{"cells":[{"cell_type":"code","execution_count":1,"id":"1501e9da","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1501e9da","executionInfo":{"status":"ok","timestamp":1770445564298,"user_tz":-420,"elapsed":31080,"user":{"displayName":"Htut Ko Ko","userId":"13068088192988605156"}},"outputId":"e1a55fc7-d53f-4146-d258-c5091a6800d5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Running in Google Colab\n","Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n","Requirement already satisfied: sentencepiece in /usr/local/lib/python3.12/dist-packages (0.2.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from datasets) (3.20.3)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from datasets) (2.0.2)\n","Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.12/dist-packages (from datasets) (18.1.0)\n","Requirement already 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/usr/local/lib/python3.12/dist-packages (from huggingface-hub>=0.24.0->datasets) (4.15.0)\n","Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.32.2->datasets) (3.4.4)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.32.2->datasets) (3.11)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.32.2->datasets) (2.5.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests>=2.32.2->datasets) (2026.1.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2.9.0.post0)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets) (2025.2)\n","Requirement already satisfied: tzdata>=2022.7 in 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(6.7.1)\n","Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (0.4.1)\n","Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets) (1.22.0)\n","Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.24.0->datasets) (4.12.1)\n","Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub>=0.24.0->datasets) (1.0.9)\n","Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface-hub>=0.24.0->datasets) (0.16.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n","Requirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.12/dist-packages (from typer-slim->huggingface-hub>=0.24.0->datasets) (8.3.1)\n","Mounted at /content/drive\n"]}],"source":["# Google Colab Setup\n","try:\n","    import google.colab\n","    IN_COLAB = True\n","    print(\"Running in Google Colab\")\n","    !pip install datasets sentencepiece\n","    from google.colab import drive\n","    drive.mount('/content/drive')\n","    # Optional: Change to your project directory if needed\n","    # import os\n","    # os.chdir('/content/drive/MyDrive/NLP/Project_A3/A3_Burmese_English_Puffer')\n","except ImportError:\n","    IN_COLAB = False\n","    print(\"Running Locally\")"]},{"cell_type":"markdown","id":"87f40e8e","metadata":{"id":"87f40e8e"},"source":["# Task 2: Experiment with Attention Mechanisms\n","\n","**Student**: Htut Ko Ko  \n","**Course**: Natural Language Understanding  \n","\n","## Objective\n","Implement a sequence-to-sequence neural network for the translation task and compare the performance of two attention mechanisms:\n","1. **General Attention (Dot Product)**: $e = s^T h_i$ (where $d_1 = d_2$)\n","2. **Additive Attention (Bahdanau)**: $e = v^T \\tanh(W_1 h_i + W_2 s)$\n","\n","We will use **Burmese-English (ALT Dataset)** for this experiment as it is the student's native language pair."]},{"cell_type":"code","execution_count":2,"id":"d91bbec5","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d91bbec5","executionInfo":{"status":"ok","timestamp":1770445569670,"user_tz":-420,"elapsed":5369,"user":{"displayName":"Htut Ko Ko","userId":"13068088192988605156"}},"outputId":"090f28a3-4db3-49d2-d20a-6a83a5ff90ed"},"outputs":[{"output_type":"stream","name":"stdout","text":["Using device: cuda\n"]}],"source":["import os\n","import math\n","import time\n","import random\n","import numpy as np\n","import pandas as pd\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","from torch.utils.data import Dataset, DataLoader\n","from torch.nn.utils.rnn import pad_sequence\n","from datasets import load_dataset\n","import sentencepiece as spm\n","import matplotlib.pyplot as plt\n","import matplotlib.ticker as ticker\n","\n","# Check for GPU\n","device = torch.device('mps' if torch.backends.mps.is_available() else ('cuda' if torch.cuda.is_available() else 'cpu'))\n","print(f\"Using device: {device}\")\n","\n","SEED = 1234\n","random.seed(SEED)\n","np.random.seed(SEED)\n","torch.manual_seed(SEED)\n","torch.cuda.manual_seed(SEED)\n","torch.backends.cudnn.deterministic = True"]},{"cell_type":"markdown","id":"9dc15d75","metadata":{"id":"9dc15d75"},"source":["## 1. Data Loading (Burmese - ALT)"]},{"cell_type":"code","execution_count":3,"id":"153fd269","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":524,"referenced_widgets":["ce8f82a3b3154d139c63ed287e6f467a","05e99ffe2ae64f9b91cd9f424fec29b1","25d7efb785944e92b63cbc66ae4eeb12","3cfe7da1331349b089064e9318d94f29","b97f54ec90514e379ee5d98331beba7f","f3f5740de4be440680999dfceb7b886d","811efe3abcbe4ad49e4ad95bbc48c5d0","b736e94d72524bccb7489ec50fcc7caf","db45805b757d47759073ff47785dc8e1","aa49d956330b4af1a70a7facf396d513","c98c5d8414e94516b499ec4fb53ac870","2f086ef5a86947a982bd4d628e0334c9","dfabcc7d459048ff8a8fc12560121d54","0d351fb5df094a0cb18990438a37ad01","b24e82b2c6a344edba5962428434bad1","4b5b6ac73d51497f934b70cfee070cc4","7d26c4dd7cc646639180cdce6666a890","49b5263a31944d86b8b601da39e9e00d","4a746f03708649f5b734500a962ee6cd","3d471c9cd60d47019c342e4179bda023","692d5af1054046cca12009be089a4fd8","395f6b40e4234cf3a44cf6ef1cbe14a9","1885bfbcfeac4492ad409af32810e3fa","8aca0e8257be48d185bad0b009853f73","5daec1d6f9564e7a8b96fb416fdf048c","2f3897f4b7ad438fbb97c0ef1954ea6d","a6f6103bf0d54b548c48f98da7f9b8af","00e89fddc33e451f9c74888011247872","97c338f5486443658710a56274cedc21","588e794e042a47898f42ae0e481ac91d","18fbb87f48a445d184c39934fd35ce2c","1f2dc2a2b3e74abfba290cf3ff0f7ccd","05b13e25b7dd4258aca6e1ecc73654ea","319130045747436c8a211478b210c24e","603035b700b44a4498ee3e8a4fe2a2bc","fd152c6e53e544a38a697c9c739e5bd8","4513651e08924884b1ff91c12a89fbfc","5154e539890b46f89baa04a006080fb4","e381579c50914895bed304beffab8a1e","690ad5977c1c42ca8f267251764d275b","611342ec51f94445b2c9cda738f13a52","b071ed0a7c1f4fd5941e50e3350209cc","40a4c1d7c86d4494805b335a36d287d6","7ae34b85575b4ee4ac9a162824a184d3","4b3f71c3defa4e81a57e786645c7febd","6bfda53a8aef44a38f4f0869e6790fdc","c878d6ddfb6f4b50917f1f45937d1950","07ad4ea062244368a8f5a0d734e23f15","57ac48019a104d8a8f7575c7062c168e","e21b8fb2864744eeaf66c25c8405ad19","56c47d713303468cab129c2d70ab0451","2c6b72079ac545c48e308b12d0697a81","0842a011c61b431a8a05ae360dd40362","72eb657e7b7b442c9499bf8be5567017","a555ac38318e4c74adef2acc1b95a3f0","b1388d981c8a477fab3640aada2fd38e","41122571f7fa418a9cdf8b34b634a00f","d1ecc1b4e2054edcbf7dc06cb1cba5f4","f5c45a9b99ce451fa742754e878bbbeb","a20cd7a2cd6d44c7bae2b4e222a81e5b","09d90871d15a4d4f81e254d7c049e61a","54c830375b1e489ea471d59663d73b3f","6b546c1298dc4f1996862c87e2c78c39","19627a42b18f4beab0b5f12af3394fc1","97dd7b6858934f35b9a248e0d2e760db","7f314e115b14424595876b725884c21f","e8587d893ca94a6bab4c598fafc0cb54","f448849060404b1083d01b77f569f7e3","44790e034f8e4271a35d520dfdeaa1b9","8b66ba4df574499e92d1715f8a6e9ce4","1e5a7f0abb9b4911b3879c0bf635fd47","4f01d079381645f283dfe6f8ecb4ce6d","6c603972ca92451dbdd00f6a819b4971","4b465a10519a49fe9c4e85951293ce89","0e1717fbb6eb4acbbc91a18456e15cb8","951c29a4dd0c41d19732a1effac7d1bf","7876524db73248e18525a6c408cb34a8"]},"id":"153fd269","executionInfo":{"status":"ok","timestamp":1770445582648,"user_tz":-420,"elapsed":12975,"user":{"displayName":"Htut Ko Ko","userId":"13068088192988605156"}},"outputId":"8542d6f0-d836-441e-a42c-b06b89d5fc17"},"outputs":[{"output_type":"stream","name":"stdout","text":["Loading ALT Dataset (Burmese-English)...\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["README.md: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ce8f82a3b3154d139c63ed287e6f467a"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n","WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"]},{"output_type":"display_data","data":{"text/plain":["alt-parallel/train-00000-of-00001.parque(…):   0%|          | 0.00/31.2M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2f086ef5a86947a982bd4d628e0334c9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["alt-parallel/validation-00000-of-00001.p(…):   0%|          | 0.00/1.71M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1885bfbcfeac4492ad409af32810e3fa"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["alt-parallel/test-00000-of-00001.parquet:   0%|          | 0.00/1.79M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"319130045747436c8a211478b210c24e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/18088 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4b3f71c3defa4e81a57e786645c7febd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/1000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b1388d981c8a477fab3640aada2fd38e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/1019 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e8587d893ca94a6bab4c598fafc0cb54"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Extracted 20107 Burmese-English pairs.\n"]}],"source":["print(\"Loading ALT Dataset (Burmese-English)...\")\n","try:\n","    dataset = load_dataset(\"alt\", split=\"train+validation+test\")\n","    data = []\n","    for item in dataset:\n","        if 'translation' in item:\n","            if 'my' in item['translation'] and 'en' in item['translation']:\n","                data.append({\n","                    'my': item['translation']['my'],\n","                    'en': item['translation']['en']\n","                })\n","    print(f\"Extracted {len(data)} Burmese-English pairs.\")\n","except Exception as e:\n","    print(f\"Error loading from HF: {e}\")\n","\n","df = pd.DataFrame(data)\n","df = df.dropna()\n","df['my'] = df['my'].astype(str)\n","df['en'] = df['en'].astype(str)"]},{"cell_type":"markdown","id":"a37d37a8","metadata":{"id":"a37d37a8"},"source":["## 2. Tokenization"]},{"cell_type":"code","execution_count":4,"id":"52c0ddc3","metadata":{"id":"52c0ddc3","executionInfo":{"status":"ok","timestamp":1770445587875,"user_tz":-420,"elapsed":5217,"user":{"displayName":"Htut Ko Ko","userId":"13068088192988605156"}}},"outputs":[],"source":["# We will use existing trained tokenizers for Burmese and English (from previous tasks)\n","# Or retrain quickly if not found. Let's rely on creating them here to be self-contained.\n","with open('train_my_att.txt', 'w', encoding='utf-8') as f:\n","    for line in df['my']: f.write(line + '\\n')\n","\n","with open('train_en_att.txt', 'w', encoding='utf-8') as f:\n","    for line in df['en']: f.write(line + '\\n')\n","\n","vocab_size = 4000\n","spm.SentencePieceTrainer.train(input='train_my_att.txt', model_prefix='spm_my_att', vocab_size=vocab_size, model_type='bpe', pad_id=0, bos_id=1, eos_id=2, unk_id=3)\n","spm.SentencePieceTrainer.train(input='train_en_att.txt', model_prefix='spm_en_att', vocab_size=vocab_size, model_type='bpe', pad_id=0, bos_id=1, eos_id=2, unk_id=3)\n","\n","sp_src = spm.SentencePieceProcessor(model_file='spm_my_att.model')\n","sp_trg = spm.SentencePieceProcessor(model_file='spm_en_att.model')"]},{"cell_type":"markdown","id":"2ac2f2d4","metadata":{"id":"2ac2f2d4"},"source":["## 3. Seq2Seq Model with Attention"]},{"cell_type":"code","execution_count":5,"id":"975ac360","metadata":{"id":"975ac360","executionInfo":{"status":"ok","timestamp":1770445587915,"user_tz":-420,"elapsed":5,"user":{"displayName":"Htut Ko Ko","userId":"13068088192988605156"}}},"outputs":[],"source":["class Encoder(nn.Module):\n","    def __init__(self, input_dim, emb_dim, enc_hid_dim, dec_hid_dim, dropout):\n","        super().__init__()\n","        self.embedding = nn.Embedding(input_dim, emb_dim)\n","        self.rnn = nn.GRU(emb_dim, enc_hid_dim, bidirectional = True)\n","        self.fc = nn.Linear(enc_hid_dim * 2, dec_hid_dim)\n","        self.dropout = nn.Dropout(dropout)\n","\n","    def forward(self, src):\n","        # src = [src len, batch size]\n","        embedded = self.dropout(self.embedding(src))\n","        outputs, hidden = self.rnn(embedded)\n","        # outputs = [src len, batch size, hid dim * num directions]\n","        # hidden = [n layers * num directions, batch size, hid dim]\n","        hidden = torch.tanh(self.fc(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)))\n","        return outputs, hidden\n","\n","class Attention(nn.Module):\n","    def __init__(self, enc_hid_dim, dec_hid_dim, method='general'):\n","        super().__init__()\n","        self.method = method\n","        self.enc_hid_dim = enc_hid_dim\n","        self.dec_hid_dim = dec_hid_dim\n","\n","        # Additive Attention Layers\n","        self.attn = nn.Linear((enc_hid_dim * 2) + dec_hid_dim, dec_hid_dim)\n","        self.v = nn.Linear(dec_hid_dim, 1, bias = False)\n","\n","        # General Attention Layer\n","        self.general_attn = nn.Linear(enc_hid_dim * 2, dec_hid_dim)\n","\n","    def forward(self, hidden, encoder_outputs):\n","        # hidden = [batch size, dec hid dim]\n","        # encoder_outputs = [src len, batch size, enc hid dim * 2]\n","\n","        if self.method == 'general':\n","            # encoder_outputs: [src len, batch, enc_hid*2]\n","            # Permute for linear layer: [batch, src_len, enc_hid*2]\n","            encoder_outputs = encoder_outputs.permute(1, 0, 2)\n","\n","            # Project to dec_hid_dim matching hidden: [batch, src_len, dec_hid]\n","            energy = self.general_attn(encoder_outputs)\n","\n","            # hidden: [batch, dec_hid] -> [batch, dec_hid, 1]\n","            hidden = hidden.unsqueeze(2)\n","\n","            # bmm: [batch, src_len, dec_hid] x [batch, dec_hid, 1] -> [batch, src_len, 1]\n","            attention = torch.bmm(energy, hidden).squeeze(2)\n","            return torch.softmax(attention, dim=1)\n","\n","        elif self.method == 'additive':\n","            src_len = encoder_outputs.shape[0]\n","            hidden = hidden.unsqueeze(1).repeat(1, src_len, 1)\n","            encoder_outputs = encoder_outputs.permute(1, 0, 2)\n","            energy = torch.tanh(self.attn(torch.cat((hidden, encoder_outputs), dim = 2)))\n","            attention = self.v(energy).squeeze(2)\n","            return torch.softmax(attention, dim=1)\n","\n","class Decoder(nn.Module):\n","    def __init__(self, output_dim, emb_dim, enc_hid_dim, dec_hid_dim, dropout, attention):\n","        super().__init__()\n","        self.output_dim = output_dim\n","        self.attention = attention\n","        self.embedding = nn.Embedding(output_dim, emb_dim)\n","        self.rnn = nn.GRU((enc_hid_dim * 2) + emb_dim, dec_hid_dim)\n","        self.fc_out = nn.Linear((enc_hid_dim * 2) + dec_hid_dim + emb_dim, output_dim)\n","        self.dropout = nn.Dropout(dropout)\n","\n","    def forward(self, input, hidden, encoder_outputs):\n","        input = input.unsqueeze(0)\n","        embedded = self.dropout(self.embedding(input))\n","        a = self.attention(hidden, encoder_outputs)\n","        a = a.unsqueeze(1)\n","\n","        # For weighted sum, we need encoder_outputs as [batch, src_len, enc_hid*2]\n","        # If method was 'general', we permuted it inside attention, but here we need original shape or permuted\n","        # encoder_outputs passed here is [src_len, batch, enc_hid*2]\n","        encoder_outputs = encoder_outputs.permute(1, 0, 2)\n","\n","        weighted = torch.bmm(a, encoder_outputs)\n","        weighted = weighted.permute(1, 0, 2)\n","        rnn_input = torch.cat((embedded, weighted), dim = 2)\n","        output, hidden = self.rnn(rnn_input, hidden.unsqueeze(0))\n","        embedded = embedded.squeeze(0)\n","        output = output.squeeze(0)\n","        weighted = weighted.squeeze(0)\n","        prediction = self.fc_out(torch.cat((output, weighted, embedded), dim = 1))\n","        return prediction, hidden.squeeze(0), a.squeeze(1)\n","\n","class Seq2Seq(nn.Module):\n","    def __init__(self, encoder, decoder, device):\n","        super().__init__()\n","        self.encoder = encoder\n","        self.decoder = decoder\n","        self.device = device\n","\n","    def forward(self, src, trg, teacher_forcing_ratio = 0.5):\n","        batch_size = src.shape[1]\n","        trg_len = trg.shape[0]\n","        trg_vocab_size = self.decoder.output_dim\n","        outputs = torch.zeros(trg_len, batch_size, trg_vocab_size).to(self.device)\n","        encoder_outputs, hidden = self.encoder(src)\n","        input = trg[0, :]\n","\n","        # Store attentions\n","        attentions = torch.zeros(trg_len, batch_size, src.shape[0]).to(self.device)\n","\n","        for t in range(1, trg_len):\n","            output, hidden, attention = self.decoder(input, hidden, encoder_outputs)\n","            outputs[t] = output\n","            attentions[t] = attention\n","            teacher_force = random.random() < teacher_forcing_ratio\n","            top1 = output.argmax(1)\n","            input = trg[t, :] if teacher_force else top1\n","        return outputs, attentions"]},{"cell_type":"markdown","id":"93580f33","metadata":{"id":"93580f33"},"source":["## 4. Training Loop"]},{"cell_type":"code","execution_count":6,"id":"7ae04390","metadata":{"id":"7ae04390","executionInfo":{"status":"ok","timestamp":1770445587936,"user_tz":-420,"elapsed":18,"user":{"displayName":"Htut Ko Ko","userId":"13068088192988605156"}}},"outputs":[],"source":["def train(model, iterator, optimizer, criterion, clip):\n","    model.train()\n","    epoch_loss = 0\n","    for i, (src, trg) in enumerate(iterator):\n","        src, trg = src.to(device), trg.to(device)\n","        # Transpose for RNN: [src len, batch size]\n","        src = src.transpose(0, 1)\n","        trg = trg.transpose(0, 1)\n","\n","        optimizer.zero_grad()\n","        output, _ = model(src, trg)\n","        output_dim = output.shape[-1]\n","        output = output[1:].view(-1, output_dim)\n","        trg = trg[1:].reshape(-1)\n","        loss = criterion(output, trg)\n","        loss.backward()\n","        torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n","        optimizer.step()\n","        epoch_loss += loss.item()\n","    return epoch_loss / len(iterator)\n","\n","def evaluate(model, iterator, criterion):\n","    model.eval()\n","    epoch_loss = 0\n","    with torch.no_grad():\n","        for i, (src, trg) in enumerate(iterator):\n","            src, trg = src.to(device), trg.to(device)\n","            src = src.transpose(0, 1)\n","            trg = trg.transpose(0, 1)\n","            output, _ = model(src, trg, 0) # No teacher forcing\n","            output_dim = output.shape[-1]\n","            output = output[1:].view(-1, output_dim)\n","            trg = trg[1:].reshape(-1)\n","            loss = criterion(output, trg)\n","            epoch_loss += loss.item()\n","    return epoch_loss / len(iterator)"]},{"cell_type":"markdown","id":"c34c7d1c","metadata":{"id":"c34c7d1c"},"source":["## 5. Experiment Execution"]},{"cell_type":"code","execution_count":7,"id":"f55aaa5c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f55aaa5c","executionInfo":{"status":"ok","timestamp":1770447533424,"user_tz":-420,"elapsed":1945474,"user":{"displayName":"Htut Ko Ko","userId":"13068088192988605156"}},"outputId":"9915c3b2-903a-4cdc-9db2-30158cec1e5d"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Training with GENERAL Attention...\n","Epoch: 01 | Time: 89s\n","\tTrain Loss: 6.661 | Train PPL: 781.340\n","\t Val. Loss: 6.747 |  Val. PPL: 851.477\n","Epoch: 02 | Time: 89s\n","\tTrain Loss: 6.192 | Train PPL: 488.955\n","\t Val. Loss: 6.665 |  Val. PPL: 784.275\n","Epoch: 03 | Time: 89s\n","\tTrain Loss: 5.918 | Train PPL: 371.519\n","\t Val. Loss: 6.555 |  Val. PPL: 702.852\n","Epoch: 04 | Time: 89s\n","\tTrain Loss: 5.708 | Train PPL: 301.269\n","\t Val. Loss: 6.555 |  Val. PPL: 702.480\n","Epoch: 05 | Time: 88s\n","\tTrain Loss: 5.499 | Train PPL: 244.412\n","\t Val. Loss: 6.533 |  Val. PPL: 687.687\n","Epoch: 06 | Time: 88s\n","\tTrain Loss: 5.318 | Train PPL: 203.981\n","\t Val. Loss: 6.548 |  Val. PPL: 697.513\n","Epoch: 07 | Time: 89s\n","\tTrain Loss: 5.162 | Train PPL: 174.458\n","\t Val. Loss: 6.566 |  Val. PPL: 710.258\n","Epoch: 08 | Time: 88s\n","\tTrain Loss: 5.052 | Train PPL: 156.289\n","\t Val. Loss: 6.581 |  Val. PPL: 720.939\n","Epoch: 09 | Time: 88s\n","\tTrain Loss: 4.924 | Train PPL: 137.557\n","\t Val. Loss: 6.602 |  Val. PPL: 736.354\n","Epoch: 10 | Time: 89s\n","\tTrain Loss: 4.819 | Train PPL: 123.868\n","\t Val. Loss: 6.662 |  Val. PPL: 782.166\n","\n","Training with ADDITIVE Attention...\n","Epoch: 01 | Time: 105s\n","\tTrain Loss: 6.577 | Train PPL: 718.289\n","\t Val. Loss: 6.619 |  Val. PPL: 749.233\n","Epoch: 02 | Time: 106s\n","\tTrain Loss: 6.019 | Train PPL: 410.993\n","\t Val. Loss: 6.459 |  Val. PPL: 638.221\n","Epoch: 03 | Time: 106s\n","\tTrain Loss: 5.651 | Train PPL: 284.472\n","\t Val. Loss: 6.367 |  Val. PPL: 582.333\n","Epoch: 04 | Time: 106s\n","\tTrain Loss: 5.361 | Train PPL: 212.926\n","\t Val. Loss: 6.394 |  Val. PPL: 598.471\n","Epoch: 05 | Time: 105s\n","\tTrain Loss: 5.143 | Train PPL: 171.248\n","\t Val. Loss: 6.336 |  Val. PPL: 564.446\n","Epoch: 06 | Time: 105s\n","\tTrain Loss: 4.956 | Train PPL: 141.959\n","\t Val. Loss: 6.316 |  Val. PPL: 553.585\n","Epoch: 07 | Time: 105s\n","\tTrain Loss: 4.796 | Train PPL: 120.984\n","\t Val. Loss: 6.376 |  Val. PPL: 587.425\n","Epoch: 08 | Time: 106s\n","\tTrain Loss: 4.661 | Train PPL: 105.699\n","\t Val. Loss: 6.408 |  Val. PPL: 606.503\n","Epoch: 09 | Time: 106s\n","\tTrain Loss: 4.553 | Train PPL:  94.890\n","\t Val. Loss: 6.445 |  Val. PPL: 629.355\n","Epoch: 10 | Time: 106s\n","\tTrain Loss: 4.447 | Train PPL:  85.368\n","\t Val. Loss: 6.440 |  Val. PPL: 626.673\n"]}],"source":["class TranslationDataset(Dataset):\n","    def __init__(self, df, sp_src, sp_trg):\n","        self.data = df\n","        self.sp_src = sp_src\n","        self.sp_trg = sp_trg\n","    def __len__(self): return len(self.data)\n","    def __getitem__(self, idx):\n","        src_text = self.data.iloc[idx]['my']\n","        trg_text = self.data.iloc[idx]['en']\n","        src_ids = [self.sp_src.bos_id()] + self.sp_src.encode(src_text, out_type=int) + [self.sp_src.eos_id()]\n","        trg_ids = [self.sp_trg.bos_id()] + self.sp_trg.encode(trg_text, out_type=int) + [self.sp_trg.eos_id()]\n","        return torch.tensor(src_ids), torch.tensor(trg_ids)\n","\n","def collate_fn(batch):\n","    src_batch, trg_batch = [], []\n","    for src, trg in batch:\n","        src_batch.append(src)\n","        trg_batch.append(trg)\n","    src_pad = pad_sequence(src_batch, batch_first=True, padding_value=0)\n","    trg_pad = pad_sequence(trg_batch, batch_first=True, padding_value=0)\n","    return src_pad, trg_pad\n","\n","dataset_full = TranslationDataset(df, sp_src, sp_trg)\n","train_size = int(0.8 * len(dataset_full))\n","valid_size = len(dataset_full) - train_size\n","train_data, valid_data = torch.utils.data.random_split(dataset_full, [train_size, valid_size])\n","train_loader = DataLoader(train_data, batch_size=64, shuffle=True, collate_fn=collate_fn)\n","valid_loader = DataLoader(valid_data, batch_size=64, shuffle=False, collate_fn=collate_fn)\n","\n","INPUT_DIM = vocab_size\n","OUTPUT_DIM = vocab_size\n","ENC_EMB_DIM = 256\n","DEC_EMB_DIM = 256\n","ENC_HID_DIM = 512\n","DEC_HID_DIM = 512\n","ENC_DROPOUT = 0.5\n","DEC_DROPOUT = 0.5\n","\n","results = {}\n","\n","for method in ['general', 'additive']:\n","    print(f\"\\nTraining with {method.upper()} Attention...\")\n","    attn = Attention(ENC_HID_DIM, DEC_HID_DIM, method=method)\n","    enc = Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, ENC_DROPOUT)\n","    dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, ENC_HID_DIM, DEC_HID_DIM, DEC_DROPOUT, attn)\n","    model = Seq2Seq(enc, dec, device).to(device)\n","\n","    optimizer = optim.Adam(model.parameters())\n","    criterion = nn.CrossEntropyLoss(ignore_index=0)\n","\n","    train_losses = []\n","    valid_losses = []\n","\n","    for epoch in range(10): # 10 Epochs\n","        start_time = time.time()\n","        train_loss = train(model, train_loader, optimizer, criterion, 1)\n","        valid_loss = evaluate(model, valid_loader, criterion)\n","        end_time = time.time()\n","\n","        train_losses.append(train_loss)\n","        valid_losses.append(valid_loss)\n","\n","        print(f'Epoch: {epoch+1:02} | Time: {end_time - start_time:.0f}s')\n","        print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n","        print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. PPL: {math.exp(valid_loss):7.3f}')\n","\n","    results[method] = {\n","        'train_losses': train_losses,\n","        'valid_losses': valid_losses,\n","        'model': model\n","    }"]},{"cell_type":"markdown","metadata":{"id":"HwPilWgXwJ2o"},"source":["## 6. Visualization & Comparison"],"id":"HwPilWgXwJ2o"},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"92EW_rwBwJ2o","executionInfo":{"status":"ok","timestamp":1770447534693,"user_tz":-420,"elapsed":1274,"user":{"displayName":"Htut Ko Ko","userId":"13068088192988605156"}},"outputId":"4d169ec9-52d3-4d71-c5bd-ca18095294cb"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Source: ပြင်သစ်နိုင်ငံ ပါရီမြို့ ပါ့ဒက်စ် ပရင့်စက် ၌ ၂၀၀၇ခုနှစ် ရပ်ဘီ ကမ္ဘာ့ ဖလား တွင် အီတလီ သည် ပေါ်တူဂီ ကို ၃၁-၅ ဂိုး ဖြင့် ရေကူးကန် စီ တွင် ရှုံးနိမ့်သွားပါသည် ။\n","Target: Italy have defeated Portugal 31-5 in Pool C of the 2007 Rugby World Cup at Parc des Princes, Paris, France.\n","\n","Method: general\n","Translation: The French defeated Port ug defeated Port ug defeated Port ug in Italy in Italy in Italy in Italy in Italy in the 2007 2007 2007 2007 in Italy , Italy , Italy , Italy Cup the 2007 . \n"]},{"output_type":"stream","name":"stderr","text":["/tmp/ipython-input-463267404.py:20: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n","  ax.set_xticklabels([''] + sentence, rotation=45)\n","/tmp/ipython-input-463267404.py:21: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n","  ax.set_yticklabels([''] + translation)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4117 (\\N{MYANMAR LETTER PA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4156 (\\N{MYANMAR CONSONANT SIGN MEDIAL RA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4100 (\\N{MYANMAR LETTER NGA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4154 (\\N{MYANMAR SIGN ASAT}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4126 (\\N{MYANMAR LETTER SA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4101 (\\N{MYANMAR LETTER CA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4116 (\\N{MYANMAR LETTER NA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4141 (\\N{MYANMAR VOWEL SIGN I}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4143 (\\N{MYANMAR VOWEL SIGN U}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4150 (\\N{MYANMAR SIGN ANUSVARA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4139 (\\N{MYANMAR VOWEL SIGN TALL AA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4123 (\\N{MYANMAR LETTER RA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4142 (\\N{MYANMAR VOWEL SIGN II}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4121 (\\N{MYANMAR LETTER MA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4151 (\\N{MYANMAR SIGN DOT BELOW}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4114 (\\N{MYANMAR LETTER DA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4096 (\\N{MYANMAR LETTER KA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4172 (\\N{MYANMAR SYMBOL LOCATIVE}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4162 (\\N{MYANMAR DIGIT TWO}) missing from font(s) DejaVu Sans.\n"," 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BHA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4153 (\\N{MYANMAR SIGN VIRAMA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4140 (\\N{MYANMAR VOWEL SIGN AA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4118 (\\N{MYANMAR LETTER PHA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4124 (\\N{MYANMAR LETTER LA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4152 (\\N{MYANMAR SIGN VISARGA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4112 (\\N{MYANMAR LETTER TA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4157 (\\N{MYANMAR CONSONANT SIGN MEDIAL WA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4129 (\\N{MYANMAR LETTER A}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4106 (\\N{MYANMAR LETTER NNYA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4145 (\\N{MYANMAR VOWEL SIGN E}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4144 (\\N{MYANMAR VOWEL SIGN UU}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4098 (\\N{MYANMAR LETTER GA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4163 (\\N{MYANMAR DIGIT THREE}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4161 (\\N{MYANMAR DIGIT ONE}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4165 (\\N{MYANMAR DIGIT FIVE}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4171 (\\N{MYANMAR SIGN SECTION}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x1000 with 1 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A3LlzceXKFSQlJaFz584ICQnBtm3b8Pzzz+Ojjz7C+++/j7CwMMTGxmL27NkIDw+3u979+vXDAw88gMzMTHzwwQcQEYuJYKNGjbBjxw5MmzYNc+bMQUFBAeLj4/HBBx+YfEPQE9LT03H27FksWLAAGzduRMOGDfHBBx/go48+QlZWlsn8o0aNgo+PD+bNm4c///wTbdq0weuvv46qVasWuayxY8eif//+OHnypNEtse+//z6qVKmCjIwMfPLJJ+jSpQs+/PBD3HHHHQgKCtLmq1y5Mvbu3Yunn34aq1evxptvvonIyEg0atQIL774okPt79SpE3bv3o1nnnkGr7/+Oq5evYoqVaogPj4eEyZMsKmMFStW4IEHHsAbb7wBEUHXrl2xfv16VKtWrcjfFhQUYNWqVbj77rv5shgiKjYUcdeT80RERORR2dnZqF27Nl566SU8+uijDpWRn5+Phg0bYtCgQXjmmWesznvgwAHExcXhgw8+wPDhwx1aXnHw6aefYtiwYTh69KhNyTQRkTfgM4JERERkM19fXzz99NN44403jG47/fvvv03mnTdvHnx8fNCxY0d3VtHtXnzxRUycOJFJIBEVK7w1lIiIiOwyePBgDB482Gjc3LlzsW/fPiQnJ8PPzw/r16/H+vXrMX78eJe9VdVbWHopDxGRN2MiSERERE5r27YtNm/ejGeeeQZXr15FzZo1MWvWLEyfPt3TVSMiIjP4jCAREREREVEpw2cEiYiIiIiIShkmgkRERERERKUME0EiIiIiIqJShokgERERERGVSHwdimVMBImIiIiIqMT46quvMH/+fACAoihMBi3g5yOIiIiIiKhE+Oyzz9CrVy8oioKcnBw8+eSTWjKoKIqnq+dVmAgSEREREVGxt27dOvTp0wdz5sxBgwYNMHHiRBQUFGDGjBlMBs1gIkhERERERMWamgS++OKLePTRR3H79m3MmzcPDz/8MAAwGTSDiSARERERERVbhZNAAPD390fPnj0BgMmgBUwEiYiIiIioWDKXBKqYDFrHRJCIiIiIiIqdL774An369MG8efPwwAMPmJ2HyaBlivB9qkREREREVIycOnUKnTp1QpUqVbBjxw4AQEFBAXx8zH8d7/bt21i3bh0efvhh3HPPPZgxYwYAlOpkkN8RJCIiIiKiYuPWrVuIjIzEuHHjEB4ejueeew4A4OPjg4KCArO/Ua8M/uc//8G7776LWbNmASjd3xnkraFERERERFQsbN26FZ999hkefvhhPPjggwgICMD69esBANOnT9eSQXNXBv39/dGjRw8A/7tNdNasWaX2NlEmgkRERERE5PXWrVuH3r17o1KlSvD398fEiRMxYcIEAGAy6AAmgkRERERE5NXUt4MuWLAAdevWxcyZM5Gfn4+HHnqIyaCDmAgSEREREZHXUpPAF154AePGjQMA5OXl4emnnwYAp5JBEcGjjz4KX19fPPXUU6UmCQSYCBIRERERkZcq/J1A9Ypd165dAQDPPPMMAMeTwX79+uHPP//Ev/71LyQkJCAlJcVNLfM8JoJEREREROR1zH0s3vD2TT2SwZMnT+KVV15BWlpaqUoCASaCRERERETkZT777DMMHDjQKAlUGd6+6UwyePz4caSmpqJOnTpYt24dAOvfIixp+EF5IiIiIiLyCgUFBbh16xZq166NsmXL4rfffrPpd5s2bcIzzzyDhIQEPPTQQ4iMjMSCBQuwfv16dOzYEdOnT9fK9/HxQXZ2Nrp27Yo6depgw4YNRtNKi9LTUiIiIiIi8mp///03goKCsGnTJly4cAH33nsv8vLyivxd165d8dRTT+Gbb77Bf/7zH1y8eBETJkxAt27dsH37dqOPzh89erTUJ4EAbw0lIiIiIiIvsH79ekycOBHLli1D27ZtsX37diQmJkJE8MYbb8DPz3rqUtRton5+fhg6dCh69uxZ6pNAgFcEiYiIiIjIw9atW4devXohPDwcI0aMwK5du9CkSRPs3r0by5cvx8SJE526Mti9e3ds3rwZtWrVQs2aNUt9EgjwiiAREREREXmQ+nbQuXPnYty4cZg9ezYGDx6MDz/8EG3btsXu3buRmJgIAHj99ddtvjKofmfw4YcfxoQJE3Dt2jU0adIEr776KoDSnQQCfFkMERERERF5iOHH4qdMmQIAuHTpEp577jmsXLlSSwYPHz6MxMREjBgxwmoyqH5aAvjnBTLPP/88WrdujQceeADVqlXTflfak0CAVwSJiIiIiMgDzH0nEADKly+vveXT3iuDhb8zeObMGYwZMwbNmzfH8OHDAfyTLJb2JBDgFUEiIiIiInIzc1cCAeMres5cGQT++U5g7969UbVqVWzcuNHlbSpueEWQiIiIiIjcZuvWrejduzfefPNN3HvvvUbJn+HH4p25Mqh+J7Bu3bp8MYwFTASJiIiIiMgtbty4gczMTNSqVQsxMTEA/kn+LCVpjiSDTAJtw0SQXOLs2bMICQlBWFiYp6tCRERERF7g7NmzqFChAiZNmoSgoCC88soruH37Nu666y74+PgYXRk0ZE8yqCaB/E5g0RgR0t3nn3+O5ORkZGRkICcnx9PVISIiIiIP+/zzz5GUlIR3330XMTEx+Ne//oXatWtj/vz5WsKmvujFHDUZHDRoEAYPHmz0ncEPPvgADz/8MI4cOcIk0A6MCulq3bp16N27N8LCwvDxxx9j9erVTAaJiIiISjH1/DAiIgIrV67E+++/j3r16uGBBx7QLRlctmwZYmNjERsbyyTQRowM6UZ9+9Mrr7yCPXv2oG3btnj//feZDBIRERGVUoXPD9u3b48lS5Y4lQw+8cQTJsngzp07cf/99+Ozzz4DwCTQFvx8BOnC0ndgZs6cie3bt2PUqFHo168fwsPDPVhLIiIiInIXa+eHWVlZSE9Px6hRo3DkyBHMnz8fx48fxwMPPIC77roLACw+MwgAFy9exEsvvYSMjAxkZmZqzwoCTAJtxQiR08xt5AUFBQCA2bNno2PHjrwySERERFSKFHV+2KlTJ6euDEZGRmLw4ME4efIkPv74Y6NpTAJtwyuC5JTPPvsMvXv3NvlLD2D81xheGSQiIiIqHew5P3T0yuCJEyeQkpKCmJgYfP755+5pWAnDRJActmnTJtx111149dVX8dBDD5mdh8kgERERUenhyPmhvclgdnY2UlJS+J1AJzFa5LBff/0VAFC3bl2L8/j4+BjdBpCUlISlS5di1apVvE2UiEocdX9HRFRaOXJ+mJycrN0mWrduXTz44IPabaLr168HAKMkkB+L1wcjRnb7+eefAQATJ07E008/jX79+uGTTz6xOL/hxj5r1ix06tQJ77//PpNBIiox/vvf/wIw3t8REZUmzp4fqsngsmXLUKdOHbPJoHolkN8J1AejRnb54osv0KhRI9x5550AgCeffBJPPvkkBg8ezGSQiEqlL774Ag0bNkTXrl0BMBkkotJHr/NDc8lgnTp18NZbb2Hx4sXo0aMHrwTqiJEjm61btw69evXCgw8+CBHRTnpmzJjBZJCISiXD/WJeXh66desGgMkgEZUeep8fmksGa9asibvvvhs1atRgEqgnIbLB2rVrxcfHR15++WURETl06JC0a9dOUlJStHlmz54t/v7+snr1aqtl5efna/8/c+ZMSUpKkvfee08uX77smsoTEbmApf3iXXfdpc1juL8jIippXHl+2LFjR1m8eLHcvn1bfv75Z3n33XfNzkuOYyJIRVI38pdeeslo/MGDB3XZ2J955hnp0KGDzJ8/X/7++2/ZvHmz5Obm6tsIIiIdFbVfZDJIRCWdO84P27VrJ6+//rrReO5T9cNEkKyytJGrnPnLT15enoiInD17VhRFkUceeUSaN28uNWrUkGXLljEZJCKvZOt+kckgEZVU7jw/nDx5sn4VJyNMBMmitWvXip+fn7z44otW53NmYz9y5IjUr19fUlNTJS4uTtq1aycPPfSQ3HnnnbJ06VImg0TkVezdLzIZJKKSxt3nh+Q6TATJrP3794uiKPLEE0+ISNEnMI5s7MePH5eYmBhJTU2V5s2bS3x8vFy5ckVERB5//HHp3Lkzk0Ei8hqO7heZDBJRSeHu80MV952uwVftkFlnzpxBhw4dcPHiRZw9e7bIN+A1adIEb731Fq5fv27T26KOHz+Orl27ok6dOjh37hyCgoKwbt06lC1bFgAwZ84ctGnTBkuXLsUnn3yCK1euuK6xREQ2cHS/eOXKFb5NlIhKBHeeH/LtoK7HqJKRw4cPY8+ePejevTseffRR/Pnnn5g+fTrOnDmj28aenZ2N1NRUoyRw7dq1qFixIgAgPz8fAJNBIvIOeuwXi1sy+MMPP2DHjh2ergaR2+jZ50vi9uPu80MmgW7i6UuS5D3Wrl0riqJIamqqfPPNNyIismbNGunbt6+MHTtWzpw5IyKO3wYQHBwsb731ljRs2FBSU1OlVatWkpCQIH/++adJGYbL4G2iROQpeu8Xi8Ntomqbu3fvLtu2bfN0dYhcTs8+XxK3H3efH6q8dR9ZkigiIp5ORsnz1q1bh379+mHKlCn47bff4OPjg0mTJiEhIQFr167FkiVLUK5cOTz33HOoUqVKkX+hOXz4MO677z6EhIRg06ZNAIBnn30WM2bMQGpqKi5evIjTp0/jhx9+QEREhNkyDJcxbdo07N27F6NHj0bfvn0RGhqqewyIiAy5ar8YGhqK9evXA/C+v3YXbrO/vz8mTJiApKQkT1eNyCX07PMlcftx9/mht+4bSyxPZ6LkeeorgNW3P506dUp69+4tgwYNkt27d4uIY3/5Ub8jY/jXnc2bN4uIyNSpUyU4OFg2bdpktQxeGSQiT3D1frF79+7auIKCAhe1wj6W2jx06FDJysrycO2I9Kdnny+J248nzg9t+T3ph4lgKVf4OzDqxvfHH3/osrH//PPP0rJlS0lOThYRkX//+9/a76ZOnSpBQUHyxRdfWC2DySBR8fLKK694ugpOcdd+0fD2KE8rqs3F+WSWyBw9+3xJ3H7cfX6oYhLoXkwESzFLHwNVP+Spx19+jh07JtWrV5c+ffpI69atRVEU6dOnjzb9scceYzJIVIKo2/mAAQM8XRWHuHu/6A1sbXNxPJklMkfPPl8St5/SuB8srZgIllKfffaZ+Pn5mWzkKnXjdWZjP3bsmMTGxspdd90lzZs3l4SEBNmyZYskJCRIjx49tPmYDBKVDOp2/uWXX0pCQkKxO8C7e7+o8uStofa2uTidzBKZo2efL4nbT2ncD5ZmTARLoUOHDomiKDJ9+nSr8zmzsRf+WHxCQoJcuHBBRET27dsnrVu31i0ZXLZsGZNBIg9Tt/NLly6JyP+28+KSDLp7v1i4PE9wtM3F4WSWyBw9+3xJ3H5K436wtGMiWErFxcVJmzZt5I8//rA6n60b++nTp7XfmEsCz507p00vKCiQffv2SZs2bRxKBtVbE0REpk+fLrGxsZKZmcm/JhE5QI/tRt3Oz58/b1Suup0Xl2TQXftFkX/io9fJjzPr0NE2u/tklvt3MuQtfb64bD/2KKpNauz12A8alkOewfeyliJZWVl45513AAD79+9HQUEBevTogVOnTln8jfqR0GrVquHNN9/EzZs38eqrr+Kbb75Br169kJ6ejr/++gtPPvkkLl68iL/++gspKSmoU6cO/vzzT+1j8ZUqVdLKVBQFLVq0wFtvvYU///wTPXv2BAC8+OKLePDBB9GvXz/t9cHm+Pr6ah8uzcvLw2+//YaWLVtCURQ9wkRU4mVlZWHJkiUA/tkexYmvCLVo0QJBQUH47LPPUKFCBW284XZ++vRp9O3b19lqu4Q794sREREYMmQIAEBEnNpnObMO9Wjz9evXsWDBAmzbts3hNthST736KRV/3tLn582bVyy2H3sUFR9zsXfm/JAfi/cinsxCyX3WrVsniqKYPPzbqlUrad68udN/Ae/fv78MHDhQqlevrl0JTEpK0m4LsMSZK4OTJk2S4OBg+e6776zOR0T/Y7gv+M9//qONd+Qv7Op2bnjF3xxvvTLozv1iXFyctqx58+ZpZTgSd2fWod5tdtWVDT37KRV/3tLn4+PjBYDXbz/2KCo+S5YssRp7e88PVbwS6B2YCJYC6tuf5syZIxkZGVKtWjXtmzAi+pz0rF27Vpo1ayZ9+vSRBx98UBRFkZ9//tnod5Y4kgw+8sgjEhQUxCSQyA7m9gWGn3qw5yRb3c5/+eUXEbF9O/eWZNCd+8X4+HhtWcuXL3cq7s6sQ1e1We+TWT37KRV/3tLn1bIaNGggbdu2lQoVKnjl9mOPouJTr149ASCPP/641djben6oYhLoPZgIlnCFXwF848YNef/993U7ARgyZIi2sR84cEBERFasWCG9evWS++67T3tBjJ7JIJNAIvtZ2xc4cpK9YsUK6d27t0PbuaeTQXfuF//zn/9oy8rPz3cq7s6sQ1e3efjw4bqczOrdT6l485Y+b1jWqVOnpEePHpKYmKhbMqjX9mOPouKjTq9Ro4Y0b95cjh49ajX2tpwfGs5H3oGJYAlm6Tswzp4AqC9rOX/+vHTp0kU6dOggR44ckVmzZsnu3buloKBAPv74Y+ndu7eMHz9e12TQ25PAp59+WrZu3erpahAZsWVfYOtJtrqdi4hT27mnkkF37hcXLFigLcswpo7E3Zl16K42d+rUSbtC7Ag9+ykVf97S5w3LMuzznTt3lvr160ulSpW8YvuxR1HxKV++vCiKok1X21RUMmjp/LDwfN6otJ6/MREsoSxt5KqbN2/KsmXLHD4BEBG5fPmyVKtWTfr06SPNmjUTRVFkxIgRWpL20UcfSe/evWXChAlOJ4NhYWFy1113SdmyZb02CVRjMGrUKPn66689XR0iEbFvX1DUSbbe27m7k0F37hfV20H1iLsz69DdxwJH6dlPqfjzlj5vqSy1z/fq1csrth97FBWfVatWiaIoEhYWZrZNx44dK3JbdHebnFWaz9+YCJZAn3/+ufj6+ho91GvOzZs3Hf5r8PHjx6VevXqSmpoqcXFxkpycLN9995307NlThg0b5vRJornvDCqKIvv27Suy/Z6gxmDfvn3Ss2dPGTlypOzYscPT1aJSzpF9gaUDe+E+rtd27q4TBXfuF1u0aKEty1qiYkvcnVmH7j4WmKu/LfTsp1T8eUuft1RW4T5/8+ZNWbp0qce2H3sUFR91+ssvv2w1PseOHbO4Lbq7Tc4q7edvTARLmN9//11q1aolycnJ2jhrJ2SGfxl74YUXtPGtW7eWZs2amd2BmftOoPp20OPHj0taWppTJ4mWvj928eJFG6PgXoW/lajGoLTtTMi7OLMvePnll7XxBQUFFvu4Xtv5gAEDnGprUdy5X+zYsaO2LHUZjsbdmXo/+eST2m8LP8Ojd5udeROgnv2Uij9v6fMNGjSQqKgok3pY6vOe2n7sUVRsC083THDNtcnwyqC6LRa37wTy/I2JYIly+/ZtuXLlisyePVu6d+9udJB05gTg5MmT2rhjx46ZJIF//vmn0TKys7O1k8Rvv/1WRIyfJVI/Ol3UzuH7778XX19fue+++0TEOw/0agwKn/iqMRgxYkSp2ZmQ99BjX6D+pqg+7sx2fuPGDW07nzhxonONtsCd+8WUlBRtWd26dXMq7rdv35bc3FyH6r1kyRKpUqWKpKamar/VIxm01GZb6mWOnv1UxDuPEYXduHHD01XwWs70Bz37/O3bt6VFixZSuXJl6dy5s1aPY8eOmVzpMizb0XMpW9rorKJia2l6Ucng0aNHtTY/8cQTWptsib2n8fztH0wES4itW7fKE088IWfPnpXc3Fx54YUXpGvXrrqd9Jw7d05+//13i0lg4WXocZI4efJkKVOmjBw8eND+gLhB4Z2IqrTuTMg76LkvqFatmtbHDU+y9djOt27dKk8++aSMHTvWZdu5O/eLrVu31pZ1+fJlp5Z13333aWXl5OTYVdbWrVtl6tSp8vrrr0uVKlUkLS1N+60eyWDhNj/55JNy5MgRu0/49Fw3xSUZVPu84csz6B/O9Ac9+/y4ceO0erRo0UKqVq0qnTp1kqeeeko7/7F2pd/ec6mi2qaHomK7detWmTZtmsXpRSWDJ0+elHnz5omvr6/ccccd2nT1xTHeiOdv/8NEsARYu3atKIoiVapUkRkzZsi5c+d0PemJj4+XO+64Q2rVqiV33XWXtG7dWtq2bWuSBBZehjMniZMnT/bqt4OqMSi8E1GVxp0JeZ6e+4I6deqIv7+/PP3009p4vZJBtZ5lypQRPz8/+fTTT/UJgAF37hdbtGihLeupp55yalmTJk0SABIaGqrV29Zk0LDN06dP1z0ZNNfmqlWrysyZM+Xo0aMWyyvMFevG25NBtc1Vq1aVWbNm2RWvks6Z/qBnnze37bVq1UoqVKggQUFBRknO7du3rZZly7mUpTbpqajYjh071qZ9l6VkUG1TdHS0NG3aVNsW1TZ547bI8zdjTASLOfXtTwsWLJB169ZJ27Zt5cknn9T9pKdFixaSkpKivbTlhx9+sFovZ04Svf0TEWoMfvrpJ6vzlbadCXmWnvsCtY+/8MILVk+yHdnO1XqmpKSIv7+/NGvWTGbMmKHribE794vNmzfXlrVmzRqnlqXWe8yYMVK+fHmpVauWVlZRyaC5Nk+bNk33ZNBcm9u1a2fzOnTluvHWZLBwm+2JV0nnTH/Qs88X3vaio6O1ejRr1kzq1aunlWXvs7/mzqUKt8UViort6NGjBYAMGDBAmz59+nS7k0G1TWqbq1at6rXbIs/fTDERLMbUjXzu3LnauNWrVzt9YL1x44YsW7ZMoqKizL5eOC4uTtq2bStnz561Wj9HThK9PQlUORqDkrwzIc9xxb4gLi5OEhIS5PXXX5eoqCiZN2+e2fns2c7Venbs2FHbztV66nVi7M79ouGy1N86uizDstRlRUZGSu3atYss69NPP7XY5scff1xef/11qV69uvTo0cOlbS5qHbpj3Vjqp55irc2lPRl0pj/o2ectbXuGyaBaj5SUFN3OpdyRBFqK7fvvvy+Koki3bt20+KjTrSWDN27ckKVLl1ps0/Xr1712W1Tx/M0YE8FiqvB3YAz/4qLHgbWgoEBeeeUVURRFtm7dKt26dZPXXntNW06TJk0kPj5et2TwwoUL8tRTT3l1EqjGQOVoDErqzqQo3bp1k7ffftvT1Shx9NwXWOrjs2fPFkVRZPv27WbrYMt2npqaKj4+PtKlSxcJCgrSpqv11OMqiTv3i88995zRx+KdWZa5eqvLAiCNGze2WFbhEz5z9Zg+fbrMnj1bAMiECRNc1mZr69Cd68ZSP3W3otpcmq8MOtMf9OzzRW17jRo1svmqvDmFz6UMx7tKUbGtX7++AJCZM2dKbm6uzJkzx65ksKCgQF5++WWLbXLVtujMOQTP3yxjIlgMWfoYqOHOyNkDq/owc48ePaRly5ZSoUIFqVatmi4bUuGTxP79+0vNmjUlICDAa5NAvWNQml5NLGIcv7feesvT1Skx9NwXWOrj9evXl6CgIKPbmcyxtp0//vjjAkAiIiKMtvPC9XTmxNid+8XWrVvrtiz1ykbhsrKzs7V9sKWymjdvLoqiaL81d8va6tWrpWXLllK+fHlJSUlxeZvNrUN3H7O8ga1tLo3JoDP9Qc8+78y25639sqjYqs8MFr7aaU8yqLYpLS3NbB1c0WZnziF4/mYdE8FiZt26dRIYGGj2kryIPn9lLfyJiMTERPn9999l8eLFUqVKFaMPkeqRDGZmZkpqaqocOHDAvmC4iRqDP/74Q9cYlMS/LJljLn5MBp2n577A0nau7gvKli2r9XFrf/02t52/8sorEhgYKMOHDze7netxlcSd+8W4uDhtWeZiYc+y1Hob3r5luKyuXbtq4z7++GOjsj788EPx8/OT2NhYs/VW63Hs2DGpWrWqREREaL+19a2mjrbZcB26+5hlaR53srfNpSkZdKY/6Nnnndn2vLVfFhXbtWvXatPNbWu2JIPt27eXChUqaG3Kz883apMr2uzMOQTP34rGRLCYyM/Pl6tXr0pYWJg0bdrU6rzOHFjNfSxefTvo33//LYsWLdJtQxo5cqTs3r1bRESuXLlisQ2eVPgVw3rFQP1oaXp6utGtFSWNtfjZkwx6S3/wBnrvC0JDQyU6OlpbR9euXZNFixZJxYoVpWLFitoB3d4+fuzYMUlNTRV/f3+pV6+eiJhu55bqaeuJsTv3i/Xq1ZM777xTW1ZRt4NZW9bzzz8vnTt3lqCgIK3e5vbBalmGz+IlJibKY489JqGhodK4cWOr9TY8KbPlli+92/z4449L2bJl3XbMKjzN3Zzpj0X1+eK+D3QmNnr2eWe2PW/rl4YvwLEWW8PpTZo00cbbmwwePnxYKlSoIJGRkVqbDRNBV7TZmXMIV52/qeewJeX8jYlgMfH333+LiMiOHTukTJky8uijj1o9MNhyYC380PORI0eK/E6gnhtSRESEjBw50uwHdtVxnvyrrqXvzOgRgxs3bkh2draEh4fL6NGj5fr16y5pgyfZEj9bk0Fv6A/eQs99QdOmTaVmzZqSlJRktC/48ccfpVKlShIQEKD18fz8fLv6+N9//y3Z2dlStmxZ8fPzk0mTJtlVT1uSQXfuF7t06aLbsv7880954YUXpFWrVhIQEKCVZbgPVuXn5xuVlZmZKW3btpVRo0ZJSEiIPPjggzJnzhyL9bblyoYr2pyZmSnt2rWT9PR0CQkJcfkxyzBenuJsf7TW54v7PtCZ2OjZ553Z9rytX6p94tq1ayJiObaFY//II49o0y0lg2qb1On/+te/pE6dOnLnnXdqbTa88uiKNjtzDuHK8zeR/53DloTzNyaCxcAXX3whjRo1kkOHDonIPxuz4Q7MElt3YK+99pqcOnVK6tevL6mpqRIXFyfx8fEWvxOox4Y0c+ZM8fPzM/sR6S+++EIaN24sP//8sza/u6kxsPSdGWdioLbv/vvvFz8/P/n+++/1rr7H2RO/opJBb+gP3kLPfUGVKlWkRYsWcvz4cbP7gi5dumjryPDtb7b28UaNGsm9994rfn5+snjxYofqae3E2J37xRYtWmjLys/Pd2pZQ4YMkTvuuEN2796tnZD6+vrKvffeK3fccYfFE6mCggKtza+++qp2YhwQEGB0Ylx4f67+tqgrG3q22bCeajLoymOWuXi5m1790VyfL+77QGdio2efd2bbU3lLv1T7xIIFC6RRo0baLfeFY2sp9rYmg6+99posWrRIgoODpU6dOkZXVl3ZZmfOIVx5/iZS9DlsccNE0MutXbtW/Pz8pGHDhhITE6P7Sc+LL74oycnJoiiKlgS2b99eLl++bLVezmxI1j4RYdje2NhY7Vsv7jzwqTHIzc21Op8jMVDbFxkZKYqiyEcffSQixe/Abo0j8bOUDHpDf/AWeu4LateuLaGhoTJlyhSL+wKRf9bRwoULnerjmZmZDtfT2stH3LVfjIuL021Z06ZNE0VRpEKFClKnTh3ZvXu3vPjiixIXFycApFatWha/d1a4zeZOjF944QWTdaiW5a42m6unnsmguX5qLl7upHd/NOzzxX0f6Exs9Ozzzmx7It7VL9W4REVFCQCpW7euxMTEmCSDffv2FV9fX4uxLyoZNIxt8+bNtelnzpxxaZudOYdw5fmbqrh85sxWTAS9mOHbny5evCgTJkyQ2rVr637S89RTT8nEiRNl+fLlUq5cOZkzZ442f15ensVyHdmQikoCC7e3Tp06bj3wqTEw/FiqXjEw/IZaYGCg9OvXz+3tczVn4lc4GfSG/uAt9NwXqOtoxIgR2r7g9OnTRvsClXqLpzN9XK99luGJsbv2i2lpadqyzp8/79Sy1HqPGjVKWrduLS1atJCaNWvK7t275amnnpJ+/fppZRXeZgzbfOHCBa0e5k6Mn3zySZN16K42F1VPvZJBc/3UU1zZHwcPHlys94HOxEbPPu/MtmfIG/ql4YfvfXx8JCEhQaKjo2XQoEFSu3ZtLRl84YUXBIAkJSUZxc/eZNAwturzmq5sszPnEGPGjHHZ+ZuqpCWBIkwEvZa5j4GeO3dOt4NMhw4dtL/s3LhxQ3Jzc+XSpUvy7LPPSmpqqsyfP1+b39qGbc+GNHny5CKTQHPtddeBT43Bc889p3sM1PZ16NBBi4G72+dqesRPTQZd2R8s3fLsCD3LskTPfUFubq5cvHhRW0f33HOPti84deqU0fO6hrF1to/rsc/q2LGjdmLsjv3iqlWrTD6c7uiyCq/D1atXS3x8vHZCqu4TzZVlbv2fPXvW6MS4Q4cO2omx4bOYtlzZ0KvNttSzY8eOkp6eLv7+/k4fs1TekAS6oj9OmzZNAEiXLl3kt99+Myq7OBwznImNnn3emW3PHGf7pTPHDLUt6enpWpvUmBomg/PmzRMfHx+57777tDYZxq+oZLBwmwxj68pt0ZlziNdff10qVaokvXr18sg5bHHGRNALWfoOjIh+B5k333xTFEWRlStXSo8ePWTy5Mly4cIF+euvv+SZZ56RlJQU3Takc+fOyWOPPWbTlUBL7XX1gU+NwcWLF3WPQWxsrPj4+EinTp1MYlCcDuzW6Bm/++67z2X9oWfPnvLoo4/KiRMn7Gid68uyRM99geE6unTpkraOBg0apO0LCr+G3ZCzfdzZfda9994rAGTkyJFOx8LSMtT94mOPPWb04XRnlmW4DgsvSz3RV8sqKCgwKmvNmjUW17/hid306dNFURR5+umntd/a+oZPPdrsTD0dOWaZm+5urjxOq2X37dvX6BECw7K9+ZjhTGz07PPObHuu6JfOHDMKXwk0jI9hMti+fXsBIJMmTRIR49jamgyaa5NhbF2xLTpzDtGjRw958MEHZd68eVKpUiXp2bOnW89hizsmgl7G3F/CRIw3MmcOMiL/vOK3SZMm0rVrV4mLi5Py5ctLQkKCPP3007ong3FxcaIoipQtW9bmK4GW2uuqA58rY7B27VoBIACMYuDO9rmanvGbOHGiAJC+ffsaTdMjXmo9ExMT5ZlnnpHs7Gx7m+qSsizRc19gaR1NnjxZypYtK/Xr19fKdGUfd3Sf9c477wgAiYmJcToWlqj7xebNm+sWd0vr8NixY9o+2FJZfn5+oiiKvPjii1brMWzYMPH395fExESPtNmd9TR8G6QnufI4rZb9+OOPG7W5uBwznImNnn3JmW3PFf3SmWOGuSuBhnEpKCiQc+fOSWpqqgCQpk2bGt0mun37dpuSwUcffdQoPs622VbOnEMY/nbGjBm6J4NFncOWBEwEvciXX34piqLI22+/LSLW/8Li6A5M/TaU4Sci8vLyZOXKlZKYmCizZ8/WPRns1auX7Nu3T5f26n3gU2NQUFCgewzuv/9+rX29evWyuhPx5gO7NXrGT+0PI0eOlCpVqsibb75pcbn2xqtwPdu2betwAqdnWZbouS+wtI72798vtWvXlnr16mnrSI2hK/u4vfus5cuXCwBp2LChFgtL9XN2v9iqVSutPYVfNmHvsrZs2WJ2HRrug1VnzpwxKktd/76+vlav7h0/flxq1aolNWrU0H5r6xs+9WizO+vpLS+GceVxWi37ueeek3r16hmdbBduszceM5yJjZ59yZltT8T+cymVpTo7c8xQ4/Lwww+Loija4xOW+k6HDh3MPjNoy5VBf39/iYiIsNgmV2yLzpxDmPvtU089pXsyaOkctqRgIuglrl27JqNHj5batWvLzp07tfF6HmTMfSz+zJkz2vQVK1bolghFRUUZbUh6tlevA58ag3Pnzmnj9IrBG2+8ISEhIRIZGemx9rmanvEz7A9fffWV1of0SAYt1dORBE7PsizRc1+gKIpUrVrV6DmHFStWSIsWLaR8+fKSnJysraMuXbq4rY/bus/66aefJDQ0VIKDg7VlOJugWdovdunSRYv7jh07nGqPv7+/NGrUyGQdGn7sWlX4Wbzo6Gjp1auX1K5dW958802Lt70Z7s890WZ31tNbkkBXHqfVsqOioqRGjRpmP4NgqWxvOGY4Exs9+5Iz254r+qUzxww1ptHR0dKtWzetTYZXAg3nq127tnz22WcmzwzakgweP35catSoIYqiaG0unAS66juBjpxDWPutmgxWr17d4WSwqHPYkoSJoBc4d+6cFBQUyIEDB+Tee++Vnj17yrZt22z+rS07MHNJoPrQsuFOLiMjQ5dEqH379lK5cmXJyckx+7yJs+119sBX+GOjesbg3Llzcv36dWnevLkEBwdLt27dJCsry63tczU942euP2zevNmoD1lSVLyKqqc9CZyeZZ07d077CHDh8XrtC5o3by6NGjXSXsyhxuX48eNSpUoViYiI0NbRxYsX3d7Hi9pnffvtt1KvXj1JTEzUYrF161Zdl6HuFzt16qTFfcKECU7F/dy5c7J9+3bx8/OTZs2aaWXZciL1ww8/yPjx46VatWoycOBA6dmzp8yfP9/q/lxl65UNPdrsznp6SxLoyuP02bNnpaCgQD7//HMJDw+XihUramUX1WZvOGY4Exs9+5Iz254j51KWylI5c8xQY/rVV1/JhAkTpGPHjtKrVy+T2JqL/aeffmpXMlijRg2Jjo6W1NRUs88MujIJdOQcwpZz2Keeekri4+OlYsWK8uSTTzp1Dnv27Fmzx+uSgomgh33++efSpEkTWbp0qRQUFMjBgwdl3Lhx0qNHD10OMtOmTTP6S5i5v6KI6JsITZkyRUJCQmT//v0uba+jBz41BufPnzcar0cM1PZ169ZNgoODJTMz0+3tczU947du3Tqz/aFu3boSGBhotg8VZilettbTlgROz7I+//xzadasmSxevFiuXr1qNF6vbSMgIECaNm0q58+fN9oXHD16VNsXqOto1qxZHuvjlvZZ7777rgQEBEjjxo21WNxzzz0u2S+2aNFCi3teXp5Tca9cubLExMTI0qVLtRPS5s2bS+fOnY2u8ohYjuv8+fNl/PjxUr16denfv7/06NFDOzEuvD8vXJY72uzOenpLEujK47Sfn59UrFhR/v3vf0u9evWkbdu2Wtn2/uHDE8cMZ2KjZ19yZttT6dkvnTlmqHGZPHmyNGnSRJ599lm55557pGPHjtKjRw8tttZib0syOG3aNNm7d6+Eh4dLcHCw1uZt27a5dFt05hwiNDRUatWqpf3W3AvOMjIypFq1auLv7y9fffWVU+ewlo7XJQkTQQ9SHwBu3ry5pKSkyIoVK4w2Zmf/4vj1119LUFCQKIoiaWlp0rhxY2nfvr3F1xc7ciJf+BsttnwiQs/22nvgU2Og/hVKzxio7atYsaL4+PjIs88+6/b2uZqe8bPUH0aOHCm+vr7SoUMHh+Nlbz2tJXB6lmXY5tTUVPnggw/k6tWrum4b9evXlypVqkjNmjXN7gu6d++uzbt8+XKP9/HC+yx/f38BIGFhYVosDJMVPfeLrVq10uLepUsXp+L+/vvvi6IoEhAQIImJibJixQrthAuAVKpUyeJVnsLr/6233jI6Me7Zs6e8/vrrRvtzlS1XNvRqszvrae237uTK47RatqIoAkCaN29u9IcPbz9mOBMbPfuSM9teYXr0S2eOGWpcateuLQAkOjpaUlJS5IUXXpB77rlHkpKSpEePHtKmTRvtQ++WYm8tGTRsU5cuXbQ2m5uu57bozDlEgwYNJDo6WpKSkswen9TfTp48Wfz9/aVp06ZOncNaOl6XNEwEPUTtYGqHfPTRRyU5OVn3ZHDnzp0ybtw4+fHHH0VRFHnkkUesluHMVTFbPhbvivbaeuBTY/Doo4/qHoNPP/1UfHx8pHPnzhIUFCQjR450e/tcTc/4NW/eXBRF0WL4yCOPSHJysnTv3l0CAwMlIyPD4XgZPudg6yv0LSVwjrbZXFnmtoE777xTJk+erNu2odb3X//6l8V9gVpfNTbe0McLnxi3bdvW6VhYWoYai65du2pxz8/Pd2pZar2ff/55mTBhgoSHh0vr1q1lxYoV8vXXX0u/fv20sgpf5bG0bzR3YvzGG29o61DEvisbzrbZnfW05bfu4MrjVuXKlbWyd+7cKU2bNtXKdvYPH+44ZjgTGz37kjPbniXO9EtnjhnDhw8XHx8fGT9+vLavTU5OlrS0NElOTtaSwUaNGgkAady4sWzbts1q7K0lg4ZtOnfunIwfP96l26Iz5xDqi8kmTpxo9TzUMJFz5hzW0vG6JCaDTAQ9wNI3dvQ4yJw9e1buv/9+qVu3rhw+fFhERLsNdOPGjeLv7y9PPfWU1TIcSYQmTZpk93cC9WxvvXr15L///a/FvzKpMdi8ebPuMTD3IW1Xtc9T9IzfihUrRFEUiYmJMepDLVu2dPoq0+HDh+X+++/Xbguxt56GCZyzbTb3V97C20Dfvn0FgAwdOtTo4OJI3ylc30mTJpnsC1SFX7jiDX28SpUqJjFSk5X27dvrul80XB+GJzeOtKfwulWXFR4eLm3atDEqq/BVnqL2jW+99Zb861//kho1amgnxrZe2dCzze6spyFrv7W0r7eXpXJcedxSr2BFRkYatbm4HDOciY2efcmZba8ojvRLZ44Zjz76qACQuLg4ozapcVGTwVGjRgkAqVOnjvTo0UNrk7XYf/LJJ3L//fdLrVq1ZNiwYRbb5Oi2aAtnYvP2229LYmKijBo1Svz9/WXKlClWz0O//fZbbZwj57DW+mhJTAaZCLqZuQ5meGlajwPBt99+K4qiyOOPPy6tW7eWypUry2+//SYi/ySDvr6+MmPGDKtl2HqS+MYbb8i0adMkODjY5iTQVe2tWrWqVKlSRY4fP2403ZUxUK9sde7cWYuBK9enJ+gZv2HDhomPj488/fTTRjtjtQ85c5VJrefq1atFURQZPHiwQ/Vs27atVK9eXSpUqOB0mw3/ylt4G1C3jaSkJO3gYvhiHHv6jqV1dM899xj1ncIvbvKWPv7yyy8LAElOTtbqpS6jdevWEhISot3y5ex21L9/f5PviznaHkv7N3VZbdq0keTkZFm+fLlJWc8//7xN+8Znn31WAMjYsWM90mZ31tNSPy2sdevWZvf19rJUjiuPW2rZDz74oNbmvLw8rc3efsxwJjZ69iVntj1X9EtnjpOGxwIAkpqaKkePHtXapMalVatWAkC6d++u3SaalpZmUzK4YMECURRFRo8ebbVN9m6LtnAmNupvX331VUlMTNQeHXnsscesnoc6eg5rS/8uackgE0E3Wrdundm/MoiI03+VVmVnZ0tMTIykpaVJ8+bNJS4uToYOHSqxsbHy66+/ioh+idDzzz8v0dHR4uPjYzYJdGd7w8LCtLbWr19fjh07JiLi0hhkZGSIoigSERFhEgNXrE9P0DN+U6ZMEQDStWtXkz6kKIpTV5nUevbq1Uv8/f2lU6dODtczOjpaypQpI02aNJE6dero0ubU1FSj20QLbxuGB5fc3FxtPltiYWkdvf/++2Y/wm6tvp7o4++++64AMPqovapZs2Za29SPMTuzHanPx+mxT7K0f8vOzpZ69epp26y5srp27SoA5L777rNaj/Hjx0twcLDRs2PubLM762nrPs6wvxvu6+1lqRxXHrfUsp944gmTNheHY4YzsdGzLzmz7bkijs4cJ9W2jBkzRnx8fGT48OHa3SRqn8zPz9fuHmnQoIHRbaK2JINDhw6VMmXKSFxcnNU2uaLvOBObwr995ZVXTJJBa+eh9p7D2tO/S1IyyETQTTZs2CCKosi8efMszmPY0aZMmWL016xDhw7J+PHjpUePHkavaTfs6IU/Fp+YmCh//vmnnDt3TtLT0yUmJsapk1rD77tcvHhRLl26JP/+97/N3n7izvaGhoZKYmKiXLhwQc6cOSPp6ekSGxsrDRs21MbrHYORI0eKoijy/PPPW4yBnuvTXJmupvYhPeKn9oeRI0da7EOOxkut5/79+6VWrVpSrVo1rZ75+fl21VMta8GCBdKqVSuJi4uT2rVrO91m9cB+4sQJo22jcJvNXRm0Fgv18woXLlyQs2fPauvoq6++knr16knLli21+tr6vKQ7+/iSJUtMkkDDjwUX3oeVK1dOEhMTHdqOWrZsaTbujrRn7ty5WlmWtlm1rPz8fKOy1q9fL4qiSGJiosV65+fna2XVqlVL+21eXp7b2uzOetq6jzO3T4qNjbU7GbRUzpIlS1x23FK3+xkzZlhsszcfM5w5puvZl5zZ9lwRR2eOk2pMJ0yYYBRbw+8MHj9+XDZs2CAATJ4ZnDNnjowbN87obaJZWVlaMvjBBx/IsWPHJCoqSqpUqSIhISHSunVrrS2GV7pc9bF4R2Nj+FvDY9vLL79slAxOmTLF4vFJxPZzWEf6t3q8vnLliqMh8gpMBN1E3XkV9bCyIwcCEfPfCTT8iPTp06dlzJgxUq9ePfnll19ERGTTpk3i5+cnTz75pNU6GW5ImZmZ0qFDB5kyZYpcunTJ4m0D7mpvaGio9j0ZtazTp09L+fLlTe4V1zMG6hu91q5da/XWCT3Wp7myXK3wd3pEnIvfiy++qPUHa33I3nip9fzuu++0eDlaz8JtzszMlPj4eGnSpInUqlXL6TZ37NhRpk6dKtOnTzfaNhxNBiMjIyUiIkI+/fRTbb7Tp0/LwIEDxd/fX9q3b29XfT3RxytUqCAATGJReF3k5+dr69XWZFBdhtov1H3Sl19+aTUOtrSnQYMGJvs3c9vs7du3TcoaOnSoKIoi7777rtV6G56UObrvcKbN7qynuXoUZm2fZE8yaK2cChUquOy4pa6L6tWrW22ztx4znDmm69mXnNn29I6js8dJNaZqIvjVV19p0wyPGU888YS2HattKSoZnDJliiQmJkrlypWlQ4cOEh0dLTVq1JC0tDTJysoyapMrvxPoSGzM/fbUqVPab1955RXp0KGDDBw4UPz8/GT69OlW62LLOayj/bskJINMBN1o2rRpEhISIps2bbI6n70HgmPHjhldCSy8Aan0SoQ6duwoUVFRFj9D4a72qknghQsXjMpo3ry5tGzZUrudwPAEQc8YhIaGunx9mivD1Qp/6NWQM/F7/PHHtXhZ60O2xqtevXqSkJAg+/btM4nXH3/8YVc9DdtceD1Xq1ZNhg4dqmubC28b9iaDzZs3l6ZNm8qoUaNM+k6tWrWkevXqWn3z8/O9uo+rsdiwYYOIWO5/9iaD5rYjw/XhbHvq1asnQUFBsmnTJqvbrLmyevXqJSEhIbJgwQKr9bb3yobebXZnPW1JAq3tk2xJBm0pp3z58hIcHOySPl+uXDnx9fU1u907W7Y7jhnOHNP17EvObHt6xVGv46Qa0yFDhkhISIhs3LhRm2Z4zFC34w0bNtiUDGZkZEhERISEh4dL5cqVJTU1VQ4cOCDjx4/XkkF722wrZ2Jj7beGyWCrVq0kKipKVq5cqds5rKP9u7gng0wE3cDw8vtjjz2m24lInz59ZMWKFdK4cWNJTU2VuLg4kyuBhTmbCD3yyCMSEhJi9plAd7bX3JVAEdFi8Oeff1o8QXA2BpMmTdJi4Mr1ae63rmYYP0vsjZ9hf5gyZYr4+flJYGCg1T5UVLwiIyOlXLly8vbbb1uMl631NNfmwn1drzbv2bNHG1+479iaDJYtW1bq1asn586ds9h3ikMf79q1q9H8ISEhUrduXav9z5Zk0Nx2lJeXp9XP2fbcvn1bW1ZMTIwEBgZKrVq1bLrKk5eXp5XVo0cPoxNjS9u/LVc29G6zO+tpLl6G7NknWUsGiyonLy/PqG/plQz26dNHPvjgA63N6rpQ//DhbNnuOGY4c0zXsy85s+0VLsuZOOpxnFRjmpeXp8V00KBBWjKoHn8MjxnqftJaMpicnCzJyckSGhoqTZs2lQoVKkj58uVl2bJlUlBQoCWDruo7zsQGgFSvXt3qb0+dOiWNGjUSRVFk1apVJnG1xtI5rB7nrMU5GWQi6CaGnUavE6tx48aJoijStWtX6dy5s0RHRxvtQC1x9CTR2ncCrdVd7/aWL19eoqOjJT8/3+g3agwMx+mdDKox2Lt3r8vaZ3iC7M4k0Fz8LLE3fmqZjzzyiPj6+mp/zbXGUrw6d+4s1apV096IaS1eRdXTUpvN9XW92mxt2ygqGezcubOEhYVJp06diuw79l4VLdx2d/Rxw224Zs2aoiiKTSfI1pJBS9uRnvsk9bmlcePGCQDx8fGx+SqP4ZUOwxNja9u/o/sOZ9rsznpa4sg+yVwyaGs5hfuWHsmg2uaUlBSj+YrbMcNb+pIz217hshyJo57HScO2GiaDfn5+4u/vrx1/CtffWjJ4zz33aAmV2ib1mUE1GXRV33EmNp07d5bKlSuLr69vkcenwMBA6dmzp67nsHocH4prMshE0I30OhFR3wZ14MABefvtt+XYsWOSlpYm3bt3Nzp5s8bek1p7kkBzddervc8++6ykpaVJWlqafPPNN9q8agzS0tJMYqBXMlg4Bq5an+bmdTVr8bPE3vgZfujV0XglJCRIXFycpKWlyQcffGBTvCzV09fXV3vDnGGbrfV1vdpsbzK4fPlyOXTokLaORowYYVPfKQ59PD8/X+t/derUsfmPBIWTQVu2Iz3bk5eXpy3L3qs8hifGPXv2lJCQEHnrrbes9mdH9x16ncC7up6FObNPMtzX21uOXsmguTYbJufF8ZjhLX3JmW2vcFn2xNEVx8nCyaCfn5/4+PiY7AeLSgZ79OihJYNvvfWWHDhwQN58803td4XfJqp333EmNtHR0dKpUydJS0uT+fPn23R8csU5rF7J4PLly4vV20SZCLqZIx3N8LL1tGnTJCkpSd59910REdm+fbuIiHz33Xdy9913S+/evWXXrl021cXWDcmRJFClZ3vVtu7fv1/Gjh2rtdVwvKUYOJsMWoqB3uvTXLnbtm2Ta9euWS3TVubKsiV+ltgbP0euMqnx2r59u0ybNk1atGghHTp0MKpnUQexwvXcvn27lgw2b95cK8uWvq5Xm631ncJ9JC4uTt555x3ZtWuXto5GjhxZZN9xpL7u7uPbt2+X/Px8rf8ZPv9T1DIMn+uKj48vMhZ6t8fcyZmtZeXn52tl3XXXXRb/GGBLPSy1z9w4e9vsznqq9NgnxcbGSmZmpkPlOJIM2tpmdxwz9GLueOEtfcmZba9wWZbaZ8iVx0l1mZMnT9buHlFvE7WUDD766KMSEhIi69ev19rSvXt3ozbl5+cb3SLuqr7jTGw++eQTGTNmjERFRcmAAQOkd+/eMm/ePJuOT644h9Vj+0xOTpZ33nlHbt68WXQAvAATQQ9wpqP98ccfoiiKPPbYY9qr7T/++GMREdm3b59RgmQLaxvSzJkzZcqUKQ4ngSo92lupUiWzbQ0PD5dq1arZFANHkkFbYqDX+jQnLi5O6tatKx9++KFcv37daplFMVeWJ/qQI/FS66l+FHfUqFEO1zMwMFBq1KghH3/8sVEyWK9ePQkICLCpr+vVZluSwcaNGwsA6dOnj4iI7N27V8aOHSupqakCwGLfcaa+hbmqjxv2v/z8fK3/2ZsMDho0SADI+PHjrc7vivY4U9bvv/+ulaX3vqOw4lJPvfZJ5cuXF39/f3njjTccKseZZLCoNrvymKEXa8ceb+lL7oqjO46Tjz76qPaHw8LPDBaVDH7wwQeiKIrcd999Ztvkyr7jTGzU377zzjsyZswYqVGjhvTv398oGSzq+OSKc1g9+tXjjz9u8/I8jYmghzjS0Y4cOSINGjTQXgyTmJgo48ePl5SUFPnoo49ERL8d1JYtW0RRFFEURfbt2+dgK//Hmfaq3wks3NY77rhDKlWqJNHR0dK6dWubYmBPMmhPDJxdn+YYruPU1FTJzMx0OBk0V1azZs081ofsiZda9yFDhkiZMmWkWbNmDtezcePGUrFiRQkLC5N27drJRx99JFu2bBEAAkCSkpLc3mZrsWjYsKEEBwdLjRo1jNbRp59+KuHh4VKpUiW3bed693Fz+zBHksEjR45ITEyMVK9e3a7PCOjZHmfKMnxGR699hyXeXk+9jmtxcXHSsmVLiY2NlZCQEHn99dcdKseRZNDWNrvimKEXW4493tKXXB1Hd55rqZ+7KvzMoLVkcPz48aIoijRu3Nhqm1zRd5yJTeHfLly40CQZfO2112w6PrniHNabt0+9MRH0IHs6WuHvBMbHx2u3bDz44INy5513OrWDMnwtr/qxzz179shPP/3kTBONONLe0NBQiY+Pl7///lvy8/O1ttaqVUvi4+Nl165dMnbsWKlTp460atXKphiobY2NjZXjx4/rFgNH16e536vr+O+//xaRf9ZxSkqKQ8mgubJCQ0OlXr16cunSJW2cu/uQLfFS6/7f//5XYmJipGbNmlo9DZMGW+qplnX06FEZM2aMhIeHS2Jionz00UeyZ88e+fjjjz3WZnPja9asKUFBQdKlSxcR+d86euONNyQmJkYSExO1+n799dcuqa+t9TfHWh831yfNrdeikkHDZahti4mJMdqu3dEeZ8uy59kxZ7/55a31tNYnRGzfJxmWc+rUKWnQoIGEhIQ4dWVQ7VtFJYP2tlnP/qcXe4493tKXXBVHvfqkoaL2wYWfGbSWDKptUV8M06pVK6PfO9JmWzkTG0u/XbBggZYMqreJLlq0yKbjkyvOYb1x+3QFJoIeZktHO3bsmMnH4gu/XlePHdTp06flnnvukapVq8qJEyecbJl59rTX0ncCK1SoIGFhYfLee++JyP/aak8yqLa1WrVqcvr0aZPxjsbA3vVp7neWvqPjSDJorqzmzZtLlSpVJCkpyagsT/Qha/FS675v3z6jeDlSz8JxUOsZEhKiXRn0dJsNx0dGRgoA7eqn+rv09HQJDg42uio6ZswYt27nzvZxa/3bnmTQ3DL++OMPs9u1K9ujV1l61qMo3lZPW/qESNHbp7lyTp8+LY0aNZKAgABZuHChTeUUpl4ZvOeee6Rs2bJmk0FH140713tRHDn2eEtf0juOevVJc4raB9uSDKrf/+vQoYPExMRI7dq1zR5LXNF3nIlNUb9duHCh3HPPPVKhQgUZNGiQx89hvWn7dBUmgl7A2kPPha8EFk4CDR9S1WMHNX78ePH19ZWjR4/q0TSzbGmvuSQwLy9Pi8G4ceO0tubl5TmUDFpqq7MxsHV9mpvf3E6y8Dq2NRm0lASq49SyVqxYoWsyaG/8zMVL/Z7c/v37tXgZvnDA3r8+moupWs/Ro0d7RZvVA3jDhg0lKChIoqKitPG3bt3S+o4zV0Udra+t9Rcp+kpgUf3blmTQ2jIcaZsz26yeZelZj6J4Sz3t6RMilrdPa+Wot8+1bdvW4e08Pz9f61vjxo3Tdd24c71b4syxx1v6kl5l6dUnrSlqP2WYDKptGThwoISEhMj7778vMTEx0rFjR61N6nyGf6Rw1ZVAR2MTExNj02+7dOkivr6+smbNGq84h/WG7dOVmAh6CcOOM3v2bClXrpysWLFCYmJipGvXrtKyZUuLH+nUawelPqysxzOBRbHW3rCwMLNXAlu2bCnx8fHaTsSwrfYmg5baqlcMilqf5uZT13Hhv5SJ2J8MmitLHXfu3DmTsvRKBh2Nn2EcqlWrJr6+vrJgwQKTeN26dcuo7kXV01JMH330UQkMDNTq6ek2q32kdu3aEhgYKJ06dTIav2LFCqlXr54WC2/Yzu3t4/b0b3PJYP369SU0NNTqMpxpmyPbrCvK0rMe3l5PR/qEiGl/t1aO+gmXffv2Ob3dGO4z9F437lzvhelx7PF0X9KrLL36pDW27qcMk0G1LQMGDBAfHx9p0qSJ1ib1+9GG87mi7zgTm8jISClXrpysX7/e6m9btGghPj4+8uKLL2q/9YZzWE9un67GRNCLqCe5586dk+DgYFEURXr06CEtWrSQ2rVry19//WXxt87uoJz5RISjzLU3PDxcateuLTk5OUYblBqDnJwci221NRkcOnSo2bbqHQNL61NlqX2W2HpANldW4XHmynI2GXQ2frdu3ZIWLVpoz8ZZipetff2OO+4wG1PDenp6uzHsI4qiCABJTU3Vxp85c8ao7zh6VVSv+lqrv7U+7kj/Vtt269Yt2bdvnwwdOlR8fX0tLkOPttmzzbqyLD3r4a31dKZPiJi+OdpcOWqf2LNnT5Hl2LrduHLduHO9q/Q89nhLn3e0LL36pJ77YLUtf/zxhwQHBwsA8fHx0dqktuXGjRsOtdlWzsSmRYsWUr16dRkxYoTF2OTl5WmxGTJkiMePbeZ4Yvt0ByaCXkLtQN9//700a9ZMAgICJDIyUk6fPi2ff/65hIWFFfnXEFt3UDt37jT6nSeSQEvtXbZsmYSFhZm8/KJwDBxNBhs3biw+Pj4m39HROwbW1qfhdEvts8SWA7K5ssyNcyYZ1LsPqfF47bXXxMfHR/z8/KzGy5a+npCQIGXKlDFqs7l6emq7KdxH/Pz8RFEUWbNmjbZsc33H1quirt7O7enjjvbvzp07y0cffSTff/+9xMbGio+PjwQEBGgx0jMJtHebdVVZetbDm+up13EtJSVF/Pz8ZMGCBUa/K9wn9NjOXblu3LneDel17PGWPu9MWd52rqXWVT0W+Pn5ia+vr9amP/74Q0REuyLoyr7jTGymTZsmYWFhsnjxYquxCQwM1GLjbeewnto+3YGJoBdQO9DBgweladOm2l+9fXx85MyZMyIi8sUXX0hYWJj24U5L7P1r1aRJk9x2O6iqqPZaamvh8fYmg5MmTZKAgADp3bu3S2Ngy/o0x9F1bC4ZNFeWuXF6XBl0Nn6F4+Xj4yMARFEUq/GyJxncvn271Xq6e7ux1EcASGhoqCxevNhq3/H0du5IH7e3f6tXPdu0aSPR0dHaMhRFkdTUVF3b5ug2q3dZetajONRTr+Oamgy++eabImK5Tziz3bhy3bh6vRcUFFid7uyx5+rVqw7X31u2PZW3nGsZJh6Gx0X12Ki2yZ37DGdi88QTT0hYWJgsWrTIamw8fWwzx50x9gQmgh5mqYNVrFjR5LW36l9k9NhB9evXT3r37i0hISFekQQWbq+lthYeb0syWLduXalQoYIEBATIvn37XBoDe9anOY6uY2tXBg3LMjfO0WRQj/hZildYWJiULVtWl77etm1b8fPzM3q+x9GyXNnmChUqyE8//SSvv/66+Pj4aLf/WOo7ntrOnenjtvZv9arnwYMHpUKFCloCWLFiRe0zH3q1zdltVq+y9KxHcaqnXsc1NRls37691T7hyHbjynXj6vW+a9cu+eCDD7RX9Vvi6Hro0qWLZGZmyp49ezzal/Qsy9PnWkUlgWqb3LnPUDkTG/XKoJoMWoqNN53DeiLG7sZE0IMsdbDIyEiLHUyPHdT+/fulb9++EhkZ6RVJoKX26pEMqm0NCgqSNm3auDQGjqxPc4pDMqhH/IqKl559PTQ0VLsy6GxZrmhz+fLl5aeffjK5KhoWFma177h7O9ejjxe1XtVl7N+/X1sGAPH395dXX31Vm6ZH2/TaZp0tS896FMd66rWtJyYmiqIosmjRIqfKMexbrlw3rl7vmzZtkoiICElMTNS+GWuNvetBvWqfkJAgNWvW9FhfckUcPXWuVVQSqLbJnfuMwvRKBq3FxhvOYT0ZY3diIughznQwZzbCjz/+WEREfvjhB+3+cndwtL3OJIMff/yx5OXlaW11ZQz03mE4kwyuXLlSrl27ZrUsW5PBzMxMrSw942drvPTs657ebmxNAtXxoaGhDl8V9fY+bmldWFvGiBEjdG2bt5yIlvYkUKXX9rl06VLdtvPinARu2bJFqlWrJuHh4XLw4EGbf+fsVfuSkASq3H3MKA5JoMqZ2Ki3iS5dutRqbDx5DusNMXYXJoIe4I6/qqsMN6RJkyZJw4YNtRctuIuz7XUkGTRsq6tj4E1/JZ80aZI0btxYPvnkkyLLKioZVMtSd7zqOGfjp9eV4cJsWc+e2m7sTQKduSpaXPp44bbZsgy92uYtJ6JMAo3p1d/1KKc4J4GfffaZVKxYUSIjI+Xw4cN2/97Rq/a+vr7y2muvWS3bW7Y9W7lrH1yckkCVM7F56qmnvOLYZo43xdgdmAi6mTv+ql6YuiE98sgjoiiKSz8WX5irb5e0lAwWbqurYuBtfyW31j57k0HDsvSKn95XhgtzNA6OlmULR5NAT9XX1vrr+Rf3rKwsq8vQs23eciLKJNA8vfq7M+UU5yRQRGTnzp2iKIocOnTI4TLsvWofFBQkAKxum96y7dnL1fvg4pgEqpyJjaePbeZ4Y4xdjYmgG3nyPvaHH37Ya14Mo/ftkoXHW2qr3jHwtr+S29I+W5NBc2U5Gz9XXRkuzNE4OFqWNc4mge6ur63117OPr1u3Tnu1uLVl6NE2bzkRZRJonV793ZFyinsSqFLf5OkMW6/aBwUFFfnmRm/Z9hzlqn1wcU4CVc7ExpvOYb05xq7ERNBNPHkf++TJk73mExGuul1SHT9o0CCzbdU7Bt72V3J72ldUMmiuLGfj564X6TgbB0fLMkevJNBd9bW1/nr2cXUZ6ptSLS1Dj7Z5y4kok0Db6NXf7SmnpCSBeirqqn1QUJD2Rm5LvGXbc5be++CSkASqnImNN5zDFocYuwoTQTf6/vvvJS4uzq33sXviO4EqV7XXUlv79OkjAEzeGOeqGLhyfZqj5zq2lAwGBASYHNT1ip9e8XJ1HBwtyxxLbXY0Fu7ezt3Rx9VlqCdB4eHhRsvQs216tseZsty57ygu9TRHr/5uTzmubLOn4+koNX7vvvuuUf1tSQJV3rLtOUvvfXDh/V/hJLDwfN7cd5yJjTecwxaHGLsCE0E3+euvvyQ+Pt6uN2vZq/CG5IkrgSpXt9dSW//zn/+4JQbuWJ/m6LmOzZXl7+9v9JkFveKnd7xcHQdXttnZWLhrO3dHHy+8jLCwMJf0P3PLcqY9zpTlzn1HcamnNXr1d1vKcWWbvSWejvrwww+1E2R7k0Bv2fb0olefVNuiJoCWkkBvaLOtnImNJ89hi1OM9cZE0E3y8vJk6dKloiiK+Pv7y88//+yS5Xz++ecSGRkpvXv3lrJly3okCRRxT3sttdUdMXDX+jRHz/aZK8sV8XNFvFwdB2dZarMesSgpfdzcMlzVNj3b40xZ7tx3FJd6FkWvPlFUOa5sszfF0xF5eXkyefJkLVmx53ktb9n29KRHn1Tbor5x1VKbvKXNtnImNp46hy1uMdYTE0E3un37tmRkZMh///tfly7n888/F0VRZP/+/S5dTlHc0V5LbXVHDNy1Ps3Rs33mynJF/FwRL1fHwVmW2qxHLEpKHze3DFe1Tc/2OFOWO/cdxaWeRdGrTxRVjivb7E3xdMTt27flscceEwB2rwdv2fb0pEefVNvyww8/WG2Tt7TZVs7ExlPnsMUtxnpRRERAJc7169cREhLi6Wq4haW2lvQY6Nk+c2UVl/i5Og7erLjV1x4luW3kGL36BPuWcxi//2EsLHMmNoyr+zARJCIiIiIiKmV8PF0BIiIiIiIici8mgkRERERERKUME8FS4ubNm5g1axZu3rzpFeWwLJZVXMryxjqxLJblyrK8sU4si2UVl7K8sU4si2VZ5Nl31ZC75OTkCADJycnxinJYFssqLmV5Y51YFstyZVneWCeWxbKKS1neWCeWxbIs4RVBIiIiIiKiUoaJIBERERERUSnj5+kKkHMKCgpw+vRphIaGQlEUi/Pl5uYa/ddRepXDslhWcSnLG+vEsliWK8vyxjqxLJZVXMryxjqxrNJVlojgypUrqFatGnx8rF/z43cEi7k//vgDUVFRnq4GERERERF5iZMnT6JGjRpW5+EVwWIuNDRU1/JycnJ0Kys8PFy3soiIiIiIyDa25AilPhG0djulOdHR0cjOzkZWVhaSk5MxevRoLFmyxDWVs4G99S9KWFiYruUREREREZF72ZIjlPpEcPTo0Sbjvv76axw9ehTNmjVD8+bNjaZVqFDBTTUjIiIiIiJyjVKfCJq7mpeeno6jR4+iT58+mDVrltvrRERERERE5Er8fAQREREREVEpw0RQB5cuXcJ9992HqlWrIjAwEI0bN8Z7771ncf6TJ09i4sSJqFu3LoKCglC+fHn06NEDu3btcmOtiYiIiIiotCr1t4Y66/Lly0hMTMTVq1fRoUMHXLhwAdu3b8fdd9+NgoIC3HPPPUbz7969G2lpafjrr79wxx13IC0tDefPn8fGjRuxYcMGLF++HIMHD/ZQa4iIiIiIqDTgFUEnrVmzBi1atMCxY8ewcuVKfPXVV/j4448BAM8884zRvLm5uejfvz9yc3PxwQcf4L///S9WrVqF7du3Y/fu3QgLC8M999yD8+fPW1zezZs3kZubazQQERERERHZg4mgk8LCwvD6668jMDBQG9enTx80btwYv//+O7Kzs7Xx7733Hs6cOYOHH34Yw4cPNyqnVatWeOqpp3D16lV88MEHFpc3Z84chIeHawM/Jk9ERERERPZiIuikli1bIjIy0mR8bGwsAODMmTPauE2bNgEA+vXrZ7asDh06AAD27t1rcXnTpk1DTk6ONpw8edLhuhMRERERUenEZwSdVKNGDbPjQ0NDAfxzK6dKvTrYrl07q2VeuHDB4rTAwECjq49ERERERET2YiLoJB8f2y+qFhQUAAAGDBiAMmXKWJyvfv36TteLiIiIiIjIEiaCblSjRg388ssvePzxx9GyZUtPV4eIiIiIiEopPiPoRikpKQCATz75xMM1ISIiIiKi0oyJoBtNmDABlSpVwty5c7Fw4ULtVlFVXl4eNm7ciB9++MFDNSQiIiIiotKAiaAbRUREYM2aNQgPD8eECRNQq1YtdO/eHcOHD8edd96JihUr4q677sKRI0c8XVUiIiIiIirB+IygmyUkJODw4cN49dVX8fnnn2Pbtm0AgKpVqyIpKQl9+/ZFly5dPFxLIiIiIiIqyRQREU9XghyXm5uL8PBwT1fDLD27lqIoupVFRERERFSS5eTkICwszOo8vDWUiIiIiIiolGEiSEREREREVMp4XSKoKIrVoVOnTp6uolMURUGtWrU8XQ0iIiIiIirFvPZlMaNHjzY7vn79+m6uCRERERERUcnitYngkiVLPF0FIiIiIiKiEsnrbg0lIiIiIiIi1yq2iWB6ejoURUFWVhY2btyI5ORkREREQFEUXL58WZtvw4YNSEtLQ8WKFREYGIg6depg8uTJuHjxotUyt2/fjs6dOyM0NBRhYWFIS0vDTz/9ZLE+GzZsQK9evVC5cmUEBgYiKioKPXr0wKpVq8zOn5+fjxdffBGxsbHa/FOnTsXNmzedjg0REREREZFV4mUAiC3VGj16tACQcePGiaIo0rp1axkyZIi0bt1aLl++LCIiU6dOFQASEBAg7dq1kwEDBkhMTIwAkLp168rZs2fNljl58mTx9fWV+Ph4GTRokMTGxgoAiYyMlDNnzpjUZfLkyQJAfHx8pF27djJ06FBJSkqSiIgIadasmUn7oqOjZdCgQVK2bFnp0aOH9OjRQ8LDwwWADB8+3K545eTkaDHztkFPnm4LBw4cOHDgwIEDBw7FZcjJySn6/FrXs3UdqJUvipq0AZDMzEyT6StXrhQA0rhxY/ntt9+08QUFBTJjxgwBIIMHDzZbpo+Pj3zyySfa+Ly8POnfv78AkKeeesroN8uWLRMAUq1aNfn++++Npl2/fl02bdpktn0NGjQwSiqPHTsmERERAkCOHDlisd03btyQnJwcbTh58qTHO5qlQU+ebgsHDhw4cODAgQMHDsVlKNaJoKXh+PHjIvK/pC0tLc1sOc2aNRMAcvjwYZNpBQUF0rx5c/H19ZXz589r49UyzV2V++677wSAJCUlGY1v0KCBAOaTUWvt27x5s8m0iRMnCgBZvHixxd/PnDnT4x3L1kFPnm4LBw4cOHDgwIEDBw7FZbAlEfTat4Za+nxE2bJljf7dq1cvk3n+/PNPHDx4EDExMWjcuLHJdEVR0K5dOxw4cAD79u1Damqq0fSuXbua/CY2NhYAcObMGW3c6dOn8fPPPyMiIgKDBg0qulH/z9/fH8nJyTYto7Bp06Zh8uTJ2r9zc3MRFRVl87KJiIiIiIi8NhG09fMRNWvWNBmXnZ0NAPjtt9+gKIrV31+4cMFkXI0aNUzGhYaGAoDRy1xOnjwJAKhTp06RyzFUpUoV+Pr62rSMwgIDAxEYGGjzsoiIiIiIiArz2kTQVkFBQSbjCgoKAPyTcBW+2ldYdHS0yTgfH9e+TNXV5RMREREREVlT7BNBc9QrehUqVHDph+nVWzKPHTsGEbHrqiAREREREZGnlMhLUzVq1ED9+vXx008/4ddff3XZcqpVq4YGDRrg8uXL+Oijj1y2HCIiIiIiIj2VyEQQAJ566ikUFBSgf//+OHDggMn0ixcv4p133nF6OY8//jgAYPLkyTh06JDRtBs3bmDz5s1OL4OIiIiIiEhPJfLWUAAYNmwYfvzxRzz//PNo2bIlmjdvjrp160JEcPToURw6dAhly5bFuHHjnFrOqFGj8N1332H+/Plo0aIFEhMTERUVhTNnzuDAgQOIjo42m4gSERERERF5SolNBAHgueeeQ2pqKl5//XXs3LkThw8fRlhYGKpXr4777rsPAwcO1GU5r732Grp06YI333wT3377Lfbu3YtKlSqhffv2GDt2rC7LICIiIiIi0ovy/x/rpmIqNzcX4eHhnq6GG+j3Ip78gnxdyvHl21+JiIiIyAvl5OQgLCzM6jw8kyUiIiIiIiplmAgSERERERGVMromgrNmzYKiKLp8uy8jIwMtW7ZESEgIFEVBrVq1nC7Tmy1ZsgSKomDWrFmergoREREREZVwXvmymG+//RYjRoxAUFAQunbtioiICFSoUMGtdVAUBdHR0cjOznbrcomIiIiIiFzNKxPBdevWoaCgAPPnz+dbN4mIiIiIiHTmlc8I/vHHHwCAOnXqeLgmREREREREJY9DieDatWuRmJiIkJAQREZGon///vj1118tzn/9+nXMmTMHcXFxKFu2LMqWLYuEhAQsXbrUaD71ObnFixcDAJKTk6EoislzhyKCjIwMdO7cGeXKlUNQUBAaNGiAWbNm4fr16ybLP3LkCGbNmoXExERUqVIFAQEBqFGjBkaNGmVSb7UOAHDixAlt+YqioFOnTg61y9DOnTvRpUsXhIaGIiIiAqmpqdizZ4/F+YmIiIiIiPRm962hb7/9Nu677z4oioIOHTqgatWq+Oabb9CmTRv07NnTZP4///wTKSkpOHToEKpUqYKkpCSICHbt2oX09HR89913mD9/PgCgXr16GD16NL7++mscPXoUqampqFKlijYNAAoKCjBixAhkZGSgbNmyaNWqFcqVK4fvvvsOs2fPxvr165GVlYXg4GCtDosWLcLcuXPRuHFjtG7dGoGBgfjpp5+wbNkyrFmzBjt27EDTpk2N6rB06VKUKVMGAwYM0MqpX7++Q+1SffbZZ+jbty/y8vLQpk0b1KlTBwcPHkTHjh2Rnp5u76ogIiIiIiJyjNghOztbgoKCxN/fXzZs2KCNv3XrlgwfPlwACABZvHixNq179+4CQB566CG5ceOGNv7s2bPSqlUrASDr1683Ws7o0aMFgGzdutWkDnPnzhUA0qlTJzlz5ow2/ubNm3L33XcLAJk6darRb3bv3i3Hjh0zKeu9994TAJKcnGwyDYBER0dbjIW97crNzZWKFSsKAHnvvfe08QUFBTJ16lQtdjNnzrS4TBGRGzduSE5OjjacPHlS+23JHhTdhvyCAl0Gz8eEAwcOHDhw4MCBAwfTIScnx2pOISJiVyI4Y8YMASCjRo0ymXbhwgUJCQkR4H+J4Pfffy8ApHXr1pKfn2/ym/379wsA6dWrl9F4S4ng7du3pUKFClKmTBk5e/asSXnXr1+XKlWqSLly5cwuz5x27dqJoihy+fJlo/GA5UTQkXapSWfHjh1N5r9165bUqFFDgKITwZkzZ3q8Y3lmYCLIgQMHDhw4cODAgYMtgy2JoF3PCO7YsQMAMGTIEJNpkZGR6Nq1q9G4TZs2AQD69OkDHx/TRanP1u3du9em5e/fvx8XLlxA27ZtUblyZZPpwcHBaNmyJf766y/89ttvRtOuXr2KjIwMTJ06FePGjUN6ejrS09Nx5swZiAiOHj1qUx0cbZe12Pn7+xvdgmrNtGnTkJOTow0nT560ud5ERERERESAnc8Inj59GgAQHR1tdnrhj76r3+CbPn06pk+fbrHcGzdu2LR8tbzNmzdrL3Sx5MKFC7jjjjsAAF999RWGDBmC8+fPW5z/ypUrNtXBsB72tMve2FkSGBiIwMBA2ypKRERERERkhku/I1hQUAAAaN++PerWratbefXq1UO7du2szhsZGQngnyuBgwYNwqVLlzBjxgwMGTIE0dHRCA4OhqIoGDZsGDIyMvDP3aD21UOvdhEREREREbmTXYlg1apV8csvv+DEiRNo2LChyfQTJ04Y/btGjRoA/rmF8pFHHnGimsbl1a9f3+hzEtbs2LEDFy9exIABAzB79myT6ceOHXO4Hva0q2rVqgBMY6SyNJ6IiIiIiEhvdj0j2KFDBwDAypUrTaZdunRJe3ZOlZKSAgD45JNPHK2fkdatWyM8PBzbtm3DpUuXbPrNX3/9BeB/yZuhI0eOYP/+/WZ/5+/vj7y8PLPTHGmXtdjl5eVh1apVNpdFRERERETkDLsSwTFjxiAwMBDLly/Hli1btPG3b9/GpEmTcO3aNaP54+PjkZKSgp07d+L+++9Hbm6uSZkHDx7Ehg0bbFp+YGAgHnvsMVy5cgX9+vUzezXv1KlTWLZsmfbv2NhYAMDq1auNnhG8fPky7r77bty+fdvssqpVq4Zz587h8uXLJtMcadfAgQMRGRmJrKwsow/OiwhmzpyJ33//vegAEBERERER6aHI94oW8vrrrwsA8fHxkU6dOsmQIUOkVq1aEh4ern1L0PA7gufOnZO4uDgBIBEREdKpUycZNmyYpKWlSVRUlAD/fIvPkLXvCObn58vIkSMFgAQEBEh8fLwMGTJE+vXrJ40aNRJFUaRZs2ZGv0lJSdGW36dPH+nTp49ERERIvXr1pHfv3maX9cADDwgAqV27tgwfPlzuvvtumTt3rlPt+vTTT8XX11cASHx8vAwdOlQaNmwo/v7+Mm7cOAGK/nxEYTk5OR5/Pa17Bn4+ggMHDhw4cODAgQMHWwbdvyOo+uSTTyQ+Pl6Cg4OlXLly0rt3b/n555+1b9wZJoIiIn///be89tpr0rZtWwkPD5eAgACJioqSpKQkeemll+TkyZNG81tLBFVr1qyRtLQ0qVSpkvj7+0ulSpWkZcuW8thjj8m+ffuM5r1+/bpMnz5dYmJiJDAwUKKiouTee++VCxcuWFzW1atXZeLEiRIVFSV+fn4CQJKSkpxql4jI9u3bJTk5WcqUKSNhYWFy5513yq5du2Tx4sUCMBG0PDAR5MCBAwcOHDhw4MDBlsGWRFARseN1meR1cnNzER4e7ulqELmVnrutoj5FQ0RERFTc5OTkICwszOo8dj0jSERERERERMUfE0EiIiIiIqJSptQngoqiGA0+Pj6IiIhAhw4dsGjRIl1vQSMiIiIiIvIGdn1QviQbPXo0ACA/Px9Hjx7Fzp078fXXX+PLL79ERkaGS5etKAqio6ORnZ3t0uUQEREREREBQKl/WYz6oojCYdi8eTO6d++OvLw8rFu3Dj169HBpHRxNBPmyGCqN+LIYIiIiIsv4shgnpKSkYOTIkQCATz/91LOVISIiIiIi0hETQSvi4uIAACdPnjQav2zZMrRv3x5hYWEICQlB06ZNMWfOHNy4ccOkjPT0dCiKgqysLGzcuBHJycmIiIiAoiiYN2+edjXixIkTRs8qdurUyeXtIyIiIiKi0onPCFpx5coVAEBgYKA2bsKECVi4cCGCgoLQuXNnhISEICsrC0888QTWrVuHLVu2ICQkxKSsFStWYNGiRWjVqhW6deuGo0ePIi4uDqNHj8bSpUtRpkwZDBgwQJu/fv36rm8gERERERGVTkV+cr6EAyDmwlBQUCCJiYkCQKZPny4iIh9//LEAkGrVqsmvv/6qzXv58mVp3769AJBHHnnEqJzRo0dry8jMzLRYh+joaJvqe+PGDcnJydGGkydPauVz4FBaBj15ui0cOHDgwIEDBw56Dzk5OUWeA/HW0ELy8/Px22+/YezYsdi9ezcCAwMxZswYAMBrr70GAJg5cyZiYmK034SHh+ONN96AoihYsGCB2VtE09LSMHjwYKfrN2fOHISHh2tDVFSU02USEREREVHpwkTw/6nP5vn5+SE2NhZLlixBaGgoMjIyULduXdy+fRvffPMNAGD48OEmv2/atCmaNm2Kq1ev4sCBAybTe/XqpUs9p02bhpycHG0o/PwiERERERFRUfiM4P9TvyPo4+ODsLAwNGnSBP369UO5cuUAABcvXsStW7dQoUIFlClTxmwZtWrVwsGDB3Hq1CmTaTVr1tSlnoGBgUbPLBIREREREdmLieD/W7JkidNlWPseWVBQkNPlExERERER6YG3htooMjISAQEBuHDhAq5du2Z2HvWD8NWrV3djzYiIiIiIiOzDRNBG/v7+SEhIAABkZmaaTP/hhx9w8OBBlC1bFs2bN7e77Ly8PD2qSUREREREVCQmgnZ44IEHAACzZs3CsWPHtPFXrlzBxIkTISKYMGGC3beBVqtWDefOncPly5f1rC4REREREZFZfEbQDgMGDMD48eOxcOFCNG7c2OiD8ufPn0dCQgKefvppu8vt1asX5s+fjxYtWqBt27YICgrCHXfcgSlTprigFUREREREVNoxEbTTggUL0L59e7z99tvYtm0b8vLyULduXTz88MOYNGkSgoOD7S5zzpw5EBGsWbMGH374IfLy8pCUlMREkIiIiIiIXEIREfF0Jchxubm5CA8P93Q1iNxKz92Wtbf9EhERERVHOTk5CAsLszoPnxEkIiIiIiIqZZgIEhERERERlTJMBImIiIiIiEoZJoJERERERESlDBNBIiIiIiKiUoaJIBERERERUSnDRNAJWVlZUBQF6enpZqenp6dDURRkZWUZjd+2bRs6d+6M0NBQlCtXDt27d8d3332HJUuWQFEUzJo1y+V1JyIiIiKi0osflHez1atXY9CgQcjPz0dCQgJq1aqFw4cPo3379hgzZoynq0dERERERKUAE0E3ys3Nxbhx45Cfn4/ly5dj2LBh2rQZM2bgmWeeKbKMmzdv4ubNm0ZlEhERERER2YO3hrrRypUrcenSJdx5551GSSDwTyIYHR1dZBlz5sxBeHi4NkRFRbmqukREREREVEIxEXSjnTt3AgAGDhxoMs3Pzw/9+/cvsoxp06YhJydHG06ePKl7PYmIiIiIqGTjraFudObMGQCweBWvZs2aRZYRGBiIwMBAXetFRERERESlC68IulBBQYGnq0BERERERGSCiaATAgICAABXr141O73wbZtVq1Y1O97S/ERERERERK7ARNAJamL366+/mky7dOkS9u/fbzSuXbt2AIBVq1aZzJ+fn4/Vq1e7oJZERERERETGmAg6oXbt2qhZsyYOHz6MNWvWaOOvXbuG8ePHm3zaYeDAgShfvjw2b96MzMxMo2nPPvssjh8/7pZ6ExERERFR6cZE0EkzZ84EAPTv3x+dO3dGr169ULduXRw6dAi9e/c2mjc8PBzvvPMOfH19MXToULRt2xbDhg1DkyZN8Pzzz2P8+PEA/nfLKRERERERkSswEXTS2LFjsXjxYjRo0AA7d+7E3r170bNnT+zevRsREREm8/fr1w9btmxBp06dcOjQIXz++eeoVq0aduzYob01NDIy0s2tICIiIiKi0kQREfF0Jegfd911FzZu3IhvvvkG8fHxNv0mNzcX4eHhLq4ZkXfRc7elKIpuZRERERF5g5ycHISFhVmdh1cE3ezUqVM4d+6c0biCggK8+uqr2LhxI2JjY9GmTRsP1Y6IiIiIiEoDflDezXbs2IERI0YgLi4O0dHRuHnzJn744QdkZ2cjJCQEixYt4hUKIiIiIiJyKV2vCM6aNQuKomDJkiVOl5WRkYGWLVsiJCQEiqKgVq1aTpfpDVq2bIlRo0bh8uXL2LRpEzZu3Ij8/Hy0bdsW169fx5dffunpKhIRERERUQnnlVcEv/32W4wYMQJBQUHo2rUrIiIiUKFCBbfWQVEUREdHIzs7W9dyY2Ji8N5775mMX7JkCXbt2qXrsoiIiIiIiMzxykRw3bp1KCgowPz58zF27FhPV4eIiIiIiKhE8cqXxfzxxx8AgDp16ni4JkRERERERCWPQ4ng2rVrkZiYiJCQEERGRqJ///749ddfLc5//fp1zJkzB3FxcShbtizKli2LhIQELF261Gi+JUuWQFEULF68GACQnJwMRVFMnjsUEWRkZKBz584oV64cgoKC0KBBA8yaNQvXr183Wf6RI0cwa9YsJCYmokqVKggICECNGjUwatQok3qrdQCAEydOaMtXFAWdOnVyqF2Gdu7ciS5duiA0NBQRERFITU3Fnj17LM5PRERERESkO7HTW2+9JQBEURTp2LGjDB48WKKjoyU8PFxGjBghAGTx4sXa/OfOnZOmTZsKAKlSpYp0795dunXrJuHh4QJAJk6cqM27Y8cOGT16tNStW1cASGpqqowePVpGjx4tO3bsEBGR/Px8GTp0qACQsmXLSqdOnaRv374SFRUlAKRNmzZy/fp1ozpPnTpVFEWRJk2aSI8ePaR///7SoEEDASBhYWFy8OBBkzoAkDJlymjLHz16tMyZM8ehdqnWrVsnfn5+Wj2HDBkiDRo0kICAABk/frwAkJkzZ9q1PnJycgQABw6latCTp9vCgQMHDhw4cOCg95CTk1P0OZA9J0zZ2dkSFBQk/v7+smHDBm38rVu3ZPjw4dqCDRPB7t27CwB56KGH5MaNG9r4s2fPSqtWrQSArF+/3mg5aiK2detWkzrMnTtXAEinTp3kzJkz2vibN2/K3XffLQBk6tSpRr/ZvXu3HDt2zKSs9957TwBIcnKyyTQAEh0dbTEW9rYrNzdXKlasKADkvffe08YXFBTI1KlTtdgVlQjeuHFDcnJytOHkyZMe72gcOLh70JOn28KBAwcOHDhw4KD3oHsiOGPGDAEgo0aNMpl24cIFCQkJEeB/ieD3338vAKR169aSn59v8pv9+/cLAOnVq5fReEuJ4O3bt6VChQpSpkwZOXv2rEl5169flypVqki5cuXMLs+cdu3aiaIocvnyZaPxgOVE0JF2qUlnx44dTea/deuW1KhRQ4CiE8GZM2d6vGNx4ODpQU+ebgsHDhw4cODAgYPegy2JoF3PCO7YsQMAMGTIEJNpkZGR6Nq1q9G4TZs2AQD69OkDHx/TRanP1u3du9em5e/fvx8XLlxA27ZtUblyZZPpwcHBaNmyJf766y/89ttvRtOuXr2KjIwMTJ06FePGjUN6ejrS09Nx5swZiAiOHj1qUx0cbZe12Pn7+2PAgAE2LXvatGnIycnRhpMnT9pcbyIiIiIiIsDOz0ecPn0aABAdHW12euGPvqvf4Js+fTqmT59usdwbN27YtHy1vM2bN2svdLHkwoULuOOOOwAAX331FYYMGYLz589bnP/KlSs21cGwHva0y97YWRIYGIjAwEDbKkpERERERGSGS78jWFBQAABo37496tatq1t59erVQ7t27azOGxkZCeCfK4GDBg3CpUuXMGPGDAwZMgTR0dEIDg6GoigYNmwYMjIy8M8dYvbVQ692ERERERERuZNdiWDVqlXxyy+/4MSJE2jYsKHJ9BMnThj9u0aNGgD+uYXykUcecaKaxuXVr1/f6HMS1uzYsQMXL17EgAEDMHv2bJPpx44dc7ge9rSratWqAExjpLI0noiIiIiISG92PSPYoUMHAMDKlStNpl26dEl7dk6VkpICAPjkk08crZ+R1q1bIzw8HNu2bcOlS5ds+s1ff/0F4H/Jm6EjR45g//79Zn/n7++PvLw8s9McaZe12OXl5WHVqlU2l0VEREREROQMuxLBMWPGIDAwEMuXL8eWLVu08bdv38akSZNw7do1o/nj4+ORkpKCnTt34v7770dubq5JmQcPHsSGDRtsWn5gYCAee+wxXLlyBf369TN7Ne/UqVNYtmyZ9u/Y2FgAwOrVq42eEbx8+TLuvvtu3L592+yyqlWrhnPnzuHy5csm0xxp18CBAxEZGYmsrCyjD86LCGbOnInff/+96AAQERERERHpwd5Xrb/++usCQHx8fKRTp04yZMgQqVWrloSHh2vfEiz8Qfm4uDgBIBEREdKpUycZNmyYpKWlaR+Bf+ihh4yWYe07gvn5+TJy5EgBIAEBARIfHy9DhgyRfv36SaNGjURRFGnWrJnRb1JSUrTl9+nTR/r06SMRERFSr1496d27t9llPfDAAwJAateuLcOHD5e7775b5s6d61S7Pv30U/H19RUAEh8fL0OHDpWGDRuKv7+/jBs3TgB+UJ4DB1sGPXm6LRw4cODAgQMHDnoPun9HUPXJJ59IfHy8BAcHS7ly5aR3797y888/a9+4M0wERUT+/vtvee2116Rt27YSHh4uAQEBEhUVJUlJSfLSSy/JyZMnjea3lgiq1qxZI2lpaVKpUiXx9/eXSpUqScuWLeWxxx6Tffv2Gc17/fp1mT59usTExEhgYKBERUXJvffeKxcuXLC4rKtXr8rEiRMlKipK/Pz8BIAkJSU51S4Rke3bt0tycrKUKVNGwsLC5M4775Rdu3bJ4sWLBWAiyIGDLYOePN0WDhw4cODAgQMHvQdbEkHl/0+EqJjKzc1FeHi4p6tB5FZ67raK+hQNERERUXGTk5ODsLAwq/PY9YwgERERERERFX9MBImIiIiIiEqZUp8IKopiNPj4+CAiIgIdOnTAokWLdL0FjYiIiIiIyBvY9UH5kmz06NEAgPz8fBw9ehQ7d+7E119/jS+//BIZGRkuXbaiKIiOjkZ2drZLl0NERERERAQApf5lMeqLIgqHYfPmzejevTvy8vKwbt069OjRw6V1cDQR5MtiqDTiy2KIiIiILOPLYpyQkpKCkSNHAgA+/fRTz1aGiIiIiIhIR0wErYiLiwMAnDx50mj8smXL0L59e4SFhSEkJARNmzbFnDlzcOPGDZMy0tPToSgKsrKysHHjRiQnJyMiIgKKomDevHna1YgTJ04YPavYqVMnl7ePiIiIiIhKJz4jaMWVK1cAAIGBgdq4CRMmYOHChQgKCkLnzp0REhKCrKwsPPHEE1i3bh22bNmCkJAQk7JWrFiBRYsWoVWrVujWrRuOHj2KuLg4jB49GkuXLkWZMmUwYMAAbf769eu7voFERERERFQ6FfnJ+RIOgJgLQ0FBgSQmJgoAmT59uoiIfPzxxwJAqlWrJr/++qs27+XLl6V9+/YCQB555BGjckaPHq0tIzMz02IdoqOjbarvjRs3JCcnRxtOnjyplc+BQ2kZ9OTptnDgwIEDBw4cOOg95OTkFHkOxFtDC8nPz8dvv/2GsWPHYvfu3QgMDMSYMWMAAK+99hoAYObMmYiJidF+Ex4ejjfeeAOKomDBggVmbxFNS0vD4MGDna7fnDlzEB4erg1RUVFOl0lERERERKULE8H/pz6b5+fnh9jYWCxZsgShoaHIyMhA3bp1cfv2bXzzzTcAgOHDh5v8vmnTpmjatCmuXr2KAwcOmEzv1auXLvWcNm0acnJytKHw84tERERERERF4TOC/0/9jqCPjw/CwsLQpEkT9OvXD+XKlQMAXLx4Ebdu3UKFChVQpkwZs2XUqlULBw8exKlTp0ym1axZU5d6BgYGGj2zSEREREREZC8mgv9vyZIlTpdh7XtkQUFBTpdPRERERESkB94aaqPIyEgEBATgwoULuHbtmtl51A/CV69e3Y01IyIiIiIisg8TQRv5+/sjISEBAJCZmWky/YcffsDBgwdRtmxZNG/e3O6y8/Ly9KgmERERERFRkZgI2uGBBx4AAMyaNQvHjh3Txl+5cgUTJ06EiGDChAl23wZarVo1nDt3DpcvX9azukRERERERGbxGUE7DBgwAOPHj8fChQvRuHFjow/Knz9/HgkJCXj66aftLrdXr16YP38+WrRogbZt2yIoKAh33HEHpkyZ4oJWEBERERFRacdE0E4LFixA+/bt8fbbb2Pbtm3Iy8tD3bp18fDDD2PSpEkIDg62u8w5c+ZARLBmzRp8+OGHyMvLQ1JSEhNBIiIiIiJyCUVExNOVIMfl5uYiPDzc09Ugcis9d1vW3vZLREREVBzl5OQgLCzM6jx8RpCIiIiIiKiUYSJIRERERERUyjARJCIiIiIiKmWYCBIREREREZUyTASJiIiIiIhKGSaCREREREREpQwTQSdkZWVBURSkp6ebnZ6eng5FUZCVlWU0ftu2bejcuTNCQ0NRrlw5dO/eHd999x2WLFkCRVEwa9Ysl9ediIiIiIhKL35Q3s1Wr16NQYMGIT8/HwkJCahVqxYOHz6M9u3bY8yYMZ6uHhERERERlQJMBN0oNzcX48aNQ35+PpYvX45hw4Zp02bMmIFnnnmmyDJu3ryJmzdvGpVJRERERERkD94a6kYrV67EpUuXcOeddxolgcA/iWB0dHSRZcyZMwfh4eHaEBUV5arqEhERERFRCcVE0I127twJABg4cKDJND8/P/Tv37/IMqZNm4acnBxtOHnypO71JCIiIiKiko23hrrRmTNnAMDiVbyaNWsWWUZgYCACAwN1rRcREREREZUuvCLoQgUFBZ6uAhERERERkQkmgk4ICAgAAFy9etXs9MK3bVatWtXseEvzExERERERuQITQSeoid2vv/5qMu3SpUvYv3+/0bh27doBAFatWmUyf35+PlavXu2CWhIRERERERljIuiE2rVro2bNmjh8+DDWrFmjjb927RrGjx9v8mmHgQMHonz58ti8eTMyMzONpj377LM4fvy4W+pNRERERESlGxNBJ82cORMA0L9/f3Tu3Bm9evVC3bp1cejQIfTu3dto3vDwcLzzzjvw9fXF0KFD0bZtWwwbNgxNmjTB888/j/HjxwP43y2nRERERERErsC3hjpp7Nix8PHxwcsvv4ydO3eiXLly6NmzJ1544QU88sgjJvP369cPW7ZswezZs/Htt9/ixx9/REJCAt59911s3rwZABAZGenuZhAVK2dzcjxdBbMCAoJ0K+vWrZu6lQWIjmWVbIqi399HRfR7YZi+feuGbmUREVHxpYgIzxC8xF133YWNGzfim2++QXx8vE2/yc3NRXh4uItrRuRdzly+rFtZVSMidCuLiWDxx0SQiIhKgpycHISFhVmdh7eGutmpU6dw7tw5o3EFBQV49dVXsXHjRsTGxqJNmzYeqh0REREREZUGvDXUzXbs2IERI0YgLi4O0dHRuHnzJn744QdkZ2cjJCQEixYtgqIonq4mERERERGVYLpeEZw1axYURcGSJUucLisjIwMtW7ZESEgIFEVBrVq1nC7TG7Rs2RKjRo3C5cuXsWnTJmzcuBH5+flo27Ytrl+/ji+//NLTVSQiIiIiohLOK68IfvvttxgxYgSCgoLQtWtXREREoEKFCm6tg6IoiI6ORnZ2tq7lxsTE4L333jMZv2TJEuzatUvXZREREREREZnjlYngunXrUFBQgPnz52Ps2LGerg4REREREVGJ4pUvi/njjz8AAHXq1PFwTYiIiIiIiEoehxLBtWvXIjExESEhIYiMjET//v3x66+/Wpz/+vXrmDNnDuLi4lC2bFmULVsWCQkJWLp0qdF8S5YsgaIoWLx4MQAgOTkZiqKYPHcoIsjIyEDnzp1Rrlw5BAUFoUGDBpg1axauX79usvwjR45g1qxZSExMRJUqVRAQEIAaNWpg1KhRJvVW6wAAJ06c0JavKAo6derkULsM7dy5E126dEFoaCgiIiKQmpqKPXv2WJyfiIiIiIhIb3bfGvr222/jvvvug6Io6NChA6pWrYpvvvkGbdq0Qc+ePU3m//PPP5GSkoJDhw6hSpUqSEpKgohg165dSE9Px3fffYf58+cDAOrVq4fRo0fj66+/xtGjR5GamooqVapo04B/PrUwYsQIZGRkoGzZsmjVqhXKlSuH7777DrNnz8b69euRlZWF4OBgrQ6LFi3C3Llz0bhxY7Ru3RqBgYH46aefsGzZMqxZswY7duxA06ZNjeqwdOlSlClTBgMGDNDKqV+/vkPtUn322Wfo27cv8vLy0KZNG9SpUwcHDx5Ex44dkZ6ebu+qICIiIiIicozYITs7W4KCgsTf3182bNigjb9165YMHz5c8M9Xi2Xx4sXatO7duwsAeeihh+TGjRva+LNnz0qrVq0EgKxfv95oOaNHjxYAsnXrVpM6zJ07VwBIp06d5MyZM9r4mzdvyt133y0AZOrUqUa/2b17txw7dsykrPfee08ASHJyssk0ABIdHW0xFva2Kzc3VypWrCgA5L333tPGFxQUyNSpU7XYzZw50+IyRURu3LghOTk52nDy5Enttxw4lJbhzOXLug161isgIEi3AVB0HDy/zorLoCg+ug3e27c8H2cOHDhw4ODaIScnx2pOISJiVyI4Y8YMASCjRo0ymXbhwgUJCQkR4H+J4Pfffy8ApHXr1pKfn2/ym/379wsA6dWrl9F4S4ng7du3pUKFClKmTBk5e/asSXnXr1+XKlWqSLly5cwuz5x27dqJoihy+fJlo/GA5UTQkXapSWfHjh1N5r9165bUqFFDgKITwZkzZ3q8Y3Hg4OmBiSATQVcNTAQ5cODAgUNJGGxJBO16RnDHjh0AgCFDhphMi4yMRNeuXY3Gbdq0CQDQp08f+PiYLkp9tm7v3r02LX///v24cOEC2rZti8qVK5tMDw4ORsuWLfHXX3/ht99+M5p29epVZGRkYOrUqRg3bhzS09ORnp6OM2fOQERw9OhRm+rgaLusxc7f39/oFlRrpk2bhpycHG04efKkzfUmIiIiIiIC7HxG8PTp0wCA6Ohos9MLf/Rd/Qbf9OnTMX36dIvl3rhxw6blq+Vt3rxZe6GLJRcuXMAdd9wBAPjqq68wZMgQnD9/3uL8V65csakOhvWwp132xs6SwMBABAYG2lZRIiIiIiIiM1z6HcGCggIAQPv27VG3bl3dyqtXrx7atWtndd7IyEgA/1wJHDRoEC5duoQZM2ZgyJAhiI6ORnBwMBRFwbBhw5CRkYF/7ga1rx56tYuIiIiIiMid7EoEq1atil9++QUnTpxAw4YNTaafOHHC6N81atQA8M8tlI888ogT1TQur379+kafk7Bmx44duHjxIgYMGIDZs2ebTD927JjD9bCnXVWrVgVgGiOVpfFERERERER6s+sZwQ4dOgAAVq5caTLt0qVL2rNzqpSUFADAJ5984mj9jLRu3Rrh4eHYtm0bLl26ZNNv/vrrLwD/S94MHTlyBPv37zf7O39/f+Tl5Zmd5ki7rMUuLy8Pq1atsrksIiIiIiIiZ9iVCI4ZMwaBgYFYvnw5tmzZoo2/ffs2Jk2ahGvXrhnNHx8fj5SUFOzcuRP3338/cnNzTco8ePAgNmzYYNPyAwMD8dhjj+HKlSvo16+f2at5p06dwrJly7R/x8bGAgBWr15t9Izg5cuXcffdd+P27dtml1WtWjWcO3cOly9fNpnmSLsGDhyIyMhIZGVlGX1wXkQwc+ZM/P7770UHgIiIiIiISAd2JYK1a9fGyy+/jNu3byM1NRXJyckYOnQoYmNjsWbNGgwfPtzkNx988AHi4uLw5ptvIjo6GsnJyRg+fDh69OiBmjVronnz5jYnggDw+OOPY+TIkdi2bRsaNGiAhIQEDB06FP3790fjxo0RFRWFl19+WZu/VatWSElJwe+//47Y2Fj07dsXffv2Re3atXH69Gn07t3b7HJ69eqFvLw8tGjRAiNGjMA999yDl156yeF2hYaG4t1334Wvry/S09ORkJCAYcOGoXHjxnjppZcwbtw4m2NARERERETkDLtfFnP//fejevXqeOGFF7Bnzx4EBQWhY8eOeOGFF5CZmWkyf6VKlbBr1y688847yMzMxPfff49du3ahcuXKqFOnDh588EGzn1SwxMfHB++//z4GDBiAhQsX4ttvv8X+/ftRrlw5REVFYcqUKRg8eLDRb9asWYPnnnsOK1euxPr161GpUiUMGTIEzz77rMVn/ObMmQMRwZo1a/Dhhx8iLy8PSUlJmDJlisPt6t27N7Zu3YqZM2di7969+Pnnn9G6dWssWrQIv/zyC9555x2b40BUmm06cNDTVTCrWbPOupV16FCWbmXdvHldt7JKOpECT1fBrFu3bHu7NhERka0Used1meR1cnNzER4e7ulqELnV0qxtupU1ulOSbmW1bt1dt7KYCBIREZGjcnJyEBYWZnUeu24NJSIiIiIiouKv1CeCiqIYDT4+PoiIiECHDh2waNEiu74vSEREREREVBy49IPyxcno0aMBAPn5+Th69Ch27tyJr7/+Gl9++SUyMjJcumxFURAdHY3s7GyXLoeIiIiIiAhgIqgp/IH6zZs3o3v37sjMzNTeBkpERERERFQSlPpbQy1JSUnByJEjAQCffvqpZytDRERERESkIyaCVsTFxQEATp48aTR+2bJlaN++PcLCwhASEoKmTZtizpw5uHHD9PXe6enpUBQFWVlZ2LhxI5KTkxEREQFFUTBv3jwoigIAOHHihNGzip06dXJ5+4iIiIiIqHTiraFWXLlyBQAQGBiojZswYQIWLlyIoKAgdO7cGSEhIcjKysITTzyBdevWYcuWLQgJCTEpa8WKFVi0aBFatWqFbt264ejRo4iLi8Po0aOxdOlSlClTBgMGDNDmr1+/vusbSEREREREpRITQQtEBJ999hkAoGnTpgCAVatWYeHChahWrRqysrIQExMD4J/vdPTo0QNff/01ZsyYgX//+98m5akfni/8sfukpCQsXboUFSpUMHlOkYiIiIiIyBV4a2gh+fn5+O233zB27Fjs3r0bgYGBGDNmDADgtddeAwDMnDlTSwIBIDw8HG+88QYURcGCBQvM3iKalpZmkgQ64ubNm8jNzTUaiIiIiIiI7MFE8P+pz+b5+fkhNjYWS5YsQWhoKDIyMlC3bl3cvn0b33zzDQBg+PDhJr9v2rQpmjZtiqtXr+LAgQMm03v16qVLPefMmYPw8HBtiIqK0qVcIiIiIiIqPXhr6P9TvyPo4+ODsLAwNGnSBP369UO5cuUAABcvXsStW7dQoUIFlClTxmwZtWrVwsGDB3Hq1CmTaTVr1tSlntOmTcPkyZO1f+fm5jIZJCIiIiIiuzAR/H96PJ+nvgHUnKCgIKfLB/55cY3hy2uIiIiIiIjsxVtDbRQZGYmAgABcuHAB165dMztPdnY2AKB69epurBkREREREZF9mAjayN/fHwkJCQCAzMxMk+k//PADDh48iLJly6J58+Z2l52Xl6dHNYmIiIiIiIrERNAODzzwAABg1qxZOHbsmDb+ypUrmDhxIkQEEyZMsPs20GrVquHcuXO4fPmyntUlIiIiIiIyi88I2mHAgAEYP348Fi5ciMaNGxt9UP78+fNISEjA008/bXe5vXr1wvz589GiRQu0bdsWQUFBuOOOOzBlyhQXtIKIiIiIiEo7JoJ2WrBgAdq3b4+3334b27ZtQ15eHurWrYuHH34YkyZNQnBwsN1lzpkzByKCNWvW4MMPP0ReXh6SkpKYCBIRERERkUsoIiKergQ5Ljc3F+Hh4Z6uBpFb3dLxmdoAP/3+HqYo+t1tL1KgW1lERERUuuTk5CAsLMzqPHxGkIiIiIiIqJRhIkhERERERFTKMBEkIiIiIiIqZZgIEhERERERlTJMBImIiIiIiEoZJoJERERERESlDBNBJ2RlZUFRFKSnp5udnp6eDkVRkJWVZTR+27Zt6Ny5M0JDQ1GuXDl0794d3333HZYsWQJFUTBr1iyX152IiIiIiEovflDezVavXo1BgwYhPz8fCQkJqFWrFg4fPoz27dtjzJgxnq4eERERERGVAkwE3Sg3Nxfjxo1Dfn4+li9fjmHDhmnTZsyYgWeeecaDtSMiIiIiotKCt4a60cqVK3Hp0iXceeedRkkg8E8iGB0dXWQZN2/eRG5urtFARERERERkDyaCbrRz504AwMCBA02m+fn5oX///kWWMWfOHISHh2tDVFSU7vUkIiIiIqKSjYmgG505cwYALCZvNWvWLLKMadOmIScnRxtOnjypax2JiIiIiKjk4zOCLlRQUKB7mYGBgQgMDNS9XCIiIiIiKj14RdAJAQEBAICrV6+anV74al3VqlXNjrc0PxERERERkSswEXSCmtj9+uuvJtMuXbqE/fv3G41r164dAGDVqlUm8+fn52P16tUuqCUREREREZExJoJOqF27NmrWrInDhw9jzZo12vhr165h/PjxJm/0HDhwIMqXL4/NmzcjMzPTaNqzzz6L48ePu6XeRERERERUujERdNLMmTMBAP3790fnzp3Rq1cv1K1bF4cOHULv3r2N5g0PD8c777wDX19fDB06FG3btsWwYcPQpEkTPP/88xg/fjyA/91ySkRERERE5ApMBJ00duxYLF68GA0aNMDOnTuxd+9e9OzZE7t370ZERITJ/P369cOWLVvQqVMnHDp0CJ9//jmqVauGHTt2aG8NjYyMdHMriIiIiIioNFFERDxdCfrHXXfdhY0bN+Kbb75BfHy8Tb/Jzc1FeHi4i2tGRFRaKDqWpd/h9etfftGtrNHdh+hW1tGj3+tWFhER6ScnJwdhYWFW5+EVQTc7deoUzp07ZzSuoKAAr776KjZu3IjY2Fi0adPGQ7UjIiIiIqLSgN8RdLMdO3ZgxIgRiIuLQ3R0NG7evIkffvgB2dnZCAkJwaJFi6Aoev5FmoiIiIiIyBivCLqIoiioVauWyfiWLVti1KhRuHz5MjZt2oSNGzciPz8fI0eOxLfffosOHTq4v7JERERERFSq8Iqgm8XExOC9997zdDWIiIiIiKgUYyLoIj///DP8/f09XQ0iIiIiIiITTARdpH79+p6uAhERERERkVl8RtBFzD0jmJWVBUVRkJ6ejkuXLuG+++5D1apVERgYiMaNG/9fe38fXFV57///r0VINgkhG0gM4CQSjhqgMBCxWvFLSkSCQdoDR/gUQYXkJCBWvsjAqRBtSxT9Ri0yUOk5g5AbGG4qECCgOVgiEhiBj54PFn9+kEIlkRiDyk32JsLegWT9/qDZZZvs3LHDhqznY+aaMdda13u9r0zH+s61rnXxyigAAACAG4IVwQCoqqrS8OHDVV1drcTERJ05c0b79u1Tenq66urqlJGREegUAQAAAHRgrAgGQGFhoYYNG6aTJ09q06ZN2rNnj7Zs2SJJWrx4cZNj3W63nE6nVwMAAACA1qAQDICIiAitWLFCNpvN0zdhwgQNHjxYp06dUllZmc+x2dnZstvtnhYbG3sDMgYAAADQkVAIBsC9996ryMjIBv3x8fGSpMrKSp9jMzMz5XA4PK28vLzd8gQAAADQMbFHMABiYmIa7e/WrZukq69/+mKz2bxWEgEAAACgtVgRDIBOnfi1AwAAAAgcKhIAAAAAsBgKQQAAAACwGApBAAAAALAYCkEAAAAAsBgKQQAAAACwGMM0TTPQSaDtnE6n7HZ7oNMAbqjOnUP8FuvKlRq/xZqW/nu/xYqJb/yYmbb4/xbM9FssAABw83M4HIqIiGjyHlYEAQAAAMBiKAQBAAAAwGIsVwgahiHDMNr9OWVlZTIMQ0lJSe3+LAAAAABoDcsVgo2haAMAAABgJRSCAAAAAGAxFIIAAAAAYDGWLwSzsrLUr18/SVJJSYlnD6FhGEpNTfXct3//fs2ePVtDhgxRjx49FBoaqgEDBmjhwoWqqqpq0bOWLFkiwzD0wgsv+LxnzJgxMgxDH3744fVMCwAAAAB8snwhmJCQoIkTJ0qSevXqpenTp3vaiBEjPPf95je/UU5OjkJDQ/Xwww/r4YcfltPp1Ouvv64RI0aourq62WelpqbKZrMpLy9PV65caXC9tLRUxcXFuvvuu/XQQw/5b5IAAAAAcI3OgU4g0CZMmKCEhAQVFBRowIABys/Pb/S+RYsW6cEHH/Q6vN3tdmvOnDl6++23tXTpUv3+900fJh0VFaWJEydqw4YNevfddzVhwgSv6zk5OTJNUxkZGT5juN1uud1uz89Op7P5SQIAAADANSy/IthSY8eO9SoCJclms2nZsmXq3LmzCgsLWxRn1qxZkqRVq1Z59dfW1io/P1/BwcFer6T+WHZ2tux2u6fFxsa2biIAAAAALM/yK4KtUVFRoZ07d+rYsWNyOp2qq6uTJIWEhOjEiRMtipGYmKhBgwZp165dKi8v9xRyRUVFqqio0KRJkxQdHe1zfGZmpubNm+f52el0UgwCAAAAaBUKwRZaunSpFi5cqMuXL193rKefflpz5sxRbm6uFi1aJOmfK4QzZsxocqzNZpPNZrvuHAAAAABYF6+GtsChQ4c0f/58hYWFKT8/X2VlZXK5XDJNU6Zpqk+fPq2KN23aNIWFhSk3N1d1dXX65ptvVFRUpLi4OCUnJ7fTLAAAAADgKlYEW2Dbtm2SpFdffVXTp0/3unbp0iWdPn26VfHsdrsef/xx5ebm6v3339fhw4dVW1urjIwMGYbht7wBAAAAoDGsCOrqHj9JjR7pIEnnz5+XJMXExDS4tnnzZpmm2epn1n80ZuXKlcrJyVFQUJDS0tJaHQcAAAAAWotCUFePdQgODtaXX36p2traBtfj4+MlXT3e4do9gkePHtWCBQva9Mz77rtPw4YNU2FhoUpLSzVu3DjdfvvtbZsAAAAAALQChaCurgimpKTo9OnTGjp0qKZNm6aMjAzl5eVJktLS0tS7d2/t3LlT/fv31+TJk5WcnKyEhAQlJiaqb9++bXpu/aqgJM2cOdMvcwEAAACA5lAI/sPq1av11FNP6ezZs9qwYYNycnJUUlIiSYqMjNQnn3yiqVOnqqamRjt27FBFRYUWL16sjRs3tvmZo0aNknT1ldOUlBS/zAMAAAAAmmO5j8X42s8XHR2ttWvX+hwXExOj9evXN3qtrKysQV9cXFyzewc3bdokSUpPT1dQUFCT9wL4p9tvv8tvsU6dOuq3WB/savsfhn5s2HeP+C0WOgL/fUjsf03+D7/F2vzOH/wWCwBwY7EiGCBOp1NvvfWWQkJCeC0UAAAAwA1luRXBQMvLy1NJSYn27dunyspKzZ07l4/EAAAAALihWBFsJ4ZhKC4urkF/SUmJ1qxZo+rqaj377LN67bXXbnxyAAAAACyNFcEbLD8/X/n5+YFOAwAAAICFUQi2ky+++ELBwcGBTgMAAAAAGqAQbCcDBgwIdAoAAAAA0Cj2CLaTxvYI7t27V4ZhKDU1VefOndMzzzyjPn36yGazafDgwcrNzQ1MsgAAAAAshRXBAKiqqtLw4cNVXV2txMREnTlzRvv27VN6errq6uqUkZER6BQBAAAAdGCsCAZAYWGhhg0bppMnT2rTpk3as2ePtmzZIklavHhxk2PdbrecTqdXAwAAAIDWoBAMgIiICK1YsUI2m83TN2HCBA0ePFinTp1SWVmZz7HZ2dmy2+2eFhsbewMyBgAAANCRUAgGwL333qvIyMgG/fHx8ZKkyspKn2MzMzPlcDg8rby8vN3yBAAAANAxsUcwAGJiYhrt79atm6Srr3/6YrPZvFYSAQAAAKC1WBEMgE6d+LUDAAAACBwqEgAAAACwGApBAAAAALAYCkEAAAAAsBgKQQAAAACwGApBAAAAALAYwzRNM9BJoO2cTqfsdnug0wAAtCPD8N/fbf/fhUv8FuuP2fP8Fksy/Bbp/13gvzm+9fp8v8UCgBvF4XAoIiKiyXtYEQQAAAAAi6EQBAAAAACLsVwhaBiGDMN/r5/4UlZWJsMwlJSU1O7PAgAAAIDWsFwh2BiKNgAAAABWQiEIAAAAABZDIQgAAAAAFmP5QjArK0v9+vWTJJWUlHj2EBqGodTUVM99+/fv1+zZszVkyBD16NFDoaGhGjBggBYuXKiqqqoWPWvJkiUyDEMvvPCCz3vGjBkjwzD04YcfXs+0AAAAAMAnyxeCCQkJmjhxoiSpV69emj59uqeNGDHCc99vfvMb5eTkKDQ0VA8//LAefvhhOZ1Ovf766xoxYoSqq6ubfVZqaqpsNpvy8vJ05cqVBtdLS0tVXFysu+++Ww899JD/JgkAAAAA1+gc6AQCbcKECUpISFBBQYEGDBig/Pz8Ru9btGiRHnzwQa/D291ut+bMmaO3335bS5cu1e9///smnxUVFaWJEydqw4YNevfddzVhwgSv6zk5OTJNUxkZGT5juN1uud1uz89Op7P5SQIAAADANSy/IthSY8eO9SoCJclms2nZsmXq3LmzCgsLWxRn1qxZkqRVq1Z59dfW1io/P1/BwcFer6T+WHZ2tux2u6fFxsa2biIAAAAALM/yK4KtUVFRoZ07d+rYsWNyOp2qq6uTJIWEhOjEiRMtipGYmKhBgwZp165dKi8v9xRyRUVFqqio0KRJkxQdHe1zfGZmpubNm+f52el0UgwCAAAAaBUKwRZaunSpFi5cqMuXL193rKefflpz5sxRbm6uFi1aJOmfK4QzZsxocqzNZpPNZrvuHAAAAABYF6+GtsChQ4c0f/58hYWFKT8/X2VlZXK5XDJNU6Zpqk+fPq2KN23aNIWFhSk3N1d1dXX65ptvVFRUpLi4OCUnJ7fTLAAAAADgKlYEW2Dbtm2SpFdffVXTp0/3unbp0iWdPn26VfHsdrsef/xx5ebm6v3339fhw4dVW1urjIwMGYbht7wBAAAAoDGsCOrqHj9JjR7pIEnnz5+XJMXExDS4tnnzZpmm2epn1n80ZuXKlcrJyVFQUJDS0tJaHQcAAAAAWotCUFePdQgODtaXX36p2traBtfj4+MlXT3e4do9gkePHtWCBQva9Mz77rtPw4YNU2FhoUpLSzVu3DjdfvvtbZsAAAAAALQChaCurgimpKTo9OnTGjp0qKZNm6aMjAzl5eVJktLS0tS7d2/t3LlT/fv31+TJk5WcnKyEhAQlJiaqb9++bXpu/aqgJM2cOdMvcwEAAACA5lAI/sPq1av11FNP6ezZs9qwYYNycnJUUlIiSYqMjNQnn3yiqVOnqqamRjt27FBFRYUWL16sjRs3tvmZo0aNknT1ldOUlBS/zAMAAAAAmmO5j8X42s8XHR2ttWvX+hwXExOj9evXN3qtrKysQV9cXFyzewc3bdokSUpPT1dQUFCT9wIArMs06/wW64/Z85q/KQCWbdzmt1h7Nn7gt1gA0FGxIhggTqdTb731lkJCQngtFAAAAMANZbkVwUDLy8tTSUmJ9u3bp8rKSs2dO5ePxAAAAAC4oVgRbCeGYSguLq5Bf0lJidasWaPq6mo9++yzeu211258cgAAAAAsjRXBGyw/P1/5+fmBTgMAAACAhVEItpMvvvhCwcHBgU4DAAAAABqgEGwnAwYMCHQKAAAAANAo9gi2k8b2CO7du1eGYSg1NVXnzp3TM888oz59+shms2nw4MHKzc0NTLIAAAAALIUVwQCoqqrS8OHDVV1drcTERJ05c0b79u1Tenq66urqlJGREegUAQAAAHRgrAgGQGFhoYYNG6aTJ09q06ZN2rNnj7Zs2SJJWrx4cZNj3W63nE6nVwMAAACA1qAQDICIiAitWLFCNpvN0zdhwgQNHjxYp06dUllZmc+x2dnZstvtnhYbG3sDMgYAAADQkVAIBsC9996ryMjIBv3x8fGSpMrKSp9jMzMz5XA4PK28vLzd8gQAAADQMbFHMABiYmIa7e/WrZukq69/+mKz2bxWEgEAAACgtVgRDIBOnfi1AwAAAAgcKhIAAAAAsBgKQQAAAACwGApBAAAAALAYCkEAAAAAsBgKQQAAAACwGMM0TTPQSaDtnE6n7HZ7oNMAAOC6BAf772ikqgsOv8Xq2qWL32IBwI3icDgUERHR5D2sCAIAAACAxVAIAgAAAIDFWK4QNAxDhmG0+3PKyspkGIaSkpLa/VkAAAAA0BqWKwQbQ9EGAAAAwEooBAEAAADAYigEAQAAAMBiLF8IZmVlqV+/fpKkkpISzx5CwzCUmprquW///v2aPXu2hgwZoh49eig0NFQDBgzQwoULVVVV1aJnLVmyRIZh6IUXXvB5z5gxY2QYhj788MPrmRYAAAAA+GS5cwTrPxRTP+3t27dr3bp1KigoUK9evZSSkuK5d8SIEcrIyJAkPfDAAzpy5IiGDBmi2NhYuVwuHT58WJWVlRo0aJAOHTqk8PBwz9iysjL169dPI0eO1N69eyVJZ86cUUxMjHr06KHy8nJ17tzZK7fS0lLdeeeduuuuu3T8+PEWzYdzBAEAHQHnCAKA/7TkHMHOTV61gAkTJighIUEFBQUaMGCA8vPzG71v0aJFevDBB72KLrfbrTlz5ujtt9/W0qVL9fvf/77JZ0VFRWnixInasGGD3n33XU2YMMHrek5OjkzT9BSfjXG73XK73Z6fnU5n85MEAAAAgGtY/tXQlho7dmyDlTebzaZly5apc+fOKiwsbFGcWbNmSZJWrVrl1V9bW6v8/HwFBwd7vZL6Y9nZ2bLb7Z4WGxvbuokAAAAAsDzLrwi2RkVFhXbu3Kljx47J6XSqrq5OkhQSEqITJ060KEZiYqIGDRqkXbt2qby83FPIFRUVqaKiQpMmTVJ0dLTP8ZmZmZo3b57nZ6fTSTEIAAAAoFUoBFto6dKlWrhwoS5fvnzdsZ5++mnNmTNHubm5WrRokaR/rhDOmDGjybE2m002m//2UQAAAACwHl4NbYFDhw5p/vz5CgsLU35+vsrKyuRyuWSapkzTVJ8+fVoVb9q0aQoLC1Nubq7q6ur0zTffqKioSHFxcUpOTm6nWQAAAADAVawItsC2bdskSa+++qqmT5/ude3SpUs6ffp0q+LZ7XY9/vjjys3N1fvvv6/Dhw+rtrZWGRkZnq+aAgAAAEB7YUVQV/f4SdKVK1cavX7+/HlJUkxMTINrmzdvVltO4Kj/aMzKlSuVk5OjoKAgpaWltToOAAAAALQWhaCuHusQHBysL7/8UrW1tQ2ux8fHS7p6vMO1ewSPHj2qBQsWtOmZ9913n4YNG6bCwkKVlpZq3Lhxuv3229s2AQAAAABoBQpBXV0RTElJ0enTpzV06FBNmzZNGRkZysvLkySlpaWpd+/e2rlzp/r376/JkycrOTlZCQkJSkxMVN++fdv03PpVQUmaOXOmX+YCAAAAAM2hEPyH1atX66mnntLZs2e1YcMG5eTkqKSkRJIUGRmpTz75RFOnTlVNTY127NihiooKLV68WBs3bmzzM0eNGiXp6iunKSkpfpkHAAAAADTHch+L8bWfLzo6WmvXrvU5LiYmRuvXr2/0WllZWYO+uLi4ZvcObtq0SZKUnp6uoKCgJu8FAKAjy3orx2+xZsx4yW+xAKCjYkUwQJxOp9566y2FhITwWigAAACAG8pyK4KBlpeXp5KSEu3bt0+VlZWaO3cuH4kBAAAAcEOxIthODMNQXFxcg/6SkhKtWbNG1dXVevbZZ/Xaa6/d+OQAAAAAWBorgjdYfn6+8vPzA50GAAAAAAujEGwnX3zxhYKDgwOdBgAAAAA0QCHYTgYMGBDoFAAAAACgUewRbCeN7RHcu3evDMNQamqqzp07p2eeeUZ9+vSRzWbT4MGDlZubG5hkAQAAAFgKK4IBUFVVpeHDh6u6ulqJiYk6c+aM9u3bp/T0dNXV1SkjIyPQKQIAAADowFgRDIDCwkINGzZMJ0+e1KZNm7Rnzx5t2bJFkrR48eImx7rdbjmdTq8GAAAAAK1BIRgAERERWrFihWw2m6dvwoQJGjx4sE6dOqWysjKfY7Ozs2W32z0tNjb2BmQMAAAAoCOhEAyAe++9V5GRkQ364+PjJUmVlZU+x2ZmZsrhcHhaeXl5u+UJAAAAoGNij2AAxMTENNrfrVs3SVdf//TFZrN5rSQCAAAAQGuxIhgAnTrxawcAAAAQOFQkAAAAAGAxFIIAAAAAYDEUggAAAABgMRSCAAAAAGAxFIIAAAAAYDGGaZpmoJNA2zmdTtnt9kCnAQDAdTEM//1t2n25xm+xQjpz0haAW4/D4VBEREST97AiCAAAAAAWQyEIAAAAABZjuULQMAwZhtHuzykrK5NhGEpKSmr3ZwEAAABAa1iuEGwMRRsAAAAAK6EQBAAAAACLoRAEAAAAAIuxfCGYlZWlfv36SZJKSko8ewgNw1Bqaqrnvv3792v27NkaMmSIevToodDQUA0YMEALFy5UVVVVi561ZMkSGYahF154wec9Y8aMkWEY+vDDD69nWgAAAADgk+XOEaz/UEz9tLdv365169apoKBAvXr1UkpKiufeESNGKCMjQ5L0wAMP6MiRIxoyZIhiY2Plcrl0+PBhVVZWatCgQTp06JDCw8M9Y8vKytSvXz+NHDlSe/fulSSdOXNGMTEx6tGjh8rLy9X5R2cTlZaW6s4779Rdd92l48ePt2g+nCMIAOgIOEcQAPynJecIyrQYSeaPp11aWmpKMkeOHOlzXFFRkVlVVeXV53K5zJkzZ5qSzJdeeqlFMadOnWpKMrdt29bgGS+++KIpyXz99dd95uFyuUyHw+Fp5eXlnjnRaDQajXarNsPo5LdWc+WK31qgfy80Go3WluZwOHzWE/Us/2poS40dO7bBypvNZtOyZcvUuXNnFRYWtijOrFmzJEmrVq3y6q+trVV+fr6Cg4O9Xkn9sezsbNntdk+LjY1t3UQAAAAAWB7vO7RCRUWFdu7cqWPHjsnpdKqurk6SFBISohMnTrQoRmJiogYNGqRdu3apvLzcU8gVFRWpoqJCkyZNUnR0tM/xmZmZmjdvnudnp9NJMQgAAACgVSgEW2jp0qVauHChLl++fN2xnn76ac2ZM0e5ublatGiRpH+uEM6YMaPJsTabTTab7bpzAAAAAGBdvBraAocOHdL8+fMVFham/Px8lZWVyeVyyTRNmaapPn36tCretGnTFBYWptzcXNXV1embb75RUVGR4uLilJyc3E6zAAAAAICrWBFsgW3btkmSXn31VU2fPt3r2qVLl3T69OlWxbPb7Xr88ceVm5ur999/X4cPH1Ztba0yMjI8XzUFAAAAgPbCiqCu7vGTpCtXrjR6/fz585KkmJiYBtc2b97sOYqiNeo/GrNy5Url5OQoKChIaWlprY4DAAAAAK1FISgpKipKwcHB+vLLL1VbW9vgenx8vCQpJyfHa4/g0aNHtWDBgjY987777tOwYcNUWFio0tJSjRs3TrfffnvbJgAAAAAArUAhqKsrgikpKTp9+rSGDh2qadOmKSMjQ3l5eZKktLQ09e7dWzt37lT//v01efJkJScnKyEhQYmJierbt2+bnlu/KihJM2fO9MtcAAAAAKA5FIL/sHr1aj311FM6e/asNmzYoJycHJWUlEiSIiMj9cknn2jq1KmqqanRjh07VFFRocWLF2vjxo1tfuaoUaMkXX3lNCUlxS/zAAAAAIDmWO5jMb7280VHR2vt2rU+x8XExGj9+vWNXisrK2vQFxcX1+zewU2bNkmS0tPTFRQU1OS9AAB0ZM+98KbfYv3m93/0WywA6KhYEQwQp9Opt956SyEhIbwWCgAAAOCGohBsJ4ZhKC4urkF/Xl6eUlNTlZCQoMrKSv3617/mIzEAAAAAbigKwRuspKREa9asUXV1tZ599lm99tprgU4JAAAAgMVYbo/gjfLFF18oODi4QX9+fr7y8/NvfEIAAAAA8A8Ugu1kwIABgU4BAAAAABrFq6HtpLE9gnv37pVhGEpNTdW5c+f0zDPPqE+fPrLZbBo8eLByc3MDkywAAAAAS2FFMACqqqo0fPhwVVdXKzExUWfOnNG+ffuUnp6uuro6ZWRkBDpFAAAAAB0YK4IBUFhYqGHDhunkyZPatGmT9uzZoy1btkiSFi9eHODsAAAAAHR0FIIBEBERoRUrVshms3n6JkyYoMGDB+vUqVONHlBfz+12y+l0ejUAAAAAaA0KwQC49957FRkZ2aA/Pj5eklRZWelzbHZ2tux2u6fFxsa2W54AAAAAOiYKwQCIiYlptL9bt26Srq76+ZKZmSmHw+Fp5eXl7ZIjAAAAgI6Lj8UEQKdOba+/bTab1yulAAAAANBarAgCAAAAgMVQCAIAAACAxVAIAgAAAIDFUAgCAAAAgMVQCAIAAACAxRimaZqBTgJt53Q6ZbfbA50GAADXyfBbpCu1V/wWq3NQkN9iAcCN4nA4FBER0eQ9rAgCAAAAgMVQCAIAAACAxViuEDQMQ4bhv9dPfCkrK5NhGEpKSmr3ZwEAAABAa1iuEGwMRRsAAAAAK6EQBAAAAACLoRAEAAAAAIuxfCGYlZWlfv36SZJKSko8ewgNw1Bqaqrnvv3792v27NkaMmSIevToodDQUA0YMEALFy5UVVVVi561ZMkSGYahF154wec9Y8aMkWEY+vDDD69nWgAAAADgk+XOEaz/UEz9tLdv365169apoKBAvXr1UkpKiufeESNGKCMjQ5L0wAMP6MiRIxoyZIhiY2Plcrl0+PBhVVZWatCgQTp06JDCw8M9Y8vKytSvXz+NHDlSe/fulSSdOXNGMTEx6tGjh8rLy9W5c2ev3EpLS3XnnXfqrrvu0vHjx1s0H84RBAB0DJwjCAD+0pJzBGVajCTzx9MuLS01JZkjR470Oa6oqMisqqry6nO5XObMmTNNSeZLL73UophTp041JZnbtm1r8IwXX3zRlGS+/vrrLZ6Pw+HwzIlGo9FotFu3GX5rV2pr/dYC/3uh0Wi01jeHw9FsHWH5V0NbauzYsQ1W3mw2m5YtW6bOnTursLCwRXFmzZolSVq1apVXf21trfLz8xUcHOz1SuqPud1uOZ1OrwYAAAAArdG5+VtQr6KiQjt37tSxY8fkdDpVV1cnSQoJCdGJEydaFCMxMVGDBg3Srl27VF5ertjYWElSUVGRKioqNGnSJEVHR/scn52drZdeeun6JwMAAADAsigEW2jp0qVauHChLl++fN2xnn76ac2ZM0e5ublatGiRpH+uEM6YMaPJsZmZmZo3b57nZ6fT6SkmAQAAAKAleDW0BQ4dOqT58+crLCxM+fn5Kisrk8vlkmmaMk1Tffr0aVW8adOmKSwsTLm5uaqrq9M333yjoqIixcXFKTk5ucmxNptNERERXg0AAAAAWoMVwRbYtm2bJOnVV1/V9OnTva5dunRJp0+fblU8u92uxx9/XLm5uXr//fd1+PBh1dbWKiMjw/NVUwAAAABoL6wI6uoeP0m6cqXxz02fP39ekhQTE9Pg2ubNmz1HUbRG/UdjVq5cqZycHAUFBSktLa3VcQAAAACgtSgEJUVFRSk4OFhffvmlamtrG1yPj4+XJOXk5HjtETx69KgWLFjQpmfed999GjZsmAoLC1VaWqpx48bp9ttvb9sEAAAAAKAVKAR1dUUwJSVFp0+f1tChQzVt2jRlZGQoLy9PkpSWlqbevXtr586d6t+/vyZPnqzk5GQlJCQoMTFRffv2bdNz61cFJWnmzJl+mQsAAAAANIdC8B9Wr16tp556SmfPntWGDRuUk5OjkpISSVJkZKQ++eQTTZ06VTU1NdqxY4cqKiq0ePFibdy4sc3PHDVqlKSrr5ympKT4ZR4AAAAA0BzLfSzG136+6OhorV271ue4mJgYrV+/vtFrZWVlDfri4uKa3Tu4adMmSVJ6erqCgoKavBcAgI4sdcYiv8V6+Y9r/BYLADoqVgQDxOl06q233lJISAivhQIAAAC4oSy3IhhoeXl5Kikp0b59+1RZWam5c+fykRgAAAAANxQrgu3EMAzFxcU16C8pKdGaNWtUXV2tZ599Vq+99tqNTw4AAACApbEieIPl5+crPz8/0GkAAAAAsDAKwXbyxRdfKDg4ONBpAAAAAEADFILtZMCAAYFOAQAAAAAaxR7BdtLYHsG9e/fKMAylpqbq3LlzeuaZZ9SnTx/ZbDYNHjxYubm5gUkWAAAAgKWwIhgAVVVVGj58uKqrq5WYmKgzZ85o3759Sk9PV11dnTIyMgKdIgAAAIAOjBXBACgsLNSwYcN08uRJbdq0SXv27NGWLVskSYsXL25yrNvtltPp9GoAAAAA0BoUggEQERGhFStWyGazefomTJigwYMH69SpUyorK/M5Njs7W3a73dNiY2NvQMYAAAAAOhIKwQC49957FRkZ2aA/Pj5eklRZWelzbGZmphwOh6eVl5e3W54AAAAAOib2CAZATExMo/3dunWTdPX1T19sNpvXSiIAAAAAtBYrggHQqRO/dgAAAACBQ0UCAAAAABZDIQgAAAAAFkMhCAAAAAAWQyEIAAAAABZDIQgAAAAAFmOYpmkGOgm0ndPplN1uD3QaAABcJ8Nvka7UXvFbrM5BQX6LBQA3isPhUERERJP3sCIIAAAAABZDIQgAAAAAFkMheB0Mw1BcXFyg0wAAAACAVqEQbEJZWZkMw1BSUlKgUwEAAAAAv6EQBAAAAACLoRAEAAAAAIuhEPQhKytL/fr1kySVlJTIMAxPS01N9bq3trZWr7/+uuLj42Wz2RQbG6sFCxbI7XY3GvvixYvKzs7WPffco/DwcIWHh+uBBx7QmjVr2ntaAAAAAKDOgU7gZpWQkKCJEyeqoKBAvXr1UkpKiufaiBEjvO6dOnWqioqKlJSUpP79+2v//v164403VFFRoXXr1nnd+9133yk5OVmfffaZevfurZEjR8o0TR04cECpqan6n//5H7311ls3ZI4AAAAALMqET6WlpaYkc+TIkY1el2RKMgcOHGhWVlZ6+k+ePGl2797dlGT+/e9/9xrz6KOPmpLM5557znS5XJ7+06dPmz/96U9NSeZ///d/+8zJ5XKZDofD08rLyz150Gg0Go126zbDb+1Kba3fWuB/LzQajdb65nA4mq11eDXUD/74xz+qd+/enp/79eunJ598UpK0f/9+T/9f//pXFRUV6b777tPSpUtls9k813r16qW3335bkvRf//VfPp+VnZ0tu93uabGxsf6eDgAAAIAOjkLwOgUHB+uhhx5q0B8fHy9Jqqys9PT95S9/kSRNmDBBnTo1/NXX7xn8+OOPfT4vMzNTDofD08rLy693CgAAAAAshkLwOvXu3VtBQUEN+rt16yZJXh+MKSsrkyS9+OKLXh+fubZVV1frzJkzPp9ns9kUERHh1QAAAACgNfhYzHVqbGXPl7q6OklXPzZz5513tldKAAAAANAkCsEbKCYmRtLVV0Pnz58f4GwAAAAAWBWvhjYhJCREknTlyhW/xEtOTpYkbdu2zS/xAAAAAKAtKASbEBUVpeDgYH355Zeqra297ng/+9nPlJycrI8++kjPPvusnE5ng3uOHDmiXbt2XfezAAAAAMAXCsEmhISEKCUlRadPn9bQoUM1bdo0ZWRkKC8vr80x161bp3vuuUf/+Z//qb59++qhhx7SE088oV/84he64447lJCQQCEIAAAAoF2xR7AZq1ev1n/8x39o9+7d2rBhg2pra3XlyhWlpaW1KV50dLQOHDigVatW6c9//rM+/fRTHThwQL169dK//Mu/aM6cOXr88cf9PAsAAAAA+CfDNE0z0Emg7ZxOp+x2e6DTAADgukya5L+PqN0/7md+i/V82q/8FgsAbhSHw9HsMXO8GgoAAAAAFkMhCAAAAAAW0+EKwYsXL2r79u1KT09X//791aVLF3Xt2lVDhw7Vyy+/rOrqap9j8/Pzdf/99ys8PFw9e/bUo48+qgMHDjT5vI8++kiPPvqoevbsqfDwcN1///1au3Zto/dmZWXJMIxm2759+67rdwAAAAAATelwH4vZsGGDZsyYIUkaOHCg/vVf/1VOp1MHDhzQokWLtHHjRpWUlCg6Otpr3Ny5c7V8+XKFhoZqzJgxcrlc2r17t/7yl79oy5YtmjBhQoNnFRQUaPLkyaqrq9PPf/5zRUVF6YMPPtD06dP12WefacmSJV73JyQkaPr06Y3mXVFRoeLiYoWFhWnYsGH++WUAAAAAQCM6XCEYHBysmTNnau7cuRo4cKCnv7KyUuPGjdOnn36quXPnasOGDZ5rxcXFWr58uSIjI3Xw4EHdfffdkqSDBw8qKSlJaWlpSkpKUvfu3T1jzp07p3//939XbW2tCgoK9Nhjj0mSvv32W40YMUJvvvmmfvGLXygpKckzZsKECY0WlJK0YMECFRcX69/+7d8UHh7uv18IAAAAAPxIh3s1dPr06Vq5cqVXEShJffr00Z/+9CdJ0tatW1VTU+O5tnTpUknSb3/7W08RKEnDhw/XrFmzVFVVpZycHK94q1evltPp1Pjx4z1FoCT16tVLb7zxhiTpzTffbFHOpmlq48aNkqSnnnqqpVMFAAAAgDbpcIVgU4YOHSpJcrvdOnv2rCTp0qVL2rNnjyRp0qRJDcbU9+3cudOr/7333vM5Zty4cerSpYuKi4vlcrmazWvv3r0qLy9X7969NXr06FbMCAAAAABaz1KF4MmTJyVdfX20Z8+ekqS//e1vcrvduu222xQTE9NgTP1+vc8++8yr/8iRI17XrxUSEqLBgwfL5XLp+PHjzea1bt06SdKUKVMUFBTUihkBAAAAQOtZqhBcvny5JCklJUU2m02SdOrUKUlqtAiUpK5du6p79+46f/68Lly4IOnqIe4Oh6PJcfX9X331VZM5uVwuFRQUSGrZa6Fut1tOp9OrAQAAAEBrWKYQLCoqUk5OjoKDg7V48WJPf/1xEmFhYT7Hdu3aVZI8heC1R1D4GvfjMb7s2LFDDodDgwYN0j333NPsPLKzs2W32z0tNja22TEAAAAAcC1LFILHjh3Tk08+KdM09Yc//MGzV/BmUP9aaEs/EpOZmSmHw+Fp5eXl7ZkeAAAAgA6owx0f8WMVFRVKSUnR+fPnNW/ePD333HNe1+uParh48aLPGD/88IMkqVu3bl5j6sdFREQ0O6YxZ8+e1a5du9SpUyc98cQTLZqPzWbzvNYKAAAAAG3RoVcEz507pzFjxuirr75SWlpagwPeJemOO+6QJH399deNxvjhhx9UVVWlHj16eIq6iIgI2e32JsfV9/ft29dnfu+8844uX76skSNH+txrCAAAAAD+1mELwerqao0dO1ZHjx7VY489plWrVskwjAb39e/fXzabTd9//70qKioaXD98+LAkaciQIV799a+X1l+/1uXLl/X555+rS5cuio+P95lja18LBQAAAAB/6JCFoNvt1vjx4/Xxxx/rkUce0caNG30eyxAaGqpRo0ZJkjZv3tzg+pYtWyRJv/zlL736x40b53X9Wu+++65cLpdGjx6tLl26NPrckydP6uDBgwoNDdXEiRNbPjkAAAAAuE4drhCsra3VlClTtGfPHiUmJmrr1q0KCQlpcsy8efMkSa+88opOnDjh6T948KBWrlyp7t27Kz093WtMRkaGIiIiVFhYqK1bt3r6v/vuOz3//POSpPnz5/t8Zv1q4Pjx4xvdYwgAAAAA7aXDfSxmxYoV2rZtmyQpKipKv/71rxu9b8mSJYqKipIkjR49Ws8995yWL1+uhIQEJScnq6amRrt375ZpmsrLy1P37t29xvfs2VO5ubn61a9+pUmTJikpKUmRkZEqLi5WVVWV5s2bp6SkJJ95rl+/XhKvhQIAAAC48TpcIXj+/HnPP9cXhI3JysryFIKStGzZMiUkJGjFihXavXu3QkJCNHr0aP3ud7/Tgw8+2GiMiRMnat++fXrllVd06NAh1dTU6Cc/+Ylmz56t6dOn+3z2xx9/rOPHjys6OlpjxoxpwywBAAAAoO0M0zTNQCeBtnM6nZ4vmAIAcKvq3LnpbRyt8eKbK/0W66Xn0vwWy1+emPaC32KtX/v/+S0WgJuHw+FodvtZh9sjCAAAAABoGoUgAAAAAFhMhysEL168qO3btys9PV39+/dXly5d1LVrVw0dOlQvv/yyqqurfY7Nz8/X/fffr/DwcPXs2VOPPvqoDhw40OTzPvroIz366KPq2bOnwsPDdf/992vt2rWN3puVlSXDMJpt+/btu67fAQAAAAA0pcN9LGbDhg2aMWOGJGngwIH613/9VzmdTh04cECLFi3Sxo0bVVJSoujoaK9xc+fO1fLlyxUaGqoxY8bI5XJp9+7d+stf/qItW7ZowoQJDZ5VUFCgyZMnq66uTj//+c8VFRWlDz74QNOnT9dnn32mJUuWeN2fkJDg8yMyFRUVKi4uVlhYmIYNG+afXwYAAAAANKLDFYLBwcGaOXOm5s6dq4EDB3r6KysrNW7cOH366aeaO3euNmzY4LlWXFys5cuXKzIyUgcPHtTdd98t6eo5gklJSUpLS1NSUpLXERLnzp3Tv//7v6u2tlYFBQV67LHHJEnffvutRowYoTfffFO/+MUvvI6QmDBhQqMFpSQtWLBAxcXF+rd/+zeFh4f77xcCAAAAAD/S4V4NnT59ulauXOlVBEpSnz599Kc//UmStHXrVtXU1HiuLV26VJL029/+1lMEStLw4cM1a9YsVVVVKScnxyve6tWr5XQ6NX78eE8RKEm9evXSG2+8IUl68803W5SzaZrauHGjJM4VBAAAAND+Olwh2JShQ4dKktxut86ePStJunTpkvbs2SNJmjRpUoMx9X07d+706n/vvfd8jhk3bpy6dOmi4uJiuVyuZvPau3evysvL1bt3b40ePboVMwIAAACA1rNUIXjy5ElJV18f7dmzpyTpb3/7m9xut2677TbFxMQ0GFO/X++zzz7z6j9y5IjX9WuFhIRo8ODBcrlcOn78eLN5rVu3TpI0ZcoUBQUFtWJGAAAAANB6lioEly9fLklKSUmRzWaTJJ06dUqSGi0CJalr167q3r27zp8/rwsXLki6eoi7w+Foclx9/1dffdVkTi6XSwUFBZJa9lqo2+2W0+n0agAAAADQGpYpBIuKipSTk6Pg4GAtXrzY019/nERYWJjPsV27dpUkTyF47REUvsb9eIwvO3bskMPh0KBBg3TPPfc0O4/s7GzZ7XZPi42NbXYMAAAAAFzLEoXgsWPH9OSTT8o0Tf3hD3/w7BW8GdS/FtrSj8RkZmbK4XB4Wnl5eXumBwAAAKAD6nDHR/xYRUWFUlJSdP78ec2bN0/PPfec1/X6oxouXrzoM8YPP/wgSerWrZvXmPpxERERzY5pzNmzZ7Vr1y516tRJTzzxRIvmY7PZPK+1AgAAAEBbdOgVwXPnzmnMmDH66quvlJaW1uCAd0m64447JElff/11ozF++OEHVVVVqUePHp6iLiIiQna7vclx9f19+/b1md8777yjy5cva+TIkT73GgIAAACAv3XYQrC6ulpjx47V0aNH9dhjj2nVqlUyDKPBff3795fNZtP333+vioqKBtcPHz4sSRoyZIhXf/3rpfXXr3X58mV9/vnn6tKli+Lj433m2NrXQgEAAADAHzpkIeh2uzV+/Hh9/PHHeuSRR7Rx40afxzKEhoZq1KhRkqTNmzc3uL5lyxZJ0i9/+Uuv/nHjxnldv9a7774rl8ul0aNHq0uXLo0+9+TJkzp48KBCQ0M1ceLElk8OAAAAAK5ThysEa2trNWXKFO3Zs0eJiYnaunWrQkJCmhwzb948SdIrr7yiEydOePoPHjyolStXqnv37kpPT/cak5GRoYiICBUWFmrr1q2e/u+++07PP/+8JGn+/Pk+n1m/Gjh+/PhG9xgCAAAAQHvpcB+LWbFihbZt2yZJioqK0q9//etG71uyZImioqIkSaNHj9Zzzz2n5cuXKyEhQcnJyaqpqdHu3btlmqby8vLUvXt3r/E9e/ZUbm6ufvWrX2nSpElKSkpSZGSkiouLVVVVpXnz5ikpKclnnuvXr5fEa6EAAAAAbrwOVwieP3/e88/1BWFjsrKyPIWgJC1btkwJCQlasWKFdu/erZCQEI0ePVq/+93v9OCDDzYaY+LEidq3b59eeeUVHTp0SDU1NfrJT36i2bNna/r06T6f/fHHH+v48eOKjo7WmDFj2jBLAAAAAGg7wzRNM9BJoO2cTqfnC6YAANyqOnVqfC9/W3x99ozfYt3eo4ffYvlLeLj/cqquPt/8TQBuOQ6Ho9ntZx1ujyAAAAAAoGkUggAAAABgMR2uELx48aK2b9+u9PR09e/fX126dFHXrl01dOhQvfzyy6qurvY5Nj8/X/fff7/Cw8PVs2dPPfroozpw4ECTz/voo4/06KOPqmfPngoPD9f999+vtWvXNnpvVlaWDMNotu3bt++6fgcAAAAA0JQOt0dw9erVmjFjhiRp4MCBGjx4sJxOpw4cOKALFy5owIABKikpUXR0tNe4uXPnavny5QoNDdWYMWPkcrn0wQcfyDRNbdmyRRMmTGjwrIKCAk2ePFl1dXX6+c9/rqioKH3wwQeqqqrS/PnztWTJEq/7t2/fru3btzead0VFhYqLixUWFqZvv/1W4eHhLZovewQBAB0BewRbjj2CAJrTkj2CHa4QXLNmjQ4cOKC5c+dq4MCBnv7KykqNGzdOn376qaZMmaINGzZ4rhUXFys5OVmRkZE6ePCg7r77bklXzxFMSkpSWFiYSktLvY6QOHfunPr16yen06mCggI99thjkqRvv/1WI0aM0N///nd9+OGHTR4hca0FCxbojTfe0BNPPOE5Y7AlKAQBAB0BhWDLUQgCaI4lPxYzffp0rVy50qsIlKQ+ffroT3/6kyRp69atqqmp8VxbunSpJOm3v/2tpwiUpOHDh2vWrFmqqqpSTk6OV7zVq1fL6XRq/PjxniJQknr16qU33nhDkvTmm2+2KGfTNLVx40ZJnCsIAAAAoP11uEKwKUOHDpUkud1unT17VpJ06dIl7dmzR5I0adKkBmPq+3bu3OnV/9577/kcM27cOHXp0kXFxcVyuVzN5rV3716Vl5erd+/eGj16dCtmBAAAAACtZ6lC8OTJk5Kk4OBg9ezZU5L0t7/9TW63W7fddptiYmIajBk2bJgk6bPPPvPqP3LkiNf1a4WEhGjw4MFyuVw6fvx4s3nVvwo6ZcoUBQX579UYAAAAAGiMpQrB5cuXS5JSUlJks9kkSadOnZKkRotASeratau6d++u8+fP68KFC5Ku7stzOBxNjqvv/+qrr5rMyeVyqaCgQFLLXgt1u91yOp1eDQAAAABawzKFYFFRkXJychQcHKzFixd7+uuPkwgLC/M5tmvXrpLkKQSvPYLC17gfj/Flx44dcjgcGjRokO65555m55GdnS273e5psbGxzY4BAAAAgGtZohA8duyYnnzySZmmqT/84Q+evYI3g/rXQlv6kZjMzEw5HA5PKy8vb8/0AAAAAHRAnQOdQHurqKhQSkqKzp8/r3nz5um5557zul5/Xt/Fixd9xvjhhx8kSd26dfMaUz+usU+z/nhMY86ePatdu3apU6dOeuKJJ1o0H5vN5nmtFQAAAADaokOvCJ47d05jxozRV199pbS0tAYHvEvSHXfcIUn6+uuvG43xww8/qKqqSj169PAUdREREZ6z+3yNq+/v27evz/zeeecdXb58WSNHjvS51xAAAAAA/K3DFoLV1dUaO3asjh49qscee0yrVq2SYRgN7uvfv79sNpu+//57VVRUNLh++PBhSdKQIUO8+utfL62/fq3Lly/r888/V5cuXRQfH+8zx9a+FgoAAAAA/tAhC0G3263x48fr448/1iOPPKKNGzf6PJYhNDRUo0aNkiRt3ry5wfUtW7ZIkn75y1969Y8bN87r+rXeffdduVwujR49Wl26dGn0uSdPntTBgwcVGhqqiRMntnxyAAAAAHCdOlwhWFtbqylTpmjPnj1KTEzU1q1bFRIS0uSYefPmSZJeeeUVnThxwtN/8OBBrVy5Ut27d1d6errXmIyMDEVERKiwsFBbt2719H/33Xd6/vnnJUnz58/3+cz61cDx48c3uscQAAAAANpLh/tYzIoVK7Rt2zZJUlRUlH796183et+SJUsUFRUlSRo9erSee+45LV++XAkJCUpOTlZNTY12794t0zSVl5en7t27e43v2bOncnNz9atf/UqTJk1SUlKSIiMjVVxcrKqqKs2bN09JSUk+81y/fr0kXgsFAAAAcOMZpmmagU7Cn7KysvTSSy81e19paani4uK8+vLz87VixQp98cUXCgkJ0QMPPKDf/e53evDBB33G+eijj/TKK6/o0KFDqqmp0U9+8hPNnj1b06dP9znm448/1s9+9jNFR0eroqJCnTu3vR53Op2eD9cAAHCr6tSp8S0cbfH12TN+i3V7jx5+i+Uv4eH+y6m6+rzfYgG4eTgcjmbfOuxwhaDVUAgCADqC2267w2+xDvz/PvZbrLt79/ZbLP9p+PG7tuM/A4GOqCWFYIfbIwgAAAAAaBqFIAAAAABYTIcrBC9evKjt27crPT1d/fv3V5cuXdS1a1cNHTpUL7/8sqqrq32Ozc/P1/3336/w8HD17NlTjz76qA4cONDk8z766CM9+uij6tmzp8LDw3X//fdr7dq1jd6blZUlwzCabfv27buu3wEAAAAANKXD7RFcvXq1ZsyYIUkaOHCgBg8eLKfTqQMHDujChQsaMGCASkpKFB0d7TVu7ty5Wr58uUJDQzVmzBi5XC598MEHMk1TW7Zs0YQJExo8q6CgQJMnT1ZdXZ1+/vOfKyoqSh988IGqqqo0f/58LVmyxOv+7du3a/v27Y3mXVFRoeLiYoWFhenbb79VeHh4i+bLHkEAQEfAHsHWYI8ggKZZ8mMxa9as0YEDBzR37lwNHDjQ019ZWalx48bp008/1ZQpU7RhwwbPteLiYiUnJysyMlIHDx7U3XffLenqOYJJSUkKCwtTaWmp1xES586dU79+/eR0OlVQUKDHHntMkvTtt99qxIgR+vvf/64PP/ywySMkrrVgwQK98cYbeuKJJzxnDLYEhSAAoCOgEGwNCkEATbPkx2KmT5+ulStXehWBktSnTx/96U9/kiRt3bpVNTU1nmtLly6VJP32t7/1FIGSNHz4cM2aNUtVVVXKycnxird69Wo5nU6NHz/eUwRKUq9evfTGG29Ikt58880W5WyapjZu3CiJcwUBAAAAtL8OVwg2ZejQoZIkt9uts2fPSpIuXbqkPXv2SJImTZrUYEx9386dO73633vvPZ9jxo0bpy5duqi4uFgul6vZvPbu3avy8nL17t1bo0ePbsWMAAAAAKD1LFUInjx5UpIUHBysnj17SpL+9re/ye1267bbblNMTEyDMcOGDZMkffbZZ179R44c8bp+rZCQEA0ePFgul0vHjx9vNq/6V0GnTJmioCD/HagLAAAAAI2xVCG4fPlySVJKSopsNpsk6dSpU5LUaBEoSV27dlX37t11/vx5XbhwQdLVfXkOh6PJcfX9X331VZM5uVwuFRQUSGrZa6Fut1tOp9OrAQAAAEBrWKYQLCoqUk5OjoKDg7V48WJPf/1xEmFhYT7Hdu3aVZI8heC1R1D4GvfjMb7s2LFDDodDgwYN0j333NPsPLKzs2W32z0tNja22TEAAAAAcC1LFILHjh3Tk08+KdM09Yc//MGzV/BmUP9aaEs/EpOZmSmHw+Fp5eXl7ZkeAAAAgA6oc6ATaG8VFRVKSUnR+fPnNW/ePD333HNe1+vP67t48aLPGD/88IMkqVu3bl5j6sc19mnWH49pzNmzZ7Vr1y516tRJTzzxRIvmY7PZPK+1AgAAAEBbdOgVwXPnzmnMmDH66quvlJaW1uCAd0m6446r5xZ9/fXXjcb44YcfVFVVpR49eniKuoiICM/Zfb7G1ff37dvXZ37vvPOOLl++rJEjR/rcawgAAAAA/tZhC8Hq6mqNHTtWR48e1WOPPaZVq1bJMBoewNq/f3/ZbDZ9//33qqioaHD98OHDkqQhQ4Z49de/Xlp//VqXL1/W559/ri5duig+Pt5njq19LRQAAAAA/KFDFoJut1vjx4/Xxx9/rEceeUQbN270eSxDaGioRo0aJUnavHlzg+tbtmyRJP3yl7/06h83bpzX9Wu9++67crlcGj16tLp06dLoc0+ePKmDBw8qNDRUEydObPnkAAAAAOA6dbhCsLa2VlOmTNGePXuUmJiorVu3KiQkpMkx8+bNkyS98sorOnHihKf/4MGDWrlypbp376709HSvMRkZGYqIiFBhYaG2bt3q6f/uu+/0/PPPS5Lmz5/v85n1q4Hjx49vdI8hAAAAALSXDvexmBUrVmjbtm2SpKioKP36179u9L4lS5YoKipKkjR69Gg999xzWr58uRISEpScnKyamhrt3r1bpmkqLy9P3bt39xrfs2dP5ebm6le/+pUmTZqkpKQkRUZGqri4WFVVVZo3b56SkpJ85rl+/XpJvBYKAAAA4MbrcIXg+fPnPf9cXxA2Jisry1MIStKyZcuUkJCgFStWaPfu3QoJCdHo0aP1u9/9Tg8++GCjMSZOnKh9+/bplVde0aFDh1RTU6Of/OQnmj17tqZPn+7z2R9//LGOHz+u6OhojRkzpg2zBAAAAIC2M0zTNAOdBNrO6XR6vmAKAMCt6he/eMZvscZMS/FbrDm/Gu+3WABwozgcjma3n3W4PYIAAAAAgKZRCAIAAACAxVAIthPDMBQXFxfoNAAAAACgAQpBAAAAALCYDvfV0JvFF198oeDg4ECnAQAAAAANUAi2kwEDBgQ6BQAAAABoFK+GtpPG9gju3btXhmEoNTVV586d0zPPPKM+ffrIZrNp8ODBys3NDUyyAAAAACyFFcEAqKqq0vDhw1VdXa3ExESdOXNG+/btU3p6uurq6pSRkRHoFAEAAAB0YKwIBkBhYaGGDRumkydPatOmTdqzZ4+2bNkiSVq8eHGTY91ut5xOp1cDAAAAgNagEAyAiIgIrVixQjabzdM3YcIEDR48WKdOnVJZWZnPsdnZ2bLb7Z4WGxt7AzIGAAAA0JFQCAbAvffeq8jIyAb98fHxkqTKykqfYzMzM+VwODytvLy83fIEAAAA0DGxRzAAYmJiGu3v1q2bpKuvf/pis9m8VhIBAAAAoLVYEQyATp34tQMAAAAIHCoSAAAAALAYCkEAAAAAsBgKQQAAAACwGApBAAAAALAYCkEAAAAAsBiOj2gnpmk26EtKSmq0v15+fr7y8/PbMSsAAG5Oly5V+y1WaLcwv8UCgI6KFUEAAAAAsBgKQQAAAACwGMsVgoZhyDCMdn9OWVmZDMNQUlJSuz8LAAAAAFrDcoVgYyjaAAAAAFgJhSAAAAAAWAyFIAAAAABYjOULwaysLPXr10+SVFJS4tlDaBiGUlNTPfft379fs2fP1pAhQ9SjRw+FhoZqwIABWrhwoaqqqlr0rCVLlsgwDL3wwgs+7xkzZowMw9CHH354PdMCAAAAAJ8sXwgmJCRo4sSJkqRevXpp+vTpnjZixAjPfb/5zW+Uk5Oj0NBQPfzww3r44YfldDr1+uuva8SIEaqubv78o9TUVNlsNuXl5enKlSsNrpeWlqq4uFh33323HnroIf9NEgAAAACuYfkD5SdMmKCEhAQVFBRowIABPg90X7RokR588EHZ7XZPn9vt1pw5c/T2229r6dKl+v3vf9/ks6KiojRx4kRt2LBB7777riZMmOB1PScnR6ZpKiMjw2cMt9stt9vt+dnpdDY/SQAAAAC4huVXBFtq7NixXkWgJNlsNi1btkydO3dWYWFhi+LMmjVLkrRq1Sqv/traWuXn5ys4ONjrldQfy87Olt1u97TY2NjWTQQAAACA5Vl+RbA1KioqtHPnTh07dkxOp1N1dXWSpJCQEJ04caJFMRITEzVo0CDt2rVL5eXlnkKuqKhIFRUVmjRpkqKjo32Oz8zM1Lx58zw/O51OikEAAAAArUIh2EJLly7VwoULdfny5euO9fTTT2vOnDnKzc3VokWLJP1zhXDGjBlNjrXZbLLZbNedAwAAAADr4tXQFjh06JDmz5+vsLAw5efnq6ysTC6XS6ZpyjRN9enTp1Xxpk2bprCwMOXm5qqurk7ffPONioqKFBcXp+Tk5HaaBQAAAABcxYpgC2zbtk2S9Oqrr2r69Ole1y5duqTTp0+3Kp7dbtfjjz+u3Nxcvf/++zp8+LBqa2uVkZEhwzD8ljcAAAAANIYVQV3d4yep0SMdJOn8+fOSpJiYmAbXNm/eLNM0W/3M+o/GrFy5Ujk5OQoKClJaWlqr4wAAAABAa1EI6uqxDsHBwfryyy9VW1vb4Hp8fLykq8c7XLtH8OjRo1qwYEGbnnnfffdp2LBhKiwsVGlpqcaNG6fbb7+9bRMAAAAAgFagENTVFcGUlBSdPn1aQ4cO1bRp05SRkaG8vDxJUlpamnr37q2dO3eqf//+mjx5spKTk5WQkKDExET17du3Tc+tXxWUpJkzZ/plLgAAAADQHArBf1i9erWeeuopnT17Vhs2bFBOTo5KSkokSZGRkfrkk080depU1dTUaMeOHaqoqNDixYu1cePGNj9z1KhRkq6+cpqSkuKXeQAAAABAcyz3sRhf+/mio6O1du1an+NiYmK0fv36Rq+VlZU16IuLi2t27+CmTZskSenp6QoKCmryXgAAOrKcPy/1W6x7Bwz1WywA6KhYEQwQp9Opt956SyEhIbwWCgAAAOCGstyKYKDl5eWppKRE+/btU2VlpebOnctHYgAAAADcUKwI3mAlJSVas2aNqqur9eyzz+q1114LdEoAAAAALMYw23IIHm4aTqdTdrs90GkAAHBdyr7/3m+x/LlH8OzZb/wWCwBuFIfDoYiIiCbvYUUQAAAAACyGQhAAAAAALIZCEAAAAAAshkIQAAAAACyG4yNuMW63W2632/Oz0+kMYDYAAAAAbkWsCN5isrOzZbfbPS02NjbQKQEAAAC4xXB8xC2msRVBikEAwK2O4yMAwH9acnwEr4beYmw2m2w2W6DTAAAAAHAL49VQAAAAALAYCkEAAAAAsBgKQQAAAACwGArBm0hWVpYMw1BqamqgUwEAAADQgVEI3kTq6uokScHBwQHOBAAAAEBHRiF4Ezly5Igkadq0aQHOBAAAAEBHxjmCN4m6ujpFRkbqpz/9qXbv3t3icU6nU3a7vR0zAwCg/XXqFOS3WJfcLr/Fst2Ub+kYfozFfwYCHVFLzhFkRfAm8emnn6qqqkovv/xyoFMBAAAA0MFZrhA0DEOG4c+/pDWurKxMhmEoKSmpRfffe++9Mk1Tw4cPb9/EAAAAAFie5QrBxrS2aAMAAACAWxmFIAAAAABYDIUgAAAAAFiM5QvBrKws9evXT5JUUlLi2UP444Pd9+/fr9mzZ2vIkCHq0aOHQkNDNWDAAC1cuFBVVVUtetaSJUtkGIZeeOEFn/eMGTNGhmHoww8/vJ5pAQAAAIBPljs+ov5DMfXT3r59u9atW6eCggL16tVLKSkpnntHjBihjIwMSdIDDzygI0eOaMiQIYqNjZXL5dLhw4dVWVmpQYMG6dChQwoPD/eMLSsrU79+/TRy5Ejt3btXknTmzBnFxMSoR48eKi8vV+fOnb1yKy0t1Z133qm77rpLx48fb9F8OD4CANARcHxEa3B8BICmteT4CJkWo6v/xvPqKy0tNSWZI0eO9DmuqKjIrKqq8upzuVzmzJkzTUnmSy+91KKYU6dONSWZ27Zta/CMF1980ZRkvv766y2ej8Ph8MyJRqPRaLRbtXXqFOS35r582W8t0L+XxpvhxxboudBotPZoDoej2TrC8q+GttTYsWMbrLzZbDYtW7ZMnTt3VmFhYYvizJo1S5K0atUqr/7a2lrl5+crODjY65XUH3O73XI6nV4NAAAAAFqjc/O3oF5FRYV27typY8eOyel0qq6uTpIUEhKiEydOtChGYmKiBg0apF27dqm8vFyxsbGSpKKiIlVUVGjSpEmKjo72OT47O1svvfTS9U8GAAAAgGVRCLbQ0qVLtXDhQl2+fPm6Yz399NOaM2eOcnNztWjRIkn/XCGcMWNGk2MzMzM1b948z89Op9NTTAIAAABAS/BqaAscOnRI8+fPV1hYmPLz81VWViaXyyXTNGWapvr06dOqeNOmTVNYWJhyc3NVV1enb775RkVFRYqLi1NycnKTY202myIiIrwaAAAAALQGK4ItsG3bNknSq6++qunTp3tdu3Tpkk6fPt2qeHa7XY8//rhyc3P1/vvv6/Dhw6qtrVVGRobnq6YAAAAA0F5YEdTVPX6SdOXKlUavnz9/XpIUExPT4NrmzZs9R1G0Rv1HY1auXKmcnBwFBQUpLS2t1XEAAAAAoLUoBCVFRUUpODhYX375pWpraxtcj4+PlyTl5OR47RE8evSoFixY0KZn3nfffRo2bJgKCwtVWlqqcePG6fbbb2/bBAAAAACgFSgEdXVFMCUlRadPn9bQoUM1bdo0ZWRkKC8vT5KUlpam3r17a+fOnerfv78mT56s5ORkJSQkKDExUX379m3Tc+tXBSVp5syZfpkLAAAAADSHQvAfVq9eraeeekpnz57Vhg0blJOTo5KSEklSZGSkPvnkE02dOlU1NTXasWOHKioqtHjxYm3cuLHNzxw1apSkq6+cpqSk+GUeAAAAANAcy30sxtd+vujoaK1du9bnuJiYGK1fv77Ra2VlZQ364uLimt07uGnTJklSenq6goKCmrwXAAAAAPzFcoXgzcLpdOqtt95SSEgIr4UCACzvvwrf81usXzza9Jm8t77Wf6QOAH6MQvAGy8vLU0lJifbt26fKykrNnTuXj8QAAAAAuKHYI3iDlZSUaM2aNaqurtazzz6r1157LdApAQAAALAYVgRvsPz8fOXn5wc6DQAAAAAWxoogAAAAAFgMhSAAAAAAWAyFIAAAAABYDIUgAAAAAFgMH4u5xbjdbrndbs/PTqczgNkAAAAAuBWxIniLyc7Olt1u97TY2NhApwQAAADgFkMheIvJzMyUw+HwtPLy8kCnBAAAAOAWw6uhtxibzSabzRboNAAAAADcwlgRBAAAAACLoRAEAAAAAIuhEAQAAAAAi6EQvIlkZWXJMAylpqYGOhUAAAAAHRiF4E2krq5OkhQcHBzgTAAAAAB0ZBSCN5EjR45IkqZNmxbgTAAAAAB0ZBSCN4m6ujrt27dPo0ePVmJiYqDTAQAAANCBGaZpmoFOAtL/+T//Rz/96U914MABDR8+vMXjnE6n7HZ7O2YGAMCNYPgtUm1drd9iBXXib+YAbj0Oh0MRERFN3sOB8jeJe++9V9TkAAAAAG4Ey/2ZyzAMGYb//uroS1lZmQzDUFJSUrs/CwAAAABaw3KFYGMo2gAAAABYCYUgAAAAAFgMhSAAAAAAWIzlC8GsrCz169dPklRSUuLZQ2gYhlJTUz337d+/X7Nnz9aQIUPUo0cPhYaGasCAAVq4cKGqqqpa9KwlS5bIMAy98MILPu8ZM2aMDMPQhx9+eD3TAgAAAACfLHd8RP2HYuqnvX37dq1bt04FBQXq1auXUlJSPPeOGDFCGRkZkqQHHnhAR44c0ZAhQxQbGyuXy6XDhw+rsrJSgwYN0qFDhxQeHu4ZW1ZWpn79+mnkyJHau3evJOnMmTOKiYlRjx49VF5ers6dvT/aWlpaqjvvvFN33XWXjh8/3qL5cHwEAKBj4PgIAPCXlhwfIdNiJJk/nnZpaakpyRw5cqTPcUVFRWZVVZVXn8vlMmfOnGlKMl966aUWxZw6daopydy2bVuDZ7z44oumJPP111/3mYfL5TIdDoenlZeXe+ZEo9FoNNqt2wy/tdq6Or+1wP9eaDQarfXN4XD4rCfq8WeuFho7dmyDlTebzaZly5apc+fOKiwsbFGcWbNmSZJWrVrl1V9bW6v8/HwFBwd7vZL6Y9nZ2bLb7Z4WGxvbuokAAAAAsDwOlG+FiooK7dy5U8eOHZPT6VRdXZ0kKSQkRCdOnGhRjMTERA0aNEi7du1SeXm5p5ArKipSRUWFJk2apOjoaJ/jMzMzNW/ePM/PTqeTYhAAAABAq1AIttDSpUu1cOFCXb58+bpjPf3005ozZ45yc3O1aNEiSf9cIZwxY0aTY202m2w223XnAAAAAMC6eDW0BQ4dOqT58+crLCxM+fn5Kisrk8vlkmmaMk1Tffr0aVW8adOmKSwsTLm5uaqrq9M333yjoqIixcXFKTk5uZ1mAQAAAABXsSLYAtu2bZMkvfrqq5o+fbrXtUuXLun06dOtime32/X4448rNzdX77//vg4fPqza2lplZGR4vmoKAAAAAO2FFUFd3eMnSVeuXGn0+vnz5yVJMTExDa5t3rzZcxRFa9R/NGblypXKyclRUFCQ0tLSWh0HAAAAAFqLQlBSVFSUgoOD9eWXX6q2tuHZQ/Hx8ZKknJwcrz2CR48e1YIFC9r0zPvuu0/Dhg1TYWGhSktLNW7cON1+++1tmwAAAAAAtAKFoK6uCKakpOj06dMaOnSopk2bpoyMDOXl5UmS0tLS1Lt3b+3cuVP9+/fX5MmTlZycrISEBCUmJqpv375tem79qqAkzZw50y9zAQAAAIDmUAj+w+rVq/XUU0/p7Nmz2rBhg3JyclRSUiJJioyM1CeffKKpU6eqpqZGO3bsUEVFhRYvXqyNGze2+ZmjRo2SdPWV05SUFL/MAwAAAACaY7mPxfjazxcdHa21a9f6HBcTE6P169c3eq2srKxBX1xcXLN7Bzdt2iRJSk9PV1BQUJP3AgDQkY0Z47998qve/YvfYgFAR8WKYIA4nU699dZbCgkJ4bVQAAAAADeU5VYEAy0vL08lJSXat2+fKisrNXfuXD4SAwAAAOCGYkXwBispKdGaNWtUXV2tZ599Vq+99lqgUwIAAABgMawI3mD5+fnKz88PdBoAAAAALIwVQQAAAACwGApBAAAAALAYCkEAAAAAsBgKQQAAAACwGD4Wc4txu91yu92en51OZwCzAQAAAHArYkXwFpOdnS273e5psbGxgU4JAAAAwC2GQvAWk5mZKYfD4Wnl5eWBTgkAAADALYZXQ28xNptNNpst0GkAAAAAuIWxIggAAAAAFkMhCAAAAAAWQyEIAAAAABZDIXgTycrKkmEYSk1NDXQqAAAAADowCsGbSF1dnSQpODg4wJkAAAAA6MgoBG8iR44ckSRNmzYtwJkAAAAA6MgM0zTNQCeBq6uBkZGR+ulPf6rdu3e3eJzT6ZTdbm/HzAAAuLX48z9tDMPwWywAuFEcDociIiKavIcVwZvEp59+qqqqKr388suBTgUAAABAB8eB8jeJe++9169/wQQAAAAAXyy3ImgYxg15zaOsrEyGYSgpKandnwUAAAAArWG5QrAxFG0AAAAArIRCEAAAAAAshkIQAAAAACzG8oVgVlaW+vXrJ0kqKSnx7CE0DEOpqame+/bv36/Zs2dryJAh6tGjh0JDQzVgwAAtXLhQVVVVLXrWkiVLZBiGXnjhBZ/3jBkzRoZh6MMPP7yeaQEAAACAT5Y7R7D+QzH1096+fbvWrVungoIC9erVSykpKZ57R4wYoYyMDEnSAw88oCNHjmjIkCGKjY2Vy+XS4cOHVVlZqUGDBunQoUMKDw/3jC0rK1O/fv00cuRI7d27V5J05swZxcTEqEePHiovL1fnzt4fbS0tLdWdd96pu+66S8ePH2/RfDhHEAAAb5wjCMDqWnKOoEyLkWT+eNqlpaWmJHPkyJE+xxUVFZlVVVVefS6Xy5w5c6YpyXzppZdaFHPq1KmmJHPbtm0NnvHiiy+akszXX3/dZx4ul8t0OByeVl5e7pkTjUaj0Wg0//6nTaDnQqPRaG1pDoej2X+/Wf7V0JYaO3Zsg5U3m82mZcuWqXPnziosLGxRnFmzZkmSVq1a5dVfW1ur/Px8BQcHe72S+mPZ2dmy2+2eFhsb27qJAAAAALA8DpRvhYqKCu3cuVPHjh2T0+lUXV2dJCkkJEQnTpxoUYzExEQNGjRIu3btUnl5uaeQKyoqUkVFhSZNmqTo6Gif4zMzMzVv3jzPz06nk2IQAAAAQKtQCLbQ0qVLtXDhQl2+fPm6Yz399NOaM2eOcnNztWjRIkn/XCGcMWNGk2NtNptsNtt15wAAAADAung1tAUOHTqk+fPnKywsTPn5+SorK5PL5ZJpmjJNU3369GlVvGnTpiksLEy5ubmqq6vTN998o6KiIsXFxSk5ObmdZgEAAAAAV7Ei2ALbtm2TJL366quaPn2617VLly7p9OnTrYpnt9v1+OOPKzc3V++//74OHz6s2tpaZWRk8HUyAAAAAO2OFUFd3eMnSVeuXGn0+vnz5yVJMTExDa5t3ry5TZ+prv9ozMqVK5WTk6OgoCClpaW1Og4AAAAAtBaFoKSoqCgFBwfryy+/VG1tbYPr8fHxkqScnByvPYJHjx7VggUL2vTM++67T8OGDVNhYaFKS0s1btw43X777W2bAAAAAAC0AoWgrq4IpqSk6PTp0xo6dKimTZumjIwM5eXlSZLS0tLUu3dv7dy5U/3799fkyZOVnJyshIQEJSYmqm/fvm16bv2qoCTNnDnTL3MBAAAAgOZQCP7D6tWr9dRTT+ns2bPasGGDcnJyVFJSIkmKjIzUJ598oqlTp6qmpkY7duxQRUWFFi9erI0bN7b5maNGjZJ09ZXTlJQUv8wDAAAAAJpjuY/F+NrPFx0drbVr1/ocFxMTo/Xr1zd6raysrEFfXFxcs3sHN23aJElKT09XUFBQk/cCANCR9es3xG+x/ufkSb/FAoCOihXBAHE6nXrrrbcUEhLCa6EAAAAAbijLrQgGWl5enkpKSrRv3z5VVlZq7ty5fCQGAAAAwA3FiuA1fvjhBy1dulQPPfSQevXqpZCQEPXo0UPDhw/X73//e506deq6n1FSUqI1a9aourpazz77rF577TU/ZA4AAAAALWeYbTkErwM6cOCAJk6cqNOnTyssLEwPPPCAevXqJYfDoU8++UTff/+9bDab3n33XY0ePTrQ6Xo4nU7Z7fZApwEAwHXx5x7BTcXb/Bbrvjvv9FssALhRHA6HIiIimryHV0Ml/fWvf9XDDz8sl8ulBQsW6He/+526du3quV5XV6ft27fr+eef19dffx3ATAEAAADg+lm+EDRNU0899ZRcLpeysrK0aNGiBvd06tRJjz32mB5++GGVl5cHIEsAAAAA8B/L7xHctWuXPv/8c8XExOjFF19s8l673a7BgwdLkpKSkmQYRqNHR5SVlckwDCUlJXn1Z2VlyTAM5efn63//7/+tRx55RN27d1dERISSk5N16NAhf00LAAAAAHyyfCH43nvvSZL+1//6X+rc+cYskB44cEA///nP9fXXX2vs2LHq37+/iouLNXLkSP3lL3+5ITkAAAAAsC7LF4J//etfJUnDhg27Yc9ctWqVfvOb3+jzzz/Xxo0b9cknn+g///M/VVNTo9TUVF26dMnnWLfbLafT6dUAAAAAoDUsXwiePXtWknTbbbfdsGf27dvX85povWeeeUY/+9nPVFlZqYKCAp9js7OzZbfbPS02NvZGpAwAAACgA7F8IRgIEydObPQ11ClTpkiS9u/f73NsZmamHA6Hp/HxGgAAAACtZfmvhkZGRkqSvv/++xv2zL59+zbaHxcXJ0n65ptvfI612Wyy2WztkRYAAAAAi7D8imBCQoIk6fDhw36LWVdX57dYAAAAAOBvli8Ex40bJ0navHmzrly50uJxISEhkqTq6uoG15p7XfOrr75qsv/2229vcR4AAAAA0FqWLwRTUlI0aNAgff3113r11VebvNfpdOr//t//K0nq06ePJOn48eMN7tu9e3eTcbZu3ara2toG/X/+858lSSNGjGhR7gAAAADQFpYvBA3D0Lp169SlSxdlZWUpMzNTP/zwg9c9pmlqx44d+ulPf6pPPvlEkjRy5EhJ0ptvvqmLFy967t2zZ4+WLVvW5DPLysr00ksvefW9/fbbOnjwoHr16qWJEyf6YWYAAAAA0DjLF4LS1X2CxcXF6tWrl1577TVFR0dr9OjReuKJJ/SLX/xCffr00fjx41VeXu45rmHKlCnq37+/Dhw4oIEDB2rSpEl64IEHlJycrGeeeabJ582YMUOvvfaaBg8erKlTp+r+++/X008/reDgYOXn5yssLOxGTBsAAACARVEI/sP/8//8P/r73/+uJUuW6L777tNnn32mTZs26aOPPlJcXJwWLVqkEydO6OGHH5YkhYaG6oMPPtCUKVN04cIFFRUVqba2Vu+8846effbZJp/14IMPqqSkRL1799a7776rL774Qg8//LD27t2rlJSUGzFdAAAAABZmmKZpBjoJq8jKytJLL72kvLw8paam+iWm0+mU3W73SywAADoCf/6njWEYfosFADeKw+FQREREk/ewIggAAAAAFkMhCAAAAAAWQyF4HQzDUFxcXKDTAAAAAIBWYY9gE8rKytSvXz+NHDlSe/fubXDdMAz17dtXZWVlNzy3euwRBADAG3sEAVgdewQBAAAAAA1QCAIAAACAxVAI+pCVlaV+/fpJkkpKSmQYhqf9+OiH2tpavf7664qPj5fNZlNsbKwWLFggt9vdaOyLFy8qOztb99xzj8LDwxUeHq4HHnhAa9asae9pAQAAAIA6BzqBm1VCQoImTpyogoIC9erVy+ug9xEjRnjdO3XqVBUVFSkpKUn9+/fX/v379cYbb6iiokLr1q3zuve7775TcnKyPvvsM/Xu3VsjR46UaZo6cOCAUlNT9T//8z966623bsgcAQAAAFiUCZ9KS0tNSebIkSMbvS7JlGQOHDjQrKys9PSfPHnS7N69uynJ/Pvf/+415tFHHzUlmc8995zpcrk8/adPnzZ/+tOfmpLM//7v//aZk8vlMh0Oh6eVl5d78qDRaDQajebf/7QJ9FxoNBqtLc3hcDT77zdeDfWDP/7xj+rdu7fn5379+unJJ5+UJO3fv9/T/9e//lVFRUW67777tHTpUtlsNs+1Xr166e2335Yk/dd//ZfPZ2VnZ8tut3tabGysv6cDAAAAoIOjELxOwcHBeuihhxr0x8fHS5IqKys9fX/5y18kSRMmTFCnTg1/9fV7Bj/++GOfz8vMzJTD4fC08vLy650CAAAAAIuhELxOvXv3VlBQUIP+bt26SZLXB2Pqzxt88cUXvT4+c22rrq7WmTNnfD7PZrMpIiLCqwEAAABAa/CxmOvU2MqeL3V1dZKufmzmzjvvbK+UAAAAAKBJFII3UExMjKSrr4bOnz8/wNkAAAAAsCpeDW1CSEiIJOnKlSt+iZecnCxJ2rZtm1/iAQAAAEBbUAg2ISoqSsHBwfryyy9VW1t73fF+9rOfKTk5WR999JGeffZZOZ3OBvccOXJEu3btuu5nAQAAAIAvFIJNCAkJUUpKik6fPq2hQ4dq2rRpysjIUF5eXptjrlu3Tvfcc4/+8z//U3379tVDDz2kJ554Qr/4xS90xx13KCEhgUIQAAAAQLtij2AzVq9erf/4j//Q7t27tWHDBtXW1urKlStKS0trU7zo6GgdOHBAq1at0p///Gd9+umnOnDggHr16qV/+Zd/0Zw5c/T444/7eRYAAAAA8E+GaZpmoJNA2zmdTtnt9kCnAQDAdenVK85vsfb/9aDfYsX36eO3WABwozgcjmaPmePVUAAAAACwGApBAAAAALCYDlcIXrx4Udu3b1d6err69++vLl26qGvXrho6dKhefvllVVdX+xybn5+v+++/X+Hh4erZs6ceffRRHThwoMnnffTRR3r00UfVs2dPhYeH6/7779fatWsbvTcrK0uGYTTb9u3bd12/AwAAAABoSofbI7h69WrNmDFDkjRw4EANHjxYTqdTBw4c0IULFzRgwACVlJQoOjraa9zcuXO1fPlyhYaGasyYMXK5XPrggw9kmqa2bNmiCRMmNHhWQUGBJk+erLq6Ov385z9XVFSUPvjgA1VVVWn+/PlasmSJ1/3bt2/X9u3bG827oqJCxcXFCgsL07fffqvw8PAWzZc9ggCAjoA9ggDgPy3ZI9jhCsE1a9bowIEDmjt3rgYOHOjpr6ys1Lhx4/Tpp59qypQp2rBhg+dacXGxkpOTFRkZqYMHD+ruu++WJB08eFBJSUkKCwtTaWmpunfv7hlz7tw59evXT06nUwUFBXrsscckSd9++61GjBihv//97/rwww+VlJTUorwXLFigN954Q0888YTWrVvX4vlSCAIAOgIKQQDwH0t+LGb69OlauXKlVxEoSX369NGf/vQnSdLWrVtVU1PjubZ06VJJ0m9/+1tPEShJw4cP16xZs1RVVaWcnByveKtXr5bT6dT48eM9RaAk9erVS2+88YYk6c0332xRzqZpauPGjZKkp556qqVTBQAAAIA26XCFYFOGDh0qSXK73Tp79qwk6dKlS9qzZ48kadKkSQ3G1Pft3LnTq/+9997zOWbcuHHq0qWLiouL5XK5ms1r7969Ki8vV+/evTV69OhWzAgAAAAAWs9SheDJkyclScHBwerZs6ck6W9/+5vcbrduu+02xcTENBgzbNgwSdJnn33m1X/kyBGv69cKCQnR4MGD5XK5dPz48Wbzqn8VdMqUKQoKCmrFjAAAAACg9SxVCC5fvlySlJKSIpvNJkk6deqUJDVaBEpS165d1b17d50/f14XLlyQdHVfnsPhaHJcff9XX33VZE4ul0sFBQWSWvZaqNvtltPp9GoAAAAA0BqWKQSLioqUk5Oj4OBgLV682NNff5xEWFiYz7Fdu3aVJE8heO0RFL7G/XiMLzt27JDD4dCgQYN0zz33NDuP7Oxs2e12T4uNjW12DAAAAABcyxKF4LFjx/Tkk0/KNE394Q9/8OwVvBnUvxba0o/EZGZmyuFweFp5eXl7pgcAAACgA+oc6ATaW0VFhVJSUnT+/HnNmzdPzz33nNf1+vP6Ll686DPGDz/8IEnq1q2b15j6cY19mvXHYxpz9uxZ7dq1S506ddITTzzRovnYbDbPa60AAAAA0BYdekXw3LlzGjNmjL766iulpaU1OOBdku644w5J0tdff91ojB9++EFVVVXq0aOHp6iLiIjwnN3na1x9f9++fX3m98477+jy5csaOXKkz72GAAAAAOBvHbYQrK6u1tixY3X06FE99thjWrVqlQzDaHBf//79ZbPZ9P3336uioqLB9cOHD0uShgwZ4tVf/3pp/fVrXb58WZ9//rm6dOmi+Ph4nzm29rVQAAAAAPCHDlkIut1ujR8/Xh9//LEeeeQRbdy40eexDKGhoRo1apQkafPmzQ2ub9myRZL0y1/+0qt/3LhxXtev9e6778rlcmn06NHq0qVLo889efKkDh48qNDQUE2cOLHlkwMAAACA69ThCsHa2lpNmTJFe/bsUWJiorZu3aqQkJAmx8ybN0+S9Morr+jEiROe/oMHD2rlypXq3r270tPTvcZkZGQoIiJChYWF2rp1q6f/u+++0/PPPy9Jmj9/vs9n1q8Gjh8/vtE9hgAAAADQXjrcx2JWrFihbdu2SZKioqL061//utH7lixZoqioKEnS6NGj9dxzz2n58uVKSEhQcnKyampqtHv3bpmmqby8PHXv3t1rfM+ePZWbm6tf/epXmjRpkpKSkhQZGani4mJVVVVp3rx5SkpK8pnn+vXrJfFaKAAAAIAbr8MVgufPn/f8c31B2JisrCxPIShJy5YtU0JCglasWKHdu3crJCREo0eP1u9+9zs9+OCDjcaYOHGi9u3bp1deeUWHDh1STU2NfvKTn2j27NmaPn26z2d//PHHOn78uKKjozVmzJg2zBIAAAAA2s4wTdMMdBJoO6fT6fmCKQAAt6o77viJ32JtK3nPb7Hu7dfPb7EA4EZxOBzNbj/rcHsEAQAAAABNoxAEAAAAAIuhEAQAAAAAi6EQBAAAAACLoRAEAAAAAIuhEAQAAAAAi6EQBAAAAACLoRAEAAAAAIvpHOgE0Dput1tut9vzs9PpDGA2AAAAAG5FrAjeYrKzs2W32z0tNjY20CkBAAAAuMVQCN5iMjMz5XA4PK28vDzQKQEAAAC4xfBq6C3GZrPJZrMFOg0AAAAAtzBWBAEAAADAYigEAQAAAMBiKAQBAAAAwGIoBG8i06ZN04ABA7Rt27ZApwIAAACgA6MQvImcOnVKf/vb3+RwOAKdCgAAAIAOjEIQAAAAACzGME3TDHQSaDun0ym73R7oNAAAuC6G4b+/TV+49IPfYoV3CfVbLH/p3DnEb7GuXKnxWywANw+Hw6GIiIgm72FFEAAAAAAshkIQAAAAACyGQhAAAAAALIZCEAAAAAAshkIQAAAAACyGQhAAAAAALIZCEAAAAAAshkIQAAAAACymc6ATQOu43W653W7Pz06nM4DZAAAAALgVsSJ4i8nOzpbdbve02NjYQKcEAAAA4BZjmKZpBjoJtFxjK4IUgwCAW51h+O9v0xcu/eC3WOFdQv0Wy186dw7xW6wrV2r8FgvAzcPhcCgiIqLJe3g19BZjs9lks9kCnQYAAACAWxivhgIAAACAxVAIAgAAAIDFUAgCAAAAgMVQCI9SdsQAAAFsSURBVAIAAACAxVAIAgAAAIDF8NXQWxynfwAAOgJ//v+Z0+n0W6ybEf/fD6A5Lfn3BIXgLe7ChQuBTgEAAD/wX3Fze3Qvv8W6GdXWXg50CgBuchcuXJDdbm/yHg6Uv8XV1dXpm2++Ubdu3WQYhs/76g+eLy8vb/Zwyab4Kw6xiHWrxLoZcyIWsdoz1s2YE7GIdavEuhlzIpa1YpmmqQsXLuj2229Xp05N7wJkRfAW16lTJ8XExLT4/oiIiOv+H6E/4xCLWLdKrJsxJ2IRqz1j3Yw5EYtYt0qsmzEnYlknVnMrgfX4WAwAAAAAWAyFIAAAAABYDIWgRdhsNi1atEg2m+2miEMsYt0qsW7GnIhFrPaMdTPmRCxi3SqxbsaciEUsX/hYDAAAAABYDCuCAAAAAGAxFIIAAAAAYDEUggAAAABgMRSCAAAAAGAxFIIAAAAAYDEUggAAAABgMRSCAAAAAGAxFIIAAAAAYDH/f4l+Kx1GVvAJAAAAAElFTkSuQmCC\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Method: additive\n","Translation: Italy defeated Port ug defeated Port ug Port ug Port ug in Port ug in Port ug in Port ug in Port ug Par as Port ug in France in France . \n"]},{"output_type":"stream","name":"stderr","text":["/tmp/ipython-input-463267404.py:20: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n","  ax.set_xticklabels([''] + sentence, rotation=45)\n","/tmp/ipython-input-463267404.py:21: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n","  ax.set_yticklabels([''] + translation)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4117 (\\N{MYANMAR LETTER PA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4156 (\\N{MYANMAR CONSONANT SIGN MEDIAL RA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4100 (\\N{MYANMAR LETTER NGA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4154 (\\N{MYANMAR SIGN ASAT}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4126 (\\N{MYANMAR LETTER SA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4101 (\\N{MYANMAR LETTER CA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4116 (\\N{MYANMAR LETTER NA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4141 (\\N{MYANMAR VOWEL SIGN I}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4143 (\\N{MYANMAR VOWEL SIGN U}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4150 (\\N{MYANMAR SIGN ANUSVARA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4139 (\\N{MYANMAR VOWEL SIGN TALL AA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4123 (\\N{MYANMAR LETTER RA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4142 (\\N{MYANMAR VOWEL SIGN II}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4121 (\\N{MYANMAR LETTER MA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4151 (\\N{MYANMAR SIGN DOT BELOW}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4114 (\\N{MYANMAR LETTER DA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4096 (\\N{MYANMAR LETTER KA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4172 (\\N{MYANMAR SYMBOL LOCATIVE}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4162 (\\N{MYANMAR DIGIT TWO}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4160 (\\N{MYANMAR DIGIT ZERO}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4167 (\\N{MYANMAR DIGIT SEVEN}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4097 (\\N{MYANMAR LETTER KHA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4158 (\\N{MYANMAR CONSONANT SIGN MEDIAL HA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4120 (\\N{MYANMAR LETTER BHA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4153 (\\N{MYANMAR SIGN VIRAMA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4140 (\\N{MYANMAR VOWEL SIGN AA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4118 (\\N{MYANMAR LETTER PHA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4124 (\\N{MYANMAR LETTER LA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4152 (\\N{MYANMAR SIGN VISARGA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4112 (\\N{MYANMAR LETTER TA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4157 (\\N{MYANMAR CONSONANT SIGN MEDIAL WA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4129 (\\N{MYANMAR LETTER A}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4106 (\\N{MYANMAR LETTER NNYA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4145 (\\N{MYANMAR VOWEL SIGN E}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4144 (\\N{MYANMAR VOWEL SIGN UU}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4098 (\\N{MYANMAR LETTER GA}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4163 (\\N{MYANMAR DIGIT THREE}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4161 (\\N{MYANMAR DIGIT ONE}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4165 (\\N{MYANMAR DIGIT FIVE}) missing from font(s) DejaVu Sans.\n","  fig.canvas.print_figure(bytes_io, **kw)\n","/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 4171 (\\N{MYANMAR SIGN 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QEIBevXph/PjxuHLlChYtWoRKlSrh9OnTxnoVK1bElClTkJqaiuTkZPTo0QM//PADPvvss0L77/HHH8eKFSvQvXt3PProo8brI6KionDgwAGb+axbty7KlSuHd955B6GhoShTpgzatGlj8X5J3eDBgzF//nzMmjULTZo0KXQmb8SIEVi9ejX+/ve/Y/v27Wjfvj1MJhN+/vlnrF69Gps3b0bLli2N9bdu3Yr27dsbl9MSEXmtYnxiKRERlWC9evWSoKAguXr1qtV1UlJSxN/fX86fPy8iIosWLZI6deqIr6+v2askzpw5Iz179pTQ0FABYPZKgMuXL8v06dMlJiZGAgICJDIyUtq1aycvvfSS3Lx5U0T+93qEF198sVAeUOCVELm5ufLwww9LxYoVRdM0s1dJFFxXRGTfvn2SlJQkZcuWlZCQEImPj5evvvrKbB399RHffvut2fLt27dbfWVGfvlfH2ELCrw+Ii8vT5577jmJioqSwMBAiY2NlY0bNxZ6VUL++nn55ZelZs2aEhgYKB07dpT9+/fb3KZu7dq1omma8coO3fr166Vp06YSFBQk0dHRMnfuXHn33XcLverBZDLJnDlzpGrVqhIcHCxdunSRQ4cOSVRUlNnrI0REDhw4IJ07d5agoCCpXr26PPPMM7JkyZIiXx8hcuuVIY0aNRI/Pz+zV0kUrJP8dVizZk0BIM8++6zFst+8eVPmzp0rd955pwQGBkr58uWlRYsWMmfOHMnMzDTWu3TpkgQEBMjixYuLrE8iouKmiXjoDnoiIiIqFseOHUPt2rXx4osvYsqUKU6lYTKZ0KhRI9x777145plnFOfw9jBv3jy88MILOHz4sNlrNYiIvBHvESQiIqIi+fr64umnn8abb75p8bLT0i4nJwevvPIKnnzySQaBRFQi8B5BIiIissvgwYMLPUyFbvH398fvv/9e3NkgIrIbzwgSERERERGVMrxHkIiIiIiIqJThGUEiIiIiIqJShoEgERERERFRKcNAkIiIiIiIqJRhIEhERERERLclPg7FOgaCRERERER02/jiiy8wf/58AICmaQwGreB7BImIiIiI6LawceNG9O7dG5qmITMzE08++aQRDGqaVtzZ8yoMBImIiIiIqMTbsGED+vbti9TUVDRs2BATJkxAXl4eZs6cyWDQAgaCRERERERUoulB4Ny5czFlyhTk5ORg3rx5mDhxIgAwGLSAgSAREREREZVYBYNAAPD390evXr0AgMGgFQwEiYiIiIioRLIUBOoYDNrGQJCIiIiIiEqcTz/9FH379sW8efPw8MMPW1yHwaB1mvB5qkREREREVIKcPHkSXbp0QZUqVbBz504AQF5eHnx8LL8dLycnBxs2bMDEiRNx//33Y+bMmQBQqoNBvkeQiIiIiIhKjJs3byIiIgJjx45FeHg4/vnPfwIAfHx8kJeXZ/E7+pnB1157DUuWLMHs2bMBlO73DPLSUCIiIiIiKhG2b9+OjRs3YuLEiXjkkUcQEBCAzz77DAAwY8YMIxi0dGbQ398fycnJAP53mejs2bNL7WWiDASJiIiIiMjrbdiwAX369EGlSpXg7++PCRMmYPz48QDAYNAJDASJiIiIiMir6U8HXbBgAerWrYtZs2bBZDLh0UcfZTDoJAaCRERERETktfQg8Pnnn8fYsWMBALm5uXj66acBwKVgUEQwZcoU+Pr64qmnnio1QSDAQJCIiIiIiLxUwfcE6mfsunXrBgB45plnADgfDPbv3x9//vknHnzwQcTFxSExMdFDJSt+DASJiIiIiMjrWHpZfP7LN1UEgydOnMArr7yCnj17lqogEGAgSEREREREXmbjxo0YNGiQWRCoy3/5pivB4NGjR5GUlIQ6depgw4YNAGy/i/B2wxfKExERERGRV8jLy8PNmzdRu3ZtlC1bFr/++qtd39uyZQueeeYZxMXF4dFHH0VERAQWLFiAzz77DJ06dcKMGTOM9H18fHDs2DF069YNderUweeff272WWlRekpKRERERERe7fr16wgKCsKWLVtw/vx5/P3vf0dubm6R3+vWrRueeuop7N27F6+99houXLiA8ePH45577sGXX35p9tL5w4cPl/ogEOCloURERERE5AU+++wzTJgwAStWrEC7du3w5Zdfom3bthARvPnmm/Dzsx26FHWZqJ+fH4YOHYpevXqV+iAQ4BlBIiIiIiIqZhs2bEDv3r0RHh6O4cOH46uvvkKTJk2wZ88erFy5EhMmTHDpzGCPHj2wdetWREdHo1atWqU+CAR4RpCIiIiIiIqR/nTQF154AWPHjsWcOXMwePBgfPDBB2jXrh327NmDtm3bAgDeeOMNu88M6u8ZnDhxIsaPH4+rV6+iSZMmePXVVwGU7iAQ4MNiiIiIiIiomOR/WfzUqVMBABcvXsQ///lPrF692ggGDx48iLZt22L48OE2g0H91RLArQfIPPfcc2jVqhUefvhhVKtWzfheaQ8CAZ4RJCIiIiKiYmDpPYEAUKFCBeMpn46eGSz4nsHTp09j9OjRaNasGe677z4At4LF0h4EAjwjSEREREREHmbpTCBgfkbPlTODwK33BPbp0wdVq1bF5s2b3V6mkoZnBImIiIiIyGO2b9+OPn364K233sLf//53s+Av/8viXTkzqL8nsG7dunwwjBUMBImIiIiIyCNu3LiB9PR0REdHo169egBuBX/WgjRngkEGgfZhIEhucebMGYSEhCAsLKy4s0JEREREXuDMmTOIjIzEpEmTEBQUhFdeeQU5OTno3r07fHx8zM4M5udIMKgHgXxPYNFYI6Tcpk2bEB8fj7S0NGRmZhZ3doiIiIiomG3atAmdO3fGkiVLUK9ePTz44IOoXbs25s+fbwRs+oNeLNGDwXvvvReDBw82e8/g+++/j4kTJ+K3335jEOgA1goptWHDBvTp0wdhYWH46KOPsHbtWgaDRERERKWYPj8sV64cVq9ejffeew8xMTF4+OGHlQWDK1asQP369VG/fn0GgXZizZAy+tOfXnnlFXz99ddo164d3nvvPQaDRERERKVUwflhhw4dsGzZMpeCwSeeeKJQMLh792489NBD2LhxIwAGgfbg6yNICWvvgZk1axa+/PJLjBw5Ev3790d4eHgx5pKIiIiIPMXW/DAjIwMpKSkYOXIkfvvtN8yfPx9Hjx7Fww8/jO7duwOA1XsGAeDChQt48cUXkZaWhvT0dONeQYBBoL1YQ+QyS508Ly8PADBnzhx06tSJZwaJiIiISpGi5oddunRx6cxgREQEBg8ejBMnTuCjjz4y+4xBoH14RpBcsnHjRvTp06fQLz2A+a8xPDNIREREVDo4Mj909szg8ePHkZiYiHr16mHTpk2eKdhthoEgOW3Lli3o3r07Xn31VTz66KMW12EwSERERFR6ODM/dDQYPHbsGBITE/meQBextshpv/zyCwCgbt26Vtfx8fExuwygc+fOWL58OdasWcPLRInotqOPd0REpZUz88P4+HjjMtG6devikUceMS4T/eyzzwDALAjky+LVYI2Rw/79738DACZMmICnn34a/fv3x8cff2x1/fydffbs2ejSpQvee+89BoNEdNv4+eefAZiPd0REpYmr80M9GFyxYgXq1KljMRjUzwTyPYFqsNbIIZ9++inuvPNO3H333QCAJ598Ek8++SQGDx7MYJCISqVPP/0UjRo1Qrdu3QAwGCSi0kfV/NBSMFinTh28/fbbWLp0KZKTk3kmUCHWHNltw4YN6N27Nx555BGIiDHpmTlzJoNBIiqV8o+Lubm5uOeeewAwGCSi0kP1/NBSMFirVi387W9/Q40aNRgEqiREdli/fr34+PjIyy+/LCIiBw4ckPbt20tiYqKxzpw5c8Tf31/Wrl1rMy2TyWT8/6xZs6Rz587y7rvvyqVLl9yTeSIiN7A2Lnbv3t1YJ/94R0R0u3Hn/LBTp06ydOlSycnJkX//+9+yZMkSi+uS8xgIUpH0Tv7iiy+aLd+/f7+Szv7MM89Ix44dZf78+XL9+nXZunWrZGVlqS0EEZFCRY2LDAaJ6Hbniflh+/bt5Y033jBbzjFVHQaCZJO1Tq5z5Zef3NxcERE5c+aMaJomjz32mDRr1kxq1KghK1asYDBIRF7J3nGRwSAR3a48OT+cPHmyuoyTGQaCZNX69evFz89P5s6da3M9Vzr7b7/9Jg0aNJCkpCSJjY2V9u3by6OPPip33323LF++nMEgEXkVR8dFBoNEdLvx9PyQ3IeBIFm0b98+0TRNnnjiCREpegLjTGc/evSo1KtXT5KSkqRZs2bSpk0buXz5soiITJs2TRISEhgMEpHXcHZcZDBIRLcLT88PdRw73YOP2iGLTp8+jY4dO+LChQs4c+ZMkU/Aa9KkCd5++21cu3bNrqdFHT16FN26dUOdOnVw9uxZBAUFYcOGDShbtiwAIDU1Fa1bt8by5cvx8ccf4/Lly+4rLBGRHZwdFy9fvsyniRLRbcGT80M+HdT9WKtk5uDBg/j666/Ro0cPTJkyBX/++SdmzJiB06dPK+vsx44dQ1JSklkQuH79elSsWBEAYDKZADAYJCLvoGJcLGnB4KFDh7Bz587izgaRx6hs87dj//H0/JBBoIcU9ylJ8h7r168XTdMkKSlJ9u7dKyIi69atk379+smYMWPk9OnTIuL8ZQDBwcHy9ttvS6NGjSQpKUlatmwpcXFx8ueffxZKI/82eJkoERUX1eNiSbhMVC9zjx49ZMeOHcWdHSK3U9nmb8f+4+n5oc5bx8jbiSYiUtzBKBW/DRs2oH///pg6dSp+/fVX+Pj4YNKkSYiLi8P69euxbNkylC9fHv/85z9RpUqVIn+hOXjwIB544AGEhIRgy5YtAIBnn30WM2fORFJSEi5cuIBTp07h0KFDKFeunMU08m9j+vTp+OabbzBq1Cj069cPoaGhyuuAiCg/d42LoaGh+OyzzwB436/dBcvs7++P8ePHo3PnzsWdNSK3UNnmb8f+4+n5obeOjbet4o5EqfjpjwDWn/508uRJ6dOnj9x7772yZ88eEXHulx/9PTL5f93ZunWriIj84x//kODgYNmyZYvNNHhmkIiKg7vHxR49ehjL8vLy3FQKx1gr89ChQyUjI6OYc0eknso2fzv2n+KYH9rzfVKHgWApV/A9MHrn++OPP5R09n//+9/SokULiY+PFxGRl156yfjeP/7xDwkKCpJPP/3UZhoMBolKlldeeaW4s+AST42L+S+PKm5FlbkkT2aJLFHZ5m/H/uPp+aGOQaBnMRAsxay9DFR/kaeKX36OHDki1atXl759+0qrVq1E0zTp27ev8fnjjz/OYJDoNqL384EDBxZ3Vpzi6XHRG9hb5pI4mSWyRGWbvx37T2kcB0srBoKl1MaNG8XPz69QJ9fpndeVzn7kyBGpX7++dO/eXZo1ayZxcXGybds2iYuLk+TkZGM9BoNEtwe9n//rX/+SuLi4EneA9/S4qCvOS0MdLXNJmswSWaKyzd+O/ac0joOlGQPBUujAgQOiaZrMmDHD5nqudPaCL4uPi4uT8+fPi4jI999/L61atVIWDK5YsYLBIFEx0/v5xYsXReR//bykBIOeHhcLplccnC1zSZjMElmiss3fjv2nNI6DpR0DwVIqNjZWWrduLX/88YfN9ezt7KdOnTK+YykIPHv2rPF5Xl6efP/999K6dWungkH90gQRkRkzZkj9+vUlPT2dvyYROUFFv9H7+blz58zS1ft5SQkGPTUuityqH1WTH1f2obNl9vRkluM75ectbb6k9B9HFFUmve5VjIP506HiweeyliIZGRlYtGgRAGDfvn3Iy8tDcnIyTp48afU7+ktCq1WrhrfeegvZ2dl49dVXsXfvXvTu3RspKSn466+/8OSTT+LChQv466+/kJiYiDp16uDPP/80XhZfqVIlI01N09C8eXO8/fbb+PPPP9GrVy8AwNy5c/HII4+gf//+xuODLfH19TVeXJqbm4tff/0VLVq0gKZpKqqJ6LaXkZGBZcuWAbjVH8WFtwg1b94cQUFB2LhxIyIjI43l+fv5qVOn0K9fP1ez7RaeHBfLlSuHIUOGAABExKUxy5V9qKLM165dw4IFC7Bjxw6ny2BPPlW1Uyr5vKXNz5s3r0T0H0cUVT+W6t6V+SFfFu9FijMKJc/ZsGGDaJpW6Obfli1bSrNmzVz+BXzAgAEyaNAgqV69unEmsHPnzsZlAda4cmZw0qRJEhwcLN99953N9Yjof/KPBa+99pqx3Jlf2PV+nv+MvyXeembQk+NibGyssa158+YZaThT767sQ9VldteZDZXtlEo+b2nzbdq0EQBe338cUVT9LFu2zGbdOzo/1PFMoHdgIFgK6E9/Sk1NlbS0NKlWrZrxThgRNZOe9evXy1133SV9+/aVRx55RDRNk3//+99m37PGmWDwsccek6CgIAaBRA6wNBbkf9WDI5NsvZ//5z//ERH7+7m3BIOeHBfbtGljbGvlypUu1bsr+9BdZVY9mVXZTqnk85Y2r6fVsGFDadeunURGRnpl/3FEUfUTExMjAGTatGk2697e+aGOQaD3YCB4myv4COAbN27Ie++9p2wCMGTIEKOz//jjjyIismrVKundu7c88MADxgNiVAaDDAKJHGdrLHBmkr1q1Srp06ePU/28uINBT46Lr732mrEtk8nkUr27sg/dXeb77rtPyWRWdTulks1b2nz+tE6ePCnJycnStm1bZcGgqv7jiKLqR/+8Ro0a0qxZMzl8+LDNurdnfph/PfIODARvY9beA+PqBEB/WMu5c+eka9eu0rFjR/ntt99k9uzZsmfPHsnLy5OPPvpI+vTpI+PGjVMaDHp7EPj000/L9u3bizsbRGbsGQvsnWTr/VxEXOrnxRUMenJcXLBggbGt/HXqTL27sg89VeYuXboYZ4idobKdUsnnLW0+f1r523xCQoI0aNBAKlWq5BX9xxFF1U+FChVE0zTjc71MRQWD1uaHBdfzRqV1/sZA8DZlrZPrsrOzZcWKFU5PAERELl26JNWqVZO+ffvKXXfdJZqmyfDhw40g7cMPP5Q+ffrI+PHjXQ4Gw8LCpHv37lK2bFmvDQL1Ohg5cqTs2rWruLNDJCKOjQVFTbJV93NPB4OeHBf1y0FV1Lsr+9DTxwJnqWynVPJ5S5u3lpbe5nv37u0V/ccRRdXPmjVrRNM0CQsLs1imI0eOFNkXPV0mV5Xm+RsDwdvQpk2bxNfX1+ymXkuys7Od/jX46NGjEhMTI0lJSRIbGyvx8fHy3XffSa9evWTYsGEuTxItvWdQ0zT5/vvviyx/cdDr4Pvvv5devXrJiBEjZOfOncWdLSrlnBkLrB3YC7ZxVf3cUxMFT46LzZs3N7ZlK1Cxp95d2YeePhZYyr89VLZTKvm8pc1bS6tgm8/Ozpbly5cXW/9xRFH1o3/+8ssv26yfI0eOWO2Lni6Tq0r7/I2B4G3m999/l+joaImPjzeW2ZqQ5f9l7PnnnzeWt2rVSu666y6LA5il9wTqTwc9evSo9OzZ06VJorX3j124cMHOWvCsgu9K1OugtA0m5F1cGQtefvllY3leXp7VNq6qnw8cONClshbFk+Nip06djG3p23C23l3J95NPPml8t+A9PKrL7MqTAFW2Uyr5vKXNN2zYUGrWrFkoH9bafHH1H0cUVbcFP88f4FoqU/4zg3pfLGnvCeT8jYHgbSUnJ0cuX74sc+bMkR49epgdJF2ZAJw4ccJYduTIkUJB4J9//mm2jWPHjhmTxG+//VZEzO8l0l86XdTg8MMPP4ivr6888MADIuKdB3q9DgpOfPU6GD58eKkZTMh7qBgL9O8U1cZd6ec3btww+vmECRNcK7QVnhwXExMTjW3dc889LtV7Tk6OZGVlOZXvZcuWSZUqVSQpKcn4ropg0FqZ7cmXJSrbqYh3HiMKunHjRnFnwWu50h5UtvmcnBxp3ry5VK5cWRISEox8HDlypNCZrvxpOzuXsqeMriqqbq19XlQwePjwYaPMTzzxhFEme+q+uHH+dgsDwdvE9u3b5YknnpAzZ85IVlaWPP/889KtWzdlk56zZ8/K77//bjUILLgNFZPEyZMnS5kyZWT//v2OV4gHFBxEdKV1MCHvoHIsqFatmtHG80+yVfTz7du3y5NPPiljxoxxWz/35LjYqlUrY1uXLl1yaVsPPPCAkVZmZqZDaW3fvl3+8Y9/yBtvvCFVqlSRnj17Gt9VEQwWLPOTTz4pv/32m8MTPpX7pqQEg3qbz//wDLrFlfagss2PHTvWyEfz5s2latWq0qVLF3nqqaeM+Y+tM/2OzqWKKpsKRdXt9u3bZfr06VY/LyoYPHHihMybN098fX3ljjvuMD7XHxzjjTh/+x8GgreB9evXi6ZpUqVKFZk5c6acPXtW6aSnTZs2cscdd0h0dLR0795dWrVqJe3atSsUBBbchiuTxMmTJ3v100H1Oig4iOhK42BCxU/lWFCnTh3x9/eXp59+2liuKhjU81mmTBnx8/OTTz75RE0F5OPJcbF58+bGtp566imXtjVp0iQBIKGhoUa+7Q0G85d5xowZyoNBS2WuWrWqzJo1Sw4fPmw1vYLcsW+8PRjUy1y1alWZPXu2Q/V1u3OlPahs85b6XsuWLSUyMlKCgoLMgpycnBybadkzl7JWJpWKqtsxY8bYNXZZCwb1MkVFRUnTpk2NvqiXyRv7Iudv5hgIlnD6058WLFggGzZskHbt2smTTz6pfNLTvHlzSUxMNB7acujQIZv5cmWS6O2viNDr4KeffrK5XmkbTKh4qRwL9Db+/PPP25xkO9PP9XwmJiaKv7+/3HXXXTJz5kylE2NPjovNmjUztrVu3TqXtqXne/To0VKhQgWJjo420ioqGLRU5unTpysPBi2VuX379nbvQ3fuG28NBguW2ZH6ut250h5UtvmCfS8qKsrIx1133SUxMTFGWo7e+2tpLlWwLO5QVN2OGjVKAMjAgQONz2fMmOFwMKiXSS9z1apVvbYvcv5WGAPBEkzv5C+88IKxbO3atS4fWG/cuCErVqyQmjVrWny8cGxsrLRr107OnDljM3/OTBK9PQjUOVsHt/NgQsXHHWNBbGysxMXFyRtvvCE1a9aUefPmWVzPkX6u57NTp05GP9fzqWpi7MlxMf+29O86u638aenbioiIkNq1axeZ1ieffGK1zNOmTZM33nhDqlevLsnJyW4tc1H70BP7xlo7LS62ylzag0FX2oPKNm+t7+UPBvV8JCYmKptLeSIItFa37733nmiaJvfcc49RP/rntoLBGzduyPLly62W6dq1a17bF3Wcv5ljIFhCFXwPTP5fXFQcWPPy8uSVV14RTdNk+/btcs8998jrr79ubKdJkybSpk0bZcHg+fPn5amnnvLqIFCvA52zdXC7DiZFueeee+Sdd94p7mzcdlSOBdba+Jw5c0TTNPnyyy8t5sGefp6UlCQ+Pj7StWtXCQoKMj7X86niLIknx8V//vOfZi+Ld2VblvKtbwuANG7c2GpaBSd8lvIxY8YMmTNnjgCQ8ePHu63MtvahJ/eNtXbqaUWVuTSfGXSlPahs80X1vTvvvNPus/KWFJxL5V/uLkXVbYMGDQSAzJo1S7KysiQ1NdWhYDAvL09efvllq2VyV190ZQ7B+Zt1DARLIGsvA80/GLl6YNVvZk5OTpYWLVpIZGSkVKtWTUlHKjhJHDBggNSqVUsCAgK8NghUXQel6dHEIub19/bbbxd3dm4bKscCa228QYMGEhQUZHY5kyW2+vm0adMEgJQrV86snxfMpysTY0+Oi61atVK2Lf3MRsG0jh07ZozB1tJq1qyZaJpmfNfSJWtr166VFi1aSIUKFSQxMdHtZba0Dz19zPIG9pa5NAaDrrQHlW3elb7nre2yqLrV7xkseLbTkWBQL1PPnj0t5sEdZXZlDsH5m20MBEuYDRs2SGBgoMVT8iJqfmUt+IqItm3byu+//y5Lly6VKlWqmL2IVEUwmJ6eLklJSfLjjz86VhkeotfBH3/8obQObsdfliyxVH8MBl2nciyw1s/1saBs2bJGG7f167elfv7KK69IYGCg3HfffRb7uYqzJJ4cF2NjY41tWaoLR7al5zv/5Vv5t9WtWzdj2UcffWSW1gcffCB+fn5Sv359i/nW83HkyBGpWrWqlCtXzviuvU81dbbM+fehp49Z1tbxJEfLXJqCQVfag8o270rf89Z2WVTdrl+/3vjcUl+zJxjs0KGDREZGGmUymUxmZXJHmV2ZQ3D+VjQGgiWEyWSSK1euSFhYmDRt2tTmuq4cWC29LF5/Ouj169dl8eLFyjrSiBEjZM+ePSIicvnyZatlKE4FHzGsqg70l5ampKSYXVpxu7FVf44Eg97SHryB6rEgNDRUoqKijH109epVWbx4sVSsWFEqVqxoHNAdbeNHjhyRpKQk8ff3l5iYGBEp3M+t5dPeibEnx8WYmBi5++67jW0VdTmYrW0999xzkpCQIEFBQUa+LY3Belr578Vr27atPP744xIaGiqNGze2me/8kzJ7LvlSXeZp06ZJ2bJlPXbMKviZp7nSHotq8yV9DHSlblS2eVf6nre1y/wPwLFVt/k/b9KkibHc0WDw4MGDEhkZKREREUaZ8weC7iizK3MId83f9Dns7TJ/YyBYQly/fl1ERHbu3CllypSRKVOm2Dww2HNgLXjT82+//VbkewJVdqRy5crJiBEjLL5gV19WnL/qWnvPjIo6uHHjhhw7dkzCw8Nl1KhRcu3aNbeUoTjZU3/2BoPe0B68hcqxoGnTplKrVi3p3Lmz2Vjwf//3f1KpUiUJCAgw2rjJZHKojV+/fl2OHTsmZcuWFT8/P5k0aZJD+bQnGPTkuNi1a1dl2/rzzz/l+eefl5YtW0pAQICRVv4xWGcymczSSk9Pl3bt2snIkSMlJCREHnnkEUlNTbWab3vObLijzOnp6dK+fXtJSUmRkJAQtx+z8tdXcXG1Pdpq8yV9DHSlblS2eVf6nre1S71NXL16VUSs123Bun/ssceMz60Fg3qZ9M8ffPBBqVOnjtx9991GmfOfeXRHmV2ZQ7hz/ibyvzns7TB/YyBYAnz66ady5513yoEDB0TkVmfOP4BZY+8A9vrrr8vJkyelQYMGkpSUJLGxsdKmTRur7wlU0ZFmzZolfn5+Fl8i/emnn0rjxo3l3//+t7G+p+l1YO09M67UgV6+hx56SPz8/OSHH35Qnf1i50j9FRUMekN78BYqx4IqVapI8+bN5ejRoxbHgq5duxr7KP/T3+xt43feeaf8/e9/Fz8/P1m6dKlT+bQ1MfbkuNi8eXNjWyaTyaVtDRkyRO644w7Zs2ePMSH19fWVv//973LHHXdYnUjl5eUZZX711VeNiXFAQIDZxLjgeK5/t6gzGyrLnD+fejDozmOWpfryNFXt0VKbL+ljoCt1o7LNu9L3dN7SLvU2sWDBArnzzjuNS+4L1q21urc3GHz99ddl8eLFEhwcLHXq1DE7s+rOMrsyh3Dn/E2k6DlsScNA0MutX79e/Pz8pFGjRlKvXj3lk565c+dKfHy8aJpmBIEdOnSQS5cu2cyXKx3J1isi8pe3fv36xrtePHng0+sgKyvL5nrO1IFevoiICNE0TT788EMRKXkHdlucqT9rwaA3tAdvoXIsqF27toSGhsrUqVOtjgUit/bRwoULXWrj6enpTufT1sNHPDUuxsbGKtvW9OnTRdM0iYyMlDp16siePXtk7ty5EhsbKwAkOjra6vvOCpbZ0sT4+eefL7QP9bQ8VWZL+VQZDFpqp5bqy5NUt8f8bb6kj4Gu1I3KNu9K3xPxrnap10vNmjUFgNStW1fq1atXKBjs16+f+Pr6Wq37ooLB/HXbrFkz4/PTp0+7tcyuzCHcOX/TlZTXnNmLgaAXy//0pwsXLsj48eOldu3ayic9Tz31lEyYMEFWrlwp5cuXl9TUVGP93Nxcq+k605GKCgILlrdOnToePfDpdZD/Zamq6iD/O9QCAwOlf//+Hi+fu7lSfwWDQW9oD95C5Vig76Phw4cbY8GpU6fMxgKdfomnK21c1ZiVf2LsqXGxZ8+exrbOnTvn0rb0fI8cOVJatWolzZs3l1q1asmePXvkqaeekv79+xtpFewz+ct8/vx5Ix+WJsZPPvlkoX3oqTIXlU9VwaCldlpc3NkeBw8eXKLHQFfqRmWbd6Xv5ecN7TL/i+99fHwkLi5OoqKi5N5775XatWsbweDzzz8vAKRz585m9edoMJi/bvX7Nd1ZZlfmEKNHj3bb/E13uwWBIgwEvZall4GePXtW2UGmY8eOxi87N27ckKysLLl48aI8++yzkpSUJPPnzzfWt9WxHelIkydPLjIItFReTx349Dr45z//qbwO9PJ17NjRqANPl8/dVNSfHgy6sz1Yu+TZGSrTskblWJCVlSUXLlww9tH9999vjAUnT540u183f9262sZVjFmdOnUyJsaeGBfXrFlT6MXpzm6r4D5cu3attGnTxpiQ6mOipbQs7f8zZ86YTYw7duxoTIzz34tpz5kNVWW2J5+dOnWSlJQU8ff3d/mYpfOGINAd7XH69OkCQLp27Sq//vqrWdol4ZjhSt2obPOu9D1LXG2Xrhwz9LKkpKQYZdLrNH8wOG/ePPHx8ZEHHnjAKFP++isqGCxYpvx1686+6Moc4o033pBKlSpJ7969i2UOW5IxEPRC1t4DI6LuIPPWW2+JpmmyevVqSU5OlsmTJ8v58+flr7/+kmeeeUYSExOVdaSzZ8/K448/bteZQGvldfeBT6+DCxcuKK+D+vXri4+Pj3Tp0qVQHZSkA7stKuvvgQcecFt76NWrl0yZMkWOHz/uQOncn5Y1KseC/Pvo4sWLxj669957jbGg4GPY83O1jbs6Zv39738XADJixAiX68LaNvRx8fHHHzd7cbor28q/DwtuS5/o62nl5eWZpbVu3Tqr+z//xG7GjBmiaZo8/fTTxnftfcKnijK7kk9njlmWPvc0dx6n9bT79etndgtB/rS9+ZjhSt2obPOu9D13tEtXjhkFzwTmr5/8wWCHDh0EgEyaNElEzOvW3mDQUpny1607+qIrc4jk5GR55JFHZN68eVKpUiXp1auXR+ewJR0DQS9j6ZcwEfNO5spBRuTWI36bNGki3bp1k9jYWKlQoYLExcXJ008/rTwYjI2NFU3TpGzZsnafCbRWXncd+NxZB+vXrxcAAsCsDjxZPndTWX8TJkwQANKvXz+zz1TUl57Ptm3byjPPPCPHjh1ztKhuScsalWOBtX00efJkKVu2rDRo0MBI051t3Nkxa9GiRQJA6tWr53JdWKOPi82aNVNW79b24ZEjR4wx2Fpafn5+ommazJ0712Y+hg0bJv7+/tK2bdtiKbMn85n/aZDFyZ3HaT3tadOmmZW5pBwzXKkblW3Jlb7njnbpyjHD0pnA/PWSl5cnZ8+elaSkJAEgTZs2NbtM9Msvv7QrGJwyZYpZ/bhaZnu5MofI/92ZM2cqDwaLmsPeDhgIepF//etfommavPPOOyJi+xcWZwcw/d1Q+V8RkZubK6tXr5a2bdvKnDlzlAeDvXv3lu+//15JeVUf+PQ6yMvLU14HDz30kFG+3r172xxEvPnAbovK+tPbw4gRI6RKlSry1ltvWd2uo/VVMJ/t2rVzOoBTmZY1KscCa/to3759Urt2bYmJiTH2kV6H7mzjjo5ZK1euFADSqFEjoy6s5c/VcbFly5ZGeQo+bMLRbW3bts3iPsw/ButOnz5tlpa+/319fW2e3Tt69KhER0dLjRo1jO/a+4RPFWX2ZD695cEw7jxO62n/85//lJiYGLPJdsEye+Mxw5W6UdmWXOl7Io7PpXTW8uzKMUOvl4kTJ4qmacbtE9baTseOHS3eM2jPmUF/f38pV66c1TK5oy+6Moew9N2nnnpKeTBobQ57u2Ag6CWuXr0qo0aNktq1a8vu3buN5SoPMpZeFn/69Gnj81WrVikLhGrWrGnWkVSWV9WBT6+Ds2fPGstU1cGbb74pISEhEhERUWzlczeV9Ze/PXzxxRdGG1IRDFrLpzMBnMq0rFE5FmiaJlWrVjW7z2HVqlXSvHlzqVChgsTHxxv7qGvXrh5r4/aOWT/99JOEhoZKcHCwsQ1XAzRr42LXrl2Net+5c6dL5fH395c777yz0D7M/7JrXcF78aKioqR3795Su3Zteeutt6xe9pZ/PC+OMnsyn94SBLrzOK2nXbNmTalRo4bF1yBYS9sbjhmu1I3KtuRK33NHu3TlmKHXaVRUlNxzzz1GmfKfCcy/Xu3atWXjxo2F7hm0Jxg8evSo1KhRQzRNM8pcMAh013sCnZlD2PquHgxWr17d6WCwqDns7YSBoBc4e/as5OXlyY8//ih///vfpVevXrJjxw67v2vPAGYpCNRvWs4/yKWlpSkJhDp06CCVK1eWzMxMi/ebuFpeVw98BV82qrIOzp49K9euXZNmzZpJcHCw3HPPPZKRkeHR8rmbyvqz1B62bt1q1oasKaq+isqnIwGcyrTOnj1rvAS44HJVY0GzZs3kzjvvNB7ModfL0aNHpUqVKlKuXDljH124cMHjbbyoMevbb7+VmJgYadu2rVEX27dvV7oNfVzs0qWLUe/jx493qd7Pnj0rX375pfj5+cldd91lpGXPROrQoUMybtw4qVatmgwaNEh69eol8+fPtzme6+w9s6GizJ7Mp7cEge48Tp85c0by8vJk06ZNEh4eLhUrVjTSLqrM3nDMcKVuVLYlV/qeM3Mpa2npXDlm6HX6xRdfyPjx46VTp07Su3fvQnVrqe4/+eQTh4LBGjVqSFRUlCQlJVm8Z9CdQaAzcwh75rBPPfWUtGnTRipWrChPPvmkS3PYM2fOWDxe3y4YCBazTZs2SZMmTWT58uWSl5cn+/fvl7Fjx0pycrKSg8z06dPNfgmz9CuKiNpAaOrUqRISEiL79u1za3mdPfDpdXDu3Dmz5SrqQC/fPffcI8HBwZKenu7x8rmbyvrbsGGDxfZQt25dCQwMtNiGCrJWX/bm054ATmVamzZtkrvuukuWLl0qV65cMVuuqm8EBARI06ZN5dy5c2ZjweHDh42xQN9Hs2fPLrY2bm3MWrJkiQQEBEjjxo2Nurj//vvdMi42b97cqPfc3FyX6r1y5cpSr149Wb58uTEhbdasmSQkJJid5RGxXq/z58+XcePGSfXq1WXAgAGSnJxsTIwLjucF0/JEmT2ZT28JAt15nPbz85OKFSvKSy+9JDExMdKuXTsjbUd/+CiOY4YrdaOyLbnS93Qq26Urxwy9XiZPnixNmjSRZ599Vu6//37p1KmTJCcnG3Vrq+7tCQanT58u33zzjYSHh0twcLBR5h07dri1L7oyhwgNDZXo6Gjju5YecJaWlibVqlUTf39/+eKLL1yaw1o7Xt9OGAgWI/0G4GbNmkliYqKsWrXKrDO7+ovjrl27JCgoSDRNk549e0rjxo2lQ4cOVh9f7MxEvuA7Wux5RYTK8jp64NPrQP8VSmUd6OWrWLGi+Pj4yLPPPuvx8rmbyvqz1h5GjBghvr6+0rFjR6fry9F82grgVKaVv8xJSUny/vvvy5UrV5T2jQYNGkiVKlWkVq1aFseCHj16GOuuXLmy2Nt4wTHL399fAEhYWJhRF/mDFZXjYsuWLY1679q1q0v1/t5774mmaRIQECBt27aVVatWGRMuAFKpUiWrZ3kK7v+3337bbGLcq1cveeONN8zGc509ZzZUldmT+bT1XU9y53FaT1vTNAEgzZo1M/vhw9uPGa7Ujcq25ErfK0hFu3TlmKHXS+3atQWAREVFSWJiojz//PNy//33S+fOnSU5OVlat25tvOjdWt3bCgbzl6lr165GmS19rrIvujKHaNiwoURFRUnnzp0tHp/0706ePFn8/f2ladOmLs1hrR2vbzcMBIuJ3sD0BjllyhSJj49XHgzu3r1bxo4dK//3f/8nmqbJY489ZjMNV86K2fOyeHeU194Dn14HU6ZMUV4Hn3zyifj4+EhCQoIEBQXJiBEjPF4+d1NZf82aNRNN04w6fOyxxyQ+Pl569OghgYGBkpaW5nR95b/Pwd5H6FsL4Jwts6W0LPWBu+++WyZPnqysb+j5ffDBB62OBXp+9brxhjZecGLcrl07l+vC2jb0uujWrZtR7yaTyaVt6fl+7rnnZPz48RIeHi6tWrWSVatWya5du6R///5GWgXP8lgbGy1NjN98801jH4o4dmbD1TJ7Mp/2fNcT3Hncqly5spH27t27pWnTpkbarv7w4Yljhit1o7ItudL3rHGlXbpyzLjvvvvEx8dHxo0bZ4y18fHx0rNnT4mPjzeCwTvvvFMASOPGjWXHjh02695WMJi/TGfPnpVx48a5tS+6MofQH0w2YcIEm/PQ/IGcK3NYa8fr2zEYZCBYDKy9Y0fFQebMmTPy0EMPSd26deXgwYMiIsZloJs3bxZ/f3956qmnbKbhTCA0adIkh98TqLK8MTEx8vPPP1v9lUmvg61btyqvA0sv0nZX+YqLyvpbtWqVaJom9erVM2tDLVq0cPks08GDB+Whhx4yLgtxNJ/5AzhXy2zpV96CfaBfv34CQIYOHWp2cHGm7RTM76RJkwqNBbqCD1zxhjZepUqVQnWkBysdOnRQOi7m3x/5JzfOlKfgvtW3FR4eLq1btzZLq+BZnqLGxrffflsefPBBqVGjhjExtvfMhsoyezKf+dn6rrWx3lHW0nHncUs/gxUREWFW5pJyzHClblS2JVf6XlGcaZeuHDOmTJkiACQ2NtasTHq96MHgyJEjBYDUqVNHkpOTjTLZqvuPP/5YHnroIYmOjpZhw4ZZLZOzfdEertTNO++8I23btpWRI0eKv7+/TJ061eY89NtvvzWWOTOHtdVGb8dgkIGgh1lqYPlPTas4EHz77beiaZpMmzZNWrVqJZUrV5Zff/1VRG4Fg76+vjJz5kybadg7SXzzzTdl+vTpEhwcbHcQ6K7yVq1aVapUqSJHjx41+9yddaCf2UpISDDqwJ37sziorL9hw4aJj4+PPP3002aDsd6GXDnLpOdz7dq1ommaDB482Kl8tmvXTqpXry6RkZEulzn/r7wF+4DeNzp37mwcXPI/GMeRtmNtH91///1mbafgg5u8pY2//PLLAkDi4+ONfOnbaNWqlYSEhBiXfLnajwYMGFDo/WLOlsfa+KZvq3Xr1hIfHy8rV64slNZzzz1n19j47LPPCgAZM2ZMsZTZk/m01k4LatWqlcWx3lHW0nHncUtP+5FHHjHKnJuba5TZ248ZrtSNyrbkSt9zR7t05TiZ/1gAQJKSkuTw4cNGmfR6admypQCQHj16GJeJ9uzZ065gcMGCBaJpmowaNcpmmRzti/ZwpW7077766qvStm1b49aRxx9/3OY81Nk5rD3t+3YLBhkIetCGDRss/sogIi7/Kq07duyY1KtXT3r27CnNmjWT2NhYGTp0qNSvX19++eUXEVEXCD333HMSFRUlPj4+FoNAT5Y3LCzMKGuDBg3kyJEjIiJurYO0tDTRNE3KlStXqA7csT+Lg8r6mzp1qgCQbt26FWpDmqa5dJZJz2fv3r3F399funTp4nQ+o6KipEyZMtKkSROpU6eOkjInJSWZXSZasG/kP7hkZWUZ69lTF9b20XvvvWfxJey28lscbXzJkiUCwOyl9rq77rrLKJv+MmZX+pF+f5yKMcna+Hbs2DGJiYkx+qyltLp16yYA5IEHHrCZj3HjxklwcLDZvWOeLLMn82nvGJe/vecf6x1lLR13Hrf0tJ944olCZS4JxwxX6kZlW3Kl77mjHl05TuplGT16tPj4+Mh9991nXE2it0mTyWRcPdKwYUOzy0TtCQaHDh0qZcqUkdjYWJtlckfbcaVuCn73lVdeKRQM2pqHOjqHdaR9307BIANBD/n8889F0zSZN2+e1XXyN7SpU6ea/Zp14MABGTdunCQnJ5s9pj1/Qy/4svi2bdvKn3/+KWfPnpWUlBSpV6+eS5Pa/O93uXDhgly8eFFeeukli5efeLK8oaGh0rZtWzl//rycPn1aUlJSpH79+tKoUSNjueo6GDFihGiaJs8995zVOlC5Py2l6W56G1JRf3p7GDFihNU25Gx96fnct2+fREdHS7Vq1Yx8mkwmh/Kpp7VgwQJp2bKlxMbGSu3atV0us35gP378uFnfKFhmS2cGbdWF/nqF8+fPy5kzZ4x99MUXX0hMTIy0aNHCyK+990t6so0vW7asUBCY/2XBBcew8uXLS9u2bZ3qRy1atLBY786U54UXXjDSstZn9bRMJpNZWp999plomiZt27a1mm+TyWSkFR0dbXw3NzfXY2X2ZD7tHeMsjUn169d3OBi0ls6yZcvcdtzS+/3MmTOtltmbjxmuHNNVtiVX+p476tGV46Rep+PHjzer2/zvGTx69Kh8/vnnAqDQPYOpqakyduxYs6eJZmRkGMHg+++/L0eOHJGaNWtKlSpVJCQkRFq1amWUJf+ZLne9LN7Zusn/3fzHtpdfftksGJw6darV45OI/XNYZ9q3fry+fPmys1XkFRgIeog+eBV1s7IzBwIRy+8JzP8S6VOnTsno0aMlJiZG/vOf/4iIyJYtW8TPz0+efPJJm3nK35HS09OlY8eOMnXqVLl48aLVywY8Vd7Q0FDjfTJ6WqdOnZIKFSoUulZcZR3oT/Rav369zUsnVOxPS2m5W8H39Ii4Vn9z58412oOtNuRofen5/O6774z6cjafBcucnp4ubdq0kSZNmkh0dLTLZe7UqZP84x//kBkzZpj1DWeDwYiICClXrpx88sknxnqnTp2SQYMGib+/v3To0MGh/BZHG4+MjBQAheqi4L4wmUzGfrU3GNS3obcLfUz617/+ZbMe7ClPw4YNC41vlvpsTk5OobSGDh0qmqbJkiVLbOY7/6TM2bHDlTJ7Mp+W8lGQrTHJkWDQVjqRkZFuO27p+6J69eo2y+ytxwxXjukq25IrfU91Pbp6nNTrVA8Ev/jiC+Oz/MeMJ554wujHelmKCganTp0qbdu2lcqVK0vHjh0lKipKatSoIT179pSMjAyzMrnzPYHO1I2l7548edL47iuvvCIdO3aUQYMGiZ+fn8yYMcNmXuyZwzrbvm+HYJCBoAdNnz5dQkJCZMuWLTbXc/RAcOTIEbMzgQU7kE5VINSpUyepWbOm1ddQeKq8ehB4/vx5szSaNWsmLVq0MC4nyD9BUFkHoaGhbt+fltJwt4Ives3PlfqbNm2aUV+22pC99RUTEyNxcXHy/fffF6qvP/74w6F85i9zwf1crVo1GTp0qNIyF+wbjgaDzZo1k6ZNm8rIkSMLtZ3o6GipXr26kV+TyeTVbVyvi88//1xErLc/R4NBS/0o//5wtTwxMTESFBQkW7ZssdlnLaXVu3dvCQkJkQULFtjMt6NnNlSX2ZP5tCcItDUm2RMM2pNOhQoVJDg42C1tvnz58uLr62ux37uatieOGa4c01W2JVf6nqp6VHWc1Ot0yJAhEhISIps3bzY+y3/M0Pvx559/blcwmJaWJuXKlZPw8HCpXLmyJCUlyY8//ijjxo0zgkFHy2wvV+rG1nfzB4MtW7aUmjVryurVq5XNYZ1t3yU9GGQg6AH5T78//vjjyiYiffv2lVWrVknjxo0lKSlJYmNjC50JLMjVQOixxx6TkJAQi/cEerK8ls4EiohRB3/++afVCYKrdTBp0iSjDty5Py19193y1581jtZf/vYwdepU8fPzk8DAQJttqKj6ioiIkPLly8s777xjtb7szaelMhds66rK/PXXXxvLC7Yde4PBsmXLSkxMjJw9e9Zq2ykJbbxbt25m64eEhEjdunVttj97gkFL/Sg3N9fIn6vlycnJMbZVr149CQwMlOjoaLvO8uTm5hppJScnm02MrfV/e85sqC6zJ/Npqb7yc2RMshUMFpVObm6uWdtSFQz27dtX3n//faPM+r7Qf/hwNW1PHDNcOaarbEuu9L2CablSjyqOk3qd5ubmGnV67733GsGgfvzJf8zQx0lbwWB8fLzEx8dLaGioNG3aVCIjI6VChQqyYsUKycvLM4JBd7UdV+oGgFSvXt3md0+ePCl33nmnaJoma9asKVSvtlibw6qYs5bkYJCBoIfkbzSqJlZjx44VTdOkW7dukpCQIFFRUWYDqDXOThJtvSfQVt5Vl7dChQoSFRUlJpPJ7Dt6HeRfpjoY1Ovgm2++cVv58k+QPRkEWqo/axytPz3Nxx57THx9fY1fc22xVl8JCQlSrVo144mYtuqrqHxaK7Oltq6qzLb6RlHBYEJCgoSFhUmXLl2KbDuOnhUtWHZPtPH8fbhWrVqiaZpdE2RbwaC1fqRyTNLvWxo7dqwAEB8fH7vP8uQ/05F/Ymyr/zs7drhSZk/m0xpnxiRLwaC96RRsWyqCQb3MiYmJZuuVtGOGt7QlV/pewbScqUeVx8n8Zc0fDPr5+Ym/v79x/CmYf1vB4P33328EVHqZ9HsG9WDQXW3HlbpJSEiQypUri6+vb5HHp8DAQOnVq5fSOayK40NJDQYZCHqQqomI/jSoH3/8Ud555x05cuSI9OzZU3r06GE2ebPF0UmtI0GgpbyrKu+zzz4rPXv2lJ49e8revXuNdfU66NmzZ6E6UBUMFqwDd+1PS+u6m636s8bR+sv/oldn6ysuLk5iY2OlZ8+e8v7779tVX9by6evrazxhLn+ZbbV1VWV2NBhcuXKlHDhwwNhHw4cPt6vtlIQ2bjKZjPZXp04du38kKBgM2tOPVJYnNzfX2JajZ3nyT4x79eolISEh8vbbb9tsz86OHaom8O7OZ0GujEn5x3pH01EVDFoqc/7gvCQeM7ylLbnS9wqm5Ug9uuM4WTAY9PPzEx8fn0LjYFHBYHJyshEMvv322/Ljjz/KW2+9ZXyv4NNEVbcdV+omKipKunTpIj179pT58+fbdXxyxxxWVTC4cuXKEvU0UQaCHuZMQ8t/2nr69OnSuXNnWbJkiYiIfPnllyIi8t1338nf/vY36dOnj3z11Vd25cXejuRMEKhTWV69rPv27ZMxY8YYZc2/3FoduBoMWqsD1fvTUro7duyQq1ev2kzTXpbSsqf+rHG0/pw5y6TX15dffinTp0+X5s2bS8eOHc3yWdRBrGA+v/zySyMYbNasmZGWPW1dVZlttZ2CbSQ2NlYWLVokX331lbGPRowYUWTbcSa/nm7jX375pZhMJqP95b//p6ht5L+vq02bNkXWheryWJqc2ZuWyWQy0urevbvVHwPsyYe18lla5miZPZlPnYoxqX79+pKenu5UOs4Eg/aW2RPHDFUsHS+8pS250vcKpmWtfPm58zipb3Py5MnG1SP6ZaLWgsEpU6ZISEiIfPbZZ0ZZevToYVYmk8lkdom4u9qOK3Xz8ccfy+jRo6VmzZoycOBA6dOnj8ybN8+u45M75rAq+md8fLwsWrRIsrOzi64AL8BAsBi40tD++OMP0TRNHn/8cePR9h999JGIiHz//fdmAZI9bHWkWbNmydSpU50OAnUqylupUiWLZQ0PD5dq1arZVQfOBIP21IGq/WlJbGys1K1bVz744AO5du2azTSLYimt4mhDztSXnk/9pbgjR450Op+BgYFSo0YN+eijj8yCwZiYGAkICLCrrasqsz3BYOPGjQWA9O3bV0REvvnmGxkzZowkJSUJAKttx5X8FuSuNp6//ZlMJqP9ORoM3nvvvQJAxo0bZ3N9d5THlbR+//13Iy3VY0dBJSWfqsakChUqiL+/v7z55ptOpeNKMFhUmd15zFDF1rHHW9qSp+rRE8fJKVOmGD8cFrxnsKhg8P333xdN0+SBBx6wWCZ3th1X6kb/7qJFi2T06NFSo0YNGTBggFkwWNTxyR1zWBXtatq0aXZvr7gxECwmzjS03377TRo2bGg8GKZt27Yybtw4SUxMlA8//FBE1A1Q27ZtE03TRNM0+f77750s5f+4Ul79PYEFy3rHHXdIpUqVJCoqSlq1amVXHTgSDDpSB67uT0vy7+OkpCRJT093Ohi0lNZdd91VbG3IkfrS8z5kyBApU6aM3HXXXU7ns3HjxlKxYkUJCwuT9u3by4cffijbtm0TAAJAOnfu7PEy26qLRo0aSXBwsNSoUcNsH33yyScSHh4ulSpV8lg/V93GLY1hzgSDv/32m9SrV0+qV6/u0GsEVJbHlbTy36Ojauywxtvzqeq4FhsbKy1atJD69etLSEiIvPHGG06l40wwaG+Z3XHMUMWeY4+3tCV316Mn51r6664K3jNoKxgcN26caJomjRs3tlkmd7QdV+qm4HcXLlxYKBh8/fXX7To+uWMO6839UzUGgsXIkYZW8D2Bbdq0MS7ZeOSRR+Tuu+92aYDK/1he/WWfX3/9tfz000+uFNGMM+UNDQ2VNm3ayPXr18VkMhlljY6OljZt2shXX30lY8aMkTp16kjLli3tqgO9rPXr15ejR48qqwNn96el7+v7+Pr16yJyax8nJiY6FQxaSis0NFRiYmLk4sWLxjJPtyF76kvP+88//yz16tWTWrVqGfnMHzTYk089rcOHD8vo0aMlPDxc2rZtKx9++KF8/fXX8tFHHxVbmS0tr1WrlgQFBUnXrl1F5H/76M0335R69epJ27Ztjfzu2rXLLfm1N/+W2Grjltqkpf1aVDCYfxt62erVq2fWrz1RHlfTcuTeMVff+eWt+bTVJkTsH5Pyp3Py5Elp2LChhISEuHRmUG9bRQWDjpZZZftTxZFjj7e0JXfVo6o2mV9RY3DBewZtBYN6WfQHw7Rs2dLs+86U2V6u1I217y5YsMAIBvXLRBcvXmzX8ckdc1hv7J/uwECwmNnT0I4cOVLoZfEFH6+rYoA6deqU3H///VK1alU5fvy4iyWzzJHyWntPYGRkpISFhcm7774rIv8rqyPBoF7WatWqyalTpwotd7YOHN2flr5n7T06zgSDltJq1qyZVKlSRTp37myWVnG0IVv1pef9+++/N6svZ/JZsB70fIaEhBhnBou7zPmXR0RECADj7Kf+vZSUFAkODjY7Kzp69GiP9nNX27it9u1IMGhpG3/88YfFfu3O8qhKS2U+iuJt+bSnTYgU3T8tpXPq1Cm58847JSAgQBYuXGhXOgXpZwbvv/9+KVu2rMVg0Nl948n9XhRnjj3e0pZU16OqNmlJUWOwPcGg/v6/jh07Sr169aR27doWjyXuaDuu1E1R3124cKHcf//9EhkZKffee2+xz2G9qX+6CwNBL2DrpueCZwILBoH5b1JVMUCNGzdOfH195fDhwyqKZpE95bUUBObm5hp1MHbsWKOsubm5TgWD1srqah3Yuz8trW9pkCy4j+0NBq0FgfoyPa1Vq1YpDQYdrT9L9aW/T27fvn1GfeV/4ICjvz5aqlM9n6NGjfKKMusH8EaNGklQUJDUrFnTWH7z5k2j7bhyVtTZ/Nqbf5GizwQW1b7tCQZtbcOZsrnSZ1WmpTIfRfGWfDrSJkSs909b6eiXz7Vr187pfm4ymYy2NXbsWKX7xpP73RpXjj3e0pZUpaWqTdpS1DiVPxjUyzJo0CAJCQmR9957T+rVqyedOnUyyqSvl/9HCnedCXS2burVq2fXd7t27Sq+vr6ybt06r5jDekP/dCcGgl4if8OZM2eOlC9fXlatWiX16tWTbt26SYsWLay+pFPVAKXfrKzinsCi2CpvWFiYxTOBLVq0kDZt2hiDSP6yOhoMWiurqjooan9aWk/fxwV/KRNxPBi0lJa+7OzZs4XSUhUMOlt/+euhWrVq4uvrKwsWLChUXzdv3jTLe1H5tFanU6ZMkcDAQCOfxV1mvY3Url1bAgMDpUuXLmbLV61aJTExMUZdeEM/d7SNO9K+LQWDDRo0kNDQUJvbcKVszvRZd6SlMh/enk9n2oRI4fZuKx39FS7ff/+9y/0m/5ihet94cr8XpOLYU9xtSVVaqtqkLfaOU/mDQb0sAwcOFB8fH2nSpIlRJv390fnXc0fbcaVuIiIipHz58vLZZ5/Z/G7z5s3Fx8dH5s6da3zXG+awxdk/3Y2BoBfRJ7lnz56V4OBg0TRNkpOTpXnz5lK7dm3566+/rH7X1QHKlVdEOMtSecPDw6V27dqSmZlp1qH0OsjMzLRaVnuDwaFDh1osq+o6sLY/ddbKZ429B2RLaRVcZiktV4NBV+vv5s2b0rx5c+PeOGv1ZW9bv+OOOyzWaf58Fne/yd9GNE0TAJKUlGQsP336tFnbcfasqKr82sq/rTbuTPvWy3bz5k35/vvvZejQoeLr62t1GyrK5kifdWdaKvPhrfl0pU2IFH5ytKV09Dbx9ddfF5mOvf3GnfvGk/tdp/LY4y1t3tm0VLVJlWOwXpY//vhDgoODBYD4+PgYZdLLcuPGDafKbC9X6qZ58+ZSvXp1GT58uNW6yc3NNepmyJAhxX5ss6Q4+qcnMBD0EnoD+uGHH+Suu+6SgIAAiYiIkFOnTsmmTZskLCysyF9D7B2gdu/ebfa94ggCrZV3xYoVEhYWVujhFwXrwNlgsHHjxuLj41PoPTqq68DW/sz/ubXyWWPPAdlSWpaWuRIMqm5Den28/vrr4uPjI35+fjbry562HhcXJ2XKlDErs6V8Fle/KdhG/Pz8RNM0WbdunbFtS23H3rOi7u7njrRxZ9t3QkKCfPjhh/LDDz9I/fr1xcfHRwICAow6UhkEOtpn3ZWWynx4cz5VHdcSExPFz89PFixYYPa9gm1CRT93577x5H7PT9Wxx1vavCtpedtcS8+rfizw8/MTX19fo0x//PGHiIhxRtCdbceVupk+fbqEhYXJ0qVLbdZNYGCgUTfeNoctrv7pCQwEvYDegPbv3y9NmzY1fvX28fGR06dPi4jIp59+KmFhYcaLO61x9NeqSZMmeexyUF1R5bVW1oLLHQ0GJ02aJAEBAdKnTx+31oE9+9MSZ/expWDQUlqWlqk4M+hq/RWsLx8fHwEgmqbZrC9HgsEvv/zSZj493W+stREAEhoaKkuXLrXZdoq7nzvTxh1t3/pZz9atW0tUVJSxDU3TJCkpSWnZnO2zqtNSmY+SkE9VxzU9GHzrrbdExHqbcKXfuHPfuHu/5+Xl2fzc1WPPlStXnM6/t/Q9nbfMtfIHHvmPi/qxUS+TJ8cMV+rmiSeekLCwMFm8eLHNuinuY5slnqzj4sBAsJhZa2AVK1Ys9Nhb/RcZFQNU//79pU+fPhISEuIVQWDB8lora8Hl9gSDdevWlcjISAkICJDvv//erXXgyP60xNl9bOvMYP60LC1zNhhUUX/W6issLEzKli2rpK23a9dO/Pz8zO7vcTYtd5Y5MjJSfvrpJ3njjTfEx8fHuPzHWtsprn7uShu3t33rZz33798vkZGRRgBYsWJF4zUfqsrmap9VlZbKfJSkfKo6runBYIcOHWy2CWf6jTv3jbv3+1dffSXvv/++8ah+a5zdD127dpX09HT5+uuvi7UtqUyruOdaRQWBepk8OWboXKkb/cygHgxaqxtvmsMWRx17GgPBYmStgUVERFhtYCoGqH379km/fv0kIiLCK4JAa+VVEQzqZQ0KCpLWrVu7tQ6c2Z+WlIRgUEX9FVVfKtt6aGiocWbQ1bTcUeYKFSrITz/9VOisaFhYmM224+l+rqKNF7Vf9W3s27fP2AYA8ff3l1dffdX4TEXZVPVZV9NSmY+SmE9Vfb1t27aiaZosXrzYpXTyty137ht37/ctW7ZIuXLlpG3btsY7Y21xdD/oZ+3j4uKkVq1axdaW3FGPxTXXKioI1MvkyTGjIFXBoK268YY5bHHWsScxECwmrjQwVzrhRx99JCIihw4dMq4v9wRny+tKMPjRRx9Jbm6uUVZ31oHqAcOVYHD16tVy9epVm2nZGwymp6cbaamsP3vrS2VbL+5+Y28QqC8PDQ11+qyot7dxa/vC1jaGDx+utGzeMhEt7UGgTlX/XL58ubJ+XpKDwG3btkm1atUkPDxc9u/fb/f3XD1rfzsEgTpPHzNKQhCoc6Vu9MtEly9fbrNuinMO6w117CkMBIuBJ35V1+XvSJMmTZJGjRoZD1rwFFfL60wwmL+s7q4Db/qVfNKkSdK4cWP5+OOPi0yrqGBQT0sfePVlrtafqjPDBdmzn4ur3zgaBLpyVrSktPGCZbNnG6rK5i0TUQaB5lS1dxXplOQgcOPGjVKxYkWJiIiQgwcPOvx9Z8/a+/r6yuuvv24zbW/pe/by1BhckoJAnSt189RTT3nFsc0Sb6pjT2Ag6GGe+FW9IL0jPfbYY6JpmltfFl+Quy+XtBYMFiyru+rA234lt1U+R4PB/Gmpqj/VZ4YLcrYenE3LHs4GgcWVX3vzr/IX94yMDJvbUFk2b5mIMgi0TFV7dyWdkhwEiojs3r1bNE2TAwcOOJ2Go2ftg4KCBIDNvuktfc9R7h6DS2IQqHOlbor72GaJN9axuzEQ9KDivI594sSJXvNgGNWXSxZcbq2squvA234lt6d89gaDltJytf7cdWa4IGfrwdm0bHE1CPR0fu3Nv8o2vmHDBuPR4ra2oaJs3jIRZRBom6r27kw6JT0I1OlP8nSFvWftg4KCinxyo7f0PWe5awwuyUGgzpW68aY5rDfXsTsxEPSQ4ryOffLkyV7zigh3XS6pL7/33nstllV1HXjbr+SOlK+oYNBSWq7Wn6cepONqPTibliWqgkBP5dfe/Kts4/o29CelWtuGirJ5y0SUQaB9VLV3R9K5XYJAlYo6ax8UFGQ8kdsab+l7rlI9Bt8OQaDOlbrxhjlsSahjd2Eg6EE//PCDxMbGevQ69uJ4T6DOXeW1Vta+ffsKgEJPjHNXHbhzf1qich9bCwYDAgIKHdRV1Z+q+nJ3PTibliXWyuxsXXi6n3uijevb0CdB4eHhZttQWTaV5XElLU+OHSUln5aoau+OpOPOMhd3fTpLr78lS5aY5d+eIFDnLX3PVarH4ILjX8EgsOB63tx2XKkbb5jDloQ6dgcGgh7y119/SZs2bRx6spajCnak4jgTqHN3ea2V9bXXXvNIHXhif1qich9bSsvf39/sNQuq6k91fbm7HtxZZlfrwlP93BNtvOA2wsLC3NL+LG3LlfK4kpYnx46Skk9bVLV3e9JxZ5m9pT6d9cEHHxgTZEeDQG/pe6qoapN6WfQA0FoQ6A1ltpcrdVOcc9iSVMeqMRD0kNzcXFm+fLlomib+/v7y73//2y3b2bRpk0REREifPn2kbNmyxRIEinimvNbK6ok68NT+tERl+Syl5Y76c0d9ubseXGWtzCrq4nZp45a24a6yqSyPK2l5cuwoKfksiqo2UVQ67iyzN9WnM3Jzc2Xy5MlGsOLI/Vre0vdUUtEm9bLoT1y1ViZvKbO9XKmb4prDlrQ6VomBoAfl5ORIWlqa/Pzzz27dzqZNm0TTNNm3b59bt1MUT5TXWlk9UQee2p+WqCyfpbTcUX/uqC9314OrrJVZRV3cLm3c0jbcVTaV5XElLU+OHSUln0VR1SaKSsedZfam+nRGTk6OPP744wLA4f3gLX1PJRVtUi/LoUOHbJbJW8psL1fqprjmsCWtjlXRRERAt51r164hJCSkuLPhEdbKervXgcryWUqrpNSfu+vBm5W0/Dridi4bOUdVm2Dbcg3r739YF9a5UjesV89hIEhERERERFTK+BR3BoiIiIiIiMizGAgSERERERGVMgwES4ns7GzMnj0b2dnZXpEO02JaJSUtb8wT02Ja7kzLG/PEtJhWSUnLG/PEtJiWVcX7rBrylMzMTAEgmZmZXpEO02JaJSUtb8wT02Ja7kzLG/PEtJhWSUnLG/PEtJiWNTwjSEREREREVMowECQiIiIiIipl/Io7A+SavLw8nDp1CqGhodA0zep6WVlZZv91lqp0mBbTKilpeWOemBbTcmda3pgnpsW0Skpa3pgnplW60hIRXL58GdWqVYOPj+1zfnyPYAn3xx9/oGbNmsWdDSIiIiIi8hInTpxAjRo1bK7DM4IlXGhoaHFngYjotqFp6u6Y8PcLUJdWQJCytE6dOq4srfDwcGVpqeTj41vcWSgkL89U3FkgolLEnhih1AWC+uWT7j4ReuzYMdSuXRudO3dGRkaG27Zj63JQIqLSQd04qHJM9da0wsLClKXlrXhsJKLSzp5xkA+Lwa2gTdM0dOnSpbizQkRERERE5HYMBImIiIiIiEoZBoJERERERESlTKkPBGfPno3atWsDAHbs2AFN04y/lJQUY72dO3diwoQJaNq0KcqXL4/g4GA0aNAA06ZNw6VLl+za1ksvvQRN0/DEE09YXadbt27QNA3bt293pVhERERERERWlbrXRxR8WMwnn3yC999/H2vWrEHlypXRvXt3Y90OHTrg/vvvBwDExcVh//79aNq0KWrWrIkbN25g3759OH36NO68807s3bsXZcuWNb5r6WEx58+fR40aNVC+fHmcOHECfn7mz+o5evQo6tati5iYGPzyyy92lScrK8trn9pGROQZ6h4MUtQ7lxzhrU8NvXz5orK0vPWhLL6+3vcsPJMpt7izQESlSGZmZpEPB/O+kdLD+vbti2bNmmHNmjVo0KABli1bZnG9WbNmoV27dmZBV3Z2Nh555BEsXLgQr7zyCmbOnGlzW5GRkRgwYABWrVqFjRs3om/fvmafL1myBCJiBJ9ERERERETuUOovDbXXPffcU+jMW2BgIObNmwc/Pz+sW7fOrnT+/ve/AwAWLVpkttxkMmHZsmXw9/c3uyS1oOzsbGRlZZn9EREREREROaLUnxF0xMmTJ7Fhwwb8/PPPyMrKQl5eHgAgICAAv/76q11pdOzYEXfeeSc+//xznDhxAjVr1gQAfPrppzh58iQGDhyISpUqWf1+amoq5syZ43phiIiIiIio1GIgaKdXXnkF06ZNQ05OjstpjR8/Ho888gjeffddzJo1C8D/zhCOHTvW5nenT5+OyZMnG//OysoygkkiIiIiIiJ78NJQO+zduxePPfYYQkJCsGzZMhw7dgw3btyAiEBEULVqVYfSGzlyJEJCQvDuu+8iLy8Pp06dwqefforo6GgkJiba/G5gYCDCwsLM/oiIiIiIiBzBM4J2+PjjjwEA//znPzFq1Cizz65fv44zZ844lF54eDiGDBmCd999F5s3b8a+fftgMplw//33e+0T2IiIiIiI6PbBM4K4dY8fAOTmWn60819//QUAqFGjRqHPPvzwQzjzBg79oTELFizAkiVL4Ovri9GjRzucDhERERERkaMYCOLWax38/f1x+PBhmEymQp/Xr18fwK3XO+S/R/Cnn37CP/7xD6e22apVKzRv3hzr1q3D0aNH0bNnT1SrVs25AhARERERETmAgSBunRHs3r07zpw5g7vuugsjR47E/fffj6VLlwIARo8ejSpVqmDDhg244447MHjwYCQmJqJZs2bo2LEjoqKinNquflYQAMaNG6ekLEREREREREVhIPhfixcvxogRI3DhwgWsWrUKS5YswY4dOwAAERER+PbbbzFs2DDcvHkT69evx8mTJ/HMM88gLS3N6W0mJCQAuHXJaffu3ZWUg4iIiIiIqCil7mEx1u7nq1SpEt577z2r36tRowZWrlxp8bNjx44VWhYdHV3kvYOrV68GAPztb3+Dr6+vzXWJiIiIiIhUKXWBoLfIysrC/PnzERAQ4GWXhap8aqnjD9EhIvV8fb1vqHfmIVvW+Pn5K0urbNnyytLy9VWXr8CAIGVpvfHRBmVpqaXu+OOnsO59FPWf69cvK0mHiEgV75sd3OaWLl2KHTt24Msvv8Tp06cxceJEPiSGiIiIiIg8Suk9grNnz4amaVi2bJnLaaWlpaFFixYICQmBpmmIjo52OU1vsGPHDixfvhxXrlzBQw89hOeffx4AsGzZMmiahtmzZxdvBomIiIiI6LbnlWcEv/32WwwfPhxBQUHo1q0bypUrh8jISI/mQdM0REVFWbz/zxXLli1TEigTERERERE5yysDwQ0bNiAvLw/z58/HmDFjijs7REREREREtxWvfH3EH3/8AQCoU6dOMeeEiIiIiIjo9uNUILh+/Xq0bdsWISEhiIiIwIABA/DLL79YXf/atWtITU1FbGwsypYti7JlyyIuLg7Lly83W0+/T05/kXt8fDw0TSt036GIIC0tDQkJCShfvjyCgoLQsGFDzJ49G9euXSu0/d9++w2zZ89G27ZtUaVKFQQEBKBGjRoYOXJkoXzreQCA48ePG9vXNA1dunRxqlz57d69G127dkVoaCjKlSuHpKQkfP3111bXJyIiIiIiUs3hS0PfeecdPPDAA9A0DR07dkTVqlWxd+9etG7dGr169Sq0/p9//onExEQcOHAAVapUQefOnSEi+Oqrr5CSkoLvvvsO8+fPBwDExMRg1KhR2LVrFw4fPoykpCRUqVLF+AwA8vLyMHz4cKSlpaFs2bJo2bIlypcvj++++w5z5szBZ599hoyMDAQHBxt5WLx4MV544QU0btwYrVq1QmBgIH766SesWLEC69atw86dO9G0aVOzPCxfvhxlypTBwIEDjXQaNGjgVLl0GzduRL9+/ZCbm4vWrVujTp062L9/Pzp16oSUlBRHdwUREREREZFTHAoEjx8/jkmTJsHf3x8bNmxAUlISACAnJwejR4/G+++/X+g7o0ePxoEDB/Doo49i7ty5CAwMBACcPXsWycnJeOONN9CzZ090794dHTp0QIcOHZCSkoLDhw9j2rRphc7Cvfzyy0hLS0OXLl2QlpZmBIo3b97Egw8+iCVLlmDOnDnG0zgBoG/fvhg/fjxq165tltbSpUsxZswYTJw4EV988QUAGHlYvnw5IiMjrT7YxZFyAcDly5cxZswY5Obm4t1338Xo0aMB3Dq7OX36dMydO9eRXUFEREREROQ0hy4Nfffdd3Hjxg0MHTrUCAIBwN/fH6+99hpCQkLM1v/xxx/x6aefolWrVnjllVeMYAkAKleujIULFwIA3n77bbu2n5ubixdeeAFlypRBenq6EQQCQEBAAObPn48qVapg4cKFyMvLMz6Li4srFAQCt4K59u3bIyMjA5mZmfZVgpPl+uijj3Du3Dl06tTJCAKBW08nfeaZZ1CjRg27tp2dnY2srCyzPyIiIiIiIkc4FAju3LkTADBkyJBCn0VERKBbt25my7Zs2QLg1hk5H5/Cm9Lvrfvmm2/s2v6+fftw/vx5tGvXDpUrVy70eXBwMFq0aIG//voLv/76q9lnV65cQVpaGv7xj39g7NixSElJQUpKCk6fPg0RweHDh+3Kg7PlslV3/v7+Zpeg2pKamorw8HDjr2bNmnbnm4iIiIiICHDw0tBTp04BAKKioix+XvCl7/o7+GbMmIEZM2ZYTffGjRt2bV9Pb+vWrcYDXaw5f/487rjjDgDAF198gSFDhuDcuXNW1798+bJdecifD0fK5WjdWTN9+nRMnjzZ+HdWVhaDQSIiIiIicohb3yOoX57ZoUMH1K1bV1l6MTExaN++vc11IyIiANw6E3jvvffi4sWLmDlzJoYMGYKoqCgEBwdD0zQMGzYMaWlpEBGH86GqXI4IDAw0uxSViIiIiIjIUQ4FglWrVsV//vMfHD9+HI0aNSr0+fHjx83+rd/31rdvXzz22GMuZNM8vQYNGlh9iEtBO3fuxIULFzBw4EDMmTOn0OdHjhxxOh+OlKtq1aoACteRztpyIiIiIiIi1Ry6R7Bjx44AgNWrVxf67OLFi8a9c7rExEQAwMcff+xs/sy0atUK4eHh2LFjBy5evGjXd/766y8AsPgwlt9++w379u2z+D1/f3/k5uZa/MyZctmqu9zcXKxZs8butIiIiIiIiFzhUCA4evRoBAYGYuXKldi2bZuxPCcnB5MmTcLVq1fN1m/Tpg0SExOxe/duPPTQQxafcLl//358/vnndm0/MDAQjz/+OC5fvoz+/ftbPJt38uRJrFixwvh3/fr1AQBr1641u0fw0qVL+Nvf/oacnByL26pWrRrOnj2LS5cuFfrMmXINGjQIERERyMjIMHvhvIhg1qxZ+P3334uuACIiIiIiIgUcCgRr166Nl19+GTk5OUhKSkJ8fDyGDh2K+vXrY926dbjvvvsKfef9999HbGws3nrrLURFRSE+Ph733XcfkpOTUatWLTRr1szuQBAApk2bhhEjRmDHjh1o2LAh4uLiMHToUAwYMACNGzdGzZo18fLLLxvrt2zZEomJifj9999Rv3599OvXD/369UPt2rVx6tQp9OnTx+J2evfujdzcXDRv3hzDhw/H/fffjxdffNHpcoWGhmLJkiXw9fVFSkoK4uLiMGzYMDRu3Bgvvvgixo4da3cdEBERERERucKhQBAAHnroIXz88cdo1aoVvv76a2zevBl33XUX9u7di5iYmELrV6pUCV999RVef/11NGrUCD/88AM++ugjHDhwAHXq1MGLL76IKVOm2J9hHx+89957WLduHRITE3H06FGsWbMGu3btQlBQEKZOnYp3333X7Dvr1q3DjBkzULFiRXz22Wf4/vvvMWTIEOzduxflypWzuJ3U1FRMmDABubm5+OCDD7BkyRJs2rTJpXL16dMH27dvR3x8PA4dOoRNmzahatWq2LFjB9q1a2d3HRAREREREblCE0cel0leJysrC+Hh4QpTtP1aDsewaRF5A19ftz4g2ikqDz1+fv7K0ipbtryytHx91eUrMCBIWVr/mPeSsrQeHtRbWVoqjz8q68tHUf+5ft3+11QREbkqMzMTYWFhNtfxvtkB3UbUHdSLem+kI9RNQBnoUsmgaQ5f/GGRSJ6SdAC1fTooqKyytEJC1P2wFhCg7lU/ly//pSytT9/dqCwtlXx81LRTAPDzD1CWlqr+o5a6/qOy3lX2a5PJpCwttcdr/mBur/LlqyhL69KlP5WlpfJYVtJ54+hGREREREREblTqA0FN08z+fHx8UK5cOXTs2BGLFy9WevkSERERERGRN+Clof81atQoALcuRTh8+DB2796NXbt24V//+hfS0tLcum1N0xAVFYVjx465dTtEREREREQAA0HDsmXLzP69detW9OjRA+np6cZrIYiIiIiIiG4Hpf7SUGsSExMxYsQIAMAnn3xSvJkhIiIiIiJSiIGgDbGxsQCAEydOmC1fsWIFOnTogLCwMISEhKBp06ZITU3FjRs3CqWRkpICTdOQkZGBzZs3Iz4+HuXKlYOmaZg3b57xhK3jx4+b3avYpUsXt5ePiIiIiIhKJ14aasPly7fe+RMY+L9HgI8fPx4LFy5EUFAQEhISEBISgoyMDDzxxBPYsGEDtm3bhpCQkEJprVq1CosXL0bLli1xzz334PDhw4iNjcWoUaOwfPlylClTBgMHDjTWb9CggfsLSEREREREpRIDQStEBBs33nrXUtOmTQEAa9aswcKFC1GtWjVkZGSgXr16AG69sDE5ORm7du3CzJkz8dJLhV/Wu2jRIqSnp2Pw4MFmyzt37ozly5cjMjKy0H2KRERERERE7sBLQwswmUz49ddfMWbMGOzZsweBgYEYPXo0AOD1118HAMyaNcsIAgEgPDwcb775JjRNw4IFCyxeItqzZ89CQaAzsrOzkZWVZfZHRERERETkCAaC/6Xfm+fn54f69etj2bJlCA0NRVpaGurWrYucnBzs3bsXAHDfffcV+n7Tpk3RtGlTXLlyBT/++GOhz3v37q0kn6mpqQgPDzf+atasqSRdIiIiIiIqPXhp6H/p7xH08fFBWFgYmjRpgv79+6N8+fIAgAsXLuDmzZuIjIxEmTJlLKYRHR2N/fv34+TJk4U+q1WrlpJ8Tp8+HZMnTzb+nZWVxWCQiIiIiIgcwkDwv1Tcn6c/AdSSoKAgl9MHbj24Jv/Da4iIiIiIiBzFS0PtFBERgYCAAJw/fx5Xr161uM6xY8cAANWrV/dgzoiIiIiIiBzDQNBO/v7+iIuLAwCkp6cX+vzQoUPYv38/ypYti2bNmjmcdm5uropsEhERERERFYmBoAMefvhhAMDs2bNx5MgRY/nly5cxYcIEiAjGjx/v8GWg1apVw9mzZ3Hp0iWV2SUiIiIiIrKI9wg6YODAgRg3bhwWLlyIxo0bm71Q/ty5c4iLi8PTTz/tcLq9e/fG/Pnz0bx5c7Rr1w5BQUG44447MHXqVDeUgoiIiIiISjsGgg5asGABOnTogHfeeQc7duxAbm4u6tati4kTJ2LSpEkIDg52OM3U1FSICNatW4cPPvgAubm56Ny5MwNBIiIiIiJyC01EpLgzQc7LyspCeHi4whStP/m0ONl6Iquj1DV5dh0qGfz8ApSkI5KnJB3VypQppyytsLBIZWkFBKh7wvPly38pS6tly+7K0vrss4XK0vLx8VWWVnBwWWVpaZqau2iuXFG3D1Ueq3181N0lpPJYbTKZlKWl9nitcp50e88jypevoiytS5f+VJaWtx7LVMvMzERYWJjNdXhGkArwzkFJ7c8V3llGInfJy1MzofLW3w2vX7+sLC2VE4Tg4FBlaamcrN+4YfnJ18VNZSAYFGT5fb/OUPVDitpAUB2VwZuqoPlWWur6oreOXSqDSrU/mKup+8qVo5WkA6jtPzk52crSKun4sBgiIiIiIqJShoEgERERERFRKcNAkIiIiIiIqJRhIEhERERERFTKMBAkIiIiIiIqZRgIuiAjIwOapiElJcXi5ykpKdA0DRkZGWbLd+zYgYSEBISGhqJ8+fLo0aMHvvvuOyxbtgyapmH27NluzzsREREREZVefH2Eh61duxb33nsvTCYT4uLiEB0djYMHD6JDhw4YPXp0cWePiIiIiIhKAQaCHpSVlYWxY8fCZDJh5cqVGDZsmPHZzJkz8cwzzxRj7oiIiIiIqLTgpaEetHr1aly8eBF33323WRAI3AoEo6KiikwjOzsbWVlZZn9ERERERESOYCDoQbt37wYADBo0qNBnfn5+GDBgQJFppKamIjw83PirWbOm8nwSEREREdHtjYGgB50+fRoArAZvtWrVKjKN6dOnIzMz0/g7ceKE0jwSEREREdHtj/cIulFeXp7yNAMDAxEYGKg8XSIiIiIiKj14RtAFAQEBAIArV65Y/Lzg2bqqVataXG5tfSIiIiIiIndgIOgCPbD75ZdfCn128eJF7Nu3z2xZ+/btAQBr1qwptL7JZMLatWvdkEsiIiIiIiJzDARdULt2bdSqVQsHDx7EunXrjOVXr17FuHHjCj3Rc9CgQahQoQK2bt2K9PR0s8+effZZHD161CP5JiIiIiKi0o2BoItmzZoFABgwYAASEhLQu3dv1K1bFwcOHECfPn3M1g0PD8eiRYvg6+uLoUOHol27dhg2bBiaNGmC5557DuPGjQPwv0tOiYiIiIiI3IGBoIvGjBmDpUuXomHDhti9eze++eYb9OrVC3v27EG5cuUKrd+/f39s27YNXbp0wYEDB7Bp0yZUq1YNO3fuNJ4aGhER4eFSEBERERFRaaKJiBR3JuiW7t27Y/Pmzdi7dy/atGlj13eysrIQHh7u5px5A01hWmzyVLr4+PgqScdbDxd+fv7K0goKKqMsreDgUGVpaZq6MbBRo/bK0tq+faWytPz81F0NEx4eqSwtVfk6e/aYknRuUdcefH3VjA8AoGnqzi+YTLnK0hJR+RR3lfMRdVSOEarqq0GDOCXpAMDhwz8oSysnJ1tZWt4sMzMTYWFhNtfh6yM87OTJk/Dz80PlypWNZXl5eXjttdewefNm1K9fH61bty7GHHonlQOcNw7iaifY3jlZJ0epa6eqAkGVVE7MfH3VBYL+/upez6Oy3nNzbypLS21Aoo7KcT7nprqJnjteBeUqlXWlsp2qzJevj7q+eFPhxN/XV93UOS/PpCwtteNNjpJ0srOvKUmH3IeBoIft3LkTw4cPR2xsLKKiopCdnY1Dhw7h2LFjCAkJweLFixUHPUREREREROaU3iM4e/ZsaJqGZcuWuZxWWloaWrRogZCQEGiahujoaJfT9AYtWrTAyJEjcenSJWzZsgWbN2+GyWRCu3btcO3aNfzrX/8q7iwSEREREdFtzivPCH777bcYPnw4goKC0K1bN5QrVw6Rkequ97eHpmmIiorCsWPHlKZbr149vPvuu4WWL1u2DF999ZXSbREREREREVnilYHghg0bkJeXh/nz52PMmDHFnR0iIiIiIqLbile+PuKPP/4AANSpU6eYc0JERERERHT7cSoQXL9+Pdq2bYuQkBBERERgwIAB+OWXX6yuf+3aNaSmpiI2NhZly5ZF2bJlERcXh+XLl5utt2zZMmiahqVLlwIA4uPjoWlaofsORQRpaWlISEhA+fLlERQUhIYNG2L27Nm4dq3wE4p+++03zJ49G23btkWVKlUQEBCAGjVqYOTIkYXyrecBAI4fP25sX9M0dOnSxaly5bd792507doVoaGhKFeuHJKSkvD1119bXZ+IiIiIiEg1hy8Nfeedd/DAAw9A0zR07NgRVatWxd69e9G6dWv06tWr0Pp//vknEhMTceDAAVSpUgWdO3eGiOCrr75CSkoKvvvuO8yfPx8AEBMTg1GjRmHXrl04fPgwkpKSUKVKFeMz4NZjnIcPH460tDSULVsWLVu2RPny5fHdd99hzpw5+Oyzz5CRkYHg4GAjD4sXL8YLL7yAxo0bo1WrVggMDMRPP/2EFStWYN26ddi5cyeaNm1qlofly5ejTJkyGDhwoJFOgwYNnCqXbuPGjejXrx9yc3PRunVr1KlTB/v370enTp2QkpLi6K4gIiIiIiJyikOB4PHjxzFp0iT4+/tjw4YNSEpKAgDk5ORg9OjReP/99wt9Z/To0Thw4AAeffRRzJ07F4GBt94Lc/bsWSQnJ+ONN95Az5490b17d3To0AEdOnRASkoKDh8+jGnTphU6C/fyyy8jLS0NXbp0QVpamhEo3rx5Ew8++CCWLFmCOXPm4Pnnnze+07dvX4wfPx61a9c2S2vp0qUYM2YMJk6ciC+++AIAjDwsX74ckZGRVp+A6ki5AODy5csYM2YMcnNz8e6772L06NEAbp3dnD59OubOnWvXPsjOzkZ29v/eh5OVlWXX94iIiIiIiHQOXRr67rvv4saNGxg6dKgRBAKAv78/XnvtNYSEhJit/+OPP+LTTz9Fq1at8MorrxjBEgBUrlwZCxcuBAC8/fbbdm0/NzcXL7zwAsqUKYP09HQjCASAgIAAzJ8/H1WqVMHChQvNXgAbFxdXKAgEbgVz7du3R0ZGBjIzM+2rBCfL9dFHH+HcuXPo1KmTEQQCt55O+swzz6BGjRp2bTs1NRXh4eHGX82aNe3ONxEREREREeBgILhz504AwJAhQwp9FhERgW7dupkt27JlC4BbZ+R8fApvSr+37ptvvrFr+/v27cP58+fRrl07VK5cudDnwcHBaNGiBf766y/8+uuvZp9duXIFaWlp+Mc//oGxY8ciJSUFKSkpOH36NEQEhw8ftisPzpbLVt35+/ubXYJqy/Tp05GZmWn8nThxwu58ExERERERAQ5eGnrq1CkAQFRUlMXPC770XX8H34wZMzBjxgyr6d64ccOu7evpbd261XigizXnz5/HHXfcAQD44osvMGTIEJw7d87q+pcvX7YrD/nz4Ui5HK07awIDA83OQBIRERERETnKre8R1C/P7NChA+rWrassvZiYGLRv397muhEREQBunQm89957cfHiRcycORNDhgxBVFQUgoODoWkahg0bhrS0NIiIw/lQVS4iIiIiIiJPcigQrFq1Kv7zn//g+PHjaNSoUaHPjx8/bvZv/b63vn374rHHHnMhm+bpNWjQwOpDXArauXMnLly4gIEDB2LOnDmFPj9y5IjT+XCkXFWrVgVQuI501pYTERERERGp5tA9gh07dgQArF69utBnFy9eNO6d0yUmJgIAPv74Y2fzZ6ZVq1YIDw/Hjh07cPHiRbu+89dffwGAxYex/Pbbb9i3b5/F7/n7+yM3N9fiZ86Uy1bd5ebmYs2aNXanRURERERE5AqHAsHRo0cjMDAQK1euxLZt24zlOTk5mDRpEq5evWq2fps2bZCYmIjdu3fjoYcesviqg/379+Pzzz+3a/uBgYF4/PHHcfnyZfTv39/i2byTJ09ixYoVxr/r168PAFi7dq3ZPYKXLl3C3/72N+Tk5FjcVrVq1XD27FlcunSp0GfOlGvQoEGIiIhARkaG2QvnRQSzZs3C77//XnQFEBERERERKeBQIFi7dm28/PLLyMnJQVJSEuLj4zF06FDUr18f69atw3333VfoO++//z5iY2Px1ltvISoqCvHx8bjvvvuQnJyMWrVqoVmzZnYHggAwbdo0jBgxAjt27EDDhg0RFxeHoUOHYsCAAWjcuDFq1qyJl19+2Vi/ZcuWSExMxO+//4769eujX79+6NevH2rXro1Tp06hT58+FrfTu3dv5Obmonnz5hg+fDjuv/9+vPjii06XKzQ0FEuWLIGvry9SUlIQFxeHYcOGoXHjxnjxxRcxduxYu+uAiIiIiIjIFQ4FggDw0EMP4eOPP0arVq3w9ddfY/Pmzbjrrruwd+9exMTEFFq/UqVK+Oqrr/D666+jUaNG+OGHH/DRRx/hwIEDqFOnDl588UVMmTLF/gz7+OC9997DunXrkJiYiKNHj2LNmjXYtWsXgoKCMHXqVLz77rtm31m3bh1mzJiBihUr4rPPPsP333+PIUOGYO/evShXrpzF7aSmpmLChAnIzc3FBx98gCVLlmDTpk0ulatPnz7Yvn074uPjcejQIWzatAlVq1bFjh070K5dO7vrgIiIiIiIyBWaOPK4TPI6WVlZCA8PL+5suJ2mOfybRYmithuyS98ebL8ixxF+fv7K0lJFJE9ZWv7+QcrSCgkJVZZWQECwsrRyc28qS6tSJcuvMXLGTz/tVpaWv7+6VyMFB5VVlpavov7z119nlKQDqD0mqhwfinq1l0NpKRwDb+ZkK0vL11fdA/fz8kzK0vLx8VWWVm6u5dumHFW7dhMl6QDAH3/8R1laOQrbgzfLzMxEWFiYzXXc+voIIm+k8kClitpA0PvKp97tH+wqnVApSstbfzdUWVeqJkAA4OurboKtcuJy/br97831JJX7UW7zMcIbx4dbaXnnj7Yqy+jjo66MKtNSWfcmk+WHJTpKZaBL7uGdPZaIiIiIiIjcptQHgpqmmf35+PigXLly6NixIxYvXuy1v4ATERERERE5i5eG/teoUaMAACaTCYcPH8bu3buxa9cu/Otf/0JaWppbt61pGqKionDs2DG3boeIiIiIiAhgIGhYtmyZ2b+3bt2KHj16ID093XgtBBERERER0e2g1F8aak1iYiJGjBgBAPjkk0+KNzNEREREREQKMRC0ITY2FgBw4sQJs+UrVqxAhw4dEBYWhpCQEDRt2hSpqam4ceNGoTRSUlKgaRoyMjKwefNmxMfHo1y5ctA0DfPmzTOeZHX8+HGzexW7dOni9vIREREREVHpxEtDbbh8+dYjtgMD//duo/Hjx2PhwoUICgpCQkICQkJCkJGRgSeeeAIbNmzAtm3bEBISUiitVatWYfHixWjZsiXuueceHD58GLGxsRg1ahSWL1+OMmXKYODAgcb6DRo0cH8BiYiIiIioVGIgaIWIYOPGjQCApk2bAgDWrFmDhQsXolq1asjIyEC9evUA3HphY3JyMnbt2oWZM2fipZdeKpTeokWLkJ6ejsGDB5st79y5M5YvX47IyMhC9ylakp2djezs/71PKisry9kiEhERERFRKcVLQwswmUz49ddfMWbMGOzZsweBgYEYPXo0AOD1118HAMyaNcsIAgEgPDwcb775JjRNw4IFCyxeItqzZ89CQaAzUlNTER4ebvzVrFnT5TSJiIiIiKh0YSD4X/q9eX5+fqhfvz6WLVuG0NBQpKWloW7dusjJycHevXsBAPfdd1+h7zdt2hRNmzbFlStX8OOPPxb6vHfv3kryOX36dGRmZhp/Be9fJCIiIiIiKgovDf0v/T2CPj4+CAsLQ5MmTdC/f3+UL18eAHDhwgXcvHkTkZGRKFOmjMU0oqOjsX//fpw8ebLQZ7Vq1VKSz8DAQLN7FomIiIiIiBzFQPC/7Lk/ryj6E0AtCQoKcjl9IiIiIiIiFXhpqJ0iIiIQEBCA8+fP4+rVqxbXOXbsGACgevXqHswZERERERGRYxgI2snf3x9xcXEAgPT09EKfHzp0CPv370fZsmXRrFkzh9POzc1VkU0iIiIiIqIiMRB0wMMPPwwAmD17No4cOWIsv3z5MiZMmAARwfjx4x2+DLRatWo4e/YsLl26pDK7REREREREFvEeQQcMHDgQ48aNw8KFC9G4cWOzF8qfO3cOcXFxePrppx1Ot3fv3pg/fz6aN2+Odu3aISgoCHfccQemTp3qhlIQEREREVFpx0DQQQsWLECHDh3wzjvvYMeOHcjNzUXdunUxceJETJo0CcHBwQ6nmZqaChHBunXr8MEHHyA3NxedO3dmIEhERERERG6hiYgUdybIeVlZWQgPDy/ubLidpqm7itnW012Li8puyC7tKO+sL5Vt3s/PX0k6KvuOynbq76/ulTremlZubo6ytEJDKyhL68SJfytLKzDA8R9SrQkIVJeWr6+a/nPp0lkl6QCAr6+63/FVpqXB+46vAJBrUtd//BS1BwAQhccflceMmzdvKEmnWrW6StIBgLNnjytLKzf3prK0vFlmZibCwsJsrsN7BImIiIiIiEoZBoJERERERESlDANBIiIiIiKiUoaBIBERERERUSnDQJCIiIiIiKiUYSBIRERERERUyjAQdEFGRgY0TUNKSorFz1NSUqBpGjIyMsyW79ixAwkJCQgNDUX58uXRo0cPfPfdd1i2bBk0TcPs2bPdnnciIiIiIiq9+EJ5D1u7di3uvfdemEwmxMXFITo6GgcPHkSHDh0wevTo4s4eERERERGVAgwEPSgrKwtjx46FyWTCypUrMWzYMOOzmTNn4plnnikyjezsbGRnZ5ulSURERERE5AheGupBq1evxsWLF3H33XebBYHArUAwKiqqyDRSU1MRHh5u/NWsWdNd2SUiIiIiotsUA0EP2r17NwBg0KBBhT7z8/PDgAEDikxj+vTpyMzMNP5OnDihPJ9ERERERHR746WhHnT69GkAsHoWr1atWkWmERgYiMDAQKX5IiIiIiKi0oVnBN0oLy+vuLNARERERERUCANBFwQEBAAArly5YvHzgpdtVq1a1eJya+sTERERERG5AwNBF+iB3S+//FLos4sXL2Lfvn1my9q3bw8AWLNmTaH1TSYT1q5d64ZcEhERERERmWMg6ILatWujVq1aOHjwINatW2csv3r1KsaNG1fo1Q6DBg1ChQoVsHXrVqSnp5t99uyzz+Lo0aMeyTcREREREZVuDARdNGvWLADAgAEDkJCQgN69e6Nu3bo4cOAA+vTpY7ZueHg4Fi1aBF9fXwwdOhTt2rXDsGHD0KRJEzz33HMYN24cgP9dckpEREREROQODARdNGbMGCxduhQNGzbE7t278c0336BXr17Ys2cPypUrV2j9/v37Y9u2bejSpQsOHDiATZs2oVq1ati5c6fx1NCIiAgPl4KIiIiIiEoTTUSkuDNBt3Tv3h2bN2/G3r170aZNG7u+k5WVhfDwcDfnrPhpmrrfLDRN86p0gNLxhFmV9aVy2FI7BKpLy8fHV1lafn7+StLRoHAfKqyrwMAQZWn5+qqpq1tpqduHOTezlaUVUkbdMePMGXW3NAQGBClLy1dRmwcAX181b9q6fPmiknQAwM9P3ZVDPj7qjq8+PureSiai7riYm3tTWVp+CscIlfz81bWJ69ctPwTRURUqVFWSDgBcvHhaWVp5eSZlaaml6hh76/iamZmJsLAwm2vyjKCHnTx5EmfPnjVblpeXh1dffRWbN29G/fr10bp162LKHRERERERlQZ8obyH7dy5E8OHD0dsbCyioqKQnZ2NQ4cO4dixYwgJCcHixYuVnjkhIiIiIiIqqNSfEdQ0zezPx8cH5cqVQ8eOHbF48WLFl40BLVq0wMiRI3Hp0iVs2bIFmzdvhslkwogRI/Dtt9+iY8eOSrdHRERERERUUKm/R1A/+zZq1CgAt97nd/jwYezduxcigiFDhiAtLc3teYiKisKxY8cc/i7vEXQmLd4jWBx4j6BjeI+g/XiPoGN4j6CDafEeQQfS4j2CxYX3CNqP9wj+Dy8N/a9ly5aZ/Xvr1q3o0aMH0tPTcd999yE5Obl4MkZERERERKRYqb801JrExESMGDECAPDJJ58Ub2aIiIiIiIgUYiBoQ2xsLADgxIkTZstXrFiBDh06ICwsDCEhIWjatClSU1Nx48aNQmmkpKRA0zRkZGRg8+bNiI+PR7ly5aBpGubNm2dcLnf8+HGzexW7dOni9vIREREREVHpxEtDbbh8+TIAIDAw0Fg2fvx4LFy4EEFBQUhISEBISAgyMjLwxBNPYMOGDdi2bRtCQgrfr7Jq1SosXrwYLVu2xD333IPDhw8jNjYWo0aNwvLly1GmTBkMHDjQWL9BgwbuLyAREREREZVKDAStEBFs3LgRANC0aVMAwJo1a7Bw4UJUq1YNGRkZqFevHoBbN2MmJydj165dmDlzJl566aVC6S1atAjp6ekYPHiw2fLOnTtj+fLliIyMLHSfoiXZ2dnIzv7fQwOysrKcLSIREREREZVSvDS0AJPJhF9//RVjxozBnj17EBgYiNGjRwMAXn/9dQDArFmzjCAQAMLDw/Hmm29C0zQsWLDA4iWiPXv2LBQEOiM1NRXh4eHGX82aNV1Ok4iIiIiIShcGgv+l35vn5+eH+vXrY9myZQgNDUVaWhrq1q2LnJwc7N27FwBw3333Ffp+06ZN0bRpU1y5cgU//vhjoc979+6tJJ/Tp09HZmam8Vfw/kUiIiIiIqKi8NLQ/9LfI+jj44OwsDA0adIE/fv3R/ny5QEAFy5cwM2bNxEZGYkyZcpYTCM6Ohr79+/HyZMnC31Wq1YtJfkMDAw0u2eRiIiIiIjIUQwE/8ue+/OKYuuF2UFB6l6US0RERERE5ApeGmqniIgIBAQE4Pz587h69arFdY4dOwYAqF69ugdzRkRERERE5BgGgnby9/dHXFwcACA9Pb3Q54cOHcL+/ftRtmxZNGvWzOG0c3NzVWSTiIiIiIioSAwEHfDwww8DAGbPno0jR44Yyy9fvowJEyZARDB+/HiHLwOtVq0azp49i0uXLqnMLhERERERkUW8R9ABAwcOxLhx47Bw4UI0btzY7IXy586dQ1xcHJ5++mmH0+3duzfmz5+P5s2bo127dggKCsIdd9yBqVOnuqEURERERERU2jEQdNCCBQvQoUMHvPPOO9ixYwdyc3NRt25dTJw4EZMmTUJwcLDDaaampkJEsG7dOnzwwQfIzc1F586dGQgSEREREZFbaCIixZ0Jcl5WVhbCw8OLOxseYP2JrI7y8VFzRbStp8Q6SmU39NYu7eur7ncnk0ndPbXeWl9+fv7K0vL18VWSjkBdXWkK+3RgkOVX+jhDZb9WuQ+vX7+iLK0ghfV18eJpZWkFBZVVlpaqcf5WWmr6z5UrfylJBwD8/dW9RkpV+QC143xeXp6ytHJzbypLS9V4CgBQON4EBKh7Ov3Vq5lK0gkNraAkHQDIyrqgLC0RdW3Lm2VmZiIsLMzmOrxHkIiIiIiIqJRhIEhERERERFTKMBAkIiIiIiIqZRgIEhERERERlTIMBImIiIiIiEoZBoJERERERESlDANBF2RkZEDTNKSkpFj8PCUlBZqmISMjw2z5jh07kJCQgNDQUJQvXx49evTAd999h2XLlkHTNMyePdvteSciIiIiotKLL5T3sLVr1+Lee++FyWRCXFwcoqOjcfDgQXTo0AGjR48u7uwREREREVEpwEDQg7KysjB27FiYTCasXLkSw4YNMz6bOXMmnnnmmSLTyM7ORnZ2tlmaREREREREjuCloR60evVqXLx4EXfffbdZEAjcCgSjoqKKTCM1NRXh4eHGX82aNd2VXSIiIiIiuk0xEPSg3bt3AwAGDRpU6DM/Pz8MGDCgyDSmT5+OzMxM4+/EiRPK80lERERERLc3XhrqQadPnwYAq2fxatWqVWQagYGBCAwMVJovIiIiIiIqXXhG0I3y8vKKOwtERERERESFMBB0QUBAAADgypUrFj8veNlm1apVLS63tj4REREREZE7MBB0gR7Y/fLLL4U+u3jxIvbt22e2rH379gCANWvWFFrfZDJh7dq1bsglERERERGROQaCLqhduzZq1aqFgwcPYt26dcbyq1evYty4cYVe7TBo0CBUqFABW7duRXp6utlnzz77LI4ePeqRfBMRERERUenGQNBFs2bNAgAMGDAACQkJ6N27N+rWrYsDBw6gT58+ZuuGh4dj0aJF8PX1xdChQ9GuXTsMGzYMTZo0wXPPPYdx48YB+N8lp0RERERERO7AQNBFY8aMwdKlS9GwYUPs3r0b33zzDXr16oU9e/agXLlyhdbv378/tm3bhi5duuDAgQPYtGkTqlWrhp07dxpPDY2IiPBwKYiIiIiIqDTRRESKOxN0S/fu3bF582bs3bsXbdq0ses7WVlZCA8Pd3POvIGmLCUfn9v79w+VXVplXfn4+CpLKy/PpDAtdU/3VVn3fn7+CtNSc5WBiHc+CTk4OFRZWprmnWPNtWuXlaUVHFxWWVoXL55WllZgYIiytDRNXd2rahPXr6vbh/7+6l4j5aOwrnwVjlsmU65XpuWr8FgGheNNQECQsrSuXcsqeiU7qBybr1y5pCwtwFtDH1Xt4Vb5MjMzERYWZnPN23tG7IVOnjyJs2fPmi3Ly8vDq6++is2bN6N+/fpo3bp1MeWOiIiIiIhKg1L/QvmCv/RpmoawsDA0adIEo0aNwt/+9jelvxDv3LkTw4cPR2xsLKKiopCdnY1Dhw7h2LFjCAkJweLFi5Vuj4iIiIiIqKBSHwjqRo0aBeDWaxwOHz6M3bt3Y9euXfjXv/6FtLQ0Zdtp0aIFRo4ciZ07d+I///kPbty4gZycHJQpUwbffPMNGjVqpGxbRERERERElpT6ewT1s28Fq2Hr1q3o0aMHcnNzsWHDBiQnJ7s1D1FRUTh27JjD3+U9go7jPYL24z2CjuE9gsWD9wg6hvcIOpoW7xG0F+8RdBDvEbQb7xF0BO8RdFliYiJGjBgBAPjkk0+KNzNEREREREQKMRC0ITY2FgBw4sQJs+UrVqxAhw4dEBYWhpCQEDRt2hSpqam4ceNGoTRSUlKgaRoyMjKwefNmxMfHo1y5ctA0DfPmzTN+aTx+/Dg0TTP+unTp4vbyERERERFR6cR7BG24fPnWZRyBgf+7BGP8+PFYuHAhgoKCkJCQgJCQEGRkZOCJJ57Ahg0bsG3bNoSEFL68ZdWqVVi8eDFatmyJe+65B4cPH0ZsbCxGjRqF5cuXo0yZMhg4cKCxfoMGDdxfQCIiIiIiKpUYCFohIti4cSMAoGnTpgCANWvWYOHChahWrRoyMjJQr149ALeuwU1OTsauXbswc+ZMvPTSS4XSW7RoEdLT0zF48GCz5Z07d8by5csRGRmJZcuWubdQRERERERE4KWhhZhMJvz6668YM2YM9uzZg8DAQIwePRoA8PrrrwMAZs2aZQSBABAeHo4333wTmqZhwYIFFi8R7dmzZ6Eg0BnZ2dnIysoy+yMiIiIiInIEA8H/0u/N8/PzQ/369bFs2TKEhoYiLS0NdevWRU5ODvbu3QsAuO+++wp9v2nTpmjatCmuXLmCH3/8sdDnvXv3VpLP1NRUhIeHG381a9ZUki4REREREZUevDT0v/T3CPr4+BgvlO/fvz/Kly8PALhw4QJu3ryJyMhIlClTxmIa0dHR2L9/P06ePFnos1q1ainJ5/Tp0zF58mTj31lZWQwGiYiIiIjIIQwE/0vF/Xm23jUUFKTm/S6BgYFmD68hIiIiIiJyFC8NtVNERAQCAgJw/vx5XL161eI6+gvhq1ev7sGcEREREREROYaBoJ38/f0RFxcHAEhPTy/0+aFDh7B//36ULVsWzZo1czjt3NxcFdkkIiIiIiIqEgNBBzz88MMAgNmzZ+PIkSPG8suXL2PChAkQEYwfP97hy0CrVauGs2fP4tKlSyqzS0REREREZBHvEXTAwIEDMW7cOCxcuBCNGzc2e6H8uXPnEBcXh6efftrhdHv37o358+ejefPmaNeuHYKCgnDHHXdg6tSpbigFERERERGVdgwEHbRgwQJ06NAB77zzDnbs2IHc3FzUrVsXEydOxKRJkxAcHOxwmqmpqRARrFu3Dh988AFyc3PRuXNnBoJEREREROQWmohIcWeCnJeVlYXw8PDizoYHWH8iq6N8fG7vK6JVdmmVdeXj46ssrbw8k8K08pSlpbLu/fz8FaYVoCQdEXV1pVJwcKiytGw9/dlRKvvPtWuXlaUVHFxWWVoXL55WllZgYIiytDRNXd2rahPXr6vbh/7+6p4e7qOwrnwVjlsmk7pnJ6hMy1fhsQwKx5uAADVPpweAa9eylKSjcmy+cuWSsrQAbw19VLWHW+XLzMxEWFiYzTV5RpCIyAt5Y0BiMqkLBFX+MODrq/JHBnVl9NYfGXJzbipLqzTwxt/LVY4PKoMRlflSGcwrVQrKqKrNqwzASwfPjzXe2QKJiIiIiIjIbRgIEhERERERlTIMBImIiIiIiEoZBoJERERERESlDANBIiIiIiKiUoaBoAsyMjKgaRpSUlIsfp6SkgJN05CRkWG2fMeOHUhISEBoaCjKly+PHj164LvvvsOyZcugaRpmz57t9rwTEREREVHpxddHeNjatWtx7733wmQyIS4uDtHR0Th48CA6dOiA0aNHF3f2iIiIiIioFGAg6EFZWVkYO3YsTCYTVq5ciWHDhhmfzZw5E88880wx5o6IiIiIiEoLXhrqQatXr8bFixdx9913mwWBwK1AMCoqqsg0srOzkZWVZfZHRERERETkCAaCHrR7924AwKBBgwp95ufnhwEDBhSZRmpqKsLDw42/mjVrKs8nERERERHd3hgIetDp06cBwGrwVqtWrSLTmD59OjIzM42/EydOKM0jERERERHd/niPoBvl5eUpTzMwMBCBgYHK0yUiIiIiotKDZwRdEBAQAAC4cuWKxc8Lnq2rWrWqxeXW1iciIiIiInIHBoIu0AO7X375pdBnFy9exL59+8yWtW/fHgCwZs2aQuubTCasXbvWDbkkIiIiIiIyx0DQBbVr10atWrVw8OBBrFu3zlh+9epVjBs3rtATPQcNGoQKFSpg69atSE9PN/vs2WefxdGjRz2SbyIiIiIiKt0YCLpo1qxZAIABAwYgISEBvXv3Rt26dXHgwAH06dPHbN3w8HAsWrQIvr6+GDp0KNq1a4dhw4ahSZMmeO655zBu3DgA/7vklIiIiIiIyB0YCLpozJgxWLp0KRo2bIjdu3fjm2++Qa9evbBnzx6UK1eu0Pr9+/fHtm3b0KVLFxw4cACbNm1CtWrVsHPnTuOpoRERER4uBRERERERlSaaiEhxZ4Ju6d69OzZv3oy9e/eiTZs2dn0nKysL4eHhbs6ZN9CUpeTjc3v//qGyS6usKx8fX2Vp5eWZFKal7um+Kuve31/dlQH+/mqeNGwy5SpJB1DbHkJCQpWl5Y6nPatw/brlh5I5I0BRewCArMsXlKUVGBiiLC1N875x/sYNhfswIEhZWirrys/PX1laJpO6cT4396aytHx91T1wX2XdqzxmXL2aqSQdlX1a5RgIlI7QJzMzE2FhYTbX4esjPOzkyZPw8/ND5cqVjWV5eXl47bXXsHnzZtSvXx+tW7cuxhwSuYemqQvmVR48NU3dAUFlIKi2vtSkpbbe2R4cS8s7A1SlVP4ura553f51r7SdloI2r7KMUFdGlT+QqsJzTY5SNXDZX+8MBD1s586dGD58OGJjYxEVFYXs7GwcOnQIx44dQ0hICBYvXqx0gkRERERERFSQ9107cZvQNA3R0dGFlrdo0QIjR47EpUuXsGXLFmzevBkmkwkjRozAt99+i44dO3o+s0REREREVKrwjKCH1atXD++++25xZ4OI/r+9e4+Oqk7z/f/ZlUuFYKrAsI4SCeCkCXAmZARsRSCLgMOMIqLNRVoQCRdhnFaX2j9b4x9cxDEz3bbtdR0JqNAuJdKCRHT6RNATBA4ssT1yWa1HDhDN0IwjxlQlASpJVf3+QMqOSSAFT1JF6v1aqxaLXbueempn7+/ez97fvb8AAAAJjEKwk3z22WdKSbG7aRoAAAAArFAIdpIhQ4bEOgUAAAAAaBP3CHaStu4RrKyslOM4KioqUk1Nje6++2717dtXbrdbeXl5dBkFAAAA0CW4IhgDtbW1uu6661RfX6+CggIdP35cH374oRYsWKBQKKSFCxfGOkUAAAAA3RhXBGOgvLxcI0aM0OHDh7V+/Xp98MEHevPNNyVJK1asiHF2AAAAALo7CsEY8Hg8ev755+V2uyPTbr31VuXl5emrr75SVVVVu58NBALy+/0tXgAAAAAQDQrBGBg5cqQyMzNbTc/NzZUkHTt2rN3PlpSUyOv1Rl7Z2dmdlicAAACA7olCMAb69evX5vSMjAxJp6/6tae4uFg+ny/yqq6u7pQcAQAAAHRfPCwmBlyu86+/3W53iy6lAAAAABAtrggCAAAAQIKhEAQAAACABEMhCAAAAAAJhkIQAAAAABIMhSAAAAAAJBgnHA6HY50Ezp/f75fX6411Gl3AMYt0IU9tvRhYbtKWyyopye4hxaFQyDBW0DCWXV6pqXZPB05JsYkVDNotK8t1Kz3dYxbLcn2w3BZPnPCbxXKn9jCL5a/71iyWZV6OK8ksVjhss10HAidM4khSamqaWSzHcP+alJxiFisYbI7LWEmG65Ycu2WfbLjsT56sN4mTarhNnzrVYBZLitfSx2p9OP37fD6fPJ6z7x8ZPgIJx+rgzDFswBPhfIzlwQai4zg2RZfj2BW6lttPvK5bptu1YazmYJNZrHhlVbwlBMNtMV5ZtjeWy8uqbbaOZYXtMP7F31oDAAAAAOhUCV8IOo7T4uVyudSrVy8VFBRo9erVCXGlBgAAAEBioWvo9+bOnSvp9D0whw4d0s6dO7Vjxw69//77WrduXad+t+M4GjBggKqqqjr1ewAAAABAohCMWLNmTYv/b9myRZMmTVJZWZlmz56tyZMnxyYxAAAAADCW8F1D2zNx4kTNmTNHkrRp06bYJgMAAAAAhigEz2L48OGSpOrq6hbTX331VY0dO1Yej0fp6enKz89XSUmJTp061SpGUVGRHMdRZWWlKioqNH78ePXq1UuO4+jpp5+OPMnqyy+/bHGvYmFhYaf/PgAAAACJia6hZ1FXVydJcrt/GINr8eLFKi0tVVpamiZMmKD09HRVVlbq0Ucf1ebNm7V161alp6e3ivX6669r9erVuvrqq3XjjTfq0KFDGj58uObOnau1a9eqZ8+emj59emT+IUOGdP4PBAAAAJCQKATbEQ6H9c4770iS8vPzJUkbNmxQaWmpsrKyVFlZqUGDBkk6PWDj5MmTtWPHDi1ZskRPPvlkq3irVq1SWVmZZs6c2WL6uHHjtHbtWvXp06fVfYoAAAAA0BnoGvojwWBQBw8e1Pz587Vr1y653W7NmzdPkvTss89KkpYuXRopAiXJ6/XqhRdekOM4WrlyZZtdRG+66aZWReD5CAQC8vv9LV4AAAAAEA0Kwe+duTcvOTlZubm5WrNmjTIyMrRu3Trl5OSoqalJu3fvliTNnj271efz8/OVn5+v+vp6ffrpp63enzJlikmeJSUl8nq9kVd2drZJXAAAAACJg66h3zszjqDL5ZLH49GwYcM0depU9e7dW5L07bffqrGxUX369FHPnj3bjDFw4EDt3btXR48ebfVe//79TfIsLi7Wgw8+GPm/3++nGAQAAAAQFQrB71ncn3fmCaBtSUtLu+D40ukH1/z1w2sAAAAAIFp0De2gzMxMpaam6vjx42poaGhznqqqKknSFVdc0YWZAQAAAEB0KAQ7KCUlRaNGjZIklZWVtXr/wIED2rt3ry655BJdddVVUcdubm62SBMAAAAAzolCMAr33nuvJGnZsmU6fPhwZHpdXZ3uuecehcNhLV68OOpuoFlZWfr6669VW1trmS4AAAAAtIl7BKMwffp0LVq0SKWlpcrLy2sxoPw333yjUaNG6bHHHos67pQpU/Tcc89pxIgRGj16tNLS0jR48GA99NBDnfArAAAAACQ6CsEorVy5UmPHjtWLL76obdu2qbm5WTk5Obr//vv1wAMPqEePHlHHLCkpUTgcVnl5ud544w01Nzdr3LhxFIIAAAAAOoUTDofDsU4C58/v98vr9cY6jS7Q/hNZo450lqe7xiKOJMXrZuhy2fUeT05KMYsVDAXNYoVMY4XMYqWm2j0dODU1+hNUbQkG7e5ltly30nt4zGIFQ3a/0XJ9OHnCbxYrKdluWzxhmJfbaD2VJBm2z1YCgRNmsVJTbZ5ELkmOY7ctJiXZXV+wbG8s23mXK8ksluWyT47D7TolxW4/FgicNIslxecxl92x7unf5/P55PGcff/IFUF0ovgr3qxjoeNCYbuD4nhlup4abj+WBy5WLA8YU912B8WNjQGzWC6X3TofNjxwaW5uNIsVr8KG7U08nqizPMmQ5LJrayzzitu/oWEsx3TZ2xW7VsvLtq2Jv+3QXtf/Rh4WAwAAAAAJhkIQAAAAABIMhSAAAAAAJBgKQQAAAABIMBSCAAAAAJBgKAQvQGVlpRzHUVFRUZvvFxUVyXEcVVZWtpi+bds2TZgwQRkZGerdu7cmTZqkjz/+WGvWrJHjOFq2bFmn5w4AAAAgcTF8RBfbuHGjbrvtNgWDQY0aNUoDBw7U/v37NXbsWM2bNy/W6QEAAABIABSCXcjv9+uuu+5SMBjUa6+9plmzZkXeW7JkiVasWBHD7AAAAAAkCrqGdqH169erpqZG119/fYsiUDpdCA4YMOCcMQKBgPx+f4sXAAAAAESDQrAL7dy5U5I0Y8aMVu8lJydr2rRp54xRUlIir9cbeWVnZ5vnCQAAAKB7oxDsQseOHZOkdou3/v37nzNGcXGxfD5f5FVdXW2aIwAAAIDuj3sEO1EoFDKP6Xa75Xa7zeMCAAAASBxcEbwAqampkqT6+vo23//x1bq+ffu2Ob29+QEAAACgM1AIXoAzhd0XX3zR6r2amhp98sknLaaNGTNGkrRhw4ZW8weDQW3cuLETsgQAAACAligEL8CVV16p/v37a//+/SovL49Mb2ho0KJFi1o90XPGjBm69NJLtWXLFpWVlbV47/HHH9eRI0e6JG8AAAAAiY1C8AItXbpUkjRt2jRNmDBBU6ZMUU5Ojvbt26dbbrmlxbxer1erVq1SUlKSbr/9do0ePVqzZs3SsGHD9MQTT2jRokWSfuhyCgAAAACdgULwAs2fP1+vvPKKhg4dqp07d+qjjz7SzTffrF27dqlXr16t5p86daq2bt2qwsJC7du3T++++66ysrK0ffv2yFNDMzMzu/hXAAAAAEgkTjgcDsc6CZx2ww03qKKiQrt379a1117boc/4/X55vd5Ozux8OXaRnPiMZSVeN0OXy+5ckcuVZBbLcnmFQkGzWJZ5pabYPR3YndbTJI7lskpKsnto9SWX9DKL1dgYMIsVDts9OdrvP24Wy1Jj4ymzWO7UHmaxwrLbFq2266Ymu3UrOdmu51CSYdvsGMYKhZoNY9lti5bLy2XYDloe25w82fZDEKNleQwRDNqtD4nC5/PJ4/GcdR6Gj+hiR48eVXJysi677LLItFAopGeeeUYVFRXKzc3VNddcE8MMLVkWN/FXvFmybMAtixHHic9C0LIgiVeWB7LNzY0mcTpjSBwL8ZqXY3kyzDBWUnKKWSzLQtBynbdsuxzTfZmNeDyhiejF68k1K5Y5JUYhaLVdd7zNir+1ppvbvn277rjjDg0fPlwDBgxQIBDQgQMHVFVVpfT0dK1evZoGHgAAAECn4h7BTuI4jgYOHNhq+siRI3XnnXeqtrZW7733nioqKhQMBjVnzhzt2bNHBQUFXZ8sAAAAgITCFcEuNmjQIL388suxTgMAAABAAqMQ7CSfffaZUlLs7sMAAAAAACsUgp1kyJAhsU4BAAAAANrEPYKdpK17BCsrK+U4joqKilRTU6O7775bffv2ldvtVl5eHl1GAQAAAHQJrgjGQG1tra677jrV19eroKBAx48f14cffqgFCxYoFApp4cKFsU4RAAAAQDfGFcEYKC8v14gRI3T48GGtX79eH3zwgd58801J0ooVK8762UAgIL/f3+IFAAAAANGgEIwBj8ej559/Xm63OzLt1ltvVV5enr766itVVVW1+9mSkhJ5vd7IKzs7uwsyBgAAANCdUAjGwMiRI5WZmdlqem5uriTp2LFj7X62uLhYPp8v8qquru60PAEAAAB0T9wjGAP9+vVrc3pGRoak090/2+N2u1tcSQQAAACAaHFFMAZcLhY7AAAAgNihIgEAAACABEMhCAAAAAAJhkIQAAAAABIMhSAAAAAAJBgKQQAAAABIME44HA7HOgmcP7/fL6/XG+s0Op3j2J2zcBzHLFY8stykk5LsRpixjBUKBc1iBYPNZrEsl31KSqpZrORkm1ihUMgkjiQlJ6eYxcrIuNQsVnNzk1ksGa4Pfv9xs1hJhsv+xAm/WazU1DSzWJb7DKu/Y6DxpEkcSUpJsRtGymW5f3UlmcUKhezaZsu2K8nwN8rweMRyH3vyZL1JHMv9WGPjKbNY8ctqfTjdZvl8Pnk8nrPOyTiCSDhWB+uWBSXnY9CZrNavcNjuYMoylmnxZigsu+06ZLi8HMOD4oQQhycPLYs3y99nOTxWOBSfeVkWu5bHEd39JDc6B11DAQAAACDBJHwh6DhOi5fL5VKvXr1UUFCg1atXc6UGAAAAQLdD19DvzZ07V5IUDAZ16NAh7dy5Uzt27ND777+vdevWdep3O46jAQMGqKqqqlO/BwAAAAAkCsGINWvWtPj/li1bNGnSJJWVlWn27NmaPHlybBIDAAAAAGMJ3zW0PRMnTtScOXMkSZs2bYptMgAAAABgiELwLIYPHy5Jqq6ubjH91Vdf1dixY+XxeJSenq78/HyVlJTo1KnWj7YtKiqS4ziqrKxURUWFxo8fr169eslxHD399NORpzx9+eWXLe5VLCws7PTfBwAAACAx0TX0LOrq6iRJbvcP4/UsXrxYpaWlSktL04QJE5Senq7Kyko9+uij2rx5s7Zu3ar09PRWsV5//XWtXr1aV199tW688UYdOnRIw4cP19y5c7V27Vr17NlT06dPj8w/ZMiQzv+BAAAAABIShWA7wuGw3nnnHUlSfn6+JGnDhg0qLS1VVlaWKisrNWjQIEmnB2ycPHmyduzYoSVLlujJJ59sFW/VqlUqKyvTzJkzW0wfN26c1q5dqz59+rS6T7EtgUBAgUAg8n+/324wXwAAAACJga6hPxIMBnXw4EHNnz9fu3btktvt1rx58yRJzz77rCRp6dKlkSJQkrxer1544QU5jqOVK1e22UX0pptualUEno+SkhJ5vd7IKzs7+4JjAgAAAEgsFILfO3NvXnJysnJzc7VmzRplZGRo3bp1ysnJUVNTk3bv3i1Jmj17dqvP5+fnKz8/X/X19fr0009bvT9lyhSTPIuLi+Xz+SKvH9+/CAAAAADnQtfQ750ZR9Dlcsnj8WjYsGGaOnWqevfuLUn69ttv1djYqD59+qhnz55txhg4cKD27t2ro0ePtnqvf//+Jnm63e4W9ywCAAAAQLQoBL/XkfvzzuXME0DbkpaWdsHxAQAAAMACXUM7KDMzU6mpqTp+/LgaGhranKeqqkqSdMUVV3RhZgAAAAAQHQrBDkpJSdGoUaMkSWVlZa3eP3DggPbu3atLLrlEV111VdSxm5ubLdIEAAAAgHOiEIzCvffeK0latmyZDh8+HJleV1ene+65R+FwWIsXL466G2hWVpa+/vpr1dbWWqYLAAAAAG3iHsEoTJ8+XYsWLVJpaany8vJaDCj/zTffaNSoUXrssceijjtlyhQ999xzGjFihEaPHq20tDQNHjxYDz30UCf8CgAAAACJjkIwSitXrtTYsWP14osvatu2bWpublZOTo7uv/9+PfDAA+rRo0fUMUtKShQOh1VeXq433nhDzc3NGjduHIUgAAAAgE7hhMPhcKyTwPnz+/3yer2xTqPTOU789WI+21NioxWvm2FSkt25IstYoVDQLFYwaHd/ruXfMSUl1SxWUlKKSRzL5Z6cbJOTJKWnx2cbGA6HzGL5fN+YxbJaHyTp1Kl6s1ipqXZP147HfUYgcMIsljs1+pPO7TLcl1m288HmJrNYYdm1zS6X3W+0PI5wuezW+RMn6kziWO7HGhtPmcWKX1brw+n13efzyePxnHVOrgiiE9k1cJYsG150nOVBcbwWzpYcw+3H6gDB9qAlySyW5cGnJcvC2VIwaHeAbclyne/u7U3I8Pe5DB8XYbmsLIs307wsl71hURmP62mzYTGfGLr+bxh/p8wAAAAAAJ2KQhAAAAAAEgyFIAAAAAAkGApBAAAAAEgwFIIAAAAAkGAoBC9AZWWlHMdRUVFRm+8XFRXJcRxVVla2mL5t2zZNmDBBGRkZ6t27tyZNmqSPP/5Ya9askeM4WrZsWafnDgAAACBxxeczt7uxjRs36rbbblMwGNSoUaM0cOBA7d+/X2PHjtW8efNinR4AAACABEAh2IX8fr/uuusuBYNBvfbaa5o1a1bkvSVLlmjFihXnjBEIBBQIBFrEBAAAAIBo0DW0C61fv141NTW6/vrrWxSB0ulCcMCAAeeMUVJSIq/XG3llZ2d3VroAAAAAuikKwS60c+dOSdKMGTNavZecnKxp06adM0ZxcbF8Pl/kVV1dbZ4nAAAAgO6NrqFd6NixY5LU7lW8/v37nzOG2+2W2+02zQsAAABAYuGKYCcKhUKxTgEAAAAAWqEQvACpqamSpPr6+jbf/3G3zb59+7Y5vb35AQAAAKAzUAhegDOF3RdffNHqvZqaGn3yySctpo0ZM0aStGHDhlbzB4NBbdy4sROyBAAAAICWKAQvwJVXXqn+/ftr//79Ki8vj0xvaGjQokWLWg3tMGPGDF166aXasmWLysrKWrz3+OOP68iRI12SNwAAAIDERiF4gZYuXSpJmjZtmiZMmKApU6YoJydH+/bt0y233NJiXq/Xq1WrVikpKUm33367Ro8erVmzZmnYsGF64okntGjRIkk/dDkFAAAAgM5AIXiB5s+fr1deeUVDhw7Vzp079dFHH+nmm2/Wrl271KtXr1bzT506VVu3blVhYaH27dund999V1lZWdq+fXvkqaGZmZld/CsAAAAAJBInHA6HY50ETrvhhhtUUVGh3bt369prr+3QZ/x+v7xebydndr4cu0hO/MWyzMlyM7SMlZRkN8JMcrLdle5gsMkwVrNZLMtln5piN0yM40oyiRMO2z0J2WWUkyS53elmsVIM19Ow7NYHn+8bs1gphuvWiRP+c8/UQe7UHmaxrNZ5S6dOtf1gufNhuaxkuC+z3GcEm+3a+ZBh25WUlGIWy/I4wuWyu7Zz4kSdSZzkZLtl1dQUMIsVv6zWh9P7Hp/PJ4/Hc9Y5uSLYxY4ePaqvv/66xbRQKKTf/e53qqioUG5urq655poYZQcAAAAgETCgfBfbvn277rjjDg0fPlwDBgxQIBDQgQMHVFVVpfT0dK1evdr0DBEAAAAA/BhXBDuJ4zgaOHBgq+kjR47UnXfeqdraWr333nuqqKhQMBjUnDlztGfPHhUUFHR9sgAAAAASClcEu9igQYP08ssvxzoNAAAAAAmMQrCTfPbZZ0pJsbtJFgAAAACsUAh2kiFDhsQ6BQAAAABoE/cIdpK27hGsrKyU4zgqKipSTU2N7r77bvXt21dut1t5eXl0GQUAAADQJbgiGAO1tbW67rrrVF9fr4KCAh0/flwffvihFixYoFAopIULF8Y6RQAAAADdGFcEY6C8vFwjRozQ4cOHtX79en3wwQd68803JUkrVqw462cDgYD8fn+LFwAAAABEg0IwBjwej55//nm53e7ItFtvvVV5eXn66quvVFVV1e5nS0pK5PV6I6/s7OwuyBgAAABAd0IhGAMjR45UZmZmq+m5ubmSpGPHjrX72eLiYvl8vsirurq60/IEAAAA0D1xj2AM9OvXr83pGRkZkk53/2yP2+1ucSURAAAAAKLFFcEYcLlY7AAAAABih4oEAAAAABIMhSAAAAAAJBgKQQAAAABIMBSCAAAAAJBgKAQBAAAAIME44XA4HOskcP78fr+8Xm+s02iHYxfJib9YljlZboaWsZKS7EaYsYwVCgXjNFbILFZqit0wMY4rySRO2HBZWeUkSSkpqYax4nN4Hr//W7NYlttiIHDCLJY7tYdZLBm2zy6jdfXkyTqTOJLkdqebxbKUnJxiFisYtGtvwmG7ttly+7E9trG7ttPQ4DOJY7k+NDW1P7Ra92G1Ppw+DvT5fPJ4PGedkyuCAAAAAJBgKAQBAAAAIMEkfCHoOE6Ll8vlUq9evVRQUKDVq1ebdrMDAAAAgHhg19H5Ijd37lxJp/ukHzp0SDt37tSOHTv0/vvva926dZ363Y7jaMCAAaqqqurU7wEAAAAAiUIwYs2aNS3+v2XLFk2aNEllZWWaPXu2Jk+eHJvEAAAAAMBYwncNbc/EiRM1Z84cSdKmTZtimwwAAAAAGKIQPIvhw4dLkqqrq1tMf/XVVzV27Fh5PB6lp6crPz9fJSUlOnXqVKsYRUVFchxHlZWVqqio0Pjx49WrVy85jqOnn3468ujgL7/8ssW9ioWFhZ3++wAAAAAkJrqGnkVd3ekxf9zuH8aVWrx4sUpLS5WWlqYJEyYoPT1dlZWVevTRR7V582Zt3bpV6emtx/d5/fXXtXr1al199dW68cYbdejQIQ0fPlxz587V2rVr1bNnT02fPj0y/5AhQzr/BwIAAABISBSC7QiHw3rnnXckSfn5+ZKkDRs2qLS0VFlZWaqsrNSgQYMknR6wcfLkydqxY4eWLFmiJ598slW8VatWqaysTDNnzmwxfdy4cVq7dq369OnT6j7FtgQCAQUCPwyq6ff7z/cnAgAAAEhQdA39kWAwqIMHD2r+/PnatWuX3G635s2bJ0l69tlnJUlLly6NFIGS5PV69cILL8hxHK1cubLNLqI33XRTqyLwfJSUlMjr9UZe2dnZFxwTAAAAQGKhEPzemXvzkpOTlZubqzVr1igjI0Pr1q1TTk6OmpqatHv3bknS7NmzW30+Pz9f+fn5qq+v16efftrq/SlTppjkWVxcLJ/PF3n9+P5FAAAAADgXuoZ+78w4gi6XSx6PR8OGDdPUqVPVu3dvSdK3336rxsZG9enTRz179mwzxsCBA7V3714dPXq01Xv9+/c3ydPtdre4ZxEAAAAAokUh+L2O3J93LmeeANqWtLS0C44PAAAAABboGtpBmZmZSk1N1fHjx9XQ0NDmPFVVVZKkK664ogszAwAAAIDoUAh2UEpKikaNGiVJKisra/X+gQMHtHfvXl1yySW66qqroo7d3NxskSYAAAAAnBOFYBTuvfdeSdKyZct0+PDhyPS6ujrdc889CofDWrx4cdTdQLOysvT111+rtrbWMl0AAAAAaBP3CEZh+vTpWrRokUpLS5WXl9diQPlvvvlGo0aN0mOPPRZ13ClTpui5557TiBEjNHr0aKWlpWnw4MF66KGHOuFXAAAAAEh0FIJRWrlypcaOHasXX3xR27ZtU3Nzs3JycnT//ffrgQceUI8ePaKOWVJSonA4rPLycr3xxhtqbm7WuHHjKAQBAAAAdAonHA6HY50Ezp/f75fX6zWM2P6TT6OOdJanqHaXWFYsN0PLWElJdueKklxJZrFC4ZBdrFDQLJblsk9Njf6kUnus1nnLbcdyWaWk2A2p4zZc7pa+q/3aLJbl37GpKWAWy+1ON4sVNmwjHKP9YqDxpEkcyXg9NVwfLPcZwaDdsxMs14ekpBSzWMnJdrEs92UnTtSZxLFsayx/X6Lw+XzyeDxnnYd7BAEAAAAgwVAIAgAAAECCoRAEAAAAgARDIQgAAAAACYZCEAAAAAASDIXgBaisrJTjOCoqKmrz/aKiIjmOo8rKyhbTt23bpgkTJigjI0O9e/fWpEmT9PHHH2vNmjVyHEfLli3r9NwBAAAAJC7GEexiGzdu1G233aZgMKhRo0Zp4MCB2r9/v8aOHat58+bFOj0AAAAACYBCsAv5/X7dddddCgaDeu211zRr1qzIe0uWLNGKFStimB0AAACAREHX0C60fv161dTU6Prrr29RBEqnC8EBAwacM0YgEJDf72/xAgAAAIBoUAh2oZ07d0qSZsyY0eq95ORkTZs27ZwxSkpK5PV6I6/s7GzzPAEAAAB0bxSCXejYsWOS1G7x1r9//3PGKC4uls/ni7yqq6tNcwQAAADQ/XGPYCcKhULmMd1ut9xut3lcAAAAAImDK4IXIDU1VZJUX1/f5vs/vlrXt2/fNqe3Nz8AAAAAdAYKwQtwprD74osvWr1XU1OjTz75pMW0MWPGSJI2bNjQav5gMKiNGzd2QpYAAAAA0BKF4AW48sor1b9/f+3fv1/l5eWR6Q0NDVq0aFGrJ3rOmDFDl156qbZs2aKysrIW7z3++OM6cuRIl+QNAAAAILFRCF6gpUuXSpKmTZumCRMmaMqUKcrJydG+fft0yy23tJjX6/Vq1apVSkpK0u23367Ro0dr1qxZGjZsmJ544gktWrRI0g9dTgEAAACgM1AIXqD58+frlVde0dChQ7Vz50599NFHuvnmm7Vr1y716tWr1fxTp07V1q1bVVhYqH379undd99VVlaWtm/fHnlqaGZmZhf/CgAAAACJxAmHw+FYJ4HTbrjhBlVUVGj37t269tprO/QZv98vr9drmIVjF8np/rGsWG6GlrGSkuweLJzkSjKLFQrbPZE3FAqaxbJc9qmpPcxiWa3zltuO5bJKSbF7krLbcLlb+q72a7NYln/HpqaAWSy3O90sVtiwjXCM9ouBxpMmcSTj9dRwfbDcZwSDzWaxLNeHpKQUs1jJyXaxLPdlJ07UmcSxbGssf1+i8Pl88ng8Z52H4SO62NGjR5WcnKzLLrssMi0UCumZZ55RRUWFcnNzdc0118QwQ8uCxCxUXBZvjmN5Qd1+qBELpgW4YSHohLr/+SvLAxezbTFOzxs2x+mJIts2wo7lgaxlIWjJ8jearROGhaBl8WbJcvtxWZ48DMZn22VZ7FouLyumf8MEKASt9hmnT7R2bJ2nEOxi27dv1x133KHhw4drwIABCgQCOnDggKqqqpSenq7Vq1fHZdEDAAAAoPuIz9OV3YDjOBo4cGCr6SNHjtSdd96p2tpavffee6qoqFAwGNScOXO0Z88eFRQUdH2yAAAAABIKVwS72KBBg/Tyyy/HOg0AAAAACYxCsJN89tlnSkmxu0cBAAAAAKxQCHaSIUOGxDoFAAAAAGgT9wh2krbuEaysrJTjOCoqKlJNTY3uvvtu9e3bV263W3l5eXQZBQAAANAluCIYA7W1tbruuutUX1+vgoICHT9+XB9++KEWLFigUCikhQsXxjpFAAAAAN0YVwRjoLy8XCNGjNDhw4e1fv16ffDBB3rzzTclSStWrIhxdgAAAAC6OwrBGPB4PHr++efldrsj02699Vbl5eXpq6++UlVVVbufDQQC8vv9LV4AAAAAEA0KwRgYOXKkMjMzW03Pzc2VJB07dqzdz5aUlMjr9UZe2dnZnZYnAAAAgO6JQjAG+vXr1+b0jIwMSaev+rWnuLhYPp8v8qquru6UHAEAAAB0XzwsJgZcrvOvv91ud4supQAAAAAQLa4IAgAAAECCoRAEAAAAgARDIQgAAAAACYZCEAAAAAASDIUgAAAAACQYJxwOh2OdBM6f3++X1+uNdRrtcMwiXciTVn/McWzychy7nMLhkGEsu006KcnuwcJJSSlmsUKhZrNYwaBdLMtln5ISh08HjtPdRVKy3bpludwt24j6+u/MYiUnp5rFOnWq3iyW251uFsvlSjKLZbXPOHHCbxJHsl1WlpINt8VQyG6/GDJs512G+0WrdUuyXecbGnwmcSyPIZqbG81ixSurfcbpY5GwfD6fPB7PWedl+Aj8iF2jhI6zLSqDZrHilWO4nsbtso/Doius+MtJsj1gDFvGcuyWVyhkt26FDWPFK8vlZXcwG5/tliXLvAzP/8rycDdeizdXnK4TiG+sNQAAAACQYBK+EHQcp8XL5XKpV69eKigo0OrVq027egEAAABAPKBr6Pfmzp0rSQoGgzp06JB27typHTt26P3339e6des69bsdx9GAAQNUVVXVqd8DAAAAABKFYMSaNWta/H/Lli2aNGmSysrKNHv2bE2ePDk2iQEAAACAsYTvGtqeiRMnas6cOZKkTZs2xTYZAAAAADBEIXgWw4cPlyRVV1e3mP7qq69q7Nix8ng8Sk9PV35+vkpKSnTq1KlWMYqKiuQ4jiorK1VRUaHx48erV69echxHTz/9dOTpU19++WWLexULCws7/fcBAAAASEx0DT2Luro6SZLb/cO4UosXL1ZpaanS0tI0YcIEpaenq7KyUo8++qg2b96srVu3Kj299fg+r7/+ulavXq2rr75aN954ow4dOqThw4dr7ty5Wrt2rXr27Knp06dH5h8yZEjn/0AAAAAACYlCsB3hcFjvvPOOJCk/P1+StGHDBpWWliorK0uVlZUaNGiQJMnn82ny5MnasWOHlixZoieffLJVvFWrVqmsrEwzZ85sMX3cuHFau3at+vTp0+o+RQAAAADoDHQN/ZFgMKiDBw9q/vz52rVrl9xut+bNmydJevbZZyVJS5cujRSBkuT1evXCCy/IcRytXLmyzS6iN910U6si8HwEAgH5/f4WLwAAAACIBoXg987cm5ecnKzc3FytWbNGGRkZWrdunXJyctTU1KTdu3dLkmbPnt3q8/n5+crPz1d9fb0+/fTTVu9PmTLFJM+SkhJ5vd7IKzs72yQuAAAAgMRB19DvnRlH0OVyyePxaNiwYZo6dap69+4tSfr222/V2NioPn36qGfPnm3GGDhwoPbu3aujR4+2eq9///4meRYXF+vBBx+M/N/v91MMAgAAAIgKheD3LO7PO/ME0LakpaVdcHzp9INr/vrhNQAAAAAQLbqGdlBmZqZSU1N1/PhxNTQ0tDlPVVWVJOmKK67owswAAAAAIDoUgh2UkpKiUaNGSZLKyspavX/gwAHt3btXl1xyia666qqoYzc3N1ukCQAAAADnRCEYhXvvvVeStGzZMh0+fDgyva6uTvfcc4/C4bAWL14cdTfQrKwsff3116qtrbVMFwAAAADaxD2CUZg+fboWLVqk0tJS5eXltRhQ/ptvvtGoUaP02GOPRR13ypQpeu655zRixAiNHj1aaWlpGjx4sB566KFO+BUAAAAAEh2FYJRWrlypsWPH6sUXX9S2bdvU3NysnJwc3X///XrggQfUo0ePqGOWlJQoHA6rvLxcb7zxhpqbmzVu3DgKQQAAAACdwgmHw+FYJ4Hz5/f75fV6DSO2/+TTWHK57Hoxn+3prtHFic+e1aFQ0CxWUpLduaKkpBSzWGHD3xg0jGW57FOSU81iWQkrPncXLpfdepqaYvhUZqO2RpLq678zi2W5bgUaT5rFcrvTzWJZsmoHT5yoM4kjSWlpbQ9jFWvJyYbtfDhkFisUsotldQwhSS5Xkl0sw2OSOqP2xvIYorm50SxWvLI6rjxd2oXl8/nk8XjOOi9XBNGCbQNnWbzZxbLauVguK8vzMZZ52f5Gux1xvDJd9oYHCGbrvOWJIsN13rKtcRkeuMQry5Mf8cqyvQk2NxlFslvnLU+GWZ6wsDwZZrlftMwrXou3eDxRFwzyIMRoxOI4KT4vaQAAAAAAOg2FIAAAAAAkGApBAAAAAEgwFIIAAAAAkGAoBAEAAAAgwVAIXoDKyko5jqOioqI23y8qKpLjOKqsrGwxfdu2bZowYYIyMjLUu3dvTZo0SR9//LHWrFkjx3G0bNmyTs8dAAAAQOLq/s/JjjMbN27UbbfdpmAwqFGjRmngwIHav3+/xo4dq3nz5sU6PQAAAAAJgEKwC/n9ft11110KBoN67bXXNGvWrMh7S5Ys0YoVK2KYHQAAAIBEQdfQLrR+/XrV1NTo+uuvb1EESqcLwQEDBpwzRiAQkN/vb/ECAAAAgGhQCHahnTt3SpJmzJjR6r3k5GRNmzbtnDFKSkrk9Xojr+zsbPM8AQAAAHRvFIJd6NixY5LUbvHWv3//c8YoLi6Wz+eLvKqrq01zBAAAAND9cY9gJwqFQuYx3W633G63eVwAAAAAiYMrghcgNTVVklRfX9/m+z++Wte3b982p7c3PwAAAAB0BgrBC3CmsPviiy9avVdTU6NPPvmkxbQxY8ZIkjZs2NBq/mAwqI0bN3ZClgAAAADQEoXgBbjyyivVv39/7d+/X+Xl5ZHpDQ0NWrRoUasnes6YMUOXXnqptmzZorKyshbvPf744zpy5EiX5A0AAAAgsVEIXqClS5dKkqZNm6YJEyZoypQpysnJ0b59+3TLLbe0mNfr9WrVqlVKSkrS7bffrtGjR2vWrFkaNmyYnnjiCS1atEjSD11OAQAAAKAzUAheoPnz5+uVV17R0KFDtXPnTn300Ue6+eabtWvXLvXq1avV/FOnTtXWrVtVWFioffv26d1331VWVpa2b98eeWpoZmZmF/8KAAAAAInECYfD4VgngdNuuOEGVVRUaPfu3br22ms79Bm/3y+v12uWg+PYnRtwuexiWeYVDts8zdXlSjKJI0mWm6HV75OkpCS7Bwtb/g1luLyCoaBZLMtln5KSZhbLMi8zhn/DpOQUs1ipqT3MYlny+4+bxbJsu5qbG81iud3pZrEs13lHjkmcQONJkziS5LZcTx2b3yfZ7jMs94shw3becvtJSbbrARaW3fKqr681i2UlLvdjcc7n88nj8Zx1HoaP6GJHjx5VcnKyLrvsssi0UCikZ555RhUVFcrNzdU111wTs/xsG17LgiQ+i0orjuGOOF5P7VgeIFiuW47pziX+Tlh8H8wkTChed8TBZrNQTU2nzGJZHjBathGWJ+lMGTZetic17f6O8chyWdm283bFW7wKhuzariRX/B3SJ8KxjSWrbfH0sXzHFlj8rTXd3Pbt23XHHXdo+PDhGjBggAKBgA4cOKCqqiqlp6dr9erVphsOAAAAAPxYnJ4WjB+O47R4uVwu9erVSwUFBVq9enXUV9BGjhypO++8U7W1tXrvvfdUUVGhYDCoOXPmaM+ePSooKOikXwIAAAAAp3FFsIPmzp0r6fR4f4cOHdLOnTu1Y8cOvf/++1q3bl2H4wwaNEgvv/xyZ6UJAAAAAOfEw2LO4Uw3zR8vpi1btmjSpElqbm7W5s2bNXny5FikZ/6wGBndFC/Z9g23vO/ASrx24bW8r8JyuScb3hRveY9gMNhkFsuyOTVd57v5PYKW93AlGz54xjKvEyf8556pgyzXrcZGu3sq4/UBKFZ/x5Mn60ziSLbLyrF8+EmKZTtvty+z3GdYsrxf1/Iewbr670ziWB4nJcI9o9b3CHbkYTF0DT1PEydO1Jw5cyRJmzZtim0yAAAAABAFCsELMHz4cElSdXW1JKm2tlbPPfec/vEf/1EDBgyQ2+1WZmambrjhBm3ZsqXNGIWFhXIcR1VVVXr99dc1atQoZWRktDkGIQAAAABYoBC8AHV1p7t5uN1uSdLu3bt133336YsvvtDgwYP1s5/9TIMHD9Z7772nf/zHfzzrvYElJSWaM2eOUlNTNXnyZOXl5XXJbwAAAACQeOLvxquLRDgc1jvvvCNJys/PlyQNHjxYu3bt0qhRo1rM+3/+z//RhAkT9MADD+i2227TJZdc0ire73//e33wwQcaN25c5ycPAAAAIKFxRTBKwWBQBw8e1Pz587Vr1y653W7NmzdPknTllVe2KgKl011If/GLX8jv9+t//a//1WbcBQsWdKgIDAQC8vv9LV4AAAAAEA2uCHZQW08+ysjI0Nq1a5WTkxOZFgwG9f777+t//+//rWPHjikQCEiSDh482OLfH5syZUqH8igpKdHy5cujTR8AAAAAIigEO+jMOIIul0sej0fDhg3T1KlT1bt378g8//Ef/6HJkydr79697cY5c1/hj/Xv379DeRQXF+vBBx+M/N/v9ys7O7tDnwUAAAAAiUKww9asWXPOeRYuXKi9e/dq2rRp+tWvfqXBgwcrIyNDLpdLpaWlWrx4cbvjjKWlpXUoD7fbHXk4DQAAAACcDwpBIw0NDdqyZYsuu+wyvfHGG0pKajlA6+HDh2OUGQAAAAC0xMNijPh8PoVCIfXt27dVEdjU1KS33norRpkBAAAAQEsUgkb+23/7b/J6vTpw4IB27twZmR4MBvXwww/riy++iGF2AAAAAPADCkEjycnJ+tWvfqXm5maNGzdO//AP/6Cf//zn+slPfqIXX3xRv/jFL2KdIgAAAABIohA09eijj2rt2rXKz8/Xzp07tXXrVv3d3/2ddu/erauvvjrW6QEAAACAJMkJt/cYS1wU/H6/vF6vYcTW4yWed6Q2xl48X0lJ8fdcI8vfZykUCprFslzuycmpZrFCoZBZrGCwySyWZXNqus4b5RUK2y13Sy5X0rln6qDk5BSzWJZ5nTjhN4tluW41Np4yi+VO7WEWS4bts9Xf8eTJtoePOh+Wy8oxXE9TUizbebt9meU+w5LLZXc9Jsllt13X1X9nEsfyOMlyfYhXjmOzPpw+FgnL5/PJ4/Gcdd74O7oGLhKWDZxlAWHVkEiSE6cnBizF69/RqniTpLDi73yf5bIKGxaolnnF64GsyxV/64Nku546cfkTDdvAOG1PLZnuy+JzhTA9WZQI6wTs0TUUAAAAABIMhSAAAAAAJBgKQQAAAABIMBSCAAAAAJBgKAQBAAAAIMFQCBp59913NX/+fA0dOlQej0c9e/bU3/3d3+mJJ55QIBBoNf+///u/a+LEibriiivkdruVlZWlsWPHavny5THIHgAAAEAiYRxBI5dffrlOnjypvLw89evXTz6fTx999JG+++47TZgwQe+9956Skk4/JviFF17QPffco6SkJI0ZM0ZXXHGFjh8/rs8++0z/8R//EdXjyxlHMHYsx/+xfZS+4Vh2luOzGY4vFQxaPpa/2TCW3SP+LZe91WP543U9tdwWLce7tGwDT5ywG4POcqzEpqbWJzrPV2pqmlksy6FvXEb7n5Mn603iSJLbbTiOoOEwDZbjCMbrUC6WLI9tLP+OdXU1JnEYRzA6jCN4EVu5cqX+4R/+QT16/NA419XVadasWXrnnXf02muv6c4775Qk/frXv5bjONq9e7euvvrqyPzhcFjbtm076/cEAoEWVxj9frtBhgEAAAAkBrqGGrnllltaFIGSlJGRod/97neSpPLy8sj0b775Rr169WpRBEqnz5wUFhae9XtKSkrk9Xojr+zsbJsfAAAAACBhcEXQ0MGDB/Xv//7v+n//7/+poaFBoVAo0rXh4MGDkflGjhypHTt2aMGCBXrwwQf1t3/7tx3+juLiYj344IOR//v9fopBAAAAAFHhHkED4XBY/9//9//pd7/7Xbt92gcOHKgjR45Ikvbt26dbb7018v/LLrtM48aN09SpUzV9+vTIvYQdwT2CscM9gtHhHsHocI9gx3GPYHS4RzA63CPYcdwjGB3uEey4eP0bWorFPYJ0DTXwxhtv6KmnnlK/fv305ptv6ujRo2psbFQ4HI7cz/fXDVp+fr7+/Oc/66233tJdd90lj8ej9evX6+c//7kKCgrU2NgYq58CAAAAIAFQCBp46623JEn/43/8D02bNk1ZWVlKSTl9xvXw4cNtfiYtLU233nqrSktL9cUXX+jAgQPKz8/Xrl27tHr16i7LHQAAAEDioRA08N1330mS+vXr1+q99evXdyjG3/7t3+oXv/iFJOnAgQN2yQEAAADAj1AIGsjNzZUklZaWtugCun37dv3mN79pMe+JEyf07LPPqra2tsX0UCik//k//6ck8fAXAAAAAJ2Kh8UY+OKLLzRixAg1NDTov//3/678/HwdPXpUO3bs0C9/+Us9+eSTGjBggKqqqlRbW6vevXsrJSVFI0eO1MCBA9XY2Kg9e/aourpaAwcO1Mcff6zMzMwOfTcPi4kdHhYTHR4WEx0eFtNxPCwmOjwsJjo8LKbjeFhMdHhYTMfF69/QEg+LuUjl5ubq448/1s0336zjx4/r7bffVn19vVauXNnqiuAll1yiF154QTfffLO++eYbvf322/rggw/Uu3dvLV++XH/60586XAQCAAAAwPngiuBFjiuCscMVwehwRTA6XBHsOK4IRocrgtHhimDHcUUwOlwR7Lh4/RtaisUVwfg7ugbaYNmYWLE80LAMZSnJ8IAxJcXuIC8cPmkWy7IZdGS3o7Iq3iS7dTUUtit0LblclgdThgWEYTGfCIJBu5MyluzWrvgsbBzHbrsOhQxPYMXpiSfbk1iWeRnuf7hGFBN2y73jcegaCgAAAAAJJuELQcdxWrxcLpd69eqlgoICrV69mrMiAAAAALoduoZ+b+7cuZJO33t06NAh7dy5Uzt27ND777+vdevWdep3O44TeaooAAAAAHQ2CsHvrVmzpsX/t2zZokmTJqmsrEyzZ8/W5MmTY5MYAAAAABhL+K6h7Zk4caLmzJkjSdq0aVNskwEAAAAAQxSCZzF8+HBJUnV1dYvpr776qsaOHSuPx6P09HTl5+erpKREp06dahWjqKhIjuOosrJSFRUVGj9+vHr16iXHcfT0009HnlD35ZdftrhXsbCwsNN/HwAAAIDERNfQs6irOz1uk9vtjkxbvHixSktLlZaWpgkTJig9PV2VlZV69NFHtXnzZm3dulXp6emtYr3++utavXq1rr76at144406dOiQhg8frrlz52rt2rXq2bOnpk+fHpl/yJAhnf8DAQAAACQkCsF2hMNhvfPOO5Kk/Px8SdKGDRtUWlqqrKwsVVZWatCgQZIkn8+nyZMna8eOHVqyZImefPLJVvFWrVqlsrIyzZw5s8X0cePGae3aterTp0+r+xTbEggEFAj8MICv3+8/358IAAAAIEHRNfRHgsGgDh48qPnz52vXrl1yu92aN2+eJOnZZ5+VJC1dujRSBEqS1+vVCy+8IMdxtHLlyja7iN50002tisDzUVJSIq/XG3llZ2dfcEwAAAAAiYVC8Htn7s1LTk5Wbm6u1qxZo4yMDK1bt045OTlqamrS7t27JUmzZ89u9fn8/Hzl5+ervr5en376aav3p0yZYpJncXGxfD5f5PXj+xcBAAAA4FzoGvq9M+MIulwueTweDRs2TFOnTlXv3r0lSd9++60aGxvVp08f9ezZs80YAwcO1N69e3X06NFW7/Xv398kT7fb3eKeRQAAAACIFoXg9zpyf965nHkCaFvS0tIuOD4AAAAAWKBraAdlZmYqNTVVx48fV0NDQ5vzVFVVSZKuuOKKLswMAAAAAKJDIdhBKSkpGjVqlCSprKys1fsHDhzQ3r17dckll+iqq66KOnZzc7NFmgAAAABwThSCUbj33nslScuWLdPhw4cj0+vq6nTPPfcoHA5r8eLFUXcDzcrK0tdff63a2lrLdAEAAACgTdwjGIXp06dr0aJFKi0tVV5eXosB5b/55huNGjVKjz32WNRxp0yZoueee04jRozQ6NGjlZaWpsGDB+uhhx7qhF8BAAAAINFRCEZp5cqVGjt2rF588UVt27ZNzc3NysnJ0f33368HHnhAPXr0iDpmSUmJwuGwysvL9cYbb6i5uVnjxo2jEAQAAADQKZxwOByOdRI4f36/X16v1zBi+08+jTrSWZ6iGq3k5BSzWFZcjl3P6rDsNkPLTTolxW6oktTU6E+StKex8aRZrFAoZBYrHAraxTJcJxyj7Tpo+PssJSenmsVKSkoyi+Vy2cWqr681i2XZnjY1BcxiJSXF57np5CSb5RUwbLcs22bLfbVlXpb7sniNFY/HNpJde2O5boXDdvvq+GW1vE6voz6fTx6P56xzxmerC3Qis4bJsIFzDE/HWBYQ8cpy52IpHos3S7Y7dcsDM7sDhFAo/pa7tUQ4/+sYnqjr7izbGsvlHg4bnliL03U+XttUJA5aSgAAAABIMBSCAAAAAJBgKAQBAAAAIMFQCAIAAABAgqEQBAAAAIAEQyF4ASorK+U4joqKitp8v6ioSI7jqLKyssX0bdu2acKECcrIyFDv3r01adIkffzxx1qzZo0cx9GyZcs6PXcAAAAAiYvhI7rYxo0bddtttykYDGrUqFEaOHCg9u/fr7Fjx2revHmxTg8AAABAAqAQ7EJ+v1933XWXgsGgXnvtNc2aNSvy3pIlS7RixYpzxggEAgoEfhjA1+/3d0quAAAAALovuoZ2ofXr16umpkbXX399iyJQOl0IDhgw4JwxSkpK5PV6I6/s7OzOShcAAABAN0Uh2IV27twpSZoxY0ar95KTkzVt2rRzxiguLpbP54u8qqurzfMEAAAA0L3RNbQLHTt2TJLavYrXv3//c8Zwu91yu92meQEAAABILFwR7EShUCjWKQAAAABAKxSCFyA1NVWSVF9f3+b7P+622bdv3zantzc/AAAAAHQGCsELcKaw++KLL1q9V1NTo08++aTFtDFjxkiSNmzY0Gr+YDCojRs3dkKWAAAAANASheAFuPLKK9W/f3/t379f5eXlkekNDQ1atGhRq6EdZsyYoUsvvVRbtmxRWVlZi/cef/xxHTlypEvyBgAAAJDYKAQv0NKlSyVJ06ZN04QJEzRlyhTl5ORo3759uuWWW1rM6/V6tWrVKiUlJen222/X6NGjNWvWLA0bNkxPPPGEFi1aJOmHLqcAAAAA0BkoBC/Q/Pnz9corr2jo0KHauXOnPvroI918883atWuXevXq1Wr+qVOnauvWrSosLNS+ffv07rvvKisrS9u3b488NTQzM7OLfwUAAACAROKEw+FwrJPAaTfccIMqKiq0e/duXXvttR36jN/vl9frNczCsYvk2MVKTk4xi2WVl+MYnkcx3AxDYbun1aak2A1VkprawyxWU9Mps1jBYNAsVijUbBbLMdwWrViuW5a7nqQku5GQXK4kw1h2bcSJE3VmsSyXV3Nzo1ksy7ws2+cko3Ui0HjSJI5k2za7DJdVSmqaWaxQyLJtjs+nuKek2PUAs2xT6+trTeJYHgeGDfc/8ctqeZ1eF3w+nzwez1nnZBzBLnb06FElJyfrsssui0wLhUJ65plnVFFRodzcXF1zzTUxzNCOZQNgGstoQ7M80AiH7XZ4tg2v3Y7FshGP1/NX8Vi8WbJct0xZnkgxPPiM1+WVGAdUhuLw7xiv61Yi7H9s9/3x+RuROCgEu9j27dt1xx13aPjw4RowYIACgYAOHDigqqoqpaena/Xq1XHbwAMAAADoHrhHsJM4jqOBAwe2mj5y5Ejdeeedqq2t1XvvvaeKigoFg0HNmTNHe/bsUUFBQdcnCwAAACChcEWwiw0aNEgvv/xyrNMAAAAAkMAoBDvJZ599ppQUuwecAAAAAIAVCsFOMmTIkFinAAAAAABt4h7BTtLWPYKVlZVyHEdFRUWqqanR3Xffrb59+8rtdisvL48uowAAAAC6BFcEY6C2tlbXXXed6uvrVVBQoOPHj+vDDz/UggULFAqFtHDhwlinCAAAAKAb44pgDJSXl2vEiBE6fPiw1q9frw8++EBvvvmmJGnFihVn/WwgEJDf72/xAgAAAIBoUAjGgMfj0fPPPy+32x2ZduuttyovL09fffWVqqqq2v1sSUmJvF5v5JWdnd0FGQMAAADoTigEY2DkyJHKzMxsNT03N1eSdOzYsXY/W1xcLJ/PF3lVV1d3Wp4AAAAAuifuEYyBfv36tTk9IyND0unun+1xu90triQCAAAAQLS4IhgDLheLHQAAAEDsUJEAAAAAQIKhEAQAAACABEMhCAAAAAAJhkIQAAAAABIMhSAAAAAAJBgnHA6HY50Ezp/f75fX6zWL5zh25wYcxzGLlZKcahZLRnm5XEkmcSQpFGw2ixWW3SbtctmNMJOammYWq7m50SxWKBQyixUOBe1iGf4drcTr7sKyrXFkGMuwjTh1qsEsluWTq0OG63xSkl17Y9k+W8UKBE6YxJEkd2oPs1iWUgzbect1y7Kdt5SaYjccmOU+o67uO5M4lm1zOByff0NbVsvr9Lrg8/nk8XjOOidXBAEAAAAgwVAIAgAAAECCibtC0HGcs74KCwtjnSIAAAAAXNTsOuQbmzt3bpvThwwZ0sWZAAAAAED3EreF4Jo1a2KdAgAAAAB0S3HXNRQAAAAA0Lku2kKwqKhIjuOosrJSFRUVGj9+vHr16iXHcVRbWytJ2r59u+655x7l5+erd+/e6tGjh4YMGaJHHnkkMs9fq6yslOM4KioqUk1Nje6++2717dtXbrdbeXl5evnll9vNp7q6Wvfdd59yc3PVo0cPXXrppbr66qu1fPly+f3+FvOGw2GtW7dOEyZMUO/evZWWlqahQ4dq2bJlOnHC7vHSAAAAANCWi7YQPOP111/XjTfeqIaGBt1444366U9/Ghm35KGHHtJLL72kHj166Prrr9f1118vv9+vf/u3f9PYsWNVX1/fZsza2lpdd911evvtt1VQUKAxY8bo888/14IFC7R69epW82/fvl35+fl67rnn1NTUpJtvvlljxoyRz+fTsmXLdPjw4ci8oVBIs2fP1qxZs7Rnzx5dddVVmjRpkhoaGrR8+XKNHz9eJ0+e7JyFBQAAAACK43sEO2rVqlUqKyvTzJkzW723dOlSjR49usWA64FAQPfdd59KS0v11FNPacmSJa0+V15erp///Odas2aN3O7Tg31u2rRJP/vZz7RixQotXLgwMm9NTY2mTZum2tpa/eY3v9GDDz7YYrDeXbt2KSsrK/L/3/72t1q3bp0KCwu1bt06XX755ZKkxsZG/fM//7NeeuklLV++XP/6r//a5u8NBAIKBAKR///4aiMAAAAAnIsTDofDsU7ir525mteeI0eOaODAgSoqKtLatWt100036Z133onqO06ePCmPx6P8/Hz96U9/ikyvrKzU+PHj5fF4dPjwYWVmZrb43LBhw3TgwIFIDpL061//Wg8//LBuuOEG/fGPfzzr9zY3N6tv3746efKkDh06pMsuu6xVXn/zN3+jQCCg48ePtygoz1i2bJmWL18e1e+NhuPYXSQ+198yGinJqWaxZJSXy5VkEkeSQsFms1hh2W3SLpfduaLU1DSzWM3NjWaxQqGQWaxwKGgXy/DvaCXOdhcRlm2NI8NYhm3EqVMNZrHa2recr5DhOp+UZNfeWLbPVrECAbtbP9ypPcxiWUoxbOct1y3Ldt5SaorbLJblPqOu7juTOJZtczgcn39DW1bL6/S64PP55PF4zjpn3F4RbG/4iEsuuaTF/6dMmXLWOEePHtXmzZv1+eefy+/3RxqD1NRUHTx4sM3PjBw5slURKEm5ubk6cOCAjh07FikEt27dKklavHjxWfOQpE8++UTHjx/XxIkTWxWBktSjRw+NHDlS7777rg4ePKjBgwe3mqe4uFgPPvhg5P9+v1/Z2dnn/G4AAAAAOCNuC8GODh/Rv3//dt976qmn9Mgjj6ipqSmq7+7Xr1+b0zMyMiSpRdfM6upqSVJOTs4541ZVVUmStmzZcs6zJMePH2+zEHS73ZHuqgAAAABwPuK2EOyotLS2uyHs3r1bv/zlL+X1evXMM8+osLBQl19+eaSIysrK0rFjx9r8rGW3mb925mrkT37yE40ZM+as87Z1RRIAAAAALFz0hWB73nrrLUnSv/zLv7TqZnry5En953/+p8n3ZGdn6/PPP9ehQ4c0bNiws8575krjkCFDOnzFEwAAAACsXfTDR7Tnu+9O3+jaVjfPP/zhD2YPPfj7v/97SVJpaek55/3pT38qr9erbdu2qaamxuT7AQAAACBa3bYQzM3NlSS99NJLLe4R/POf/6yHH37Y7HsWLlyoPn366I9//KOefvrpVgXm7t279V//9V+STt/f96tf/Up1dXWaOnVqi/EFzzh69KheffVVs/wAAAAA4Me6bSE4b948XX755dq8ebMGDx6smTNnauLEibrqqqtUUFCgAQMGmHzPpZdeqj/84Q/KyMjQAw88oJycHM2cOVNTpkzRoEGDdN111+kvf/lLZP5HHnlEc+bM0bZt2zR06FCNGjVKt99+u6ZNm6a8vDxlZ2frt7/9rUluAAAAANCWblsIZmZmas+ePZo1a5YaGxv19ttv6+jRo1qxYoXWrVtn+l2FhYXau3ev/umf/knhcFibNm3Szp075fV69dhjj7V4oqjL5dLvf/97lZeXa+LEiTpy5Ig2bNigHTt2KC0tTQ899JBefvll0/wAAAAA4K/F3YDyiI7f75fX6zWLl2w4cHuS4YC+ySl2ecXjwLKWA6RbshyUOcVw4FzLgYaDwWazWN19wFvLwdYtBz+O10HgLQdTPnmy3iyWLbu/o+WA8paHNlbtoGU7n2o4cLulVMOB7i3bZst9huPYXUNJS+tpFitkuLz8dfH4HAvKlWh1ZED5bntFEAAAAADQNgpBAAAAAEgwFIKdxHEcDRw4MNZpAAAAAEArFIIAAAAAkGDs7sxGC5999plSUlJinQYAAAAAtEIh2EmGDBkS6xQAAAAAoE10De0kbd0jWFlZKcdxVFRUpJqaGt19993q27ev3G638vLyGD8QAAAAQJfgimAM1NbW6rrrrlN9fb0KCgp0/Phxffjhh1qwYIFCoZAWLlwY6xQBAAAAdGNcEYyB8vJyjRgxQocPH9b69ev1wQcf6M0335QkrVix4qyfDQQC8vv9LV4AAAAAEA0KwRjweDx6/vnn5Xa7I9NuvfVW5eXl6auvvlJVVVW7ny0pKZHX6428srOzuyBjAAAAAN0JhWAMjBw5UpmZma2m5+bmSpKOHTvW7meLi4vl8/kir+rq6k7LEwAAAED3xD2CMdCvX782p2dkZEg63f2zPW63u8WVRAAAAACIFlcEY8DlYrEDAAAAiB0qEgAAAABIMBSCAAAAAJBgKAQBAAAAIMFQCAIAAABAgqEQBAAAAIAE44TD4XCsk8D58/v98nq9ZvGSkuxGFLGMlZJiN2SG49ic/wiHQyZxJKmpqf0hQ2LJ5bL7GyYnp5jFCgabzWKFQkGzWJbrhGXT7DiOSZx4zEmK37wc2cUKNJ4yi2W77O3Wect9hlU7L9k96bvR8G/oTu1hFstSqtsuL8t23rKNsIxluV+03BYbGvwmceK1rYlfVsvr9Drq8/nk8XjOOidXBAEAAAAgwcRdIeg4zllfhYWFsU4RAAAAAC5qdv0wjM2dO7fN6UOGDOniTAAAAACge4nbQnDNmjWxTgEAAAAAuqW46xoKAAAAAOhcF20hWFRUJMdxVFlZqYqKCo0fP169evWS4ziqra2VJG3fvl333HOP8vPz1bt3b/Xo0UNDhgzRI488Epnnr1VWVspxHBUVFammpkZ33323+vbtK7fbrby8PL388svt5lNdXa377rtPubm56tGjhy699FJdffXVWr58ufz+lk9fCofDWrdunSZMmKDevXsrLS1NQ4cO1bJly3TixAnLxQQAAAAArVy0heAZr7/+um688UY1NDToxhtv1E9/+tPI42ofeughvfTSS+rRo4euv/56XX/99fL7/fq3f/s3jR07VvX19W3GrK2t1XXXXae3335bBQUFGjNmjD7//HMtWLBAq1evbjX/9u3blZ+fr+eee05NTU26+eabNWbMGPl8Pi1btkyHDx+OzBsKhTR79mzNmjVLe/bs0VVXXaVJkyapoaFBy5cv1/jx43Xy5MnOWVgAAAAAoDi+R7CjVq1apbKyMs2cObPVe0uXLtXo0aNbjLMXCAR03333qbS0VE899ZSWLFnS6nPl5eX6+c9/rjVr1sjtPj1+3aZNm/Szn/1MK1as0MKFCyPz1tTUaNq0aaqtrdVvfvMbPfjggy3GHNq1a5eysrIi///tb3+rdevWqbCwUOvWrdPll18uSWpsbNQ///M/66WXXtLy5cv1r//6rxe+cAAAAACgDXE3oPy5Bp88cuSIBg4cqKKiIq1du1Y33XST3nnnnai+4+TJk/J4PMrPz9ef/vSnyPTKykqNHz9eHo9Hhw8fVmZmZovPDRs2TAcOHIjkIEm//vWv9fDDD+uGG27QH//4x7N+b3Nzs/r27auTJ0/q0KFDuuyyy1rl9Td/8zcKBAI6fvx4m4PYBgIBBQI/DD7u9/uVnZ0d1e8/GwaU7zgGlI8OA8pHGyv+BkmPx5yk+M2LAeWjw4DyHceA8tFhQPnoMKB8rHT9gPJxe0WwveEjLrnkkhb/nzJlylnjHD16VJs3b9bnn38uv9+vUOj0ipSamqqDBw+2+ZmRI0e2KgIlKTc3VwcOHNCxY8ciheDWrVslSYsXLz5rHpL0ySef6Pjx45o4cWKrIlCSevTooZEjR+rdd9/VwYMHNXjw4FbzlJSUaPny5ef8LgAAAABoT9wWgh0dPqJ///7tvvfUU0/pkUceUVNTU1Tf3a9fvzanZ2RkSFKLK3LV1dWSpJycnHPGraqqkiRt2bLlnGdJjh8/3mYhWFxcrAcffDDyf+srggAAAAC6v7gtBDsqLS2tzem7d+/WL3/5S3m9Xj3zzDMqLCzU5ZdfHrnnLysrS8eOHWvzs1bdQH7szNXIn/zkJxozZsxZ523riqQkud3uyG8AAAAAgPNx0ReC7XnrrbckSf/yL//SqpvpyZMn9Z//+Z8m35Odna3PP/9chw4d0rBhw84675krjUOGDOnwFU8AAAAAsHbRDx/Rnu+++05S2908//CHP5jd7Pv3f//3kqTS0tJzzvvTn/5UXq9X27ZtU01Njcn3AwAAAEC0um0hmJubK0l66aWXWtwj+Oc//1kPP/yw2fcsXLhQffr00R//+Ec9/fTTrQrM3bt367/+678kne7W+atf/Up1dXWaOnVqi/EFzzh69KheffVVs/wAAAAA4Me6bSE4b948XX755dq8ebMGDx6smTNnauLEibrqqqtUUFCgAQMGmHzPpZdeqj/84Q/KyMjQAw88oJycHM2cOVNTpkzRoEGDdN111+kvf/lLZP5HHnlEc+bM0bZt2zR06FCNGjVKt99+u6ZNm6a8vDxlZ2frt7/9rUluAAAAANCWblsIZmZmas+ePZo1a5YaGxv19ttv6+jRo1qxYoXWrVtn+l2FhYXau3ev/umf/knhcFibNm3Szp075fV69dhjj7V4oqjL5dLvf/97lZeXa+LEiTpy5Ig2bNigHTt2KC0tTQ899JBefvll0/wAAAAA4K/F3YDyiI7f75fX6zWLx4DyHceA8tFhQPloY8XfIOnxmJMUv3kxoHx0GFC+4xhQPjoMKB8dBpSPFQaUR4ylpLQ9HMf5SEpKMouVarjTa2w8aRInGLQrICx3eJYHQJJdXsGg3Q4BseEyXLdCcbpTtyzeHJddG2gpEc7/2p4sou3qKJfhOm97ks6yELRruyzb1GbD5WUlEdoaW12/vLpt11AAAAAAQNsoBAEAAAAgwVAIAgAAAECCoRAEAAAAgARDIQgAAAAACYZCEAAAAAASDIUgAAAAACQYCkEAAAAASDAMKH+RCQQCCgQCkf/7/f4YZgMAAADgYsQVwYtMSUmJvF5v5JWdnR3rlAAAAABcZCgELzLFxcXy+XyRV3V1daxTAgAAAHCRoWvoRcbtdsvtdsc6DQAAAAAXMa4IAgAAAECCoRAEAAAAgARDIRhH7rzzTg0ZMkRvvfVWrFMBAAAA0I1RCMaRr776Sv/3//5f+Xy+WKcCAAAAoBujEAQAAACABMNTQ+NIZWVlrFMAAAAAkAAoBNFCU9Mps1jhcKpZLJcrySxWUlKKSRzLnEKhZrNYlhzHrtOAy2UXKxQKmcWKV+Fw2CyW4zgmccKyyylehcJ265YrTldTq/VBsl1PLVm2N0lJNodKjY1BkzjWLLdry/UhXtctS0HDfb/ldm0lEdqaix1dQwEAAAAgwVAIAgAAAECCoRAEAAAAgARDIQgAAAAACYZCEAAAAAASDIUgAAAAACQYCkEAAAAASDAUggAAAACQYBhQ/iITCAQUCAQi//f7/THMBgAAAMDFiCuCF5mSkhJ5vd7IKzs7O9YpAQAAALjIUAheZIqLi+Xz+SKv6urqWKcEAAAA4CJD19CLjNvtltvtjnUaAAAAAC5iXBEEAAAAgARDIQgAAAAACYZCEAAAAAASDIUgAAAAACQYCkEAAAAASDA8NfQiFw6H4zZevMaSQiZR4vf3WYrP30is2MWyEo85WQsbbj+226JZKFPxuk7EY16m7YPpumWzfz0dKz7bwHiNZcsmr7j9eQmiI+sXheBFrq6uzjReKBQ0i9XYeDIuYyE2+BvGTtBus0a3EJ9HZ5b7H0vxmFdj06lYp9CmpqZArFO4qHT//WJ8tjWJoq6uTl6v96zzOOH4PR2BDgiFQvrLX/6ijIwMOY7T7nx+v1/Z2dmqrq6Wx+M57++zikMsYl0sseIxJ2IRqzNjxWNOxCLWxRIrHnMiVmLFCofDqqurU1ZWllyus98FyBXBi5zL5VK/fv06PL/H47ngldAyDrGIdbHEiseciEWszowVjzkRi1gXS6x4zIlYiRPrXFcCz+BhMQAAAACQYCgEAQAAACDBUAgmCLfbraVLl8rtdsdFHGIR62KJFY85EYtYnRkrHnMiFrEulljxmBOxiNUeHhYDAAAAAAmGK4IAAAAAkGAoBAEAAAAgwVAIAgAAAECCoRAEAAAAgARDIQgAAAAACYZCEAAAAAASDIUgAAAAACQYCkEAAAAASDD/P7Q2kanxXv1/AAAAAElFTkSuQmCC\n"},"metadata":{}}],"source":["# Plot Losses\n","plt.figure(figsize=(10, 5))\n","for method in results:\n","    plt.plot(results[method]['train_losses'], label=f'{method} Train')\n","    plt.plot(results[method]['valid_losses'], label=f'{method} Valid', linestyle='--')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()\n","plt.title('Training & Validation Loss Comparison')\n","plt.savefig('attention_loss_comparison.png')\n","plt.show()\n","\n","# Display Attention Maps\n","def display_attention(sentence, translation, attention, method_name):\n","    fig = plt.figure(figsize=(10, 10))\n","    ax = fig.add_subplot(111)\n","    attention = attention.squeeze(1).cpu().detach().numpy()\n","    cax = ax.matshow(attention, cmap='bone')\n","    ax.tick_params(labelsize=15)\n","    ax.set_xticklabels([''] + sentence, rotation=45)\n","    ax.set_yticklabels([''] + translation)\n","    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n","    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n","    plt.title(f'Attention Map ({method_name})')\n","    plt.show()\n","\n","def translate_sentence(sentence, model, device, max_len=50):\n","    model.eval()\n","    tokens = [sp_src.bos_id()] + sp_src.encode(sentence, out_type=int) + [sp_src.eos_id()]\n","    src_tensor = torch.LongTensor(tokens).unsqueeze(1).to(device)\n","    with torch.no_grad():\n","        encoder_outputs, hidden = model.encoder(src_tensor)\n","        attentions = torch.zeros(max_len, 1, len(tokens)).to(device)\n","        trg_indexes = [sp_trg.bos_id()]\n","        for i in range(max_len):\n","            trg_tensor = torch.LongTensor([trg_indexes[-1]]).to(device)\n","            output, hidden, attention = model.decoder(trg_tensor, hidden, encoder_outputs)\n","            attentions[i] = attention\n","            pred_token = output.argmax(1).item()\n","            trg_indexes.append(pred_token)\n","            if pred_token == sp_trg.eos_id():\n","                break\n","    trg_tokens = [sp_trg.decode([i]) for i in trg_indexes]\n","    return trg_tokens[1:], attentions[:len(trg_tokens)-1]\n","\n","# Example Translation\n","example_idx = 0\n","src_text = df.iloc[example_idx]['my']\n","tgt_text = df.iloc[example_idx]['en']\n","print(f\"Source: {src_text}\")\n","print(f\"Target: {tgt_text}\")\n","\n","for method in results:\n","    print(f\"\\nMethod: {method}\")\n","    trans, attn = translate_sentence(src_text, results[method]['model'], device)\n","    print(f\"Translation: {' '.join(trans)}\")\n","    display_attention(sp_src.encode_as_pieces(src_text), trans, attn, method)"],"id":"92EW_rwBwJ2o"},{"cell_type":"markdown","metadata":{"id":"zPIy9Q5pwJ2o"},"source":["## 7. Results Table\n","Please copy the following table to your README.md."],"id":"zPIy9Q5pwJ2o"},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iULsjQxCwJ2o","executionInfo":{"status":"ok","timestamp":1770447534705,"user_tz":-420,"elapsed":12,"user":{"displayName":"Htut Ko Ko","userId":"13068088192988605156"}},"outputId":"55f45597-0419-4b83-e64b-f38ae6451177"},"outputs":[{"output_type":"stream","name":"stdout","text":["Attentions          | Training Loss | Training PPL | Validation Loss | Validation PPL\n","------------------------------------------------------------------------------------\n","General Attention    | 4.819         | 123.868      | 6.662           | 782.166       \n","Additive Attention   | 4.447         | 85.368       | 6.440           | 626.673       \n"]}],"source":["print(\"Attentions          | Training Loss | Training PPL | Validation Loss | Validation PPL\")\n","print(\"------------------------------------------------------------------------------------\")\n","for method in results:\n","    tl = results[method]['train_losses'][-1]\n","    vl = results[method]['valid_losses'][-1]\n","    print(f\"{method.title() + ' Attention':<20} | {tl:<13.3f} | {math.exp(tl):<12.3f} | {vl:<15.3f} | {math.exp(vl):<14.3f}\")"],"id":"iULsjQxCwJ2o"}],"metadata":{"kernelspec":{"display_name":"Python 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