(pt):\n",
" pt[:, 1] = -pt[:, 1]\n",
" return\n",
"\n",
"\n",
"for stroke_dir in ['strokes_background', 'strokes_evaluation']:\n",
" num_strokes = 0\n",
" max_num_strokes = 0\n",
" num_valid_strokes = 0 # valid data needs to have more than one stroke and the length of each stroke should be more than 20 steps\n",
" stroke_lengths = []\n",
" train_valid_fnames = []\n",
" test_valid_fnames = []\n",
"\n",
" alphabet_names = [a for a in os.listdir(stroke_dir) if a[0] != '.'] # get folder names\n",
"\n",
" for alpha_name in alphabet_names: # for each alphabet\n",
"\n",
" char_dirs = sorted(os.listdir(os.path.join(stroke_dir, alpha_name)))\n",
" for char_dir in char_dirs:\n",
" if \"char\" not in char_dir: # protect against useless folders, .DS_STORE\n",
" continue\n",
"\n",
" char_renditions = os.listdir(os.path.join(stroke_dir, alpha_name, char_dir))\n",
" valid_data = []\n",
" for char_rendition in char_renditions:\n",
" fname_stroke = os.path.join(stroke_dir, alpha_name, char_dir, char_rendition)\n",
" strokes = load_motor(fname_stroke)\n",
" num_strokes += 1\n",
"\n",
" if len(strokes) > 1:\n",
" valid_strokes = True\n",
" for stroke in strokes:\n",
" if len(stroke) < 10 or len(stroke) > 100:\n",
" valid_strokes = False\n",
" break\n",
"\n",
" if valid_strokes:\n",
" num_valid_strokes += 1\n",
" if len(strokes) > max_num_strokes:\n",
" max_num_strokes = len(strokes)\n",
"\n",
" for stroke in strokes:\n",
" stroke_lengths.append(len(stroke))\n",
"\n",
" valid_data.append(fname_stroke)\n",
"\n",
" if len(valid_data) > 0:\n",
" if len(valid_data) > 3:\n",
" num_train = int(0.75*len(valid_data))\n",
" train_valid_fnames += valid_data[:num_train]\n",
" test_valid_fnames += valid_data[num_train:]\n",
" else:\n",
" train_valid_fnames += valid_data\n",
"\n",
" print(\"Number of points\", num_strokes)\n",
" print(\"Number of valid points\", num_valid_strokes)\n",
" print(\"Max Number of strokes\", max_num_strokes)\n",
"\n",
" with open(stroke_dir+'_train.txt', 'w') as f:\n",
" for item in train_valid_fnames:\n",
" f.write(\"%s\\n\" % item)\n",
"\n",
" with open(stroke_dir+'_test.txt', 'w') as f:\n",
" for item in test_valid_fnames:\n",
" f.write(\"%s\\n\" % item)\n"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of points 19280\n",
"Number of valid points 6019\n",
"Max Number of strokes 12\n",
"Number of points 13180\n",
"Number of valid points 4644\n",
"Max Number of strokes 11\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jl8rVC3x4q0M"
},
"source": [
"## Creating a pytorch dataloader"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Xhfn61kk10cy",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"outputId": "b1f843e7-ecd6-4f11-b855-cc51c6924ae1"
},
"source": [
"!pip install -q torch==1.9.0 torchvision==0.10.0"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[K |████████████████████████████████| 831.4 MB 2.7 kB/s \n",
"\u001b[K |████████████████████████████████| 22.1 MB 30.4 MB/s \n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"torchtext 0.11.0 requires torch==1.10.0, but you have torch 1.9.0 which is incompatible.\n",
"torchaudio 0.10.0+cu111 requires torch==1.10.0, but you have torch 1.9.0 which is incompatible.\u001b[0m\n",
"\u001b[?25h"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pdfn8RYUwkNM"
},
"source": [
"import os\n",
"from torch.utils.data import Dataset\n",
"import numpy as np\n",
"from scipy import signal\n",
"\n",
"\n",
"class OmniGlotDataset(Dataset):\n",
" def __init__(self, dset_path=\".\", in_seq_len=10, entire_seq_len=20, split_name='train'):\n",
" self.in_seq_len = in_seq_len\n",
"\n",
" with open(os.path.join(dset_path, \"strokes_background_{}.txt\".format(split_name)), 'r') as fid:\n",
" lines = fid.readlines()\n",
" char_fnames = [l.strip() for l in lines]\n",
"\n",
" with open(os.path.join(dset_path, \"strokes_evaluation_{}.txt\".format(split_name)), 'r') as fid:\n",
" lines = fid.readlines()\n",
" char_fnames += [l.strip() for l in lines]\n",
"\n",
" max_num_strokes = 0\n",
" self.data = []\n",
" self.fnames = []\n",
" for char_fname in char_fnames:\n",
" self.fnames.append(char_fname)\n",
" char_data = self.load_motor(os.path.join(dset_path, char_fname))\n",
" new_char_data = []\n",
" for i in range(len(char_data)):\n",
" new_char_data.append(signal.resample(char_data[i], num=entire_seq_len))\n",
" new_char_data[-1][0] = char_data[i][0]\n",
"\n",
" curr_data = np.array(new_char_data)\n",
" curr_data[:, :, 2] = 1.0\n",
" curr_data = self.normalize(curr_data)\n",
"\n",
" if len(curr_data) > max_num_strokes:\n",
" max_num_strokes = len(curr_data)\n",
" if len(curr_data) < 12:\n",
" new_curr_data = np.zeros((12, entire_seq_len, 3))\n",
" new_curr_data[:len(curr_data)] = curr_data\n",
" self.data.append(new_curr_data)\n",
" else:\n",
" self.data.append(curr_data)\n",
"\n",
" def normalize(self, data):\n",
" min_x = np.min(data[:, :, 0])\n",
" max_x = np.max(data[:, :, 0])\n",
" min_y = np.min(data[:, :, 1])\n",
" max_y = np.max(data[:, :, 1])\n",
"\n",
" data[:, :, 0] -= min_x\n",
" data[:, :, 0] /= (max_x - min_x)\n",
" data[:, :, 0] *= 10.0 # factor for stability\n",
"\n",
" data[:, :, 1] -= min_y\n",
" data[:, :, 1] /= (max_y - min_y)\n",
" data[:, :, 1] *= 10.0 # factor for stability\n",
" return data\n",
"\n",
" def load_motor(self, fn):\n",
" motor = []\n",
" with open(fn, 'r') as fid:\n",
" lines = fid.readlines()\n",
" lines = [l.strip() for l in lines]\n",
" for myline in lines:\n",
" if myline == 'START': # beginning of character\n",
" stk = []\n",
" elif myline == 'BREAK': # break between strokes\n",
" stk = np.array(stk)\n",
" motor.append(stk) # add to list of strokes\n",
" stk = []\n",
" else:\n",
" arr = np.fromstring(myline, dtype=float, sep=',')\n",
" stk.append(arr)\n",
" return motor\n",
"\n",
" def __getitem__(self, idx: int):\n",
" data = self.data[idx].transpose((1, 0, 2))\n",
" past = data[:self.in_seq_len]\n",
" future = data[self.in_seq_len:]\n",
" fname = self.fnames[idx]\n",
" return past, future, fname\n",
"\n",
" def __len__(self):\n",
" return len(self.data)"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "91iVloE0wwn7",
"outputId": "8bdd44c4-09d7-4db5-dcc3-25207e7bbffd"
},
"source": [
"dset = OmniGlotDataset(split_name=\"train\")\n",
"past, future, fname = dset[0]\n",
"print(past.shape)\n",
"print(future.shape)\n",
"print(fname)"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(10, 12, 3)\n",
"(10, 12, 3)\n",
"strokes_background/Asomtavruli_(Georgian)/character01/0068_16.txt\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qihb0PM-4kte"
},
"source": [
"## Model Code"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Hds29xte8Hpl"
},
"source": [
"import math\n",
"\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"\n",
"def init(module, weight_init, bias_init, gain=1):\n",
" weight_init(module.weight.data, gain=gain)\n",
" bias_init(module.bias.data)\n",
" return module\n",
"\n",
"\n",
"class PositionalEncoding(nn.Module):\n",
" def __init__(self, d_model, dropout=0.1, max_len=20):\n",
" super(PositionalEncoding, self).__init__()\n",
" self.dropout = nn.Dropout(p=dropout)\n",
" pe = torch.zeros(max_len, d_model)\n",
" position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n",
" div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n",
" pe[:, 0::2] = torch.sin(position * div_term)\n",
" pe[:, 1::2] = torch.cos(position * div_term)\n",
" pe = pe.unsqueeze(0).transpose(0, 1)\n",
" self.register_buffer('pe', pe)\n",
"\n",
" def forward(self, x):\n",
" '''\n",
" :param x: must be (T, B, H)\n",
" :return:\n",
" '''\n",
" x = x + self.pe[:x.size(0), :]\n",
" return self.dropout(x)\n",
"\n",
"\n",
"class OutputModel(nn.Module):\n",
" def __init__(self, hidden_size=64):\n",
" super(OutputModel, self).__init__()\n",
" self.hidden_size = hidden_size\n",
" init_ = lambda m: init(m, nn.init.xavier_normal_, lambda x: nn.init.constant_(x, 0), np.sqrt(2))\n",
" self.observation_model = nn.Sequential(\n",
" init_(nn.Linear(hidden_size, hidden_size)), nn.ReLU(),\n",
" init_(nn.Linear(hidden_size, hidden_size)), nn.ReLU(),\n",
" init_(nn.Linear(hidden_size, 5))\n",
" )\n",
" self.min_stdev = 0.01\n",
"\n",
" def forward(self, agent_latent_state):\n",
" T = agent_latent_state.shape[0]\n",
" BK = agent_latent_state.shape[1]\n",
" pred_obs = self.observation_model(agent_latent_state.reshape(-1, self.hidden_size)).reshape(T, BK, -1)\n",
" x_mean = pred_obs[:, :, 0]\n",
" y_mean = pred_obs[:, :, 1]\n",
" x_sigma = F.softplus(pred_obs[:, :, 2]) + self.min_stdev\n",
" y_sigma = F.softplus(pred_obs[:, :, 3]) + self.min_stdev\n",
" rho = torch.tanh(pred_obs[:, :, 4]) * 0.9 # for stability\n",
" return torch.stack([x_mean, y_mean, x_sigma, y_sigma, rho], dim=2)\n",
"\n",
"\n",
"class AutoBotJoint(nn.Module):\n",
" def __init__(self, d_k=128, M=5, c=5, T=30, L_enc=1, dropout=0.0, num_heads=16, L_dec=1, tx_hidden_size=384):\n",
" super(AutoBotJoint, self).__init__()\n",
" init_ = lambda m: init(m, nn.init.xavier_normal_, lambda x: nn.init.constant_(x, 0), np.sqrt(2))\n",
"\n",
" self.d_k = d_k\n",
" self.M = M\n",
" self.c = c\n",
" self.T = T\n",
" self.L_enc = L_enc\n",
" self.dropout = dropout\n",
" self.num_heads = num_heads\n",
" self.L_dec = L_dec\n",
" self.tx_hidden_size = tx_hidden_size\n",
"\n",
" # INPUT ENCODERS\n",
" self.agents_dynamic_encoder = nn.Sequential(init_(nn.Linear(2, self.d_k)))\n",
"\n",
" self.social_attn_layers = []\n",
" self.temporal_attn_layers = []\n",
" for _ in range(self.L_enc):\n",
" tx_encoder_layer = nn.TransformerEncoderLayer(d_model=self.d_k, nhead=self.num_heads, dropout=self.dropout, dim_feedforward=self.tx_hidden_size)\n",
" self.temporal_attn_layers.append(nn.TransformerEncoder(tx_encoder_layer, num_layers=1))\n",
" tx_encoder_layer = nn.TransformerEncoderLayer(d_model=self.d_k, nhead=self.num_heads, dropout=self.dropout, dim_feedforward=self.tx_hidden_size)\n",
" self.social_attn_layers.append(nn.TransformerEncoder(tx_encoder_layer, num_layers=1))\n",
"\n",
" self.temporal_attn_layers = nn.ModuleList(self.temporal_attn_layers)\n",
" self.social_attn_layers = nn.ModuleList(self.social_attn_layers)\n",
"\n",
" # DECODER MODELS\n",
" self.Q = nn.Parameter(torch.Tensor(self.T, 1, self.c, 1, self.d_k), requires_grad=True) # Decoder seed parameters\n",
" nn.init.xavier_uniform_(self.Q)\n",
" self.social_attn_decoder_layers = []\n",
" self.temporal_attn_decoder_layers = []\n",
" for _ in range(self.L_dec):\n",
" tx_decoder_layer = nn.TransformerDecoderLayer(d_model=self.d_k, nhead=self.num_heads, dropout=self.dropout, dim_feedforward=self.tx_hidden_size)\n",
" self.temporal_attn_decoder_layers.append(nn.TransformerDecoder(tx_decoder_layer, num_layers=1))\n",
" tx_encoder_layer = nn.TransformerEncoderLayer(d_model=self.d_k, nhead=self.num_heads, dropout=self.dropout, dim_feedforward=self.tx_hidden_size)\n",
" self.social_attn_decoder_layers.append(nn.TransformerEncoder(tx_encoder_layer, num_layers=1))\n",
"\n",
" self.temporal_attn_decoder_layers = nn.ModuleList(self.temporal_attn_decoder_layers)\n",
" self.social_attn_decoder_layers = nn.ModuleList(self.social_attn_decoder_layers)\n",
" self.pos_encoder = PositionalEncoding(self.d_k, dropout=0.0)\n",
"\n",
" # OUTPUT MODEL\n",
" self.output_model = OutputModel(hidden_size=self.d_k)\n",
"\n",
" # Mode Prediction Models\n",
" self.P = nn.Parameter(torch.Tensor(1, self.c, 1, self.d_k), requires_grad=True)\n",
" nn.init.xavier_uniform_(self.P)\n",
" self.prob_decoder = nn.TransformerDecoderLayer(d_model=self.d_k, nhead=self.num_heads, activation=\"relu\", dim_feedforward=self.tx_hidden_size)\n",
" self.prob_predictor = init_(nn.Linear(self.d_k, 1))\n",
" self.train()\n",
"\n",
" def process_observations(self, in_set_seqs):\n",
" '''\n",
" :param in_set_seqs: (B, T, M, k+1)\n",
" where k is the number of attributes; here it's (x,y, mask).\n",
" '''\n",
" in_set_tensors = in_set_seqs[:, :, :, :2]\n",
" in_set_masks = (1.0 - in_set_seqs[:, :, :, 2]).type(torch.BoolTensor).to(in_set_seqs.device)\n",
" return in_set_tensors, in_set_masks\n",
"\n",
" def generate_decoder_mask(self, seq_len, device):\n",
" ''' For masking out the subsequent info. '''\n",
" subsequent_mask = (torch.triu(torch.ones((seq_len, seq_len), device=device), diagonal=1)).bool()\n",
" return subsequent_mask\n",
"\n",
" def temporal_attn_fn(self, agents_emb, agent_masks, layer):\n",
" '''\n",
" :param agents_emb: (t, B, M, d_k)\n",
" :param agent_masks: (B, t, M)\n",
" :return: (t, B, M, d_k)\n",
" '''\n",
" t = agents_emb.size(0)\n",
" B = agent_masks.size(0)\n",
" agent_masks = agent_masks.permute(0, 2, 1).reshape(-1, t)\n",
" agent_masks[:, -1][agent_masks.sum(-1) == t] = False # Ensures that agent's that don't exist don't cause NaNs.\n",
" agents_temp_emb = layer(self.pos_encoder(agents_emb.reshape(t, B * (self.M), -1)), src_key_padding_mask=agent_masks)\n",
" return agents_temp_emb.view(t, B, self.M, -1)\n",
"\n",
" def social_attn_fn(self, agents_emb, agent_masks, layer):\n",
" '''\n",
" :param agents_emb: (t, B, M, d_k)\n",
" :param agent_masks: (B, t, M)\n",
" :return: (t, B, M, d_k)\n",
" '''\n",
" t = agents_emb.size(0)\n",
" B = agent_masks.size(0)\n",
" agents_emb = agents_emb.permute(2, 1, 0, 3).reshape(self.M, B * t, -1)\n",
" agents_soc_emb = layer(agents_emb, src_key_padding_mask=agent_masks.view(-1, self.M))\n",
" agents_soc_emb = agents_soc_emb.view(self.M, B, t, -1).permute(2, 1, 0, 3)\n",
" return agents_soc_emb\n",
"\n",
" def temporal_attn_decoder_fn(self, agents_emb, context, agent_masks, layer):\n",
" '''\n",
" :param agents_emb: (T, Bc, M, d_k)\n",
" :param context: (t, Bc, M, d_k)\n",
" :param agent_masks: (Bc, T, M)\n",
" :return: (T, Bc, M, d_k)\n",
" '''\n",
" t = context.size(0)\n",
" Bc = agent_masks.size(0)\n",
" time_masks = self.generate_decoder_mask(seq_len=self.T, device=agents_emb.device)\n",
" agent_masks = agent_masks.permute(0, 2, 1).reshape(-1, t)\n",
" agent_masks[:, -1][agent_masks.sum(-1) == t] = False # Ensure that agent's that don't exist don't make NaN.\n",
" agents_emb = agents_emb.reshape(self.T, -1, self.d_k) # [T, BxcxM, d_k]\n",
" context = context.view(-1, Bc*self.M, self.d_k)\n",
"\n",
" agents_temp_emb = layer(agents_emb, context, tgt_mask=time_masks, memory_key_padding_mask=agent_masks)\n",
" agents_temp_emb = agents_temp_emb.view(self.T, Bc, self.M, -1)\n",
"\n",
" return agents_temp_emb\n",
"\n",
" def social_attn_decoder_fn(self, agents_emb, agent_masks, layer):\n",
" '''\n",
" :param agents_emb: (T, Bc, M, d_k)\n",
" :param agent_masks: (Bc, T, M)\n",
" :return: (T, Bc, M, d_k)\n",
" '''\n",
" Bc = agent_masks.size(0)\n",
" agent_masks = agent_masks[:, -1:].repeat(1, self.T, 1).view(-1, self.M) # take last timestep of all agents.\n",
" agents_emb = agents_emb.permute(2, 1, 0, 3).reshape(self.M, Bc * self.T, -1)\n",
" agents_soc_emb = layer(agents_emb, src_key_padding_mask=agent_masks)\n",
" agents_soc_emb = agents_soc_emb.view(self.M, Bc, self.T, -1).permute(2, 1, 0, 3)\n",
" return agents_soc_emb\n",
"\n",
" def forward(self, in_set_seqs):\n",
" B = in_set_seqs.size(0) # batch_size\n",
" t = in_set_seqs.size(1)\n",
"\n",
" # Encode all input observations\n",
" in_set_tensors, in_set_masks = self.process_observations(in_set_seqs)\n",
" in_sets_emb = self.agents_dynamic_encoder(in_set_tensors).permute(1, 0, 2, 3) # element-wise MLP\n",
"\n",
" for i in range(self.L_enc):\n",
" in_sets_emb = self.temporal_attn_fn(in_sets_emb, in_set_masks, layer=self.temporal_attn_layers[i])\n",
" in_sets_emb = self.social_attn_fn(in_sets_emb, in_set_masks, layer=self.social_attn_layers[i])\n",
"\n",
" # Repeat the tensors for the number of modes.\n",
" in_set_masks_modes = in_set_masks.unsqueeze(1).repeat(1, self.c, 1, 1).view(B*self.c, t, -1)\n",
" context = in_sets_emb.unsqueeze(2).repeat(1, 1, self.c, 1, 1).view(t, B*self.c, self.M, self.d_k)\n",
"\n",
" # Decoding\n",
" dec_parameters = self.Q.repeat(1, B, 1, self.M, 1).view(self.T, B*self.c, self.M, -1)\n",
" for i in range(self.L_dec):\n",
" dec_parameters = self.temporal_attn_decoder_fn(dec_parameters, context, in_set_masks_modes, layer=self.temporal_attn_decoder_layers[i])\n",
" dec_parameters = self.social_attn_decoder_fn(dec_parameters, in_set_masks_modes, layer=self.social_attn_decoder_layers[i])\n",
" \n",
" out_dists = self.output_model(dec_parameters.reshape(self.T, -1, self.d_k))\n",
" out_dists = out_dists.reshape(self.T, B, self.c, self.M, -1).permute(2, 0, 1, 3, 4)\n",
"\n",
" # Mode prediction\n",
" mode_params_emb = self.P.repeat(B, 1, self.M, 1).transpose(0, 1).reshape(self.c, -1, self.d_k)\n",
" mode_par_masks = torch.eye(self.c).to(in_set_seqs.device)\n",
" mode_probs = self.prob_decoder(mode_params_emb, in_sets_emb.reshape(-1, B*self.M, self.d_k), tgt_mask=mode_par_masks).transpose(0, 1)\n",
" mode_probs = self.prob_predictor(mode_probs).squeeze(-1).view(B, self.M, -1).sum(1)\n",
" mode_probs = F.softmax(mode_probs, dim=1)\n",
"\n",
" # return # [c, T, B, M, 5], [B, c]\n",
" return out_dists, mode_probs\n"
],
"execution_count": 25,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "5yRdFh2R48_P"
},
"source": [
"## Loss Functions\n",
"We define some utility functions for calculating the multimodal loss of the generated characters.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "m7wxpzvpA-fI"
},
"source": [
"import torch\n",
"from scipy import special\n",
"import torch.distributions as D\n",
"from torch.distributions import MultivariateNormal\n",
"\n",
"\n",
"def get_BVG_distributions(pred):\n",
" B = pred.size(0)\n",
" T = pred.size(1)\n",
" N = pred.size(2)\n",
" mu_x = pred[:, :, :, 0].unsqueeze(3)\n",
" mu_y = pred[:, :, :, 1].unsqueeze(3)\n",
" sigma_x = pred[:, :, :, 2]\n",
" sigma_y = pred[:, :, :, 3]\n",
" rho = pred[:, :, :, 4]\n",
"\n",
" cov = torch.zeros((B, T, N, 2, 2)).to(pred.device)\n",
" cov[:, :, :, 0, 0] = sigma_x ** 2\n",
" cov[:, :, :, 1, 1] = sigma_y ** 2\n",
" cov_val = rho * sigma_x * sigma_y\n",
" cov[:, :, :, 0, 1] = cov_val\n",
" cov[:, :, :, 1, 0] = cov_val\n",
"\n",
" biv_gauss_dist = MultivariateNormal(loc=torch.cat((mu_x, mu_y), dim=-1), covariance_matrix=cov)\n",
" return biv_gauss_dist\n",
"\n",
"\n",
"def nll_pytorch_dist(pred, data, agents_masks):\n",
" biv_gauss_dist = get_BVG_distributions(pred)\n",
" num_active_agents_per_timestep = agents_masks.sum(2)\n",
" loss = (((-biv_gauss_dist.log_prob(data) * agents_masks).sum(2)) / num_active_agents_per_timestep).sum(1)\n",
" return loss\n",
"\n",
"\n",
"def nll_loss_multimodes(pred, agents_data, modes_pred, entropy_weight=1.0, kl_weight=1.0):\n",
" gt_agents = agents_data[:, :, :, :2]\n",
" modes = len(pred)\n",
" nSteps, batch_sz, M, dim = pred[0].shape\n",
"\n",
" agents_masks = agents_data[:, :, :, 2]\n",
"\n",
" modes_pred = modes_pred\n",
" log_lik = np.zeros((batch_sz, modes))\n",
"\n",
" with torch.no_grad():\n",
" for kk in range(modes):\n",
" nll = nll_pytorch_dist(pred[kk].transpose(0, 1), gt_agents, agents_masks)\n",
" log_lik[:, kk] = -nll.cpu().numpy()\n",
"\n",
" priors = modes_pred.detach().cpu().numpy()\n",
" \n",
" log_posterior_unnorm = log_lik + np.log(priors)\n",
" log_posterior = log_posterior_unnorm - special.logsumexp(log_posterior_unnorm, axis=1).reshape((batch_sz, 1))\n",
" post_pr = np.exp(log_posterior)\n",
"\n",
" post_pr = torch.tensor(post_pr).float().to(gt_agents.device)\n",
" post_entropy = torch.mean(D.Categorical(post_pr).entropy()).item()\n",
"\n",
" loss = 0.0\n",
" for kk in range(modes):\n",
" nll_k = nll_pytorch_dist(pred[kk].transpose(0, 1), gt_agents, agents_masks) * post_pr[:, kk]\n",
" loss += nll_k.sum() / float(batch_sz*M)\n",
"\n",
" kl_loss = torch.nn.KLDivLoss(reduction='batchmean') # type: ignore\n",
" loss += kl_weight*kl_loss(torch.log(modes_pred), post_pr)\n",
"\n",
" entropy_vals = []\n",
" for kk in range(modes):\n",
" entropy_vals.append(get_BVG_distributions(pred[kk]).entropy())\n",
" entropy_loss = torch.mean(torch.stack(entropy_vals).permute(2, 0, 3, 1).sum(3).mean(2).max(1)[0])\n",
" loss += entropy_weight*entropy_loss\n",
"\n",
" return loss, post_entropy\n"
],
"execution_count": 38,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "6Qf-IiT8646l"
},
"source": [
"## Training Loop\n",
"The training loop takes about 45-60 minutes on a single-GPU machine."
]
},
{
"cell_type": "code",
"metadata": {
"id": "TQU44dSO1vXV"
},
"source": [
"import torch\n",
"from torch import optim\n",
"import torch.nn as nn\n",
"import time\n",
"\n",
"\n",
"# Model parameters\n",
"d_k = 128\n",
"c = 4\n",
"M = 12\n",
"T = 10\n",
"L_enc = 1\n",
"L_dec = 1\n",
"dropout = 0.2\n",
"\n",
"# Training parameters\n",
"batch_size = 64\n",
"learning_rate = 5e-4\n",
"adam_epsilon = 1e-4\n",
"entropy_weight = 1.0\n",
"kl_weight = 1.0\n",
"num_epochs = 40\n",
"\n",
"\n",
"if torch.cuda.is_available():\n",
" device = torch.device(\"cuda\")\n",
" torch.cuda.manual_seed(0)\n",
"else:\n",
" device = torch.device(\"cpu\")\n",
"\n",
"\n",
"# Defining models\n",
"autobot_model = AutoBotJoint(d_k=d_k, M=M, c=c, T=T, L_enc=L_enc, L_dec=L_dec, dropout=dropout).to(device)\n",
"optimiser = optim.Adam(autobot_model.parameters(), lr=learning_rate, eps=adam_epsilon)\n",
"\n",
"# Initialize dataloader\n",
"dset = OmniGlotDataset(dset_path=\".\", split_name=\"train\")\n",
"print(\"Number of Characters:\", len(dset))\n",
"train_loader = torch.utils.data.DataLoader(dset, batch_size=batch_size, shuffle=True, num_workers=2, drop_last=False, pin_memory=True)\n",
"\n",
"start_time = time.time()\n",
"total_steps = 0\n",
"losses = []\n",
"for train_iter in range(0, num_epochs):\n",
" print(\"Epoch:\", train_iter, \"Entropy Weight:\", entropy_weight, \"KL weight:\", kl_weight)\n",
" print(\"time since start:\", (time.time() - start_time) / 60.0, \"minutes.\")\n",
" for i, data in enumerate(train_loader):\n",
" in_set_seqs, out_set_seqs, _ = data\n",
" in_set_seqs = in_set_seqs.float().to(device)\n",
" out_set_seqs = out_set_seqs.float().to(device)\n",
"\n",
" # Run through model.\n",
" pred_obs, mode_probs = autobot_model(in_set_seqs)\n",
" \n",
" # Compute loss\n",
" loss, post_entropy = nll_loss_multimodes(pred_obs, out_set_seqs, mode_probs, entropy_weight=entropy_weight, kl_weight=kl_weight)\n",
" sigmas = pred_obs[:, :, :, :, 2:4]\n",
" sigma_magnitude = torch.mean(torch.norm(sigmas, dim=-1))\n",
"\n",
" # Backprop\n",
" optimiser.zero_grad()\n",
" loss.backward()\n",
" nn.utils.clip_grad_norm_(autobot_model.parameters(), 5.0)\n",
" optimiser.step()\n",
"\n",
" losses.append([loss.item()])\n",
" if i % 5 == 0:\n",
" print(i, \"Obs_Loss\", losses[-1][0], \"Sigma Magnitude\", sigma_magnitude.item())"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8m8q_EM_7DuV"
},
"source": [
"## Testing Learned Model\n",
"\n",
"This consistutes the results shown in Figures 3 (left), 13-14 of the paper.\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 3775
},
"id": "myz8KkJ8Y1dd",
"outputId": "d585955f-f832-46a4-c2fc-256636d10317"
},
"source": [
"import os\n",
"from matplotlib import pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"\n",
"%matplotlib inline\n",
"\n",
"autobot_model.eval()\n",
"dset = OmniGlotDataset(dset_path=\".\", split_name='test')\n",
"test_loader = torch.utils.data.DataLoader(\n",
" dset, batch_size=batch_size, shuffle=True, num_workers=12, drop_last=False, pin_memory=True\n",
")\n",
"pred_colors = ['r', 'y', 'g', 'm']\n",
"with torch.no_grad():\n",
" for i, data in enumerate(test_loader):\n",
" in_set_seqs, out_set_seqs, fname = data\n",
" in_set_seqs = in_set_seqs.float().to(device)\n",
" out_set_seqs = out_set_seqs.float().to(device)\n",
" pred_obs, mode_probs = autobot_model(in_set_seqs)\n",
" img_fname = fname[0].replace(\".txt\", \".png\").replace(\"strokes\", \"images\")\n",
" char_image = mpimg.imread(img_fname)\n",
"\n",
" num_strokes = int(in_set_seqs[0, 0, :, 2].sum())\n",
" gt_past = in_set_seqs[0].cpu().numpy()\n",
" gt_future = out_set_seqs[0].cpu().numpy()\n",
" prediction = pred_obs[:, :, 0].cpu().numpy()\n",
"\n",
" fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(15,15))\n",
" ax[0, 0].imshow(char_image)\n",
" ax[0,0].title.set_text('Char Image')\n",
" for m in range(num_strokes):\n",
" ax[0, 1].plot(gt_past[:, m, 0], gt_past[:, m, 1], color='b')\n",
" \n",
" for m in range(num_strokes):\n",
" ax[0, 1].plot(gt_future[:, m, 0], gt_future[:, m, 1], color='k')\n",
" ax[0, 1].axis(xmin=-1, xmax=11, ymin=-1, ymax=11)\n",
" ax[0, 1].title.set_text('GT Strokes')\n",
" \n",
" row = 0\n",
" for k in range(c):\n",
" col = (k+2) % 3\n",
" if (k + 2) % 3 == 0:\n",
" row += 1\n",
" for m in range(num_strokes):\n",
" ax[row, col].plot(gt_past[:, m, 0], gt_past[:, m, 1], color='b')\n",
" ax[row, col].plot(prediction[k, :, m, 0], prediction[k, :, m, 1], color=pred_colors[k])\n",
" \n",
" ax[row, col].axis(xmin=-1, xmax=11, ymin=-1, ymax=11)\n",
" ax[row, col].title.set_text('Prediction '+str(k+1))\n",
" plt.show()\n",
" if i == 5:\n",
" break\n"
],
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 12 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
" cpuset_checked))\n"
]
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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ySunVlNJ7wD3AsNKx304p/TmltDSltIQsFI4AiIj+wG7AT1NKK1JKj5EFztWOAe4ufa+qlNL9wBTggI2sUVIdPPQQvPaa80UkFUtK2dDIvfeGT3wi72qkjWdoax3eBnpsxFyzuTUeLwXar/5MRHw9IqaWhhi+S9YT1qPG9rPqUNe8Go8/XMfzzqVjd4yISyPijYhYDDwKbB4R5UAf4J2U0tJaatkG+Orquku17w3U7CWU1MCuuAK6dYNDD93wtpLUVB59FKZP94SSmh9DW+vwBLAcOKQuH46IbYDLgQlA95TS5mQ9YzUXyW3Mq5x8FxgM7JFS6gJ8fnVpwBygW0R0rLF9vxqPZwHXppQ2r3HrlFI6uxHrlVq1hQvhttvga1+D9u3zrkaSql1+OXTtCocdlncl0qYxtLUCpeGGPwUuiohDSj1XbSLiSxHxq43YRSeyULYAICKOJetpayqbkfW8vRsR3YAzV7+RUnqDbLjjzyKibUTsBRxc47PXAQdHxKiIKI+I9hGxT82FTCQ1rMcegxUrYMyYvCuRpDU9+CCMHAkdO254W6lIDG2tRErp18C/AT8mC1+zyHrObt+Iz74E/Jqsx24esDPwf41W7MedB3QAFgJPAveu9f7RZIutvE224MiNZD2LpJRmkV3W4D+o/t7/jn/3pUaze2m261NP5VuHJK1tzz2ztik15vggqRFE8m+tWpiIuBH4Z0rpzA1uLK3H8OHD05QpU/Iuo1naYw+IgCefzLuS1m2ffbL7hx/Os4riiYhnUkrDN7xlcdk+1c3VV2fXjnzmGfj0p/OupvWybVq39bVN9jao2YuI3UrXeCuLiNFkPWsb7EGU1HjGjMnOZs+Zk3clklTtwAOhrAzuvHPD20pFYmhTS9AbeBh4H/gtcHJK6blcK5JaubFjs/tJk/KtQ5Jq6tkTPvOZ7OLaUnNiaFOzl1KamFLql1LqmFLaIaV0Vd41Sa3djjvCwIH+MJJUPGPGwNSp8MYbeVcibbxGCW0RMToipkXEjIj4QWMcQ5JUXBFZb9sDD8AHH+RdjSRVWz0SYOLEfOuQNkWDh7bSBY8vAr4EDAGOjIghDX0cSVKxjR0Ly5fD5Ml5VyJJ1XbYAT7xCUcCqHmpaIR97g7MSCm9ChARN5AtDPFSbR/o0a08DejXphFKkZSX12etZOE7lbHhLdVS7b03bLFF9sPo0EPzrkaSqo0ZA7/5Dbz3XnaxbanoGiO09SW7FtZqbwJ7rL1RRJwInAjQv28Ff7uvXyOUIikvu4+ateGN1KJVVGQrtU2aBJWVUF6ed0WSlBk7Fn71K7jnHjjiiLyrkTYst4VIUkqXpZSGp5SG9+zuv+SS1BKNGQNvvw2PP553JZJUbY89spUkXfpfzUVjhLbZQM1us61Lr0mSWpnRo6FtW38YSSqW8nI4+GC4+25YuTLvaqQNa4zQ9jSwfUQMjIi2wBGA/1xLUiu02Waw777ZvLaU8q5GkqqNGZPNaXvkkbwrkTaswUNbSmkVMAG4D3gZuCml9GJDH0eS1DyMHQvTp8O0aXlXIknV9t8fOnRwJICah0aZ05ZSurt0keNBKaVfNMYxJEnNw8EHZ/cury2pSDp2zIKbIwHUHOS2EIkkqXXYemvYdVdDm6TiGTMGZs6Ef/wj70qk9TO0SZIa3dix8OSTMG9e3pVIUrWDDoIITyqp+AxtkqRGN2ZMNvxo0qS8K5Gkar16wZ57Oq9NxWdokyQ1uk99CrbZxh9Gkopn7Fh45hl48828K5FqZ2iTJDW6iOyH0f33w9KleVcjSdXGjs3uJ07Mtw5pfQxtkqQmMWYMfPhhFtwkqSgGD4btt3dem4rN0CZJahKf/zx07eoQSUnFsnokwEMPweLFeVcjrZuhTZLUJNq0gQMOyIYgVVbmXY0kVRszBlauhPvuy7sSad0MbZKkJjN2LCxYAE89lXclklTtM5+B7t0dIqniMrRJkprM6NFZj5s/jCQVSXl5ds22u+7KetykojG0SZKaTNeusM8+hjZJxTN2LLz7Ljz2WN6VSB9naJMkNamxY2HatOwmSUXxxS9Cu3YulqRiMrRJkprUwQdn9/4wklQknTrByJHZSICU8q5GWpOhTZLUpPr3h2HDDG2SimfsWHjtNXjhhbwrkdZkaJMkNbkxY+Dxx7OVJCWpKA46KLv3pJKKxtAmSWpyY8dCVRVMmpR3JZJUbautYI89XCxJxWNokyQ1uaFDoV8/z2ZLKp4xY+Dpp+Gtt/KuRKpmaJMkNbmI7IfR5Mnw4Yd5VyNJ1caOze4nTsy3DqkmQ5skKRdjx8LSpfDgg3lXIknVhgyBQYMcCaBiMbRJknIxYgR06eLcEUnFsnokwIMPwvvv512NlDG0SWpVIuL0iHgxIl6IiD9FRPu8a2qt2rbNLmZ79915VyLlz7apWA46CJYvh4ceyrsSKWNok9RqRERf4DvA8JTSTkA5cES+VbVeKcFLL0GfPnlXIuXLtql4Xnopu7d9UlFU5F2AJDWxCqBDRKwEOgKuD5aTRx7Jfhj9/vd5VyIVgm1TQaQEF18Mu+0Gw4fnXY2UsadNUquRUpoN/C8wE5gDvJdSmlxzm4g4MSKmRMSUBV75uVFdfDF06wZH2J+gVm5j2iawfWoqDz8ML78M3/pW3pVI1QxtklqNiNgCGAsMBPoAnSLimJrbpJQuSykNTykN79mzZx5ltgpvvQW33QbHHQcdOuRdjZSvjWmbwPapqVx0UXZC6fDD865EqmZok9SajAReSyktSCmtBG4FPpNzTa3SZZdBZSWcdFLelUiFYNtUEG++CbffDscf7wklFYuhTVJrMhPYMyI6RkQAXwBezrmmVmflyiy0jR6dXQtJkm1TUVx2GVRVwckn512JtCZDm6RWI6X0FHAL8CzwPFkbeFmuRbVCt90Gc+Y4X0RazbapGFasyELbAQfAwIF5VyOtydUjJbUqKaUzgTPzrqM1u+ii7AfR6NF5VyIVh21T/m69FebNg1NOybsS6ePsaZMkNZkXXoBHH82GHpWX512NJFW7+GLYdltPKKmYDG2SpCZz8cXQrl22aqQkFcXzz8Nf/5qdUCrz17EKyL+WkqQmsXgxXHttdl227t3zrkaSql10EbRv7wklFZehTZLUJP7wB3j/fRcgkVQs770H110HRx6ZXZ9NKiJDmySp0aWUDY3cbbfsJklFcc018MEHLkCiYnP1SElSo3vsMXj5Zbj66rwrkaQ1XXYZ7L47DB+edyVS7expkyQ1uvnzs/tddsm3Dkla24IFtk0qPkObJKnR9e+f3c+alW8dkrS2fv1sm1R8dQ5tEdEvIv4SES9FxIsRcWrp9W4RcX9ETC/db9Fw5UqSmqN+/bL7mTPzrUOS1ta/v22Tiq8+PW2rgO+mlIYAewLfioghwA+AB1NK2wMPlp5LklqxLbeEtm09my2peFaHtpTyrkSqXZ1DW0ppTkrp2dLjJcDLQF9gLHBNabNrgEPqW6QkqXkrK4Ott/ZstqTi6dcvuxzJe+/lXYlUuwaZ0xYRA4BhwFNAr5TSnNJbc4FeDXEMSVLz5hAkSUXknFs1B/UObRHRGfgzcFpKaXHN91JKCVhnZ3NEnBgRUyJiyoK3K+tbhiSp4JzsL6mInHOr5qBeoS0i2pAFtutTSreWXp4XEVuV3t8KmL+uz6aULkspDU8pDe/Zvbw+ZUiSmoH+/WH2bKj0PJ2kArGnTc1BfVaPDOBK4OWU0m9qvHUnMK70eBxwR93LkyS1FP37Z4FtzpwNbytJTaVXL6iosKdNxVafnrbPAl8D9ouIqaXbAcDZwP4RMR0YWXouSWrlHIIkqYjKy10oScVXUdcPppQeA6KWt79Q1/1KklomhyBJKirn3KroGmT1SEmSNsSeNklF5eq2Kro697S1ZitTJcvTyrzLUCNpE+W0izZ5lyG1OF26QNeu/jCSVDz9+lUvlFTu+ngqIENbHZw5fxg3PfQZypbXNjpUzdmWn57HXTtdT9eyDnmXIrU4DkGSVET9+8PKlTBvHvTpk3c10scZ2urg9lc+xQ6/epXK+QvyLkWNYPYZe/HekEq6OnhYanAOQZJURDXn3BraVET+LK2rqgTJW0u8dXm9isOeP5ZjZ36Of638IO+/aVKL0r+/PW2Sisc5tyo6Q5u0lq53v0jPby1n+v8O4Y4ln8q7HKlF6dcPFi6EpUvzrkSSqrm6rYrO0CatpWrJEla9PpMOc5fzfmX7vMuRWpTVP4zefDPfOiSppq5doXNne9pUXIY2SVKTcQiSpCKKcPi2is3QJklqMqt72gxtkoqmXz/bJhWXoU2S1GT69s3OaHs2W1LRuLqtiszQJklqMm3bQu/e/jCSVDz9+sH8+bBsWd6VSB/nddqktZRvvy1Ldu7Joh3KGdRuXt7lSC2O80YkFVHNhZK22y7fWqS1GdqktczZvzffmDCRHdvNZli7D4AOeZcktSj9+sHzz+ddhSStqeay/4Y2FY2hrQ46tV9B1dZbUtG5Y241pCXvU7nw7Y3evrxHd2Kzzo1YUQNbuYrKBQtJy5dv1OZl7dtT1rMHVJTX+9BL+yTGdn6ZrSs6Y2CTGl7//nD33dn17CPyrkaSMq5uqyIztNXBzz5xJxf9Zj+WrmqTWw0LHhhMv/OfpWojBl6XtW/PzOMH03Pk7CaorGHMnLcl2/22G/xt407Hr9xzCLNOXkWfbu/V67hlkRi35UP0LG9Xr/1Iql2/ftnFtd95B7p3z7saScpsvXV27/BtFZGhrQ4O7LiMAwffnWsN2845Dtq02bjZsm3asHTIMh4Ychvl0TzWnrly695c2/NgNjY6Le3dlt/s+kcO7NhQs4fzC+RSS1dz2X9Dm6SiaN8ettzSnjYVU/P4Ba96SStW0P3hdmw38SRGvXwQCys/yLskSa1YzXkjklQkLpSkojK0tQJp+XK6X/s0nzjtH8y7vT9vVdZ/3pck1ZXzRiQVlRfYVlEZ2lqJtGoVVcuWUbYyUZWc+S8pPz17Qrt2ns2WVDyrL7CdUt6VSGsytEmSmlRZWTbh37PZkoqmXz94/314r37rmkkNztDWTLVpt4rYakvKe/YkKlxPRlLzsvpstiQVSc2FkqQiMbQ1Uz/c5V7eOS/450+3pWzQgLzLkaRN0q0bvL3xl5qUpCbRrVt2v3BhvnVIazO0NVPju8znyaG38NW9n2JVj2Z00WxJAl57DQYMyLsKSVrTa69l97ZPKhpDmySpSaUE//oXDB6cdyWStKZp07KFkrbZJu9KpDUZ2iRJTeqtt7KJ/oY2SUUzbRpstx2Ue3UkFYyhTZLUpKZNy+4NbZKKZto02yYVk6FNktSkDG2SimjlSnj1VdsmFZOhTZLUpKZNg44doW/fvCuRpGqvvgqrVhnaVEyGNklSk5o2DXbYASLyrkSSqjkKQEVmaJMkNSnnjEgqIkObiszQJklqMsuWweuv+6NIUvFMmwY9e8IWW+RdifRxhjZJUpN55ZXsOm2GNklF4/UjVWSGNklSk3H4kaSicui2iszQJklqMqtD2w475FuHJNX07rswf76hTcVlaJMkNZlp06BPH9hss7wrkaRqnlBS0RnaJElNxuFHkorIodsqOkObpFYlIjaPiFsi4p8R8XJE7JV3Ta1FSoY2qTa2TfmaNg3Ky2HbbfOuRFq3irwLkKQmdj5wb0rpsIhoC3TMu6DWYuFCWLTI0CbVwrYpR9OmZYGtbdu8K5HWrd49bRFRHhHPRcSk0vOBEfFURMyIiBtLDY8k5S4iugKfB64ESCmtSCm9m29VrYfDj6R1s23Kn6MAVHQNMTzyVODlGs/PAc5NKW0HLAKOb4BjSFJDGAgsAK4qnWy6IiI61dwgIk6MiCkRMWXBggX5VNlCOdFfqtUG2yawfWoslZUwfbqhTcVWr9AWEVsDBwJXlJ4HsB9wS2mTa4BD6nMMSWpAFcCngUtSSsOAD4Af1NwgpXRZSml4Sml4z54986ixxZo2LRt6NGBA3pVIhbPBtglsnxrLzJmwfLmhTcVW356284AzgKrS8+7AuymlVaXnbwJ963kMSWoobwJvppSeKj2/heyHkprAP/4B222XTfaXtAbbphz94x/ZvaFNRVbn0BYRBwHzU0rP1KAon5EAACAASURBVPHz1V38b1fWtQxJ2mgppbnArIhY/U/zF4CXciyp1Zg+HSZPhrFj865EKh7bpnxdfDH06gW77553JVLt6rN65GeBMRFxANAe6EK28tHmEVFR6m3bGpi9rg+nlC4DLgMYvkv7VI86JGlTfBu4vrRI0qvAsTnX0yr8z/9kQyNPPTXvSqTCsm3KwbPPZieUzjoL2rfPuxqpdnUObSmlHwI/BIiIfYDvpZSOjoibgcOAG4BxwB0NUKckNYiU0lRgeN51tCZz5sA118Bxx2VnsyV9nG1TPn71K+jSBU4+Oe9KpPVrjItrfx/4t4iYQTbH7cpGOIYkqZk47zxYtQq+9728K5Gkaq+8AjffDCedBF275l2NtH4NcnHtlNLDwMOlx68CjgqWJPHee/C738FXvwqDBuVdjSRV+9//hYoKOO20vCuRNqwxetokSQLgkktg8WL4/vfzrkSSqs2dC1ddBePGwVZb5V2NtGGGNklSo1i2LBsauf/+MGxY3tVIUrXf/hZWrHDYtpoPQ5skqVH84Q8wbx784GOXCJak/CxenC3z/5WvwA475F2NtHEMbZKkBldZmS3zP3w47Ltv3tVIUrVLL83m2zpsW81JgyxEIklSTbfeCjNmwC23QETe1UhSZvlyOPdc+MIXspNKUnNhaJMkNaiU4OyzYfvt4ZBD8q5Gkqpde231tSOl5sTQJklqUA8+CM8+C5ddBuXleVcjSZnVw7aHDYORI/OuRto0hjZJUoM6++xsCe2vfz3vSiSp2u23w7/+BTfe6LBtNT8uRCJJajBTpmQ9baedBu3a5V2NJGVSgnPOgUGDslUjpebGnjZJUoM55xzo2hVOOinvSiSp2l/+Ak8/Db/7ncO21TzZ0yZJahC33pqtFjlhAnTpknc1kpR57z04+eRs2Pa4cXlXI9WNPW2SpHr75z+zH0O77w4/+Une1UhSpqoqa5teeSUbut2+fd4VSXVjaJMk1cvixXDoodChQ9bT5lw2SUVx1llwxx3ZtdlGjMi7GqnuDG2SpDpLCcaPh+nT4YEHoF+/vCuSpMy992Y9/0cdBaeemnc1Uv0Y2iRJdXb22XDbbfCb38A+++RdjSRlXn01C2s775xdM9Il/tXcuRCJJKlOJk+GH/0IjjgiW+Jfkopg6VL48pezkQC33gqdOuVdkVR/9rQ1I4sql3Lsq4fyj9e2/ui1Nm+2ZdC8uVTmWJek1ue11+DII2GnneCKKzyLLakYUoITT4R//APuuiu7LpvUEhjampG3KoPXbhvE4Cufr36xspLKDz/MryhJrc7qs9hVVdnQSM9iSyqKCy6A66+Hn/8cvvSl0oszZsDdd8Mjj8DQoVmq69Ur1zqlTWVoa0YqCcpWQNWSJR9/s6ycsiHbs7xPZ9q99T5VL02HKvvfJDWslLILZ//97zBpkmexJRXHo4/Cd78LXzlwGf+x6yNw6t1wzz3ZSkkAW2+djZf8+c/h8MPh29+GPfbIt2hpIzmnrYUo79KZaSdszphzH2DaCZtT3qVz3iVJaoEuugiuvRZ+9jM44IC8q5GkzNwnX+fugy7mvnYHc/ND3Sg7YHS2Asl222XdbzNmwKxZ2UUlTzoJ7rwT9twzu7jkH/4Ay5bl/RWk9TK0NQPL00pmrnqfV1f2oHxlWvdGUQZbrODgzi/AFiuy5zWUdepEeY/urOoYtImqJqhaUkvz2GNw+ulw0EHw4x/nXY2kVm3Fiuxq2d/9LlWfHELvvQZy9pJvsfcWLxHHH58Nh3znnex+woTqYQGDB8NvfwuzZ8OFF8KSJdnVt/v3z1ZWmjUr3+8l1cLhkc3An5b05Ze3f4VOs4LeT7zDuiJX1dKl9L+xnC8/dwb9/7WSqqVLP3qvrH175hy3C8s/v4Q9+z1Pn/Jagp8k1eKtt+CrX4WBA7OetjJP+UlqCinBwoXw5ptZoHr9dfjLX7ILQ77/PrRty7QtR3ApJzLqvC/xpe/ssHErI222GXzrW3DKKVn4u/DC7Bom55wDhxySBb0RI1xlSYVhaGsGnlg8iG1vXEx67sV1BjaAtHw57e56mt53lZ7XfLNNGxbvtoxXP3tt6YWOjVespBbn+eezC2gvWQL33w+bb553RZJajOXL4eWXs0C2Opitvl/9ePnyNT/Tvz8ccwzL9juAs5/al//8dWfOOAO+VJcLaEfAyJHZ7fXX4ZJLsiVx//znbHncCRPgmGOa34pL99wD//M/WRgdMiTvatQADG0FM7/yAya8MYa/z+770WvplU5s/86brNrEfVX07sWC0dvywVbBZ7Z7qWELldTizZ8PP/lJ9vula1f44x+z3zCS1CAeewy+/vXsGiKrVVRA377ZoiG77QaHHgr9+mXPS/dVW/bm+j8GPzw9G+V4zDHwi180QD0DBmQ9bT/7GfzpT9lcuJNOgu9/H447LuuZaw6rL11ySRY2q6pg9Gh4/PHsz0/NmqGtYKav7MD0Pw5m0J9e/ui1tGoVq95/f5P3tWpAL3Y86QX+s889dCurANo3YKWSWqply+D887MfQR9+mC2w9tOfQrdueVcmqUVYsQLOPDMLSAMGwHXXwfbbZ8GiVy8oL6/1o//3f3D6WHj6aRg+HG64Afbeu4Hr69AhC2nHHpsFngsvzALceedl1xH49rfhi1+s2zjxysrs1rZtAxdd2vcZZ8BvfgMHHgg/+EG2YtSXvpQtrbnFFg1/TDUZQ1sjq0xV/N/yMp5aunFnZv6+uB+d5lVSuWjRx96LigrKBg1gVY/OVMx7j8pXXs/Geq+loncvVg7szTtDOvKlzWbRv8KVJCVtWErZiKAzzshOfB98cDa6ZvDgvCuT1GK8+GLWNTZ1Khx/PJx7bja/bANeey3r8Lr55qwj7g9/gKOPbuT5tRHw2c9mt1//OluN8tJLsxC03XZw2GFZw7l0aXb74IPqx7W9tnx5VvS++8L/+39ZT2KPHvWv9YMPsj/X22/PQuW552bh9/bbs3rHjoX77ssCqZolQ1sjW55WMf7RkxlwHWzMoo1lKyrpMv011nWFtbLNu/LPCd05Yu8nuOWezzLov+etseDIagtGbcvOJz/PwZvN4rDNXgQMbZKy3xaLF8OcOTB37pr3c+Zkv6Weew523jmbuzZyZN4VS2oxqqqyLvwf/hC6dIE77oAxYz56e8UKmDevuj2q2Ta99VbWJpWXZyMXv/e9HKaY9emTHfw//iM7u3XBBdnCJe3aQceO1bdOnaof9+ix7tfffx9uuy27yPfJJ8MXvpAFuEMOqduQhrlzs7Nszz6b/Rl/5zvV7+23X7Z61BFHZCn35pvX25Op4jK0NbIqqqh4qy1tHnxynb1i61LbJbGjooL2W33At7s/zp+23P1j/9OVtW9PtG3LB32CH291LwPbdKa5BbblaSXvVa1g3squ1LrqiqT1uvvuLHyt/eNn7txsuOPa2rWDrbbKfpNceml28tt/04vrb3/7G506dWLHHXfMuxRp48ycma1m9Je/8MqOY7huxOW8csuWzLmgum16++2PfywCevaE3r3ha1/LRlTmPjWrbVs48sjsVlVV966+c87Jehtvuim7HX88fPObsP/+WYAbO3bjVn164YVsKOTChVmv2sEHf3ybww/P/pBPPTWb63bxxa6K2QwZ2pqRqsVL2OKmgYx85gz6v7CKtKx6NaWyjh2Zc/xQluz+IXsNeome5c3zP+1P5+/G7XftRcc5QZ9/zt7kxVckZcOGbrwxm77Qu3cWyD7zmerHW21V/bh37+x3gf9+Nw/vvfcen/vc51ixYgXDhw9n/PjxHHHEEXTv3j3v0qSPSwmuvz4LCqtW8a8zrmDwr46j7fT4qC3afnv4/OfXbJdWP95yS2jTJu8vsR71GZsZAcOGZbdf/jLrJVsd4MaPz774qFFZgBszJuudXNvkydm1WDp1gr/+FT796dqP953vZF2W55yTjS/1YpvNTvP8Zd9KVS1dSuebnvyo76xmv110aM+yzy3hlb1XL+vfPBcdmTzzE2x30WusmjPXwCbV0e9+B1dfDe2bZzOg9bj33ntZsWIF3/72t3n00UeZMGECp59+OgcffDDjx49n9OjRtCn0r1y1GsuXZ91jN9+cnTX6wx8Y0G8Q7/zAE0UfEwG77prdzj47W2VldYCbNCkbDjF6dNZjdvDB2RzAyy/PhlbuuGO2Tb9+Gz7OWWdlXZs/+UmWir/xjcb/bmowhrZmrmKr3ry93wA+2KqMvfo/n3c5dbI8reSn83dj8sxPsOKpbqQP5+RdktSseR21lmvixIl0796dc889l/LycqZOnco111zD9ddfz6233sqWW27J0Ucfzbhx49hll13yLlet2auvwi23ZI933BHat6dt28ZZNLFFiYDdd89uv/oVPPVUFt5uvjmbB9i+fbZs5mOPZQuM3HjjRi3k8tG+r7gimzz4zW9mK3WuazilCqkx19xRE1g+uA+7nPp3bpjwa87benLe5dTJe1UruP2uvehz0rtsc/GLVL63OO+SJKlwVq1axd13380BBxxAeWnS4dChQzn33HOZPXs2d9xxB3vvvTcXXnghQ4cOZdiwYbz55nmsWDE/58rVKn3yk/D889lcreuvh5Ur866o+Skrg732ylaCnDkzC2onnpiFrlNPhTvv3PjAtlqbNlmY3nXXbOjlE080Tu1qcPa0NXNlK6t4dUkPntxsYN6l1Nm8lV3pOCdYNWdu3qVILV5VVRUrV66kXbt2eZeiTfT444+zaNEiDl7HmfE2bdowZswYxowZw8KFC7nhhhu4+uqreeWV03n11X/nkEMOZNy4cRx44IG0tatDTWXHHbOenY1c1l/rUVZWffmB88+v3746d4a77sqGrR50UBYGP/nJhqlTjcbQ1sxVvPQGK/9rANd2bsbd21W46IjURG699Va++93v8uMf/5jx48c7/6kZmThxIm3atGHUqFHr3a5Hjx5MmDCBCRMmsNtuLzB37jU89dR13HHHHXTv3p2jjjqK8ePHM2zYMMKJRWoKBrbi6dkzu27bZz6TzZd7/PFsgRIVlqGtmatctIiyRxbR3M+ZG9ikptG7d2969+7NiSeeyFlnncVPf/pTjjnmGCoq/Oeg6CZOnMg+++xDl3WtIleLTp12YtCg/+GBB85i8uTJXH311Vx66aVccMEF7LTTTowbN47TTz/9o+GWklqRbbeFe+6BESOy+XGPPuqk6AJzTpsktSJ77703Tz75JJMmTWKLLbbg2GOPZciQIVx//fVUVtZ2lUjlbfr06UybNm2dQyM3RkVFBQcccAA33XQTc+bM4eKLL6ZTp07ccMMNBjapNRs2DG69Ff75z+zi3suW5V2RamFok6RWJiI48MADmTJlCrfddhvt27fnmGOO4VOf+hQ333wzVVVe2b5oJk6cCFDn0FZTt27dOPnkk3nyySd5+OGH670/Sc3cyJFwzTXwyCPZZRo8gVdIhjZJaqUigkMOOYSpU6dy0003kVLi8MMPZ9iwYdx+++2klDa8EzWJiRMnstNOOzFgwIAG3W/nzp03vJGklu/II+E3v8lWljz11OzC6CqUeoW2iNg8Im6JiH9GxMsRsVdEdIuI+yNieul+i4YqVpLU8MrKyvjqV7/K888/z3XXXceHH37IoYceyvDhw7nrrrsMb7WYNWsWxxxzDEcddRQPPvhgo/VQLlq0iL/+9a8N0ssmSbU6/XT43vfgoouyC3GrUOrb03Y+cG9K6RPALsDLwA+AB1NK2wMPlp63ais3r6Rsx8GUr+dWMaA/4UIAknJUXl7O0UcfzUsvvcRVV13FokWLOOigg9hrr72YPHlyocPbSy+9xKOPPsqKFSsa/VhVVVX87ne/Y8cdd+S2227j3nvvZeTIkWy//fb88pe/ZM6cOQ16vHvvvZfKykpDm6TGd845cMwx8KMfwVVX5V2NaqhzaIuIrsDngSsBUkorUkrvAmOBa0qbXQMcUt8im7MO0Zb/3O9Wul46ny6XLaj19tIPelPed6u8y5UkKioqGD9+PNOmTeOyyy7jrbfeYtSoUXz+85/nwQcfZP78+cyePZs33niDV155hWnTpvHiiy8ydepUpkyZwnPPPcfixYsbvc6qqiomTZrEyJEj2XHHHRkxYgTdunXj4IMP5sILL2T69OkNHjRnzJjBfvvtx8knn8wee+zBCy+8wOzZs7n++uvp378/P/rRj+jXrx9jx45l0qRJrFpV/7VxJ06cSM+ePdl9990b4BtI0nqUlcGVV8KoUfDOO3lXoxrq07UzEFgAXBURuwDPAKcCvVJKq08zzgV6revDEXEicCJA/74tt4epPMr4epeFfL3LQ+vd7gjgva5bEm2qL3qaKiuhysmgkvLRpk0bTjjhBL7+9a9z5ZVX8otf/IKRI0du9Od79+7N4MGD2WGHHRg8ePBHtwEDBtTr+nBLlizhmmuu4fzzz2fGjBlsvfXWnH322QwePJj777+f++67j0mTJgEwYMAARo0axahRo9hvv/3o2rVrnY5ZWVnJueeey09+8hPatWvHFVdcwXHHHffRdc6OOuoojjrqKKZPn86VV17J1VdfzZ133knfvn059thjOf744+s0H23lypXcc889HHLIIa7yKKlptG2bXXzbNqdQoq5nISNiOPAk8NmU0lMRcT6wGPh2SmnzGtstSimtd17b8F3ap7/d169OdbQUN73flR88fhgsLgXYFPR+ArrcMoXUAGdqW7olR+zJj/77ag7s6FK1RbH7qFlM+fuyZn3l3uHDh6cpU6bkXUZhLFu2jFtuuYXFixdTUVGxxq1NmzYfPV6xYgUzZsxg2rRpTJs2jX/9618sXLjwo/1UVFQwaNCgdQa6nj171nrB59dff50LLriAK664gsWLF7Pnnnty2mmn8eUvf/ljIfCVV17hvvvuY/LkyTz00EMsWbKE8vJy9txzz49C3K677rpRQeiFF17guOOO4+mnn2bMmDFccskl9OnTZ72fWblyJZMmTeLyyy/n3nvvBWDkyJGccMIJjB07lrZt267386s9/PDD7Lvvvvz5z3/my1/+8kZ9pqZ99lm9n03+aIsWEc+klIbnXUd92D6pObNtWrf1tU316eJ6E3gzpfRU6fktZPPX5kXEVimlORGxFTC/HsdoNQ7v/B6H7n8ZVWQT2StTYsd2E+h6e4WhTVIhrL40QF288847a4S41Y/vvffeNeahbb755msEuR122IHOnTtz5ZVXctttt320aMqpp57KHnvsUevxBg0axCmnnMIpp5zCypUreeKJJz4KcWeeeSY//elP6datGyNHjmTUqFF88YtfZOutt15jHytWrOCss87iF7/4BV27duWGG27g8MMPrzVU1tSmTRsOPfRQDj30UGbOnMlVV13FlVdeyeGHH06PHj0YN24c3/jGN/jEJz6x3v3ceeedtG3bli9+8YsbPKYkqeWqc08bQET8FfhGSmlaRPwM6FR66+2U0tkR8QOgW0rpjPXtx562ddvr71+h6o9bUr6yuJP/G0ubD6ro9LfXqZy3cZnfnrbisadNG6OyspKZM2d+FOJqhro333zzo+26devGN7/5TU455ZSPhatNtWDBAh544AEmT57Mfffd99HCIUOGDPmoF65z586cfPLJPP/88xx11FGcd9559OzZs97f9f777+fyyy/nzjvvZNWqVey9996ccMIJHHbYYXTs2HGN7VNK7LDDDgwaNOij3rpN5dnsdbOnTcqXbdO6NVZPG8C3gesjoi3wKnAs2eImN0XE8cAbwOH1PEar9cch1zDtzO5U0qx/99bJLQt3Y+aPdqBiI0ObpOapvLycgQMHMnDgQEaPHr3Gex988AHTp09nzpw5jBgx4mOhpq569uzJkUceyZFHHklKiRdeeOGjXriLL76Yc889F4C+ffty5513NtiqjeXl5YwePZrRo0czb948rrnmGq644grGjRvHd77zHY4++mhOOOEEhg4dCsC0adOYMWMGp512WoMcX5LUfNUrtKWUpgLrSoNfqM9+lRnYpjMD2yzPu4xczO06nWvbf6LeZxUkNV+dOnVi6NChH4WYxhAR7Lzzzuy8885873vfY+nSpTz66KPMmDGDr33ta3VeuGRDevXqxRlnnMG///u/8+ijj3LFFVfw+9//nosvvphdd92VE044gdmzZwNw0EEHNUoNkqTmw9/EkiSVdOzY8WM9fo0pIhgxYgQjRozgt7/9Lddffz2XX345J510EgC77LIL22yzTZPVI0kqJkObJEkFsMUWWzBhwgS+9a1vMWXKFK699lr233//vMuSJBWAoU2SpAKJCHbbbTd22223vEuRJBVEWd4FSFJTi4jyiHguIiblXYskrWbbJKk2hjZJrdGpwMt5FyFJa7FtkrROhjZJrUpEbA0cCFyRdy2StJptk6T1MbRJam3OA84AqvIuRJJqsG2SVCtDm6RWIyIOAuanlJ5ZzzYnRsSUiJiyYMGCJqxOUmu1MW1TaTvbJ6mVMrRJak0+C4yJiNeBG4D9IuK6mhuklC5LKQ1PKQ3v2bNnHjVKan022DaB7ZPUmhnaJLUaKaUfppS2TikNAI4AHkopHZNzWZJaOdsmSRtiaJMkSZKkAvPi2pJapZTSw8DDOZchSWuwbZK0Lva0SZIkSVKBGdokSZIkqcAMbZIkSZJUYIY2SZIkSSowQ5skSZIkFZihTZIkSZIKzNAmSZIkSQVmaJMkSZKkAjO0SZIkSVKBGdokSZIkqcAMbZIkSZJUYIY2SZIkSSowQ5skSZIkFZihTZIkSZIKzNAmSZIkSQVmaJMkSZKkAjO0SZIkSVKBGdokSZIkqcAMbZIkSZJUYIY2SZIkSSowQ5skSZIkFZihTZIkSZIKzNAmSZIkSQVmaJMkSZKkAjO0SZIkSVKBGdokSZIkqcDqFdoi4vSIeDEiXoiIP0VE+4gYGBFPRcSMiLgxIto2VLGSJEmS1NrUObRFRF/gO8DwlNJOQDlwBHAOcG5KaTtgEXB8QxQqSZIkSa1RfYdHVgAdIqIC6AjMAfYDbim9fw1wSD2PIUmSJKkl+Mc/GP7O5LyraHbqHNpSSrOB/wVmkoW194BngHdTSqtKm70J9F3X5yPixIiYEhFTFrxdWdcyJElSLWbMOI0ZM07LuwxJqnbRRfzHP7+edxXNTn2GR24BjAUGAn2ATsDojf18SumylNLwlNLwnt3L61qGJEmq1dTSTZIKIiXK2wRDh+ZdSPNSUY/PjgReSyktAIiIW4HPAptHREWpt21rYHb9y5QkSZtqu+3yrkCS1pISXbsG552XdyHNS33mtM0E9oyIjhERwBeAl4C/AIeVthkH3FG/EiVJkiS1CClBmVcd21T1mdP2FNmCI88Cz5f2dRnwfeDfImIG0B24sgHqlCRJktTcVVVBRN5VNDv1GR5JSulM4My1Xn4V2L0++5U2VnmXLkTXLizbPGgfK/MuR5IkSeuTkqGtDuoV2qRclZUz78gdKR+zkF17/IOd2y4mWw9HkiRJhWRoqxNDmworbeB/6GhTwbufTLw09I+0izYY2CRJkgrO0FYnhjYV0s7tZzHzK5V0GPYZ+jy2jPK/PPvRe+U9ujN/7A68vw3s8ukZVOAlIyRJkpoFQ1udGNpUSLu2LWfq/heysLKSg8rOoP/Dkf1PDrBld3p/7XWuHfRnOpa1oTza5FusJEmSNo6rR9aJoU2FVB5ldI0OlLOM5QOXs3z0cKKU2d7vU8H+Xf/JFuUd8y1SkiRJm8bVI+vE0KZC6xBt+ePnL2Pq7v0/em2z8mXs1+ENoHN+hUmSJGnTOTyyTgxtKrTyKGPP9rBn+9lrvWNgkyRJanYMbXXigFJJkiRJTcPQVieGNkmSJElNw9BWJ4Y2SZIkSU3D1SPrxD8xSZIkSU3D1SPrxNAmSZIkqWk4PLJODG2SJEmSmoahrU4MbZIkSZKahqGtTrxOmyRJLdQjjzySdwmStCZDW53Y0yZJkiSpabh6ZJ34JyZJkiSpadjTVieGNkmSJElNwyX/68TQJqnViIh+EfGXiHgpIl6MiFPzrkmSbJvUqpSXw4oVeVfR7LgQiaTWZBXw3ZTSsxGxGfBMRNyfUnop78KkxjBixIi8S9DGsW1S6zFkCNxzDyxbBu3b511Ns2FPm6RWI6U0J6X0bOnxEuBloG++VUlq7Wyb1KrsvjusXAlTp+ZdSbNiaJPUKkXEAGAY8NRar58YEVMiYsqCBQvyKE1SK1Zb21R6z/ZJzd/uu2f3f/tbvnU0M4Y2Sa1ORHQG/gycllJaXPO9lNJlKaXhKaXhPXv2zKdASa3S+tomsH1SC9G3L/TpY2jbRIY2/X/27jw+yurs//j3SiYhCxBWIUACFkG0raDiXsWNuoCJ3evearVWW7Wtba3P01/r08dqfVq7qi1S61p3rRbcKrWodSvgLgawCmEPWyAhZJvz++OeMAkkIYEk58zk83695nXP3Blmvhnx4r7Ofe4zQK9iZlmKDorucc494jsPAEjUJvQyhx5K09ZJNG0Aeg0zM0l/krTQOXej7zwAIFGb0Asddpi0eLG0caPvJCmDpg1Ab3KUpHMkHW9mbyRup/oOBaDXozahdxk+PNpWVvrNkUJY8h9Ar+Gce1ES3+gJICjUJvQ6tbXRtk8fvzlSCE0bAABpau7cub4jAMDOtm2LtjRtHcb0SAAAAAA9hzNtnUbTBgAAAKDn0LR1Gk0bAAAAgJ5TWytlZEgxrtTqKJo2AAAAAD2ntpazbJ1E0wYAAACg59TWSjk5vlOkFJo2AAAAAD2HM22dRtMGAAAAoOfQtHUaTRsAAACAnkPT1mk0bQAAAAB6Dk1bp9G0AQAAAOg5NG2dtsumzcxuM7O1ZvZOs32DzOzvZrY4sR2Y2G9m9lszW2Jmb5nZQd0ZHgAAtG3KlCmaMmWK7xgA0NK2bTRtndSRM223Szp5h31XSZrjuXOGYQAAIABJREFUnBsnaU7isSSdImlc4naRpFu6JiYAAACAtMCZtk7bZdPmnHte0oYddpdKuiNx/w5Jpzfbf6eLvCJpgJkVdlVYAAAAACmOpq3TdveatmHOuVWJ+6slDUvcHympvNnzlif27cTMLjKzeWY2r2J9427GAAAAAJBS8vOld9+Vqqp8J0kZe7wQiXPOSXK78edmOOcmO+cmDx2cuacxAAAAAKSCb39bWrZMeuUV30lSxu42bWuapj0mtmsT+1dIKmr2vFGJfQAAAAAgDR0abeNxvzlSyO42bY9LOi9x/zxJjzXbf25iFcnDJVU2m0YJAAAAAOik2K6eYGb3SjpW0hAzWy7px5Kul/SAmV0gaamkLyae/oSkUyUtkbRV0le7ITMAAOiAuXPn+o4AAOgCu2zanHNntPGjE1p5rpN06Z6GAgAAAABE9nghEgAAAABA96FpAwAAAICA0bQBAAAAQMBo2gAAAAD0vHhc+uEPpUWLfCcJHk0bAAAAgJ73/vvS9ddL99/vO0nwdrl6ZE+Y/1btuszCJdWS1vnOspuGiOw+kN2PjmYf3d1BAABACnvjjWi7bJnfHCkgiKbNOTfUzOY55yb7zrI7yO4H2f1I5ewAACAgd9wRbZcu9ZsjBTA9EgAAAIA/NG27RNMGAAAAwJ9lyyTnfKcIWkhN2wzfAfYA2f0gux+pnB0AAPhWW5u8P26ctG2bVFHhL08KCKZpc86l7IEg2f0gux+pnB0AAARg4cLk/dLSaMtiJO0KpmkDAAAA0AsUFibvn3ZatOW6tnbRtAEAAADoOUOGJO9/4hPRljNt7QqiaTOzk82szMyWmNlVvvO0x8yKzOw5M3vPzN41s8sT+weZ2d/NbHFiO9B31taYWaaZvW5msxKP9zazVxOf/f1mlu07Y2vMbICZPWRm75vZQjM7IoU+828n/q68Y2b3mllOqJ+7md1mZmvN7J1m+1r9nC3y28Tv8JaZHeQvOQAASBmrV0fbffeVBg6U8vM507YL3ps2M8uUdJOkUyTtL+kMM9vfb6p2NUj6rnNuf0mHS7o0kfcqSXOcc+MkzUk8DtHlkppNJNbPJf3KObePpI2SLvCSatd+I+kp59wESRMV/Q7Bf+ZmNlLSZZImO+c+ISlT0pcV7ud+u6STd9jX1ud8iqRxidtFkm7poYwAACCVLV4cbU8+WTKTRo+madsF702bpEMlLXHO/cc5VyfpPkmlnjO1yTm3yjm3IHF/i6LmYaSizIlvCNQdkk73k7BtZjZK0jRJMxOPTdLxkh5KPCXU3AWSjpH0J0lyztU55zYpBT7zhJikXDOLScqTtEqBfu7Oueclbdhhd1ufc6mkO13kFUkDzKxQAAAA7XnmmWh7/PHRdvRopkfuQghN20hJ5c0eL0/sC56ZjZF0oKRXJQ1zzq1K/Gi1pGGeYrXn15K+LymeeDxY0ibnXEPicaif/d6SKiT9OTG1c6aZ5SsFPnPn3ApJv5C0TFGzVilpvlLjc2/S1uecsv/vAgAAj+bOjbYHJa6sKC7mTNsuhNC0pSQz6yvpYUlXOOc2N/+Zc85JCuobAs1suqS1zrn5vrPshpikgyTd4pw7UFK1dpgKGeJnLkmJ679KFTWeIyTla+fphykj1M8ZAACkoJGJsd7Ro6X166Xqar95AhZC07ZCUlGzx6MS+4JlZlmKGrZ7nHOPJHavaZoaltiu9ZWvDUdJKjGzjxRNQT1e0XViAxLT9qRwP/vlkpY7515NPH5IURMX+mcuSSdK+tA5V+Gcq5f0iKL/FqnwuTdp63NOuf93AQBAQMyibXFxtGWKZJtCaNr+LWlcYjW9bEWLNDzuOVObEteB/UnSQufcjc1+9Lik8xL3z5P0WE9na49z7ofOuVHOuTGKPuN/OOfOkvScpM8nnhZcbklyzq2WVG5m+yZ2nSDpPQX+mScsk3S4meUl/u40ZQ/+c2+mrc/5cUnnJlaRPFxSZbNplAAAAK1bsEBatCj5ePToaEvT1qbYrp/SvZxzDWb2TUlPK1pZ7zbn3LueY7XnKEnnSHrbzN5I7Lta0vWSHjCzCyQtlfRFT/k66weS7jOz/5X0uhKLfQToW5LuSTT2/5H0VUWDDkF/5s65V83sIUkLFK08+rqkGZJmK8DP3czulXSspCFmtlzSj9X23+0nJJ0qaYmkrYr+mwAAALTvwANbPm4608Z1bW2y6BIVAMCOJk+e7ObNm+c7BrDbLDH1iH/rWzKz+c65yb5z7AnqE9JKQ4OUkyP94AfStdf6TuNNe7UphOmRAAAAAHqrWEw6+mipb1/fSYLlfXokAAAAgF7uued8JwgaZ9oAAAAAIGCcaQMAIE1NmTLFdwQAQBfgTBsAAAAABIymDQAAAAACRtMGAAAAAAGjaQMAAACAgNG0AQAAAEDAaNoAAAAAIGA0bQAAAAAQMJo2AL2KmZ1sZmVmtsTMrvKdBwAkahOA9tG0Aeg1zCxT0k2STpG0v6QzzGx/v6kA9HbUJgC7QtMGoDc5VNIS59x/nHN1ku6TVOo5EwBQmwC0K+Y7AAD0oJGSyps9Xi7psOZPMLOLJF0kScXFxT2XDOgGkyZN8h0BHbPL2iRRn4DejKYNAJpxzs2QNEOSJk+e7DzHAfbIr3/9a98R0IWoT0DvxfRIAL3JCklFzR6PSuwDAJ+oTQDaRdMGoDf5t6RxZra3mWVL+rKkxz1nAgBqE4B2MT0SQK/hnGsws29KelpSpqTbnHPveo4FoJejNgHYFZo2AL2Kc+4JSU/4zgEAzVGbALSH6ZEAAAAAEDCaNgAAAAAIGE0bAAAAAASMpg0AAAAAAkbTBgAAAAABo2kDAAAAgIDRtAEAAABAwGjaAAAAACBgNG0AAAAAEDCaNgAAAAAIGE0bAAAAAASMpg0AAAAAAkbTBgAAAAABo2kDAAAAgIDRtAEAAABAwGjaAAAAACBgNG0AAAAAEDCaNgAAAAAIGE0bAAAAAASMpg0AAAAAAkbTBgAAAAABo2kDAAAAgIDRtAEAAABAwGjaAAAAACBgNG0AAAAAEDBzzvnOAABBMrMKSUu7+GWHSFrXxa/Z1cjYNcjYNboj42jn3NAufs0e1Q31qbf+XehqqZBRSo2cvTFjm7WJpg0AepCZzXPOTfadoz1k7Bpk7BqpkDEdpMLnTMaukwo5ydgS0yMBAAAAIGA0bQAAAAAQMJo2AOhZM3wH6AAydg0ydo1UyJgOUuFzJmPXSYWcZGyGa9oAAAAAIGCcaQMAAACAgNG0AQAAAEDAaNoAoIeY2clmVmZmS8zsKt95dmRmRWb2nJm9Z2bvmtnlvjO1xswyzex1M5vlO0tbzGyAmT1kZu+b2UIzO8J3ph2Z2bcT/53fMbN7zSwngEy3mdlaM3un2b5BZvZ3M1uc2A70mTEdUZu6Tuj1idq025m81yaaNgDoAWaWKekmSadI2l/SGWa2v99UO2mQ9F3n3P6SDpd0aYAZJelySQt9h9iF30h6yjk3QdJEBZbXzEZKukzSZOfcJyRlSvqy31SSpNslnbzDvqskzXHOjZM0J/EYXYTa1OVCr0/Upt1zuzzXJpo2AOgZh0pa4pz7j3OuTtJ9kko9Z2rBObfKObcgcX+Lon/MR/pN1ZKZjZI0TdJM31naYmYFko6R9CdJcs7VOec2+U3VqpikXDOLScqTtNJzHjnnnpe0YYfdpZLuSNy/Q9LpPRoq/VGbukjo9YnatPtCqE00bQDQM0ZKKm/2eLkCPOhoYmZjJB0o6VW/SXbya0nflxT3HaQde0uqkPTnxDSpmWaW7ztUc865FZJ+IWmZpFWSKp1zz/hN1aZhzrlVifurJQ3zGSYNUZu6Tuj1idrUtXq0NtG0AQBaMLO+kh6WdIVzbrPvPE3MbLqktc65+b6z7EJM0kGSbnHOHSipWoFN6Utce1Gq6CBuhKR8Mzvbb6pdc9H3FPFdRb1UqLVJSpn6RG3qJj1Rm2jaAKBnrJBU1OzxqMS+oJhZlqKDonucc4/4zrODoySVmNlHiqZwHW9md/uN1KrlkpY755rOBDyk6EApJCdK+tA5V+Gcq5f0iKQjPWdqyxozK5SkxHat5zzphtrUNVKhPlGbulaP1iaaNgDoGf+WNM7M9jazbEUXVj/uOVMLZmaKrnVY6Jy70XeeHTnnfuicG+WcG6Po8/uHcy64EVjn3GpJ5Wa2b2LXCZLe8xipNcskHW5meYn/7icosAUJmnlc0nmJ++dJesxjlnREbeoCqVCfqE1drkdrU6w7XxwAEHHONZjZNyU9rWg1rNucc+96jrWjoySdI+ltM3sjse9q59wTHjOlqm9JuidxEPwfSV/1nKcF59yrZvaQpAWKVuZ7XdIMv6kkM7tX0rGShpjZckk/lnS9pAfM7AJJSyV90V/C9ENt6nWoTbshhNpk0RRMAAAAAECImB4JAAAAAAGjaQMAAACAgNG0AQAAAEDAaNoAAAAAIGA0bQAAAAAQMJo2AAAAAAgYTRsAAAAABIymDQAAAAACRtMGAAAAAAGjaQMAAACAgNG0AQAAAEDAaNoAAAAAIGA0bQAAAAAQMJo2AAAAAAgYTRsAAAAABIymDQAAAAACRtMGAAAAAAGjaQMAAACAgNG0AQAAAEDAaNoAAAAAIGA0bQAAAAAQMJo2AAAAAAgYTRsAAAAABIymDQAAAAACRtMGAAAAAAGjaQMAAACAgNG0AQAAAEDAaNoAAAAAIGA0bQAAAAAQMJo2AAAAAAgYTRsAAAAABIymDQAAAAACRtMGAAAAAAGjaQMAAACAgNG0AQAAAEDAaNoAAAAAIGA0bQAAAAAQMJo2AAAAAAgYTRsAAAAABIymDQAAAAACRtMGAAAAAAGjaQMAAACAgNG0YY+Z2e1m9r+J+0ebWdluvs4fzOxHXZsOQG9FbQIQKuoTOoumrZcws4/MrMbMqsxsTaJY9O3q93HOveCc27cDeb5iZi/u8Gcvds79tKszmdl5ZjbfzDab2XIzu8HMYl39PgA6r5fXpi+bWZmZVZrZWjO7w8z6d/X7ANg9vbk+7fC+c8zMcezkF01b73Kac66vpIMkTZb03zs+IU3/h8yTdIWkIZIOk3SCpCu9JgLQXG+tTf+SdJRzrkDSxyTFJP2v30gAdtBb65MkyczOkpTlOwdo2nol59wKSU9K+oQkJUZPLjWzxZIWJ/ZNN7M3zGyTmb1kZgc0/XkzO9DMFpjZFjO7X1JOs58da2bLmz0uMrNHzKzCzNab2e/NbD9Jf5B0RGL0alPiudunCiQeX2hmS8xsg5k9bmYjmv3MmdnFZrY4kfEmM7M2ft9bEqNYdYnf/R5JR3XFZwmg6/TC2lTunFvXbFejpH325DME0D16W31KPL9A0o8lfX9PPz/sOZq2XsjMiiSdKun1ZrtPV3QWan8zO1DSbZK+LmmwpD9KetzM+phZtqS/SrpL0iBJD0r6XBvvkylplqSlksZIGinpPufcQkkXS3rZOdfXOTeglT97vKTrJH1RUmHiNe7b4WnTJR0i6YDE807q4EdwjKR3O/hcAD2kN9YmM/uUmVVK2pLI++u2ngvAn95YnyT9TNItkla38xz0EJq23uWviZGZFyXNVfQ/Y5PrnHMbnHM1ki6S9Efn3KvOuUbn3B2SaiUdnrhlSfq1c67eOfeQpH+38X6HShoh6XvOuWrn3Dbn3IttPHdHZ0m6zTm3wDlXK+mHikaXxjR7zvXOuU3OuWWSnpM0aVcvambnK5re8IsO5gDQ/XptbXLOvZiYHjlK0v9J+qiDOQD0jF5Zn8xssqJZSb/r4Hujm6XtHFy06nTn3LNt/Ky82f3Rks4zs28125etqIg4SSucc67Zz5a28ZpFkpY65xp2I+sISQuaHjjnqsxsvaIRp48Su5uP/GyV1O7FwWZ2uqIRqBN3mJIEwK9eXZsSr7PCzJ5SNCp+0G7kAtA9el19MrMMSTdLutw519DODEr0IM60oUnzQlIu6Vrn3IBmtzzn3L2SVkkaucMc6OI2XrNcUrG1foGua2VfcysVFUBJkpnlK5pusGJXv0hrzOxkSbcquqD47d15DQBepHVt2kFM0tgueB0APSNd61N/RbOS7jez1UqeFVxuZkd38rXQRWja0JpbJV1sZodZJN/MpplZP0kvS2qQdJmZZZnZZxWdym/Na4oK1fWJ18gxs6YFQNZIGpWY592aeyV91cwmmVkfRdMRXnXOfdTZXyYxx/seSZ9zzr3W2T8PIBjpVpvOMrPixP3Rkq6VNKezrwMgCOlUnyoVnbWblLidmth/sKRXO/la6CI0bdiJc26epAsl/V7SRklLJH0l8bM6SZ9NPN4g6UuSHmnjdRolnaZoNbRlkpYnni9J/1C0GMhqM9tpqmJiKsKPJD2sqHiNlfTl3fyVfiSpQNITiRWXqszsyd18LQCepGFt2l/SS2ZWrWj5/7LE7wcgxaRTfXKR1U03SRWJH61J/C7wwFpOrwUAAAAAhIQzbQAAAAAQMJo2AAAAAAgYTRsAAAAABIymDQAAAAACFsSXaw8ZMsSNGTPGdwwAXWj+/PnrnHNDfefYE9QmID1RnwCEqL3aFETTNmbMGM2bN893DABdyMyW+s6wp6hNQHqiPgEIUXu1iemRAAAAABAwmjYAAAAACBhNGwAAAAAEjKYNAAAAAAJG0wYAAAAAAaNpAwAAAICA0bQBAAAAQMBo2gAAAAAgYDRtAAAAABAwmjYAAAAACBhNGwAAAAAEjKYNAAAAAAJG0wYAAAAAAaNpAwAAAICA0bQBAAAAQMBo2gAAwG5piDdo1ZZVvmMAQDAatzWqflN9l79urMtfEUCXcs6psbFKztUpK2twN79PtRoaNqqhYYPq6zcqFuunfv0O7rb3BNB94i6uiuoKLatcpvLN5SqvLNeyymVaX7NeVx99tcYPHr/L16ipr9HSyqVaumnp9u1/Nv1H979zv5zc9ufNPnO2Th13anf+OgDQIfH6uOrX16thfYPq19er+r1qbXhig/Y6cy8N+/KwXf7ZhsoGNWxqUGNlY3S/6dbBfa7Wqe9BfTV5/uQu/b1o2oAu5pyT5GSWPJEdj9eroaEy0RBtVEPDpu3b+vqd9zU0bEzs36SGhk2SGiVJBx74kgoKjmj3/ePxWtXXb9j+Xq3db+vnzrUcGRo8+DR98pOPd/VHBKALbK7dHDVkiWasfHO5yjeXb99XvrlcdY11Lf6MyeTkNH38dI0fPF6btm1q0ZAtrWx5f2312g5neXThoy3fy6zV55ra2N/K81t7bkFOgY4ZfUyHcgFIXc45xbdGDVj9+nrVr6tv0Yy1tq+2vFauwbX6euv/tl5b5m1pt/GK18R3mSuzb6YyCzIVK4gpVhBT1tAs5e6T22Jfzuicrv44aNqAjorORG1Wbe0q1dWtVF3dKtXWttxWVj6//fm5ueMUj9eooWGTGhur2n1ts2zFYgMViw1QVtZA1dWtVm3t8hbPyc//pLZseU0bN85pcTasoWFDsyZvg+LxmvbeSbFYQeK9Bikra6D69Bm1/X4sNkix2MDt9/v0GbUnHxmAPbSxZqPeWvOW3lrzlt6teHd7c7ascpk2125u8dxMy9SIfiNUVFCkQ0Yeos/u91kV9S9ScUGxigqi7TX/vEa///fvdfWcq3XB4xfs9Bo5sRyNLhit0QNGa9LwSdvvZ2dm60sPfanNnGc8fEa3/P6tObjwYM27aF6PvR+AndVvqteWV7eodmWtFI1Vy8Vdi63iiYHseMd+vvmVzVo/a70kKXtEturX18vVtt6ASVJm/0xlDclS1uAsZQ3JUu6+uVp7T/sDTStvXqlYQSzZYA2IKac4J7lvQGx749X08+bNWGb/TGXE/FxdRtOGPeKck3P1isdrFY9vUzxeK+eS99vbLznl5o5VXt4EZWePaHNUtid+h4aGyjYbsbq6ldsbtdYaooyMfPXpM0LZ2YXKzi5UXV10fUde3v6J5mfg9oaseWPWfF9GRs7233/Filu0ePElO71PdfXbWrLkiu3vmZXV1GANUm7uOPXrN3D7vqb9O98vkFlmN36agH+vrXhNq6tWa0DOgBa3vtl9lWFhXcrdGG/U+pr1WlO1RqurVmt11Wq9v+59vbX2Lb25+k2Vby7f/txBuYM0ZsAYjR04VseNOa5FQ1bUv0iF/QoVy2j/n/Wxg8Zq4rCJKi4o1kljT9LoAaM1ZsCY7c3Z0Lyhbdbiw0cdrk3bNu20P5pdsMM+tX6g1dpzO/v83KzcVp8LpArX6NRY3ajGqg7c2nle1uAs5Y7LVd74POWOy1Xu+FzljMnZo6bCOSdXl8hX3aj41vj2+zWLalT5cqU2v7xZW9/b2oWfyM4GnTwoasYGZyk2OLa9Mdu+b1BMGVk7/57F3y/W0muXasCxA5S3b17LBq0gpozssP4N6Axrq4D2pMmTJ7t58xg1SyUffvgjLVv2f3KutkteLzOzr3Jz91Ve3r7Ky5ugvLwJys3dR2aZ25u8qOmr3c3H2+RcneLx+sS2Ts7Vqb5+faIZ29ZKpn7Kzi5s0ZAl749Qnz7RNhbr1yWfQZPq6vdVUfHg9oZu5+ZrgDIysrv0PbuDmc13znXthO4eRm1KTec8eo7ufuvunfZnWIYK+hTs1Mw1vw3MGbjTvoKcAuVn5Ss3K1c5sZxdNn5xF9eGmg3bG7E11Ylt1Rqtrl7dYn9FdYUaXWOLP59pmZowZIImDp+oA/Y6QBOHT9TEYRM1vO9wb4Nb6Yb6hJ5W+a9KvXP6O2qsalR8266n4G2XKcX6xaIpec1uGXkZqq+o19ZFW9VYmawhFjPlfCxneyOXNTRrp+YrXp2839pjNbYdJzYwpv5H9FfBkQXqf0R/5eydI8uwaGlDU3S/2VYZianPHf25mSyz99a59moTZ9qwW/r3P1yjRl2mjIwcZWT0kVmf7feTt5zE/pY/a3quFFdNzRJt3fq+tm4t09at76uy8kWtXfuXPcqWfM/m79dHGRnZifvZiTz9lJs7rtVGLDu7ULFY3675sDopP3+C8vN/5OW9gXRww4k36NuHf1sbazZq07ZNO99qk/cXb1i8/X5VXfvTmJtkZ2YrN5a7vYlruu+c05rqNVpbvVYN8YZW/9yw/GEa3nd4NIVxxCEa1jd63LR/WN9hGl0wWn1ifbr6YwHgUdawLA39wtCdmq/tt/zW91u2tTtY45xT/bp61Syu0dZFW1WzqGb7/Y1zNkbXaGVImflRo5eZn7n9lpGfoawhWS0eZ+ZnKjOv2f1mP+szqo/yxudFDRd6HE0bdsvgwdM0ePC0PX6dnJxiDRx4fIt9jY3V2rp1sWpqlkhSGw3Yjk1g0/0sRqKBXq6wX6EK+xV2+s/VN9arsray1Uavuq5a2xq2qaahRjX1NappqNnpsSRNGj5ppyas6fGAnAHUJ6CXytsnT+Nv3vWKrZ1lZsoemq3sodkqOLKgxc9c3CleF1dGnwxqTxqgaUNwMjPz1a/fJPXrN8l3FAC9SFZmlobkDdGQvCG+owDAHrMMU2YO17Gni9S9Gg8AAAAAegGaNgAAAAAI2B41bWZ2m5mtNbN3mu0bZGZ/N7PFie3APY8JAB1HbQIQImoTgN21p2fabpd08g77rpI0xzk3TtKcxGMA6Em3i9oEIDy3i9oEYDfs0UIkzrnnzWzMDrtLJR2buH+HpH9K+sGevE9nTZ0qPfts2z+fMqX1/XPn7vmfaet5qWrSJOnXv/adAugcalP61yaJ+oTUk6q1SWq9huxObWrtz6VbfaI2oTt0xzVtw5xzqxL3V0sa1tqTzOwiM5tnZvMqKiq6IQYAtEBtAhCiDtUmifoE9GbduuS/c86ZmWvjZzMkzZCkyZMnt/qc3fX3v3flqwFIN9QmACFqrzYlft4t9YnaBISvO860rTGzQklKbNd2w3sAQGdRmwCEiNoEYJe6o2l7XNJ5ifvnSXqsG94DADqL2gQgRNQmALu0p0v+3yvpZUn7mtlyM7tA0vWSpprZYkknJh4DQI+hNgEIEbUJwO7a09Ujz2jjRyfsyesCwJ6gNgEIEbUJwO7qjumRAAAAAIAuQtMGAAAAAAGjaQMAAACAgNG0AQAAAEDAaNqAPXTFFdENAEJDfQIQImpT5+3R6pEApDfe8J0AAFpHfQIQImpT53GmDQAAAAACRtMGAAAAAAGjaUsT8bjvBADQOuoTgBBRm5BKaNrSxB//KB1yiLRpk+8kAJC0ZYu0997Sfff5TgIALV12mXT66ZJzvpMAu0bTliZuvVVqaJAKCnwnAYCk++6Tli2TRo/2nQQAkqqrpbvukvr3l8x8pwF2jaYtDcyfL73+unThhRQeAGGZOVP6+Melww/3nQQAkh58UNq8OTp2AlIBTVsamDlTys2VzjzTdxIASHrrLem11xhQAhCemTOlffeVPvUp30mAjqFpS3HV1dI990hf+II0YIDvNACQdOutUna2dPbZvpMAQNJ770n/+pf0ta8xoITUQdOW4h54ILrQn9P7AEJSUyPdfbf0uc9Jgwf7TgMASTNnSllZ0rnn+k4CdBxNW4qbOVOaMEE66ijfSQAg6eGHo9VsGVACEJLaWunOO6XSUmmvvXynATqOpi2Fvfuu9NJLnN4HEJ6ZM6V99pGOPdZ3EgBI+utfpfXrGVBC6qFpS2Gc3gcQokWLpLlzpQsuYEAJQFhuvTX6CpITT/SdBOgcmrYUtmhRVHiGDvWdBACSFi+OtpMn+80BADtatEg66CApgyNgpBj+yqawqVOlJUuiGwCE4phjolUjn3rKdxIAaGnqVGnOHKmuzncSoHNo2lJYaWm0ffxxvzkAoLl+/aTjj5cee0xyzncaAEgqLY2+VHvuXN9JgM6haUthe+8tffKTNG0AwlNaGs0CeP9930kAIOnEE6XcXI6dkHpo2lJcSYn0wgvRSkgAEIrTTou2jz3mNwcANJeXF02RZCYAUg1NW4orLZWZK+kGAAAgAElEQVTicemJJ3wnAYCkkSOjhUgYzQYQmtJSqbxcevNN30mAjqNpS3EHHywVFjKaDSA8JSXSK69Ia9b4TgIASdOmRV9HwrETUglNW4rLyIgOjJ56Stq2zXcaAEgqLY2mH/3tb76TAEDSsGHSEUcwEwCphaYtDZSWStXV0nPP+U4CAEmf/KQ0ZgwHRgDCU1oqLVgQTZMEUgFNWxo47jgpP5/T/ADCYhbNBPj736OBJQAIRUlJtGVQCamCpi0N5ORIJ58cTUGKx32nAYCk0tJo6vazz/pOAgBJEyZI48fTtCF10LSliZISaeVKaf5830kAIOnoo6UBA5gJACA8JSXRpSWVlb6TALtG05Ympk2LFiXhwAhASLKypFNPlWbNkhobfacBgKTSUqm+Xnr6ad9JgF2jaUsTgwdLn/oUp/kBhKekRKqoiJb/B4BQHHGENGQIA95IDTRtaaS0VHr7benDD30nAYCkU06JzrhxYAQgJJmZ0vTp0hNPRGfcgJDRtKWR0tJoy9k2ACHp3z9a5ZbaBCA0paXSpk3SCy/4TgK0j6YtjYwdK+2/P6PZAMJTUiKVlUU3AAjF1KnRKtwcOyF0NG1pprRUev55acMG30kAIKnpO5E4MAIQkvx86cQTo5kAzvlOA7SNpi3NlJREK7Q9+aTvJACQVFQkHXggUyQBhKekRProo2hdACBUNG1p5tBDpeHDGc0GEJ7SUumll6S1a30nAYCk006TzBhUQtho2tJMRkZUfJ56Sqqt9Z0GAJJKS6PpR7Nn+04CAEnDh0uHHcaAN8JG05aGSkqkLVukf/7TdxIASJo4USou5sAIQHhKSqR586QVK3wnAVpH05aGTjhBysvjwAhAWMyiA6NnnpG2bvWdBgCSmr426W9/85sDaAtNWxrKzZU+/WlWQgIQnpISqaZGmjPHdxIASNpvv+irk7iuDaGiaUtTpaXRKf4FC3wnAYCkKVOiL9tmJgCAkJhFx05z5kSXmAChoWlLU9OmRYuSMGIEICTZ2dIpp0RTkOJx32kAIKmkRKqrk55+2ncSYGc0bWlq6FDpyCMZzQYQntLSaNn/V1/1nQQAko46Sho0iAFvhImmLY2Vlkpvvhl9YSQAhOKUU6RYjEElAGGJxaTp06OvJWlo8J0GaImmLY2VlERbVkICEJIBA6Jr2xjNBhCakhJpwwbpX//ynQRoiaYtjY0fL02YwGg2gPCUlkoLF0qLF/tOAgBJJ50UXXvLsRNCQ9OW5kpKpLlzpU2bfCcBgKSmmQCcbQMQkr59o++75WuTEBqatjRXWhrNy37ySd9JACBp9Ghp4kRGswGEp7RU+uAD6b33fCcBkmja0txhh0UrSTKaDSA0JSXRdSPr1vlOAgBJp50WbRlUQkho2tJcZmZUfJ54IvruEQAIRWlp9F1ts2f7TgIASSNGSIccwoA3wkLT1guUlkqbN0fXtgFAKA46SBo5ktFsAOEpLY2+S3LVKt9JgEi3NG1m9m0ze9fM3jGze80spzveBx0zZUq0pWkDqE8hMYvqE7UJoDaFpunY6YUX/OYAmnR502ZmIyVdJmmyc+4TkjIlfbmr3wcd17QIyVFH+c0B+EZ9CktdnTRnjnTkkb6TAH5Rm8Lz5JNSRka0NgAQgu6aHhmTlGtmMUl5klZ20/ugA266SRo7NvruEQDUp1A8/LC0Zo106aW+kwBBoDYForZWuvVWafr0aKVbIARd3rQ551ZI+oWkZZJWSap0zj2z4/PM7CIzm2dm8yoqKro6BhLeekt68UXpG9+IRoyA3qwj9Yna1HNuuknaZx/p05/2nQTwi2OnsDz0kFRRwYASwtId0yMHSiqVtLekEZLyzezsHZ/nnJvhnJvsnJs8dOjQro6BhJtvlnJypK9+1XcSwL+O1CdqU894881ouX8GlACOnUJz883SuHHSiSf6TgIkdcc/lSdK+tA5V+Gcq5f0iCSuWPCgslK6+27pzDOlQYN8pwGCQH0KxM03S7m5DCgBCdSmQLzxhvTSS9IllzCghLB0x1/HZZION7M8MzNJJ0ha2A3vg1244w6pujoqPAAkUZ+CsGlTckBp4EDfaYAgUJsCcdNN0YDSeef5TgK01B3XtL0q6SFJCyS9nXiPGV39Pmifc9FI9mGHSQcf7DsNEAbqUxjuuEPaupUBJaAJtSkMGzdK99wjnXUWA0oIT6w7XtQ592NJP+6O10bHzJkjlZVJd97pOwkQFuqTX/F4NKB0+OHRl2sDiFCb/Lv9dqmmhgVIEKZuadrg3003SUOGSF/4gu8kAJA0Z460aJF0112+kwBAUtOA0pFHSpMm+U4D7IxLLNNQebn0+OPSBRdEK0cCQChuvlkaOpQBJQBhefZZackSpm0jXDRtaeiPf4yuabv4Yt9JACBp2bJoQOlrX5P69PGdBgCSbropGlD6/Od9JwFaR9OWZmprpVtvlaZPl8aM8Z0GAJL++Mdoy4ASgJAsXSrNmiVdeCEDSggXTVuaefhhae1aLqIFEJamAaXTTpOKi32nAYCkP/wh2n79635zAO2haUszd90l7b23NHWq7yQAkPT001JFBWfZAITnrrukadMYUELYaNrSTEaGNGBAtAWAUDTVJL77CEBoMjKoTQgfh/ZppqgoWj0SAELSNIJNfQIQGo6dkApo2tJMcbG0bp20davvJACQVFQUbZct85sDAHZUXExtQvho2tJM02j28uV+cwBAcwMGSH37MpoNIDzFxdFxUzzuOwnQNpq2NMNoNoAQmUX1idoEIDRFRdEKtxUVvpMAbaNpSzNNZ9o4MAIQGqYgAQgR19wiFdC0pZmRI6MRbQoPgNBwsT+AEDFLCamApi3NZGdLw4dTeACEp7hYWrMmmoYEAKFglhJSAU1bGmI0G0CIWCgJQIgGDZJyczl2Qtho2tIQ140ACBFTkACEyIxjJ4SPpi0NNZ1pc853EgBI4mJ/AKFilhJCR9OWhoqLoy/X3rDBdxIASBo1Ktoymg0gNJxpQ+ho2tIQF9QCCFFOjrTXXtQmAOEpLpZWr5bq6nwnAVpH05aGmq4b4TQ/gNAUF1ObAISnqCi6rGTFCt9JgNbRtKUhzrQBCFVREbUJQHi45haho2lLQ0OHRt/XRuEBEJqm60ZYKAlASFjdFqGjaUtDGRmMZgMIU1GRVFUlVVb6TgIASVxagtDRtKUpmjYAIWL6NoAQ5eVJgwdTmxAumrY0xcX+AELEaDaAULHsP0JG05amioqiFZAaGnwnAYAkzrQBCBVfsI2Q0bSlqeJiKR6XVq3ynQQAkoYPl7KyODACEB7OtCFkNG1pilWQAIQoI0MaOZLaBCA8RUXRIkmbN/tOAuyMpi1N8X0jAELFNbcAQsSxE0JG05amONMGIFSsbgsgRDRtCBlNW5rq318qKODACEB4ioul5culxkbfSQAgiQFvhIymLY0xBQlAiIqKopVt16zxnQQAkgoLpcxMjp0QJpq2NMYUJAAhYtl/ACGKxaQRI6hNCBNNWxrjTBuAEHHdCIBQceyEUNG0pbGiImn9emnrVt9JACCJ60YAhIpZSggVTVsaYzQbQIgKCqR+/ahNAMLTdKYtHvedBGiJpi2NMZoNIERmjGYDCFNRkVRXJ1VU+E4CtETTlsa42B9AqIqLqU0AwsOxE0JF05bG+vSJttu2+c0BADvq04faBCA8TcdONTV+cwA7omlLY4sWRdtx4/zmAIAdLVpEbQIQHo6dECqatjRWVhZt993Xbw4AaK6hQVqyhNoEIDxlZdFCScOH+04CtETTlsbKyqTc3OSCJAAQgo8+kurradoAhKesLKpNZr6TAC3RtKWxsrLo9H4G/5UBBIRZAABC1dS0AaHhcD6NUXgAhIimDUCIqquj72ijNiFENG1pqrZW+vBDCg+A8JSVSYMHRzcACMXixdGWYyeEiKYtTf3nP1I8TuEBEB5mAQAIEbMAEDKatjRF4QEQKpo2ACFqOnZiuX+EiKYtTdG0AQjR5s3S6tXUJgDhKSuTioulvDzfSYCd0bSlqbKy6DtG+vf3nQQAkpoGlMaP95sDAHbELACEjKYtTVF4AISIWQAAQuQcx04IG01bmqLwAAhRWVn03ZFjx/pOAgBJq1ZJVVUcOyFcNG1paP366EbhARCasjJp772lPn18JwGAJGYBIHTd0rSZ2QAze8jM3jezhWZ2RHe8D1pH4QHaRn3yi1kAQOuoTX5x7ITQxbrpdX8j6Snn3OfNLFsS6/D0IAoP0C7qkyfxePTltSec4DsJECRqk0dlZVJurjRqlO8kQOu6vGkzswJJx0j6iiQ55+ok1XX1+6BtZWVSVpY0ZozvJEBYqE9+lZdLNTUMKAE7ojb5V1YWrWqbwYVDCFR3/NXcW1KFpD+b2etmNtPM8rvhfdCGsjJpn32kWHedRwVSF/XJo0WLoi1NG7ATapNnixZRmxC27mjaYpIOknSLc+5ASdWSrtrxSWZ2kZnNM7N5FRUV3RCj9+KaEaBNu6xP1Kbuw9RtoE0cO3lUWyt9+CG1CWHrjqZtuaTlzrlXE48fUlSIWnDOzXDOTXbOTR46dGg3xOidGhulJUsoPEAbdlmfqE3dp6xM6tdPGj7cdxIgOBw7efTBB9E1txw7IWRd3rQ551ZLKjezpr/6J0h6r6vfB61bsECqr5f22893EiA81Cd/nJNefjmqTWa+0wBhoTb59fLL0ZZjJ4Ssu656+pakexKrH/1H0le76X2wg1/+UurfXzr9dN9JgGBRnzx4/nlp/nzpppt8JwGCRW3yIB6XbrxR+uQnpQMP9J0GaFu3NG3OuTckTe6O10bbPvhAevBB6XvfkwoKfKcBwkR98uPnP5eGDpW+ymEo0Cpqkx+zZ0vvvSfdfTezABA2FjZNI7/4RbRi5OWX+04CAElvvik9+WRUm3JzfacBgKTrr5dGj5a+9CXfSYD2sSh8mlizRvrzn6WvfEUqLPSdBgCSbrhB6ttXuuQS30kAIOnFF6WXXpJ+9zu+Jgnh40xbmvjtb6W6OunKK30nAYCkDz+U7r9f+vrXpYEDfacBgKSf/1waMkQ6/3zfSYBdo2lLA5s3Rxf3f+5z0rhxvtMAQNIvfyllZEjf/rbvJACQ9M470qxZ0re+JeXl+U4D7BpNWxqYMUOqrJR+8APfSQAgae1a6U9/ks45Rxo50ncaAEi64QYpP1+69FLfSYCOoWlLcbW10VK1J5wgTWbNKQAB+d3vohr1ve/5TgIASUuXSn/5i3ThhdLgwb7TAB3DZZcp7u67pVWrpDvv9J0EAJK2bImmbZ9+ujRhgu80AJB0443R8v7f+Y7vJEDHcaYthTU2Rqf3DzooOtMGAKG49VZp40ambQMIy7p1UX066yypqMh3GqDjONOWwh57TFq0KFqZjS+EBBCKurpoJHvKFOmww3ynAYCk3/9eqqmRvv9930mAzqFpS1HORUvVjh0brRoJAKH4y1+kFSukmTN9JwGApOrq6FrbkhJp//19pwE6h6YtRf3zn9Jrr0l/+IOUmek7DQBE4vFoQGniROmkk3ynAYCkmTOlDRuYto3URNOWon7+c2nYMOm883wnAYCkv/1Nev/96Gwb07YBhKK+PvreyKOPlo480ncaoPNo2lLQvHnS009L110n5eT4TgMAkXhc+tnPpL33lr7wBd9pACDp7rul8nLpllt8JwF2D01biqmslM48MzrLdvHFvtMAQNJPfxpN2/7zn6UY/7oACMSiRdIVV0iHHiqdeqrvNMDu4Z/VFBKPS+eeK334ofTcc9KAAb4TAUBk1izpJz+JpmwzbRtAKKqqpM98RsrOlh54gGnbSF00bSnk2mulxx+PVj761Kd8pwGAyOLF0tlnR98ZecstHBQBCINz0vnnR9fZPvOMNHq070TA7qNpSxFPPCH9+MfSOedIl17qOw0ARKqqpM9+NpoO+cgjUm6u70QAEPnFL6QHH5RuuEE64QTfaYA9Q9OWAj74QDrrrGgJ7T/8gVFsAGFwTvra16T33pOeeopRbADhmDNHuuoq6fOfl6680ncaYM/RtAWuujqai20WjWLn5flOBACRX/1Kuv9+6frrpalTfacBgMjSpdKXviRNmCDddhuD3UgPNG0Bc0668ELpnXekJ5+MltEGgBA895z0/e9Ln/tctAWAEGzbFtWl+nrp0Uelfv18JwK6Bk1bwH7zG+nee6PvPTrpJN9pACBSXi598YvS+PHR8v6MYgMIgXPSJZdI8+dLjz0W1SggXWT4DoDWzZ0bzcH+zGeiOdkAEIKmUezaWkaxAYRlxoxoIOlHP5JKSnynAboWZ9oCtHx5NIq9zz7S7bczig0gHN/6lvTvf0cN2777+k4DAJFXXonq0ymnRKttA+mGpi0wtbXRSkdbt0r//KfUv7/vRAAQufVWaeZM6b/+Szr9dN9pACCyenU0A6CoSLrnHikz03cioOvRtAXmssukV1+NVorcbz/faQAg8uqr0je/KZ18snTNNb7TAECkvj6anbRxY3S2beBA34mA7sE1bYFYvjz64uwZM6Qf/jC6lg0AfGtsjOrStGnSyJGMYgMIx9tvRwu1vfBCNAvggAN8JwK6D02bZ9XV0k9+Eq1w9OCD0tVXSz/9qe9UACA9+6x04IHS178u7b9/9NUjgwb5TgWgt1uzJqpLkyZJb7wh/fGP0pln+k4FdC+aNk/icenOO6ML+a+5RjrtNOn996Vrr2UUG4BfZWVRTZo6Vaqqkh56KFrRloVHAPi0bZt0/fXSuHHRl2Zfdpm0ZIl00UW+kwHdj6bNgxdekA47TDrvPGnECOnFF6X775fGjPGdDEBvtmGDdPnl0ic+IT3/vHTDDdLChdEF/qxiC8AX56QHHoiu9f/hD6XjjpPefVf61a84+4/eg4VIulBVVbSC0apV0a35/eaPKyqkUaOku+6KTudn0DoD6EYNDdLata3Xpub3V66MrmG76KJoBsBee/lODiCdOSdVVrZfl5pumzZF16w9+6x0wgm+kwM9j6ZtN61fL11yScviUlW18/OysqThw6PbmDHSEUdEI0UXXijl5fV4bAC9wC9/GR3YNB3sVFREB0c7GjRIKiyMbsccE535P/vs6EwbAHS1t96KBoSaN2fbtu38vJycZG3abz/p+OOlQw+VzjqLS0jQe9G07aacHOn116OCctBB0Xb48GSRabo/aBBn0gD0rBUrpHXrpOLi6ECnqS41r03Dhkl9+vhOCqA3qa+Prt8vLJSOOmrnutR069+fKdnAjmjadlN+vrRoke8UALCzG2/c9XOcc6rctlkbajaouKBYmRkMXwPoXgcfHF2LBqDzaNoAII0457Rx20at2rJKq6pWaeWWlS3vV63Sqi3R/ZqGGknSkLwhOnXcqZo2bpo+PfbTGpAzwPNvAQAAmqNpA4A0cdLdJ2nuR3NV21i708/6ZfdTYb9CFfYt1KEjD1Vh30IV9itU/z799fzS5zVr0Szd+eadimXE9KniT2n6uOmaNn6a9h28r4x5SgAAeEXT1kmNjdVauXKGCgvPVyxW4DsOAGx33JjjNHHYRBX2LdSIfiO2N2mF/QrVN7tvm3/uooMvUmO8Ua8sf0WzF8/WrEWzdOXfr9SVf79SYweO1bRx0zR9/HQdM/oY9YlxIRwAAD2Npq2TVq36sz744Dvq3/8wFRQc6TsOAGx31aeu2u0/m5mRqaOKj9JRxUfpZyf8TMsql2n2otmavXi2ZiyYod++9lv1ze6rqR+bqmnjpunUcaeqsF9hF6YHkI5co1Ptylr1GdWHs/bAHqBp6wTnGrV8+a/Vv//hNGwA0lpxQbG+ccg39I1DvqGt9Vv1jw//odmLZmvW4ll69P1HJUkHFx6s6eOna9q4aTp4xMHKMJbKBdBSzZIavTbhNU24a4KGnz3cdxwgZdG0dcK6dY9r27YP9LGPXec7CgD0mLysPE0fP13Tx0/Xze5mvb32bc1aNEuzF8/W/8z9H10z9xoNyx+maeOmadr4aZr6sanq16ef79gAAlD1RvQltvmfyPecBEhtNG2dUF7+S+XkjNGQIZ/xHQUAvDAzHTDsAB0w7ABdffTVWrd1nZ5a8pRmLZqlhxc+rNveuE1ZGVmaMmaKpo+brjM+eYb2yt/Ld2wAnlS9WSWLmfL3o2kD9gRzWTpo8+ZXtXnzvzRq1BXKyKDXBQAp+rqAsw84W/d9/j5VfK9C/zzvn7ri8Cu0YvMKXfH0Fdrnt/vouheuU019je+oADyoerNKefvnKaMPh5zAnuD/oA4qL79RmZkFGj78fN9RACBIWZnRGbYbpt6g9y59T+984x0dt/dxuvofV2vCTRP0l7f/oriL+44JoAdVvVmlvhPbXr0WQMfQtHVATc1Hqqh4SCNGXKRYjOs0AKAjPr7Xx/XYlx/TP879hwbnDtZZj5ylw2cerheXveg7GoAeULeuTnUr6mjagC5A09YBK1b8RmYZGjnyMt9RACDlHLf3cZp30TzdXnq7VmxZoaP/fLQ+/8Dn9cGGD3xHA9CNqt+sliTlT+R6NmBP0bTtQkNDpVatmqmhQ7+knJxRvuMAQErKsAydN+k8LfrmIl1z7DV6aslT2u+m/fTdp7+rjTUbfcdLae+ve1+1DbW+YwA7qXozWjmSM23AnqNp24WVK29VY2OVioq+4zsKAKS8/Ox8/b8p/0+Lv7VY5048V7965Vfa53f76Dev/EZ1jXW+46WUhniDrnzmSu130376+M0f16MLH5VzzncsYLuqN6qUPSJb2UOzfUcBUh5NWzvi8XqtWPEbDRhwrPr1O8h3HABIG4X9CjWzZKZe//rrOqjwIF3x9BX6+M0f11/f/yuNRwesqVqjqXdN1S9f/qXOOeAc5cRy9NkHPqvj7jhOr6963Xc8QBKLkABdiaatHRUVD6m2drlGjfqu7ygAkJYmDp+oZ85+RrPPnK2sjCx95v7P6Ng7jtW8lfN8RwvWK8tf0cEzDtYry1/RHaffoTs/c6feuPgN3TLtFr1b8a4OnnGwzn/sfK3assp3VPRi8bq4ti7cqr6TaNqArkDT1o7ly29Ubu6+Gjz4VN9RACBtmZlOHXeq3vrGW7pl2i1aWLFQh9x6iM559ByVV5b7jhcM55xu+fctOubPxyg7M1svX/Cyzp14riQplhHTxZMv1uJvLdZ3j/iu7n7rbo373TgtrVyquGv0nBy9UfV71XL1jjNtQBehaWuDc3Ft2bJAQ4aUyIyPCQC6W1PjseSyJbrqqKv04LsPavzvx+u///Hf2lK7xXe8DnPOaemmpfpo00cqryzXyi0rtaZqjdZvXa9N2zZpS+0Wba3fqtqGWjXGGzs0HbSmvkZffeyruuSJSzR17FTNv2i+Jg2ftNPzBuQM0P99+v/03qXv6dNjP62PNn6o11a8pvveuY9pp+hRrBwJdK2Y7wDhMmVk5CoeZ0UuAOhJ/fv013UnXqeLJ1+sq/9xta594VrNXDBT/3Pc/+j8A89XLCPMf7riLq5HFz6qn734My1YtaBTfzbDMpRpmYplxJSZkalMy9y+jWXEtK1hmzZt26SfTPmJfjTlR8rYxWDiPoP20SNfekSTfrVJH2xYojMePkO/ffW3+tVJv9Jhow7bk18T6JCqN6uUkZuhvHF5vqMAaSHMf/kCUF+/VvF4tXJzx/qOAgC90ugBo3XPZ+/R5Yddru88/R19fdbX9bvXfqfrTrhOJ37sROXEcnxHlKT/396dx0dZ3nsf/16zZSZkXwiBBMISdtkFlNa6Vkpdq8djj9XW2nraWrcuHmv7dNPW9tRalda29mitrZW2amufVvE5tYsrFGSRJQQCsiQEEkCyk2Xmev4YSEECZGAm9z0zn/fr5QsYZiZfovzMN9d1X7e6w91atHaR7n31XlXtqVJlQaV+cOEPlBfMUzgSVtiG1RPp6f15LI/1RHoUtmFFbERXTbpK7x/9/piy5QXzNGPoTH3sksd011/v0txH5+qa067Rvefdq/Lc8gR9RgCpbW2bQqNDMl7jdBQgJVDajqG9fZMkKRSqdDgJAKS32cNm65XrX9GzVc/qv/7yX7r4qYvl9/g1dchUzR02V3PK5mhu2VyNzh8tYwbuC8QDPQf0+KrH9d3Xvqut+7dqSskULbpika6ceKW8Hu+A5TgRI6Prp1+vKydeqe++9l3d9/p9erbqWX3hzC/ojnl3KCvANUeIv5w5Odp2zzY1/r5RxZcXOx0HSHoJK23GGK+k5ZLqrLUXJerjJEpHB6UNSFXJPp/SkTFGV0y8QheNvUgv1LygJbVLtKR2iX6+6uf64bIfSpIKQ4WaPWy25pbN1ZxhczR72Gzlh/LjnqWtq00/ffOnuu/1+1TfWq85w+boofkP6aKxFw1oaYxVdka27jn3Hn1yxid150t36u6X79ajKx/Vt8/9tq6deu0Jt1wi8VJpNo34PyO0b/E+Vd9QrexZ2QqWu2NlHEhWiVxpu1VSlaScBH6MhOno2CRjfAoGK5yOAiD+kno+pbMMX4YuG3+ZLht/maToDabXN67XktolWlq7VEvrlmpxzWJZRQ/dGFc4TnPK5mjOsOhq3GmDT5Pf6z+pj73/wH4tXLpQDy59UHs79urckefql5f/UueOPNfVZe3dRuSN0FNXPKWbZ9+s21+8XR977mNa+M+F+sGFP9B7R7zX6XjpLmVmkyfg0YRfT9CbM95U1bVVmvbSNLZKAqcgIaXNGFMm6YOSviXpc4n4GInW0bFJweBIeVx6wTuAk5MK8wn/4vP4NKVkiqaUTNGNM2+UJDV3NmtZ3TItrVuqJbVLtLhmsZ5Y/YQkKegLamLxRAW8ARkZGWP6/ePS2qVq6WrRRWMv0pff+2XNLZvr5B/9lJ1ZfqbeuOENPbXmKd350p066/Gz9NnTP6uFCxY6HS0tpeJsyqzMVOWPKrXhoxu07d5tqvhKhdORgKSVqEbygKQ7JGUf6wnGmBZIx2MAACAASURBVBsl3ShJw4cPT1CMk9fevomtkUBqOu58cvtswonlZOTovFHn6bxR50k6eAR/07be1bgNezdEj9qXlbW2zx8jkUjvryXJyurS8ZfqC2d8QVOHTHXyjxdXHuPRNVOu0eUTLtd9r9+ncYXjnI6UzpL+a6e+lFxbon0v7tPWr29V/rn5yj0z1+lIQFKKe2kzxlwkqcFa+6Yx5uxjPc9a+4ikRyRp1qxZrrp5jLVWHR01yss72+koAOKoP/PJzbMJJ8cYo4q8ClXkVejqyVc7HceVMv2Z+ur7vup0jLSVCl87HYsxRmN/PFbNbzRr/X+s16xVs+TPO7ktykA6S8RVx/MkXWKM2SppkaRzjTG/SsDHSZiurnpFIm3KzGSlDUgxST+fAKSklJ5NvhyfJj41UV11Xdr4qY3c6B04CXEvbdbaL1lry6y1FZKulvRXa+1H4v1xEomTI4HUlArzCUDqSYfZlDMnRxV3V6jxN43a9fNdTscBkg7n+/aBe7QBAADE1/A7hivv3DxtunmT2je1Ox0HSCoJLW3W2r8n431Gosf9BxQMJsdFvgBil6zzCUBqS+XZZDxGE56YIE/Qo6prqhTpjjgdCUgarLT1oaNjk0KhUYre4xIAAADxkDEsQ+MeGaeWZS3a+o2tTscBkgalrQ/R0sbWSAAAgHgrvqJYQ64fou33btf+V/c7HQdICpS2d7E2oo6OGkobAABAgox5cIyCI4Oq+kiVepp6nI4DuB6l7V06O+sUiRxQONyq1ta1ikQ6nY4EAACQUnzZPk18cqI6azu18aaNTscBXC/uN9dOBR5PSPX1j6i+/hFJHgWDFcrMHK/8/AtUVnarjDFORwQAAEhqOXNyVPHVCm392lYVfrBQJR8ucToS4FqUtncJBss1b16j2ts3qr19w8F/qtXWtlqbN9+u7OxZyst7j9MxAQAAkt7wu4Zr34v7tPHTG5V7Zq6CI4JORwJcidLWB693kLKzpys7e3rvY+Fwm954o0x1dQspbQAAAHHg8Xk04ZcTtHzaclVdW6Vpf5sm42VHE/BuXNPWT17vIJWWfkKNjc/owIFap+MAAACkhNCokCp/WKmmV5q0/b+3Ox0HcCVKWwyGDr1JktXOnT92OgoAAEDKKLm2RMVXFWvrV7eqeXmz03EA16G0xSAUqlBh4cWqr39E4fABp+MAAACkBGOMxv5krAJDAqq6pkrhtrDTkQBXobTFqKzsFnV371FDwyKnowAAAKQMf75f458Yr45NHar5XI3TcQBXobTFKC/vHGVmTlJd3UOy1vb7dV1dDers3JXAZAAAAMkt/5x8lX+xXPWP1GvPc3ucjgO4BqUtRsYYlZXdrNbWlWpufv2Ez+/padGWLV/RkiUVWrlynqyNDEBKAACA5DTy7pHKmp6lDTdsUGd9p9NxAFegtJ2EkpKPyOfLU23tQ8d8TiTSrbq6h7V06Wht3/4tDRp0mg4c2KJ33nlpAJMCAAAkF0/AowlPTlCkLaIN12+QjfR/ZxOQqihtJ+F4x/9ba7Vnz3Natuw0bdp0kzIzJ2rGjH9q2rR/yOcrUH39ow6lBgAASA6DJgzS6PtH650X31HdD+ucjgM4jtJ2koYO/YykiHbu/EnvY83N/9SqVe/T2rWXyRijyZOf07Rpf1NOzunyeoMqKfmI9uz5vbq79zoXHAAAIAkM/dRQFV5UqM13bFbrmlan4wCOorSdpFBopAoLL1F9/U/V1laldeuu1ooVc9TeXq3Kyh9r1qw1Kiq6RMaY3teUlt4ga7u0e/evHEwOAADgfsYYjXt0nHy5Pq29fK3aN7U7HQlwDKXtFBw6/n/Zsonau/ePGjHiK5ozp0bDhn1KHo/vqOdnZU1Rdvbpqq//n36fPNnZuUs1NZ9XTw83mgQAAOklMDigyX+YrHBTWCvmrtD+f+x3OhLgCErbKcjLO0fFxVeptPQTmjNnk0aOvFs+X/ZxX1NaeoPa2taqpWXZCd+/p6dFa9YsUG3t/WppWR6v2AAAAEkj94xczVgyQ4HBAa2+YLXqH693OhIw4Chtp8AYo0mTfqNx436mjIxh/XrN4MEflseTecIDSSKRbq1ff5VaW1cefISTkwAAQHoKjQ5p+hvTlXtWrqqvr9aWu7ZwqiTSCqVtgPl8OSou/jc1NDylcLitz+dYa7Vx46e0b99ilZR8tPcxAACAdOXP82vKC1NUemOptt+7Xev/fb3C7WGnYwEDgtLmgNLSGxQOt6ih4Xd9/v62bd/Url2PacSIr6q09BMHH6W0AQCA9ObxezT2J2M1+vuj1fhMo1advUqdu7gBN1Ifpc0BubnvUSg0Vrt2Hb1Fsr7+MW3d+nUNGfIxVVR8/YjTJwEAANKdMUblnyvX5D9MVtu6Nq2YvUKtb3FLAKQ2SpsDjDEqLb1BTU2vqr29uvfxvXsXq7r6RuXnv19jxz5ysLAdKm2stAEAABxSdEmRpr86XTZitXLeSu19nvvgInVR2hxSUnKdjPH1HkjS0rJC69Zdqays0zRp0tPyePwHn0lpAwAA6Ev29GzNXDpTocqQ1ly8Rrue2OV0JCAhKG0OycgYosLCi7Rr1y/U3r5Jb721QH5/oU477fk+bxvAQSQAAABHyxiWoemvTFfe2Xmq/mS1mpdxb1ukHkqbg0pLP6Hu7gatWDFb1nZqypTFysgofdezWGlzu9NO+7WGDVvqdAwAANKWd5BXk347SYHSgNZduU5de7qcjoQTYUEiJpQ2B+XnX6hAYKjC4XZNnvxHDRo04ajn/OsgEv7DdqsFC27SlClPOh0DAHr1RHpU21yrsJfDGZA+/IV+TX5msrp2d6nqw1WyYb52cqO29W36ysv/0MQ9jU5HSSqUNgd5PD5Nnvycpk9/WXl57z3Gszg90u3a24uUmbnH6RgA0GtH0w6V/6BcjcXPOB0FGFDZM7M19uGxeucv7+jtr77tdBz0wTvIK5+1yujhHnux8DkdIN3l5Mzq5zP5bpFbRUsb3y0C4B5BX1CSFPEccDgJMPBKP16q5iXN2v7t7cqZnaOiS4ucjoTDeHO8kqSMcI/DSZILK22uF11p4yAS9/L7i1RYyEobAPcI+UOSpNLyA5o2zeEwgAPGPDRG2bOyVXVdldo3tjsdB4fxZkdL24hiVtpiQWlzOb+/WEOGXK+MjDKno+AYpk0rUkkJpQ2AexxaafvAxR164AGHwwAO8Aa9mvTMJBm/0doPrVW4jYLgFh6fR55Mjy6Yx0pbLChtLhcKVWj8+MeUnc23St3K7y9Sd/ceVkMBuEaGN0OSdKCH7ZFIX8HhQU1cNFHtVe2q/kQ1/592EV+OT+FminQsKG3AKfL7ixWJHFAkwvYLAO5gjFGGN4PShrRXcH6BRn5rpBoWNajuoTqn4+Agb7ZXPc2stMWC0gacIr8/eoFzdzdbJAG4R8gfUkd3h9MxAMcN/6/hKrqsSJu/sFn7X9nvdBwoehhJuIWVtlhQ2oBTRGkD4EZBX5CVNkDRlefxj49XcFRQ669ar876TqcjpT22R8aO0gacIkobADcK+oI6EKa0AZLky/Vp8rOT1dPco3X/tk6R7ojTkdKaN4ftkbGitAGniNIGwI1YaQOONGjSII1/bLyaX2vW5i9sdjpOWmOlLXaUNuAUUdoAuFHIxzVtwLsN/vfBKru9THUP1Wn3r3c7HSdtcRBJ7ChtwCny+fIkedTV1eh0FADoxUob0LdR3x2l3PfmqvqT1Wpd0xrTa23E6kDtAW4fcIp8OT6FW8J8HmNAaQNOkTEe+f2FrLQBcBVKG9A3j9+jib+dKF+uT+s+tE7d+7uP+dye5h7t+9992vqNrVo9f7VeLXhVS8qXaOfDOwcwcerx5nhlu60inVxb2F8+pwMAqeDQDbYBwC2CvqAa29kBAPQlY0iGJv1ukladvUobrtugyX+YLBmpY1OHml5vUvMbzWp+o1lta9skK8lIgyYP0uB/H6y2t9q05a4tKr6yWIGSgNN/lKTky4lWkHBzWN6g1+E0yYHSBsSB319MaQPgKqy0AceXOy9Xo+8frZpbarRi7gp1bOlQz97odVbeXK9y5uao+Mpi5ZyRo5zZOfLlRr9sbt/YrmWnLdPmL27WhCcmOPlHSFrenGhR62nuUWAwxbc/KG1AHPj9RWpvr3Y6BgD0CvlDlDbgBIZ9dpja17dr/8v7VXRpkXLOyFHuGbnKnJAp4zF9viZzbKbKv1iu7d/artJPlCrvrLwBTp38Dl9pQ/9Q2oA4iG6PfM3pGADQK+hlpQ04EWOMxv54bMyvG3HXCO3+1W5tummTZq6YKY+fYyJi4c2OrrSFWyht/cV/YUAcHLqmjVOQALhF0BfkyH8gQbyZXlU+WKm2tW2qW1jndJykc/j2SPQPpQ2Ig+i92sLq6WlyOgoASOKaNiDRCi8pVMEHC7T1a1vVubPzpN4jXU9PZHtk7ChtQBxwg20AbsM1bUBiGWNU+WClIt0Rbf785phe272vW2suXaNXC15V/eP1CUroXqy0xY7SBsTBv0obx2sDcIegL6iwDas7fOx7UAE4NaHRIY340gg1LGrQO399p1+vaXqtScunLde+F/Ypc1ymqq+vVtVHq9TTmj4FhpW22FHagDhgpQ2A2wR9QUlitQ1IsPI7yhUcFdSmmzYp0nXs7Y42YrXtO9u08n0rZfxG01+frpnLZmrE10Zo9y93a8XpK9S6pnUAkzvHE/JIXqmnJX2K6qmitAFx4PcXS6K0AXAPShswMLwhryoXVqp9Q7tqf1Db53O6Grr01gfe0ttfelvFVxRr1opZypmVI+M1Gvn1kZr6l6nq2d+jFbNXaOfPdqb8wWbGGPmyfay0xYDSBsRBIDBEHk9Qra2rnI4CAJKkkC8kidIGDITCBYUquqxIW7+5VQd2HPl37p2/vqPlU5dr/z/2a+xPxmrioom9N+o+JP/cfM1aNUu5783Vxhs3quqaqpRfhfLmeLmmLQaUNiAOvN6g8vMv0J49z6X8d8cAJIdDK20dPRz7DwyEMQ+MkaxUc3uNJMmGrd7+2ttaff5q+fJ8mvnPmRr6n0NlTN837Q6UBDRl8RSNvGekGn7ToDdnvKmWlS0D+UcYUL4cVtpiEffSZowpN8b8zRiz3hizzhhza7w/BuBGRUWXqbNzm1pbVzsdBcfAfEI6YXtk8mA2pYbgiKBGfGWE9jyzR/U/r9eq81Zp2ze3qeS6Es1YNkNZU7JO+B7GYzTiyyM07W/TFG4Pa8UZK1T3cF1KfkOYlbbYJGKlrUfS5621EyXNlXSTMWZiAj4O4CqFhRdL8mjPnj84HQXHxnxC2sgP5UuSGts41TYJMJtSRPnnyxUaG1L1x6vVsqxF4x8frwmPT5Avy3fiFx8m76w8zVo1S/nn5mvTTZu0/qr16mlKrYLDSlts4l7arLX11toVB3/eIqlK0rB4fxzAbQKBYuXmnqm9e59zOgqOgfmEdDKmYIwkafM7sd0/CgOP2ZQ6PBkejX98vIouK9LMN2dqyEeHnPR7BYoDOu1Pp2nUd0ep8feNWj5juZqXN8cxrbM8QY8iB9Lz5uInI6HXtBljKiRNl7S0j9+70Riz3BizvLGR7wIiNRQVXabW1lXq6NjqdBScwLHmE7MJqaIsp0wZ3gzV7KtxOgpiwNdOyS/3jFxN/v1kDRo/6JTfy3iMht8xXNNfni7bbbXyzJWqfbA2JbZLhtvC8g7yOh0jaSSstBljsiQ9I+k2a+1R3xaw1j5irZ1lrZ1VXFycqBjAgCoo+KAkaf/+vzqcBMdzvPnEbEKq8BiPRuWPorQlEb52wrHknpmrWStnqWB+gWpuq9G6D61T9zvdTsc6JeGWsLzZlLb+SkhpM8b4FR06T1prn03ExwDcKDNzrLzebLW0rHA6Co6B+YR0MqZgDNsjkwSzCSfiL/Rr8nOTNfr+0dr7p71aPn25mpcm73bJcGtY3ixKW38l4vRII+lRSVXW2vvj/f6AmxnjUVbWdLW2vul0FPSB+YR0M6ZgjGr21aTEVqpUxmxCfxljVH57uaa/Nl3GGK18z0rt+P4O2Ujy/R0Pt7LSFotErLTNk3StpHONMasO/rMgAR8HcKXs7JlqbV2tSCS1TnlKEcwnpJXR+aPV3t2u+tZ6p6Pg+JhNiEnO7BzNXDlThRcXasf3d6hnf/J9zdHT0sNKWwxiO3+0H6y1r0rq+66BQBrIypqhSKRD7e0blJU12ek4OAzzCelmSskUSdLS2qW6fMLlDqfBsTCbcDL8eX5NemaSOus65S/wOx0nZqy0xSahp0cC6Sg7e6YkqbWV69oAOGtu2VxlB7K1uGax01EAJIAxRsGyoNMxYhbpjsh2WlbaYkBpA+IsM3OsPJ5BamnhujYAzvJ7/Tpv1Hl6cfOLXNcGwDXCrdGbalPa+o/SBsSZMV5lZU2jtAFwhfmj52tb0zZV7612OgoASJIindGbansyqCL9xWcKSIDoYSSrZG3Y6SgA0tyFYy6UJL1Y86LDSQAgyhOMVpBD5Q0nRmkDEiA7e4YikTa1t290OgqANFeRV6FxheO0eDPXtQFwh97S1kFp6y9KG5AAWVkcRgLAPeaPma9/bP2HOro7nI4CANFtkYbSFgtKG5AAmZnj5fGEuK4NgCtcOPpCdfR06JXtrzgdBQBkjJEn6FHkAKWtvyhtQAJ4PD5lZU2ltAFwhfdVvE8Z3gyO/gfgGp6gh5W2GFDagATJypqp1taVspaBBMBZmf5MnTXiLL24mcNIALiDJ8RKWywobUCCZGfPUDjcoo6OGqejAIDmj5mv9Y3rtaNph9NRAECeoEfhDk7Z7i9KG5Ag2dnRw0haWjiMBIDzLhx98Oh/VtsAuIAnxPbIWFDagATJzJwoYzLU2sp1bQCcN7F4ospyyriuDYAreENetkfGgNIGJIjH41dW1hQOIwHgCsYYzR89X3/Z8hf1RHqcjgMgzXEQSWwobUACZWfPVEvLCllrnY4CALpwzIVq6mzS0tqlTkcBkObYHhkbShuQQFlZMxQON+nAgS1ORwEAnT/qfHmNV3/a+CenowBIc5weGRtKG5BA/zqMhC2SAJyXF8zTRWMv0sJ/LtT2pu1OxwGQxjg9MjaUNiCBBg2aLJ8vT5s3f1EtLaucjgMAemD+A7Kyuun5m9i6DcAxrLTFhtIGJJDHE9DUqX+RFNHq1efLWr6jBMBZFXkVuvucu/WnjX/Snzf92ek4ANIUB5HEhtIGJFh29kwNH36nenr2qrt7r9NxAEC3zLlFxZnFWrR2kdNRAKQpb8hLaYsBpQ0YAH7/YElSV1eDw0kAQPJ5fJo/Zr4W1yxWOMIOAAADj+2RsaG0AQMgECiRJHV373Y4CQBELahcoL0de7V853KnowBIQ56gR7bbyoa5trY/KG3AAGClDYDbvH/0++UxHj2/6XmnowBIQ55QtIZwgmT/UNqAARAIREtbdzelDYA7FIQKNLdsrp6vobQBGHjeTK8kqbux2+EkyYHSBgwAny9PxvhYaQPgKgvGLNDyncu1u5Wt2wAGVv6F+TI+o+3f4Z6R/UFpAwaAMR75/cWstAFwlQWVCyRJi2sW9/n7e9r36O9b/z6AiQCki8wxmSq5rkQNv25QpIsDSU6E0gYMEL9/sLq6+G42APeYNmSaSrNK9ULNC5Kkmn01R9xw+9EVj+qcX5yj1btWOxURQAoruqRI4dawml5rOun3aPhNg6qurUr5A00obcAACQRKjtgeaW1E4fABBxMBSHfGGM0fM18vbn5Rf9/6d1UurNSTa57s/f2IjX73+5svf9OpiABSWN65eTJ+o30v7Dvp99j5s53a/avd2vGDHXFM5j6UNmCABAKDj9geWVV1nZYvn6JwuM3BVADS3YLKBdp/YL8+/edPS5Luf+P+3tW2gDcgSXq26llW2wDEnS/bp9yzcrX3+b0n9fpIT0TNS5olr7T1/2xVe3V7nBO6B6UNGCDR7ZHR0tbc/E81NDypjo5N2rbtXoeTAUhnF4y6QF7j1YY9GzQse5hW7lqpl7e9LEnye/2SouWN1TYAiVD4gUK1r2vXgR0H1LSkSd37+n+aZNuaNkXaIhr9vdHyhDza8PENKbtNktIGDJBAYLAikTaFw23asuUu+f3FKiq6Qjt2fE/t7ZuOen5d3cNqanrDgaQA0kluMFfvGf4eSdJTVzylwlChHlj6gKR/rbR9bOrH9GzVs3pr91uO5QSQmgo+UCBJ2vvnvVp19iqtvmC1wu39u3db8+vNkqTiDxVrzENj1Px6s2ofqk1YVidR2oABcugG2w0Ni7R//0saPvwuVVYulMeToZqaW4+4+F+Stm79hqqrP3nU4wAQb58743P69KxP6z3D36P/nPmfem7Dc9ryzhb5PdGVts+c/hnlZOTom/9gtQ1AfGVOyFTGiAw1PNkg22nVuqI1umLWj69/ml5vUmBoQBnDM1RyTYkKLy7U23e9rfZNqbdNktIGDJBDN9jevPkOZWSUa+jQTykjo1QVFV/Xvn0vaO/e/3vE8z2eoNrb12nfvhediAsgjVwy7hI9/MGHZYzRZ07/jLwerxYuXdi7PTIrkKXb5tymZ6qeYbUNQFwZY1T4gUI1vRo9QTLnjBw1/qZR27994vu3Nb3WpNwzc2WMkTFGY38yVp6gR9Ufr5aNpNY3vSltwAA5tNLW07NPFRVfl9cblCQNG3azMjMnqabmVoXDHb3P93hCkqQdO+4b+LAA0tawnGG6atJVenTlozrQEz3htjvSrdvm3sZqG4CEOLRFUpIqvlGhko+U6O2vvK3GPzQe8zWddZ3q3NapnHk5vY9lDM3QmAfGqOnVJtX9sC6RkQccpQ0YIIFAiSQpFBqnkpLreh/3ePyqrPyhDhzYqh07/rv3ca83U5K0f/9LamlZObBhAaS12+bcppauFv3qrV9JkrrCXcoP5evWObey2gYg7vLOzZMJGEmSv9ivsT8bq+zZ2ar6SJVa32rt8zVNb0RX5nLPzD3i8ZLrSlSwoEBb7tyi9prU2SZJaQMGSCAwVAUFC1RZ+UN5PL4jfi8//2wNHny1tm//jjo63pYUXWnLypoprzdLO3Z834nIANLU6cNO17zyeXpl+yuSpO5w9DS3Q6ttd798t5PxAKQYX5ZPeWflSZICxQF5g15N/v1k+XJ9WnPJGnU1dh31mubXmuUJepQ1LeuIx40xGvfIOJmAUfUNqbNNktIGDBCPx6cpU/6sgoLz+/z9UaO+J8mrmprbJEVX2jyegEpLP6mGhkU6cCC1bxoJwF1um3tb78+7I9HSVhAq0K1zbtXT65/Wmt1rnIoGIAUNvmawAqUB+Yuj19JmDM3Q5D9MVvfubq27cp0iXZEjnt/0epOyZ2fLEzi6zmQMy9CY+8eo6eUm1T2cGtskKW2ASwSDZaqo+Kr27v2j9u59Xh5PSJFIh8rKbpUk1dY+6HBCAOnksvGXaXjucEnR7ZGH9F7bxn3bAMTRkI8O0Rl1ZxxRwnJOz9G4R8ep6eUmbbp5U++JkuGOsFpXtB61NfKI97t+iPIvzNeWO7eoY0vHMZ+XLChtgIuUld2mUGicampuleRRONyuYHCEBg++SvX1j6inp8npiADShM/j082zb5Yk9UR6eh8vCBXoltm3sNoGIK4OnQD5biX/UaLhXxqu+kfqVfej6KpZy7IW2R6rnDNzjnr+4e837mfjZDypsU2S0ga4iMcTUGXlQnV01GjfvhcUiUS/M1Re/nmFwy3aufNnDicEkE4+PevTuuecezS3bO4Rj99+xu3KDmSz2gZgQIy8Z6QKLy5UzW01eueld9T0+r9uD3A8wfKgRt8/Wvv/vl87f7pzIKImDKUNcJmCggtUVHSFrO1SOBw99Sg7e6by8s5RXd2Dihy8tgQAEm1QYJC+fNaXlenPPOLxw69tW9uw1qF0ANKF8RhNeHKCMsdnat2/rVPj040KjQspUBQ44WtLbyhV/gX52vzFzerYmrzbJCltgAuNGXN/7zVth5SXf0GdnbVqbPytg8kAIKp3tY37tgEYAL5sn07742mSkVrfPP71bIfr3SZpjKo/Ud17XVyyobQBLhQMDteoUf+trKypvY8VFMxXZuZE7dhxX9IOHACpoyBUoFvm3KLfrf8dq20ABkRoVEiTnp4kEzDKvyC/368Ljghq9H2j1VHToc66zgQmTBxKG+BSZWWf1YwZr/f+2hiPyss/r9bWVdq//68OJgOAqNvnstoGYGDln5OveXvnafDVg2N6XemNpTp97ekKlgUTlCyxKG1AEikpuUZ+f4l27LjP6SgAoMLMQt0y5xaubQMwoHxZvj5PmjweY4x8Wb4EJUo8ShuQRDyeDJWV3aJ9+xartZUvkAA47/a5tysrkKW7X77b6SgAkLIobUCSGTr0U/J4Bqm29vtORwEAFWYW6ubZN+t367i2DQAShdIGJBm/v0ClpTeoo+NtWRt2Og4A6HNnfE6DAoN0z8v3OB0FAFJS8m7sBNLY6NHfk8dz4nuTAMBAKMws1BOXPaGpQ6ae+MkAgJhR2oAkRGED4DaXT7jc6QgAkLLYHgkAAAAALkZpAwAAAAAXo7QBAAAAgItR2gAAAADAxRJS2owx840x1caYGmPMnYn4GABwMphPANyI2QTgeOJe2owxXkk/kvQBSRMlfdgYMzHeHwcAYsV8AuBGzCYAJ5KIlbbZkmqstVustV2SFkm6NAEfBwBixXwC4EbMJgDHlYjSNkzSjsN+XXvwsSMYY240xiw3xixvbGxMQAwAOMoJ5xOzCYAD+NoJwHE5dhCJtfYRa+0sa+2s4uJip2IAwBGYTQDcivkEpK9ElLY6SeWH/brs4GMA4DTmEwA3YjYBOK5ElLZlkiqNMSONMQFJV0v6YwI+DgDEivkEwI2YTQCOyxfvN7TW9hhjPivpRUleSY9Za9fF++MAQKyYTwDciNkE4ETiXtokyVr7Bcl5ZgAABcdJREFUvKTnE/HeAHAqmE8A3IjZBOB4HDuIBAAAAABwYpQ2AAAAAHAxShsAAAAAuBilDQAAAABcjNIGAAAAAC5GaQMAAAAAF6O0AQAAAICLUdoAAAAAwMUobQAAAADgYpQ2AAAAAHAxShsAAAAAuBilDQAAAABcjNIGAAAAAC5GaQMAAAAAF6O0AQAAAICLUdoAAAAAwMUobQAAAADgYpQ2AAAAAHAxShsAAAAAuBilDQAAAABcjNIGAAAAAC5GaQMAAAAAF6O0AQAAAICLUdoAAAAAwMUobQAAAADgYsZa63QGGWMaJW2L89sWSdoT5/eMNzLGBxnjI94ZR1hri+P4fgOO2eRqZIyPdM3IfDpauv63EG/JkFFKjpzpmPGYs8kVpS0RjDHLrbWznM5xPGSMDzLGRzJkTAXJ8HkmY3yQMT6SIWMqSIbPMxnjJxlykvFIbI8EAAAAABejtAEAAACAi6VyaXvE6QD9QMb4IGN8JEPGVJAMn2cyxgcZ4yMZMqaCZPg8kzF+kiEnGQ+Tste0AQAAAEAqSOWVNgAAAABIepQ2AAAAAHCxlCttxpj5xphqY0yNMeZOp/O8mzGm3BjzN2PMemPMOmPMrU5nOhZjjNcYs9IY8yens/TFGJNnjHnaGLPBGFNljDnD6UzvZoy5/eC/57XGmKeMMUGnM0mSMeYxY0yDMWbtYY8VGGP+1xiz6eCP+U5mTEXMp/hw+2ySmE+nkInZ5ABmU/y4fT4xm046k+OzKaVKmzHGK+lHkj4gaaKkDxtjJjqb6ig9kj5vrZ0oaa6km1yY8ZBbJVU5HeI4HpS02Fo7XtJUuSyrMWaYpFskzbLWTpbklXS1s6l6PS5p/rseu1PSS9baSkkvHfw14oT5FFdun00S8+lkPS5m04BiNsWd2+cTs+nkPC6HZ1NKlTZJsyXVWGu3WGu7JC2SdKnDmY5gra231q44+PMWRf+yDHM21dGMMWWSPijpf5zO0hdjTK6ksyQ9KknW2i5r7X5nU/XJJylkjPFJypS00+E8kiRr7cuS9r3r4Usl/eLgz38h6bIBDZX6mE9x4PbZJDGfTgWzyRHMpjhx+3xiNp08N8ymVCttwyTtOOzXtXLhX+pDjDEVkqZLWupskj49IOkOSRGngxzDSEmNkn5+cBvC/xhjBjkd6nDW2jpJ90naLqleUpO19v85m+q4Sqy19Qd/vktSiZNhUhDzKT7cPpsk5lO8MZsSi9kUP26fT8ym+BrQ2ZRqpS1pGGOyJD0j6TZrbbPTeQ5njLlIUoO19k2nsxyHT9IMST+21k6X1CaXbZk5uLf5UkWH5FBJg4wxH3E2Vf/Y6L1AuB9ImnLrfEqS2SQxnxKG2ZTe3DqbpKSZT8ymBBmI2ZRqpa1OUvlhvy47+JirGGP8ig6dJ621zzqdpw/zJF1ijNmq6DaJc40xv3I20lFqJdVaaw99p+1pRQeRm5wv6W1rbaO1tlvSs5LOdDjT8ew2xpRK0sEfGxzOk2qYT6cuGWaTxHyKN2ZTYjGb4iMZ5hOzKb4GdDalWmlbJqnSGDPSGBNQ9MLFPzqc6QjGGKPoXuIqa+39Tufpi7X2S9baMmtthaKfw79aa131XQ5r7S5JO4wx4w4+dJ6k9Q5G6st2SXONMZkH/72fJ5dd8Psuf5T00YM//6ik5xzMkoqYT6coGWaTxHxKAGZTYjGb4iAZ5hOzKe4GdDb5EvnmA81a22OM+aykFxU9beYxa+06h2O92zxJ10paY4xZdfCxu6y1zzuYKVndLOnJg/+T2SLpeofzHMFau9QY87SkFYqefLVS0iPOpooyxjwl6WxJRcaYWklfk/QdSb81xtwgaZukq5xLmHqYT2mH+XQSmE0Dj9mUdphNJ8ENs8lEt2ACAAAAANwo1bZHAgAAAEBKobQBAAAAgItR2gAAAADAxShtAAAAAOBilDYAAAAAcDFKGwAAAAC4GKUNAAAAAFzs/wMKznq/95BvqAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1080x1080 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
}
]
}
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