{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# X-Pert for Drug Perturbation Prediction\n",
        "\n",
        "This notebook is adapted from the gene perturbation workflow for **drug perturbation** analysis. It:\n",
        "- adapts perturbation format to handle drug names and dosages\n",
        "- loads drug embeddings (GROVER, RDKit, ECFP, etc.) instead of gene embeddings\n",
        "- incorporates dosage information into the model\n",
        "- maintains the same clean structure and organization\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 1. Imports\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Core\n",
        "import os, json, time, copy, warnings\n",
        "from pathlib import Path\n",
        "from typing import Dict\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torch import nn\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# X-Pert package\n",
        "import xpert\n",
        "from xpert.models import TransformerGenerator\n",
        "from xpert.loss import masked_relative_error, masked_mse_loss, masked_huber_loss\n",
        "from xpert.external_model.scgpt.gene_tokenizer import GeneVocab\n",
        "from xpert.external_model.gears.inference import compute_metrics\n",
        "# from xpert.data import Byte_Pert_Data\n",
        "\n",
        "# Utils\n",
        "from xpert.utils import fix_seed, merge_plot\n",
        "from xpert.external_model.scgpt.util import set_seed, map_raw_id_to_vocab_id, add_file_handler\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2. Global Settings and Logger\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:xpert:Running on 2025-11-01 17:32:36\n"
          ]
        }
      ],
      "source": [
        "# Reproducibility\n",
        "set_seed(42)\n",
        "fix_seed(2024)\n",
        "\n",
        "# Logger to file\n",
        "save_root = Path(\"./L1000_phase1/model_mode_scFlamingo_drug_v1\")\n",
        "save_root.mkdir(parents=True, exist_ok=True)\n",
        "logger = xpert.logger\n",
        "add_file_handler(logger, save_root / \"run.log\")\n",
        "logger.info(f\"Running on {time.strftime('%Y-%m-%d %H:%M:%S')}\")\n",
        "\n",
        "# Plot default\n",
        "plt.rcdefaults()\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 3. Data Configuration\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Dataset and split\n",
        "prefix = 'L1000_phase1'  # or 'sciplex_3', etc.\n",
        "add_control = False\n",
        "\n",
        "# Data split column (for drug perturbation datasets)\n",
        "# data_split_0: random split perts\n",
        "# data_split_1: split drugs\n",
        "# data_split_2: split cell types\n",
        "split_col = 'data_split_1'\n",
        "\n",
        "# Paths\n",
        "# Drug perturbation data path\n",
        "data_dir = Path('../../data') / prefix\n",
        "pert_data_version = 'pert_data.pkl'\n",
        "\n",
        "# Drug embedding directory\n",
        "drug_embed_dir = data_dir\n",
        "\n",
        "# scGPT pretrained model\n",
        "load_model_dir = Path('../../data/scGPT_human')\n",
        "model_config_file = load_model_dir / 'args.json'\n",
        "model_file = load_model_dir / 'best_model.pt'\n",
        "vocab_file = load_model_dir / 'vocab.json'\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4. Model Hyperparameters\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note: For drug perturbation, we typically use fewer layers (nlayers = 4-12) depending on computational resources. Adjust `device_ids` based on available GPUs.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Training hyperparameters\n",
        "batch_size = 64\n",
        "eval_batch_size = 64\n",
        "log_interval = 100\n",
        "\n",
        "# Cell encoder hyperparameters\n",
        "embsize = 512\n",
        "d_hid = 512\n",
        "nlayers = 2  # Can reduce to 2 for faster training\n",
        "nhead = 8\n",
        "n_layers_cls = 3\n",
        "dropout = 0.2\n",
        "use_fast_transformer = True\n",
        "amp = True\n",
        "device_ids = [0, 1, 2, 3]  # Adjust based on available GPUs\n",
        "\n",
        "# Drug embedding settings\n",
        "drug_embed_mode = 'ecfp'  # Options: 'grover', 'rdkit', 'morgan', 'ecfp', 'chemberta_st', 'grover+rdkit'\n",
        "# Embedding dimension depends on the embedding method (will be loaded dynamically)\n",
        "gpt_emb_dim = None  # Will be set after loading embeddings\n",
        "\n",
        "# add-token settings\n",
        "load_encoder_plus = True  # copy encoder weights to encoder_plus when adding tokens\n",
        "include_zero_gene = \"all\"\n",
        "\n",
        "# Task settings\n",
        "# X-Pert settings for DRUG perturbation\n",
        "pert_mode = 'drug'  # Changed from 'gene'\n",
        "drug_embed_mode = drug_embed_mode  # Set above\n",
        "pert_flag_mode = True  # Use drug-gene interaction matrix when True\n",
        "delta_mode = False\n",
        "attn_gate_mode = True\n",
        "load_cxg_weight = True\n",
        "mask_mode = True\n",
        "use_scgpt_layer = True\n",
        "use_scgpt_input = True\n",
        "add_token = True\n",
        "init_mode = False\n",
        "cross_mode = True\n",
        "\n",
        "# Dosage settings\n",
        "dosage_mode = True  # Enable dosage information\n",
        "dosage_mode_type = 1  # 0: direct multiply; 1: learnable vector; 2: additive with learnable\n",
        "\n",
        "# Scheduler\n",
        "epochs = 2\n",
        "lr = 1e-4\n",
        "scheduler_type = 'cosine_warm'  # or 'steplr', 'cosine'\n",
        "schedule_interval = 5  # for steplr\n",
        "\n",
        "# Gradient clipping\n",
        "max_norm = 1.0\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 5. Helper Losses and Training/Eval Routines"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "# LR scheduler (cosine with warmup)\n",
        "from torch.optim.lr_scheduler import LambdaLR\n",
        "import math\n",
        "\n",
        "def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps):\n",
        "    def lr_lambda(current_step):\n",
        "        if current_step < num_warmup_steps:\n",
        "            return float(current_step) / float(max(1, num_warmup_steps))\n",
        "        progress = (current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))\n",
        "        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * progress)))\n",
        "    return LambdaLR(optimizer, lr_lambda)\n",
        "\n",
        "# Training / Evaluation routines for DRUG perturbation\n",
        "def train(model: nn.Module, train_loader: torch.utils.data.DataLoader) -> None:\n",
        "    model.train()\n",
        "    total_loss, total_mse = 0.0, 0.0\n",
        "    start_time = time.time()\n",
        "    num_batches = len(train_loader)\n",
        "\n",
        "    for batch, batch_data in enumerate(tqdm(train_loader)):\n",
        "        batch_size = len(batch_data.y)\n",
        "        batch_data.to(device)\n",
        "        x: torch.Tensor = batch_data.x\n",
        "        ori_gene_values = x\n",
        "        # Handle pert_flags for drug mode\n",
        "        if pert_mode == 'drug' and pert_flag_mode:\n",
        "            pert_flags = batch_data.pert_flags\n",
        "        else:\n",
        "            pert_flags = batch_data.pert_flags.long()\n",
        "        target_gene_values = batch_data.y\n",
        "\n",
        "        # Prepare drug perturbation embeddings and dosages\n",
        "        # Format: \"drug1; drug2 | dosage1; dosage2\"\n",
        "        batch_perts = batch_data.pert\n",
        "        batch_perts = [i.split(' | ')[0].split(\"; \") for i in batch_perts]\n",
        "        # Extract dosages and apply log10 transform\n",
        "        batch_dosages = [[np.log10(float(j)+1) for j in i.split(' | ')[1].split(\"; \")] \n",
        "                        for i in batch_data.pert]\n",
        "        \n",
        "        max_pert_len = max([len(perts) for perts in batch_perts])\n",
        "        batch_pert_embed = torch.zeros(batch_size, max_pert_len, gpt_emb_dim).float()\n",
        "        pert_mask = torch.ones(batch_size, max_pert_len)\n",
        "        batch_dosages_pad = torch.zeros(batch_size, max_pert_len).float()\n",
        "        \n",
        "        for i, perts in enumerate(batch_perts):\n",
        "            for j, pert in enumerate(perts):\n",
        "                batch_pert_embed[i, j, :] = torch.tensor(np.array(pert_embed_dict[pert]))\n",
        "                pert_mask[i, j] = 0\n",
        "                batch_dosages_pad[i, j] = batch_dosages[i][j]\n",
        "        batch_pert_embed = batch_pert_embed.to(device)\n",
        "        batch_dosages_pad = batch_dosages_pad.to(device)\n",
        "        \n",
        "        # Set to None if dosage_mode is False\n",
        "        if not dosage_mode:\n",
        "            batch_dosages_pad = None\n",
        "\n",
        "        # Select input genes\n",
        "        if include_zero_gene in [\"all\", \"batch-wise\"]:\n",
        "            if include_zero_gene == \"all\":\n",
        "                input_gene_ids = torch.arange(n_genes, device=device, dtype=torch.long)\n",
        "            else:\n",
        "                input_gene_ids = (\n",
        "                    ori_gene_values.nonzero()[:, 1].flatten().unique().sort()[0]\n",
        "                )\n",
        "            if len(input_gene_ids) > max_seq_len:\n",
        "                input_gene_ids = torch.randperm(len(input_gene_ids), device=device)[:max_seq_len]\n",
        "            input_values = ori_gene_values[:, input_gene_ids]\n",
        "            input_pert_flags = pert_flags[:, input_gene_ids]\n",
        "            target_values = target_gene_values[:, input_gene_ids]\n",
        "            if delta_mode:\n",
        "                target_values = target_values - input_values\n",
        "            mapped_input_gene_ids = map_raw_id_to_vocab_id(input_gene_ids, gene_ids)\n",
        "            mapped_input_gene_ids = mapped_input_gene_ids.repeat(batch_size, 1)\n",
        "            src_key_padding_mask = torch.zeros_like(input_values, dtype=torch.bool, device=device)\n",
        "\n",
        "        with torch.cuda.amp.autocast(enabled=amp):\n",
        "            output_dict = model(\n",
        "                mapped_input_gene_ids,\n",
        "                input_values,\n",
        "                input_pert_flags,\n",
        "                src_key_padding_mask=src_key_padding_mask,\n",
        "                batch_pert_embed=batch_pert_embed,\n",
        "                pert_mask=pert_mask,\n",
        "                batch_dosages_pad=batch_dosages_pad,  # Now includes dosage information\n",
        "            )\n",
        "            output_values = output_dict[\"mlm_output\"]\n",
        "            masked_positions = torch.ones_like(input_values, dtype=torch.bool)\n",
        "            loss = loss_mse = criterion(output_values, target_values, masked_positions)\n",
        "\n",
        "        model.zero_grad()\n",
        "        scaler.scale(loss).backward()\n",
        "        scaler.unscale_(optimizer)\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm, error_if_nonfinite=False if scaler.is_enabled() else True)\n",
        "        scaler.step(optimizer)\n",
        "        scaler.update()\n",
        "        if scheduler_type in ['cosine', 'cosine_warm']:\n",
        "            scheduler.step()\n",
        "\n",
        "        total_loss += loss.item()\n",
        "        total_mse += loss_mse.item()\n",
        "        if batch % log_interval == 0 and batch > 0:\n",
        "            lr_ = scheduler.get_last_lr()[0] if scheduler_type != 'steplr' else scheduler.get_last_lr()[0]\n",
        "            ms_per_batch = (time.time() - start_time) * 1000 / log_interval\n",
        "            cur_loss = total_loss / log_interval\n",
        "            cur_mse = total_mse / log_interval\n",
        "            logger.info(f\"| epoch {epoch:3d} | {batch:3d}/{num_batches:3d} batches | lr {lr_:07.6f} | ms/batch {ms_per_batch:5.2f} | loss {cur_loss:5.5f} | mse {cur_mse:5.5f} |\")\n",
        "            total_loss = 0\n",
        "            total_mse = 0\n",
        "            start_time = time.time()\n",
        "\n",
        "@torch.no_grad()\n",
        "def evaluate(model: nn.Module, val_loader: torch.utils.data.DataLoader):\n",
        "    model.eval()\n",
        "    total_loss = 0.0\n",
        "    total_error = 0.0\n",
        "    for batch, batch_data in enumerate(val_loader):\n",
        "        batch_size = len(batch_data.y)\n",
        "        batch_data.to(device)\n",
        "        x: torch.Tensor = batch_data.x\n",
        "        ori_gene_values = x\n",
        "        if pert_mode == 'drug' and pert_flag_mode:\n",
        "            pert_flags = batch_data.pert_flags\n",
        "        else:\n",
        "            pert_flags = batch_data.pert_flags.long()\n",
        "        target_gene_values = batch_data.y\n",
        "\n",
        "        batch_perts = batch_data.pert\n",
        "        batch_perts = [i.split(' | ')[0].split(\"; \") for i in batch_perts]\n",
        "        batch_dosages = [[np.log10(float(j)+1) for j in i.split(' | ')[1].split(\"; \")] \n",
        "                        for i in batch_data.pert]\n",
        "        max_pert_len = max([len(perts) for perts in batch_perts])\n",
        "        batch_pert_embed = torch.zeros(batch_size, max_pert_len, gpt_emb_dim).float()\n",
        "        pert_mask = torch.ones(batch_size, max_pert_len)\n",
        "        batch_dosages_pad = torch.zeros(batch_size, max_pert_len).float()\n",
        "        for i, perts in enumerate(batch_perts):\n",
        "            for j, pert in enumerate(perts):\n",
        "                batch_pert_embed[i, j, :] = torch.tensor(np.array(pert_embed_dict[pert]))\n",
        "                pert_mask[i, j] = 0\n",
        "                batch_dosages_pad[i, j] = batch_dosages[i][j]\n",
        "        batch_pert_embed = batch_pert_embed.to(device)\n",
        "        batch_dosages_pad = batch_dosages_pad.to(device)\n",
        "        if not dosage_mode:\n",
        "            batch_dosages_pad = None\n",
        "\n",
        "        if include_zero_gene in [\"all\", \"batch-wise\"]:\n",
        "            if include_zero_gene == \"all\":\n",
        "                input_gene_ids = torch.arange(n_genes, device=device)\n",
        "            else:\n",
        "                input_gene_ids = (\n",
        "                    ori_gene_values.nonzero()[:, 1].flatten().unique().sort()[0]\n",
        "                )\n",
        "            if len(input_gene_ids) > max_seq_len:\n",
        "                input_gene_ids = torch.randperm(len(input_gene_ids), device=device)[:max_seq_len]\n",
        "            input_values = ori_gene_values[:, input_gene_ids]\n",
        "            input_pert_flags = pert_flags[:, input_gene_ids]\n",
        "            target_values = target_gene_values[:, input_gene_ids]\n",
        "            if delta_mode:\n",
        "                target_values = target_values - input_values\n",
        "            mapped_input_gene_ids = map_raw_id_to_vocab_id(input_gene_ids, gene_ids)\n",
        "            mapped_input_gene_ids = mapped_input_gene_ids.repeat(batch_size, 1)\n",
        "            src_key_padding_mask = torch.zeros_like(input_values, dtype=torch.bool, device=input_values.device)\n",
        "\n",
        "        with torch.cuda.amp.autocast(enabled=amp):\n",
        "            output_dict = model(\n",
        "                mapped_input_gene_ids,\n",
        "                input_values,\n",
        "                input_pert_flags,\n",
        "                src_key_padding_mask=src_key_padding_mask,\n",
        "                batch_pert_embed=batch_pert_embed,\n",
        "                pert_mask=pert_mask,\n",
        "                batch_dosages_pad=batch_dosages_pad,\n",
        "            )\n",
        "            output_values = output_dict[\"mlm_output\"]\n",
        "            masked_positions = torch.ones_like(input_values, dtype=torch.bool, device=input_values.device)\n",
        "            loss = criterion(output_values, target_values, masked_positions)\n",
        "        total_loss += loss.item()\n",
        "        total_error += masked_relative_error(output_values, target_values, masked_positions).item()\n",
        "    return total_loss / len(val_loader), total_error / len(val_loader)\n",
        "\n",
        "@torch.no_grad()\n",
        "def pred_perturb_new(model, batch_data, include_zero_gene=\"batch-wise\", gene_ids=None, amp=True):\n",
        "    model.eval()\n",
        "    device = next(model.parameters()).device\n",
        "    batch_data.to(device)\n",
        "    batch_size = len(batch_data.pert)\n",
        "    x: torch.Tensor = batch_data.x\n",
        "    ori_gene_values = x\n",
        "    if pert_mode == 'drug' and pert_flag_mode:\n",
        "        pert_flags = batch_data.pert_flags\n",
        "    else:\n",
        "        pert_flags = batch_data.pert_flags.long()\n",
        "\n",
        "    batch_perts = batch_data.pert\n",
        "    batch_perts = [i.split(' | ')[0].split(\"; \") for i in batch_perts]\n",
        "    batch_dosages = [[np.log10(float(j)+1) for j in i.split(' | ')[1].split(\"; \")] \n",
        "                    for i in batch_data.pert]\n",
        "    max_pert_len = max([len(perts) for perts in batch_perts])\n",
        "    batch_pert_embed = torch.zeros(batch_size, max_pert_len, gpt_emb_dim).float()\n",
        "    pert_mask = torch.ones(batch_size, max_pert_len)\n",
        "    batch_dosages_pad = torch.zeros(batch_size, max_pert_len).float()\n",
        "    for i, perts in enumerate(batch_perts):\n",
        "        for j, pert in enumerate(perts):\n",
        "            batch_pert_embed[i, j, :] = torch.tensor(np.array(pert_embed_dict[pert]))\n",
        "            pert_mask[i, j] = 0\n",
        "            batch_dosages_pad[i, j] = batch_dosages[i][j]\n",
        "    batch_pert_embed = batch_pert_embed.to(device)\n",
        "    batch_dosages_pad = batch_dosages_pad.to(device)\n",
        "    if not dosage_mode:\n",
        "        batch_dosages_pad = None\n",
        "\n",
        "    if include_zero_gene in [\"all\", \"batch-wise\"]:\n",
        "        assert gene_ids is not None\n",
        "        if include_zero_gene == \"all\":\n",
        "            input_gene_ids = torch.arange(ori_gene_values.size(1), device=device)\n",
        "        else:\n",
        "            input_gene_ids = (\n",
        "                ori_gene_values.nonzero()[:, 1].flatten().unique().sort()[0]\n",
        "            )\n",
        "        input_values = ori_gene_values[:, input_gene_ids]\n",
        "        input_pert_flags = pert_flags[:, input_gene_ids]\n",
        "        mapped_input_gene_ids = map_raw_id_to_vocab_id(input_gene_ids, gene_ids)\n",
        "        mapped_input_gene_ids = mapped_input_gene_ids.repeat(batch_size, 1)\n",
        "        src_key_padding_mask = torch.zeros_like(input_values, dtype=torch.bool, device=device)\n",
        "        with torch.cuda.amp.autocast(enabled=amp):\n",
        "            output_dict = model(\n",
        "                mapped_input_gene_ids,\n",
        "                input_values,\n",
        "                input_pert_flags,\n",
        "                src_key_padding_mask=src_key_padding_mask,\n",
        "                batch_pert_embed=batch_pert_embed,\n",
        "                pert_mask=pert_mask,\n",
        "                batch_dosages_pad=batch_dosages_pad,\n",
        "            )\n",
        "        output_values = output_dict[\"mlm_output\"].float()\n",
        "        pred_gene_values = torch.zeros_like(ori_gene_values)\n",
        "        pred_gene_values[:, input_gene_ids] = output_values\n",
        "        if delta_mode:\n",
        "            pred_gene_values = input_values + pred_gene_values\n",
        "    return pred_gene_values\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "def eval_perturb_new(\n",
        "    loader,\n",
        "    model,\n",
        "    device: torch.device,\n",
        "):\n",
        "    \"\"\"\n",
        "    Run model in inference mode using a given data loader.\n",
        "    Returns a dict with predictions on all genes and DE gene subsets.\n",
        "    \"\"\"\n",
        "    model.eval()\n",
        "    model.to(device)\n",
        "\n",
        "    pert_cat = []\n",
        "    pred = []\n",
        "    truth = []\n",
        "    pred_de = []\n",
        "    truth_de = []\n",
        "    results = {}\n",
        "\n",
        "    for itr, batch in enumerate(loader):\n",
        "        batch.to(device)\n",
        "        pert_cat.extend(batch.pert)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            p = pred_perturb_new(model, batch, include_zero_gene, gene_ids=gene_ids)\n",
        "            t = batch.y\n",
        "            pred.extend(p.cpu())\n",
        "            truth.extend(t.cpu())\n",
        "\n",
        "            # Differentially expressed genes\n",
        "            for bidx, de_idx in enumerate(batch.de_idx):\n",
        "                pred_de.append(p[bidx, de_idx])\n",
        "                truth_de.append(t[bidx, de_idx])\n",
        "\n",
        "    # all genes\n",
        "    results[\"pert_cat\"] = np.array(pert_cat)\n",
        "    pred = torch.stack(pred)\n",
        "    truth = torch.stack(truth)\n",
        "    results[\"pred\"] = pred.detach().cpu().numpy().astype(np.float64)\n",
        "    results[\"truth\"] = truth.detach().cpu().numpy().astype(np.float64)\n",
        "\n",
        "    # DE genes\n",
        "    pred_de = torch.stack(pred_de)\n",
        "    truth_de = torch.stack(truth_de)\n",
        "    results[\"pred_de\"] = pred_de.detach().cpu().numpy().astype(np.float64)\n",
        "    results[\"truth_de\"] = truth_de.detach().cpu().numpy().astype(np.float64)\n",
        "\n",
        "    return results\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 6. Data Loading and Tokenization\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 101775/101775 [09:15<00:00, 183.18it/s]\n",
            "100%|██████████| 26950/26950 [02:32<00:00, 176.48it/s]\n",
            "0it [00:00, ?it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "========== get Data_scGPT finished!\n"
          ]
        }
      ],
      "source": [
        "# Build dataset via Byte_Pert_Data\n",
        "seed = 2024\n",
        "bs_train = 32\n",
        "bs_test = bs_train * 2\n",
        "\n",
        "# Load preprocessed pert_data object\n",
        "import pickle\n",
        "pert_data = pickle.load(open(data_dir / pert_data_version, 'rb'))\n",
        "# pert_data = pickle.load(open(\"/nfs/public/lichen/results/single_cell_perturbation/perturbation_drug/data/L1000_phase1/pert_data_v1.pkl\", 'rb'))\n",
        "\n",
        "# Set var_genes\n",
        "pert_data.var_genes = pert_data.adata_split.var_names\n",
        "\n",
        "# Build X-Pert training datasets\n",
        "# Note: get_Data_scgpt needs to be adapted for drug format with split_col\n",
        "pert_data.get_Data_scgpt_2(\n",
        "    num_de_genes=pert_data.num_de_genes,\n",
        "    dataset_name=['train', 'test', 'val'],\n",
        "    add_control=add_control,\n",
        "    split_col=split_col,  # Drug datasets use specific split columns\n",
        "    pert_flag_mode=pert_flag_mode,  # For drug-gene interaction matrix\n",
        "    drug_embed_dir=drug_embed_dir,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "# Set adata\n",
        "pert_data.adata = pert_data.adata_split\n",
        "pert_data.adata.var[\"gene_name\"] = pert_data.adata.var_names\n",
        "\n",
        "# DataLoaders\n",
        "trainloader, testloader, valloader = pert_data.get_dataloader(\n",
        "    mode='all', \n",
        "    bs_train=int(bs_train)*len(device_ids), \n",
        "    bs_test=int(bs_test)*len(device_ids)\n",
        ")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(4029, 530, 0)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(trainloader), len(testloader), len(valloader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(4029, 530, 530)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(pert_data.dataloader[\"train_loader\"]), len(pert_data.dataloader[\"val_loader\"]), len(pert_data.dataloader[\"test_loader\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 7. Drug Embeddings and Vocabulary\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:xpert:Loaded 17200 drug embeddings with dimension 2048\n",
            "INFO:xpert:match 978/978 genes in vocabulary of size 60726.\n"
          ]
        }
      ],
      "source": [
        "# Load drug embeddings based on drug_embed_mode\n",
        "if drug_embed_mode == 'grover':\n",
        "    pert_embed = pd.read_csv(drug_embed_dir / 'embed_grover.csv', sep=',', index_col=0)\n",
        "elif drug_embed_mode == 'rdkit':\n",
        "    pert_embed = pd.read_csv(drug_embed_dir / 'embed_rdkit.csv', sep=',', index_col=0)\n",
        "elif drug_embed_mode == 'morgan':\n",
        "    pert_embed = pd.read_csv(drug_embed_dir / 'embed_morgan.csv', sep=',', index_col=0)\n",
        "elif drug_embed_mode == 'ecfp':\n",
        "    pert_embed = pd.read_csv(drug_embed_dir / 'embed_ecfp.csv', sep=',', index_col=0)\n",
        "elif drug_embed_mode == 'chemberta_st':\n",
        "    pert_embed = pd.read_csv(drug_embed_dir / 'embed_chemberta_st.csv', sep=',', index_col=0)\n",
        "elif drug_embed_mode == 'grover+rdkit':\n",
        "    pert_embed_1 = pd.read_csv(drug_embed_dir / 'embed_grover.csv', sep=',', index_col=0)\n",
        "    pert_embed_2 = pd.read_csv(drug_embed_dir / 'embed_rdkit.csv', sep=',', index_col=0)\n",
        "    pert_embed = pd.concat([pert_embed_1, pert_embed_2], axis=0)\n",
        "    from sklearn.preprocessing import StandardScaler\n",
        "    scaler = StandardScaler()\n",
        "    pert_embed = pd.DataFrame(\n",
        "        scaler.fit_transform(pert_embed.T).T, \n",
        "        index=pert_embed.index, \n",
        "        columns=pert_embed.columns\n",
        "    )\n",
        "else:\n",
        "    raise ValueError(f\"Unknown drug_embed_mode: {drug_embed_mode}\")\n",
        "\n",
        "# Set embedding dimension\n",
        "gpt_emb_dim = pert_embed.shape[0]\n",
        "\n",
        "# Collect all drug names (from perturbation_group format: \"drug1; drug2 | dosage1; dosage2\")\n",
        "total_perts = [i.split(' | ')[0] for i in pert_data.filter_perturbation_list]\n",
        "# Split multi-drug perturbations\n",
        "all_drugs = []\n",
        "for pert_str in total_perts:\n",
        "    all_drugs.extend(pert_str.split('; '))\n",
        "total_perts = np.unique(all_drugs)\n",
        "\n",
        "# Create pert_embed_dict\n",
        "pert_embed_dict: Dict[str, np.ndarray] = {}\n",
        "np.random.seed(2024)\n",
        "for pert in total_perts:\n",
        "    if pert in pert_embed.columns:\n",
        "        pert_embed_dict[pert] = pert_embed.loc[:, pert].values\n",
        "    else:\n",
        "        logger.warning(f'{pert} not in pert_embed, using random embedding')\n",
        "        pert_embed_dict[pert] = pert_embed.loc[:, np.random.choice(pert_embed.columns, 1)[0]].values\n",
        "\n",
        "logger.info(f\"Loaded {len(pert_embed_dict)} drug embeddings with dimension {gpt_emb_dim}\")\n",
        "\n",
        "# Load vocab and extend if needed\n",
        "vocab = GeneVocab.from_file(vocab_file)\n",
        "special_tokens = [\"<pad>\", \"<cls>\", \"<eoc>\"]\n",
        "for s in special_tokens:\n",
        "    if s not in vocab:\n",
        "        vocab.append_token(s)\n",
        "        \n",
        "vocab_ori = copy.deepcopy(vocab)\n",
        "if add_token:\n",
        "    # add non-overlap genes to the vocab\n",
        "    add_genes = np.setdiff1d(pert_data.adata.var_names, list(vocab.get_stoi().keys()))\n",
        "    for gene in add_genes:\n",
        "        if gene not in vocab:\n",
        "            vocab.append_token(gene)\n",
        "\n",
        "# Mark genes present in vocab\n",
        "pert_data.adata.var[\"id_in_vocab\"] = [1 if gene in vocab else -1 for gene in pert_data.adata.var_names]\n",
        "gene_ids_in_vocab = np.array(pert_data.adata.var[\"id_in_vocab\"])\n",
        "logger.info(f\"match {np.sum(gene_ids_in_vocab >= 0)}/{len(gene_ids_in_vocab)} genes in vocabulary of size {len(vocab)}.\")\n",
        "\n",
        "# Build token ids for input genes\n",
        "genes = pert_data.adata.var[\"gene_name\"].tolist()\n",
        "vocab.set_default_index(vocab[\"<pad>\"])\n",
        "gene_ids = np.array([vocab[g] if g in vocab else vocab[\"<pad>\"] for g in genes], dtype=int)\n",
        "n_genes = len(genes)\n",
        "\n",
        "# Sequence length\n",
        "max_seq_len = 6000\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 8. Build Model, Optimizer and Scheduler\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using simple batchnorm instead of domain specific batchnorm\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:xpert:Missing keys: 0\n",
            "INFO:xpert:Unexpected keys: 120\n"
          ]
        }
      ],
      "source": [
        "# Load model config\n",
        "with open(model_config_file, 'r') as f:\n",
        "    model_configs = json.load(f)\n",
        "embsize = model_configs.get('embsize', embsize)\n",
        "d_hid = model_configs.get('d_hid', d_hid)\n",
        "n_layers_cls = model_configs.get('n_layers_cls', n_layers_cls)\n",
        "\n",
        "# Token count\n",
        "ntokens = len(vocab_ori)  # using extended vocab if add_token True\n",
        "\n",
        "# Model\n",
        "model = TransformerGenerator(\n",
        "    ntokens,\n",
        "    embsize,\n",
        "    nhead,\n",
        "    d_hid,\n",
        "    nlayers,\n",
        "    nlayers_cls=n_layers_cls,\n",
        "    n_cls=1,\n",
        "    vocab=vocab,\n",
        "    dropout=dropout,\n",
        "    pad_token='<pad>',\n",
        "    pad_value=0,\n",
        "    pert_pad_id=2,\n",
        "    use_fast_transformer=use_fast_transformer,\n",
        "    pert_embed_dict=pert_embed_dict,\n",
        "    gpt_emb_dim=gpt_emb_dim,\n",
        "    model_mode='scFlamingo_drug_v1',  # or specify your model mode\n",
        "    attn_gate_mode=attn_gate_mode,\n",
        "    pert_mode=pert_mode,\n",
        "    drug_embed_mode=drug_embed_mode,\n",
        "    pert_flag_mode=pert_flag_mode,\n",
        "    use_scgpt_layer=use_scgpt_layer,\n",
        "    use_scgpt_input=use_scgpt_input,\n",
        "    mask_mode=mask_mode,\n",
        "    add_token=add_token,\n",
        "    init_mode=init_mode,\n",
        "    dosage_mode_type=dosage_mode_type,\n",
        "    cross_mode=cross_mode,\n",
        ")\n",
        "\n",
        "# Optionally load pretrained partial weights\n",
        "if load_cxg_weight:\n",
        "    load_param_prefixs = [\"encoder\", \"value_encoder\", \"transformer_encoder\"]\n",
        "    state = torch.load(model_file)\n",
        "    model_dict = model.state_dict()\n",
        "    pretrained_dict = {k: v for k, v in state.items() if any([k.startswith(p) for p in load_param_prefixs])}\n",
        "    model_dict.update(pretrained_dict)\n",
        "    missing = model.load_state_dict(model_dict, strict=False)\n",
        "    logger.info(f\"Missing keys: {len(missing.missing_keys)}\")\n",
        "    logger.info(f\"Unexpected keys: {len(missing.unexpected_keys)}\")\n",
        "\n",
        "# If adding tokens, optionally load encoder weights into encoder_plus and init new tokens\n",
        "if add_token and load_encoder_plus:\n",
        "    with torch.no_grad():\n",
        "        n = model.encoder.embedding.num_embeddings\n",
        "        # copy base encoder weights and norms\n",
        "        model.encoder_plus.embedding.weight[:n] = model.encoder.embedding.weight\n",
        "        model.encoder_plus.enc_norm.weight[:] = model.encoder.enc_norm.weight\n",
        "        model.encoder_plus.enc_norm.bias[:] = model.encoder.enc_norm.bias\n",
        "        # initialize newly added tokens with mean/std of pretrained embeddings\n",
        "        pretrained_embed = model.encoder.embedding.weight  # (n, d)\n",
        "        mean = pretrained_embed.mean(dim=0)\n",
        "        std = pretrained_embed.std(dim=0)\n",
        "        m = model.encoder_plus.embedding.num_embeddings - n\n",
        "        if m > 0:\n",
        "            model.encoder_plus.embedding.weight[n:] = torch.normal(\n",
        "                mean=mean.expand(m, -1),\n",
        "                std=std.expand(m, -1)\n",
        "            )\n",
        "\n",
        "# Device & DP\n",
        "device = torch.device(f\"cuda:{device_ids[0]}\" if torch.cuda.is_available() else \"cpu\")\n",
        "if torch.cuda.device_count() > 1:\n",
        "    try:\n",
        "        device_ids  # use pre-defined list if exists\n",
        "    except NameError:\n",
        "        device_ids = list(range(torch.cuda.device_count()))\n",
        "    model = torch.nn.DataParallel(model, device_ids=device_ids).to(device)\n",
        "else:\n",
        "    model = model.to(device)\n",
        "\n",
        "# Loss, optimizer, scheduler\n",
        "loss_type = 'mse'\n",
        "criterion = masked_mse_loss if loss_type == 'mse' else masked_huber_loss\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
        "if scheduler_type == 'steplr':\n",
        "    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, schedule_interval, gamma=0.9)\n",
        "elif scheduler_type == 'cosine':\n",
        "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=len(trainloader)*epochs)\n",
        "elif scheduler_type == 'cosine_warm':\n",
        "    scheduler = get_cosine_schedule_with_warmup(\n",
        "        optimizer,\n",
        "        num_warmup_steps=int(len(trainloader)*epochs*0.05),\n",
        "        num_training_steps=len(trainloader)*epochs,\n",
        "    )\n",
        "else:\n",
        "    raise ValueError('Unknown scheduler')\n",
        "\n",
        "scaler = torch.cuda.amp.GradScaler(enabled=amp)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 9. Training Loop and Validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  2%|▏         | 100/4029 [00:17<08:15,  7.94it/s]INFO:xpert:| epoch   1 | 100/4029 batches | lr 0.000025 | ms/batch 171.49 | loss 65.00415 | mse 65.00415 |\n",
            "  5%|▍         | 200/4029 [00:29<08:00,  7.98it/s]INFO:xpert:| epoch   1 | 200/4029 batches | lr 0.000050 | ms/batch 126.04 | loss 4.40624 | mse 4.40624 |\n",
            "  7%|▋         | 300/4029 [00:42<07:48,  7.96it/s]INFO:xpert:| epoch   1 | 300/4029 batches | lr 0.000075 | ms/batch 126.08 | loss 1.59986 | mse 1.59986 |\n",
            " 10%|▉         | 400/4029 [00:54<07:35,  7.96it/s]INFO:xpert:| epoch   1 | 400/4029 batches | lr 0.000100 | ms/batch 125.96 | loss 1.56468 | mse 1.56468 |\n",
            " 12%|█▏        | 500/4029 [01:07<07:20,  8.01it/s]INFO:xpert:| epoch   1 | 500/4029 batches | lr 0.000100 | ms/batch 125.36 | loss 1.52894 | mse 1.52894 |\n",
            " 15%|█▍        | 600/4029 [01:19<07:08,  7.99it/s]INFO:xpert:| epoch   1 | 600/4029 batches | lr 0.000100 | ms/batch 124.99 | loss 1.51555 | mse 1.51555 |\n",
            " 17%|█▋        | 700/4029 [01:32<06:55,  8.01it/s]INFO:xpert:| epoch   1 | 700/4029 batches | lr 0.000100 | ms/batch 124.94 | loss 1.48804 | mse 1.48804 |\n",
            " 20%|█▉        | 800/4029 [01:44<06:45,  7.97it/s]INFO:xpert:| epoch   1 | 800/4029 batches | lr 0.000099 | ms/batch 125.23 | loss 1.47788 | mse 1.47788 |\n",
            " 22%|██▏       | 900/4029 [01:57<06:30,  8.02it/s]INFO:xpert:| epoch   1 | 900/4029 batches | lr 0.000099 | ms/batch 125.18 | loss 1.45252 | mse 1.45252 |\n",
            " 25%|██▍       | 1000/4029 [02:09<06:17,  8.03it/s]INFO:xpert:| epoch   1 | 1000/4029 batches | lr 0.000098 | ms/batch 124.68 | loss 1.45935 | mse 1.45935 |\n",
            " 27%|██▋       | 1100/4029 [02:22<06:04,  8.03it/s]INFO:xpert:| epoch   1 | 1100/4029 batches | lr 0.000098 | ms/batch 124.91 | loss 1.44462 | mse 1.44462 |\n",
            " 30%|██▉       | 1200/4029 [02:34<05:55,  7.96it/s]INFO:xpert:| epoch   1 | 1200/4029 batches | lr 0.000097 | ms/batch 126.25 | loss 1.44614 | mse 1.44614 |\n",
            " 32%|███▏      | 1300/4029 [02:47<05:43,  7.95it/s]INFO:xpert:| epoch   1 | 1300/4029 batches | lr 0.000097 | ms/batch 125.06 | loss 1.43164 | mse 1.43164 |\n",
            " 35%|███▍      | 1400/4029 [03:00<05:36,  7.82it/s]INFO:xpert:| epoch   1 | 1400/4029 batches | lr 0.000096 | ms/batch 125.45 | loss 1.42629 | mse 1.42629 |\n",
            " 37%|███▋      | 1500/4029 [03:25<05:18,  7.93it/s]  INFO:xpert:| epoch   1 | 1500/4029 batches | lr 0.000095 | ms/batch 251.57 | loss 1.41329 | mse 1.41329 |\n",
            " 40%|███▉      | 1600/4029 [03:37<05:08,  7.88it/s]INFO:xpert:| epoch   1 | 1600/4029 batches | lr 0.000094 | ms/batch 127.07 | loss 1.41402 | mse 1.41402 |\n",
            " 42%|████▏     | 1700/4029 [03:50<04:56,  7.85it/s]INFO:xpert:| epoch   1 | 1700/4029 batches | lr 0.000093 | ms/batch 127.20 | loss 1.39660 | mse 1.39660 |\n",
            " 45%|████▍     | 1800/4029 [04:03<04:42,  7.90it/s]INFO:xpert:| epoch   1 | 1800/4029 batches | lr 0.000092 | ms/batch 127.55 | loss 1.39432 | mse 1.39432 |\n",
            " 47%|████▋     | 1900/4029 [04:16<04:30,  7.86it/s]INFO:xpert:| epoch   1 | 1900/4029 batches | lr 0.000091 | ms/batch 127.03 | loss 1.35867 | mse 1.35867 |\n",
            " 50%|████▉     | 2000/4029 [04:28<04:19,  7.81it/s]INFO:xpert:| epoch   1 | 2000/4029 batches | lr 0.000090 | ms/batch 127.62 | loss 1.35443 | mse 1.35443 |\n",
            " 52%|█████▏    | 2100/4029 [04:41<04:04,  7.89it/s]INFO:xpert:| epoch   1 | 2100/4029 batches | lr 0.000088 | ms/batch 126.89 | loss 1.36290 | mse 1.36290 |\n",
            " 55%|█████▍    | 2200/4029 [04:54<03:54,  7.78it/s]INFO:xpert:| epoch   1 | 2200/4029 batches | lr 0.000087 | ms/batch 128.15 | loss 1.37226 | mse 1.37226 |\n",
            " 57%|█████▋    | 2300/4029 [05:07<03:41,  7.79it/s]INFO:xpert:| epoch   1 | 2300/4029 batches | lr 0.000086 | ms/batch 128.02 | loss 1.35059 | mse 1.35059 |\n",
            " 60%|█████▉    | 2400/4029 [05:19<03:27,  7.87it/s]INFO:xpert:| epoch   1 | 2400/4029 batches | lr 0.000084 | ms/batch 127.50 | loss 1.33494 | mse 1.33494 |\n",
            " 62%|██████▏   | 2500/4029 [05:32<03:14,  7.85it/s]INFO:xpert:| epoch   1 | 2500/4029 batches | lr 0.000083 | ms/batch 127.01 | loss 1.35183 | mse 1.35183 |\n",
            " 65%|██████▍   | 2600/4029 [05:45<03:01,  7.89it/s]INFO:xpert:| epoch   1 | 2600/4029 batches | lr 0.000081 | ms/batch 127.15 | loss 1.33104 | mse 1.33104 |\n",
            " 67%|██████▋   | 2700/4029 [05:57<02:47,  7.92it/s]INFO:xpert:| epoch   1 | 2700/4029 batches | lr 0.000079 | ms/batch 126.44 | loss 1.33160 | mse 1.33160 |\n",
            " 69%|██████▉   | 2800/4029 [06:10<02:35,  7.91it/s]INFO:xpert:| epoch   1 | 2800/4029 batches | lr 0.000078 | ms/batch 126.05 | loss 1.32298 | mse 1.32298 |\n",
            " 72%|███████▏  | 2900/4029 [06:23<02:22,  7.95it/s]INFO:xpert:| epoch   1 | 2900/4029 batches | lr 0.000076 | ms/batch 126.09 | loss 1.32695 | mse 1.32695 |\n",
            " 74%|███████▍  | 3000/4029 [06:35<02:09,  7.95it/s]INFO:xpert:| epoch   1 | 3000/4029 batches | lr 0.000074 | ms/batch 125.98 | loss 1.29774 | mse 1.29774 |\n",
            " 77%|███████▋  | 3100/4029 [06:48<01:56,  7.98it/s]INFO:xpert:| epoch   1 | 3100/4029 batches | lr 0.000072 | ms/batch 125.83 | loss 1.30883 | mse 1.30883 |\n",
            " 79%|███████▉  | 3200/4029 [07:00<01:43,  7.98it/s]INFO:xpert:| epoch   1 | 3200/4029 batches | lr 0.000070 | ms/batch 125.62 | loss 1.29395 | mse 1.29395 |\n",
            " 82%|████████▏ | 3300/4029 [07:25<01:33,  7.76it/s]INFO:xpert:| epoch   1 | 3300/4029 batches | lr 0.000069 | ms/batch 246.89 | loss 1.31994 | mse 1.31994 |\n",
            " 84%|████████▍ | 3400/4029 [07:38<01:19,  7.89it/s]INFO:xpert:| epoch   1 | 3400/4029 batches | lr 0.000067 | ms/batch 126.65 | loss 1.27893 | mse 1.27893 |\n",
            " 87%|████████▋ | 3500/4029 [07:51<01:07,  7.84it/s]INFO:xpert:| epoch   1 | 3500/4029 batches | lr 0.000065 | ms/batch 127.62 | loss 1.29397 | mse 1.29397 |\n",
            " 89%|████████▉ | 3600/4029 [08:03<00:55,  7.78it/s]INFO:xpert:| epoch   1 | 3600/4029 batches | lr 0.000063 | ms/batch 127.38 | loss 1.28557 | mse 1.28557 |\n",
            " 92%|█████████▏| 3700/4029 [08:16<00:41,  7.95it/s]INFO:xpert:| epoch   1 | 3700/4029 batches | lr 0.000061 | ms/batch 127.48 | loss 1.29124 | mse 1.29124 |\n",
            " 94%|█████████▍| 3800/4029 [08:29<00:28,  7.91it/s]INFO:xpert:| epoch   1 | 3800/4029 batches | lr 0.000059 | ms/batch 127.04 | loss 1.28448 | mse 1.28448 |\n",
            " 97%|█████████▋| 3900/4029 [08:41<00:16,  7.96it/s]INFO:xpert:| epoch   1 | 3900/4029 batches | lr 0.000057 | ms/batch 126.53 | loss 1.29239 | mse 1.29239 |\n",
            " 99%|█████████▉| 4000/4029 [08:54<00:03,  7.91it/s]INFO:xpert:| epoch   1 | 4000/4029 batches | lr 0.000055 | ms/batch 126.29 | loss 1.30030 | mse 1.30030 |\n",
            "100%|██████████| 4029/4029 [08:58<00:00,  7.49it/s]\n",
            "INFO:xpert:-----------------------------------------------------------------------------------------\n",
            "INFO:xpert:| end of epoch   1 | time: 580.58s | valid loss/mse 1.2698 |\n",
            "INFO:xpert:-----------------------------------------------------------------------------------------\n",
            "INFO:xpert:Best model with score 1.2698\n",
            "  2%|▏         | 100/4029 [00:13<08:24,  7.79it/s]INFO:xpert:| epoch   2 | 100/4029 batches | lr 0.000052 | ms/batch 131.97 | loss 1.28284 | mse 1.28284 |\n",
            "  5%|▍         | 200/4029 [00:25<08:12,  7.78it/s]INFO:xpert:| epoch   2 | 200/4029 batches | lr 0.000050 | ms/batch 128.38 | loss 1.27764 | mse 1.27764 |\n",
            "  7%|▋         | 300/4029 [00:38<07:58,  7.79it/s]INFO:xpert:| epoch   2 | 300/4029 batches | lr 0.000048 | ms/batch 128.84 | loss 1.28330 | mse 1.28330 |\n",
            " 10%|▉         | 400/4029 [00:51<07:42,  7.85it/s]INFO:xpert:| epoch   2 | 400/4029 batches | lr 0.000046 | ms/batch 127.93 | loss 1.27734 | mse 1.27734 |\n",
            " 12%|█▏        | 500/4029 [01:04<07:28,  7.88it/s]INFO:xpert:| epoch   2 | 500/4029 batches | lr 0.000044 | ms/batch 127.14 | loss 1.25229 | mse 1.25229 |\n",
            " 15%|█▍        | 600/4029 [01:29<07:18,  7.82it/s]  INFO:xpert:| epoch   2 | 600/4029 batches | lr 0.000042 | ms/batch 256.45 | loss 1.27088 | mse 1.27088 |\n",
            " 17%|█▋        | 700/4029 [01:42<07:04,  7.85it/s]INFO:xpert:| epoch   2 | 700/4029 batches | lr 0.000040 | ms/batch 128.22 | loss 1.24518 | mse 1.24518 |\n",
            " 20%|█▉        | 800/4029 [01:55<06:51,  7.85it/s]INFO:xpert:| epoch   2 | 800/4029 batches | lr 0.000038 | ms/batch 126.93 | loss 1.25825 | mse 1.25825 |\n",
            " 22%|██▏       | 900/4029 [02:08<06:39,  7.84it/s]INFO:xpert:| epoch   2 | 900/4029 batches | lr 0.000036 | ms/batch 129.29 | loss 1.25058 | mse 1.25058 |\n",
            " 25%|██▍       | 1000/4029 [02:21<06:32,  7.71it/s]INFO:xpert:| epoch   2 | 1000/4029 batches | lr 0.000034 | ms/batch 128.92 | loss 1.24740 | mse 1.24740 |\n",
            " 27%|██▋       | 1100/4029 [02:34<06:13,  7.83it/s]INFO:xpert:| epoch   2 | 1100/4029 batches | lr 0.000032 | ms/batch 128.40 | loss 1.26223 | mse 1.26223 |\n",
            " 30%|██▉       | 1200/4029 [02:46<06:00,  7.85it/s]INFO:xpert:| epoch   2 | 1200/4029 batches | lr 0.000030 | ms/batch 127.96 | loss 1.24876 | mse 1.24876 |\n",
            " 32%|███▏      | 1300/4029 [02:59<05:59,  7.58it/s]INFO:xpert:| epoch   2 | 1300/4029 batches | lr 0.000028 | ms/batch 128.64 | loss 1.24424 | mse 1.24424 |\n",
            " 35%|███▍      | 1400/4029 [03:12<05:38,  7.77it/s]INFO:xpert:| epoch   2 | 1400/4029 batches | lr 0.000026 | ms/batch 128.46 | loss 1.24871 | mse 1.24871 |\n",
            " 37%|███▋      | 1500/4029 [03:25<05:20,  7.90it/s]INFO:xpert:| epoch   2 | 1500/4029 batches | lr 0.000025 | ms/batch 127.30 | loss 1.24852 | mse 1.24852 |\n",
            " 40%|███▉      | 1600/4029 [03:38<05:07,  7.91it/s]INFO:xpert:| epoch   2 | 1600/4029 batches | lr 0.000023 | ms/batch 126.50 | loss 1.24752 | mse 1.24752 |\n",
            " 42%|████▏     | 1700/4029 [03:50<04:54,  7.91it/s]INFO:xpert:| epoch   2 | 1700/4029 batches | lr 0.000021 | ms/batch 126.44 | loss 1.23685 | mse 1.23685 |\n",
            " 45%|████▍     | 1800/4029 [04:03<04:40,  7.95it/s]INFO:xpert:| epoch   2 | 1800/4029 batches | lr 0.000019 | ms/batch 126.10 | loss 1.25094 | mse 1.25094 |\n",
            " 47%|████▋     | 1900/4029 [04:15<04:30,  7.86it/s]INFO:xpert:| epoch   2 | 1900/4029 batches | lr 0.000018 | ms/batch 126.22 | loss 1.23449 | mse 1.23449 |\n",
            " 50%|████▉     | 2000/4029 [04:28<04:16,  7.92it/s]INFO:xpert:| epoch   2 | 2000/4029 batches | lr 0.000016 | ms/batch 126.30 | loss 1.22911 | mse 1.22911 |\n",
            " 52%|█████▏    | 2100/4029 [04:41<04:02,  7.97it/s]INFO:xpert:| epoch   2 | 2100/4029 batches | lr 0.000015 | ms/batch 125.58 | loss 1.23448 | mse 1.23448 |\n",
            " 55%|█████▍    | 2200/4029 [04:53<03:49,  7.97it/s]INFO:xpert:| epoch   2 | 2200/4029 batches | lr 0.000013 | ms/batch 125.53 | loss 1.24162 | mse 1.24162 |\n",
            " 57%|█████▋    | 2300/4029 [05:06<03:36,  7.98it/s]INFO:xpert:| epoch   2 | 2300/4029 batches | lr 0.000012 | ms/batch 125.33 | loss 1.23597 | mse 1.23597 |\n",
            " 60%|█████▉    | 2400/4029 [05:30<03:24,  7.98it/s]  INFO:xpert:| epoch   2 | 2400/4029 batches | lr 0.000011 | ms/batch 240.08 | loss 1.24450 | mse 1.24450 |\n",
            " 62%|██████▏   | 2500/4029 [05:42<03:12,  7.95it/s]INFO:xpert:| epoch   2 | 2500/4029 batches | lr 0.000010 | ms/batch 125.79 | loss 1.23866 | mse 1.23866 |\n",
            " 65%|██████▍   | 2600/4029 [05:55<02:59,  7.95it/s]INFO:xpert:| epoch   2 | 2600/4029 batches | lr 0.000008 | ms/batch 125.71 | loss 1.24764 | mse 1.24764 |\n",
            " 67%|██████▋   | 2700/4029 [06:07<02:47,  7.94it/s]INFO:xpert:| epoch   2 | 2700/4029 batches | lr 0.000007 | ms/batch 125.76 | loss 1.23106 | mse 1.23106 |\n",
            " 69%|██████▉   | 2800/4029 [06:20<02:34,  7.95it/s]INFO:xpert:| epoch   2 | 2800/4029 batches | lr 0.000006 | ms/batch 125.84 | loss 1.22217 | mse 1.22217 |\n",
            " 72%|███████▏  | 2900/4029 [06:33<02:22,  7.94it/s]INFO:xpert:| epoch   2 | 2900/4029 batches | lr 0.000005 | ms/batch 125.71 | loss 1.21713 | mse 1.21713 |\n",
            " 74%|███████▍  | 3000/4029 [06:45<02:09,  7.96it/s]INFO:xpert:| epoch   2 | 3000/4029 batches | lr 0.000004 | ms/batch 125.72 | loss 1.22231 | mse 1.22231 |\n",
            " 77%|███████▋  | 3100/4029 [06:58<01:56,  7.96it/s]INFO:xpert:| epoch   2 | 3100/4029 batches | lr 0.000004 | ms/batch 125.68 | loss 1.22234 | mse 1.22234 |\n",
            " 79%|███████▉  | 3200/4029 [07:10<01:44,  7.95it/s]INFO:xpert:| epoch   2 | 3200/4029 batches | lr 0.000003 | ms/batch 125.63 | loss 1.21855 | mse 1.21855 |\n",
            " 82%|████████▏ | 3300/4029 [07:23<01:31,  7.95it/s]INFO:xpert:| epoch   2 | 3300/4029 batches | lr 0.000002 | ms/batch 125.79 | loss 1.22944 | mse 1.22944 |\n",
            " 84%|████████▍ | 3400/4029 [07:35<01:19,  7.95it/s]INFO:xpert:| epoch   2 | 3400/4029 batches | lr 0.000002 | ms/batch 125.79 | loss 1.21225 | mse 1.21225 |\n",
            " 87%|████████▋ | 3500/4029 [07:48<01:06,  7.98it/s]INFO:xpert:| epoch   2 | 3500/4029 batches | lr 0.000001 | ms/batch 125.37 | loss 1.22758 | mse 1.22758 |\n",
            " 89%|████████▉ | 3600/4029 [08:01<00:53,  7.98it/s]INFO:xpert:| epoch   2 | 3600/4029 batches | lr 0.000001 | ms/batch 125.28 | loss 1.23964 | mse 1.23964 |\n",
            " 92%|█████████▏| 3700/4029 [08:13<00:41,  7.97it/s]INFO:xpert:| epoch   2 | 3700/4029 batches | lr 0.000000 | ms/batch 125.40 | loss 1.24234 | mse 1.24234 |\n",
            " 94%|█████████▍| 3800/4029 [08:26<00:28,  7.97it/s]INFO:xpert:| epoch   2 | 3800/4029 batches | lr 0.000000 | ms/batch 125.62 | loss 1.21881 | mse 1.21881 |\n",
            " 97%|█████████▋| 3900/4029 [08:38<00:16,  7.97it/s]INFO:xpert:| epoch   2 | 3900/4029 batches | lr 0.000000 | ms/batch 125.30 | loss 1.22792 | mse 1.22792 |\n",
            " 99%|█████████▉| 4000/4029 [08:51<00:03,  7.81it/s]INFO:xpert:| epoch   2 | 4000/4029 batches | lr 0.000000 | ms/batch 127.13 | loss 1.24660 | mse 1.24660 |\n",
            "100%|██████████| 4029/4029 [08:55<00:00,  7.53it/s]\n",
            "INFO:xpert:-----------------------------------------------------------------------------------------\n",
            "INFO:xpert:| end of epoch   2 | time: 586.06s | valid loss/mse 1.2105 |\n",
            "INFO:xpert:-----------------------------------------------------------------------------------------\n",
            "INFO:xpert:Best model with score 1.2105\n"
          ]
        }
      ],
      "source": [
        "# Training loop\n",
        "best_val_loss = float('inf')\n",
        "best_model = None\n",
        "patience = 0\n",
        "early_stop = epochs\n",
        "\n",
        "train_metrics_list, train_metrics_pert_list = [], []\n",
        "val_metrics_list, val_metrics_pert_list = [], []\n",
        "\n",
        "for epoch in range(1, epochs + 1):\n",
        "    epoch_start_time = time.time()\n",
        "    train_loader = pert_data.dataloader[\"train_loader\"]\n",
        "    valid_loader = pert_data.dataloader[\"val_loader\"]\n",
        "\n",
        "    train(model, train_loader)\n",
        "    val_loss, val_mre = evaluate(model, valid_loader)\n",
        "    elapsed = time.time() - epoch_start_time\n",
        "    logger.info(\"-\" * 89)\n",
        "    logger.info(f\"| end of epoch {epoch:3d} | time: {elapsed:5.2f}s | valid loss/mse {val_loss:5.4f} |\")\n",
        "    logger.info(\"-\" * 89)\n",
        "\n",
        "    if val_loss < best_val_loss:\n",
        "        best_val_loss = val_loss\n",
        "        best_model = copy.deepcopy(model)\n",
        "        logger.info(f\"Best model with score {best_val_loss:5.4f}\")\n",
        "        patience = 0\n",
        "        torch.save(best_model.state_dict(), save_root / \"model_best.pt\")\n",
        "    else:\n",
        "        patience += 1\n",
        "        if patience >= early_stop:\n",
        "            logger.info(f\"Early stop at epoch {epoch}\")\n",
        "            break\n",
        "\n",
        "    # Check for NaN loss\n",
        "    if np.isnan(val_loss):\n",
        "        logger.warning(f\"NaN loss detected at epoch {epoch}, stopping training\")\n",
        "        break\n",
        "\n",
        "    # Collect metrics for plotting later (optional, can be expensive)\n",
        "    train_res = eval_perturb_new(train_loader, model, device)\n",
        "    val_res = eval_perturb_new(valid_loader, model, device)\n",
        "    if train_res is not None:\n",
        "        train_metrics, train_metrics_pert = compute_metrics(train_res)\n",
        "        train_metrics_list.append(train_metrics)\n",
        "        train_metrics_pert_list.append(train_metrics_pert)\n",
        "    if val_res is not None:\n",
        "        val_metrics, val_metrics_pert = compute_metrics(val_res)\n",
        "        val_metrics_list.append(val_metrics)\n",
        "        val_metrics_pert_list.append(val_metrics_pert)\n",
        "\n",
        "    if scheduler_type == 'steplr':\n",
        "        scheduler.step()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "530"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(valid_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 10. Final Evaluation and Save Artifacts\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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xxBNPMG7cOEqWLImnpyc///wzy5Ytu+88BAcHU758eUJDQ4mJicnwmBqk/ZLwzjvv8Oabb3Ly5EmefPJJSpYsyYULF9i+fTuOjo6MHTv2vs97N7Vr12bDhg2sWrUKd3d3nJycsv0re0hICPPmzaN69erUqVOHiIgIPvroI8qXL59rcYmISP5XqVIlFi9eTJcuXahXrx5DhgzB19cXgIMHDzJnzhwMw6BDhw7A/c29dzNx4kROnjxJ7969WbduHR06dMDV1ZXLly8TFhbG3LlzWbx4MXXq1KFBgwZ4e3vz6quvkpycTMmSJVm+fDnh4eH3fa3dunVj+PDhdOvWjYSEhEwf4woKCmLAgAH06dOHnTt38uijj+Lo6Eh0dDTh4eHUrl2bwYMH3/d5s+Lk5ISnpycrV66kRYsWlCpVChcXl/Q/NGTF2dmZRx99lI8++ih9399++40vv/xST6uJ+ZhxYTeRQuHvq5rfyz9XNT948KDxn//8x6hfv75RunRpw8rKyihZsqTRrFkzY8GCBVme425fn3/+eY5iXblypfHUU09lOF/z5s2NmTNnGgkJCen7RUdHG506dTJKlSplFC9e3OjRo4exc+fOLFc1d3R0vOc533jjDQMwPDw8jJSUlCz3WbFihdG8eXPD2dnZsLW1NTw9PY1OnToZP/30U46uK6f27NljBAUFGQ4ODgaQ/t/jXv8Nr169avTr188oU6aM4eDgYDRp0sTYuHGj0axZswz/Pe+1qvmlS5cyjHm3lWdFROThy+k8fseJEyeM0NBQo2rVqoatra1hb29v1KhRwxg+fHiW/1/P6dx7N8nJycZXX31lPP7440apUqUMKysro3Tp0kbr1q2Nb775JsPcevToUSM4ONhwdnY2SpcubQwdOtRYs2ZNlqua16xZ857n7d69uwEYQUFBd91nzpw5RqNGjQxHR0fD3t7eqFKlitGrVy9j586d2V7X/fjpp58MX19fw9bW1gDSO5TcbZ41DMM4d+6c8cwzzxglS5Y0nJycjCeffNLYv3+/4enpmaHDyd1WNc/q95s75xP5N0yG8YDLCIqIiIiIiIjIXekz3iIiIiIiIiJ5SIW3iIiIiIiISB5S4S0iIiIiIiKSh1R4i4iIiIiIiOQhFd4iIiIiIiIieUiFt4iIiIiIiEgesjJ3ADkxffp0PvroI6Kjo6lZsyaTJk2iadOmWe67YcMGmjdvnmn7oUOHqF69OgCff/458+fPZ//+/QD4+/vzwQcf0LBhwxzHlJqayvnz53FycsJkMv2LqxIREQHDMLhx4wZly5bFwkJ/D38YNIeLiEhuuJ85PN8X3kuWLGHYsGFMnz6doKAgZs2aRevWrTl48CAVKlS463FHjhzB2dk5/XXp0qXT/33Dhg1069aNxo0bY2dnx4QJEwgODubAgQOUK1cuR3GdP38eDw+Pf39hIiIif3P27FnKly9v7jCKBM3hIiKSm3Iyh5sMwzAeUjz/SqNGjfDz82PGjBnp23x8fGjfvj3jxo3LtP+dO95Xr16lRIkSOTpHSkoKJUuWZOrUqfTq1StHx1y/fp0SJUpw9uzZDAX+v5GUlMT69esJDg7G2tr6gcYqrJSj7ClHOaM8ZU85ypncylNsbCweHh5cu3aN4sWL52KEcje5NYfrZyVnlKfsKUc5ozxlTznKGXPM4fn6jndiYiIRERGMHDkyw/bg4GA2b958z2N9fX2Jj4+nRo0avPXWW1k+fn5HXFwcSUlJlCpVKsex3Xk0zdnZOVcKbwcHB5ydnfUDchfKUfaUo5xRnrKnHOVMbudJjzw/PLk1h+tnJWeUp+wpRzmjPGVPOcoZc8zh+brwvnz5MikpKbi6umbY7urqSkxMTJbHuLu7M3v2bPz9/UlISGDBggW0aNGCDRs28Oijj2Z5zMiRIylXrhxPPPHEXWNJSEggISEh/XVsbCyQ9h8tKSnpfi8tgzvHP+g4hZlylD3lKGeUp+wpRzmTW3lSnkVERAq/fF143/HPvyAYhnHXvyp4e3vj7e2d/jowMJCzZ8/y8ccfZ1l4T5gwgUWLFrFhwwbs7OzuGsO4ceMYO3Zspu3r16/HwcEhp5dyT2FhYbkyTmGmHGVPOcoZ5Sl7ylHOPGie4uLicikSERERya/ydeHt4uKCpaVlprvbFy9ezHQX/F4CAgJYuHBhpu0ff/wxH3zwAT/99BN16tS55xijRo1i+PDh6a/vPM8fHBycK4+ah4WF0bJlSz0SchfKUfaUo5xRnrKnHOVMbuXpzhNUIiIiUnjl68LbxsYGf39/wsLC6NChQ/r2sLAw2rVrl+Nxdu/ejbu7e4ZtH330Ee+99x7r1q2jfv362Y5ha2uLra1tpu3W1ta59otpbo5VWClH2VOOckZ5yp5ylDMPmiflWEREpPDL14U3wPDhw+nZsyf169cnMDCQ2bNnExkZyaBBg4C0O9FRUVHMnz8fgEmTJlGxYkVq1qxJYmIiCxcuZOnSpSxdujR9zAkTJjB69Gi++eYbKlasmH5HvVixYhQrVuzhX6SIiIiIiIgUWvm+8O7SpQtXrlzhnXfeITo6mlq1arF27Vo8PT0BiI6OJjIyMn3/xMREXn31VaKiorC3t6dmzZqsWbOGNm3apO8zffp0EhMT6dSpU4Zzvf3224wZM+ahXJeIiIiIiIgUDfm+8AYIDQ0lNDQ0y/fmzZuX4fWIESMYMWLEPcc7ffp0LkUmIiIiIiIicm8W5g5AREREREREpDBT4S0iIiIiIiKSh1R4i4iIiIiIiOQhFd5m9uetREYu38+tJHNHIiIiIiIiUrjFJ6UwdvUhLtx+uOctEIurFWYjl/7B+oMX+MXGkur1r+NX0cXcIYmIiIiIiBQ6Z/+MY9DCCA6cj8Xd3pJeqQbWD+ncuuNtZq+0rIZnKQeuJpro8vl2Fm2PxDAMc4clIiIiIiJSaPx6+CIhU8I5cD6Wkg7WdKiYiqWF6aGdX4W3mfm4O7N8cCNql0wlKcVg1LJ9jPj+D+KTUswdmoiIiIiISIGWkmowMewofb/awfXbSdTzKMHK0EC8Szzcm50qvPMBJztr+nqn8mpLLyxM8F3EOTpO30zklThzhyYiIiIiIlIgXb2VSJ95O5j88zEMA3oGeLJkYADuxe0eeiwqvPMJCxMMfLQSC/s14hFHGw5GxxIyZSM/H7pg7tBEREREREQKlD/OXSNkSji/H72EnbUFn3apy7vta2FrZWmWeFR45zONq7qw+qUm+FYoQWx8Mv2+2skn64+QkqrPfYuIiIiIiNyLYRgs2h5JpxlbiLp2m4qPOLA8NIgOvuXNGpcK73zIvbg9SwYE8nygJwBTfjlO77nb+fNWopkjExERERERyZ/ik1IY8f0fjFq2j8SUVFrWcGXlkCb4uDubOzQV3vmVjZUFY9vVYlKXethbW7Lx2GXaTgln79lr5g5NREREREQkX4m8EkfH6Zv5LuIcFiYY8aQ3s3r4U9z+YTUMuzcV3vlce99yLH+xMZVcHIm6dptnZ27h621n1HJMREREREQE+OXwBUKmbORgdCyPONqwoF8jQh+risVDbBeWHRXeBUB1N2dWDgmiVU1XElNSeXP5fl797g9uJ6rlmIiIiIiIFE0pqQYT1x+h77ydxMYn41uhBKtfakJQVRdzh5aJCu8CwtnOmpk9/BnZujoWJli66xwdZ2zmzJVb5g5NRERERETkofrzViK9525n8i/HAegV6MmSAYG4F7c3c2RZU+FdgJhMJgY1q8LC/o1wKWbDoehYQqaE89NBtRwTEREREZGiYe/Za7SdEs7GY5exs7ZgUpd6vNOuFjZW+be8zb+RyV01ruLC6qFN8atQghvxyfSfv5OP16nlmIiIiIiIFF6GYfD1tjM8O/P/W4WteDGI9r7lzB1atlR4F1Buxe1YPCCQ3o0rAjD11+M8P2c7V24mmDcwERERERGRXHY7MYVXv/uDN5fvJzElleAarvwwtAnV3czfKiwnVHgXYDZWFox5uiafdU1rORZ+PK3l2B61HBMRERERkULizJVbdJyxmaW70lqFjWxdnVk9/XG2yx+twnJChXch0K5eOVa8GERlF0fOX4/n2ZmbWbhVLcdERERERKRg+/nQBUKmhHMoOhaXYjYs7N+IQc2qYDLln1ZhOaHCu5DwdnNi5ZAgnqzpRlKKwVsr9vOf7/aq5ZiIiIiIiBQ4KakGH687Qr+vdnIjPhm/CiVYPbQpjavkv1ZhOaHCuxBxsrNmRg8/Rv3VcmzZrig6TN/E6ctqOSYiIiIiIgXDnVZhU39NaxXWu3FFFg8IxK24nZkj+/dUeBcyJpOJgc2q8HX/AFyK2XA45gZtp4YTppZjIiIiIiKSz+05e42QyRvZeOwy9taWfNa1HmOerpmvW4XlRMGOXu4qsMojrHmpKf6eJbkRn8wL83cy4cfDajkmIiI5Mn36dCpVqoSdnR3+/v5s3LjxnvtPmzYNHx8f7O3t8fb2Zv78+RnenzdvHiaTKdNXfHx8+j43btxg2LBheHp6Ym9vT+PGjdmxY0eGcXr37p1pjICAgNy7cBERMQvDMFi49QzPztzM+evxVHZxZMWLQbSrl/9bheWElbkDkLzj6mzH4gEBfLD2EHM3nWb6hhPsPXeNyV19eaSYrbnDExGRfGrJkiUMGzaM6dOnExQUxKxZs2jdujUHDx6kQoUKmfafMWMGo0aN4vPPP6dBgwZs376dF154gZIlS9K2bdv0/ZydnTly5EiGY+3s/v+xwf79+7N//34WLFhA2bJlWbhwIU888QQHDx6kXLn//8XrySefZO7cuemvbWxscvPyRUTkIbudmMKbK/axbFcUAE/WdOOjZ+vgVIBWLc+O7ngXctaWFrzdtiaTu/niYGPJpuNXCJkSzu7Iq+YOTURE8qmJEyfSr18/+vfvj4+PD5MmTcLDw4MZM2Zkuf+CBQsYOHAgXbp0oXLlynTt2pV+/foxfvz4DPuZTCbc3NwyfN1x+/Ztli5dyoQJE3j00UepWrUqY8aMoVKlSpnOa2trm2GMUqVK5X4SRETkoTh9+RYdpm9i2a4oLEwwqnV1ZvTwK1RFN+iOd5HxdN2yVHdzYtDCCE5eukXnWVv4b0gNegR4Fril+EVEJO8kJiYSERHByJEjM2wPDg5m8+bNWR6TkJCQ4c41gL29Pdu3bycpKQlr67Rfnm7evImnpycpKSnUq1ePd999F19fXwCSk5NJSUnJcpzw8PAM2zZs2ECZMmUoUaIEzZo14/3336dMmTJ3vaaEhAQSEhLSX8fGxgKQlJREUlLSvdJxT3eOfZAxigLlKXvKUc4oT9kraDn6+dBFXlu2nxvxyTziaMNnXerQqFIpkpOT8/S8uZWn+zlehXcRUs3ViZUvBjHi+z/43/4YRq88wK7Ia3zQoTb2NpbmDk9ERPKBy5cvk5KSgqura4btrq6uxMTEZHlMq1at+OKLL2jfvj1+fn5EREQwZ84ckpKSuHz5Mu7u7lSvXp158+ZRu3ZtYmNj+eyzzwgKCmLv3r14eXnh5OREYGAg7777Lj4+Pri6urJo0SK2bduGl5dX+rlat27Ns88+i6enJ6dOnWL06NE8/vjjREREYGub9ceoxo0bx9ixYzNtX79+PQ4ODg+QrTRhYWEPPEZRoDxlTznKGeUpe/k9R6kGrD1rQVhU2gPYlZwMenvFceXQVtYeenhxPGie4uLicryvCu8ixsnOmunP+fHFxlN8+ONhlu+O4lB0LDN6+FPJxdHc4YmISD7xz6ehDMO46xNSo0ePJiYmhoCAAAzDwNXVld69ezNhwgQsLdP+sBsQEJBhEbSgoCD8/PyYMmUKkydPBtIeWe/bty/lypXD0tISPz8/unfvzq5du9KP69KlS/q/16pVi/r16+Pp6cmaNWvo2LFjlvGNGjWK4cOHp7+OjY3Fw8OD4OBgnJ2d7zMz/y8pKYmwsDBatmyZfldfMlOesqcc5YzylL2CkKMrtxIZ/u0fbI76E4BeARV4vVW1h7pqeW7l6c4TVDmhwrsIMplMvPBoZWqXL86Qb3ZzOOYGT08J55POdQmu6Zb9ACIiUmi5uLhgaWmZ6e72xYsXM90Fv8Pe3p45c+Ywa9YsLly4gLu7O7Nnz8bJyQkXF5csj7GwsKBBgwYcO3YsfVuVKlX47bffuHXrFrGxsbi7u9OlSxcqVap013jd3d3x9PTMMM4/2draZnk33NraOld+Mc2tcQo75Sl7ylHOKE/Zy6852h15ldCvdxF9PR4HG0s+fKYOT9cta7Z4HjRP93OsFlcrwgIqP8Kal5pQ37MkNxKSGbAggg//d5jklFRzhyYiImZiY2ODv79/psfvwsLCaNy48T2Ptba2pnz58lhaWrJ48WJCQkKwsMj6Vw3DMNizZw/u7u6Z3nN0dMTd3Z2rV6+ybt062rVrd9dzXrlyhbNnz2Y5joiI5A+GYbBgy2k6z9pC9PV4KpdOaxVmzqL7YdMd7yLO1dmORQMCGLf2MHM2nWLmbyf449w1JnfzxUUtx0REiqThw4fTs2dP6tevT2BgILNnzyYyMpJBgwYBaY9uR0VFpffqPnr0KNu3b6dRo0ZcvXqViRMnsn//fr766qv0MceOHUtAQABeXl7ExsYyefJk9uzZw7Rp09L3WbduHYZh4O3tzfHjx3nttdfw9vamT58+QNribGPGjOGZZ57B3d2d06dP88Ybb+Di4kKHDh0eYoZERCSnbiem8MbyfSzfndYqrHUtNyZ0KlytwnJChbdgbWnBf9vWwM+zBCO+/4PNJ64QMjmc6T388KtQ0tzhiYjIQ9alSxeuXLnCO++8Q3R0NLVq1WLt2rV4enoCEB0dTWRkZPr+KSkpfPLJJxw5cgRra2uaN2/O5s2bqVixYvo+165dY8CAAcTExFC8eHF8fX35/fffadiwYfo+169fZ9SoUZw7d45SpUrxzDPP8P7776c/ymdpacm+ffuYP38+165dw93dnebNm7NkyRKcnJweTnJERCTHTl2+xeCFERyOuYGlhYmRT1anf9NKRbKrkgpvSRdSpyzermktx05cukWXWVt466ka9ApUyzERkaImNDSU0NDQLN+bN29ehtc+Pj7s3r37nuN9+umnfPrpp/fcp3PnznTu3Pmu79vb27Nu3bp7jiEiIvnD+gMx/OfbvdxISMalmC1Tu/sSUPkRc4dlNvqMt2Tg5erEyiFNaFPbjaQUg7d/OMArS/YQl5i3vfRERERERKTgS05JZfyPhxmwIIIbCcnU9yzJmpeaFOmiG1R4SxaK2Voxrbsfbz3lg6WFiRV7ztNh2mZOXrpp7tBERERERCSfunwzgV5ztjNjwwkA+gZVYtGAAFyd7cwcmfmp8JYsmUwm+jetzKIXAijtZMuRCzdoN3UTP+6Pyf5gEREREREpUnZFXiVkcjibT1zBwcaSKd18+W/bGlhbquQEFd6SjYaVSrFmaBMaVizFjYRkBi2MYNz/DqnlmIiIiIiIYBgGX20+TZdZW4iJjadKaUdWvhhE2yLUKiwnVHhLtso42/H1C43o16QSALN+O0nPL7dz6UaCmSMTERERERFziUtM5pUle3j7hwMkpRi0qe3GyiFN8HJVp4l/KhCF9/Tp06lUqRJ2dnb4+/uzcePGu+67YcMGTCZTpq/Dhw+n73PgwAGeeeYZKlasiMlkYtKkSQ/hKgo2a0sLRofUYFp3PxxtLNly8gohUzYSceZPc4cmIiIiIiIP2clLN+kwbTMr9pzH0sLEW0/5MK27H8Vs1TgrK/m+8F6yZAnDhg3jzTffZPfu3TRt2pTWrVtn6B+alSNHjhAdHZ3+5eXllf5eXFwclStX5sMPP8TNzS2vL6FQeaqOOyuHBFG1TDEuxCbQZdZW5m06hWEY5g5NREREREQegh/3x9Bu6iaOXLhBaSdbvunfiP5NK6sF8T3k+8J74sSJ9OvXj/79++Pj48OkSZPw8PBgxowZ9zyuTJkyuLm5pX9ZWlqmv9egQQM++ugjunbtiq2tbV5fQqFTtYwTK14M4qk67iSnGoxZdZCXF6vlmIiIiIhIYZacksq4/x1i0MK0VmENKpZkzdAmNCrircJyIl8X3omJiURERBAcHJxhe3BwMJs3b77nsb6+vri7u9OiRQt+/fXXvAyzSCpma8XUbr6MDqmBlYWJH/aep/20TWo5JiIiIiJSCF26kUDPL7cz67eTAPRrUolvXgigjFqF5Ui+fgD/8uXLpKSk4OrqmmG7q6srMTFZt7Vyd3dn9uzZ+Pv7k5CQwIIFC2jRogUbNmzg0Ucf/dexJCQkkJDw/4uJxcbGApCUlERSUtK/HvfOGH//Z0HSq1F5arg58vKSPzh64SZtp4YzvkMtWtV0zf7g+1CQc/SwKEc5ozxlTznKmdzKk/IsIiL5XcSZq4R+HcGF2AQcbSyZ0KkuT9VxN3dYBUq+Lrzv+OdnBQzDuOvnB7y9vfH29k5/HRgYyNmzZ/n4448fqPAeN24cY8eOzbR9/fr1ODg4/Otx/y4sLCxXxjGHodVg3lFLTtxIYcjivTxeNpWQCqlY5vLHPApyjh4W5ShnlKfsKUc586B5iouLy6VIREREctedVmHvrTlEcqpB1TLFmNnDj6pltGr5/crXhbeLiwuWlpaZ7m5fvHgx013wewkICGDhwoUPFMuoUaMYPnx4+uvY2Fg8PDwIDg7G2dn5gcZOSkoiLCyMli1bYm1t/UBjmdMzKal8EnaMLzed4ZfzFtyyfYRJnevgUuzBP0dfWHKUl5SjnFGesqcc5Uxu5enOE1QiIiL5SVxiMiOX7uOHvecBeKq2O+M71dGq5f9Svs6ajY0N/v7+hIWF0aFDh/TtYWFhtGvXLsfj7N69G3f3B3sUwtbWNsuF2KytrXPtF9PcHMscrK1hdNta+Fd8hNe+28u2U1dpN30r05/zo37FUrl0joKdo4dBOcoZ5Sl7ylHOPGielGMREclvTl66yaCFERy9cBMrCxOj2vjQN6iiVi1/APm68AYYPnw4PXv2pH79+gQGBjJ79mwiIyMZNGgQkHYnOioqivnz5wMwadIkKlasSM2aNUlMTGThwoUsXbqUpUuXpo+ZmJjIwYMH0/89KiqKPXv2UKxYMapWrfrwL7KQaVPbnWquTgxaGMHxizfpOnsrb7TxoY9+WEVERERE8rUf90fz6nd/cDMhmdJOtkx/zo8GuXQTrSjL94V3ly5duHLlCu+88w7R0dHUqlWLtWvX4unpCUB0dHSGnt6JiYm8+uqrREVFYW9vT82aNVmzZg1t2rRJ3+f8+fP4+vqmv/7444/5+OOPadasGRs2bHho11aYVS1TjJUvBvH60j9Y/Uc076w+yO6z1/iwY20c9XiKiIiIiEi+kpySykfrjjDr97RVyxtWKsXU7r6UcdKq5bmhQFRAoaGhhIaGZvnevHnzMrweMWIEI0aMuOd4FStWxDCM3ApP7sLR1oop3Xzx9yzJ+2sOsWrveQ5HxzKjhz9VyxQzd3giIiIiIkJaq7Chi3ax9eSfALzQtBIjnqyOtWW+7j5doCiTkqdMJhN9giqxeEAArs62HLt4k3ZTw/nfvmhzhyYiIiIiUuRFnPmTkCkb2XryTxxtLJn+nB9vPlVDRXcuUzbloahfsRSrhzalUaVS3EpMYfDXu3h/zUGSU1LNHZqIiIiISJFjGAZzN52iy6ytXIhNSPuo6JAmtKmt/tx5QYW3PDSlnWz5un8jBj5aGYDPN56i+xfbuHgj3syRiYiIiIgUHbcSknlp8R7GrjpIcqpBSB13Vr4YpI+D5iEV3vJQWVlaMKqNDzN7+FHM1ortp/4kZHI4O07/ae7QREREREQKveMXb9J+2iZW7T2PlYWJ/4bUYEo3Xy2AnMdUeItZPFnLnZVDgqjmWoyLNxLoNnsrX4af0qJ3IiIiIiJ55H/7omk3NZxjF29SxsmWxQMC6Nukklr+PgQqvMVsqpQuxvLQIJ6uW5bkVIN3Vx9kyKLd3ExINndoIiIiIiKFRnJKKu+vOcjgr3dxKzGFRpVKsfqlJtRXf+6HRoW3mJWjrRWfda3HmLY1sLIwseaPaNpP28TxizfMHZqIiIiISIF38UY83b/YxucbTwEw8NHKfN2/kfpzP2QqvMXsTCYTvYMqsWRgWsux4xdv0m7qJtaq5ZiIiIiIyL+243TaekrbT/1JMVsrZjznx6g2PlipVdhDp4xLvuHvmdZyLKByWsux0K938d7qgySp5ZiIiIiISI4ZhsGX4afoNnsrF28k4FWmGCuHBNFarcLMRoW35CulnWxZ2K8RA5ultRz7IvwUz32+jYs3EswcmYiIiIhI/peQAq98u493V6e1Cnu6bllWvBhEldJqFWZOWjNe8h0rSwtGtfbB16Mkr363l+2n/6T99C108zR3ZCIiIiIi+deJS7f4ZJ8lF27HYGVh4q2nfHi+cUWtWp4P6I635FtP1nLjhyFBeLs6celmIlMPWDJn02m1HBMRERER+Ye1+6J5ZuZWLtw24epky5KBAfQOUquw/EKFt+RrlUsXY/mLjWlbx41UTIz78ShDvlHLMRERERERgKSUVN5bfZDQv1qFVXVOZUVoAP6eahWWn+hRc8n3HGys+KRTbWxvRLEy0oo1+6I5HBPLrJ7+VC3jZO7wRERERETM4mJsPEO+2c32038C8EKTivgkH8elmK2ZI5N/0h1vKRBMJhNN3Qy+7tsAN2c7Tly6xdNTN7H6j/PmDk1ERERE5KHbfupPnpoSzvbTaa3CZvbwZ0SraljqyfJ8SYW3FCi+FUqw+qUmBFZ+hLjEFIZ8s5t3VqnlmIiIiIgUDYZh8MXGk3T7fCuXbiTg7erED0OCeLKWm7lDk3tQ4S0FjksxWxb0a8jgx6oAMGfTKbp/vpWLsfFmjkxEREREJO/cTEhmyDe7eW/NIVJSDdrVK8vyFxtTWa3C8j0V3lIgWVla8PqT1ZnV0x8nWyt2nL5Km8nhbDt5xdyhiYiIiIjkuuMXb9Buajhr9kVjbWli7NM1mdSlHg42WrarIFDhLQVaq5pu/DC0CdXdnLh8M4HuX2zj899PquWYiIiIiBQaq/84z9NTN3Hi0i3cnO1YPCBQ/bkLGBXeUuBVcnFkWWhjOviWIyXV4P21h3jxm11qOSYiIiIiBVpSSirvrDrIkG92E5eYQmDlR1j9UhP8PUuaOzS5Tyq8pVBwsLFiYue6vNuuJtaWJtbui+HpqeEcu3DD3KGJiIiIiNy3i7HxdP98K3M2nQJgULMqLOjXUK3CCigV3lJomEwmegZWZMnAQNyL23Hy0i3aTdvEqr1qOSYiIiIiBce2k1doMzmcHaev4mRrxaye/oxsXR0rS5VvBZX+y0mh41ehJKuHNiGoalrLsaGLdjN21QESk9VyTERERETyL8Mw+Pz3k3T/YhuXb/7VKmxoE1rVVKuwgk6FtxRKjxSzZX7fRoT+1XJs7qbTdPt8KxfUckxERERE8qGbCcm8+M0u3l+b1iqsg285lr/YmEoujuYOTXKBCm8ptCwtTIx4sjqze/rjZGdFxJmrPDV5I1tOqOWYiIiIiOQfxy7c4Omp4azdF4O1pYl329VkYue6ahVWiKjwlkIvuKYbq4bcaTmWSI8vtzHrtxNqOSYiIiIiZrdq73naTdvEyUu3cC9ux5KBgfQMVKuwwkaFtxQJFV0cWR4aRMe/Wo6N+99hBi/cxY34JHOHJiIiIiJFUFJKKmNXHWDoorRWYY2rPMLqoU3wq6BWYYWRCm8pMuxtLPmkc13ebV8La0sTPx6Iod3UTRxVyzEREREReYguxMbTbfZW5m46DUDoY1VY0K8Rj6hVWKGlwluKFJPJRM8AT76903Ls8i3aTd3Eyj1R5g5NRERERIqArSev8NTkcHaeSWsVNrunPyOerI6lhR4tL8xUeEuR5PtXy7EmVV24nZTCy4v3MOYHtRwTEblj+vTpVKpUCTs7O/z9/dm4ceM99582bRo+Pj7Y29vj7e3N/PnzM7w/b948TCZTpq/4+P/vNnHjxg2GDRuGp6cn9vb2NG7cmB07dmQYxzAMxowZQ9myZbG3t+exxx7jwIEDuXfhIiJ5xDAMZv9+guf+ahVW3c2JVUObEKxWYUWCCm8psh4pZstXfRsypHlVAOZtPk3X2VuIua6WYyJStC1ZsoRhw4bx5ptvsnv3bpo2bUrr1q2JjIzMcv8ZM2YwatQoxowZw4EDBxg7diwvvvgiq1atyrCfs7Mz0dHRGb7s7OzS3+/fvz9hYWEsWLCAffv2ERwczBNPPEFU1P8/lTRhwgQmTpzI1KlT2bFjB25ubrRs2ZIbN/SxIRHJv27EJzF44S4+WHuYlFSDjr7lWB4aREW1CisyVHhLkWZpYeLVVt580as+TnZW7Iq8RsiUjWw+cdncoYmImM3EiRPp168f/fv3x8fHh0mTJuHh4cGMGTOy3H/BggUMHDiQLl26ULlyZbp27Uq/fv0YP358hv1MJhNubm4Zvu64ffs2S5cuZcKECTz66KNUrVqVMWPGUKlSpfTzGobBpEmTePPNN+nYsSO1atXiq6++Ii4ujm+++SbvEiIi8gCOXrhBu6mb+PHAX63C2tfik851sbexNHdo8hCpMZwI8EQNV1YPbcKghbs4FB1Ljy+2MeLJ6gx8tLJaOYhIkZKYmEhERAQjR47MsD04OJjNmzdneUxCQkKGO9cA9vb2bN++naSkJKytrQG4efMmnp6epKSkUK9ePd599118fX0BSE5OJiUlJctxwsPDATh16hQxMTEEBwenv29ra0uzZs3YvHkzAwcOvGt8CQkJ6a9jY2MBSEpKIinp33e3uHPsg4xRFChP2VOOcqYg5mnVH9G8ueIAt5NScXO2ZUrXutTzKEFycnKenK8g5sgccitP93O8Cm+Rv3g+4siywY15a8V+lu46x4f/O8zuyKt89GxdnO2szR2eiMhDcfnyZVJSUnB1dc2w3dXVlZiYmCyPadWqFV988QXt27fHz8+PiIgI5syZQ1JSEpcvX8bd3Z3q1aszb948ateuTWxsLJ999hlBQUHs3bsXLy8vnJycCAwM5N1338XHxwdXV1cWLVrEtm3b8PLyAkg/f1axnTlz5q7XNG7cOMaOHZtp+/r163FwcLiv/GQlLCzsgccoCpSn7ClHOVMQ8pScCivPWPB7TNoDxtWKp/K81y3O79vM+X15f/6CkKP84EHzFBcXl+N9VXiL/I29jSUfP1sHP88SjP3hIOsOXODohU3M7OGPt5uTucMTEXlo/vm0j2EYd30CaPTo0cTExBAQEIBhGLi6utK7d28mTJiApWXao5QBAQEEBASkHxMUFISfnx9Tpkxh8uTJQNoj63379qVcuXJYWlri5+dH9+7d2bVr17+ODWDUqFEMHz48/XVsbCweHh4EBwfj7Oycg2xkLSkpibCwMFq2bJl+V18yU56ypxzlTEHJU0xsPC8v+YNdMdcAGNysEi8/XvWhrFpeUHJkbrmVpztPUOWECm+RfzCZTDzXyJNaZYszeGEEpy7fov20TYzrWJv2vuXMHZ6ISJ5ycXHB0tIy093tixcvZrrTfIe9vT1z5sxh1qxZXLhwAXd3d2bPno2TkxMuLi5ZHmNhYUGDBg04duxY+rYqVarw22+/cevWLWJjY3F3d6dLly5UqlQJIP0z4TExMbi7u+coNkh7HN3WNnNvXGtr61z5xTS3xinslKfsKUc5k5/ztPnEZV5atJvLNxNxsrPi0871eKLG3f//lFfyc47ykwfN0/0cq8XVRO6irkcJVr/UlKZeaS3Hhi3Zw9sr96vlmIgUajY2Nvj7+2d6/C4sLIzGjRvf81hra2vKly+PpaUlixcvJiQkBAuLrH/VMAyDPXv2ZCig73B0dMTd3Z2rV6+ybt062rVrB0ClSpVwc3PLEFtiYiK//fZbtrGJiOQlwzCY+dsJenyxjcs3E/Fxd2b10CZmKbolf9Idb5F7KOVow7w+DZn001Gm/HKcr7ac4Y+o60x/zg/34vbmDk9EJE8MHz6cnj17Ur9+fQIDA5k9ezaRkZEMGjQISHt0OyoqKr1X99GjR9m+fTuNGjXi6tWrTJw4kf379/PVV1+ljzl27FgCAgLw8vIiNjaWyZMns2fPHqZNm5a+z7p16zAMA29vb44fP85rr72Gt7c3ffr0AdKeSBo2bBgffPABXl5eeHl58cEHH+Dg4ED37t0fYoZERP5fbHwSr323l3UHLgDwjF953mtfS6uWSwYqvEWyYWlh4j/B3tTzKMErS/awO/IaIZPDmdLNl8ZVs36EUkSkIOvSpQtXrlzhnXfeITo6mlq1arF27Vo8PT0BiI6OztDTOyUlhU8++YQjR45gbW1N8+bN2bx5MxUrVkzf59q1awwYMICYmBiKFy+Or68vv//+Ow0bNkzf5/r164waNYpz585RqlQpnnnmGd5///0Mj/KNGDGC27dvExoaytWrV2nUqBHr16/HyUnrcIjIw3ck5gaD/vpooo2lBW8/XYPuDSuoK45kosJbJIda+LiyemhTBi2M4GB0LD2+3MarrbwZ3KyK/ucqIoVOaGgooaGhWb43b968DK99fHzYvXv3Pcf79NNP+fTTT++5T+fOnencufM99zGZTIwZM4YxY8bccz8Rkby2YncUo5bt43ZSCmWL2zG9hz/1PEqYOyzJpwrEZ7ynT59OpUqVsLOzw9/fn40bN9513w0bNmAymTJ9HT58OMN+S5cupUaNGtja2lKjRg2WL1+e15chhUCFRxxYFtqYTv7lSTVgwo9HGLAggth49UoUERERKQoSk1N5e+V+hi3Zw+2kFJp6ubD6paYquuWe8n3hvWTJEoYNG8abb77J7t27adq0Ka1bt87wiFtWjhw5QnR0dPrXnR6gAFu2bKFLly707NmTvXv30rNnTzp37sy2bdvy+nKkELCztuSjTnUY17E2NpYWhB28wNNTwjkUnfN2AiIiIiJS8ERfv03X2Vv4assZAIY+XpV5fRpSytHGzJFJfpfvC++JEyfSr18/+vfvj4+PD5MmTcLDw4MZM2bc87gyZcrg5uaW/nWnjyjApEmTaNmyJaNGjaJ69eqMGjWKFi1aMGnSpDy+GiksTCYT3RpW4PvBgZQrYc/pK3F0mL6J5bvPmTs0EREREckDm49fJmRyOLsir+FsZ8WXz9fnP8HeD6U/txR8+foz3omJiURERDBy5MgM24ODg9m8efM9j/X19SU+Pp4aNWrw1ltv0bx58/T3tmzZwiuvvJJh/1atWt2z8E5ISCAhISH99Z1m6UlJSSQlPdhjxneOf9BxCrP8miMfV0eWDWrEf77fR/jxK7yyZC87T/3JqNbe2Fo93L9r5dcc5TfKU/aUo5zJrTwpzyIi+Vtaq7CTfLTuMKkG+Lg7M6uHPxUecTB3aFKA5OvC+/Lly6SkpODqmrH/naurKzExMVke4+7uzuzZs/H39ychIYEFCxbQokULNmzYwKOPPgpATEzMfY0JMG7cOMaOHZtp+/r163FwyJ0fun/2TJXM8muOnnEBx3gL1p2z4OvtZwk/GEmfaimUtH34seTXHOU3ylP2lKOcedA8xcXF5VIkIiKS22Ljk/jPt3sJO5jWKqyTf1qrMDtrtQqT+5OvC+87/rlitGEYd11F2tvbG29v7/TXgYGBnD17lo8//ji98L7fMSGtZ+nw4cPTX8fGxuLh4UFwcDDOzs73dT3/lJSURFhYGC1btszQMkX+X0HIUQiw4egl/vPdPs7cTOazw/ZM6lyHxlUeeSjnLwg5yg+Up+wpRzmTW3m68wSViIjkL4djYhm0IILTV+KwsbRgzNM16dbQQ91s5F/J14W3i4sLlpaWme5EX7x4MdMd63sJCAhg4cKF6a/d3Nzue0xbW1tsbTPfvrS2ts61X0xzc6zCKr/nqGXNsqxxL8GghREcOB9Ln68i+E9wWssxi4f0+Z/8nqP8QnnKnnKUMw+aJ+VYRCT/Wb77HKOW7SM+KZVyJeyZ/pwfdbVquTyAfL24mo2NDf7+/pke4wsLC6Nx48Y5Hmf37t24u7unvw4MDMw05vr16+9rTJG78SjlwNLBjelcP63l2Efr0lqOXb+tz3GKiIiI5GeJyamMXrGfV5bsJT4plaZeLqwa2kRFtzywfH3HG2D48OH07NmT+vXrExgYyOzZs4mMjGTQoEFA2iPgUVFRzJ8/H0hbsbxixYrUrFmTxMREFi5cyNKlS1m6dGn6mC+//DKPPvoo48ePp127dqxcuZKffvqJ8PBws1yjFD521pZM6FQXvwol+e8PB/jp0AWenhrOzB7++Lg/2EcTRERERCT3nb92m9Cvd7Hn7DUAXmrhxcstvLRqueSKfF94d+nShStXrvDOO+8QHR1NrVq1WLt2LZ6engBER0dn6OmdmJjIq6++SlRUFPb29tSsWZM1a9bQpk2b9H0aN27M4sWLeeuttxg9ejRVqlRhyZIlNGrU6KFfnxRuXRtWoGbZ4gxaGMGZv1qOvd++Ns/4lzd3aCIiIiLyl03HLzN00W7+vJWIs50Vn3X1pXn1MuYOSwqRfF94A4SGhhIaGprle/PmzcvwesSIEYwYMSLbMTt16kSnTp1yIzyRe6pdvjirhzZh2JI9/Hb0Ev/5bi+7Iq/y37Y1sLXSipgiIiIi5pKaajDjtxN8sv4IqQbUcHdmplqFSR7I15/xFiksSjraMKd3A15u4YXJBF9vi6TzrK1EXbtt7tBEREREiqTrt5MYsCCCj9alFd3P+pdnWWhjFd2SJ1R4izwklhYmXmlZjTm9G1Dc3pq9Z68RMnkj4ccumzs0ERERkSLlUHQsT08N56dDF7CxsuDDjrX56Nm66s8teUaFt8hD1ty7DKuHNqFWOWeuxiXRc842pv5yjNRUw9yhiYiIiBR6y3ado8P0TZy5Eke5EvZ8PyiQrg0rmDssKeRUeIuYgUcpB74f1Jgu9T0wDPh4/VEGLNiplmMiIiIieSQhOYW3Vuxj+LdprcKaVSvN6qFNqFO+hLlDkyJAhbeImdhZWzK+Ux3GP1MbGysLfjp0kbZTwjlw/rq5QxMREREpVKKu3abzrK0s3BqJyQQvt/BiTu8GlHS0MXdoUkSo8BYxsy4NKrB0UGPKl7Qn8s84Ok7fzPcR58wdloiIiEihEH7sMiGTN7L37DWK21sz5/kGvNKymvpzy0OlwlskH7jTcuwx79IkJKfy6nd7eWP5PhKSU8wdmoiIiEiBlJpqMPWXY/Scs42rcUnUKufM6qFN1J9bzEKFt0g+UcLBJu0vsE9Uw2SCb7ZF0nnmFrUcExEREblPaa3CdvLx+qMYBnSp78H3gxrjUUqtwsQ8VHiL5CMWFiZefsKLub0bUMLBmr3nrhMyeSO/H71k7tBERERECoSD52NpOyWcnw5dxMbKgvHP1GZ8pzpqFSZmpcJbJB96zLsMq4Y0oXa54lyNS+L5uduZ8rNajomIiIjcy/cRaa3CIv+Mo3xJe5YOakyXBmoVJuanwlskn/Io5cB3gwLp1jCt5dgnYUfpP38n1+PUckxERETk7xKSU3hj+T5e/W4vCcmpPOad1iqsdvni5g5NBFDhLZKv2VlbMq5jHSZ0qoONlQW/HL5IyNSN7I9SyzERERER+KtV2MwtfLMtrVXYsCe8mPN8A0o4qFWY5B8qvEUKgM71PVg2uDEepew5++dtnpmxmW93njV3WCIiIiJm9fvRS2mtws5dp4SDNXN7N2DYE9WwUKswyWdUeIsUELXKFWf1kKY0/6vl2Ijv/2DUsn3EJ6nlmIiIiBQtqakGU34+xvNzt3M1Lona5YqzakgTHvNWqzDJn1R4ixQgxR2s+fL5BgxvmdZybNH2SDrP2sK5q3HmDk1ERETkobh+O4kX5u/kk7C0VmHdGnrw3aBAtQqTfE2Ft0gBY2Fh4qUWXszr05ASDtb8ce46IVPC2XjssrlDExEREclT525Bhxlb+flwWquwCZ3qMK6jWoVJ/qfCW6SAalYtbbXOOuWLcy0uiX4LdvHjWZNajomIiEihtHRXFJP2WXL26m08StmzbHBjOtf3MHdYIjmiwlukACtfMq3lWPdGFTAM+N85SwZ8vZtrcYnmDk1EREQkV8QnpTBq2T5GLj9AkmGiWTUXVg9pSq1yahUmBYcKb5ECztbKkg861ObDDjWxNhn8dvQyIVPC1XJMRERECrxzV+PoPGsLi7antQpr45HC7Od8Ke5gbe7QRO6LCm+RQuIZv3IMq52CR0l7zl29TccZm/l2h1qOiYiISMH029FLhEwJ54+/WoV92dOPVuUNtQqTAkmFt0ghUt4Rlg8OoEX1MiQmpzJi6R+MXPqHWo6JiIhIgZGaajD552P0nruda3FJ1ClfnNVDm9DUy8XcoYn8ayq8RQqZ4vbWfN6rPq8Gp7UcW7zjLM/O3MLZP9VyTERERPK3a3GJ9PtqBxPTW4VV4NuBgZQvqVZhUrCp8BYphCwsTAx53Iv5fRtS0sGafVHXaTs1nA1HLpo7NBEREZEs7Y9Ka5H665FL2FpZ8FGnOozrWFutwqRQUOEtUog19SrN6peaUvevlmN95u1g0k9H1XJMRERE8pVvd5yl44zNnLvTKiy0Mc+qVZgUIiq8RQq5ciXs+XZQIM/91XJs0k/H6DNvh1qOiYiIiNnFJ6UwcukfjFj6B4nJqTxevQyrhzSlZlm1CpPCRYW3SBFga2XJ+x1q88mzdbG1suC3o5d4anI4+86p5ZiIiIiYx9k/43h25hYW7ziLyQT/aVmNL3rVV6swKZRUeIsUIc/4l2d5aBCejzgQde02z8zczOLtkeYOS0RERIqYDUcu0nZqOPuirlPSwZqv+jRkaAsvtQqTQkuFt0gRU6OsMz8MacITPmktx0Yu28eI7/eq5ZiIiIjkudRUg0k/Hf3rY29J1C1fnNUvNeXRaqXNHZpInlLhLVIEFbe3ZnbP+rzWyhsLE3y78xydZm5WyzERERHJM9fiEun71Q4m/XQMw4DnGlXg20GBlCthb+7QRPJcnhXeycnJ/PTTT8yaNYsbN24AcP78eW7evJlXpxSR+2BhYeLF5lWZ37cRpRxt2B8Vm9bC47BajokUVJp7RSS/utMqbMNfrcI+ebYu73eoja2VWoVJ0WCVF4OeOXOGJ598ksjISBISEmjZsiVOTk5MmDCB+Ph4Zs6cmRenFZF/oYmXC6uHNmHw17vYe/Yafb/awdDHvXi5hReW+pyVSIGhuVdE8qslOyIZvfIAicmpVCjlwMwe/tQo62zusEQeqjy54/3yyy9Tv359rl69ir39/z860qFDB37++ee8OKWIPICyJez5dmAAPQLSWo5N/jmt5djVW2o5JlJQaO4VkfwmPimFEd/v5fWl+0hMTuUJnzKsGtpERbcUSXlyxzs8PJxNmzZhY2OTYbunpydRUVF5cUoReUC2Vpa81742fhVK8sbyffx+9BIhU8KZ0cOPOuVLmDs8EcmG5l4RyU/O/hnH4K8j2B8Vi4UJ/hPszeBmVbRquRRZeXLHOzU1lZSUzCsknzt3Dicnp7w4pYjkko5+aS3HKv7VcqzTjC0s2h6JYRjmDk1E7iG3597p06dTqVIl7Ozs8Pf3Z+PGjffcf9q0afj4+GBvb4+3tzfz58/P8P68efMwmUyZvuLj49P3SU5O5q233qJSpUrY29tTuXJl3nnnHVJTU9P36d27d6YxAgIC7vv6RCTv/Hr4IiFTwtkfFUspRxvm923Ei82rquiWIi1PCu+WLVsyadKk9Ncmk4mbN2/y9ttv06ZNm7w4pYjkIh93Z1YOaULLGq4kpqQyatk+Rnz/h1qOieRjuTn3LlmyhGHDhvHmm2+ye/dumjZtSuvWrYmMjMxy/xkzZjBq1CjGjBnDgQMHGDt2LC+++CKrVq3KsJ+zszPR0dEZvuzs7NLfHz9+PDNnzmTq1KkcOnSICRMm8NFHHzFlypQM4zz55JMZxli7du19XZ+I5I2UVIOJYUfp+9UOrt9Ooq5HCVYPbUITLxdzhyZidnnyqPmnn35K8+bNqVGjBvHx8XTv3p1jx47h4uLCokWL8uKUIpLLittbM6uHPzN+O8En64/wXcQ5DkbHMuM5fyo84mDu8ETkH3Jz7p04cSL9+vWjf//+AEyaNIl169YxY8YMxo0bl2n/BQsWMHDgQLp06QJA5cqV2bp1K+PHj6dt27bp+5lMJtzc3O563i1bttCuXTueeuopACpWrMiiRYvYuXNnhv1sbW3vOY6IPHxXbyUybMkefjt6CYAeARUYHVJDq5aL/CVP7niXLVuWPXv28OqrrzJw4EB8fX358MMP2b17N2XKlMmLU4pIHrjTcmxBv0Y84mjDgfOxhEzZyC+HL5g7NBH5h9yaexMTE4mIiCA4ODjD9uDgYDZv3pzlMQkJCRnuXAPY29uzfft2kpKS0rfdvHkTT09PypcvT0hICLt3785wTJMmTfj55585evQoAHv37iU8PDzTHfsNGzZQpkwZqlWrxgsvvMDFi2qDKGJOf5y7RsiUcH47egk7awsmdq7Le+3VKkzk7/LkjjekTbh9+/alb9++eXUKEXlIgqq6sPqlJgxeuIs9Z6/Rd95OXnq8Ki8/UU0tx0TykdyYey9fvkxKSgqurq4Ztru6uhITE5PlMa1ateKLL76gffv2+Pn5ERERwZw5c0hKSuLy5cu4u7tTvXp15s2bR+3atYmNjeWzzz4jKCiIvXv34uXlBcDrr7/O9evXqV69OpaWlqSkpPD+++/TrVu39HO1bt2aZ599Fk9PT06dOsXo0aN5/PHHiYiIwNbWNsv4EhISSEhISH8dGxsLQFJSUoY/DNyvO8c+yBhFgfKUvYKaI8Mw+DYiirGrD5GUYlChlD3TutWjuptTnlxLQc3Tw6Qc5Uxu5el+js+TwvufC6r8U69evfLitCKSh9yL2/PtwEDeW3OQ+VvOMPmX4+w+e43PuvpSytEm+wFEJE/l9txrMmX8o5phGJm23TF69GhiYmIICAjAMAxcXV3p3bs3EyZMwNIy7Y5XQEBAhkXQgoKC8PPzY8qUKUyePBlI+2z5woUL+eabb6hZsyZ79uxh2LBhlC1blueffx4g/XF2gFq1alG/fn08PT1Zs2YNHTt2zDK+cePGMXbs2Ezb169fj4PDg390Jiws7IHHKAqUp+wVpBwlpsD3pyzYdintAdpaJVN5rsoNTu7ayMk8PndBypO5KEc586B5iouLy/G+eVJ4v/zyyxleJyUlERcXh42NDQ4ODvc9+U+fPp2PPvqI6OhoatasyaRJk2jatGm2x23atIlmzZpRq1Yt9uzZkyGecePG8dVXXxEVFYW3tzfjx4/nySefvK+4RIoaGysL3mlXC78KJRm57A82HrtM2ynhTH/Oj7oeJcwdnkiRlltzr4uLC5aWlpnubl+8eDHTXfA77O3tmTNnDrNmzeLChQu4u7sze/ZsnJyccHHJelElCwsLGjRowLFjx9K3vfbaa4wcOZKuXbsCULt2bc6cOcO4cePSC+9/cnd3x9PTM8M4/zRq1CiGDx+e/jo2NhYPDw+Cg4Nxdv73/YSTkpIICwujZcuWWFtb/+txCjvlKXsFLUeRf8YxdPFeDl66gYUJXmlRlQFNK+X5quUFLU/moBzlTG7l6c4TVDmRJ4X31atXM207duwYgwcP5rXXXruvse6srDp9+nSCgoKYNWsWrVu35uDBg1SoUOGux12/fp1evXrRokULLlzI+HnUt956i4ULF/L5559TvXp11q1bR4cOHdi8eTO+vr73FZ9IUdTetxzV3Z0YtCCC01fieHbmFsY8XZNuDT3uekdMRPJWbs29NjY2+Pv7ExYWRocOHdK3h4WF0a5du3sea21tTfny5QFYvHgxISEhWFhkvZyMYRjs2bOH2rVrp2+Li4vLtL+lpWWGdmL/dOXKFc6ePYu7u/td97G1tc3yMXRra+tc+cU0t8Yp7JSn7BWEHP1y+ALDFu8hNj6ZRxxtmNzNl6CqD3fV8oKQJ3NTjnLmQfN0P8fmyeJqWfHy8uLDDz/M9Bf57Px9ZVUfHx8mTZqEh4cHM2bMuOdxAwcOpHv37gQGBmZ6b8GCBbzxxhu0adOGypUrM3jwYFq1asUnn3xyX7GJFGXV3Zz5YWgTgv9qOfbG8n28+p1ajonkJ/927h0+fDhffPEFc+bM4dChQ7zyyitERkYyaNAgIO0O8t/voB89epSFCxdy7Ngxtm/fTteuXdm/fz8ffPBB+j5jx45l3bp1nDx5kj179tCvXz/27NmTPiZA27Ztef/991mzZg2nT59m+fLlTJw4Mf0PADdv3uTVV19ly5YtnD59mg0bNtC2bVtcXFwy/JFARHJfSqrBxPVH6DtvJ7HxydTzKMHql5o89KJbpKDKs8XVsmJpacn58+dzvP+dlVVHjhyZYfu9VlYFmDt3LidOnGDhwoW89957md6/2+qr4eHhdx0zrxZmuTPG3/8pmSlH2TNHjuwtYWrXOszeeJqJPx1j6a5zHDh/nand6uJZKn+2HNP3UvaUo5wxx8Is/8b9zr2Q9jnqK1eu8M477xAdHU2tWrVYu3Ytnp6eAERHR2fo6Z2SksInn3zCkSNHsLa2pnnz5mzevJmKFSum73Pt2jUGDBhATEwMxYsXx9fXl99//52GDRum7zNlyhRGjx5NaGgoFy9epGzZsgwcOJD//ve/6deyb98+5s+fz7Vr13B3d6d58+YsWbIEJyenB8iSiNzLn7cSeXnxbjYeuwxAzwBP3grx0arlIvchTwrvH374IcNrwzCIjo5m6tSpBAUF5Xicf7Oy6rFjxxg5ciQbN27Eyirry2vVqhUTJ07k0UcfpUqVKvz888+sXLmSlJS736nL64VZQIsg5IRylD1z5MgDGOxj4qujFhyOuUHbyRvp4ZVKrZLGQ48lp/S9lD3lKGce5sIs95Jbc+8doaGhhIaGZvnevHnzMrz28fHJ1Brsnz799FM+/fTTe+7j5OTEpEmTmDRpUpbv29vbs27dunuOISK5a+/Za4R+vYuoa7exs7ZgXMfadPAtb+6wRAqcPCm827dvn+G1yWSidOnSPP744//qce6crqyakpJC9+7dGTt2LNWqVbvreJ999hkvvPAC1atXx2QyUaVKFfr06cPcuXPvekxeLcwCWgQhJ5Sj7Jk7R22ALtfjeWnJXvacvc7nhy0Z3KwSLz9eNV+1HDN3ngoC5ShnzLEwy73k9twrIkWbYRgs2n6WMT8cIDEllYqPODCzpz/V3R7s916RoipPCu97LYJyP+53ZdUbN26wc+dOdu/ezZAhQ9JjMQwDKysr1q9fz+OPP07p0qVZsWIF8fHxXLlyhbJlyzJy5EgqVap011jyemGW3B6rsFKOsmfOHFVwsebbgY35YO0h5m0+zYzfTrH//I182XJM30vZU45y5mEuzHIvuTX3iojEJ6Xw5vL9LN11DoDgGq583LkuznaaE0T+rYe2uNq/8feVVf8uLCyMxo0bZ9rf2dmZffv2sWfPnvSvQYMG4e3tzZ49e2jUqFGG/e3s7ChXrhzJycksXbo029VaRSR7NlYWjHm6Jp91rYe9tSUbj10mZPJG9py9Zu7QREREJBuRV+LoOH0zS3edw8IErz9ZnVk9/VV0izygXLvj/ffHsLMzceLE+xq3Z8+e1K9fn8DAQGbPnp1pZdWoqCjmz5+PhYUFtWrVynB8mTJlsLOzy7B927ZtREVFUa9ePaKiohgzZgypqamMGDEix3GJyL21q1eO6m7ODFoYwanLt+g8cwv/bVuD5xpVUMsxkVySV3OviBRNPx+6wCtL/r9V2JRuvjTWquUiuSLXCu/sFlW5435/4b7flVVzIj4+nrfeeouTJ09SrFgx2rRpw4IFCyhRosR9jSMi9+bt5sTKIUG89t1e1h24wFsr9rMr8irvt6+NvY1WQhV5UHk194pI0ZKSavBp2FGm/nocAN8KJZj+nB/uxe3NHJlI4ZFrhfevv/6aW0Nlcj8rq/7TmDFjGDNmTIZtzZo14+DBg7kUnYjci7OdNTN7+DP795OM//Ewy3ZFcfB8LDN7+FPRxdHc4YkUaHk594pI0fDPVmHPB3ry5lM1sLHK159IFSlw9BMlInnOZDIxsFkVvu4fgEsxm7SWY1PDCTt4wdyhiYiIFFl7zl4jZPJGNh67jL21JZO61GNsu1oqukXyQJ6sag6wY8cOvvvuOyIjI0lMTMzw3rJly/LqtCKSjwVWeYTVQ5vy4je7iDhzlRfm7+TF5lUY3tI7X7UcEymoNPeKSE4YhsHX2yJ5Z9VBElNSqeTiyMwe/ni7OZk7NJFCK0/+nLV48WKCgoI4ePAgy5cvJykpiYMHD/LLL79QvHjxvDiliBQQbsXtWPRCAL0bVwRg2q8n6DVnG1duJpg3MJECTnOviOTE7cQU/vPdXt5asZ/ElFRa1XRl5ZAgFd0ieSxPCu8PPviATz/9lNWrV2NjY8Nnn33GoUOH6Ny5MxUqVMiLU4pIAfLPlmObjl8hZEo4uyOvmjs0kQJLc6+IZOfMlVt0mL6JZbuisDDBqNbVmdlDrcJEHoY8KbxPnDjBU089BYCtrS23bt3CZDLxyiuvMHv27Lw4pYgUQO3qlWPlkCAquzgSfT2ezrO2sGDLaQzDMHdoIgWO5l4RuZefDl4gZEo4h2Nu4FLMhoX9GzGwWRV1PRB5SPKk8C5VqhQ3btwAoFy5cuzfvx+Aa9euERcXlxenFJECqpprWsuxJ2u6kZRiMHrlAYZ/u5fbiSnmDk2kQNHcKyJZSUk1+GjdYfrP38mN+GT8KpRg9dCmNK6i/twiD1OuFt579uwBoGnTpoSFhQHQuXNnXn75ZV544QW6detGixYtcvOUIlIIONlZM6OHH2+0qY6lhYnlu6PoMH0Tpy7fMndoIvme5l4RuZsrNxN4fs52pv16AoDejSuyeEAgbsXtzByZSNGTq4W3n58f/v7++Pj40K1bNwBGjRrFq6++yoULF+jYsSNffvllbp5SRAoJk8nEgEer8HX/RrgUs+VwzA2enhLO+gMx5g5NJF/T3CsiWdkdeZWQKeGEH09rFfZZ13qMebqmWoWJmEmu/uRt2rQJPz8/Pv74Y6pUqUKPHj347bffGDFiBD/88AMTJ06kZMmSuXlKESlkAio/wpqXmlDfsyQ3EpIZsCCC8T8eJjkl1dyhieRLmntF5O8Mw2DBltN0nrWF6OvxVHZxZOWQINrVK2fu0ESKtFwtvAMDA/n888+JiYlhxowZnDt3jieeeIIqVarw/vvvc+7cudw8nYgUUq7OdiwaEECfoIoAzNhwgl5ztnNZLcdEMtHcKyJ33E5MYfi3exm98gBJKQZP1nRj5ZAgqrmqVZiIueXJsyb29vY8//zzbNiwgaNHj9KtWzdmzZpFpUqVaNOmTV6cUkQKGWtLC95uW5PJ3XxxsLFk84krhEwOZ5dajolkSXOvSNF2+nJaq7Dlu6OwtDDxRpvqzOjhh5NahYnkC3n+IY8qVaowcuRI3nzzTZydnVm3bl1en1JECpGn65Zl5YtBVC7tSExsPF1mbWG+Wo6J3JPmXpGiZf2BGNr+rVXY1/0bMeBRtQoTyU/ytPD+7bffeP7553Fzc2PEiBF07NiRTZs25eUpRaQQ8nJ1YuWLQbSpndZy7L8rD/DKkj3EJSabOzSRfEdzr0jRkZySyvgfDzNgQQQ3EpKp71mSNS81JaDyI+YOTUT+wSq3Bzx79izz5s1j3rx5nDp1isaNGzNlyhQ6d+6Mo6Njbp9ORIoIJztrpnX348vwU4z732FW7DnPoegbzOjhR+XSxcwdnohZae4VKXou30zgpUW72XziCgB9giryRhsfrC21arlIfpSrhXfLli359ddfKV26NL169aJv3754e3vn5ilEpAgzmUz0b1qZ2uWKM2TRbo5cuEG7qZv46Nm6PFnLzdzhiZiF5l6RomdX5FVCF+4iJjYeBxtLPnymDk/XLWvusETkHnK18La3t2fp0qWEhIRgaWmZm0OLiKRrVPkR1gxtwovf7GLH6asMWhjBoGZVeDW4Glb6S78UMZp7RYoOwzBYsPUM764+SFKKQeXSjszq4Y+XVi0XyfdytfD+4YcfcnM4EZG7KuNsxzcvBPDh/w7zZfgpZv52gr1nrzG5my+lnWzNHZ7IQ6O5V6RoiEtM5s3l+1m+OwqA1rXcmNCpjlYtFykgdGtIRAosa0sLRofUYGr3tJZjW05eIWTKRiLOqOWYiIgUHqcu36LDtM3prcLebOPD9OfUKkykIFHhLSIFXkidsvwwJIgqpR25EJtAl1lbmLfplFqOiYhIgbfuQAxPTwnnyIUbuBSz5Zv+jXjh0cpqFSZSwKjwFpFCoWoZJ1YOacJTtd1JTjUYs+ogLy9WyzERESmYklNS+fB/hxn4V6uwBhVLsualJjRSqzCRAkmFt4gUGsVsrZja3Ze3nvLB0sLED3vP037aJk5eumnu0ERERHLs0o0Een65nZm/nQCgX5NKfPNCAK7OdmaOTET+LRXeIlKo3Gk5tuiFAEo72XL0wk2enrqJH/dHmzs0ERGRbEWcuUrIlI1sOXkFBxtLpnb3ZXRIDfXnFing9BMsIoVSw0qlWDO0CQ0rluJmQjKDFu5i3NpDJKekmjs0ERGRTAwDFmyNpOvsLVyITaBKaUdWvhhESB315xYpDFR4i0ihVcbZjq9faET/JpUAmPX7SXp/FUFsopkDExER+Zu4xGQWHLfgnTWHSUoxeKq2OyuHNFF/bpFCRIW3iBRq1pYWvBVSg2nd/XC0sWTbqat8/IcluyKvmTs0ERERTl66SadZ24i4bIGlhYm3nvJhandfitlamTs0EclFKrxFpEh4qk7a3YMqpR25nmTiuS93MFctx0RExIx+3B/N01M3ceziLZytDRb0qU//pmoVJlIYqfAWkSKjapliLB3YCN9HUklONRi76iAvLd7DrQS1HBMRkYcnOSWVcWsPMWjhLm4mJFPfswSv1kmhQcWS5g5NRPKICm8RKVIcba143iuVN9t4Y2VhYtVfLceOX1TLMRERyXuXbiTQ48ttzPr9JAD9m1Rifp/6FLcxc2AikqdUeItIkWMyQe9ATxYNCKCMky3HLt6k3dRw/rdPLcdERCTvRJz5k5ApG9l68k8cbSyZ1t2Pt9QqTKRI0E+5iBRZDSqWYvVLTWhUqRS3ElMY/PUuPlDLMRERyWWGYTB30ym6zNrKhdgEqpYpxsohQTxVx93coYnIQ6LCW0SKtDJOdnzdvxEDHq0MwOzfT9L9i21cvBFv5shERKQwuJWQzMuL9zB21UGSU420xT5fDKJqGbUKEylKVHiLSJFnZWnBG218mPGcH8Vsrdh+6k9CJoez4/Sf5g5NREQKsBOXbtJ+2iZ+2HseKwsTo0NqMLWbL45qFSZS5KjwFhH5S+va7qwcEoRXmWJcvJFAt9lb+TJcLcdEROT+/W9fNO2mbuLYxZuUcbJl0YAA+jWppFZhIkWUCm8Rkb+pUroYK14Mom3dsiSnGry7+iBDF+1WyzEREcmR5JRUPlh7iMFfp7UKa1QpbT2RBhVLmTs0ETEjFd4iIv/gaGvF5K71eLttDawsTKz+I5p2ajkmIiLZuHgjnue+2Mbsv1qFDXi0Ml/3b0QZJzszRyYi5qbCW0QkCyaTiT5BlVg8IABXZ1uO/9VybK1ajomISBZ2nk5bH2TbqbRWYdOf8+ONNj5YqVWYiKDCW0TknupXLMXqoU0JqJzWciz06128t/ogSWo5JiIipLUKmxN+iq6zt3LxRgJeZYrxw9AmtKmtVmEi8v9UeIuIZKO0ky0L+zVi4F8tx74IP8Vzn2/jYqxajomIFGW3EpIZumg376xOaxXWtm5ZVrwYRJXSxcwdmojkMyq8RURywMrSglFtfJjZ46+WY6f/5Kkp4Ww/pZZjIiJF0fGLN2k3bROr/4jGysLE221rMLlrPbUKE5EsFYjCe/r06VSqVAk7Ozv8/f3ZuHFjjo7btGkTVlZW1KtXL9N7kyZNwtvbG3t7ezw8PHjllVeIj9fdKxG5tydrufPDkCCquRbj0o0Eun2+lS82nlTLMSl07nfunTZtGj4+Ptjb2+Pt7c38+fMzvD9v3jxMJlOmr7/PvcnJybz11ltUqlQJe3t7KleuzDvvvENq6v9/tMMwDMaMGUPZsmWxt7fnscce48CBA7l78SLZWLsvmnZTwzl+8SauzrYsHhBAnyC1ChORu8v3hfeSJUsYNmwYb775Jrt376Zp06a0bt2ayMjIex53/fp1evXqRYsWLTK99/XXXzNy5EjefvttDh06xJdffsmSJUsYNWpUXl2GiBQilf9qOfZ03bKkpBq8t+YQQ77ZzU21HJNC4n7n3hkzZjBq1CjGjBnDgQMHGDt2LC+++CKrVq3KsJ+zszPR0dEZvuzs/n+15/HjxzNz5kymTp3KoUOHmDBhAh999BFTpkxJ32fChAlMnDiRqVOnsmPHDtzc3GjZsiU3btzIm2SI/E1SSirvrT5I6Ne7uJWYktYqbGhT6qtVmIhkI98X3hMnTqRfv370798fHx8fJk2ahIeHBzNmzLjncQMHDqR79+4EBgZmem/Lli0EBQXRvXt3KlasSHBwMN26dWPnzp15dRkiUsg42FjxWdd6jH26JlYWJtak3/3QL/9S8N3v3LtgwQIGDhxIly5dqFy5Ml27dqVfv36MHz8+w34mkwk3N7cMX3+3ZcsW2rVrx1NPPUXFihXp1KkTwcHB6fOzYRhMmjSJN998k44dO1KrVi2++uor4uLi+Oabb/ImGSJ/uXgjnuc+38YX4acAGPhXq7DSTrZmjkxECoJ8/SGUxMREIiIiGDlyZIbtwcHBbN68+a7HzZ07lxMnTrBw4ULee++9TO83adKEhQsXsn37dho2bMjJkydZu3Ytzz//fK5fg4gUXiaTiecbV6RWueKEfh3BiUu3aDd1E+M71SGkTllzhyfyr/ybuTchISHDnWsAe3t7tm/fTlJSEtbW1gDcvHkTT09PUlJSqFevHu+++y6+vr7pxzRp0oSZM2dy9OhRqlWrxt69ewkPD2fSpEkAnDp1ipiYGIKDg9OPsbW1pVmzZmzevJmBAwfeNb6EhIT017GxsQAkJSWRlJSUw8xkdufYBxmjKCgMedpx+iovL9nLpZuJONpaMr5DLVrVdMVITSEpNeWBxy8MOXoYlKfsKUc5k1t5up/j83XhffnyZVJSUnB1dc2w3dXVlZiYmCyPOXbsGCNHjmTjxo1YWWV9eV27duXSpUs0adIEwzBITk5m8ODBmX7J+Lu8mrTvjPH3f0pmylH2lKOcyYs81SlbjJWDAxj27R9sPXWVId/sZuepK4xoVQ3rAti/Vd9LOWOOSfth+Ddzb6tWrfjiiy9o3749fn5+REREMGfOHJKSkrh8+TLu7u5Ur16defPmUbt2bWJjY/nss88ICgpi7969eHl5AfD6669z/fp1qlevjqWlJSkpKbz//vt069YNIP38WcV25syZu17TuHHjGDt2bKbt69evx8HBIefJuYuwsLAHHqMoKIh5MgzYEG3ihzMWpGLCzd6gr3cCKWciWHv3b7l/rSDmyByUp+wpRznzoHmKi4vL8b75uvC+458LVRiGkeXiFSkpKXTv3p2xY8dSrVq1u463YcMG3n//faZPn06jRo04fvw4L7/8Mu7u7owePTrLY/J60gb9gOSEcpQ95Shn8iJPnV3BMcGCn89bMG9LJL/vP0PvaikUt8n1Uz0U+l7KmYc5aT9MOZ17AUaPHk1MTAwBAQEYhoGrqyu9e/dmwoQJWFpaAhAQEEBAQED6MUFBQfj5+TFlyhQmT54MpH22fOHChXzzzTfUrFmTPXv2MGzYMMqWLZvhqbT7iQ1g1KhRDB8+PP11bGwsHh4eBAcH4+zsnMOMZJaUlERYWBgtW7ZMv6svmRXUPN1MSObNFQdYe+YCACG13Xi/fQ0cbHL/1+eCmqOHTXnKnnKUM7mVpzs3Y3MiXxfeLi4uWFpaZvoL+8WLFzP9tRvgxo0b7Ny5k927dzNkyBAAUlNTMQwDKysr1q9fz+OPP87o0aPp2bMn/fv3B6B27drcunWLAQMG8Oabb2JhkfkOVV5N2qAfkJxQjrKnHOVMXuepLRB28CIjlu3n5I1kJh9x4LMudWhYgBbe0fdSzphj0n4Y7nfuhbTHyufMmcOsWbO4cOEC7u7uzJ49GycnJ1xcXLI8xsLCggYNGnDs2LH0ba+99hojR46ka9euQNr8fObMGcaNG8fzzz+f/pnwmJgY3N3dcxQbpD2Obmub+XO41tbWufI9nlvjFHYFKU/HL95g4IK0jxBZWZgYHVKDXoGeeb5qeUHKkTkpT9lTjnLmQfN0P8fm68LbxsYGf39/wsLC6NChQ/r2sLAw2rVrl2l/Z2dn9u3bl2Hb9OnT+eWXX/j++++pVKkSkHZ34Z/FtaWlJYZh3LUlUF5P2rk9VmGlHGVPOcqZvMxTm7rl8ClXgkELIjhy4Qa95kYw8snq9G9asFrN6HspZx7mpP0w3O/c+3fW1taUL18egMWLFxMSEpLlH7Mh7S71nj17qF27dvq2u83Pd9qJVapUCTc3N8LCwtI/G56YmMhvv/2WaSE3kX9r9R/nef37P7iVmIKrsy3Tn/PH37OkucMSkQIuXxfeAMOHD6dnz57Ur1+fwMBAZs+eTWRkJIMGDQLS7kRHRUUxf/58LCwsqFWrVobjy5Qpg52dXYbtbdu2ZeLEifj6+qY/aj569Giefvrp9EfiREQeRCUXR5a/2Jg3lu1jxZ7zvL/2ELvPXmVCp7oUs833/+uVIu5+5l6Ao0ePsn37dho1asTVq1eZOHEi+/fv56uvvkofc+zYsQQEBODl5UVsbCyTJ09mz549TJs2LX2ftm3b8v7771OhQgVq1qzJ7t27mThxIn379gXSHjEfNmwYH3zwAV5eXnh5efHBBx/g4OBA9+7dH2KGpDBKSkll3NrDzNmUtmp5QOVSTOnmp1XLRSRX5Pvf/rp06cKVK1d45513iI6OplatWqxduxZPT08AoqOjs+3p/U9vvfUWJpOJt956i6ioKEqXLp0+2YuI5BYHGys+7VIPP8+SvLv6IGv3xXA45gazevjj5epk7vBE7up+596UlBQ++eQTjhw5grW1Nc2bN2fz5s1UrFgxfZ9r164xYMAAYmJiKF68OL6+vvz+++80bNgwfZ8pU6YwevRoQkNDuXjxImXLlmXgwIH897//Td9nxIgR3L59m9DQUK5evUqjRo1Yv349Tk76mZJ/72JsPC9+s4sdp68CMKhZFV4NroZVAVwgU0Typ3xfeAOEhoYSGhqa5Xvz5s2757FjxoxhzJgxGbZZWVnx9ttv8/bbb+dShCIiWTOZTPQK/Kvl2MJdnLx0i3bTNjH+mTq0rauWY5J/3c/c6+Pjw+7du+853qeffsqnn356z32cnJyYNGlSevuwrJhMpizndpF/a9vJKwxZtJtLNxJwsrXi4851aVXTLfsDRUTug/6MJyLyEPhVKMnql5rQuMojxCWmMHTRbsauOkBSSqq5QxMRKZIMw+Dz30/S/YttXLqRgLerEyuHBKnoFpE8ocJbROQhcSlmy/y+DRn8WBUA5m46TbfZW7kQG2/myEREipabCcm8+M0u3l97iJRUg/b1yrL8xcZULl3M3KGJSCGlwltE5CGysrTg9SerM7unP062Vuw8c5WnJoez9eQVc4cmIlIkHLtwg6enhrN2XwzWlibeaVeTT7vUy5P+3CIid6jwFhExg+CabvwwtAnV3Zy4fDOB577YxuzfT9y1paGIiDy4VXvP027aJk5euoWbsx2LBwTSK7BigWr1KCIFkwpvEREzqeTiyPLQIDr4liMl1eCDtYcZvHAXN+KTzB2aiEihkpSSythVBxi6aDdxiSk0rvIIq19qov7cIvLQqPAWETEjextLJnauy7vta2FtaeLHAzG0m7qJoxdumDs0EZFC4UJsPN1mb2XuptMADH6sCvP7NsSlmPpzi8jDo8JbRMTMTCYTPQM8+XZgIO7F7Th5+Rbtpm5i5Z4oc4cmIlKgbT15hacmh7PzzFWcbK2Y3dOf15+srv7cIvLQ6f86IiL5hG+Fkqwe2oQmVV24nZTCy4v3MOaHAyQmq+WYiMj9MAyD2b+f4LkvtnH5ZgLV3Zz4YWgTgtUqTETMRIW3iEg+8kgxW77q25AXm6e1HJu3+TTdPt9KzHW1HBMRyYkb8UmEfr2LD9YeJiXVoINvOZaHBlHJxdHcoYlIEabCW0Qkn7G0MPFaq+p83qs+TnZWRJy5SsiUjWw+cdncoYmI5GtHL9yg3dRN/G9/Wquwd9vVZGLnutjbWJo7NBEp4lR4i4jkUy1ruLJqyJ2WY4n0+GIbM39TyzERkaz8sPc87aZu4uTlW7gXt2PJwEB6qlWYiOQTKrxFRPKxin+1HOvoV45UAz7832EGLYwgVi3HREQASExOZcwPB3hp0W5uJ6UQVPURVg9tgl8FtQoTkfxDhbeISD5nb2PJJ8/W5b32tbCxtGDdgQu0m7qJIzFqOSYiRVvM9Xi6fb6VeZtPA/Bi8yrM79uIR9QqTETyGRXeIiIFgMlkokeAJ98OCqRscTtOXb5F+2lqOSYiRdfmE5cJmbKRiDNXcbKz4vNe9XmtVXUsLfRouYjkPyq8RUQKkHoeJVj9UtMMLcfeXrlfLcdEpMgwDIOZv52gxxfbuHwzkepuTqwa0oSWNVzNHZqIyF2p8BYRKWBKOdrwVd+GDGleFYCvtpyh6+wtRF+/bebIRETyVmx8EoMWRvDh/w6TakDHv1qFVVSrMBHJ51R4i4gUQJYWJl5t5c0Xf7Uc2xV5jZDJ4Ww+rpZjIlI4HYlJaxW27sAFrC1NvNe+Fp+oVZiIFBAqvEVECrAnariyemgTfNyduXIrkR5fbmPGBrUcE5HCZeWeKNpP28Spy7coW9yO7wY1pkeAp1qFiUiBocJbRKSA83zEkeWhjXnGrzypBoz/8TADF6jlmIgUfInJqby9cj8vL97D7aQUmlR1YfVLTannUcLcoYmI3BcV3iIihYCdtSUfP1uHDzrUxsbSgvUHL/D0lHAOx8SaOzQRkX8l+vptus7ewldbzgAwpHlVvurbkFKONmaOTETk/qnwFhEpJEwmE90bVeC7QYGUK2HP6StxtJ+2ieW7z5k7NBGR+7L5+GVCJoezK/IaTnZWfNGrPq+28larMBEpsFR4i4gUMnU9SrBqaBOaerkQn5TKK0v2MnqFWo6JSP5nGAYzNpygx5fbuHIrER93Z1YPbcITahUmIgWcCm8RkUKolKMN8/o05KXH01qOLdh6hs6ztnD+mlqOiUj+FBufxMAFEYz/Ma1V2DN+5Vk2uDGej6hVmIgUfCq8RUQKKUsLE8ODvZnTuz7OdlbsOXuNkCnhbFLLMRHJZw7HxPL0lHDWH7yAjaUF73eoxcfP1lGrMBEpNFR4i4gUco9Xd2X10KbUcHfmz1uJ9PxyG9N+PU5qqlqOiYj5rdid1irs9JU4ypWw57tBgTzXSK3CRKRwUeEtIlIEVHjEgWWhjXnWP63l2EfrjjBgQQTXb6vlmIiYR2JyKv9duZ9hS/YQn5RKUy8XVg1tQl21ChORQkiFt4hIEWFnbcmETnUY1zGt5dhPhy7w9NRwDkWr5ZiIPFzR12/TedYW5v/VKmzo41WZ10etwkSk8FLhLSJShJhMJro1rMD3g9Najp25EkeH6ZtYtkstx0Tk4dh0/DJPTQ5nz9lrONtZMad3ff4TrFZhIlK4qfAWESmC6pQvweqhTXi0Wmnik1IZ/u1e3lqxj4TkFHOHJiKFVGqqwbRfj9Pzy238eSuRGu7OrB7alMerq1WYiBR+KrxFRIqoko42zO3dgJdaeAGwcGsknWdtJfp6vJkjE5HCJi4ZQhft4aN1R0g14Fn/8iwLbUyFRxzMHZqIyEOhwltEpAiztDAxvGU15vZuQHF7a/aevUa76Vs4ck2PfIpI7jgcc4NP9lny8+FL2FhaMK5jbSZ0qoOdtVqFiUjRocJbRERoXr0Mq4c2oWZZZ67GJTHjkAUzfjuplmMi8kCW7TrHs7O3cTneRLkSdnw/OJBuDSuoVZiIFDkqvEVEBACPUg4sHdyYZ/3LYWBi4k/HGbBgp1qOich9S0hO4a0V+xj+7V7ik1KpXjyV5YMDqFO+hLlDExExCxXeIiKSzs7akg/a16Rr5RRsrCz46dBF2k4J5+B5tRwTkZw5f+02nWdtZeHWSACGPFaZgT6plHRQqzARKbpUeIuISCaBrgZL+jekfEl7Iv9Mazn2fYRajonIvYUfu0zIlHD2nr1GcXtr5vZuwMstqqJOYSJS1KnwFhGRLNUq58zqoU1oVq00CcmpvPrdXt5YrpZjIpLZnVZhveaktQqrWTbt/x/Nq5cxd2giIvmCCm8REbmrEg5pLceGPeGFyQTfbIuk88wtRF27be7QRCSfuH47iQELdqa3CutcvzxLBzfGo5RahYmI3KHCW0RE7snCwsSwJ/7WcuzcdUImb+T3o5fMHZqImNnB87E8PTWcnw5dxMbKgg871mZCp7pqFSYi8g8qvEVEJEce805rOVarXFrLsefnbmfKz8fUckykiFoacY4O0zdx5koc5UrYs3RQY7o2rGDusERE8iUV3iIikmMepRz4flBjujbwwDDgk7CjvDB/J9fj1HJMpKhISE7hzeX7+M93e0lITqVZtdKsHtqE2uWLmzs0EZF8q0AU3tOnT6dSpUrY2dnh7+/Pxo0bc3Tcpk2bsLKyol69ehm2P/bYY5hMpkxfTz31VB5ELyJSuNhZW/LhM3WY8EwdbKws+PnwRdpODefA+evmDk1E8ljUtdt0nrmFr7dFYjLByy28mNu7ASUd1SpMRORe8n3hvWTJEoYNG8abb77J7t27adq0Ka1btyYyMvKex12/fp1evXrRokWLTO8tW7aM6Ojo9K/9+/djaWnJs88+m1eXISJS6HRu4MGywY3TW451nL6Z73aeNXdYIpJHfj96iZDJG9l77jrF7a2Z07sBr7SshoV6hYmIZCvfF94TJ06kX79+9O/fHx8fHyZNmoSHhwczZsy453EDBw6ke/fuBAYGZnqvVKlSuLm5pX+FhYXh4OCgwltE5D7VKlc8rWWQd1rLsde+/4NRy/YRn6SWYyKFRWqqwZSfj/H83O1cjUtKbzXY3FutwkREcsrK3AHcS2JiIhEREYwcOTLD9uDgYDZv3nzX4+bOncuJEydYuHAh7733Xrbn+fLLL+natSuOjo533SchIYGEhIT017GxsQAkJSWRlPRgn228c/yDjlOYKUfZU45yRnnK3v3myNHaxMzu9Zj+20km/3qCRdsj2R91jSld61KuhH1ehmpWufW9pO9Fyc+uxyUx/Ns9/Hz4IgBdG3gw5umaWrVcROQ+5evC+/Lly6SkpODq6pphu6urKzExMVkec+zYMUaOHMnGjRuxssr+8rZv387+/fv58ssv77nfuHHjGDt2bKbt69evx8Ehd/pUhoWF5co4hZlylD3lKGeUp+zdb44qAwOrm5h/zIJ9UbG0+ex3enml4lOicK96/qDfS3FxcbkUiUjuOnD+OoMX7iLyzzhsrCx4r10tOjfwMHdYIiIFUr4uvO8wmTJ+dsgwjEzbAFJSUujevTtjx46lWrVqORr7yy+/pFatWjRs2PCe+40aNYrhw4env46NjcXDw4Pg4GCcnZ1zdK67SUpKIiwsjJYtW2Jtbf1AYxVWylH2lKOcUZ6y9yA5agN0uXqboYv3sv98LLMOW/JS8yqENqtc6D4HmlvfS3eeoMpvpk+fzkcffUR0dDQ1a9Zk0qRJNG3a9K77T5s2jalTp3L69GkqVKjAm2++Sa9evdLfnzdvHn369Ml03O3bt7GzswOgYsWKnDlzJtM+oaGhTJs2DYDevXvz1VdfZXi/UaNGbN269V9dp2Ttu51neWvFfhKSUylf0p6ZPfypVU6rlouI/Fv5uvB2cXHB0tIy093tixcvZroLDnDjxg127tzJ7t27GTJkCACpqakYhoGVlRXr16/n8ccfT98/Li6OxYsX884772Qbi62tLba2tpm2W1tb59ov77k5VmGlHGVPOcoZ5Sl7/zZHlcpY8/3gxoxddZBF2yP57JcT/BEVy6dd6lHCofCtfPyg30v58fvwzsKm06dPJygoiFmzZtG6dWsOHjxIhQqZ+zTPmDGDUaNG8fnnn9OgQQO2b9/OCy+8QMmSJWnbtm36fs7Ozhw5ciTDsXeKboAdO3aQkvL/6wPs37+fli1bZlqD5cknn2Tu3Lnpr21sCt/3lbnEJ6Wk/+wCNPcuXWh/dkVEHqZ8XXjb2Njg7+9PWFgYHTp0SN8eFhZGu3btMu3v7OzMvn37MmybPn06v/zyC99//z2VKlXK8N63335LQkICPXr0yJsLEBEpouysLRnXsTa+FUowesV+fj1yiZAp4bprVkD8fWFTgEmTJrFu3TpmzJjBuHHjMu2/YMECBg4cSJcuXQCoXLkyW7duZfz48RkKb5PJhJub213PW7p06QyvP/zwQ6pUqUKzZs0ybLe1tb3nOPLvnLsaR+jXu/jj3HVMJhjWohpDH69a6J5WERExh3xdeAMMHz6cnj17Ur9+fQIDA5k9ezaRkZEMGjQISHsEPCoqivnz52NhYUGtWrUyHF+mTBns7OwybYe0x8zbt2/PI4888lCuRUSkqOlc34Ma7s4M/jqCs3/epuOMzfqcaD73bxY2TUhIyHDnGsDe3p7t27eTlJSUflf/5s2beHp6kpKSQr169Xj33Xfx9fW9axwLFy5k+PDhmT5etmHDBsqUKUOJEiVo1qwZ77//PmXK3H2F7bxaILUwLda48dhlhn+3j2u3kyhhb80nz9bmUS8XUlKSSXnAJgWFKU95RTnKGeUpe8pRzphjgdR8X3h36dKFK1eu8M477xAdHU2tWrVYu3Ytnp6eAERHR2fb0zsrR48eJTw8nPXr1+d2yCIi8je1yhVn9ZCmvPLtHn45fJERS/9gV+RVrYycT/2bhU1btWrFF198Qfv27fHz8yMiIoI5c+aQlJTE5cuXcXd3p3r16sybN4/atWsTGxvLZ599RlBQEHv37sXLyyvTmCtWrODatWv07t07w/bWrVvz7LPP4unpyalTpxg9ejSPP/44ERERWX4kDPJ+gdSCvFhjqgFhUSb+d9YCAxMejgZ9qt3m5rHtrD2Wu+cqyHl6WJSjnFGesqcc5czDXCA13xfekLaoSmhoaJbvzZs3757HjhkzhjFjxmTaXq1aNQyjcK+0KyKSXxR3sOaLXvWZ9utxJv50lMU7znLgfCzTn/PDo1TudIaQ3JXThU0BRo8eTUxMDAEBARiGgaurK71792bChAlYWqb9cSUgIICAgID0Y4KCgvDz82PKlClMnjw505hffvklrVu3pmzZshm233mcHaBWrVrUr18fT09P1qxZQ8eOHbOML68WSC3oizVev53Eq9/vY8PZywB0qV+e0W28sc3lP4gV9Dw9DMpRzihP2VOOcsYcC6QWiMJbREQKPgsLE0NbeFHXowQvL97NvqjrtJ0azqQu9XjM++6PCcvDdb8Lm0LaY+Vz5sxh1qxZXLhwAXd3d2bPno2TkxMuLi5ZHmNhYUGDBg04dizzbdUzZ87w008/sWzZsmzjdXd3x9PTM8tx7sjrBVIL4mKN+6Oup38ExNbKgnfb16Jz/bz9CEhBzNPDphzljPKUPeUoZx7mAqkW//osIiIi/8Kj1UqzamgT6pQvzrW4JPrM28Gkn46SmqqnkPKDvy9s+ndhYWE0btz4nsdaW1tTvnx5LC0tWbx4MSEhIVhYZP2rhmEY7NmzB3d390zvzZ07lzJlyvDUU09lG++VK1c4e/ZsluNI1r7deZaOMzZz9s/beJSyZ+ngxnledIuIFHW64y0iIg9d+ZIOfDcokLGrDvLNtkgm/XSMPWevMUlti/KF+1nYFNLWTdm+fTuNGjXi6tWrTJw4kf3792fotz127FgCAgLw8vIiNjaWyZMns2fPnvT+3HekpqYyd+5cnn/+eaysMv6acvPmTcaMGcMzzzyDu7s7p0+f5o033sDFxSVD9xPJWlqrsAMs2n4WgMerl+HTzvUo7qC7YiIieU2Ft4iImIWtlSUfdKiNX4WSvLl8HxvUcizfuN+FTVNSUvjkk084cuQI1tbWNG/enM2bN1OxYsX0fa5du8aAAQOIiYmhePHi+Pr68vvvv9OwYcMM5/7pp5+IjIykb9++meKytLRk3759zJ8/n2vXruHu7k7z5s1ZsmQJTk5OeZOMQuLsn2mtwvZFpbUKG/5ENV5srlZhIiIPiwpvERExq07+5fFxd2Lwwl1E/hlHxxmbebddTbo0qGDu0Iq0+1nY1MfHh927d99zvE8//ZRPP/002/MGBwffdfFTe3t71q1bl+0YktGGIxcZtmQP1+KSKOlgzWddfXm0WunsDxQRkVyjz3iLiIjZ1SxbnFVDmtCiehkSk1N5fek+Xv/+D+KTHrCBsEgRlppq8NlPx+gzbwfX4pKoU744q4Y2UdEtImIGKrxFRCRfKO5gzee96vNqcDVMJliy8yydZm7m7J8575EpImmuxSXS96sdfPrTUQwDujeqwHeDAilfUu37RETMQYW3iIjkGxYWJoY87sX8vg0p6WDN/qhYQqaE8+uRi+YOTaTA2B91nZAp4Ww4cglbKws+6lSHDzrUxtYqd/tzi4hIzqnwFhGRfKepV2lWv9SUuuWLc/12En3n7WBi2FFS1HJM5J6W7Iik44zNnLt6mwqlHFgW2phn1SpMRMTsVHiLiEi+VK6EPd8OCqRHQAUMAyb/fIy+83Zw9VaiuUMTyXfik1J4/fs/eH3pPhKTU2lRvQyrhjShZll1CBARyQ9UeIuISL5la2XJe+1rM7FzXeysLfjtaFrLsT/OXTN3aCL5xtk/4+g0czNLdp7FZIJXg6vxea/66s8tIpKPqPAWEZF8r6NfeZYNDsLzEQeirt2m04wtLN4emf2BIoXcr0cuEjIlnP1RsZR0sGZ+34YMedxL/blFRPIZFd4iIlIg1CjrzA9DmvCEjyuJKamMXLaPEd/vVcsxKZJSUw0+DTtK33k7uH47ibrli7P6paY09VKrMBGR/EiFt4iIFBjF7a2Z3dOf11p5Y2GCb3ee45kZm4m8opZjUnRcvZVIn3k7+OznYxgGPNeoAt8OCqRcCXtzhyYiInehwltERAoUCwsTLzavyvy+jSjlaMOB87GETNnIL4cvmDs0kTy371xaq7Dfjqa1Cvvk2bq8r1ZhIiL5ngpvEREpkJp4ubB6aBPqeZQgNj6ZvvN2MnH9EbUck0Jr8fZInpmxmahrt/F8xIHloUE841/e3GGJiEgOqPAWEZECq2wJe5YMDKBngCcAk385Tu+52/lTLcekEIlPSmHE93sZuWwfiSmpPOFThh+GNKFGWWdzhyYiIjmkwltERAo0WytL3m1fi0+7pLUc23jsMm2nhLP37DVzhybywCKvxPHMjM18u/McFiZ4rZU3s3vWp7i9WoWJiBQkKrxFRKRQ6OBbnuWhQVT8q+XYszO38M22SAxDj55LwfTr4YuETNnIgfOxlHK0YX7fRrzYvKpahYmIFEAqvEVEpNDwcXdm5ZAmtKyR1nLsjeX7eO37P9RyTAqUlFSDieuP0GfeDmLjk6nrUYLVQ5vQxMvF3KGJiMi/pMJbREQKleL21szq4c+IJ9Najn0fcY6O09VyTAqGq7cS6T13O5N/OQ5AzwBPvh0YQFm1ChMRKdBUeIuISKFjYWEi9LGqLOjXiEccbTgYndZy7OdDajkm+dfe/2PvvsOiOLs2gN/LslQBCwgoIIgKqNjAQjFqEjEmYI+IJdhiQbGQJlFf0RgxxhBjATURe4uxGxNFYwGxIlixFxRBxEYTWNj5/iDslxWQRRaXcv+ui+t9d/aZmTMnyMNhZp/z4AU8l0Qh8mYqdCQaCBnYGt/1aclWYURE1QALbyIiqrbcmhhj3yR3tLUqaDk2au05LDzAlmNUuQiCgE2nE/Dp8pNIfPEK1v+2CuvXjq3CiIiqCxbeRERUrZkb6WLrGBf4uhS0HFt6hC3HqPLIlubjqz8u4tudBa3Cujc3xe6J7nAwZ6swIqLqhIU3ERFVe1qaGpjduyUWebeBrkSMyJup8FwciTi2HCM1SniahX6h0fgjpqBV2Ncf2WHFUCe2CiMiqoZYeBMRUY3Rp21D7JzgCut6enj0MhsDl5/EhlP32XKM3rnD8Y/huSQSV5PSUE9fC+tHdYRfV7YKIyKqrlh4ExFRjWJvZog9/u7w+Lfl2Ixdl/HFtgt4lcuWY1Tx8mUCfjp4HaPWnkNadh7aWtXGvknucGvCVmFERNUZC28iIqpxDHUkWDHMCdN62kNDBOw4n4i+oSdw/2mmukOjauzZv63ClvzbKuwzl0bYOsYF5kZsFUZEVN2x8CYiohpJJBJhXBdbbBjdEca1tHAtOR2eS6Jw6CpbjpHqxT14Ac/FkfJWYYu822BO75bQ0uSvYkRENQF/2hMRUY3mamuMff6d0c6qNtKz8zB63Tn8eOAaW46RSgiCgA2n7mPg8pN49DIb1vX0sGuCG/q0baju0IiI6B1i4U1ERDWemZEOtoxxwXBXawDAsiO34Rt+Bk8zctQbGFVpr3Lz8eW2i5ix6zJy82XwaG6KPf7usDdjqzAiopqGhTcREREKWo4F9WqBXwYVtByLupUKryVRiE14ru7QqAq6/zQT/cKisf18QauwaT3tsWKYEwx12CqMiKgmYuFNRET0H73bNMSuCW5obKxf0HJsxUmsZ8sxKoNDVx/Dc0kU4v9tFbZhVEeM62ILkYitwoiIaioW3kRERK+xMzPA7olu+KiFGaT5AmbuuowvfmfLMXqzfJmAHw9cw+h155CenYd2VrXx56TOcGWrMCKiGo+FNxERUTEMdCQIG9oOgYUtx2ILWo7dS2XLMSrqaUYOfMPPYNmR2wCA4a7W2DLGBWZGOmqOjIiIKgMW3kRERCUQiUQY28UWG0d3krcc81oShYNXktUdGlUicQ9ewGtJFKJupUJXIsYvg9ogqFcLtgojIiI5zghERESlcLGthz8ndYZTozpIz8nDmPUxWHjwJvL5se8aTRAERCWLMHjVWTx6mY3GxvrYNcENvduwVRgRESli4U1ERKQEU0MdbP68k7zl2IrIuwi7qoEcKT/3XRMJgoBvd13FtrtiSPMF9Ghhit0T3WBnZqDu0IiIqBJi4U1ERKSkwpZji33aQk9LDBMdQFsiVndYpAYikQhN6utDBAFf92iK5UOdYMBWYUREVIIqUXiHhobCxsYGOjo6cHJyQmRkpFL7nThxApqammjTpk2R9168eIEJEybA3NwcOjo6cHBwwP79+1UcORERVUe9WjfAznGd0M9Gpu5QSI1GujbCV63y8bm7DVuFERHRG1X6wnvr1q2YMmUKpk+fjtjYWHTu3Bk9e/ZEQkLCG/d7+fIlPvvsM3zwwQdF3svNzUX37t1x7949/PHHH7h+/Tp+/fVXNGzIz2QREZFyGpvoQ1LpZ1GqSCKRCA311R0FERFVBZrqDqA0ISEhGDVqFEaPHg0AWLRoEQ4cOICwsDAEBweXuN/YsWMxePBgiMVi7Nq1S+G98PBwPHv2DNHR0ZBICh4La9SoUYVdAxEREREREdVclbrwzs3NRUxMDKZNm6aw3cPDA9HR0SXut3r1aty+fRsbNmzA3Llzi7y/Z88euLi4YMKECdi9ezdMTEwwePBgfPPNNxCLi/+sXk5ODnJycuSv09LSAABSqRRSqfRtLk+ucP/yHqc6Y45Kxxwph3kqHXOkHFXliXkmIiKq/ip14Z2amor8/HyYmpoqbDc1NUVycvE9VG/evIlp06YhMjISmprFX96dO3fwzz//YMiQIdi/fz9u3ryJCRMmIC8vD//73/+K3Sc4OBizZ88usv3gwYPQ09Mr45UVLyIiQiXHqc6Yo9IxR8phnkrHHCmnvHnKyspSUSRERERUWVXqwrvQ6wuWCIJQ7CIm+fn5GDx4MGbPno1mzZqVeDyZTIb69etj5cqVEIvFcHJywqNHj/Djjz+WWHgHBgYiICBA/jotLQ2Wlpbw8PCAoaHhW15ZAalUioiICHTv3l3+6DspYo5Kxxwph3kqHXOkHFXlqfAJKiIiIqq+KnXhbWxsDLFYXOTudkpKSpG74ACQnp6Oc+fOITY2FhMnTgRQUGQLggBNTU0cPHgQ77//PszNzSGRSBQeK3dwcEBycjJyc3OhpaVV5Nja2trQ1tYusl0ikajsF1NVHqu6Yo5Kxxwph3kqHXOknPLmiTkmIiKq/ir1eqxaWlpwcnIq8hhfREQEXF1di4w3NDTEpUuXEBcXJ/8aN24c7OzsEBcXh44dOwIA3NzccOvWLchk/98G5saNGzA3Ny+26CYiIiIiIiJ6W5X6jjcABAQEYNiwYXB2doaLiwtWrlyJhIQEjBs3DkDBI+CJiYlYt24dNDQ00LJlS4X969evDx0dHYXt48ePx5IlSzB58mT4+/vj5s2bmDdvHiZNmvROr42IiIiIiIiqv0pfeHt7e+Pp06eYM2cOkpKS0LJlS+zfv1/e/ispKanUnt6vs7S0xMGDBzF16lS0atUKDRs2xOTJk/HNN99UxCUQERERERFRDVbpC28A8PPzg5+fX7HvrVmz5o37BgUFISgoqMh2FxcXnDp1SgXREREREREREZWsUn/Gm4iIiIiIiKiqqxJ3vCsjQRAAqKYNjFQqRVZWFtLS0ri6bQmYo9IxR8phnkrHHClHVXkqnEcK5xWqeKqaw/lvRTnMU+mYI+UwT6VjjpSjjjmchfdbSk9PB1DweXEiIqLySk9Ph5GRkbrDqBE4hxMRkSopM4eLBP6J/a3IZDI8evQIBgYGEIlE5TpWWloaLC0t8eDBAxgaGqoowuqFOSodc6Qc5ql0zJFyVJUnQRCQnp6OBg0aQEODnwB7F1Q1h/PfinKYp9IxR8phnkrHHClHHXM473i/JQ0NDVhYWKj0mIaGhvwHUgrmqHTMkXKYp9IxR8pRRZ54p/vdUvUczn8rymGeSsccKYd5Kh1zpJx3OYfzT+tEREREREREFYiFNxEREREREVEFYuFdCWhra2PWrFnQ1tZWdyiVFnNUOuZIOcxT6Zgj5TBPxO8B5TBPpWOOlMM8lY45Uo468sTF1YiIiIiIiIgqEO94ExEREREREVUgFt5EREREREREFYiFNxEREREREVEFYuFNREREREREVIFYeFew48ePw8vLCw0aNIBIJMKuXbtK3efYsWNwcnKCjo4OGjdujOXLl1d8oGpU1hzt2LED3bt3h4mJCQwNDeHi4oIDBw68m2DV6G2+lwqdOHECmpqaaNOmTYXFVxm8TY5ycnIwffp0NGrUCNra2rC1tUV4eHjFB6tGb5OnjRs3onXr1tDT04O5uTlGjBiBp0+fVnywahIcHIz27dvDwMAA9evXR58+fXD9+vVS96tpP7+rO87hpeMcrhzO4aXjHK4czuGlq6xzOAvvCpaZmYnWrVtj6dKlSo2/e/cuPv74Y3Tu3BmxsbH49ttvMWnSJGzfvr2CI1Wfsubo+PHj6N69O/bv34+YmBh069YNXl5eiI2NreBI1auseSr08uVLfPbZZ/jggw8qKLLK421yNHDgQBw+fBirVq3C9evXsXnzZtjb21dglOpX1jxFRUXhs88+w6hRo3DlyhVs27YNZ8+exejRoys4UvU5duwYJkyYgFOnTiEiIgJ5eXnw8PBAZmZmifvUxJ/f1R3n8NJxDlcO5/DScQ5XDufw0lXaOVygdwaAsHPnzjeO+frrrwV7e3uFbWPHjhU6depUgZFVHsrkqDjNmzcXZs+erfqAKqmy5Mnb21uYMWOGMGvWLKF169YVGldlokyO/vrrL8HIyEh4+vTpuwmqElImTz/++KPQuHFjhW2LFy8WLCwsKjCyyiUlJUUAIBw7dqzEMTX953d1xzm8dJzDlcM5vHScw5XDOVw5lWUO5x3vSubkyZPw8PBQ2NajRw+cO3cOUqlUTVFVbjKZDOnp6ahbt666Q6l0Vq9ejdu3b2PWrFnqDqVS2rNnD5ydnbFgwQI0bNgQzZo1w5dffolXr16pO7RKxdXVFQ8fPsT+/fshCAIeP36MP/74A5988om6Q3tnXr58CQBv/DnDn9/E74Gy4xxeMs7hb8Y5XDmcwyvPHK6pkqOQyiQnJ8PU1FRhm6mpKfLy8pCamgpzc3M1RVZ5/fTTT8jMzMTAgQPVHUqlcvPmTUybNg2RkZHQ1OQ/9eLcuXMHUVFR0NHRwc6dO5Gamgo/Pz88e/as2n9GrCxcXV2xceNGeHt7Izs7G3l5eejVqxeWLFmi7tDeCUEQEBAQAHd3d7Rs2bLEcfz5TfweKDvO4cXjHF46zuHK4RxeeeZw3vGuhEQikcJrQRCK3U7A5s2bERQUhK1bt6J+/frqDqfSyM/Px+DBgzF79mw0a9ZM3eFUWjKZDCKRCBs3bkSHDh3w8ccfIyQkBGvWrOFfzP/j6tWrmDRpEv73v/8hJiYGf//9N+7evYtx48apO7R3YuLEibh48SI2b95c6lj+/CZ+DyiPc3jxOIcrh3O4cjiHV545nH9Cq2TMzMyQnJyssC0lJQWampqoV6+emqKqnLZu3YpRo0Zh27Zt+PDDD9UdTqWSnp6Oc+fOITY2FhMnTgRQMEEJggBNTU0cPHgQ77//vpqjVD9zc3M0bNgQRkZG8m0ODg4QBAEPHz5E06ZN1Rhd5REcHAw3Nzd89dVXAIBWrVpBX18fnTt3xty5c6v1XTx/f3/s2bMHx48fh4WFxRvH8uc38XtAeZzDS8Y5XDmcw5XDObzyzOEsvCsZFxcX7N27V2HbwYMH4ezsDIlEoqaoKp/Nmzdj5MiR2Lx5c436jIqyDA0NcenSJYVtoaGh+Oeff/DHH3/AxsZGTZFVLm5ubti2bRsyMjJQq1YtAMCNGzegoaFR6g/omiQrK6vIo45isRjA//81uLoRBAH+/v7YuXMnjh49qtS/Gf78Jn4PKIdz+JtxDlcO53DlcA6vRHO4ypZpo2Klp6cLsbGxQmxsrABACAkJEWJjY4X79+8LgiAI06ZNE4YNGyYff+fOHUFPT0+YOnWqcPXqVWHVqlWCRCIR/vjjD3VdQoUra442bdokaGpqCsuWLROSkpLkXy9evFDXJbwTZc3T62rCiqhlzVF6erpgYWEhDBgwQLhy5Ypw7NgxoWnTpsLo0aPVdQnvRFnztHr1akFTU1MIDQ0Vbt++LURFRQnOzs5Chw4d1HUJFW78+PGCkZGRcPToUYWfM1lZWfIx/Pld/XEOLx3ncOVwDi8d53DlcA4vXWWdw1l4V7AjR44IAIp8+fr6CoIgCL6+vkKXLl0U9jl69KjQtm1bQUtLS7C2thbCwsLefeDvUFlz1KVLlzeOr67e5nvpv2rCpP02OYqPjxc+/PBDQVdXV7CwsBACAgIUfjBXR2+Tp8WLFwvNmzcXdHV1BXNzc2HIkCHCw4cP333w70hx+QEgrF69Wj6GP7+rP87hpeMcrhzO4aXjHK4czuGlq6xzuOjf4IiIiIiIiIioAnBVcyIiIiIiIqIKxMKbiIiIiIiIqAKx8CYiIiIiIiKqQCy8iYiIiIiIiCoQC28iIiIiIiKiCsTCm4iIiIiIiKgCsfAmIiIiIiIiqkAsvImo0hGJRNi1a5e6wyAiIqIy4hxOVDwW3kSkYPjw4RCJREW+PvroI3WHRkRERG/AOZyo8tJUdwBEVPl89NFHWL16tcI2bW1tNUVDREREyuIcTlQ58Y43ERWhra0NMzMzha86deoAKHiELCwsDD179oSuri5sbGywbds2hf0vXbqE999/H7q6uqhXrx7GjBmDjIwMhTHh4eFo0aIFtLW1YW5ujokTJyq8n5qair59+0JPTw9NmzbFnj17KvaiiYiIqgHO4USVEwtvIiqzmTNnon///rhw4QKGDh0KHx8fxMfHAwCysrLw0UcfoU6dOjh79iy2bduGQ4cOKUzKYWFhmDBhAsaMGYNLly5hz549aNKkicI5Zs+ejYEDB+LixYv4+OOPMWTIEDx79uydXicREVF1wzmcSE0EIqL/8PX1FcRisaCvr6/wNWfOHEEQBAGAMG7cOIV9OnbsKIwfP14QBEFYuXKlUKdOHSEjI0P+/p9//iloaGgIycnJgiAIQoMGDYTp06eXGAMAYcaMGfLXGRkZgkgkEv766y+VXScREVF1wzmcqPLiZ7yJqIhu3bohLCxMYVvdunXl/9/FxUXhPRcXF8TFxQEA4uPj0bp1a+jr68vfd3Nzg0wmw/Xr1yESifDo0SN88MEHb4yhVatW8v+vr68PAwMDpKSkvO0lERER1Qicw4kqJxbeRFSEvr5+kcfGSiMSiQAAgiDI/39xY3R1dZU6nkQiKbKvTCYrU0xEREQ1DedwosqJn/EmojI7depUkdf29vYAgObNmyMuLg6ZmZny90+cOAENDQ00a9YMBgYGsLa2xuHDh99pzERERMQ5nEhdeMebiIrIyclBcnKywjZNTU0YGxsDALZt2wZnZ2e4u7tj48aNOHPmDFatWgUAGDJkCGbNmgVfX18EBQXhyZMn8Pf3x7Bhw2BqagoACAoKwrhx41C/fn307NkT6enpOHHiBPz9/d/thRIREVUznMOJKicW3kRUxN9//w1zc3OFbXZ2drh27RqAgtVKt2zZAj8/P5iZmWHjxo1o3rw5AEBPTw8HDhzA5MmT0b59e+jp6aF///4ICQmRH8vX1xfZ2dn4+eef8eWXX8LY2BgDBgx4dxdIRERUTXEOJ6qcRIIgCOoOgoiqDpFIhJ07d6JPnz7qDoWIiIjKgHM4kfrwM95EREREREREFYiFNxEREREREVEF4qPmRERERERERBWId7yJiIiIiIiIKhALbyIiIiIiIqIKxMKbiIiIiIiIqAKx8CYiIiIiIiKqQCy8iYiIiIiIiCoQC28iIiIiIiKiCsTCm4iIiIiIiKgCsfAmIiIiIiIiqkAsvImIiIiIiIgqEAtvIiIiIiIiogrEwpuIiIiIiIioArHwJiIiIiIiIqpALLyJiIiIiIiIKhALbyIiIiIiIqIKxMKbiIiIiIiIqAKx8Cb615o1ayASiSASiXD06NEi7wuCgCZNmkAkEqFr164K7z19+hSBgYFo3rw59PX1YWRkBHt7ewwbNgwXL14s9hzFfRV33rIaPnw4rK2ty32c10VGRmLgwIFo2LAhtLS0YGRkBFdXV4SFhSEzM1Pl56tsQkNDsWbNmgo7vkgkQlBQUIUdn4iount9jtXU1ISFhQVGjBiBxMTEIuPv3LmDiRMnolmzZtDV1YWenh5atGiBGTNmFDt+79698PLygqmpKbS0tFC3bl188MEH2LhxI6RSabnj79q1a5HfL1ShouOu7ObNm4ddu3ZVyLHv3bsHkUhUob8fUPWhqe4AiCobAwMDrFq1qsjkd+zYMdy+fRsGBgYK2zMyMtCpUydkZGTgq6++QuvWrfHq1SvcuHEDO3bsQFxcHFq1aqWwz+rVq2Fvb1/k3M2bN1f59ajCrFmzMGfOHLi6uuK7776Dra0tsrKyEB0djaCgINy4cQM///yzusOsUKGhoTA2Nsbw4cMr5PgnT56EhYVFhRybiKgmKZxjX716hePHjyM4OBjHjh3DpUuXoK+vDwDYt28fBg0aBGNjY0ycOBFt27aFSCTCpUuXEB4ejj///BOxsbEACv7wPnLkSKxZswYff/wxQkJCYGlpiZcvX+LIkSPw8/NDamoqJk+erM7LLqKqxq1q8+bNw4ABA9CnTx+VH9vc3BwnT56Era2tyo9N1ZBARIIgCMLq1asFAMLo0aMFXV1d4eXLlwrvDx06VHBxcRFatGghdOnSRb49PDxcACD8888/xR43Pz+/yDnOnj1bIdcgCILg6+srNGrUSGXH+/333wUAwqhRowSZTFbk/bS0NOHAgQMqOVdmZqZKjlMRXv/v/ia5ubmCVCqt2ICIiEhBSXPszJkzBQDChg0bBEEQhDt37gj6+vpC27ZthRcvXhQ5jkwmE7Zv3y5//cMPPwgAhNmzZxd73qSkJCEyMrLc8Xfp0kXpeUYZ7ypuQajc87e+vr7g6+ur1NisrKxif9chUgU+ak70Gh8fHwDA5s2b5dtevnyJ7du3Y+TIkUXGP336FEDBXz2Lo6FRcf/M1qxZAzs7O2hra8PBwQHr1q0rdlxubi7mzp0Le3t7aGtrw8TEBCNGjMCTJ09KPcecOXNQp04dLF68GCKRqMj7BgYG8PDwAPDmR65ef5Q6KCgIIpEI58+fx4ABA1CnTh3Y2tpi0aJFEIlEuHXrVpFjfPPNN9DS0kJqaqp826FDh/DBBx/A0NAQenp6cHNzw+HDh0u9rrKwtrbGlStXcOzYMfkjjIWP8x89ehQikQjr16/HF198gYYNG0JbWxu3bt3CkydP4Ofnh+bNm6NWrVqoX78+3n//fURGRpaan8JHJo8cOYLx48fD2NgY9erVQ79+/fDo0SOVXh8RUXXWqVMnAMD9+/cBACEhIcjMzERoaCiMjIyKjBeJROjXrx8AQCqV4ocffoC9vT1mzpxZ7PHNzMzg7u6udDyCIGDBggVo1KgRdHR00K5dO/z111/Fjk1LS8OXX34JGxsbaGlpoWHDhpgyZUqpH/Eqa9yFc9nrH3krbl4fPnw4atWqhUuXLsHDwwMGBgb44IMPMGXKFOjr6yMtLa3Iuby9vWFqaqrwaPvWrVvh4uICfX191KpVCz169JA/ZaAqIpEImZmZWLt2rXz+LnyisXCePXjwIEaOHAkTExPo6ekhJycHt27dwogRI9C0aVPo6emhYcOG8PLywqVLl0rNT+HvN1euXIGPjw+MjIxgamqKkSNH4uXLlyq9PqpaWHgTvcbQ0BADBgxAeHi4fNvmzZuhoaEBb2/vIuNdXFwAAJ999hl27dolL8TfJD8/H3l5eQpf+fn5ZYpzzZo1GDFiBBwcHLB9+3bMmDED3333Hf755x+FcTKZDL1798b8+fMxePBg/Pnnn5g/fz4iIiLQtWtXvHr1qsRzJCUl4fLly/Dw8ICenl6Z4lNWv3790KRJE2zbtg3Lly/H0KFDoaWlVaR4z8/Px4YNG+Dl5QVjY2MAwIYNG+Dh4QFDQ0OsXbsWv//+O+rWrYsePXqotPjeuXMnGjdujLZt2+LkyZM4efIkdu7cqTAmMDAQCQkJWL58Ofbu3Yv69evj2bNnAAoe1f/zzz+xevVqNG7cGF27dlX68/yjR4+GRCLBpk2bsGDBAhw9ehRDhw5V2bUREVV3hX/INTExAQAcPHgQpqam8oL8Tc6dO4dnz56hd+/exf7x+W3Mnj0b33zzDbp3745du3Zh/Pjx+Pzzz3H9+nWFcVlZWejSpQvWrl2LSZMm4a+//sI333yDNWvWoFevXhAE4Z3G/V+5ubno1asX3n//fezevRuzZ8/GyJEjkZWVhd9//11h7IsXL7B7924MHToUEokEQMHj3z4+PmjevDl+//13rF+/Hunp6ejcuTOuXr2qsjhPnjwJXV1dfPzxx/L5OzQ0VGHMyJEjIZFIsH79evzxxx+QSCR49OgR6tWrh/nz5+Pvv//GsmXLoKmpiY4dOxb571SS/v37o1mzZti+fTumTZuGTZs2YerUqSq7NqqC1H3Lnaiy+O8jakeOHBEACJcvXxYEQRDat28vDB8+XBCE4h85njNnjqClpSUAEAAINjY2wrhx44QLFy4Ue47ivsRisdKx5ufnCw0aNBDatWun8EjUvXv3BIlEovCo+ebNmwUACo/NCYIgnD17VgAghIaGlnieU6dOCQCEadOmKRXX3bt3BQDC6tWri7wHQJg1a5b89axZswQAwv/+978iY/v16ydYWFgoPKa/f/9+AYCwd+9eQRAKHmurW7eu4OXlpbBvfn6+0Lp1a6FDhw5Kxayskh41L/xeee+990o9Rl5eniCVSoUPPvhA6Nu3r8J7r+en8HvFz89PYdyCBQsEAEJSUtJbXQcRUXVV+HPz1KlTglQqFdLT04V9+/YJJiYmgoGBgZCcnCwIgiDo6OgInTp1UuqYW7ZsEQAIy5cvV0mMz58/F3R0dIrMASdOnBAAKMwzwcHBgoaGRpFH5//44w8BgLB//36VxV04lx05ckRhe3Hzuq+vrwBACA8PL3Kcdu3aCa6urgrbQkNDBQDCpUuXBEEQhISEBEFTU1Pw9/dXGJeeni6YmZkJAwcOVCpmZZX0qHnh98tnn31W6jHy8vKE3NxcoWnTpsLUqVPl24vLT+HvNwsWLFA4hp+fn6Cjo8NH2Wsw3vEmKkaXLl1ga2uL8PBwXLp0CWfPni32MfNCM2fOREJCAsLDwzF27FjUqlULy5cvh5OTk8Ij64XWrVuHs2fPKnydPn1a6fiuX7+OR48eYfDgwQp/yW7UqBFcXV0Vxu7btw+1a9eGl5eXwh32Nm3awMzMTCUrqZdH//79i2wbMWIEHj58iEOHDsm3rV69GmZmZujZsycAIDo6Gs+ePYOvr6/CdclkMnz00Uc4e/bsGx/Fe/2pA5lMpvLrAIDly5ejXbt20NHRgaamJiQSCQ4fPoz4+HiljturVy+F14UL9RU+MklERIo6deoEiUQCAwMDeHp6wszMDH/99RdMTU3VHRpOnjyJ7OxsDBkyRGG7q6srGjVqpLBt3759aNmyJdq0aaMwX/Xo0UNlnVDKo6T5Ozo6WuGu8OrVq9G+fXu0bNkSAHDgwAHk5eXhs88+U7guHR0ddOnSpdTrev2JQeENd/7f9jry8vIwb948NG/eHFpaWtDU1ISWlhZu3rxZrvk7OzsbKSkp5YqXqi6uak5UDJFIhBEjRmDx4sXIzs5Gs2bN0Llz5zfuY2pqihEjRmDEiBEAgOPHj6Nnz56YPHmy/HPjhRwcHODs7PzW8RU+zm5mZlbkPTMzM9y7d0/++vHjx3jx4gW0tLSKPdZ/Py/9OisrKwDA3bt33zrW0hT32fiePXvC3Nwcq1evhoeHB54/f449e/Zg8uTJEIvFAAquCwAGDBhQ4rGfPXsmX8H2dba2tgrF66xZs8rVzqu46wgJCcEXX3yBcePG4bvvvoOxsTHEYjFmzpyp9MRdr149hdfa2toA8MaPCBAR1WTr1q2Dg4MDNDU1YWpqWuTns5WVldLzmqrnwdLm7/96/Pgxbt26JX88+3XqnL/19PRgaGhYZPuQIUPw5ZdfYs2aNQgODsbVq1dx9uxZhce7C+fv9u3bF3vs0tbGeT0fq1evLlfHkeLm74CAACxbtgzffPMNunTpgjp16kBDQwOjR49Wev7l/E2vY+FNVILhw4fjf//7H5YvX47vv/++zPu/99578PDwwK5du5CSkoL69eurLLbCH+bJyclF3nt9W+GiXH///Xexx3q9Pdp/mZubw9HREQcPHkRWVlapn/PW0dEBAOTk5Chsf9Pn3ov77JlYLMawYcOwePFivHjxAps2bUJOTo78jxqF1wUAS5YsKfFzem+6u7F3716FOBs0aFDiWGUUdx0bNmxA165dERYWprA9PT29XOciIqKSlfbH7R49emDJkiU4depUqZ/zdnZ2Rt26dbF7924EBweX+/PSpc3fhQt3AgXznK6ursKaM/9VOA+qIu6S5u+SivuSjlenTh307t0b69atw9y5c7F69Wro6Ogo3IAojPuPP/4ocpdfGWfPnlV4bWNjU+Zj/FdJ8/dnn32GefPmKWxPTU1F7dq1y3U+qrn4qDlRCRo2bIivvvoKXl5e8PX1LXHc48ePi31MOT8/Hzdv3oSenp7Kf0jb2dnB3NwcmzdvVnjE6v79+4iOjlYY6+npiadPnyI/Px/Ozs5Fvuzs7N54rpkzZ+L58+eYNGlSsY9zZWRk4ODBgwAKCl0dHR1cvHhRYczu3bvLfI0jRoxAdnY2Nm/ejDVr1sDFxUWh97mbmxtq166Nq1evFntdzs7OJd7lBwBHR0eFsaUV3tra2mX+K7VIJJL/hbvQxYsXcfLkyTIdh4iIVGfq1KnQ19eHn59fsatMC4IgX0BTIpHgm2++wbVr1/Ddd98Ve7yUlBScOHFCqXN36tQJOjo62Lhxo8L26OjoIh8h8vT0xO3bt1GvXr1i57j/FumvK2vchcd6ff7es2ePUtf1XyNGjMCjR4+wf/9+bNiwAX379lX4PahHjx7Q1NTE7du3S5y/3+T1sa/fWX6dqubvP//8E4mJiWU6DtF/8Y430RvMnz+/1DHr16/HihUrMHjwYLRv3x5GRkZ4+PAhfvvtN1y5cgX/+9//ihSAly9fRl5eXpFj2draylddfRMNDQ189913GD16NPr27YvPP/8cL168QFBQUJFH1QYNGoSNGzfi448/xuTJk9GhQwdIJBI8fPgQR44cQe/evdG3b98Sz/Xpp59i5syZ+O6773Dt2jWMGjUKtra2yMrKwunTp7FixQp4e3vDw8MDIpEIQ4cORXh4OGxtbdG6dWucOXMGmzZtKvWaXmdvbw8XFxcEBwfjwYMHWLlypcL7tWrVwpIlS+Dr64tnz55hwIABqF+/Pp48eYILFy7gyZMnRe40l4ejoyO2bNmCrVu3onHjxtDR0YGjo+Mb9/H09MR3332HWbNmoUuXLrh+/TrmzJkDGxubYv/7ExFRxbOxscGWLVvg7e2NNm3aYOLEiWjbti0A4OrVqwgPD4cgCPK58auvvkJ8fDxmzZqFM2fOYPDgwbC0tMTLly9x/PhxrFy5ErNnz4abm1up565Tpw6+/PJLzJ07F6NHj8ann36KBw8eFDt/T5kyBdu3b8d7772HqVOnolWrVpDJZEhISMDBgwfxxRdfoGPHjiWeqyxxm5mZ4cMPP0RwcDDq1KmDRo0a4fDhw9ixY0eZ8+vh4QELCwv4+fkhOTlZ4Wk1oKDInzNnDqZPn447d+7go48+Qp06dfD48WOcOXMG+vr6mD17dpnPWxJHR0ccPXoUe/fuhbm5OQwMDEq96eDp6Yk1a9bA3t4erVq1QkxMDH788UdYWFioLC6qgdS6tBtRJfLfVc3f5PXVra9evSp88cUXgrOzs2BiYiJoamoKderUEbp06SKsX7++2HOU9PXrr7+WKebffvtNaNq0qaClpSU0a9ZMCA8PF3x9fRVWNRcEQZBKpcLChQuF1q1bCzo6OkKtWrUEe3t7YezYscLNmzeVOtexY8eEAQMGCObm5oJEIhEMDQ0FFxcX4ccffxTS0tLk416+fCmMHj1aMDU1FfT19QUvLy/h3r17Ja5q/uTJkxLPuXLlSgGAoKurK7x8+bLEuD755BOhbt26gkQiERo2bCh88sknwrZt25S6LmXdu3dP8PDwEAwMDAQA8hwXrgRb3PlycnKEL7/8UmjYsKGgo6MjtGvXTti1a1ex/41ez09J348lrTxLRFTTKTuPF7p9+7bg5+cnNGnSRNDW1hZ0dXWF5s2bCwEBAcLdu3eLjN+9e7fwySefKMz13bp1E5YvXy7k5OQoHadMJhOCg4MFS0tLQUtLS2jVqpWwd+9eoUuXLkW6Z2RkZAgzZswQ7OzsBC0tLcHIyEhwdHQUpk6dKl+lvTTKxp2UlCQMGDBAqFu3rmBkZCQMHTpUOHfuXLGrmuvr67/xnN9++60AQLC0tFToUPJfu3btErp16yYYGhoK2traQqNGjYQBAwYIhw4dUuq6lBUXFye4ubkJenp6CivHv+n75fnz58KoUaOE+vXrC3p6eoK7u7sQGRlZ5L/Rm1Y1f/33m8LzFfe9RTWDSBDKuRQgEREREREREZWIn/EmIiIiIiIiqkD8jDdRJSOTyUrtKa2pyX+6RERElUl+fv4be0qLRCJ5S0wiqnl4x5uokhk5ciQkEskbv4iIiKhy+eCDD944d9va2qo7RCJSI37Gm6iSuXfvXol9MwuV1mqDiIiI3q3r168jPT29xPe1tbVL7YZBRNUXC28iIiIiIiKiCsRHzYmIiIiIiIgqEFdoeksymQyPHj2CgYEBRCKRusMhIqIqShAEpKeno0GDBtDQ4N/D3wXO4UREpAplmcNZeL+lR48ewdLSUt1hEBFRNfHgwQNYWFioO4wagXM4ERGpkjJzOAvvt2RgYACgIMmGhoblOpZUKsXBgwfh4eHBFatLwByVjjlSDvNUOuZIOarKU1paGiwtLeXzClU8Vc3h/LeiHOapdMyRcpin0jFHylHHHM7C+y0VPppmaGioksJbT08PhoaG/AdSAuaodMyRcpin0jFHylF1nvjI87ujqjmc/1aUwzyVjjlSDvNUOuZIOeqYw/lhMiIiIiIiIqIKxMKbiIiIiIiIqAKx8CYiIiIiIiKqQCy8iYiIiIiIiCoQC28iIiIiIiKiCsTCm4iIiIiIiKgCsfAmIiIiIiIiqkAsvImIiIiIiIgqEAtvIiIiIiIiogrEwrsSuP8sS90hEBERERER1QgPnmdBJrzbc7LwVrPdcYno8csJ/PNIBEF4x//1iYiIiIiIapB9Fx/Bc+lJHHwoeqfnZeGtZqfvPkO+TMDu+2L4b7mA9GypukMiIiIiIiKqVqT5MszZexUTN8UiKzcft9NFyH+Ht71ZeKvZ931aIsjTHmKRgANXU9B72QnceJyu7rCIiIiIiIiqhZS0bAz+9RTCT9wFAIztbINxDjKINd7dXW8W3momEokwpKMVJrXIh5mhNu48yUSfZSew58IjdYdGRERERERUpZ2+8xQfL47C2XvPYaCtiRXDnPClR1OI3+2T5iy8KwtrA2CXnwvcmtRDVm4+Jm2ORdCeK8jNk6k7NCIiIiIioipFEAT8evwOBv92GqkZObA3M8Aef3f0aGGmlnhYeFci9fS1sG5kR0zoZgsAWBN9Dz6/nsLjtGw1R0ZERERERFQ1ZOTkYcKm8/h+fzzyZQL6tm2IHX6usDHWV1tMLLwrGbGGCF/1sMevnznDQEcTMfef45PFkTh5+6m6QyMiIiIiIqrUbj5OR6+lUdh/KRkSsQjf9W6BkIGtoaelqda4WHhXUt2bm2LvRHfYmxkgNSMXQ1edxopjt9lyjIiIiIiIqBh7LzxC72UncOdJJsyNdLB1rAuGuVhDJHrHH+guBgvvSszaWB87/dzQr21D5MsEBP91DeM3nGfLMSIiIiIion/l5skwe+8V+G8uaBXm1qQe9vm7o51VHXWHJsfCu5LT1RLjp4GtMbdPS0jEIvx9JRm9l7LlGBERERER0eO0bPj8egqrT9wDAPh1tcW6kR1Rr5a2egN7DQvvKkAkEmFop0bYNs4VDYx0cCc1E72XnsDuuER1h0ZERERERKQWJ28/xSeLIxFz/zkMdDSxcpgTvv7I/p3251YWC+8qpI1lbeyb1BnuTYzxSpqPyVvi2HKMiIiIiIhqFEEQsOLYbQxddRqpGbmwNzPA3onu8FBTqzBlqL3wDg0NhY2NDXR0dODk5ITIyMgSx0ZFRcHNzQ316tWDrq4u7O3t8fPPPyuM2bFjB5ydnVG7dm3o6+ujTZs2WL9+vcKYoKAgiEQihS8zs8r7H+m/6uprYe3IDpjYrQmAgpZjg1aeRPJLthwjIiIiIqLqLT1bivEbziP4r2vIlwno17Yhdvq5wVqNrcKUodY11bdu3YopU6YgNDQUbm5uWLFiBXr27ImrV6/CysqqyHh9fX1MnDgRrVq1gr6+PqKiojB27Fjo6+tjzJgxAIC6deti+vTpsLe3h5aWFvbt24cRI0agfv366NGjh/xYLVq0wKFDh+SvxWJxxV+wiog1RPiyhx3aWNbG1N/jcD7hBTyXRGKxT1u42hqrOzwiIiIiIiKVu/E4HePWx+BOaiYkYhFmebXAkI5WlWLV8tKo9Y53SEgIRo0ahdGjR8PBwQGLFi2CpaUlwsLCih3ftm1b+Pj4oEWLFrC2tsbQoUPRo0cPhbvkXbt2Rd++feHg4ABbW1tMnjwZrVq1QlRUlMKxNDU1YWZmJv8yMTGp0GutCB82N8U+f3c4mBsWtBz77TSWs+UYERERERFVM7vjEtF76QncSS1oFfb7WBcM7dSoShTdgBoL79zcXMTExMDDw0Nhu4eHB6Kjo5U6RmxsLKKjo9GlS5di3xcEAYcPH8b169fx3nvvKbx38+ZNNGjQADY2Nhg0aBDu3LnzdheiZo3q6WPHeFf0b2cBmQDM/+saxq6PQRpbjhERERERURWXmydD0J4rmLwlDq+k+XBvYox9/u5oW4lahSlDbY+ap6amIj8/H6ampgrbTU1NkZyc/MZ9LSws8OTJE+Tl5SEoKAijR49WeP/ly5do2LAhcnJyIBaLERoaiu7du8vf79ixI9atW4dmzZrh8ePHmDt3LlxdXXHlyhXUq1ev2HPm5OQgJydH/jotLQ0AIJVKIZWWr8gt3P9tj6MpAoL7OKC1hQG++/MaDl59jF5LorB0UGvYmRmUK7bKorw5qgmYI+UwT6VjjpSjqjwxz0RERMVLfpkNv40xOJ/wAgAwsVsTTO3erFKuWl4atX7GG0CRRwMEQSj1cYHIyEhkZGTg1KlTmDZtGpo0aQIfHx/5+wYGBoiLi0NGRgYOHz6MgIAANG7cGF27dgUA9OzZUz7W0dERLi4usLW1xdq1axEQEFDsOYODgzF79uwi2w8ePAg9PT1lL/eNIiIiyrW/EQD/5sDq62Lce5qFfmHR8G4sg7NJ9Xn0vLw5qgmYI+UwT6VjjpRT3jxlZWWpKBIiIqLqI/p2KiZtjkVqRi4MdDTx88A2+LC5aek7VlJqK7yNjY0hFouL3N1OSUkpchf8dTY2NgAKiubHjx8jKChIofDW0NBAkyYFq363adMG8fHxCA4Olhfer9PX14ejoyNu3rxZ4jkDAwMVivK0tDRYWlrCw8MDhoaGb4y3NFKpFBEREejevTskEkm5jgUA3pm5+OKPS4i69RTrb4mBepaY9pEdtDTVvoj9W1N1jqoj5kg5zFPpmCPlqCpPhU9QERER0b+two7fwYK/r0EmAA7mhlg+tB0a1avcq5aXRm2Ft5aWFpycnBAREYG+ffvKt0dERKB3795KH0cQBIVHwN9mTE5ODuLj49G5c+cSx2hra0NbW7vIdolEorJfTFV1LNPaEqwd2RG/HLqBxf/cwvrTD3A5KR2hQ9rB3EhXBZGqjyrzXV0xR8phnkrHHCmnvHlijomIiAqkZUvx1bYLOHDlMQCgfzsLzO3TErpaVacDVUnU+qh5QEAAhg0bBmdnZ7i4uGDlypVISEjAuHHjABTcZU5MTMS6desAAMuWLYOVlRXs7e0BFPT1XrhwIfz9/eXHDA4OhrOzM2xtbZGbm4v9+/dj3bp1Ciulf/nll/Dy8oKVlRVSUlIwd+5cpKWlwdfX9x1efcUSa4gQ4GGHNla1MWVLHGITXsBzcRSW+LSFaxO2HCMiIiIiosrjenI6xm2Iwd3UTGiJNTCrV3MM7lA1WoUpQ62Ft7e3N54+fYo5c+YgKSkJLVu2xP79+9GoUSMAQFJSEhISEuTjZTIZAgMDcffuXWhqasLW1hbz58/H2LFj5WMyMzPh5+eHhw8fQldXF/b29tiwYQO8vb3lYx4+fAgfHx+kpqbCxMQEnTp1wqlTp+TnrU7etzfFPv/OGLchBleT0jB01Wl82cMO47vYVptvYiIiIiIiqrp2xSYicMclvJLmo4GRDsKGOqG1ZW11h6VSal9czc/PD35+fsW+t2bNGoXX/v7+Cne3izN37lzMnTv3jWO2bNlSphirOqt6etjh54qZuy5jW8xDLPj7OmITXuCnga1hqMNHHImIiIiI6N3LzZPh+z+vYu3J+wCAzk2N8cugtqirr6XmyFSv6q62RWWiIxFjwYBWCO7nCC2xBiL+bTkWn8RFfYiIiIiI6N1KevkK3itPyotu//ebYM2IDtWy6AZYeNcoIpEIPh2s8Md4FzSsrYt7T7PQN/QEdsY+VHdoRERERERUQ0TfSoXn4ijEJryAoY4mwoc74wsPuyrZn1tZLLxroFYWtbHP3x3vNTNBtlSGqVsvYOauy8jJy1d3aEREREREVE0JgoCwo7cxdNVpPM3MRXNzQ+zz74z37atuf25lsfCuoeroa2H18PaY9EFTAMD6U/fhveIUHr14pebIiIiIiIiouknLlmLM+hj88G9/7gFOFtjh5wqrenrqDu2dYOFdg4k1RAjo3gyrh7eHka4EcQ9ewHNJFE7cSlV3aEREREREVE3EJ6Wh15IoRFx9DC2xBoL7OeLHAa2gI6n6/bmVxcKb0M2+Pvb5u6NFA0M8y8zFsFWnsezILchkgrpDIyIiIiKiKmxn7EP0DT2Be0+z0LC2Lv4Y7wKfatSfW1ksvAkAYFlXD9vHu2KgswVkAvDjgesYsz4GL19J1R0aERERERFVMTl5+Zi56zKmbr2AbKkM7zUzwT5/d7SyqK3u0NSChTfJFbQca435/RyhpamBQ/GP0WtpFK4+YssxIqKaJjQ0FDY2NtDR0YGTkxMiIyPfOH7ZsmVwcHCArq4u7OzssG7duiJjFi1aBDs7O+jq6sLS0hJTp05Fdna2/P3g4GC0b98eBgYGqF+/Pvr06YPr168rHEMQBAQFBaFBgwbQ1dVF165dceXKFdVcNBERqcSjF6/gveIU1p8qaBU26YOmWD28PepU01ZhymDhTUUM6mCF7eNcYVFHF/efZqFf2Alsj2HLMSKimmLr1q2YMmUKpk+fjtjYWHTu3Bk9e/ZEQkJCsePDwsIQGBiIoKAgXLlyBbNnz8aECROwd+9e+ZiNGzdi2rRpmDVrFuLj47Fq1Sps3boVgYGB8jHHjh3DhAkTcOrUKURERCAvLw8eHh7IzMyUj1mwYAFCQkKwdOlSnD17FmZmZujevTvS09MrLiFERKS0E7dS4bkkCnEPXsBIV4LVw9sjoHuzat0qTBksvKlYjhZG2Ofvji7/thz7YtsFTN95iS3HiIhqgJCQEIwaNQqjR4+Gg4MDFi1aBEtLS4SFhRU7fv369Rg7diy8vb3RuHFjDBo0CKNGjcIPP/wgH3Py5Em4ublh8ODBsLa2hoeHB3x8fHDu3Dn5mL///hvDhw9HixYt0Lp1a6xevRoJCQmIiYkBUHC3e9GiRZg+fTr69euHli1bYu3atcjKysKmTZsqNilERPRGMpmAZUduYdiq03iWmYsWDQyxz98d3ezrqzu0SkFT3QFQ5VVbr6Dl2C+Hb2LxPzex8XQCLj9KQ+iQdmhYW1fd4RERUQXIzc1FTEwMpk2bprDdw8MD0dHRxe6Tk5MDHR0dhW26uro4c+YMpFIpJBIJ3N3dsWHDBpw5cwYdOnTAnTt3sH//fvj6+pYYy8uXLwEAdevWBQDcvXsXycnJ8PDwkI/R1tZGly5dEB0djbFjx5YYX05Ojvx1WlrBR6ikUimk0rdfy6Rw3/IcoyZgnkrHHCmHeSqdunKU9kqKr3dcxuFrTwAAA9o1xCxPe+hIxJXyv5eq8lSW/Vl40xtpaIgwtXsztLGqjSlb4nDhwQt4Lo7EYp+26NzURN3hERGRiqWmpiI/Px+mpqYK201NTZGcnFzsPj169MBvv/2GPn36oF27doiJiUF4eDikUilSU1Nhbm6OQYMG4cmTJ3B3d4cgCMjLy8P48eOLFPiFBEFAQEAA3N3d0bJlSwCQn7+42O7fv1/iNQUHB2P27NlFth88eBB6euXvHxsREVHuY9QEzFPpmCPlME+le5c5SswEwq+LkZojgqZIwAAbGVy07+OfiJJ/LlcW5c1TVlaW0mNZeJNSutkVtBwbvzEGlxPT8Fn4GXzRvRn8ujaBRg3/vAYRUXX0epsXQRBKbP0yc+ZMJCcno1OnThAEAaamphg+fDgWLFgAsbigR+vRo0fx/fffIzQ0FB07dsStW7cwefJkmJubY+bMmUWOOXHiRFy8eBFRUVHlig0AAgMDERAQIH+dlpYGS0tLeHh4wNDQsOQklEIqlSIiIgLdu3eHRCJ56+NUd8xT6Zgj5TBPpXvXOdoZ+wiL915FtlSGhrV1sHRQG7Rs+PY/V98VVeWp8AkqZbDwJqVZ1tXDH+NcEbTnCracfYCFB28g7sEL/PRpGxjp8YcfEVF1YGxsDLFYXOTudkpKSpE7zYV0dXURHh6OFStW4PHjxzA3N8fKlSthYGAAY2NjAAXF+bBhwzB69GgAgKOjIzIzMzFmzBhMnz4dGhr/v+yMv78/9uzZg+PHj8PCwkK+3czMDEDBnW9zc3OlYgMKHkfX1tYusl0ikajkF1NVHae6Y55Kxxwph3kqXUXnKCcvH3P2XsXG0wWLbnZpZoJF3m2q3Krl5c1TWfbl4mpUJjoSMeb3b4UF/Vv923IsBV5Lo3Dl0Ut1h0ZERCqgpaUFJyenIo/fRUREwNXV9Y37SiQSWFhYQCwWY8uWLfD09JQX1FlZWQrFNQCIxWIIggBBEAAU3LmeOHEiduzYgX/++Qc2NjYK421sbGBmZqYQW25uLo4dO1ZqbEREpBqJL15h4IpT2Hg6ASIRMJmtwpTCO970Vga2t0TzBoYYtyEGCc+y0C80Gt/3dcQAJ4vSdyYiokotICAAw4YNg7OzM1xcXLBy5UokJCRg3LhxAAoe3U5MTJT36r5x4wbOnDmDjh074vnz5wgJCcHly5exdu1a+TG9vLwQEhKCtm3byh81nzlzJnr16iV/HH3ChAnYtGkTdu/eDQMDA/lddyMjI+jq6kIkEmHKlCmYN28emjZtiqZNm2LevHnQ09PD4MGD33GWiIhqnqibqfDffB7Ps6Qw0pVg0aA26GbHVcuVwcKb3lrLhgUtx6ZujcOR60/w5bYLOJ/wHLO8mkNbU6zu8IiI6C15e3vj6dOnmDNnDpKSktCyZUvs378fjRo1AgAkJSUp9PTOz8/HTz/9hOvXr0MikaBbt26Ijo6GtbW1fMyMGTMgEokwY8YMJCYmwsTEBF5eXvj+++/lYwrblXXt2lUhntWrV2P48OEAgK+//hqvXr2Cn58fnj9/jo4dO+LgwYMwMDComGQQERFkMgGhR2/hp4gbEASgZUNDhA1xgmXd8i9QWVOw8KZyqa2nhVW+7bHkn1tYdPgGNp1OwJXElwgd6sSWY0REVZifnx/8/PyKfW/NmjUKrx0cHBAbG/vG42lqamLWrFmYNWtWiWMKHzl/E5FIhKCgIAQFBZU6loiIyu/lKym++D0Oh+JTAADezpaY3bsFdCS80VYW/Iw3lZuGhgiTPyz4bEdtPQkuPHwJz8WROH7jibpDIyIiIiKit3Tl0Ut4LYnCofgUaGlq4If+jvhhQCsW3W+BhTepTFe7+tg70R2ODY3wPEsK39VnsPjwTchkpd/BICIiIiKiyuOPmIfoFxqNhGdZsKijix3jXeHd3krdYVVZLLxJpSzr6mHbOBf4dLCCIAAhETcwet05vMySqjs0IiIiIiIqRU5ePr7deQlfbruAnDwZutmZYJ+/O1o2NFJ3aFUaC29SOR2JGMH9HLFgQCtoa2rgn2sp8FwaicuJbDlGRERERFRZJb54hYHLT2LTv63Cpn7YDKt826O2HluFlRcLb6owA50tsX28Kyzr6uLBs1foHxaN3889UHdYRERERET0muM3nsBzcSQuPHyJ2noSrB7eHpM/bAoNDZG6Q6sWWHhThWrZ0Aj7JnbG+/b1kZMnw9d/XETgjovIluarOzQiIiIiohpPJhOw5PBN+K4+g+dZUjg2NMLeie7oyv7cKsXCmyqckZ4Ev33mjC+6N4NIBGw+8wCfLj+Jh8+z1B0aEREREVGN9TJLitHrzsn7c/t0sMS2cS7sz10BWHjTO6GhIYL/B02xdkQH1NGT4FLiS3guicIxthwjIiIiInrnLie+hOfSSPxzLQXamhpYMKAVgvuxVVhFUXvhHRoaChsbG+jo6MDJyQmRkZEljo2KioKbmxvq1asHXV1d2Nvb4+eff1YYs2PHDjg7O6N27drQ19dHmzZtsH79+nKdl1TnvWYm2OvvjlYWRniRJcXw1WfwyyG2HCMiIiIield+P/cA/cOi8eDZK1jW1cX28a4Y6Gyp7rCqNbUW3lu3bsWUKVMwffp0xMbGonPnzujZsycSEhKKHa+vr4+JEyfi+PHjiI+Px4wZMzBjxgysXLlSPqZu3bqYPn06Tp48iYsXL2LEiBEYMWIEDhw48NbnJdWyqFPQcmxwx4KWYz8fuoFRa8/iRVauukMjIiIiIqq2sqX5CNxxCV//cfH/W4VN7MxWYe+AWgvvkJAQjBo1CqNHj4aDgwMWLVoES0tLhIWFFTu+bdu28PHxQYsWLWBtbY2hQ4eiR48eCneru3btir59+8LBwQG2traYPHkyWrVqhaioqLc+L6metqYY8/o64sd/W44duf4Enkui2HKMiIiIiKgCPHyehYErTmLzmYJWYQHdC1qFGelJ1B1ajaCprhPn5uYiJiYG06ZNU9ju4eGB6OhopY4RGxuL6OhozJ07t9j3BUHAP//8g+vXr+OHH34o13lzcnKQk5Mjf52WlgYAkEqlkEqlSsVbksL9y3ucqqhPazM0q6+HiZsv4MHzV+gXFo0gTwd86tRQYVxNzpGymCPlME+lY46Uo6o8Mc9ERFTRjt14gslbYvEiS4raehL8MqgtujQzUXdYNYraCu/U1FTk5+fD1NRUYbupqSmSk5PfuK+FhQWePHmCvLw8BAUFYfTo0Qrvv3z5Eg0bNkROTg7EYjFCQ0PRvXv3cp03ODgYs2fPLrL94MGD0NNTzap/ERERKjlOVeTXBNhwSwNXngPf7rqCvdGX0N9GBslrz2TU5BwpizlSDvNUOuZIOeXNU1YWOzwQEVHFkMkELPnnFhYdLli1vJWFEUKHtINFHa5a/q6prfAuJBIpNmQXBKHIttdFRkYiIyMDp06dwrRp09CkSRP4+PjI3zcwMEBcXBwyMjJw+PBhBAQEoHHjxujatetbnzcwMBABAQHy12lpabC0tISHhwcMDQ2VudQSSaVSREREoHv37pBIau6jHv1kApYfv4tF/9zCyRQNpGkaYemgNrCoo8scKYE5Ug7zVDrmSDmqylPhE1RERESq9CIrF1O3xuHI9YIuQoM7WmGWV3Noa3LVcnVQW+FtbGwMsVhc5C5zSkpKkbvRr7OxsQEAODo64vHjxwgKClIovDU0NNCkSRMAQJs2bRAfH4/g4GB07dr1rc+rra0NbW3tItslEonKfjFV5bGqqsnd7dDOui4mbY7FlUfp6BN2CosGtYF74zoAmCNlMEfKYZ5Kxxwpp7x5Yo6JiEjVrjxKw8QtF/Dw+Stoa2pgbp+W+JSrlquV2hZX09LSgpOTU5FH9CIiIuDq6qr0cQRBUPjsdWljVHVeqjidm5pg36TOaG1hhJevpBi55iwW/3ML7DhGRERERPRmp1JEGPjrGTx8/gpWdfWww8+VRXcloNZHzQMCAjBs2DA4OzvDxcUFK1euREJCAsaNGweg4PHuxMRErFu3DgCwbNkyWFlZwd7eHkBBX++FCxfC399ffszg4GA4OzvD1tYWubm52L9/P9atW6ewYnlp5yX1a1hbF7+Pc8GcvVex8XQClhy5A4faGnDvJoWJEe8OERERERH9V7Y0H//bdQW/3xYDkOED+/oIGdiGq5ZXEmotvL29vfH06VPMmTMHSUlJaNmyJfbv349GjRoBAJKSkhR6a8tkMgQGBuLu3bvQ1NSEra0t5s+fj7Fjx8rHZGZmws/PDw8fPoSuri7s7e2xYcMGeHt7K31eqhy0NcX4vq8j2lnVwbc7LyH+BdAn7CSWD3WGowV7DRIRERERAcCDZ1nw23gelxJfQgQBUz5oCv8PmkFD481rZ9G7o/bF1fz8/ODn51fse2vWrFF47e/vr3B3uzhz584tsb2YsuelyqW/kwWamuhh5KpoJL7IRv/l0ZjTqwUGdbBSd2hERERERGp19HoKpmyNw4ssKeroSTCoUTb8ujZm0V3JqO0z3kRl4WBugC9a5eN9OxPk5skwbcclfP3HBWRL89UdGhERERHROyeTCVh06AZGrDmLF1lStLYwwq7xnWBfmwsjVUYsvKnK0NMEwga3wVc97KAhAn4/9xD9w6Lx4Bl74BIRERFRzfEiKxcj1pzFokM3IQjAkI5W+H2cCxrU1lV3aFQCFt5UpWhoiDChWxOsG9kRdfW1cOVRGjyXROHItRR1h0ZEREREVOEuPXyJTxZH4diNJ9DW1MBPn7bG930d2Z+7kmPhTVWSe1Nj7PN3R2vL2nj5SooRa84iJOIG8tlzjIiIiIiqqS1nEtB/eTQSX7xCo3p62Onnhv5OFuoOi5TAwpuqrAa1dfH72E4Y1qlgNfrFh29ixJqzeJ6Zq+bIiIiIiIhUJ1uaj6//uIBpOy4hN0+GDx3qY89EdzRvYKju0EhJLLypStPWFOO7Pi0RMrA1dCQaOH7jCTyXROHiwxfqDo2IiIiIqNwePMvCgOXR+P3cQ2iIgK962GHlMGcY6bI/d1XCwpuqhX7tLLDTzw3W9fSQ+OIVBoSdxOYzCRAEPnpORERERFXTkWsp8FwShcuJaairr4V1IztiQrcmbBVWBbHwpmrDwdwQuye6o3tzU+TmyxC44xK+/uMiW44RERERUZWSLxMQEnEDI9eexctXUrS2rI19/u5wb2qs7tDoLbHwpmrFSFeCFUOd8PVHBS3HtsU8RL/QaCQ8ZcsxIiIiIqr8nmcWtApbfLigVdiwTo3w+9hObBVWxbHwpmpHQ0MEv65NsH5UR9TT18LVpDR4LonEP9ceqzs0IiIiIqISXXz4Ap5LonD8xhPoSDQQMrA1vuvTkq3CqgEW3lRtuTUxxr5J7mhrVRtp2XkYueYcQg5eZ8sxIiIiIqpUBEHA5jMJGBB2EokvXsH631Zh/dqxVVh1wcKbqjVzI11sHeOCz1z+bTn2zy0MX30Gz9hyjIiIiIgqgYJWYRcRuOMScvNl6N7cFLsnusPBnK3CqhMW3lTtaWlqYE7vlljk3QY6Eg1E3kyF15IoXHjwQt2hEREREVENlvA0C/3DorEtpqBV2Ncf2WHFUCe2CquGWHhTjdGnbUPsmvD/Lcc+XX4SG0/fZ8sxIiIiInrn/rn2GJ5LInHlURrq6Wth/aiO8OvKVmHVFQtvqlHszQyxx98dHv+2HJu+8zK+3HYRr3LZcoyIiIiIKl6+TEDIwesYueYc0rLz0MayNvZNcodbE7YKq85YeFONY6gjwYphTpjW0x4aImD7+YfoFxaN+08z1R0aEREREVVjzzJzMXz1GSz+5xYA4DOXRvh9rAvMjdgqrLpj4U01kkgkwrguttjwb8ux+KQ0eC6JwuF4thwjIiIiItW78OAFvJZEIfJmKnQkGljk3QZzereEliZLspqA/5WpRnNtYow/J3VGO6vaSM/Ow6i157DwAFuOEREREZFqCIKATacT8Ony/28VtmuCG/q0baju0OgdYuFNNZ6ZkQ62jHHBcFdrAMDSI2w5RkRERETlly3Nx5fbLuLbnQWtwjyam2KPvzvszdgqrKZh4U2EgpZjQb1a4JdBbaArESPyZio8F0ciji3HiIiIiOgt3H+aib6h0dh+vqBV2LSe9lgxzAmGOmwVVhOx8Cb6j95tClqONTbWx6OX2fh0eTQ2nGLLMSKqeUJDQ2FjYwMdHR04OTkhMjLyjeOXLVsGBwcH6Orqws7ODuvWrSsyZtGiRbCzs4Ouri4sLS0xdepUZGdny98/fvw4vLy80KBBA4hEIuzatavIMTIyMjBx4kRYWFhAV1cXDg4OCAsLK/f1EhGp0uH4x/BcEoX4pIJWYRtGdcS4LrYQidgqrKZi4U30GjszA+ye6IYeLUwhzRcwY9dlfLHtAluOEVGNsXXrVkyZMgXTp09HbGwsOnfujJ49eyIhIaHY8WFhYQgMDERQUBCuXLmC2bNnY8KECdi7d698zMaNGzFt2jTMmjUL8fHxWLVqFbZu3YrAwED5mMzMTLRu3RpLly4tMbapU6fi77//xoYNGxAfH4+pU6fC398fu3fvVl0CiIjeUr5MwMID1zFq7TmkZ+ehrVVBqzBXtgqr8Vh4ExXDQEeC5UOdEPhvy7Ed5xPRN/QE7qWy5RgRVX8hISEYNWoURo8eDQcHByxatAiWlpYl3llev349xo4dC29vbzRu3BiDBg3CqFGj8MMPP8jHnDx5Em5ubhg8eDCsra3h4eEBHx8fnDt3Tj6mZ8+emDt3Lvr161dibCdPnoSvry+6du0Ka2trjBkzBq1bt1Y4DhGROhS2Clt6pKBV2HBXa2wdw1ZhVEBT3QEQVVYikQhju9iilUVt+G8+j2vJ6fBaGoWQgW3QvbmpusMjIqoQubm5iImJwbRp0xS2e3h4IDo6uth9cnJyoKOjo7BNV1cXZ86cgVQqhUQigbu7OzZs2IAzZ86gQ4cOuHPnDvbv3w9fX98yxefu7o49e/Zg5MiRaNCgAY4ePYobN27gl19+KXGfnJwc5OTkyF+npaUBAKRSKaRSaZnO/1+F+5bnGDUB81Q65kg5lTlPFx6+hP+WC0h6mQ1diQbm9m6BXq3NASEfUum7e2qyMueoMlFVnsqyPwtvolK42NbDPv/OmLDpPGLuP8fn685hQjdbBHS3g1iDn9MhouolNTUV+fn5MDVV/AOjqakpkpOTi92nR48e+O2339CnTx+0a9cOMTExCA8Ph1QqRWpqKszNzTFo0CA8efIE7u7uEAQBeXl5GD9+fJECvzSLFy/G559/DgsLC2hqakJDQwO//fYb3N3dS9wnODgYs2fPLrL94MGD0NPTK9P5ixMREVHuY9QEzFPpmCPlVKY8CQJw4rEIO+5pIF8Qob6OgBF2udBMjMX+xFi1xVWZclSZlTdPWVlZSo9Ve+EdGhqKH3/8EUlJSWjRogUWLVqEzp07Fzs2KioK33zzDa5du4asrCw0atQIY8eOxdSpU+Vjfv31V6xbtw6XL18GADg5OWHevHno0KGDfExQUFCRCfhNv1AQmRnpYPPnnTBvfzzWRN/DsiO3EffgBRYPaot6tbTVHR4Rkcq9vgCQIAglLgo0c+ZMJCcno1OnThAEAaamphg+fDgWLFgAsVgMADh69Ci+//57hIaGomPHjrh16xYmT54Mc3NzzJw5U+m4Fi9ejFOnTmHPnj1o1KgRjh8/Dj8/P5ibm+PDDz8sdp/AwEAEBATIX6elpcHS0hIeHh4wNHz7lj5SqRQRERHo3r07JBKuUlwS5ql0zJFyKlueXuXmY9beq9h5NwkA0N2hPn7o1xIGOuorsSpbjiorVeWp8AkqZai18C5cvCU0NBRubm5YsWIFevbsiatXr8LKyqrIeH19fUycOBGtWrWCvr4+oqKiMHbsWOjr62PMmDEACiZ2Hx8fuLq6QkdHBwsWLICHhweuXLmChg3/v0l9ixYtcOjQIfnrwl8MiEpS2HKsXaM6mLb9Ik7cegrPJVEIHdIOba3qqDs8IiKVMDY2hlgsLvLH6JSUlCJ3wQvp6uoiPDwcK1aswOPHj2Fubo6VK1fCwMAAxsYFCwrNnDkTw4YNw+jRowEAjo6OyMzMxJgxYzB9+nRoaJS+7MyrV6/w7bffYufOnfjkk08AAK1atUJcXBwWLlxYYuGtra0Nbe2ifySVSCQq+cVUVcep7pin0jFHyqkMebqXmolxG2JwLTkdGiLgm4/sMea9xpVm1fLKkKOqoLx5Ksu+al1crayLt7Rt2xY+Pj5o0aIFrK2tMXToUPTo0UOhxcnGjRvh5+eHNm3awN7eHr/++itkMhkOHz6scCxNTU2YmZnJv0xMTCr0Wqn66NW6QUHLMRN9JL3MxsAVJ7H+5D22HCOiakFLSwtOTk5FHr+LiIiAq6vrG/eVSCSwsLCAWCzGli1b4OnpKS+os7KyihTXYrEYgiAo/fOz8DPZxR1HJpMpdQwiovKKuPoYXkujcC05Hca1tLBxdCeMZaswKoXaCu/CxVs8PDwUtr9p8ZbXxcbGIjo6Gl26dClxTFZWFqRSKerWrauw/ebNm2jQoAFsbGwwaNAg3Llzp+wXQTVWM1MD7J7gho9amEGaL2Dm7isI+J0tx4ioeggICMBvv/2G8PBwecuuhIQEjBs3DkDBo9ufffaZfPyNGzewYcMG3Lx5E2fOnMGgQYNw+fJlzJs3Tz7Gy8sLYWFh2LJlC+7evYuIiAjMnDkTvXr1kj91lpGRgbi4OMTFxQEA7t69i7i4OHkbM0NDQ3Tp0gVfffUVjh49irt372LNmjVYt24d+vbt+46yQ0Q1Vb5MwI8HruHzdQWtwpwa1cE+/85wsa2n7tCoClDbo+Zvs3hLIQsLCzx58gR5eXkICgqSP7ZWnGnTpqFhw4YKj5917NgR69atQ7NmzfD48WPMnTsXrq6uuHLlCurVK/4fTkWtiFp4jP/+LxVVGXOkIwYWezsiPNoQPx68iZ2xibj66CWW+rSGdT39dx5PZcxRZcQ8lY45Uo46VkR9V7y9vfH06VPMmTMHSUlJaNmyJfbv349GjRoBAJKSkhR6eufn5+Onn37C9evXIZFI0K1bN0RHR8Pa2lo+ZsaMGRCJRJgxYwYSExNhYmICLy8vfP/99/Ix586dQ7du3eSvCz+X7evrizVr1gAAtmzZgsDAQAwZMgTPnj1Do0aN8P3338v/KEBEVBGeZuRg8pY4RN1KBVDQKuzbjx2gpcnuzKQctS+uVpbFWwpFRkYiIyMDp06dwrRp09CkSRP4+PgUGbdgwQJs3rwZR48eVWhz0rNnT/n/d3R0hIuLC2xtbbF27VqFxVf+q6JXRAW4+qAyKmOOzAH42QNrbopx/XEGvJZEYWgTGRzrqufR88qYo8qIeSodc6Scd7ki6rvk5+cHPz+/Yt8rLIILOTg4IDb2zav3ampqYtasWZg1a1aJY7p27VrqY+dmZmZYvXr1G8cQEalSbMJz+G08/2+rMDHm93dE7zYNS9+R6D/UVni/zeIthWxsbAAUFM2PHz9GUFBQkcJ74cKFmDdvHg4dOoRWrVq98Xj6+vpwdHTEzZs3SxxTUSuiAlx9UBlVIUfeadmYvPUiYhJe4LfrYoztbIMpH9hCU/xu/hJaFXJUGTBPpWOOlKOOFVGJiOjdEQQBG07dx5x9VyHNF9DYWB/LhzmhmamBukOjKkhthfd/F2/57+eyIiIi0Lt3b6WPIwiCwiPgAPDjjz9i7ty5OHDgAJydnUs9Rk5ODuLj40tsYwZU/Iqoqj5WdVWZc2RRT4ItY10QvP8awk/cxYrIu7j0KA2LfdrC+B22HKvMOapMmKfSMUfKeZcrohIR0bvxKjcf3+68hJ2xiQCAj1qY4cdPW8FAhz+z6e2o9VHzgIAADBs2DM7OznBxccHKlSuLLN6SmJiIdevWAQCWLVsGKysr2NvbAyjo671w4UL4+/vLj7lgwQLMnDkTmzZtgrW1tfyOeq1atVCrVi0AwJdffgkvLy9YWVkhJSUFc+fORVpaGnx9fd/l5VM1JBFr4H9ezdHWqja+2X4R0befwnNxFEKHtkM7thwjIiIiqvTupmZi/L+twsQaInzzkR0+71x5WoVR1aTWwrusi7fIZDIEBgbi7t270NTUhK2tLebPn4+xY8fKx4SGhiI3NxcDBgxQONesWbMQFBQEAHj48CF8fHyQmpoKExMTdOrUCadOnZKfl6i8vFo3gL2ZAcZtiMHtJ5nwXnESMz5pjs9cGvGHNhEREVEldfBKMr74/QLSc/JgXEsbSwe3RafGXLWcyk/ti6uVZfEWf39/hbvbxbl3716p59yyZYuy4RG9taamBtg90R1f/3EB+y8lY9aeK4hNeI55/Ryhp6X2f3pERERE9K+8fBl+iriBsKO3AQDOjepg2ZB2MDXUKWVPIuVw/XuiClRLWxPLBrfDjE8cINYQYVfcI/RdFo07TzLUHRoRERERAUjNyMFn4WfkRfdINxtsHtOJRTepFAtvogomEokwunNjbBrdESYG2rj+OB29l57A35ff3K+eiIiIiCrW+YTn8FwchejbT6GnJcYSn7b4n1dzSN5RVxqqOfgdRfSOdGxcD3/6u6O9dR2k5+Rh3IYYBP8Vj7x8mbpDIyIiIqpRBEHAupP34L3iJJLTsmFroo/dE9zg1bqBukOjaoqFN9E7VN9QB5s+74RR7gW96Fccu4Nhq87gSXpOKXsSERERkSpk5eZh6tY4/G/3FUjzBXzsaIbdE93RlP25qQKx8CZ6xyRiDcz0bI6lg9tCT0uMk3eewnNJJGLuP1N3aERERETV2p0nGei7LBq74h5BrCHCjE8csGxwO9TS5sK3VLFYeBOpiWerBtgz0Q22Jvp4nJYD7xWnsObEXQiCoO7QiIiIiKqdvy8no/fSE7j+OB0mBtrYNLojRrM/N70jLLyJ1KhJ/YKWY584miNPJiBo71VM3hKHrNw8dYdGREREVC3k5csw/69rGLchBuk5eWhvXQd/+rujI/tz0zvEwptIzWppa2Lp4LaY6dkcmhoi7LnwCH2WnWDLMSIiIqJyepKeg2GrzmD5sYJWYaPcbbDp806oz1Zh9I6x8CaqBEQiEUa5F/SMNDHQxo3HGei19AT+vpyk7tCIiIiIqqSY+8/huSQSJ+8UtAorvNHBVmGkDvyuI6pE2lvXxZ/+7uhgXRcZOXkYt+E8gvez5RgRERGRsgRBwJoTd+G94iQep+XA1kQfeya6wbMVW4WR+rDwJqpk6hvqYOPnHfF5539bjh2/g6GrTrPlGBEREVEpsnLzMHlLHIL2XkWeTMAnjubYPdEdTeqzVRipFwtvokpIItbA9E+aI3RIO+hriXHqzjN8sjgS5+6x5RgRERFRce48yUCfZSew58L/twpbOrgtW4VRpcDCm6gS+1j+V9paSEnPwaCVpxAexZZjRERERP/19+Uk9Fp6AjceZ8DEQBubP+/EVmFUqbDwJqrkmtSvhd0T3ODZqqDl2Jx9VzFpSxwyc9hyjIiIiGq2vHwZgvfHY9yG88jIyUOHwvVybOqqOzQiBSy8iaoAfW1NLPFpi//923Js778tx26lsOUYERER1UxP0nMwdNVprDh+BwDweWcbbPy8I1uFUaXEwpuoihCJRBjpboMtYzqhvoE2bqZkoPfSKPx1iS3HiIiIqGaJuf8MnksicerOM+hriRE6pB2mf8JWYVR58TuTqIpxtq6LfZPc0dGmLjJz8zF+43l8/+dVthwjIiKiak8QgLUn78N7xSk8Tssp+EjeRHd87Giu7tCI3ohL/BFVQfUNdLBxdEcsOHAdK4/fwa+RdxH34AV61VN3ZEREREQVIzMnD+tuauD80+sAAM9W5vihfyvoc9VyqgJ4x5uoitIUa+Dbjx0QNqQdamlr4uy95/jxohjn7j9Xd2hEREREKnX7SQYGrDiN8081oKkhwv88m2OJT1sW3VRlsPAmquJ6Oppj90Q3NDHRR5pUhGHh57CKLceIiIiomvjrUhJ6Lz2BW08yYSgRsH6kM0a627BVGFUpLLyJqgFbk1r4Y2xHtKsnQ55MwHf7rsJ/cyxbjhEREVGVlZcvw7z98Ri/sbBVWB181Sofzo3qqDs0ojJj4U1UTehra+KzpjLM+NgOmhoi7LuYhN5sOUZERERVUEp6Ngb/dhor/20VNua9xlg73AmGWmoOjOgtsfAmqkZEIsDXpRG2jOkEU0Nt3Pq35dh+thwjIiKiKuLsvWfwXByFM3efoZa2JsKGtMO3HztAk63CqArjdy9RNeRsXRf7/DujU+OClmN+G89j7r6rkLLlGBEREVVSgiBgVdRd+Kw8hZT0HDStXwu7J7qhJ1uFUTXAwpuomjIx0MaGUR0xtktjAMBvUXcx5NfTSEnLVnNkRERERIoyc/LgvzkW3+27ijyZAK/WDbBrghtsTWqpOzQilWDhTVSNaYo1ENjTAcuHOqGWtibO3HuGT5YUPLpFREREVBncSslA72UnsO9iEjQ1RJjl1RyLB7VhqzCqVtReeIeGhsLGxgY6OjpwcnJCZGRkiWOjoqLg5uaGevXqQVdXF/b29vj5558Vxvz666/o3Lkz6tSpgzp16uDDDz/EmTNnynVeoqruo5Zm2DPRDXamBniSngOfX0/ht8g7bDlGREREarX/UhJ6L43CrZQMmBpqY8uYThjhxlZhVP2otfDeunUrpkyZgunTpyM2NhadO3dGz549kZCQUOx4fX19TJw4EcePH0d8fDxmzJiBGTNmYOXKlfIxR48ehY+PD44cOYKTJ0/CysoKHh4eSExMfOvzElUHjU1qYecEV/Ru0wD5MgFz/4zHxE2xyGDLMSIiInrHpPkyzN13FX4bzyMzNx+dGhesT+NsXVfdoRFVCLUW3iEhIRg1ahRGjx4NBwcHLFq0CJaWlggLCyt2fNu2beHj44MWLVrA2toaQ4cORY8ePRTuVm/cuBF+fn5o06YN7O3t8euvv0Imk+Hw4cNvfV6i6kJPSxOLvNtgdq8W0NQQ4U/5X5nT1R0aERER1RApadkY8utp/BZ1FwAw9r3G2DCqI0wMtNUcGVHFUdsHJ3JzcxETE4Np06YpbPfw8EB0dLRSx4iNjUV0dDTmzp1b4pisrCxIpVLUrVu3XOfNyclBTk6O/HVaWhoAQCqVQiqVKhVvSQr3L+9xqjPmqHRlydHg9g3hYKoP/60XcPtJJnotPYHgPi3wsaNZRYepdvxeKh1zpBxV5Yl5JqKa5MzdZ5iw6TyepOeglrYmFn7aCh+15KrlVP2prfBOTU1Ffn4+TE1NFbabmpoiOTn5jftaWFjgyZMnyMvLQ1BQEEaPHl3i2GnTpqFhw4b48MMPy3Xe4OBgzJ49u8j2gwcPQk9P743xKisiIkIlx6nOmKPSlSVH/s2AtTc0cDMNmPz7ReyIjENvKxlqQptMfi+VjjlSTnnzlJWVpaJIiIgqr8JWYcF/XUO+TEAz01pYPtQJjblqOdUQb1V45+Xl4ejRo7h9+zYGDx4MAwMDPHr0CIaGhqhVq2z/eF5fOEEQhFIXU4iMjERGRgZOnTqFadOmoUmTJvDx8SkybsGCBdi8eTOOHj0KHR2dcp03MDAQAQEB8tdpaWmwtLSEh4cHDA0N3xhvaaRSKSIiItC9e3dIJJJyHau6Yo5K97Y56p8vw8+Hb2Fl5D0cS9JAplZd/OLdGvWr6eNe/F4qHXOkHFXlqfAJKlVQ5fxMRKQqGTl5+OaPi/jzUhIAoHebBgju5wg9La5aTjVHmb/b79+/j48++ggJCQnIyclB9+7dYWBggAULFiA7OxvLly9X6jjGxsYQi8VF7jKnpKQUuRv9OhsbGwCAo6MjHj9+jKCgoCKF98KFCzFv3jwcOnQIrVq1Kvd5tbW1oa1dtBCRSCQq+8VUlceqrpij0pU1RxIJ8O0nLeBkXQ9f/n4B5+6/QO/QU1g2uC06Nq5XgZGqF7+XSsccKae8eVJVjlU1PxMRqdKtlHSMXR+D208yoakhwkzP5vjMpRFXLacap8wPlE6ePBnOzs54/vw5dHV15dv79u2rsIBZabS0tODk5FTkEb2IiAi4uroqfRxBEBQ+ew0AP/74I7777jv8/fffcHZ2rpDzElU3PVqYYY+/O+xMDZCakYPBv53Gr8fZcoyoqlDV/ExEpCr7Lj5C76UncPtJJkwNtbF1rAt8Xa1ZdFONVObCOyoqCjNmzICWlpbC9kaNGim07FJGQEAAfvvtN4SHhyM+Ph5Tp05FQkICxo0bB6Dg8e7PPvtMPn7ZsmXYu3cvbt68iZs3b2L16tVYuHAhhg4dKh+zYMECzJgxA+Hh4bC2tkZycjKSk5ORkZGh9HmJaiobY33snOCKPv+2HPt+fzwmbDrPlmNEVYAq52cACA0NhY2NDXR0dODk5KTQQaQ4y5Ytg4ODA3R1dWFnZ4d169YVGbNo0SLY2dlBV1cXlpaWmDp1KrKzs+XvHz9+HF5eXmjQoAFEIhF27dpV7Lni4+PRq1cvGBkZwcDAAJ06dWJLUKJKRJovw5y9VzFxUywyc/Ph0rge/pzUGU6N6qg7NCK1KfOj5jKZDPn5+UW2P3z4EAYGBmU6lre3N54+fYo5c+YgKSkJLVu2xP79+9GoUSMAQFJSksJEKpPJEBgYiLt370JTUxO2traYP38+xo4dKx8TGhqK3NxcDBgwQOFcs2bNQlBQkFLnJarJ9LQ08bN3G7RrVAff7buK/ZeScS05HSuGOqGpadn+jRPRu6PK+Xnr1q2YMmUKQkND4ebmhhUrVqBnz564evUqrKysiowPCwtDYGAgfv31V7Rv3x5nzpzB559/jjp16sDLywtAQbvPadOmITw8HK6urrhx4waGDx8OAPj5558BAJmZmWjdujVGjBiB/v37Fxvb7du34e7ujlGjRmH27NkwMjJCfHx8kbVciEg9UtKyMWHTeZy99xwAMK6LLb70aAbNmrByK9EblLnw7t69OxYtWoSVK1cCKFikLCMjA7NmzcLHH39c5gD8/Pzg5+dX7Htr1qxReO3v7w9/f/83Hu/evXvlPi9RTScSifCZizVaNjTChI3ncedJJnovO4Ef+reCV+sG6g6PiIqhyvk5JCQEo0aNkncNWbRoEQ4cOICwsDAEBwcXGb9+/XqMHTsW3t7eAIDGjRvj1KlT+OGHH+SF98mTJ+Hm5obBgwcDAKytreHj44MzZ87Ij9OzZ0/07NnzjbFNnz4dH3/8MRYsWCDf1rhx4zJdHxFVjNN3nmLCplikZuTAQFsTCwe2Ro8W1b9VKZEyylx4//zzz+jWrRuaN2+O7OxsDB48GDdv3oSxsTE2b95cETESkZq0s6qDff7u8N8ci+jbT+G/ORbnE57j248dIOFfrokqFVXNz7m5uYiJicG0adMUtnt4eCA6OrrYfXJycorccdbV1cWZM2cglUohkUjg7u6ODRs24MyZM+jQoQPu3LmD/fv3w9fXV+nYZDIZ/vzzT3z99dfo0aMHYmNjYWNjg8DAQPTp06fE/XJychTWgylcSV4qlZarjzp73iuHeSpdVc+RIAgIj76PHw/eLGgVVr8Wlg1uDet6+iq9pqqep3eBOVKOqvJUlv3LXHg3aNAAcXFx2Lx5M86fPw+ZTIZRo0ZhyJAhCou5EFH1UK+WNtaP6oifDl5H6NHbWH3iHi49fIllQ9rB1JCPdhJVFqqan1NTU5Gfn1+k04epqWmRjiCFevTogd9++w19+vRBu3btEBMTg/DwcEilUqSmpsLc3ByDBg3CkydP4O7uDkEQkJeXh/Hjxxcp8N8kJSUFGRkZmD9/PubOnYsffvgBf//9N/r164cjR46gS5cuxe4XHByM2bNnF9l+8OBB6OnpKX3+krDnvXKYp9JVxRxl5wObb2kg7lnBH+SdjGXwtn6Bq6eP4WoFnbMq5uldY46UU948ZWVlKT32rZrn6erqYuTIkRg5cuTb7E5EVYxYQ4SvP7JHG8va+OL3Czh3/zk+WRyFpYPbolM1bjlGVNWocn5+fdVhQRBKXIl45syZSE5ORqdOnSAIAkxNTTF8+HAsWLAAYrEYAHD06FF8//33CA0NRceOHXHr1i1MnjwZ5ubmmDlzplIxyWQyAEDv3r0xdepUAECbNm0QHR2N5cuXl1h4BwYGIiAgQP46LS0NlpaW8PDwgKGhoVLnLg573iuHeSpdVc3RzZQMTNwchzvPsiARi/BtTzsM6WBZYauWV9U8vUvMkXJUlafCJ6iUUebCu7hVSv/rv6uQE1H14tHCDHv9DTBuQwyuJadjyG+n8c1Hdvi8c2O2BiFSM1XNz8bGxhCLxUXubqekpBS5C15IV1cX4eHhWLFiBR4/fgxzc3OsXLkSBgYGMDY2BlBQnA8bNkz+uXFHR0dkZmZizJgxmD59OjQ0Sv/4irGxMTQ1NdG8eXOF7Q4ODoiKiipxP21tbWhraxfZrqpe9ex5rxzmqXRVKUd7LzzCN9svIis3H2aGOggd2g7trN7NquVVKU/qwhwpp7x5Ksu+ZS68J0+erPBaKpUiKysLWlpa0NPTY+FNVM1ZG+tjp58bpu+8hB2xiZi3/xrO33+BHz9tBQMd/oAnUhdVzc9aWlpwcnJCREQE+vbtK98eERGB3r17v3FfiUQCCwsLAMCWLVvg6ekpL6izsrKKFNdisRiCIEAQBKVja9++Pa5fv66w/caNG+xMQvSOSPNlmLc/HqtP3AMAuNrWw2KftjCuVfSPW0T0/8pceD9//rzItps3b2L8+PH46quvVBIUEVVuulpi/DSwNdo2qoM5e6/g7yvJuPE4HcuHOaEZW44RqYUq5+eAgAAMGzYMzs7OcHFxwcqVK5GQkIBx48YBKHh0OzExUX6X/caNGzhz5gw6duyI58+fIyQkBJcvX8batWvlx/Ty8kJISAjatm0rf9R85syZ6NWrl/xx9IyMDNy6dUu+z927dxEXF4e6devK25h99dVX8Pb2xnvvvYdu3brh77//xt69e3H06NEyXSMRld3jtGxM2Hge5+4X/LwZ39UWX3RnqzAiZbzVZ7xf17RpU8yfPx9Dhw7FtWvXVHFIIqrkRCIRhnVqhJYNDOG38TzupGai99ITmN/fEb3bNFR3eESEt5+fvb298fTpU8yZMwdJSUlo2bIl9u/fL7+rnJSUhISEBPn4/Px8/PTTT7h+/TokEgm6deuG6OhoWFtby8fMmDEDIpEIM2bMQGJiIkxMTODl5YXvv/9ePubcuXPo1q2b/HXh57J9fX3lLUb79u2L5cuXIzg4GJMmTYKdnR22b98Od3f3t0kRESnp1J2nmPifVmE/DWwND7YKI1KaSgpvoOBxsUePHqnqcERURbT9t+XY5C1xiLqVislb4hCb8ALffuwALU3+BZxI3d52fvbz84Ofn1+x7xUWwYUcHBwQGxv7xuNpampi1qxZmDVrVoljunbtqtRj51zglejdEQQBv0bewQ9/X0e+TIC9mQGWD3WCtbG+ukMjqlLKXHjv2bNH4bUgCEhKSsLSpUvh5uamssCIqOqoV0sba0d2QEjEdSw7chtrou/hUuJLLBvcDmZGbDlG9C5wfiYiVUvPluLrPy7ir8sFiy32bdsQ8/o6QldLrObIiKqeMhfeffr0UXgtEolgYmKC999/Hz/99JOq4iKiKkasIcJXPezRxrIOAn6PQ8z95/BcEonFPm3hamus7vCIqj3Oz0SkSjcep2Pc+hjcSc2ERCzC/7xaYGhHK3YxIXpLZS68C3toEhEVp3tzU+yd6C5vOTb0t9P4+iN7jH2PLceIKhLnZyJSld1xiZi2/RJeSfNhbqSD0CHt0PYdtQojqq74AUwiUrnClmP92jWETADm/3UN4zbEIC1bqu7QiIiIqAS5eTIE7bmCyVvi8EqaD/cmxtjn786im0gFlLrjXbiqqDJCQkLeOhgiqj50tcT46dPWcGpUB7P3XMWBK49x4/EJLB/qBDszthwjUgXOz0SkKskvszFh03nE/NsqbEI3WwR0t4NYg0+rEamCUoV3aSuVFuJjpET0XyKRCEM6NkKLBkbw2xCDu6mZ6LPsBIL7OaJPW7YcIyovzs9EpArRt1MxaXMsUjNyYaCjiZCBbdC9uam6wyKqVpQqvI8cOVLRcRBRNdbGsjb2TeqMyVtiEXkzFVO2xiE24Tmmf9KcLceIyoHzMxGVhyAIWHH8Dhb8fQ0yAWwVRlSB+BsvEb0TdfW1sGZEB/i/3wQAsPbkfQxaeRJJL1+pOTIiIqKaJy1binEbYjD/r4Kiu1+7htjp58aim6iClHlVcwA4e/Ystm3bhoSEBOTm5iq8t2PHDpUERkTVj1hDhC887NDGsjambo3D+YQX8FwchSU+beHahC3HiMqL8zMRKeN6cjrG/fsRMC2xBv7n1RxD2CqMqEKV+Y73li1b4ObmhqtXr2Lnzp2QSqW4evUq/vnnHxgZGVVEjERUzXzgYIp9/p3hYG6Ip5m5GLrqNMKO3oYgCOoOjajK4vxMRMrYHZeIPstO4G5qJhoY6eD3cS4Y2qkRi26iClbmwnvevHn4+eefsW/fPmhpaeGXX35BfHw8Bg4cCCsrq4qIkYiqIat6etjp54oBThaQCcAPf1/DmPVsOUb0tjg/E9Gb5ObJMGv3ZXmrsM5NjbFvUme0sayt7tCIaoQyF963b9/GJ598AgDQ1tZGZmYmRCIRpk6dipUrV6o8QCKqvnQkYvw4oBXm9XWEllgDEVcfo9eSKMQnpak7NKIqh/MzEZUk6eUrDFp5EmtP3gcA+L/fBGtGdEBdfS01R0ZUc5S58K5bty7S09MBAA0bNsTly5cBAC9evEBWVpZqoyOiak8kEmFwRytsG+eChrV1ce9pFvqGnsDO2IfqDo2oSuH8TETFib6VCs/FUTif8AKGOppY5euMLzzYn5voXVO68I6LiwMAdO7cGREREQCAgQMHYvLkyfj888/h4+ODDz74oEKCJKLqr7Vlbez1d0fnpsbIlsowdesFzNx1Gbl5MnWHRlSpcX4mouIIgoCwo7cxdNVpPM3MhYO5Ifb5d8YHDuzPTaQOShfe7dq1g5OTExwcHODj4wMACAwMxJdffonHjx+jX79+WLVqVYUFSkTVX2HLsUn/thxbf+o+Bq44iUcv2HKMqCScn4nodWnZUoxdH4Mf/u3P3b+dBXb6ucKqnp66QyOqsZQuvE+cOIF27dph4cKFsLW1xdChQ3Hs2DF8/fXX2LNnD0JCQlCnTp2KjJWIagCxhggBHnYIH+4MQx1NxD14Ac8lUThxK1XdoRFVSpyfiei/riWnodeSKBy8+hhaYg3M6+uIhZ+2go5ErO7QiGo0pQtvFxcX/Prrr0hOTkZYWBgePnyIDz/8ELa2tvj+++/x8CE/j0lEqvO+fUHLsebmhniWmYthq05j2ZFbkMnYcozovzg/E1GhnbEP0WfZCdx7moWGtXWxbZwLBrM/N1GlUObF1XR1deHr64ujR4/ixo0b8PHxwYoVK2BjY4OPP/64ImIkohrKqp4edvi54tN/W479eOA6xqyPwctXbDlG9DrOz0Q1V26eDDN3XcbUrReQLZWhc1Nj7PV3R2u2CiOqNMpceP+Xra0tpk2bhunTp8PQ0BAHDhwo8zFCQ0NhY2MDHR0dODk5ITIyssSxUVFRcHNzQ7169aCrqwt7e3v8/PPPCmOuXLmC/v37w9raGiKRCIsWLSpynKCgIIhEIoUvMzOzMsdORBVPRyLGj5+2xvx+jtDS1MCh+MfotZQtx4jeRBXzMxFVDUkvX2HgipNYf6qgVdgktgojqpQ033bHY8eOITw8HNu3b4dYLMbAgQMxatSoMh1j69atmDJlCkJDQ+Hm5oYVK1agZ8+euHr1KqysrIqM19fXx8SJE9GqVSvo6+sjKioKY8eOhb6+PsaMGQMAyMrKQuPGjfHpp59i6tSpJZ67RYsWOHTokPy1WMzPvRBVZoM6WKF5A0OM33Ae9/9tOTavryP6tbNQd2hElYoq5mciqhpO3EqF/+ZYPMvMhaGOJhYNaoP37blqOVFlVKbC+8GDB1izZg3WrFmDu3fvwtXVFUuWLMHAgQOhr69f5pOHhIRg1KhRGD16NABg0aJFOHDgAMLCwhAcHFxkfNu2bdG2bVv5a2tra+zYsQORkZHywrt9+/Zo3749AGDatGklnltTU5N3uYmqmFYWtbHP3x2Tt8bh+I0nCPj9As4nPMdMz+bQ1uQfz6jmUvX8TESVm0wmIOzYbfx08DpkAtDc3BDLhzpx1XKiSkzpwrt79+44cuQITExM8Nlnn2HkyJGws7N76xPn5uYiJiamSHHs4eGB6OhopY4RGxuL6OhozJ07t8znv3nzJho0aABtbW107NgR8+bNQ+PGjUscn5OTg5ycHPnrtLSCx1ylUimk0vJ93rRw//IepzpjjkpXU3JUS0uElUPaYNnR21h69A42nErAxYcvsMS7NRrU1i11/5qSp/JgjpSjqjyVd39Vz89EVLm9fCXFF79fwKH4xwCAT50s8F2flly1nKiSU7rw1tXVxfbt2+Hp6amSx7JTU1ORn58PU1PFx2FMTU2RnJz8xn0tLCzw5MkT5OXlISgoSH7HXFkdO3bEunXr0KxZMzx+/Bhz586Fq6srrly5gnr16hW7T3BwMGbPnl1k+8GDB6Gnp5q/LkZERKjkONUZc1S6mpKjJgA+txNhw00NXHyYho9/OQ7fpjLY1VZu1fOakqfyYI6UU948ZWVllWt/Vc/PRFR5xSelYdyGGNx/mgUtsQZm926BQe0tuWo5URWgdOG9Z8+eCgng9R8UgiCU+sMjMjISGRkZOHXqFKZNm4YmTZrAx8dH6XP27NlT/v8dHR3h4uICW1tbrF27FgEBAcXuExgYqPBeWloaLC0t4eHhAUNDQ6XPXRypVIqIiAh0794dEomkXMeqrpij0tXEHH0MYNDzLPhvuYArj9IRdk2MKe83wbj3bKChUfzPkZqYp7JijpSjqjwVPkH1tipqfiaiymXH+Yf4duclZEtlaFhbF2FD26GVRW11h0VESnrrxdXKy9jYGGKxuMjd7ZSUlCJ3wV9nY2MDoKBofvz4MYKCgspUeL9OX18fjo6OuHnzZoljtLW1oa2tXWS7RCJR2S+mqjxWdcUcla6m5ahxfSNsH++GWbuvYOu5B/j58C1cepSGnwa2gZFuyXmoaXl6G8yRcsqbJ+aYiN4kJy8f3+27ig2nEgAA7zUzwS/ebVCHq5YTVSnlaidWHlpaWnByciryiF5ERARcXV2VPo4gCAqfvX4bOTk5iI+Ph7m5ebmOQ0TqoSMR44cBrfBD/8KWYynwWhKFq4/YcoyIiKquRy9eYeCKU9hwKgEiETD5g6ZYPbw9i26iKkhtd7wBICAgAMOGDYOzszNcXFywcuVKJCQkYNy4cQAKHu9OTEzEunXrAADLli2DlZUV7O3tART09V64cCH8/f3lx8zNzcXVq1fl/z8xMRFxcXGoVasWmjRpAgD48ssv4eXlBSsrK6SkpGDu3LlIS0uDr6/vu7x8IlIx7/ZWaG5uhPEbY5DwrKDl2Pd9HTHAiS3HiIioaom6mYpJWwpahRnpSrDIuw262ddXd1hE9JbUWnh7e3vj6dOnmDNnDpKSktCyZUvs378fjRo1AgAkJSUhISFBPl4mkyEwMBB3796FpqYmbG1tMX/+fIwdO1Y+5tGjRwotxxYuXIiFCxeiS5cuOHr0KADg4cOH8PHxQWpqKkxMTNCpUyecOnVKfl4iqrocLYywz98dU7bG4ej1J/hyW0HLsVlebDlGRESV3+utwlo0KGgVZlmXrcKIqjK1Ft4A4OfnBz8/v2LfW7NmjcJrf39/hbvbxbG2toYgvHlV4y1btpQpRiKqWmrraSHctz0W/3MTvxy+iU2nE3Al8SVChzqhvr7af+wREREVK+2VFN/sjMOh+BQAgLezJWb3bsFWYUTVgNo+401EVJE0NESY8mEzrB7eHrX1JLjw8CU8F0ci8laqukMjIiIqIjET6BN2CofiU6ClqYEf+jvihwGtWHQTVRMsvImoWutqVx97J7rDsaERnmdJMWrdeRx4KIJMply/byIiooq2IzYRP18S48HzV7Coo4vt41zh3d5K3WERkQqx8Caias+yrh62jXPBoPaWEARg/wMxxm6MxcssqbpDIyKiGiwnLx/f7ryEb3ZcgVQQ4b2m9bDP3x2OFkbqDo2IVIyFNxHVCDoSMeb3b4V5fVpAUyTg6I1UeC2NwpVHL9UdGhER1UCJL15h4PKT2HS6oFXYRxb5+HVoO9TWY6swouqIhTcR1SifOjXElJb5sKitg4RnWegXGo1t5x6oOywiIqpBjt94As/Fkbjw8CWMdCX4bVg79LQUoKEhUndoRFRBWHgTUY1jWQvYOd4F3exMkJMnw1d/XETgjkvIluarOzQiIqrGZDIBSw7fhO/qM3ieJYVjw4IWmO81NVZ3aERUwVh4E1GNVFtPglW+7RHQvRlEImDzmQQMXHESD59nqTs0IiKqhl5mSfH5unP4KeIGBAEY1N4S28a5sD83UQ3BwpuIaiwNDREmfdAUa0Z0QG09CS4+fAnPJVE4duOJukMjIqJq5Mqjl/BaGoXD1wpahS3o3wrz+7NVGFFNwsKbiGq8Ls1MsM/fHa0sjPAiS4rhq8/gl0M32XKMiIjKbdu5B+gXGo2EZ1mwqKOLHeNdMbC9pbrDIqJ3jIU3EREAizp6+H2sC3w6WEEQgJ8P3cCotWfxIitX3aEREVEVlC3NR+COS/jqj4vIyZOhm13BH3lbNmSrMKKaiIU3EdG/dCRiBPdzxI8DWkFbUwNHrj+B55IoXE5kyzGqeUJDQ2FjYwMdHR04OTkhMjLyjeOXLVsGBwcH6Orqws7ODuvWrSsyZtGiRbCzs4Ouri4sLS0xdepUZGdny98/fvw4vLy80KBBA4hEIuzateuN5xw7dixEIhEWLVr0NpdIVGEePs/CwBUnsflMQauwgO7NsMq3PVuFEdVgLLyJiF7zqbMldvi5wrKuLh4+f4V+YdH4/SxbjlHNsXXrVkyZMgXTp09HbGwsOnfujJ49eyIhIaHY8WFhYQgMDERQUBCuXLmC2bNnY8KECdi7d698zMaNGzFt2jTMmjUL8fHxWLVqFyyspwAAPr9JREFUFbZu3YrAwED5mMzMTLRu3RpLly4tNcZdu3bh9OnTaNCgQfkvmEiFjt0o+KPtxYcvUVtPgjUjOmDSB03ZKoyohtNUdwBERJVRiwZG2DexM6b+Hod/rqXg6+0XcT7hOYJ6teBiOFTthYSEYNSoURg9ejSAgjvVBw4cQFhYGIKDg4uMX79+PcaOHQtvb28AQOPGjXHq1Cn88MMP8PLyAgCcPHkSbm5uGDx4MADA2toaPj4+OHPmjPw4PXv2RM+ePUuNLzExERMnTsSBAwfwySeflPt6iVRBJhOw9Mgt/HyoYNXyVhZGCB3SDhZ1uGo5EbHwJiIqkZGeBL995oxlR24h5NANbDn7AFcepSF0SDu2f6FqKzc3FzExMZg2bZrCdg8PD0RHRxe7T05ODnR0dBS26erq4syZM5BKpZBIJHB3d8eGDRtw5swZdOjQAXfu3MH+/fvh6+tbpvhkMhmGDRuGr776Ci1atFBqn5ycHOTk5Mhfp6WlAQCkUimkUmmZzv9fhfuW5xg1QU3I04ssKb7cfgnHbqQCALydLTDzYztoS8RKXXdNyJEqME+lY46Uo6o8lWV/Ft5ERG+goSGC/wdN0dqyNiZvicWlxIKWMIu826CrXX11h0ekcqmpqcjPz4epqanCdlNTUyQnJxe7T48ePfDbb7+hT58+aNeuHWJiYhAeHg6pVIrU1FSYm5tj0KBBePLkCdzd3SEIAvLy8jB+/PgiBX5pfvjhB2hqamLSpElK7xMcHIzZs2cX2X7w4EHo6ZX/j2gRERHlPkZNUF3z9DATWHVdjGc5IkhEAgY0lqGT5B4OR9wr87Gqa45UjXkqHXOknPLmKSsrS+mxLLyJiJTwXjMT7PV3x4SN53Hh4UuMWHMWkz9oiknv83N7VD2JRIrf14IgFNlWaObMmUhOTkanTp0gCAJMTU0xfPhwLFiwAGJxwUczjh49iu+//x6hoaHo2LEjbt26hcmTJ8Pc3BwzZ85UKqaYmBj88ssvOH/+fImxFCcwMBABAQHy12lpabC0tISHhwcMDQ2VPs7rpFIpIiIi0L17d0gkkrc+TnVXnfP0x/lE/LI3Hrl5MljU0cXSQa3RokHZv6eqc45UiXkqHXOkHFXlqfAJKmWw8CYiUpJFHT38Ps4Fs/dexabTCVh06CbiHrzAIu82XKmWqg1jY2OIxeIid7dTUlKK3AUvpKuri/DwcKxYsQKPHz+Gubk5Vq5cCQMDAxgbGwMoKM6HDRsm/9y4o6MjMjMzMWbMGEyfPh0aGqWv9xoZGYmUlBRYWVnJt+Xn5+OLL77AokWLcO/evWL309bWhra2dpHtEolEJb+Yquo41V11ylO2NB+z917B5jMFC2++b18fPw9sAyO98l1fdcpRRWKeSsccKae8eSrLvlzVnIioDLQ1xZjX1xELP20NbU0NHGXLMapmtLS04OTkVOTxu4iICLi6ur5xX4lEAgsLC4jFYmzZsgWenp7ygjorK6tIcS0WiyEIAgRBUCq2YcOG4eLFi4iLi5N/NWjQAF999RUOHDhQhqskensPnmXh0+UnsfnMA4hEwBfdm+G3z5zLXXQTUfXGO95ERG9hgJMFmpsbYtyGGCQ8y0K/sGh817sFvNtblb4zUSUXEBCAYcOGwdnZGS4uLli5ciUSEhIwbtw4AAWPbicmJsp7dd+4cQNnzpxBx44d8fz5c4SEhODy5ctYu3at/JheXl4ICQlB27Zt5Y+az5w5E7169ZI/jp6RkYFbt27J97l79y7i4uJQt25dWFlZoV69eqhXr55CrBKJBGZmZrCzs6votBDh6PUUTNkahxdZUtTRk+CXQW3xXjMTdYdFRFUAC28iorfUvIEh9vq744vf43AoPgXfbL+EmPvPMad3S7YcoyrN29sbT58+xZw5c5CUlISWLVti//79aNSoEQAgKSlJoad3fn4+fvrpJ1y/fh0SiQTdunVDdHQ0rK2t5WNmzJgBkUiEGTNmIDExESYmJvDy8sL3338vH3Pu3Dl069ZN/rrwc9m+vr5Ys2ZNxV400RvIZAIW/3MTvxy+CUEAWlsYYRlbhRFRGbDwJiIqByNdCVYOc0bYsdv46eB1/H7uIa4mpSFsiBNbjlGV5ufnBz8/v2Lfe70IdnBwQGxs7BuPp6mpiVmzZmHWrFkljunatavSj50XKulz3USq8iIrF1O2xuHo9ScAgMEdrTDLqzm0NfkHViJSHj/jTURUThoaIkzo1gTrRnZEXX0tXE5Mg+eSKBy5lqLu0IiIqBwuJ76E55IoHL3+BNqaGlj4aWvM6+vIopuIyoyFNxGRirg3NcZef3e0tqyNl6+kGLn2LEIibiBfVrY7eEREpH5bzyagX1g0Hj5/Bau6etjh54oBThbqDouIqigW3kREKtSwti5+H9sJQztZQRCAxYdvYuSa/2vvzuOiqtc/gH8GGFbBFJQlQBYXwAURE1lMvSWKiWh5RVSy0p8LQpq3unCFC3o1W4y4KpCUpiYp18olo5RuqSASiriEu6KmsoiabALDcH5/kHMj1BmW4Qzweb9evnLOfM85zzzhfHnO9hzFvYoasUMjIiIVVMnk+PuXp/D3r06jprYOzzn1xDehPuhv1VXs0IioHWPhTUTUyvR0tLFi0kDETnWFvlQLBy/Utxw7deM3sUMjIqIn+PVuJaZ8nImUY79CSwK8NbYfPmGrMCJqBaIX3gkJCbC3t4e+vj7c3d2Rnp7+2LEZGRnw9vaGqakpDAwM4OTkhI8++qjBmLy8PLz00kuws7ODRCJBXFxci/dLRNQcLw6xxs4Qb/QyNcTN3x5gSuIRbMu+3uSHRxERkfr9dL4YE9Zm4JebpehupIstr3lg4eje0NKSiB0aEXUAohbeKSkpWLx4MZYuXYrc3FyMGDECfn5+DVqU/JGRkRFCQ0Nx6NAhnD17FpGRkYiMjERSUpJiTGVlJRwcHPDuu+/CwsKiVfZLRNRczpYm2BPqg+edzVEjr0PE16fx9penUCWTix0aEREBkNcJiE27gNc2HcX9BzK4WnfFN2E+8OljJnZoRNSBiFp4x8bGYvbs2ZgzZw6cnZ0RFxcHGxsbJCYmPnK8m5sbgoKC0L9/f9jZ2WHmzJkYO3Zsg7PVzzzzDD744ANMmzYNenp6rbJfIqKWqG855o63xvaDlgTYkXMDLyVm4vqdSrFDIyLq1O5V1OC1TUex5vf+3DOH2+I/8z3x9FMGYodGRB2MaIV3TU0NcnJy4Ovr22C5r68vMjMzVdpGbm4uMjMzMXLkyDbdLxFRUz1sOfb57PqWY3m3SjFhbTp+PFckdmhERJ3SqRu/YcLaDBy8cBv6Ui3ETnXFiklsFUZE6qEj1o5LSkogl8thbm7eYLm5uTkKCwufuK61tTVu376N2tpaxMTEYM6cOWrfb3V1NaqrqxWvS0tLAQAymQwymUzl/T/Kw/Vbup2OjDlSjjlSjdh5GtarK3YtGI6w7Sdx8sZ9vLbpGBaOckDYaEdoa8h9hGLnqL1orTwxz0Rtb3v2dfxzdx5q5HXoZWqIxBnucLEyETssIurARCu8H5JIGv6iKQhCo2V/lp6ejvLycmRlZSE8PBy9e/dGUFCQWve7atUqLFu2rNHy/fv3w9DQsEn7fpy0tLRW2U5HxhwpxxypRuw8vfw0sFOmhYwiLcQfuIL/5l5CcJ86dNGgB+eKnaP2oqV5qqzkLQdEbaVKJsc/d/+C/xy7AQB43tkcH051RVcDDfryJaIOSbTC28zMDNra2o3OMhcXFzc6G/1n9vb2AICBAweiqKgIMTExKhfezd1vREQElixZonhdWloKGxsb+Pr6wsSkZUdIZTIZ0tLSMGbMGEil/OJ/FOZIOeZINZqUp4kAdp+4hcg9Z3DuPhB/0RBrp7likLW4vWI1KUearLXy9PAKKiJSr+t3KrEgOQd5t0qhJQH+5tsPC0Y68qnlRNQmRCu8dXV14e7ujrS0NEyePFmxPC0tDQEBASpvRxCEBpeAq2u/enp6j3xYm1QqbbVfTFtzWx0Vc6Qcc6QaTcnTlGd6YYBNN8z/PAdX71Qi6NOjiJnYH0HDbJRe/aNumpIjTdfSPDHHROr347kiLN5+AqVVtehupIu1QW7w7s2nlhNR2xH1UvMlS5YgODgYQ4cOhaenJ5KSknD9+nXMnz8fQP1Z5ps3b2LLli0AgPj4eNja2sLJyQlAfV/v1atXIywsTLHNmpoanDlzRvH3mzdv4sSJE+jSpQt69+6t0n6JiNqSk4UJ9oT54G//OYm0M0X4x87TyLl2DysnD4C+lA/5ISJqLnmdgH//cAFrfrwEABhs8xQSZgyBFZ9aTkRtTNTCOzAwEHfu3MHy5ctRUFCAAQMGIDU1Fb169QIAFBQUNOitXVdXh4iICOTn50NHRweOjo549913MW/ePMWYW7duwc3NTfF69erVWL16NUaOHIkDBw6otF8iorZmoi/F+pnuWH/oCj7Ydw5fHb+BswWl+HimO2xNW+c5EkREncm9ihq8vj0X6RdLAADBw3shcoIzn1pORKIQ/eFqISEhCAkJeeR7mzZtavA6LCyswdntR7Gzs4MgCC3aLxGRGLS0JFgwyhGu1l0Rti0XZwrqW459FDgYzzk/+dkXRET0Pyd//Q0hycdx87cH0JdqYdWLAzHZzVrssIioExOtjzcRET2aV28z7H3dB262T6G0qhazNx/D6n3nIa9TflCRiKgzEwQBX/x8HX/9+Ahu/vYAdqaG2BnizaKbiETHwpuISANZdjVAylxPzPKsvwVm3U+X8Mpn2bhbUSNyZEREmqlKJsdbX57CP3aeRo28DmNczLEnzAfOluzPTUTiY+FNRKShdHW0sCxgAOICB8NAqo30iyWYsCYdJ379TezQiIg0yvU7lXgxIRNf5tyAlgT4+zgnrJ/pDhN9dg0gIs3AwpuISMNNcnsauxZ6w97MCLfuV2Hqx0ewNeuaSs+zICLq6P57tggT1qbjTEEpTI10sXW2BxaMYn9uItIsLLyJiNqBfhbG2B3qjbH9zVEjr0Pkrl/wtx0n8aBGLnZoRESikNcJWL3vPGZvPobSqlq42T6Fva/7wIv9uYlIA7HwJiJqJ0z0pfh4pjvC/ZygJQG+Pn4TkxMO49qdCrFDIyJqU3cravDKZ9lY91N9f+5Znr2QMtcTll3Zn5uINBMLbyKidkQikWD+SEdsneMBsy66OFdYhglrM/DDmSKxQyMiahMnfv0NE9akI/1iCQyk2ogLHIxlAQOgq8Nfa4lIc/EbioioHfJyNMPesBEYYvsUyqpqMWfLMXyw7xxbjhFRhyUIArZmXcPUj4/g1v0q2JsZYedCL0xye1rs0IiIlGLhTUTUTll01cf2uZ54xcsOABD/02XM2piNO+XV4gZGRNTKHtTI8bcdJxG56xfUyOswtr85dod6w8mCrcKIqH1g4U1E1I7p6mghZmJ//HtafcuxjEslmLA2A7nX74kdGhFRq7h2pwKTEw7j6+M3oSUBwv2c8DFbhRFRO8PCm4ioAwgYXN9yzMHMCAX3qzB1/RF8fuQqW44RUbv2w5kiTFibgXOFZTDrooutczwwf6QjJBK2CiOi9oWFNxFRB/Gw5di4/haQyQVE7c7Dkv+w5RgRtT/yOgEf7DuHOVuOoayqFkNsn8LesBHwcmSrMCJqn1h4ExF1IMb6UiTOHIJ/jHeCtpYEO3PrW45dLWHLMSJqH+6UV2PWxmzE/3QZAPCKlx22z/WERVd9kSMjImo+Ft5ERB2MRCLB3GcdkfyHlmP+azOwP69Q7NCIiJ4o9/o9+K/NQMal+lZh/542GDET+7NVGBG1e/wWIyLqoIY7mOLb10dgaK9uKKuuxdzPc/De9+dQK68TOzQiogYEQcDnWdcwdX19qzAHMyPsWuiNgMFsFUZEHQMLbyKiDszcRB/b5g7Hq952AIDEA5fx8sZslLDlGBFpiAc1cvztPycRtesXyOQCxvW3wO5Qb/SzMBY7NCKiVsPCm4iog5NqayHavz/WBLnBUFcbmZfvYMKaDBxnyzEiEtnVkt9bheXWtwqL8HNC4swhMGarMCLqYFh4ExF1EhNdrepbjvUwQmFpFQLXH8EWthwjIpHszyuE/x9ahSXPGY55bBVGRB0UC28iok6kr7kxdi/0ht+A+pZj/9ydhzdSTqCyplbs0Iiok6iV1+G9789h7uc5KKuuhXuvbvj29RHwdDQVOzQiIrVh4U1E1MkY60uRMGMIlo53hraWBLtO3MLk+Ezks+UYEalZSXk1Xt6YjcQD9a3CXvW2w/a5w2FuwlZhRNSxsfAmIuqEJBIJ/u9ZB3wxxwNmXfRwvqgME9dmYB9bjhGRmuT++hv812Yg8/IdGOpqY02QG6L9+0OqzV9Hiajj4zcdEVEn5uFgim9f91G0HJv3eQ4+2H8Bct72TUStRBAEpBdKMGPDURTcr4JDj/pWYRNdrcQOjYiozbDwJiLq5B62HHvN2x4AkJR+FYlntNhyjIharLKmFm999Qu+zNeGTC7Ab4AFdi/0Rl9ztgojos6FhTcREUGqrYV/+rtg3fT6lmMXS7UwKSELOdfYcoyImie/pAKT4zOx+2QBtCAgfFxfJMxgqzAi6pxYeBMRkcKEQVb4cp4HzA0EFJVVY1rSEWzOZMuxzighIQH29vbQ19eHu7s70tPTnzg+Pj4ezs7OMDAwQL9+/bBly5ZGY+Li4tCvXz8YGBjAxsYGb7zxBqqqqhTvHzp0CP7+/rCysoJEIsGuXbsarC+TyfD3v/8dAwcOhJGREaysrPDyyy/j1q1brfKZqfXsyyvExLUZOF9U3ypsoYscs73t2CqMiDotFt5ERNRAn55dsGSgHH79zSGTC4jek4fFbDnWqaSkpGDx4sVYunQpcnNzMWLECPj5+eH69euPHJ+YmIiIiAjExMQgLy8Py5Ytw8KFC/HNN98oxiQnJyM8PBzR0dE4e/YsNmzYgJSUFERERCjGVFRUwNXVFevWrXvkfiorK3H8+HFERUXh+PHj+Prrr3HhwgVMnDixdRNAzVYrr8O7353DvN9bhQ3t1Q27FgxH765iR0ZEJC7RC++mHFHPyMiAt7c3TE1NYWBgACcnJ3z00UeNxn311VdwcXGBnp4eXFxcsHPnzgbvx8TEQCKRNPhjYWHR6p+NiKi90tcG/h04CJEv1Lcc233iFibFH8aV2+Vih0ZtIDY2FrNnz8acOXPg7OyMuLg42NjYIDEx8ZHjP//8c8ybNw+BgYFwcHDAtGnTMHv2bLz33nuKMUeOHIG3tzemT58OOzs7+Pr6IigoCMeOHVOM8fPzw4oVK/Diiy8+cj9du3ZFWloapk6din79+mH48OFYu3YtcnJyHntQgNpOSXk1gjdk4+OD9a3CXvO2xza2CiMiAgDoiLnzh0fUExIS4O3tjfXr18PPzw9nzpyBra1to/FGRkYIDQ3FoEGDYGRkhIyMDMybNw9GRkaYO3cugPqJPTAwEP/6178wefJk7Ny5E1OnTkVGRgY8PDwU2+rfvz9++OEHxWttbW31f2AionZEIpFgzggHDLJ+Cgu/OI4LReWYuO4wVv91EMYNsBQ7PFKTmpoa5OTkIDw8vMFyX19fZGZmPnKd6upq6Os3LK4MDAyQnZ0NmUwGqVQKHx8fbN26FdnZ2Rg2bBiuXLmC1NRUzJo1q0Xx3r9/HxKJBE899dRjx1RXV6O6+n8PCywtLQVQf+m6TCZr9r4frtuSbXQUudd/Q1jKSRSVVsNQVxurJvXH+IEWQJ2ceVIBc6Qa5kk55kg1rZWnpqwvauH9xyPqQP29X/v27UNiYiJWrVrVaLybmxvc3NwUr+3s7PD1118jPT1dUXjHxcVhzJgxikvXIiIicPDgQcTFxWHbtm2KdXV0dHiWm4hIBcPsu+PbMB+EfpGL7Kt3MX/rccx71gFvje0HHfbf7XBKSkogl8thbm7eYLm5uTkKCx/d533s2LH49NNPMWnSJAwZMgQ5OTnYuHEjZDIZSkpKYGlpiWnTpuH27dvw8fGBIAiora3FggULGhX4TVFVVYXw8HBMnz4dJiYmjx23atUqLFu2rNHy/fv3w9DQsNn7fygtLa3F22ivBAFIL5Rg1zUtyAUJzA0EvNa3Gvj1OFJ/bTi2M+dJVcyRapgn5Zgj1bQ0T5WVlSqPFa3wbs4R9T/Lzc1FZmYmVqxYoVh25MgRvPHGGw3GjR07FnFxcQ2WXbx4EVZWVtDT04OHhwfeeecdODg4PHZf6jpa/nAbf/wvNcYcKcccqYZ5Uu5ROepmoI1NrwzB6v0XsTHzGtYfuoITv95D3NRBMOuiJ1aoohLjaHlb+vNDsARBeOyDsaKiolBYWIjhw4dDEASYm5vjlVdewfvvv6+4ouzAgQNYuXIlEhIS4OHhgUuXLmHRokWwtLREVFRUk+OTyWSYNm0a6urqkJCQ8MSxERERWLJkieJ1aWkpbGxs4Ovr+8SCXZUY0tLSMGbMGEilne9J3ZU1tYjcfQbfXK0/IDOuvzlWTe6PLnoNf73s7HlSBXOkGuZJOeZINa2Vp4c1oSpEK7ybc0T9IWtra9y+fRu1tbWIiYlRnDEHgMLCQqXb9PDwwJYtW9C3b18UFRVhxYoV8PLyQl5eHkxNTR+5T3UfLQd4ZEoVzJFyzJFqmCflHpUjVwCv9JVg2yUt/Jx/D+NiD+DVfnLYd+KWvG15tLwtmJmZQVtbu9FcXFxc3Gh+fcjAwAAbN27E+vXrUVRUBEtLSyQlJcHY2BhmZmYA6ovz4OBgxZw9cOBAVFRUYO7cuVi6dCm0tFS/ekImk2Hq1KnIz8/Hjz/+qLR41tPTg55e4wNEUqm0VX4xba3ttCdXbpdj/tYcXCgqh7aWBBF+TpjtY//Ep5Z3xjw1FXOkGuZJOeZINS3NU1PWFfVSc6BpR9QfSk9PR3l5ObKyshAeHo7evXsjKChI5W36+fkp/j5w4EB4enrC0dERmzdvbnBE/I/UdbQc4JEpVTBHyjFHqmGelFOWo/EAAovLEbr9JC7frsC6M1KEj+uLl4fbdqpWQWIcLW8Lurq6cHd3R1paGiZPnqxYnpaWhoCAgCeuK5VKYW1tDQDYvn07JkyYoCioKysrGxXX2traEAShSe3qHhbdFy9exE8//fTYA+akPt//Uog3d5xEeXUtehjrIX76EAyz7y52WEREGk20wrs5R9Qfsre3B1BfNBcVFSEmJkZReFtYWDR5m0ZGRhg4cCAuXrz42DHqPlre2tvqqJgj5Zgj1TBPyj0pR85Pd8OeUB+8/dUpfHuqACtSz+PUzTKsenEgjPREP6bbptryaHlbWbJkCYKDgzF06FB4enoiKSkJ169fx/z58wHUH4y+efOmolf3hQsXkJ2dDQ8PD9y7dw+xsbH45ZdfsHnzZsU2/f39ERsbCzc3N8Wl5lFRUZg4caLicvTy8nJcunRJsU5+fj5OnDiB7t27w9bWFrW1tZgyZQqOHz+OvXv3Qi6XK+b87t27Q1dXt61S1CnVyuvwwf7zWH/wCgBgmF13rJvuhp58ajkRkVKi/XbUkiPqfyQIQoN7rz09PZGWltbgPu/9+/fDy8vrsduorq7G2bNnMWLEiCZ+CiKizstITwfrgtwwxLYbVqWexZ6Tt3C2oBQfB7vDsUcXscOjFggMDMSdO3ewfPlyFBQUYMCAAUhNTUWvXr0AAAUFBQ3ad8nlcnz44Yc4f/48pFIpRo8ejczMTNjZ2SnGREZGQiKRIDIyEjdv3kSPHj3g7++PlStXKsYcO3YMo0ePVrx+eKXZrFmzsGnTJty4cQN79uwBAAwePLhBzD/99BNGjRrVypmgh26XVSNs23FkXbkLAJjtY49wPydI+YBFIiKViHpaoqlH1OPj42FrawsnJycA9X29V69ejbCwMMU2Fy1ahGeffRbvvfceAgICsHv3bvzwww/IyMhQjHnzzTfh7+8PW1tbFBcXY8WKFSgtLW1xSxMios5GIpFgto89Bll3xcLk47hYXI6AdYfxwZRB8BvIlmPtWUhICEJCQh753qZNmxq8dnZ2Rm5u7hO3p6Ojg+joaERHRz92zKhRo5542bmdnV2TLkun1pFz7S5Cko+jqLQaRrraeH+KK14YxH/fRERNIWrh3dQj6nV1dYiIiEB+fj50dHTg6OiId999F/PmzVOM8fLywvbt2xEZGYmoqCg4OjoiJSWlQQ/vGzduICgoCCUlJejRoweGDx+OrKwsxX6JiKhpnrHrjr2v/95yLP8uFiQfx9xnHfA2W44RtVuCIGBT5lWs/PYsausE9O7ZBR/PHILePTvx0xSJiJpJ9BvxmnJEPSwsrMHZ7ceZMmUKpkyZ8tj3t2/f3qQYiYhIuZ7G+kie44EP9p1H0qErSDp0BSd+/a3+HlBj3gNK1J5UVNci4uvT2HPyFgDghUGWeO+lQY1ahRERkWp4GoKIiFqNVFsL/xjvjIQZQ2Ckq43s/LuYsCYDR6/eFTs0IlLR5dvlmBR/GHtO3oKOlgRRE1ywLsiNRTcRUQuw8CYiolY3fqAl9oT5oE/PLiguq0ZQUhY2ZuTz/lwiDffd6QIErDuMi8Xl6Gmsh21zhyvtz01ERMqx8CYiIrVw7NEFuxZ6w9/VCrV1ApbvPYOwbbmoqK4VOzQi+pNaeR3eST2LBcnHUV5di2H29c9teMaO/bmJiFoDC28iIlIbIz0drJk2GNH+LtDRkmDvqQIExB/GpeJysUMjot8Vl1Vhxqc/I+lQfX/u/xthj+Q5Hnw2AxFRK2LhTUREaiWRSPCqtz22zx0OcxM9XCouR8C6DKSeLhA7NKJO79jV+ucw/Jx/F0a62kiYMQRLX3Bhf24iolbGb1UiImoTQ+26Y2/YCHjYd0dFjRwhycexYu8ZyOR1YodG1OkIgoCNGfmYlpSF4rJq9O7ZBbtDfTB+IPtzExGpAwtvIiJqMz2M9ZA8xwPznnUAAHyakY8Zn/yM4tIqkSMj6jwqqmsRti0Xy/eeQW2dgAmDLLF7oTd69+widmhERB0WC28iImpTOtpaiBjvjI9nDkEXPR1kX72LF9ZmIDufLceI1O1ScTkC4g9j76kC6GhJEO3vgrVBbjBiqzAiIrVi4U1ERKIYN8ASu0O90de8C26XVSPokyx8mn6FLceI1CT1dAEC1mXg0u+twrbPHY5XvdkqjIioLbDwJiIi0Tj26IKdId6Y6GoFeZ2AFd+eRei2XJSz5RhRq5HJ67Dy2zMIST6Oiho5PH5vFTaUrcKIiNoMC28iIhKVkZ4O/j1tMGJ+bzn27amHZ+XKxA6NqN0rLqvCjE9+xifp+QCAec86sFUYEZEIWHgTEZHoJBIJXvG2R8q8+pZjl29XIGDdYew9dUvs0Ijarez8u3hhTQayr95FFz0dfDxzCCLGO0OHrcKIiNocv3mJiEhjuPeqbzk23KG+5VjoF7lY/g1bjhE1hSAI+DT9CoI+ycLtsmr0Ne+C3aHeGDeArcKIiMTCwpuIiDRKD2M9bJ3tgfkjHQEAGw/nY/onWWw5RqSC8upahG7LxYpvz0JeJ2CiqxV2hnjDsQdbhRERiYmFNxERaRwdbS2E+zlhfbA7jPV0cPTqPbywNgM/X7kjdmhEGutScRkmxR/Gt7+3Covxd8G/pw1mqzAiIg3AwpuIiDTW2P4W2B3qjX7mxrhdVo3pn/6MTw6x5RjRn9U/lPAwLhWXw9xEDynzhuMVtgojItIYLLyJiEijOfTogp0LvRAwuL7l2MrUs1j4xXG2HCNCfauwf+09g4Vf1LcKG+5Q/5wE915sFUZEpElYeBMRkcYz1NVBXOBgLA/oD6m2BKmnCzFxXQYuFrHlGHVexaVVmP5JFjZk/N4qbKQDts72QA9jPZEjIyKiP2PhTURE7YJEIsHLnnbYPtcTFib6uHK7AgHxh/HNSbYco87n5yt38MLaDBy9eu/3VmHuiPBjqzAiIk3Fb2ciImpX3Ht1w97XfeDlaIrKGjnCtuVi2Td5bDlGnYIgCPjk0BVM//Rn3C6rRj9zY+wJ9ca4ARZih0ZERE/AwpuIiNodsy562PLaMCwYVd9y7LPDVxGUlIUithyjDqy8uhYLvziOlan1rcICBlth50IvOLBVGBGRxmPhTURE7ZKOthb+Pu5/LceOXbuHF9ZkIIstx6gDulhUhoB1GUg9XQiptgTLJvZHXOBgGOqyVRgRUXvAwpuIiNq1sf0tsCfMB04Wxigpr8aMT39G0qHLbDlGHcY3J28hIP4wLt+ugIWJPrbP9cQsLzu2CiMiakdYeBMRUbtnb2aEr0O8MNntacjrBLyTeg4hycdRViUTOzSiZpPJ67DsmzyEbctFZY0cng6m2Pu6D9x7dRM7NCIiaiIW3kRE1CEY6uogdqor/vV7y7HvfilEwLrDuMCWY9QOFZVWISgpC58dvgoAWDDKEZ/PHgazLmwVRkTUHoleeCckJMDe3h76+vpwd3dHenr6Y8dmZGTA29sbpqamMDAwgJOTEz766KNG47766iu4uLhAT08PLi4u2LlzZ4v2S0RE7YNEIkGwpx1S5nnCsqs+rpRUIGDdYew+cVPs0IhUlnXlDl5Yk4Fj1+7BWE8H64Pd8fdxTmwVRkTUjon6DZ6SkoLFixdj6dKlyM3NxYgRI+Dn54fr168/cryRkRFCQ0Nx6NAhnD17FpGRkYiMjERSUpJizJEjRxAYGIjg4GCcPHkSwcHBmDp1Kn7++edm75eIiNqXIbbdsDfMB969TfFAJsei7ScQsycPNbVsOUaaSxAEJB26jBmf/oyS8mo4WRhjT5gPxvZnqzAiovZO1MI7NjYWs2fPxpw5c+Ds7Iy4uDjY2NggMTHxkePd3NwQFBSE/v37w87ODjNnzsTYsWMbnK2Oi4vDmDFjEBERAScnJ0REROC5555DXFxcs/dLRETtj2kXPWx5zQMhv7cc25R5FUGfZKHwPluOkeYpq5IhJPk43kk9B3mdgMluT+PrEC/YmxmJHRoREbUC0Qrvmpoa5OTkwNfXt8FyX19fZGZmqrSN3NxcZGZmYuTIkYplR44cabTNsWPHKrbZGvslIqL2QVtLgrfHOSEp2B3G+jrIuXYPE9amI/NyidihESlcKCpDQPxhfPdLfauwfwX0R+xUV7YKIyLqQET7Ri8pKYFcLoe5uXmD5ebm5igsLHziutbW1rh9+zZqa2sRExODOXPmKN4rLCx84jabu9/q6mpUV1crXpeWlgIAZDIZZLKWPTX34fot3U5HxhwpxxyphnlSriPmaHRfU+ycPxyh207gXFE5Zn76M/42pg/+z6f5LZlaK08dKc/UdHtO3sLfvzyFBzI5LLvqI37GEAyx5VPLiYg6GtEPpf75Fx5BEJT+EpSeno7y8nJkZWUhPDwcvXv3RlBQUJO22dT9rlq1CsuWLWu0fP/+/TA0NHxivKpKS0trle10ZMyRcsyRapgn5Tpijmb3Av4j18LREi18sP8i9h07j+mOdTBowWzY0jxVVla2aH1qn2pq6/BO6llsyrwKAPDubYo109xgyqeWExF1SKIV3mZmZtDW1m50lrm4uLjR2eg/s7e3BwAMHDgQRUVFiImJURTeFhYWT9xmc/cbERGBJUuWKF6XlpbCxsYGvr6+MDExUfJpn0wmkyEtLQ1jxoyBVCpt0bY6KuZIOeZINcyTch09RwGCgG1Hb2BF6jmcuquFUkkXxAe5oq+5cZO201p5engFFXUehfersPCL48i5dg8AEDLKEX/z7QdtreZdfUFERJpPtMJbV1cX7u7uSEtLw+TJkxXL09LSEBAQoPJ2BEFocAm4p6cn0tLS8MYbbyiW7d+/H15eXi3ar56eHvT0Gh+FlkqlrfaLaWtuq6NijpRjjlTDPCnXkXM0y9sBrrbdEbI1B1fvVGLK+my8+9JABAx+usnbammeOmqO6dGOXL6DsG3HUVJeA2N9HXz4V1f48qnlREQdnqiXmi9ZsgTBwcEYOnQoPD09kZSUhOvXr2P+/PkA6s8y37x5E1u2bAEAxMfHw9bWFk5OTgDq+3qvXr0aYWFhim0uWrQIzz77LN577z0EBARg9+7d+OGHH5CRkaHyfomIqOMbbPMU9r4+Aq9vy0XGpRIs2n4Cx6/dw9IXXKCrw37J1LrqW4Vdwfv7zkNeJ8DJwhgfz3SHHZ9aTkTUKYhaeAcGBuLOnTtYvnw5CgoKMGDAAKSmpqJXr14AgIKCgga9tevq6hAREYH8/Hzo6OjA0dER7777LubNm6cY4+Xlhe3btyMyMhJRUVFwdHRESkoKPDw8VN4vERF1Dt2NdLH5tWH4KO0C1v10CZuPXMPpm/cRP2MILLsaiB0edRBlVTK8teMUvs+rv83tRbensXLyQBjoaoscGRERtRXRH64WEhKCkJCQR763adOmBq/DwsIanN1+nClTpmDKlCnN3i8REXUe2loSvDm2HwbbPIU3/nMCx6//hglrMrA2yA1evc3EDo/aufOFZZi/NQf5JRWQakvwT//+mOlh2+yn6RMRUfvEa+mIiIgAPO9ijr1hPnC2NMGdihrM3PAzEg9chiAIYodG7dTuEzcxKf4w8ksqYNlVH/+Z54ng4b1YdBMRdUIsvImIiH7Xy9QIXy/wwktDrFEnAO99fw7zPs9BaVXn67WdkJAAe3t76Ovrw93dHenp6U8cHx8fD2dnZxgYGKBfv36K57P8UVxcHPr16wcDAwPY2NjgjTfeQFVVleL9Q4cOwd/fH1ZWVpBIJNi1a1ejbQiCgJiYGFhZWcHAwACjRo1CXl5eiz9va6qprUP07l+waPsJPJDJ4dPbDHvDfODG/txERJ0WC28iIqI/MNDVxuq/DsLKyQOgq62F/WeKMHFtBs4Vdp62XykpKVi8eDGWLl2K3NxcjBgxAn5+fg2eu/JHiYmJiIiIQExMDPLy8rBs2TIsXLgQ33zzjWJMcnIywsPDER0djbNnz2LDhg1ISUlBRESEYkxFRQVcXV2xbt26x8b2/vvvIzY2FuvWrcPRo0dhYWGBMWPGoKysrPUS0AIF9x9gWtIRbD5yDQAQOro3Nr82jP25iYg6OdHv8SYiItI0EokEMzx6YYBVVyz4veXYpPjDePfFQZjk1vSWY+1NbGwsZs+ejTlz5gCoP1O9b98+JCYmYtWqVY3Gf/7555g3bx4CAwMBAA4ODsjKysJ7770Hf39/AMCRI0fg7e2N6dOnAwDs7OwQFBSE7OxsxXb8/Pzg5+f32LgEQUBcXByWLl2KF198EQCwefNmmJub44svvmjwsFUxZF4uQdgXubhTUd8q7KOpg/G8i7moMRERkWZg4U1ERPQYrr+3HFu0PRfpF0uwOOUEjl+/h8gXXNBR79KtqalBTk4OwsPDGyz39fVFZmbmI9eprq6Gvr5+g2UGBgbIzs6GTCaDVCqFj48Ptm7diuzsbAwbNgxXrlxBamoqZs2apXJs+fn5KCwshK+vr2KZnp4eRo4ciczMzMcW3tXV1aiurla8Li2tv3pBJpNBJmv+bQQP162pqcH6Q/mI/eEi6gTAycIY64Jc0au7YYu231E8zAFz8XjMkWqYJ+WYI9W0Vp6asj4LbyIioifobqSLTa8OQ9wPF7D2x0vYcuQaTt24jzWBg8QOTS1KSkogl8thbt7wTK25uTkKCwsfuc7YsWPx6aefYtKkSRgyZAhycnKwceNGyGQylJSUwNLSEtOmTcPt27fh4+MDQRBQW1uLBQsWNCrwn+Th/h8V27Vr1x673qpVq7Bs2bJGy/fv3w9DQ0OV9/8oD2qBaet+xOl79XfvDetRh7/a3kNe1gFo1p3n4ktLSxM7BI3HHKmGeVKOOVJNS/NUWVmp8lgW3kREREpoa0nwN99+cLN9Cou3n8CJX39DQMIRBNlJMF7s4NTkz0/eFgThsU/jjoqKQmFhIYYPHw5BEGBubo5XXnkF77//PrS163tVHzhwACtXrkRCQgI8PDxw6dIlLFq0CJaWloiKilJbbAAQERGBJUuWKF6XlpbCxsYGvr6+MDExadK+/yjvxj383+Zs3K6SQKotQdQLTpg21JpPLf8TmUyGtLQ0jBkzBlKpVOxwNBJzpBrmSTnmSDWtlaeHV1CpgoU3ERGRiv7iZI69YSMwf2sOzhSU4otLWgiRyTvULzdmZmbQ1tZudHa7uLi40ZnmhwwMDLBx40asX78eRUVFsLS0RFJSEoyNjWFmVt8LPSoqCsHBwYr7xgcOHIiKigrMnTsXS5cuhZaW8ue9WlhYAKg/821paalSbED95eh6eo0fbiaVSpv9/04QBKz4/iJuV0lg2VUfH890h6vNU83aVmfRknx3FsyRapgn5Zgj1bQ0T01Zl081JyIiagJbU0N8HeKFqe5PY1YfOfSk2mKH1Kp0dXXh7u7e6PK7tLQ0eHl5PXFdqVQKa2traGtrY/v27ZgwYYKioK6srGxUXGtra0MQBJV7pdvb28PCwqJBbDU1NTh48KDS2FqbRCLBey8OwGDTOuxaMJxFNxERPRHPeBMRETWRvlQbKyf1R2rq4+8rbs+WLFmC4OBgDB06FJ6enkhKSsL169cxf/58APWXbt+8eVPRq/vChQvIzs6Gh4cH7t27h9jYWPzyyy/YvHmzYpv+/v6IjY2Fm5ub4lLzqKgoTJw4UXE5enl5OS5duqRYJz8/HydOnED37t1ha2sLiUSCxYsX45133kGfPn3Qp08fvPPOOzA0NFQ8Lb0t2XY3xKt969DdSLfN901ERO0LC28iIiJqIDAwEHfu3MHy5ctRUFCAAQMGIDU1Fb169QIAFBQUNOjpLZfL8eGHH+L8+fOQSqUYPXo0MjMzYWdnpxgTGRkJiUSCyMhI3Lx5Ez169IC/vz9WrlypGHPs2DGMHj1a8frhfdmzZs3Cpk2bAABvv/02Hjx4gJCQENy7dw8eHh7Yv38/jI2N1ZgRIiKilmHhTURERI2EhIQgJCTkke89LIIfcnZ2Rm5u7hO3p6Ojg+joaERHRz92zKhRo5Redi6RSBATE4OYmJgnjiMiItIkvMebiIiIiIiISI1YeBMRERERERGpEQtvIiIiIiIiIjVi4U1ERERERESkRiy8iYiIiIiIiNSIhTcRERERERGRGrHwJiIiIiIiIlIjFt5EREREREREasTCm4iIiIiIiEiNdMQOoL0SBAEAUFpa2uJtyWQyVFZWorS0FFKptMXb64iYI+WYI9UwT8oxR6pprTw9nEceziukfq01h/PfimqYJ+WYI9UwT8oxR6oRYw5n4d1MZWVlAAAbGxuRIyEioo6grKwMXbt2FTuMToFzOBERtSZV5nCJwEPszVJXV4dbt27B2NgYEomkRdsqLS2FjY0Nfv31V5iYmLRShB0Lc6Qcc6Qa5kk55kg1rZUnQRBQVlYGKysraGnxDrC20FpzOP+tqIZ5Uo45Ug3zpBxzpBox5nCe8W4mLS0tWFtbt+o2TUxM+A9ECeZIOeZINcyTcsyRalojTzzT3bZaew7nvxXVME/KMUeqYZ6UY45U05ZzOA+tExEREREREakRC28iIiIiIiIiNWLhrQH09PQQHR0NPT09sUPRWMyRcsyRapgn5Zgj1TBPxJ8B1TBPyjFHqmGelGOOVCNGnvhwNSIiIiIiIiI14hlvIiIiIiIiIjVi4U1ERERERESkRiy8iYiIiIiIiNSIhbeaHTp0CP7+/rCysoJEIsGuXbuUrnPw4EG4u7tDX18fDg4O+Pjjj9UfqIiamqOvv/4aY8aMQY8ePWBiYgJPT0/s27evbYIVUXN+lh46fPgwdHR0MHjwYLXFpwmak6Pq6mosXboUvXr1gp6eHhwdHbFx40b1Byui5uQpOTkZrq6uMDQ0hKWlJV599VXcuXNH/cGKZNWqVXjmmWdgbGyMnj17YtKkSTh//rzS9Trb93dHxzlcOc7hquEcrhzncNVwDldOU+dwFt5qVlFRAVdXV6xbt06l8fn5+Rg/fjxGjBiB3Nxc/OMf/8Drr7+Or776Ss2RiqepOTp06BDGjBmD1NRU5OTkYPTo0fD390dubq6aIxVXU/P00P379/Hyyy/jueeeU1NkmqM5OZo6dSr++9//YsOGDTh//jy2bdsGJycnNUYpvqbmKSMjAy+//DJmz56NvLw87NixA0ePHsWcOXPUHKl4Dh48iIULFyIrKwtpaWmora2Fr68vKioqHrtOZ/z+7ug4hyvHOVw1nMOV4xyuGs7hymnsHC5QmwEg7Ny584lj3n77bcHJyanBsnnz5gnDhw9XY2SaQ5UcPYqLi4uwbNmy1g9IQzUlT4GBgUJkZKQQHR0tuLq6qjUuTaJKjr777juha9euwp07d9omKA2kSp4++OADwcHBocGyNWvWCNbW1mqMTLMUFxcLAISDBw8+dkxn//7u6DiHK8c5XDWcw5XjHK4azuGq0ZQ5nGe8NcyRI0fg6+vbYNnYsWNx7NgxyGQykaLSbHV1dSgrK0P37t3FDkXjfPbZZ7h8+TKio6PFDkUj7dmzB0OHDsX777+Pp59+Gn379sWbb76JBw8eiB2aRvHy8sKNGzeQmpoKQRBQVFSEL7/8Ei+88ILYobWZ+/fvA8ATv2f4/U38GWg6zuGPxzn8yTiHq4ZzuObM4TqtshVqNYWFhTA3N2+wzNzcHLW1tSgpKYGlpaVIkWmuDz/8EBUVFZg6darYoWiUixcvIjw8HOnp6dDR4T/1R7ly5QoyMjKgr6+PnTt3oqSkBCEhIbh7926Hv0esKby8vJCcnIzAwEBUVVWhtrYWEydOxNq1a8UOrU0IgoAlS5bAx8cHAwYMeOw4fn8TfwaajnP4o3EOV45zuGo4h2vOHM4z3hpIIpE0eC0IwiOXE7Bt2zbExMQgJSUFPXv2FDscjSGXyzF9+nQsW7YMffv2FTscjVVXVweJRILk5GQMGzYM48ePR2xsLDZt2sQj5n9w5swZvP766/jnP/+JnJwcfP/998jPz8f8+fPFDq1NhIaG4tSpU9i2bZvSsfz+Jv4MqI5z+KNxDlcN53DVcA7XnDmch9A0jIWFBQoLCxssKy4uho6ODkxNTUWKSjOlpKRg9uzZ2LFjB55//nmxw9EoZWVlOHbsGHJzcxEaGgqgfoISBAE6OjrYv38//vKXv4gcpfgsLS3x9NNPo2vXroplzs7OEAQBN27cQJ8+fUSMTnOsWrUK3t7eeOuttwAAgwYNgpGREUaMGIEVK1Z06LN4YWFh2LNnDw4dOgRra+snjuX3N/FnQHWcwx+Pc7hqOIerhnO45szhLLw1jKenJ7755psGy/bv34+hQ4dCKpWKFJXm2bZtG1577TVs27atU92joioTExOcPn26wbKEhAT8+OOP+PLLL2Fvby9SZJrF29sbO3bsQHl5Obp06QIAuHDhArS0tJR+QXcmlZWVjS511NbWBvC/o8EdjSAICAsLw86dO3HgwAGV/s3w+5v4M6AazuFPxjlcNZzDVcM5XIPm8FZ7TBs9UllZmZCbmyvk5uYKAITY2FghNzdXuHbtmiAIghAeHi4EBwcrxl+5ckUwNDQU3njjDeHMmTPChg0bBKlUKnz55ZdifQS1a2qOvvjiC0FHR0eIj48XCgoKFH9+++03sT5Cm2hqnv6sMzwRtak5KisrE6ytrYUpU6YIeXl5wsGDB4U+ffoIc+bMEesjtImm5umzzz4TdHR0hISEBOHy5ctCRkaGMHToUGHYsGFifQS1W7BggdC1a1fhwIEDDb5nKisrFWP4/d3xcQ5XjnO4ajiHK8c5XDWcw5XT1Dmchbea/fTTTwKARn9mzZolCIIgzJo1Sxg5cmSDdQ4cOCC4ubkJurq6gp2dnZCYmNj2gbehpuZo5MiRTxzfUTXnZ+mPOsOk3ZwcnT17Vnj++ecFAwMDwdraWliyZEmDL+aOqDl5WrNmjeDi4iIYGBgIlpaWwowZM4QbN260ffBt5FH5ASB89tlnijH8/u74OIcrxzlcNZzDleMcrhrO4cpp6hwu+T04IiIiIiIiIlIDPtWciIiIiIiISI1YeBMRERERERGpEQtvIiIiIiIiIjVi4U1ERERERESkRiy8iYiIiIiIiNSIhTcRERERERGRGrHwJiIiIiIiIlIjFt5EREREREREasTCm4g0jkQiwa5du8QOg4iIiJqIczjRo7HwJqIGXnnlFUgkkkZ/xo0bJ3ZoRERE9AScw4k0l47YARCR5hk3bhw+++yzBsv09PREioaIiIhUxTmcSDPxjDcRNaKnpwcLC4sGf7p16wag/hKyxMRE+Pn5wcDAAPb29tixY0eD9U+fPo2//OUvMDAwgKmpKebOnYvy8vIGYzZu3Ij+/ftDT08PlpaWCA0NbfB+SUkJJk+eDENDQ/Tp0wd79uxR74cmIiLqADiHE2kmFt5E1GRRUVF46aWXcPLkScycORNBQUE4e/YsAKCyshLjxo1Dt27dcPToUezYsQM//PBDg0k5MTERCxcuxNy5c3H69Gns2bMHvXv3brCPZcuWYerUqTh16hTGjx+PGTNm4O7du236OYmIiDoazuFEIhGIiP5g1qxZgra2tmBkZNTgz/LlywVBEAQAwvz58xus4+HhISxYsEAQBEFISkoSunXrJpSXlyve//bbbwUtLS2hsLBQEARBsLKyEpYuXfrYGAAIkZGRitfl5eWCRCIRvvvuu1b7nERERB0N53AizcV7vImokdGjRyMxMbHBsu7duyv+7unp2eA9T09PnDhxAgBw9uxZuLq6wsjISPG+t7c36urqcP78eUgkEty6dQvPPffcE2MYNGiQ4u9GRkYwNjZGcXFxcz8SERFRp8A5nEgzsfAmokaMjIwaXTamjEQiAQAIgqD4+6PGGBgYqLQ9qVTaaN26uromxURERNTZcA4n0ky8x5uImiwrK6vRaycnJwCAi4sLTpw4gYqKCsX7hw8fhpaWFvr27QtjY2PY2dnhv//9b5vGTERERJzDicTCM95E1Eh1dTUKCwsbLNPR0YGZmRkAYMeOHRg6dCh8fHyQnJyM7OxsbNiwAQAwY8YMREdHY9asWYiJicHt27cRFhaG4OBgmJubAwBiYmIwf/589OzZE35+figrK8Phw4cRFhbWth+UiIiog+EcTqSZWHgTUSPff/89LC0tGyzr168fzp07B6D+aaXbt29HSEgILCwskJycDBcXFwCAoaEh9u3bh0WLFuGZZ56BoaEhXnrpJcTGxiq2NWvWLFRVVeGjjz7Cm2++CTMzM0yZMqXtPiAREVEHxTmcSDNJBEEQxA6CiNoPiUSCnTt3YtKkSWKHQkRERE3AOZxIPLzHm4iIiIiIiEiNWHgTERERERERqREvNSciIiIiIiJSI57xJiIiIiIiIlIjFt5EREREREREasTCm4iIiIiIiEiNWHgTERERERERqRELbyIiIiIiIiI1YuFNREREREREpEYsvImIiIiIiIjUiIU3ERERERERkRqx8CYiIiIiIiJSo/8HMB43WJl+aYoAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x800 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Save best model and evaluate on test set\n",
        "torch.save(best_model.state_dict(), save_root / \"model_best.pt\")\n",
        "\n",
        "test_loader = pert_data.dataloader[\"test_loader\"]\n",
        "\n",
        "test_res = eval_perturb_new(test_loader, best_model, device)\n",
        "# test_metrics, test_pert_res = compute_metrics(test_res)  # optional\n",
        "\n",
        "# Save results\n",
        "model_prefix = f'lr_{lr}_dosage_{dosage_mode_type}'\n",
        "result_dir = save_root / f'result_{model_prefix}'\n",
        "result_dir.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "import pickle\n",
        "pickle.dump(test_res, open(result_dir / 'test_res.pkl', 'wb'))\n",
        "if len(train_metrics_list) > 0:\n",
        "    pickle.dump(train_metrics_list, open(result_dir / 'train_metrics_list.pkl', 'wb'))\n",
        "    pickle.dump(train_metrics_pert_list, open(result_dir / 'train_metrics_pert_list.pkl', 'wb'))\n",
        "    pickle.dump(val_metrics_list, open(result_dir / 'val_metrics_list.pkl', 'wb'))\n",
        "    pickle.dump(val_metrics_pert_list, open(result_dir / 'val_metrics_pert_list.pkl', 'wb'))\n",
        "\n",
        "# Optional plotting if metrics were collected\n",
        "if len(train_metrics_list) > 0:\n",
        "    try:\n",
        "        merge_plot(train_metrics_list, 'train', str(result_dir / 'train.png'))\n",
        "        merge_plot(val_metrics_list, 'val', str(result_dir / 'val.png'))\n",
        "    except Exception as e:\n",
        "        logger.warning(f\"Plotting skipped: {e}\")\n",
        "\n",
        "# Free CUDA cache\n",
        "torch.cuda.empty_cache()\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "scGPT_2",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.14"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}