{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68824f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80dacb30",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/dataset/test_data_updated_with_psa.json\",\n",
    ") as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "neg_label = [i for i in data[\"test\"] if i[\"label\"] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac0821ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(neg_label))\n",
    "pirads_label = np.array([i[\"pirads\"] for i in neg_label])\n",
    "print(np.unique(pirads_label, return_counts=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656c8242",
   "metadata": {},
   "source": [
    "### PSA Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "514753e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\n",
    "    \"/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/marksheet.csv\"\n",
    ")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "564d9290",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/dataset/PICAI_cspca_updated.json\",\n",
    ") as f:\n",
    "    json_data = json.load(f)\n",
    "\n",
    "new_train = []\n",
    "for i in json_data[\"train\"]:\n",
    "    p_id = i[\"image\"].split(\"_\")[0]\n",
    "    st_id = i[\"image\"].split(\"_\")[1].split(\".\")[0]\n",
    "    row = data[(data[\"patient_id\"] == int(p_id)) & (data[\"study_id\"] == int(st_id))]\n",
    "    pvol = row[\"prostate_volume\"].iloc[0]\n",
    "    psa = row[\"psa\"].iloc[0]\n",
    "    if pd.notna(psa) and psa != \"\" and pd.notna(pvol) and pvol != \"\":\n",
    "        i[\"psa\"] = [psa, pvol]\n",
    "        new_train.append(i)\n",
    "\n",
    "print(len(new_train))\n",
    "print(len(json_data[\"train\"]))\n",
    "\n",
    "new_test = []\n",
    "for i in json_data[\"test\"]:\n",
    "    p_id = i[\"image\"].split(\"_\")[0]\n",
    "    st_id = i[\"image\"].split(\"_\")[1].split(\".\")[0]\n",
    "    row = data[(data[\"patient_id\"] == int(p_id)) & (data[\"study_id\"] == int(st_id))]\n",
    "    pvol = row[\"prostate_volume\"].iloc[0]\n",
    "    psa = row[\"psa\"].iloc[0]\n",
    "    if pd.notna(psa) and psa != \"\" and pd.notna(pvol) and pvol != \"\":\n",
    "        i[\"psa\"] = [psa, pvol]\n",
    "        new_test.append(i)\n",
    "\n",
    "print(len(new_test))\n",
    "print(len(json_data[\"test\"]))\n",
    "\n",
    "json_data[\"train\"] = new_train\n",
    "json_data[\"test\"] = new_test\n",
    "with open(\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/dataset/PICAI_cspca_updated_with_psa.json\",\n",
    "    \"w\",\n",
    ") as f:\n",
    "    json.dump(json_data, f, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f51a6c",
   "metadata": {},
   "source": [
    "### Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f61d3ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_excel(\n",
    "    \"/sc-projects/sc-proj-cc06-ag-ki-radiologie/prostate_test/COMFORT_data_mpMRI/anon_data.xlsx\"\n",
    ")\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cae067c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b9f4825",
   "metadata": {},
   "outputs": [],
   "source": [
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8673cc2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7eec1c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/dataset/test_data_updated.json\",\n",
    ") as f:\n",
    "    json_data = json.load(f)\n",
    "\n",
    "\n",
    "new_test = []\n",
    "for i in json_data[\"test\"]:\n",
    "    p_id = i[\"image\"].split(\".\")[0]\n",
    "\n",
    "    row = test_df[(test_df[\"PATIENT_ID\"] == int(p_id))]\n",
    "\n",
    "    pvol = row[\"PROSTATE_VOLUME\"].iloc[0]\n",
    "    psa = row[\"PSA\"].iloc[0]\n",
    "    if pd.notna(psa) and psa != \"\" and pd.notna(pvol) and pvol != \"\":\n",
    "        i[\"psa\"] = [float(psa), float(pvol)]\n",
    "        new_test.append(i)\n",
    "\n",
    "print(len(new_test))\n",
    "print(len(json_data[\"test\"]))\n",
    "\n",
    "\n",
    "json_data[\"test\"] = new_test\n",
    "with open(\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/dataset/test_data_updated_with_psa.json\",\n",
    "    \"w\",\n",
    ") as f:\n",
    "    json.dump(json_data, f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "864af580",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb34fff3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4c047734",
   "metadata": {},
   "source": [
    "### Normalize PSA and PSAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a8b8c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "import torch\n",
    "import yaml\n",
    "\n",
    "from src.data.data_loader import get_dataloader\n",
    "\n",
    "# from src.model.cspca_model import CSPCAModel\n",
    "from src.model.mil import MILModel3D\n",
    "\n",
    "with open(\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/config/config_cspca_train.yaml\",\n",
    ") as f:\n",
    "    cfg = yaml.safe_load(f)\n",
    "from argparse import Namespace\n",
    "\n",
    "args = Namespace(**cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9fc1a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "args.mode = \"train\"\n",
    "args.project_dir = (\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/\"\n",
    ")\n",
    "args.checkpoint_cspca = None\n",
    "\n",
    "args.run_name = \"check_dummy\"\n",
    "\n",
    "args.logdir = os.path.join(args.project_dir, \"logs\", args.run_name)\n",
    "os.makedirs(args.logdir, exist_ok=True)\n",
    "\n",
    "\n",
    "if args.dataset_json is None:\n",
    "    print(\"Dataset path not provided. Quitting.\")\n",
    "    sys.exit(1)\n",
    "if args.checkpoint_pirads is None and args.mode == \"train\":\n",
    "    print(\"PI-RADS checkpoint path not provided. Quitting.\")\n",
    "    sys.exit(1)\n",
    "elif args.checkpoint_cspca is None and args.mode == \"test\":\n",
    "    print(\"csPCa checkpoint path not provided. Quitting.\")\n",
    "    sys.exit(1)\n",
    "\n",
    "args.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "if args.device == torch.device(\"cuda\"):\n",
    "    torch.backends.cudnn.benchmark = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98dd0762",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "args.num_classes = 4\n",
    "args.mil_mode = \"att_trans\"\n",
    "mil_model = MILModel3D(num_classes=args.num_classes, mil_mode=args.mil_mode)\n",
    "cache_dir_path = Path(os.path.join(args.logdir, \"cache\"))\n",
    "\n",
    "scaler = StandardScaler()\n",
    "with open(os.path.join(args.project_dir, \"dataset\", \"PICAI_cspca_updated_with_psa.json\")) as f:\n",
    "    dataset_json = json.load(f)\n",
    "train_clinical = [i[\"psa\"] for i in dataset_json[\"train\"]]\n",
    "_ = scaler.fit_transform(train_clinical)\n",
    "args.psa_mean = scaler.mean_.tolist()\n",
    "args.psa_std = scaler.scale_.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c410728f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from monai.utils.module import optional_import\n",
    "\n",
    "models, _ = optional_import(\"torchvision.models\")\n",
    "\n",
    "\n",
    "class SimpleNN(nn.Module):\n",
    "    \"\"\"\n",
    "    A simple Multi-Layer Perceptron (MLP) for binary classification.\n",
    "\n",
    "    This network consists of two hidden layers with ReLU activation and a dropout layer,\n",
    "    followed by a final sigmoid activation for probability output.\n",
    "\n",
    "    Args:\n",
    "        input_dim (int): The number of input features.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, input_dim: int) -> None:\n",
    "        super().__init__()\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(input_dim, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.3),\n",
    "            nn.Linear(128, 1),\n",
    "            nn.Sigmoid(),  # since binary classification\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward pass of the classifier.\n",
    "\n",
    "        Args:\n",
    "            x (torch.Tensor): Input tensor of shape (Batch, input_dim).\n",
    "\n",
    "        Returns:\n",
    "            torch.Tensor: Output probabilities of shape (Batch, 1).\n",
    "        \"\"\"\n",
    "        return self.net(x)\n",
    "\n",
    "\n",
    "class CSPCAModel(nn.Module):\n",
    "    \"\"\"\n",
    "    Clinically Significant Prostate Cancer (csPCa) risk prediction model using a MIL backbone.\n",
    "\n",
    "    This model repurposes a pre-trained Multiple Instance Learning (MIL) backbone (originally\n",
    "    designed for PI-RADS prediction) for binary csPCa risk assessment. It utilizes the\n",
    "    backbone's feature extractor, transformer, and attention mechanism to aggregate instance-level\n",
    "    features into a bag-level embedding.\n",
    "\n",
    "    The original fully connected classification head of the backbone is replaced by a\n",
    "    custom :class:`SimpleNN` head for the new task.\n",
    "\n",
    "    Args:\n",
    "        backbone (nn.Module): A pre-trained MIL model. The backbone must possess the\n",
    "            following attributes/sub-modules:\n",
    "            - ``net``: The CNN feature extractor.\n",
    "            - ``transformer``: A sequence modeling module.\n",
    "            - ``attention``: An attention mechanism for pooling.\n",
    "            - ``myfc``: The original fully connected layer (used to determine feature dimensions).\n",
    "\n",
    "    Attributes:\n",
    "        fc_cspca (SimpleNN): The new classification head for csPCa prediction.\n",
    "        backbone: The MIL based PI-RADS classifier.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, backbone: nn.Module) -> None:\n",
    "        super().__init__()\n",
    "        self.backbone = backbone\n",
    "\n",
    "        self.clinical_dim = 2\n",
    "        self.projection_dim = 16\n",
    "        self.clinical_projection = nn.Sequential(\n",
    "            nn.Linear(self.clinical_dim, self.projection_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(self.projection_dim),  # Helps stabilize the merged scale\n",
    "        )\n",
    "\n",
    "        self.fc_dim = backbone.myfc.in_features\n",
    "        self.fc_cspca = SimpleNN(input_dim=self.fc_dim + self.projection_dim)\n",
    "\n",
    "    def forward(self, x, psa_data):\n",
    "        sh = x.shape\n",
    "        x = x.reshape(sh[0] * sh[1], sh[2], sh[3], sh[4], sh[5])\n",
    "        x = self.backbone.net(x)\n",
    "        x = x.reshape(sh[0], sh[1], -1)\n",
    "        x = x.permute(1, 0, 2)\n",
    "        x = self.backbone.transformer(x)\n",
    "        x = x.permute(1, 0, 2)\n",
    "        a = self.backbone.attention(x)\n",
    "        a = torch.softmax(a, dim=1)\n",
    "        x = torch.sum(x * a, dim=1)\n",
    "\n",
    "        psa_features = self.clinical_projection(psa_data)\n",
    "        x = torch.cat((x, psa_features), dim=1)\n",
    "\n",
    "        x = self.fc_cspca(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "900c0f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "args.use_psa = True\n",
    "checkpoint = torch.load(args.checkpoint_pirads, weights_only=False, map_location=\"cpu\")\n",
    "mil_model.load_state_dict(checkpoint[\"state_dict\"])\n",
    "mil_model = mil_model.to(args.device)\n",
    "train_loader = get_dataloader(args, split=\"train\")\n",
    "valid_loader = get_dataloader(args, split=\"test\")\n",
    "cspca_model = CSPCAModel(backbone=mil_model).to(args.device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33d83352",
   "metadata": {},
   "outputs": [],
   "source": [
    "for submodule in [\n",
    "    cspca_model.backbone.net,\n",
    "    cspca_model.backbone.myfc,\n",
    "    cspca_model.backbone.transformer,\n",
    "]:\n",
    "    for param in submodule.parameters():\n",
    "        param.requires_grad = False\n",
    "\n",
    "args.optim_lr = 1e-4\n",
    "optimizer = torch.optim.AdamW(\n",
    "    filter(lambda p: p.requires_grad, cspca_model.parameters()), lr=args.optim_lr\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87608e91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from monai.metrics import Cumulative, CumulativeAverage\n",
    "\n",
    "old_loss = float(\"inf\")\n",
    "epoch = 0\n",
    "cspca_model.train()\n",
    "criterion = nn.BCELoss()\n",
    "loss = 0.0\n",
    "run_loss = CumulativeAverage()\n",
    "targets_cumulative = Cumulative()\n",
    "preds_cumulative = Cumulative()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98a632c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "for _, batch_data in enumerate(train_loader):\n",
    "    data = batch_data[\"image\"].as_subclass(torch.Tensor).to(args.device)\n",
    "    target = batch_data[\"label\"].as_subclass(torch.Tensor).to(args.device)\n",
    "    psa_data = batch_data[\"psa\"].as_subclass(torch.Tensor).to(args.device)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ef03a7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer.zero_grad()\n",
    "output = cspca_model(data, psa_data)\n",
    "output = output.squeeze(1)\n",
    "loss = criterion(output, target)\n",
    "loss.backward()\n",
    "optimizer.step()\n",
    "\n",
    "targets_cumulative.extend(target.detach().cpu())\n",
    "preds_cumulative.extend(output.detach().cpu())\n",
    "run_loss.append(loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f0a8365",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "302091ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "59ed3821",
   "metadata": {},
   "source": [
    "### Visualise Logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1adc6c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "train_loss_dict = {}\n",
    "val_loss_dict = {}\n",
    "train_auc_dict = {}\n",
    "val_auc_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc8ebd4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/logs/cspca_train_randmodel_newtrain_tcia/cspca_train_randmodel_newtrain_tcia.log\",\n",
    ") as f:\n",
    "    for line in f:\n",
    "        m = re.search(\n",
    "            r\"EPOCH (\\d+) TRAIN loss: ([0-9.]+) TRAIN ATTN LOSS: ([0-9.]+) TRAIN AUC: ([0-9.]+)\",\n",
    "            line,\n",
    "        )\n",
    "        if m:\n",
    "            e = int(m.group(1))\n",
    "            train_loss_dict[e] = float(m.group(2))\n",
    "            # train_attn_loss_dict[e] = float(m.group(3))\n",
    "            train_auc_dict[e] = float(m.group(4))\n",
    "\n",
    "        m = re.search(\n",
    "            r\"EPOCH (\\d+) VAL loss: ([0-9.]+) AUC: ([0-9.]+)\",\n",
    "            line,\n",
    "        )\n",
    "        if m:\n",
    "            e = int(m.group(1))\n",
    "            val_loss_dict[e] = float(m.group(2))\n",
    "            val_auc_dict[e] = float(m.group(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f341608a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/logs/cspca_train_randmodel_newtrain/cspca_train_randmodel_newtrain.log\",\n",
    ") as f:\n",
    "    for line in f:\n",
    "        m = re.search(r\"EPOCH (\\d+) TRAIN loss: ([0-9.]+) AUC: ([0-9.]+)\", line)\n",
    "        if m:\n",
    "            e = int(m.group(1))\n",
    "            train_loss_dict[e] = float(m.group(2))\n",
    "            train_auc_dict[e] = float(m.group(3))\n",
    "\n",
    "        m = re.search(r\"EPOCH (\\d+) VAL loss: ([0-9.]+) AUC: ([0-9.]+)\", line)\n",
    "        if m:\n",
    "            e = int(m.group(1))\n",
    "            val_loss_dict[e] = float(m.group(2))\n",
    "            val_auc_dict[e] = float(m.group(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f002002d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAHHCAYAAABEEKc/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/GU6VOAAAACXBIWXMAAA9hAAAPYQGoP6dpAADCV0lEQVR4nOzdd3iUVfbA8e/MZNI7qYSQ0BM6hGIAAaUpdlGxoqziTwQbq6u4q9hWXQu6riiKIrhr7wUEQlfpvYWEEJJQ0nufycz8/ngzkwyZJJOQZBJyPs8zTybvvOXOm5A53HvuuSqTyWRCCCGEEEJYqB3dACGEEEKI9kYCJCGEEEKI80iAJIQQQghxHgmQhBBCCCHOIwGSEEIIIcR5JEASQgghhDiPBEhCCCGEEOeRAEkIIYQQ4jwSIAkhhBBCnEcCJCGEuEjdc889eHp6OroZQnRIEiAJIZpsxYoVqFQq9uzZ4+imONQ999yDSqWy+XB1dXV084QQF8DJ0Q0QQoiOzMXFhY8++qjOdo1G44DWCCFaigRIQghxAZycnLjzzjsd3QwhRAuTITYhRKvZv38/V155Jd7e3nh6ejJp0iR27NhhtY9er+f555+nT58+uLq60qVLF8aNG0dcXJxln4yMDGbPnk23bt1wcXEhNDSU6667jpSUlHqv/cYbb6BSqUhNTa3z2sKFC3F2diY/Px+AEydOMGPGDEJCQnB1daVbt27ceuutFBYWtsh9MA9Jbt26lf/7v/+jS5cueHt7M2vWLEsbanvvvfcYMGAALi4udO3alXnz5lFQUFBnv507dzJ9+nT8/Pzw8PBg8ODB/Pvf/66z39mzZ7n++uvx9PQkMDCQxx9/HIPB0CLvTYiLlfQgCSFaxdGjR7n00kvx9vbmb3/7G1qtlg8++ICJEyeyZcsWRo8eDcBzzz3HK6+8wn333ceoUaMoKipiz5497Nu3jylTpgAwY8YMjh49ykMPPURkZCRZWVnExcWRlpZGZGSkzevfcsst/O1vf+Prr7/miSeesHrt66+/ZurUqfj5+aHT6Zg2bRqVlZU89NBDhISEcPbsWX799VcKCgrw8fFp9L3m5OTU2ebs7Iy3t7fVtvnz5+Pr68tzzz1HQkIC77//PqmpqWzevBmVSmW5H88//zyTJ09m7ty5lv12797Nn3/+iVarBSAuLo6rr76a0NBQHnnkEUJCQoiPj+fXX3/lkUcesVzTYDAwbdo0Ro8ezRtvvMH69et588036dWrF3Pnzm30vQnRaZmEEKKJPvnkExNg2r17d737XH/99SZnZ2fTyZMnLdvOnTtn8vLyMo0fP96ybciQIaarrrqq3vPk5+ebANPrr7/e5HbGxsaaYmJirLbt2rXLBJg+/fRTk8lkMu3fv98EmL755psmn//uu+82ATYf06ZNs+xnvl8xMTEmnU5n2f7aa6+ZANNPP/1kMplMpqysLJOzs7Np6tSpJoPBYNnv3XffNQGm5cuXm0wmk6mqqsrUo0cPU0REhCk/P9+qTUajsU77XnjhBat9hg0bVue+CCGsyRCbEKLFGQwG1q1bx/XXX0/Pnj0t20NDQ7n99tv5448/KCoqAsDX15ejR49y4sQJm+dyc3PD2dmZzZs32xyOasjMmTPZu3cvJ0+etGz76quvcHFx4brrrgOw9BCtXbuWsrKyJp0fwNXVlbi4uDqPV199tc6+999/v6UHCGDu3Lk4OTmxevVqANavX49Op+PRRx9Fra758zxnzhy8vb1ZtWoVoAxdnjp1ikcffRRfX1+ra5h7omp74IEHrL6/9NJLSU5ObvJ7FaIzkQBJCNHisrOzKSsro1+/fnVei46Oxmg0cvr0aQBeeOEFCgoK6Nu3L4MGDeKJJ57g0KFDlv1dXFz417/+xW+//UZwcDDjx4/ntddeIyMjo9F23HzzzajVar766isATCYT33zzjSUvCqBHjx4sWLCAjz76iICAAKZNm8aSJUvszj/SaDRMnjy5zmPo0KF19u3Tp4/V956enoSGhlpyqcz5UuffN2dnZ3r27Gl53RzwDRw4sNH2ubq6EhgYaLXNz8+vycGmEJ2NBEhCCIcaP348J0+eZPny5QwcOJCPPvqI4cOHW02df/TRR0lMTOSVV17B1dWVZ555hujoaPbv39/gubt27cqll17K119/DcCOHTtIS0tj5syZVvu9+eabHDp0iKeffpry8nIefvhhBgwYwJkzZ1r+DbcxKTcgRPNIgCSEaHGBgYG4u7uTkJBQ57Xjx4+jVqsJDw+3bPP392f27Nl88cUXnD59msGDB/Pcc89ZHderVy/++te/sm7dOo4cOYJOp+PNN99stC0zZ87k4MGDJCQk8NVXX+Hu7s4111xTZ79Bgwbxj3/8g61bt/L7779z9uxZli5d2vQ334DzhxFLSkpIT0+3JJpHREQA1LlvOp2OU6dOWV7v1asXAEeOHGnR9gkhakiAJIRocRqNhqlTp/LTTz9ZTcXPzMzk888/Z9y4cZYhrtzcXKtjPT096d27N5WVlQCUlZVRUVFhtU+vXr3w8vKy7NOQGTNmoNFo+OKLL/jmm2+4+uqr8fDwsLxeVFREVVWV1TGDBg1CrVbbdf6m+PDDD9Hr9Zbv33//faqqqrjyyisBmDx5Ms7OzrzzzjuYTCbLfh9//DGFhYVcddVVAAwfPpwePXrw9ttv15n+X/s4IUTzyTR/IUSzLV++nDVr1tTZ/sgjj/DSSy8RFxfHuHHjePDBB3FycuKDDz6gsrKS1157zbJv//79mThxIjExMfj7+7Nnzx6+/fZb5s+fD0BiYiKTJk3illtuoX///jg5OfHDDz+QmZnJrbfe2mgbg4KCuOyyy1i8eDHFxcV1htc2btzI/Pnzufnmm+nbty9VVVX897//RaPRMGPGjEbPX1VVxf/+9z+br91www1WwZhOp7O8l4SEBN577z3GjRvHtddeCyg9bwsXLuT555/niiuu4Nprr7XsN3LkSEtBSrVazfvvv88111zD0KFDmT17NqGhoRw/fpyjR4+ydu3aRtsthGiEg2fRCSE6IPO09foep0+fNplMJtO+fftM06ZNM3l6eprc3d1Nl112mWnbtm1W53rppZdMo0aNMvn6+prc3NxMUVFRpn/+85+W6fA5OTmmefPmmaKiokweHh4mHx8f0+jRo01ff/213e1dtmyZCTB5eXmZysvLrV5LTk42/eUvfzH16tXL5OrqavL39zdddtllpvXr1zd63oam+QOmU6dOWd2vLVu2mO6//36Tn5+fydPT03THHXeYcnNz65z33XffNUVFRZm0Wq0pODjYNHfu3DrT+U0mk+mPP/4wTZkyxeTl5WXy8PAwDR482PSf//zHqn0eHh51jlu0aJFJ/vwL0TCVyST9sUII0ZpWrFjB7Nmz2b17NyNGjHB0c4QQdpAcJCGEEEKI80iAJIQQQghxHgmQhBBCCCHOIzlIQgghhBDnaRc9SEuWLCEyMhJXV1dGjx7Nrl276t134sSJqFSqOg9zfRBQ6oA8++yzhIaG4ubmxuTJk+sUaMvLy+OOO+7A29sbX19f7r33XkpKSlrtPQohhBCi43B4gPTVV1+xYMECFi1axL59+xgyZAjTpk0jKyvL5v7ff/896enplseRI0fQaDTcfPPNln1ee+013nnnHZYuXcrOnTvx8PBg2rRpVsXm7rjjDo4ePUpcXBy//vorW7du5f7772/19yuEEEKI9s/hQ2yjR49m5MiRvPvuuwAYjUbCw8N56KGHeOqppxo9/u233+bZZ58lPT0dDw8PTCYTXbt25a9//SuPP/44AIWFhQQHB7NixQpuvfVW4uPj6d+/v9WU2zVr1jB9+nTOnDlD165dG72u0Wjk3LlzeHl52Vw9WwghhBDtj8lkori4mK5du6JWN9BP5LAKTCaTqbKy0qTRaEw//PCD1fZZs2aZrr32WrvOMXDgQNOcOXMs3588edIEmPbv32+13/jx400PP/ywyWQymT7++GOTr6+v1et6vd6k0WhM33//vc3rVFRUmAoLCy2PY8eONVggTh7ykIc85CEPebTfh7mgbX0cutRITk4OBoOB4OBgq+3BwcEcP3680eN37drFkSNH+Pjjjy3bMjIyLOc4/5zm1zIyMggKCrJ63cnJCX9/f8s+53vllVd4/vnn62z/6KOPcHd3b7StQgghhHC8srIy7rvvPry8vBrcr0Ovxfbxxx8zaNAgRo0a1erXWrhwIQsWLLB8X1RURHh4ONdff71l0c2WoNfriYuLY8qUKWi12hY7r6hL7nXbkvvdduRetx25122npe51UVER9913X6PpMQ4NkAICAtBoNGRmZlptz8zMJCQkpMFjS0tL+fLLL3nhhRestpuPy8zMJDQ01OqcQ4cOtexzfhJ4VVUVeXl59V7XxcUFFxeXOtu1Wm2r/KNorfOKuuRety25321H7nXbkXvddi70Xtt7rENnsTk7OxMTE8OGDRss24xGIxs2bCA2NrbBY7/55hsqKystq1ub9ejRg5CQEKtzFhUVsXPnTss5Y2NjKSgoYO/evZZ9Nm7ciNFoZPTo0S3x1oQQQgjRgTl8iG3BggXcfffdjBgxglGjRvH2229TWlrK7NmzAZg1axZhYWG88sorVsd9/PHHXH/99XTp0sVqu0ql4tFHH+Wll16iT58+9OjRg2eeeYauXbty/fXXAxAdHc0VV1zBnDlzWLp0KXq9nvnz53PrrbfaNYNNCCGEEBc3hwdIM2fOJDs7m2effZaMjAyGDh3KmjVrLEnWaWlpdabhJSQk8Mcff7Bu3Tqb5/zb3/5GaWkp999/PwUFBYwbN441a9bg6upq2eezzz5j/vz5TJo0CbVazYwZM3jnnXda740KIYRot4xGIzqdrsnH6fV6nJycqKiowGAwtELLhJm991qr1aLRaC74eg6vg9RRFRUV4ePjQ2FhYYsnaa9evZrp06fLeHYrk3vdtuR+tx25102j0+k4deoURqOxyceaTCbKy8txc3OTmnitrCn32tfXl5CQEJv72fv57fAeJCGEEMJRTCYT6enpaDQawsPDGy4caIPRaKSkpARPT88mHyuaxp57bTKZKCsrs0zEqj1Zq6kkQBJCCNFpVVVVUVZWRteuXZtV0848NOfq6ioBUiuz9167ubkBkJWVRVBQULOH2+SnKYQQotMy57I4Ozs7uCWiJZmDXb1e3+xzSIAkhBCi05P8oYtLS/w8JUASQgghhDiPBEhCCCGEIDIykrffftvRzWg3JEASQgghOhCVStXg47nnnmvWeXfv3s39999/QW2bOHEijz766AWdo72QWWztjNFo4niBiiulPJUQQggb0tPTLc+/+uornn32WRISEizbPD09Lc9NJhMGgwEnp8Y/7gMDA1u2oR2c9CC1I0ajiZs+3Mn78Rr+OJnr6OYIIYRoh0JCQiwPHx8fVCqV5fvjx4/j5eXFb7/9RkxMDC4uLvzxxx+cPHmS6667juDgYDw9PRk5ciTr16+3Ou/5Q2wqlYqPPvqIG264AXd3d/r06cPPP/98QW3/7rvvGDBgAC4uLkRGRvLmm29avf7ee+/Rp08fXF1dCQ4O5qabbrK89u233zJmzBg8PDzo0qULkydPprS09ILa0xAJkNoRtVpFTHdfAD7YesqxjRFCiE7IZDJRpqtq0qNcZ2jyMbYeLbmwxVNPPcWrr75KfHw8gwcPpqSkhOnTp7Nhwwb279/PFVdcwTXXXENaWlqD53n++ee55ZZbOHToENOnT+eOO+4gLy+vWW3au3cvt9xyC7feeiuHDx/mueee45lnnmHFihUA7Nmzh4cffpgXXniBhIQE1qxZw/jx4wGl1+yOO+7gzjvv5OjRo2zevJkbb7yxRe/Z+WSIrZ35y9hI/rsjlZ2n8tmXls/w7n6ObpIQQnQa5XoD/Z9d65BrH3thGu7OLfOx/MILLzBlyhTL9/7+/gwZMsTy/YsvvsgPP/zAzz//zPz58+s9zz333MNtt90GwMsvv8w777zDrl27uOKKK5rcpsWLFzNp0iSeeeYZAPr27cuxY8d4/fXXueeee0hLS8PDw4Orr74aLy8vIiIiGDZsGKAESFVVVVx99dVERkaiVqsZNGhQk9vQFNKD1M6E+rgyIkCJiN/ffNLBrRFCCNERjRgxwur7kpISHn/8caKjo/H19cXT05P4+PhGe5AGDx5see7h4YG3t7dlGY+mio+PZ+zYsVbbxo4dy4kTJzAYDEyZMoWIiAh69uzJXXfdxWeffUZZWRkAQ4YMYdKkSYwbN45bbrmFZcuWkZ+f36x22Et6kNqhSWFGduWoiTuWSWJmMX2DvRzdJCGE6BTctBqOvTDN7v2NRiPFRcV4eXtd8FIjbtoLX4HezMPDw+r7xx9/nLi4ON544w169+6Nm5sbN910EzqdrsHznL/YsUqlataivvbw8vJi3759bN68mXXr1vHss8/y3HPPsXv3bnx9fVm7di1xcXFs27aN//znP/z9739n586d9OjRo1XaIz1I7VCwG0yJDgJgqfQiCSFEm1GpVLg7OzXp4easafIxth6tWc37zz//5J577uGGG25g0KBBhISEkJKS0mrXsyU6Opo///yzTrv69u1rWS/NycmJyZMn89prr3Ho0CFSUlLYuHEjoPxsLrnkEp577jn279+Ps7MzP/zwQ6u1V3qQ2qkHxvdg3bEsfjp4jgVT+9LNr+mLKAohhBAAffr04fvvv+eaa65BpVLxzDPPtFpPUHZ2NgcOHLDaFhoayl//+ldGjhzJiy++yMyZM9m+fTvvvvsu7733HgC//vorycnJjB8/Hj8/P1avXo3RaKRfv37s3LmT9evXM2bMGHr06MHu3bvJzs4mOjq6Vd4DSA9SuzUozIdxvQMwGE0s25rs6OYIIYTowBYvXoyfnx9jxozhmmuuYdq0aQwfPrxVrvX5558zbNgwq8eyZcsYPnw4X3/9NV9++SUDBw7k2Wef5YUXXuCee+4BwNfXl++//57LL7+c6Oholi5dyhdffMGAAQPw9vZm69at3HLLLURFRfGPf/yDN998kyuvvLJV3gOAytSac+QuYkVFRfj4+FBYWIi3t3eLnVev17N69WqmT5/OrtRC7vhoJy5Oav586nICPF1a7DrC+l6fP84uWp7c77Yj99p+FRUVnDp1ih49euDq6trk441GI0VFRXh7e19wDpJoWFPudUM/V3s/v+Wn2Y6N6dWFId18qKwysuLPFEc3RwghhOg0JEBqx1QqFXMn9gZg5bYUcksqHdwiIYQQonOQAKmdm9o/mIFh3hRXVvGfjUmObo4QQgjRKUiA1M6p1SqevlLJ0v/fjlSSs0sc3CIhhBDi4icBUgcwpncAl/ULpMpo4rU1CY0fIIQQQogLIgFSB7FwejRqFaw5msGelOYtFCiEEEII+0iA1EH0DfZi5shwAP65Or5VVzAWQgghOjsJkDqQxyb3xd1Zw/60AlYfznB0c4QQQoiLlgRIHUiQtyv3j+8JwL/WHEdX1Tpl4oUQQojOTgKkDmbOpT0J9HIhLa+Mj/845ejmCCGE6KAmTpzIo48+6uhmtFsSIHUwHi5OPHlFFABvrU8kKavYwS0SQgjRlq655hquuOIKm6/9/vvvqFQqDh06dMHXWbFiBb6+vhd8no5KAqQOaMbwMCb0DURXZeTxbw5hMErCthBCdBb33nsvcXFxnDlzps5rn3zyCSNGjGDw4MEOaNnFRQKkDkilUvHKjYPwcnHiwOkCPv4j2dFNEkII0UauvvpqAgMDWbFihdX2kpISvvnmG+69915yc3O57bbbCAsLw93dnUGDBvHFF1+0aDvS0tK47rrr8PT0xNvbm1tuuYXMzEzL6wcPHuSyyy7Dy8sLb29vYmJi2LNnDwCpqalcc801+Pn54eHhwYABA1i9enWLtu9CSYDUQXX1deMfVysVtt9Yl0hSllTYFkKIC2Yyga60aQ99WdOPsfWws3yLk5MTs2bNYsWKFVYlX7755hsMBgO33XYbFRUVxMTEsGrVKo4cOcL999/PXXfdxa5du1rkNhmNRq677jry8vLYsmULcXFxJCcnM3PmTMs+d9xxB926dWP37t3s3buXp556Cq1WC8C8efOorKxk69atHD58mH/96194enq2SNtaipOjGyCa75YR4aw6nMHWxGz+9u1BvnlgDBq1ytHNEkKIjktfBi93tXt3NeDbUtd++hw4e9i161/+8hdef/11tmzZwsSJEwFleG3GjBn4+Pjg4+PD448/btn/oYceYu3atXz99deMGjXqgpu6YcMGDh8+zKlTpwgPV2r0ffrppwwYMIDdu3czcuRI0tLSeOKJJ4iKUvJm+/TpYzk+LS2NGTNmMGjQIAB69ux5wW1qadKD1IGpVCpevXEQni5O7EsrYLnMahNCiE4hKiqKMWPGsHz5cgCSkpL4/fffuffeewEwGAy8+OKLDBo0CH9/fzw9PVm7di1paWktcv34+HjCw8MtwRFA//798fX1JT4+HoAFCxZw3333MXnyZF599VVOnjxp2ffhhx/mpZdeYuzYsSxatKhFkspbmsN7kJYsWcLrr79ORkYGQ4YM4T//+U+D0W1BQQF///vf+f7778nLyyMiIoK3336b6dOnAxAZGUlqamqd4x588EGWLFkCKFMbt2zZYvX6//3f/7F06dIWfGdto6uvG/+4Kpqnvj/Mm3EJ3D66Ox4uDv+xCiFEx6R1V3py7GQ0GikqLsbbywu1+gL7HLTuTdr93nvv5aGHHmLJkiV88skn9OrViwkTJgDw+uuv8+9//5u3336bQYMG4eHhwaOPPopOp7uwNjbBc889x+23386qVav47bffWLRoEV9++SU33HAD9913H9OmTWPVqlWsW7eOV155hTfffJOHHnqozdrXGIf2IH311VcsWLCARYsWsW/fPoYMGcK0adPIysqyub9Op2PKlCmkpKTw7bffkpCQwLJlywgLC7Pss3v3btLT0y2PuLg4AG6++Warc82ZM8dqv9dee6313mgrmzkynO7+7lTojfyRlOPo5gghRMelUinDXE15aN2bfoyth6ppKRK33HILarWazz//nE8//ZS//OUvqKrP8eeff3Lddddx5513MmTIEHr27EliYmKL3abo6GhOnz7N6dOnLduOHTtGQUEB/fv3t2zr27cvjz32GOvWrePGG2/kk08+sbwWHh7OAw88wPfff89f//pXli1b1mLtawkO7WpYvHgxc+bMYfbs2QAsXbqUVatWsXz5cp566qk6+y9fvpy8vDy2bdtmSfSKjIy02icwMNDq+1dffdUqqjZzd3cnJCSkBd+N46hUKi6PCmLFthQ2xmcxbcDF8b6EEELUz9PTk5kzZ7Jw4UKKioq45557LK/16dOHb7/9lm3btuHn58fixYvJzMy0Cl7sYTAYOHDggNU2FxcXJk+ezKBBg7jjjjt4++23qaqq4sEHH2TChAmMGDGC8vJynnjiCW666SZ69OjBmTNn2L17NzNmzADg0Ucf5corr6Rv377k5+ezadMmoqOjL/SWtCiHBUg6nY69e/eycOFCyza1Ws3kyZPZvn27zWN+/vlnYmNjmTdvHj/99BOBgYHcfvvtPPnkk2g0GpvX+N///seCBQssUbXZZ599xv/+9z9CQkK45ppreOaZZ3B3r797s7KyksrKSsv3RUVFAOj1evR6fZPee0PM52rqOcf38WfFthQ2JWSh0+nqvF9RV3PvtWgeud9tR+61/fR6PSaTCaPRiNHY9OWbzLPIzOdoa7Nnz+bjjz/myiuvJCQkxNKGp59+mpMnTzJt2jTc3d2ZM2cO1113HYWFhVbtbKjdRqORkpIShg0bZrW9V69eJCYm8sMPP/Dwww8zfvx41Go106ZN45133sFoNKJSqcjJyWHWrFlkZmYSEBDADTfcwKJFizAajVRVVTFv3jzOnDmDt7c306ZNY/HixQ3ew6bca6PRiMlkQq/X14kP7P13oTI5aFn4c+fOERYWxrZt24iNjbVs/9vf/saWLVvYuXNnnWOioqJISUnhjjvu4MEHHyQpKYkHH3yQhx9+mEWLFtXZ/+uvv+b2228nLS2Nrl1rZiV8+OGHRERE0LVrVw4dOsSTTz7JqFGj+P777+tt73PPPcfzzz9fZ/vnn3/eYGDVVqqMsHC3Bp1RxeODqghvX7MlhRCiXXJyciIkJITw8HCcnZ0d3RzRQnQ6HadPnyYjI4Oqqiqr18rKyrj99tspLCzE29u73nN0qGxeo9FIUFAQH374IRqNhpiYGM6ePcvrr79uM0AyR9W1gyOA+++/3/J80KBBhIaGMmnSJE6ePEmvXr1sXnvhwoUsWLDA8n1RURHh4eFMnTq1wRvcVHq9nri4OKZMmWIZRrTXmqIDxMVnoQvsx/TLbL8PUeNC7rVoOrnfbUfutf0qKio4ffo0np6euLq6Nvl4k8lEcXExXl5e0nPfyppyrysqKnBzc2P8+PF1fq7mEaDGOCxACggIQKPRWFXdBMjMzKw3Nyg0NBStVmvVXRYdHU1GRgY6nc4q+k9NTWX9+vUN9gqZjR49GlCmSdYXILm4uODi4lJnu1arbZU/QM0576ToYOLis9hyIpcFU6NavE0Xq9b6GQrb5H63HbnXjTMYDKhUKtRqdbNmoZmHesznEK2nKfdarVajUqls/huw99+Ew36azs7OxMTEsGHDBss2o9HIhg0brIbcahs7dixJSUlWY4+JiYmEhobW6Rr95JNPCAoK4qqrrmq0LeYEtNDQ0Ga8k/bjsqggAA6eLiC7uLKRvYUQQghRH4eGuwsWLGDZsmWsXLmS+Ph45s6dS2lpqWVW26xZs6ySuOfOnUteXh6PPPIIiYmJrFq1ipdffpl58+ZZnddoNPLJJ59w99134+Rk3Ul28uRJXnzxRfbu3UtKSgo///wzs2bNYvz48R1+cb9gb1cGhinDfZsTbJdKEEIIIUTjHJqDNHPmTLKzs3n22WfJyMhg6NChrFmzhuDgYEApRV67Gy08PJy1a9fy2GOPMXjwYMLCwnjkkUd48sknrc67fv160tLS+Mtf/lLnms7Ozqxfv563336b0tJSwsPDmTFjBv/4xz9a9822kcujgjlytohNCVncPCK88QOEEELgoPlKopW0xM/T4Una8+fPZ/78+TZf27x5c51tsbGx7Nixo8FzTp06td6bEx4eXqeK9sXk8qgg3tlwgq2JOeiqjDg7yZi4EELUx5zTqtPpcHNzc3BrREspKysD7M83ssXhAZJoWYPDfAjwdCanRMeelDzG9A5wdJOEEKLdcnJywt3dnezsbLRabZMTrY1GIzqdjoqKCknSbmX23GuTyURZWRlZWVn4+vrarJFoLwmQLjJqtYqJ/YL4du8ZNhzPkgBJCCEaoFKpCA0N5dSpUzbX8WyMyWSivLwcNzc3mebfyppyr319fS94tQwJkC5Ck6KUAGnT8SyeubppZeWFEKKzcXZ2pk+fPs1ayFWv17N161bGjx8vJRVamb33+vxyQM0lAdJFaFyfALQaFck5pZzKKaVHgIejmySEEO2aWq1uVqFIjUZDVVUVrq6uEiC1sra+1zJgehHyctUyMtIfgHVHMxzcGiGEEKLjkQDpIjV9kFL0csmmJLKKKhzcGiGEEKJjkQDpInXryHAGhflQVFHF0z8clhofQgghRBNIgHSRctKoeePmIWg1KtbHZ/HTgXOObpIQQgjRYUiAdBHrF+LFI5P6ALDo56My1CaEEELYSQKki9wDE3oxKMyHwnI9T/9wRIbahBBCCDtIgHSRc9Koef3mwdVDbZn8fFCG2oQQQojGSIDUCUSFePPw5TVDbXmlTS+GJoQQQnQmEiB1Eg9M7EVUiBcFZXr+vT7R0c0RQggh2jUJkDoJrUbNs9XLjvxvZxpJWcUObpEQQgjRfkmA1ImM6R3A5OhgDEYTL68+7ujmCCGEEO2WBEidzNPTo3BSq9h4PIvfT2Q7ujlCCCFEuyQBUifTM9CTu2IjAPjnqngMRpn2L4QQQpxPAqRO6JFJffBx03I8o5iv95x2dHOEEEKIdkcCpPam6Bw9s9aArqTVLuHr7szD1RW231yXQEllVatdSwghhOiIJEBqZ5w+u4FBZz9HdWJtq17nrksi6BHgQU6Jjjs/2smXu9IoLNO36jWFEEKIjkICpHbGGH0dAOpjP7bqdZyd1Dx37QC0GhUHThfw1PeHGfHPOO5buZsN8Zmtem0hhBCivZMAqZ0x9r8eANXJDVBe0KrXmtA3kI1/ncgT0/oRFeKF3mBifXwW967cQ9wxCZKEEEJ0XhIgtTeB0RS5hqEy6CBhdatfLtzfnXmX9WbNo+OJe2w81wzpCsArv8WjNxhb/fpCCCFEeyQBUnujUnHWd7Ty/Mj3bXrpPsFevHzDQLp4OJOcXcqXu9La9PpCCCFEeyEBUjt0zq86QEreBGV5bXptL1ctj0xWZri9vf4ExRWSuC2EEKLzkQCpHSpxDcUUPAiMVRD/c5tf/7ZR3ekZ4EFuqY4PtiS3+fWFEEIIR5MAqZ0yJ2u39TAbKAvbPnllFAAf/ZFMemF5m7dBCCGEcCQJkNop83R/Un6Hkqw2v/7U/sGMjPSjQm/kzXWJbX59IYQQwpEkQGqv/CIhLAZMRjj2U5tfXqVS8fT0aAC+23eGY+eK2rwNQgghhKNIgNSeDbhR+eqAYTaAYd39uGpwKCYTLI6TXiQhhBCdhwRI7dmAG5Svaduh8KxDmvDw5cqMtq0nsinXGRzSBiGEEKKtSYDUnvmEQfdYwAStvPRIffoGexLm64auysiOU7kOaYMQQgjR1iRAau8sw2zfOeTyKpWK8X0DANiSkO2QNgghhBBtzeEB0pIlS4iMjMTV1ZXRo0eza9euBvcvKChg3rx5hIaG4uLiQt++fVm9umZJjueeew6VSmX1iIqKsjpHRUUF8+bNo0uXLnh6ejJjxgwyM9vp2mMDrge1E5zdCxlHHNKE8X0CAWWYTQghhOgMHBogffXVVyxYsIBFixaxb98+hgwZwrRp08jKsj2tXafTMWXKFFJSUvj2229JSEhg2bJlhIWFWe03YMAA0tPTLY8//vjD6vXHHnuMX375hW+++YYtW7Zw7tw5brzxxlZ7nxfEMwiirlae7/nYIU0Y0zsAjVpFcnYpp/PKHNIGIYQQoi05NEBavHgxc+bMYfbs2fTv35+lS5fi7u7O8uXLbe6/fPly8vLy+PHHHxk7diyRkZFMmDCBIUOGWO3n5ORESEiI5REQEGB5rbCwkI8//pjFixdz+eWXExMTwyeffMK2bdvYsWNHq77fZht5n/L10NdQ0fbT7X3ctAwL9wWkF0kIIUTn4OSoC+t0Ovbu3cvChQst29RqNZMnT2b79u02j/n555+JjY1l3rx5/PTTTwQGBnL77bfz5JNPotFoLPudOHGCrl274urqSmxsLK+88grdu3cHYO/evej1eiZPnmzZPyoqiu7du7N9+3YuueQSm9eurKyksrLS8n1RkRKo6PV69PqWW6/MfC6rc4aNximgL6qcRAz7v8A44i8tdj17jevdhT2p+Ww+nsUtw7u2+fVbg817LVqN3O+2I/e67ci9bjstda/tPd5hAVJOTg4Gg4Hg4GCr7cHBwRw/ftzmMcnJyWzcuJE77riD1atXk5SUxIMPPoher2fRokUAjB49mhUrVtCvXz/S09N5/vnnufTSSzly5AheXl5kZGTg7OyMr69vnetmZGTU295XXnmF559/vs72devW4e7u3sR337i4uDir73u6jGIQiZRueYdNmcGgUrX4NRuiLgFw4vfETH75dTUah2evtZzz77VoXXK/247c67Yj97rtXOi9LiuzL1XEYQFScxiNRoKCgvjwww/RaDTExMRw9uxZXn/9dUuAdOWVV1r2Hzx4MKNHjyYiIoKvv/6ae++9t9nXXrhwIQsWLLB8X1RURHh4OFOnTsXb27v5b+o8er2euLg4pkyZglarrXmhYiymd77Hu+IMVw3yx9Q9tsWuaQ+D0cQnJzeTX6YnZGAsIyP92vT6raHeey1ahdzvtiP3uu3IvW47LXWvzSNAjXFYgBQQEIBGo6kzeywzM5OQkBCbx4SGhqLVaq2G06Kjo8nIyECn0+Hs7FznGF9fX/r27UtSUhIAISEh6HQ6CgoKrHqRGrougIuLCy4uLnW2a7XaVvlHUee82gAYdBPs+xSn/Suh1/gWv2aD7QEu7RPIzwfPsS05nzF9gtr0+q2ptX6Gwja5321H7nXbkXvddi70Xtt7rMMGSpydnYmJiWHDhg2WbUajkQ0bNhAba7t3ZOzYsSQlJWE0Gi3bEhMTCQ0NtRkcAZSUlHDy5ElCQ0MBiImJQavVWl03ISGBtLS0eq/bboyo7gE79hOUtH2y9Pi+ynT/LYmSqC2EEOLi5tBMkgULFrBs2TJWrlxJfHw8c+fOpbS0lNmzZwMwa9YsqyTuuXPnkpeXxyOPPEJiYiKrVq3i5ZdfZt68eZZ9Hn/8cbZs2UJKSgrbtm3jhhtuQKPRcNtttwHg4+PDvffey4IFC9i0aRN79+5l9uzZxMbG1pug3W50HQphI8Coh/2ftvnlx/dRZgMeOVdIbkml1WtluiqqDEZbhwkhhBAdjkNzkGbOnEl2djbPPvssGRkZDB06lDVr1lgSt9PS0lCra2K48PBw1q5dy2OPPcbgwYMJCwvjkUce4cknn7Tsc+bMGW677TZyc3MJDAxk3Lhx7Nixg8DAQMs+b731Fmq1mhkzZlBZWcm0adN477332u6NX4iR98LZPbBnBYx9FNSaxo5oMUHerkSHehOfXsQfSTlcNzQMk8nE13tO8/wvxxjczYcv72/nvXBCCCGEHRyepD1//nzmz59v87XNmzfX2RYbG9tgvaIvv/yy0Wu6urqyZMkSlixZYnc7240BN8CahVCYBifioN8VbXr58X0DiE8vYktCNpdFBfH094f59VA6ADuS88gr1eHvYXu4UwghhOgoLqLJ2p2E1g2G36U839H2Ad6E6jykjQlZXPXO7/x6KB2NWoWXixJr70/Lb/M2CSGEEC1NAqSOaPQDyvpsp7bCuQNteukREf64O2soKNNzOq+cbn5ufPNALFcMVGYA7k2VAEkIIUTHJwFSR+TTDQZUrx237T9temlnJzWTopUcsasGhbLq4UsZ3t2PmAilLtI+6UESQghxEZAAqaMa85Dy9egPUJDWppf+14xB/PrQON69fRg+bko9CXOAdPB0IXqZzSaEEKKDkwCpowodDD0ngskAO95v00u7OzsxMMwHVa3lTnoFeuLt6kS53sDx9OI2bY8QQgjR0iRA6sjGPKx83bsSyh07tKVWqxjWXYbZhBBCXBwkQOrIel0OwQNBXwp7PnF0ayzDbJKoLYQQoqOTAKkjU6lqcpF2LoWqyob3b2XDu0uAJIQQ4uIgAVJHN+BG8OoKJZlw+BuHNmVIuA9qFZwtKCezqMKhbRFCCCEuhARIHZ2TM1wyV3m+7T9gMjmsKV6uWvqFeAOwT3qRhBBCdGASIF0MYu4GZ0/IPq4Uj3RkUyJ8ARlmE0II0bFJgHQxcPWBwTOV53s+dmhTLHlIMpNNCCFEByYB0sVi5L3K1+OroCjdYc0wz2Q7eraICr3BYe0QQgghLoQESBeL4AHQPRaMVbDvU4c1o7u/OwGezugMRo6eK3RYO4QQQogLIQHSxWREdS/S3hVgqHJIE1SqmoKRkockhBCio5IA6WLS/1pwD4Dic5D4m8OaYVm4NrXAsq2yysCXu9KIO5bpoFYJIYQQ9pMA6WLi5ALD71Ke73ZcsralonZaPiaTifXHMpn21lae+v4w9/93D0lZJQ5rmxBCCGEPCZAuNjGzARUkb4Lckw5pwqAwH5zUKrKLK7nlg+3c9+keUnLLAKVM04dbHdMuIYQQwl4SIF1s/CKgzxTl+Z7lDmmCq1bDgDAfAHan5OOsUTN3Yi/+e+8oAH7Yf5b0wnKHtE0IIYSwhwRIF6OR9ylf9/8P9I4JRK4YEALA1P7BxC0Yz5NXRHFpn0BG9/BHbzCxbOsph7RLCCGEsIcESBej3pPBpztUFMCR7x3ShLkTe3Hg2Sl8OGsEEV08LNsfvKw3AF/sSiO/VOeQtgkhhBCNkQDpYqTWwIh7lOd7VzisGb7uznW2je8TwICu3pTrDazYltL2jRJCCCHsIAHSxWronaDSwJldkBXv6NZYqFQqHpyo9CKt2JZCaaVj6jUJIYQQDZEA6WLlFQz9rlSeO7Cyti1XDAyhR4AHheV6vtiV5ujmCCGEEHVIgHQxG3638vXgF6CvcGxbatGoVfzf+J4AfPT7KSqrZM02IYQQ7YsESBez3pPAOwzK8+H4r45ujZUbhocR7O1CRlEFP+4/26RjF69L4Lp3/yAho7iVWieEEKKzkwDpYqbWwLA7lef7Vjq2LedxcdIw51KlF2nplmQMRpNdx50tKGfJ5pMcPFPITUu3sSM5tzWbKYQQopOSAOliN+xOQAWntkJesqNbY+W2Ud3xdddyKqeUVYfT7Trmv9tTMRhNqFVQXFHFrI938cvBc63cUiGEEJ2NBEgXO9/u0Oty5fm+/zq2LefxcHHiL2N7APDepiSMjfQilesMlqTud24bxpUDQ9AZjDz0xX4++r19BX9CCCE6NgmQOoOY6mTtA5+BoX1Nq787NhJPFyeOZxSz4XhWg/v+eOAsheV6wv3duHJgKO/ePpx7xkQC8NKqeN7deKINWiyEEKIzkACpM+h7JbgHQEkmnFjr6NZY8XHXcldsBADvbkrCZLLdi2QymfjkT2V5krtjI9GoVWjUKhZd058nr4gC4P3NJ6WukhBCiBYhAVJn4OQMQ29Xnu9tX8naAPeO64GrVs3B0wX8mWQ76XrbyVwSM0twd9Zwy8hwy3aVSsUDE3rSI8CDUp2BVYfsy2USQgghGiIBUmdhromUFAfFmY5ty3kCPF24dWR3AN7dZHuYzNx7dFNMN7xdtVavqVQqbh7RDYCv9pxuxZYKIYToLBweIC1ZsoTIyEhcXV0ZPXo0u3btanD/goIC5s2bR2hoKC4uLvTt25fVq1dbXn/llVcYOXIkXl5eBAUFcf3115OQkGB1jokTJ6JSqaweDzzwQKu8v3YjoDeEDgGTEZI3O7o1dfzfhJ5oNSp2JOexNzXP6rXU3FJLftLd1TlH57tpeDc0ahV7U/NJypL6SEIIIS6MQwOkr776igULFrBo0SL27dvHkCFDmDZtGllZtpN1dTodU6ZMISUlhW+//ZaEhASWLVtGWFiYZZ8tW7Ywb948duzYQVxcHHq9nqlTp1JaWmp1rjlz5pCenm55vPbaa636XtuFnpcpX9thgBTq48aM4Uov0JvrEjmdV2bJR1q5LRWTCSb0DaRXoKfN44O8XbmsXxAAX+850zaNFkIIcdFycuTFFy9ezJw5c5g9ezYAS5cuZdWqVSxfvpynnnqqzv7Lly8nLy+Pbdu2odUqwyyRkZFW+6xZs8bq+xUrVhAUFMTevXsZP368Zbu7uzshISEt/I7auZ4T4c+3IXkTmEygUjm6RVYemNCLr/ecZtvJXC59bRNBXi6MiPTj98QcAGaPjWzw+Jkjw1kfn8l3e8/w+NR+ODs5vINUCCFEB+WwAEmn07F3714WLlxo2aZWq5k8eTLbt2+3eczPP/9MbGws8+bN46effiIwMJDbb7+dJ598Eo1GY/OYwsJCAPz9/a22f/bZZ/zvf/8jJCSEa665hmeeeQZ3d/d621tZWUllZaXl+6KiIgD0ej16vd6+N20H87la8pwWXUfg5OSKqjgdfcYxCOjb8te4AGE+zrx50yBW7kjj6LkisoorWX04A4CeAe7ERvo2eF/G9fQl0NOZ7BId646cY9qA4Aav16r3WtQh97vtyL1uO3Kv205L3Wt7j3dYgJSTk4PBYCA42PpDLDg4mOPHj9s8Jjk5mY0bN3LHHXewevVqkpKSePDBB9Hr9SxatKjO/kajkUcffZSxY8cycOBAy/bbb7+diIgIunbtyqFDh3jyySdJSEjg+++/r7e9r7zyCs8//3yd7evWrWswsGquuLi4Fj8nQKxbL4KKjxK/6j1OBU5tlWtcCDUwuxvoQiGtFE4Vq0gvUzE2uIg1a35r9PjB3mo2lKh5b81+DKlGu67ZWvda2Cb3u+3IvW47cq/bzoXe67KyMrv2c+gQW1MZjUaCgoL48MMP0Wg0xMTEcPbsWV5//XWbAdK8efM4cuQIf/zxh9X2+++/3/J80KBBhIaGMmnSJE6ePEmvXr1sXnvhwoUsWLDA8n1RURHh4eFMnToVb2/vFnqHSmQbFxfHlClTLMOILUm9PQk2HmWgWzbR06e3+PkdLTqnlA3//pPjhWqGj5tIiLdrvfu29r0W1uR+tx25121H7nXbaal7bR4BaozDAqSAgAA0Gg2ZmdZTzjMzM+vNDQoNDUWr1VoNp0VHR5ORkYFOp8PZ2dmyff78+fz6669s3bqVbt26NdiW0aNHA5CUlFRvgOTi4oKLi0ud7VqttlX+UbTWeek9CTa+gDp1G2o1oLm4/kH3DfVlVKQ/u1Ly+OlgBvMv79PoMa12r4VNcr/bjtzrtiP3uu1c6L2291iHZbE6OzsTExPDhg0bLNuMRiMbNmwgNjbW5jFjx44lKSkJo7Fm6CQxMZHQ0FBLcGQymZg/fz4//PADGzdupEePHo225cCBA4ASgF30QgaDmz/oiuHsXke3plWYC0l+vedMo+u7CSGEELY4dJrPggULWLZsGStXriQ+Pp65c+dSWlpqmdU2a9YsqyTuuXPnkpeXxyOPPEJiYiKrVq3i5ZdfZt68eZZ95s2bx//+9z8+//xzvLy8yMjIICMjg/LycgBOnjzJiy++yN69e0lJSeHnn39m1qxZjB8/nsGDB7ftDXAEtRp6TlCet8Pp/i1h+qAQvFycSMsr4/eknHr3M5lM5FZQ7/ImQgghOi+HBkgzZ87kjTfe4Nlnn2Xo0KEcOHCANWvWWBK309LSSE+vWToiPDyctWvXsnv3bgYPHszDDz/MI488YlUS4P3336ewsJCJEycSGhpqeXz11VeA0nO1fv16pk6dSlRUFH/961+ZMWMGv/zyS9u+eUfqOVH5enKTQ5vRWtydnbipurL2a2uO19uL9Nyv8byw34k1R9tXZXEhhBCO5/Ak7fnz5zN//nybr23evLnOttjYWHbs2FHv+RrrDQgPD2fLli1NauNFx1ww8sxuqCgC15ZLMm8v5l/Wm2/2nOHouSJ+OniWG4ZZ56HtOpXH57uUgpJbTuRw7bBwW6cRQgjRSUklvc7ILwL8eoDJAKnbHN2aVtHF04W5E5WE+zfWJlKhN1he01UZ+cePhy3fHzlr34wGIYQQnYcESJ1VL/OyIxfnMBvAveN6EOrjytmCclZsS7Fs/+iPZBIzS/ByVTpQk7JLKdcZ6jmLEEKIzkgCpM7KnId0kSZqA7hqNfx1aj8AlmxKIr9Ux+m8Mt7ZcAKAZ6ZH4a01YTCaOJYuvUhCCCFqSIDUWfUYD6gg+zgUnXN0a1rNDcPC6B/qTXFFFe9sPMGzPx2hQm/kkp7+XD80lHBPJWft8JkCxzZUCCFEuyIBUmfl5gddhynPky/epHWNWsXT06MBWLEthU0J2Wg1Kl66fhAqlYpwDyVAOnS20JHNFEII0c5IgNSZmfOQEhtf46wjG9cngAl9AzFPcHxgQi96B3kCEK584fAZCZCEEELUkACpMxtwg/I1/hfITnRsW1rZ09OjcXFS0yvQg3mX9bZsN/cgJWWXUFpZ5ajmCSGEaGckQOrMQgZBv+lgMsLW1x3dmlbVL8SLLU9cxo/zxuKqrVnLz8cZgr1dMJng6DlJ1BZCCKFweKFI4WATnoSE1XDkW5jwNwhofHHXjirEx9Xm9kFdvcksyubw2UJG9fBv8nmNRhN/JOVQVKG3vp63KzERfqhUqma1VwghhONIgNTZdR2q9CIlrFZ6kW780NEtanMDw3xYfzy72TPZfth/lr9+c9Dma5f1C+SF6wYS7u9+AS0UQgjR1mSITSg9RwCHv4GcJMe2xQEGhSlLrTR3JtvPB5UyCb2DPLmkpz+X9PRndA9/nDVqNiVkM3nxFpZsSkJXZWyxNgshhGhd0oMklOn+fa9UZrNtfR1u/MDRLWpTA7oqAVJydinFFXq8XLV2H1tYrmfbyRwAPrgrhl6BnpbXTmaX8MyPR9h2MpfX1ybww/6zvD1zKAPDfFr2DQghhGhx0oMkFBOfVL4e/rrT9SJ18XAmzNcNaPq6bJuOZ6E3mOgT5GkVHAH0CvTks/tG8/bMoQR4OpOUVcJDX+xvdEFlIYQQjicBklB0HQZ9r+gUM9psGVTdq3P4bEGTjltzJAOAKwaG2HxdpVJx/bAw4h6bgKeLE6dyStmRnHdBbRVCCNH6JEASNSbU6kW6iNdos2VQNyVAOtSEgpHlOgObE7MAmDbAdoBk5ufhzDVDugLw5e60ZrZSCCFEW5EASdQIGw4DZyi9SJ/dAifiHN2iNjO4OkA60oRE7S2J2VTojXTzc7PkMTXk1pHhAPx2JIOCMl3zGipahNFo4vFvDvLK6nhHN0UI0U5JgCSsXf8+9LsKDJXwxW1Kle1OwDzElpJbRmGZvpG9FWuPVg+vDQixq9bR4G4+RId6o6sy8sP+s3VeL9cZeOKbg6zclmJ/w1GSwe//dA/x6VLo0l6Hzxby7d4zfLA1WWYXCiFskgBJWHNygVtWKsuQGPXw9d1w+FtHt6rV+bo70726VtGRczW9SCk5pby3OYmMwgqr/XVVRtbHZwL15x+dT6VSWXqRvtx1uk6y9utrE/hm7xleWnXM7iANYMWfKaw7lslLq47ZfUx9OksC+Z/VMw8B8qU3TwhhgwRIoi6NFm78CIbcBiYDfHcfHPnO0a1qdbXzkCqrDPx7/Qmmvr2V19YkcOfHO60qZW9PzqW4oopALxeGd/ez+xrXDw3DxUlNQmYxB04XWLbvOpXHJ9tOAaA3mPjtSLrd50zMLAbgz6RckrNL7D7ufJuOZzHq5Q088c1BDMaLO1DafjLX8jy3RAIkIURdEiAJ2zROcN17EHMPYIKNLzm6Ra1ucPUw26rD57jy7d95a30iuiojzho1SVklzP98P1UGZTjGPHttav9g1Gr7lxLxcddy1aBQQOlFAmVo7W/fHsRkUkoOAPx04Jzd50zKqgmKvtjVvATwnw6cZc6ne8guruSbvWf4x4+HL9repMoqA7tTamYS5pZWOrA1Qoj2SgIkUT+1GiY/rzzPS4aSbMe2p5WZ85COnC0iOaeUQC8X/nPbML5/cAyuWjVbE7P55+p4DEYTcccant7fkJnVw2y/HDpHSWUVb6xLICW3jBBvV/5772gAdpzKJbOooqHTAJBbUkluaU0PyLd7z1ChNzSpPZ9uT+HRrw5QZTQxuoc/ahV8ses0/1qT0KTzdBT70wqo0NfkHeWVSg+SEKIuCZBEw9x8ITBKeX5mt0Ob0toGdfPB3VmDSgV3x0aw4a8TuGZIVwaG+fDWLUMB+OTPFJ787hA5JTq8XZ24pGeXJl9nVA9/egZ4UKYz8MIvR1n+pzK09sqMQfTv6s2ICD9MJvjlYOO9SCeqe4/CfN3o6uNKfpne0rvVGJPJxL/Xn+DZn45iMsGs2Ai+mHMJL98wCIClW07ywZaTTX5/7d22WsNrADkyxCaEsEECJNG4biOVr2d2ObYdrczLVctP88YS99gEnr9uIN61lhy5clAoj0/tCyi9NACT+wej1TT9n5BKpbL0In295wwmE9wc043L+gUBcO1QpV7Sz/YESNX5R1EhXtw6qjsAn+1Mtasdi+MSeWt9IgCPTOrD89cOQK1Wceuo7jx1pRIUv/Lbcb66yOo2bUtSErS9XJWVlvJkiE0IYYMESKJx4aOUr6cv7h4kgD7BXvQO8rT52rzLenN9dfACyvT+5poR0w2n6tylYG8X/nF1f8tr0weFolGrOHSmkFM5pQ2ex9yD1DvYk5kjw9GoVexOybckbtfnbEE5729WeocWXdOfx6b0tSpV8MCEXvzfhJ4ALPz+MMfOXRwlBEorqyzJ8eafnyRpCyFskQBJNK5bdYB0bh8Y7J9+frFRqVS8OmMwk6KCiInwY3zfwGafK8DThZtiuqHVqPjXjMH4uGmtXhvXOwCAnxtJ1jYHQn2DvAj2dmVKdDAAn+9suNfn499PUWU0MaZXF2aP7WFzn6euiGJc7wCMJvgzKcfmPu1BfqmOncm5diWV707Jo8poopufG0O7+wJY5XAJIYSZBEiicQF9wdUH9GWQecTRrXEoV62Gj+8ZyXdzx+Cq1VzQuf55wyD2PjOFidVDa7VdW70syU8Hzzb4wW+ewdY32AuAOy5Rhtm+23eGMl2VzWMKynSW5U7+b0Kves+tUqkY1cMfgPiM9tuD9LfvDjHzwx12rXFnzj8a06uLZcZgbokMsQkh6pIASTROra7JQ+oEw2xtRaNWWeU51TZ1QDAuTmqSs0s5Ws/wVl6pzpJg3CvIA4CxvQLo7u9OcUUVvx60XUvpv9tTKdMZiA71ZnyfgAbbGBWiBF7H0xsesnOk49XB2760/Eb33VZdIHJs7wC6eLoA0oMkhLBNAiRhH/Mw20WeqN1eeLlqmVw9XFZfsrY5QTvc3w13ZyXhWK1WcftopRfpfztT6/Q+VegNrKheyuSBCT0bXSIlKkRZYy4pq8RSA6o9MRhNlirnjeVdFZTpLMFmbM8u+Ff3IOVJDpIQwgYJkIR9ws09SBIgtZVrqofZfjl4DqONytaJ1cNrfYK8rLbfHNMNZ42aQ2cKefW341ZB0jd7z5BbqiPM181SsLIh3fzc8HDWoDMYG00Yd4Sckkr0BuX9JWQ0HCDtSM7FZII+QZ4EebsS4KH0IBVXVlFZ1bTaUUKIi58ESMI+YSMAFRSkQnGmo1vTKUzsF4iXqxPphRXsSqmbX5NU3WPSJ9h61l0XTxeeu3YAAB9sTbYESVUGI8u2JgMw59IeONlRokCtVtGvepgtvp4ApKBMx88Hz3Emv8z+N9dCzhaUW54nZ5c22MtVO/8IwNvNyTKTUIpFCiHOJwGSsI+rNwRFK89lmK1NuGo1lqnoqw/XzSdKzLTdgwRw++juvHj9QKA6SFpznN+OZJCWV4afu5Zbqusw2SMqVBlmO55uOxfq5dXxPPzFfsb9axMz3t/Gym0pZBe3TeLzuVoBks5gJCW3/iDNPBMvtpeSd6VSqSzDbDLVXwhxPgmQhP0s9ZAkQGor5qVM1h3NrDPMdiKreop/sO26TXddEsGL11X3JG1J5qnvDgFw95hIS86SPaKre5BsDWGZTCa2JtaUANibms+in48y+uX1PPXdoVZfz612gAT15yFlFlVwMrsUlUrJPzKTRG0hRH0kQBL2syRqy0y2tjK2dwAezhoyiio4dLbQsr32DLb6ClsC3BUbaQmSSnUGXLVqZsVGNqkN/aoTtY/bCJBO55WTUVSBVqNi8+MT+cdV0Qzp5oPRBF/uPs03e87Ue96fDpy1u+p3fc4VWK9XV18e0vbq4bWBXX3wca+ZOShT/YUQ9WlWgHT69GnOnKn5w7dr1y4effRRPvzwwyafa8mSJURGRuLq6sro0aPZtavh3omCggLmzZtHaGgoLi4u9O3bl9WrVzfpnBUVFcybN48uXbrg6enJjBkzyMyUvJpGmXuQzu2HKvkfd1tw1WosdZLWHq1ZY808g62bn1ujvUHmIMlJreK+cT0tw0r2MucgnS0op7DculDozlNK4DGkmy+RAR7cd2lPfpo/jqenK0uV/HN1vM3htjVH0nnkywP8/YcjdXqBmsKcgxTRxR2ovwfJPL1/TG/rtfO6eFbPZJMeJCHEeZoVIN1+++1s2rQJgIyMDKZMmcKuXbv4+9//zgsvvGD3eb766isWLFjAokWL2LdvH0OGDGHatGlkZWXZ3F+n0zFlyhRSUlL49ttvSUhIYNmyZYSFhTXpnI899hi//PIL33zzDVu2bOHcuXPceOONzbkVnUuX3uDmB1UVkHnY0a3pNKYOUKb7WwVI5xWIbMxdsZEcfm4af61eT64pfNy0hPm6AXV7aHadUpLHzQUlzf4ytgcDunpTWK7nxV+PWb2WmlvGE98csnyfktv82XHm4Mq8jl19AZK5iOT5iwubg0VZsFYIcb5mBUhHjhxh1CilN+Hrr79m4MCBbNu2jc8++4wVK1bYfZ7FixczZ84cZs+eTf/+/Vm6dCnu7u4sX77c5v7Lly8nLy+PH3/8kbFjxxIZGcmECRMYMmSI3ecsLCzk448/ZvHixVx++eXExMTwySefsG3bNnbs2NGc29F5qFS1CkZKHlJbuSwqCK1GRXJ2KUnVeUfmHqQ+DQyvnc/NWdNo3aP6WApGnldR2zy77vwAyUmj5tUbB6NWKXWcNiUo/0HRGeChLw9SXFlT5ftMXvN7kMwB0sR+yrIvKbllVOitp+ynF5aTlleGWgUjIvysXgsw5yDJEJsQ4jz2Z2rWotfrcXFR/rCsX7+ea6+9FoCoqCjS021X7z2fTqdj7969LFy40LJNrVYzefJktm/fbvOYn3/+mdjYWObNm8dPP/1EYGAgt99+O08++SQajcauc+7duxe9Xs/kyZMt+0RFRdG9e3e2b9/OJZdcYvPalZWVVFbW/BEtKiqy3Au9vuXWJzOfqyXP2ZLUXUegObEOY9oODDH3Obo5F6S932szNw3E9vRn64lcVh86x9wJPS09JT0D3Nqk/X2CPNhwHI6dK7RcL6OogtRcJfAY3NWrTjuigt25OzaCT7al8o8fDvPTA6P4PkVNfFYx/h5ahnbzZWNCNqdyipv1Hsp0VeSXKccNCvXE29WJoooqEtMLiQ6t6VnbdiIbgP6h3rhqrH/ePq7KcjE5JRXt/vegKTrK7/bFQO5122mpe23v8c0KkAYMGMDSpUu56qqriIuL48UXXwTg3LlzdOnSpZGjFTk5ORgMBoKDg622BwcHc/z4cZvHJCcns3HjRu644w5Wr15NUlISDz74IHq9nkWLFtl1zoyMDJydnfH19a2zT0ZGBvV55ZVXeP755+tsX7duHe7u7va85SaJi4tr8XO2hIBiI2OBihO/E3de7ldH1V7vdW2hBhWg4ZvtJ4goPc6R0xpAReaJg6xOP9jq1y/PUa6/I/40q51SANhXvS3M3cTvG9fZPC7aAH7OGs4WVHDTu1tIKVGjwsSt3Ss4U5oBaNh19CSrdSea3KbMcgAnXDUm/tgUR4BWQ1GFim/W/cGIwJrZc98lqwE1AaaCOvmKKXnKe0g+m13ntYtBR/jdvljIvW47F3qvy8rsq9nWrADpX//6FzfccAOvv/46d999t2WI6+eff7YMvbUGo9FIUFAQH374IRqNhpiYGM6ePcvrr7/OokWLWu26AAsXLmTBggWW74uKiggPD2fq1Kl4e3u32HX0ej1xcXFMmTIFrdb2Ol0OpRuP6Y3XcNfnMv3SYeDVeDXm9qrd3+taRpVU8vVrWzhdqqLHsEspru4RnXXdVDxcmvXPuEn6ZJWw8sQ2snVOXHHFVNRqFTt/OQacYfKQSKZf2a/eY/36ZTPnv/tJKVGG9x6c0INHJ/fltyMZ/Jx2CKObH9Onj25ym35PyoED++jexYvp08ewveoYybvP4NG1N9On9LHs9+9//wmUcutlw5kUbb0wcEhaAR8l7MKgdWf69Eub3Ib2qiP9bnd0cq/bTkvda/MIUGOa9Zd14sSJ5OTkUFRUhJ9fzZj+/fffb3dvSkBAABqNps7ssczMTEJCQmweExoailarRaOpWUU9OjqajIwMdDqdXecMCQlBp9NRUFBg1YvU0HUBXFxcLMOKtWm12lb5R9Fa571gWj8IGgCZh9Gm/QFDb3d0iy5Yu73XtYT6aYnp7see1Hw++CMFgDBfN3w93drk+n1DfHDWqCnVGcgsqaJ7F3f2pBYAcEmvgAbv35QBXbl6cAa/HkonysfIQ5f3QavVEhmoDIOdKaho1v3PKla6ycP83NBqtUSH+gBnSMoutZwvu7iS5Byl/tElvQPrXCfYR/l7lVeqa/e/A83REX63LxZyr9vOhd5re49tVpJ2eXk5lZWVluAoNTWVt99+m4SEBIKCgho5WuHs7ExMTAwbNmywbDMajWzYsIHY2Fibx4wdO5akpCSMxprlBBITEwkNDcXZ2dmuc8bExKDVaq32SUhIIC0trd7rivMMuE75uu1daOVCgKLGtPOqatdXILI1OGnUliVN4jOKyCvVWSp5j4z0b+hQAF6/aQhv3DSIv/Qzoqle3iPcTwlOsosrKdc1fS00c4J21+oZduYZfeZ2AeyuTiLvF+yFr3vd8gb+1dP8y3SGZrVBCHHxalaAdN111/Hpp58CSl2i0aNH8+abb3L99dfz/vvv232eBQsWsGzZMlauXEl8fDxz586ltLSU2bNnAzBr1iyrhOu5c+eSl5fHI488QmJiIqtWreLll19m3rx5dp/Tx8eHe++9lwULFrBp0yb27t3L7NmziY2NrTdBW5xn5H3g7AlZR+GE7dwT0fLMAZI5JrV3in9L6VerorY58Ogb7GlXXSU3Zw3XDQnFpabzF193LV7Vw4PNWcftbHWRyJoASQng0vLKKNMps+R2Jit1ms6f3m/m5eKEc/WadLmlMpNNCFGjWQHSvn37uPRSZbz+22+/JTg4mNTUVD799FPeeecdu88zc+ZM3njjDZ599lmGDh3KgQMHWLNmjSXJOi0tzWpWXHh4OGvXrmX37t0MHjyYhx9+mEceeYSnnnrK7nMCvPXWW1x99dXMmDGD8ePHExISwvfff9+cW9E5ufnBiL8oz39f7Ni2dCLdu7hbpttDwxW0W0O0paJ2Ub31j5pCpVLRzV/pRTrdjADJ3INkrtHUxdOFgOoeoRPVvUg7G2mnSqWSYpFCCJualYNUVlaGl5fyh3rdunXceOONqNVqLrnkElJTm7Z0wPz585k/f77N1zZv3lxnW2xsbKP1iho6J4CrqytLlixhyZIlTWqrqCV2HuxcCqd3QOo2iBjj6BZ1CtMGhFiW/GjrHqSo6qnzx9OLOV1du2hUD/tmrdYn3M+N+PQiy/ma4lyh9RAbKPckpySXxMxiIrq4W+5VQ4Gcv4cz6YUVsmCtEMJKs3qQevfuzY8//sjp06dZu3YtU6dOBSArK6tFZ3SJdswrpCZBW3qR2ox5mE2tgl5t3IMUVd2DdCq3lKPnlHXhRtmRf9SQcHMPUl7TepCMRhPpliE2V8v2mjykYksvV+8gT0tBSFvMC9bmSLFIIRxCV2Xk851ppOU2vSe5NTUrQHr22Wd5/PHHiYyMZNSoUZbk5nXr1jFs2LAWbaBox8Y+Aio1JMVB+qHG9xcXLDrUi39cFc3LNwzCsw2m99cW6OVCFw9nTCYwmpT1z0J8XBs/sAHhfkrvT1OH2HJKK9EZjKhVEOxdN0BKyCxpdHjNzLxgrQyxCeEYG+IzefqHw7y06ljjO7ehZgVIN910E2lpaezZs4e1a9datk+aNIm33nqrxRon2jn/njDgBuX5n287tCmdhUql4r5Le3LrqO4OuX5UrQrVF9p7BEpeFdDkIbZz1b1Hwd6uaDU1f8b6hSi9aokZNT1Io+0MkHIlQGp3jEaZJdsZnCtU/j2fzm/+skOtoVkBEij1hIYNG8a5c+c4c+YMAKNGjSIqKqrFGic6gHGPKV+P/gC5Jx3bFtHqzMNscGEJ2mbmqf6n88owNaFkxPlT/M16BykBXEZRhWUYcHQjeVLmqf7NzUGqrDJQXNHw0gVf7z7NnR/tlF6qJjiZXcLQF9axOC7R0U0RraywTPl3kV1c4eCWWGtWgGQ0GnnhhRfw8fEhIiKCiIgIfH19efHFF61qFIlOIGQQ9J4CJiNss38Go+iYas+iayzwsEe36gCpuLKKwnL711eqL0DycdMSWj3sZ+8wYIBH9YK1zZjmbzCauGPZTmJf2cjZAtv/+zUYTfxrzXH+SMrhx/1nm3yNzuqPEzkUVVTJPesEzP/2c0t1VBnaTwzRrADp73//O++++y6vvvoq+/fvZ//+/bz88sv85z//4ZlnnmnpNor27tLqJVj2fwYFaY5ti2hVw7r7olJBZBd3wv0vvIq3m7PGkkDdlGG2s5YAqW7wU3t2X2PDa4CljlNzend+2H+WPan5lFRW8evBczb3OXC6wDJ8t+1kbpOv0VmlVSfup+WVUVAmPW8Xs4LqAMlkal+5gM0KkFauXMlHH33E3LlzGTx4MIMHD+bBBx9k2bJlrFixooWbKNq9iDHQYwIY9bDlX45ujWhFvYO8+Oy+0Sy/ZyQqlapFzmkOtJqSqH1+DaTaalcYt6eXq0szh9jKdQbeWJtg+X7NUduLXa+Pr1n6aGdybrv6H3J7llprRtPhs4UObIlobbV7j7OK289s0mYFSHl5eTZzjaKiosjLy7vgRokO6PLqnsMDX0BOkmPbIlrVmF4B9AxsuRID3Zsx1d+cpN3Vx1aAVCuR3I4epC4eNdP8m5IHtfzPU2QUVRDk5YJKBfvTCsgorJtDsf5YTYBUXFnFkXP2LZTZ2aXllVqeHzojAdLFrKCsJkDK7ugB0pAhQ3j33XfrbH/33XcZPHjwBTdKdEDhI6HvFWAywOaXHd0a0YGYE7XTmhQg2c5BAhgargwD9gz0sNRZaoi5B6myykiZneux5ZRU8v5mZVLC36+KZnh3ZV3Kdcese5FScko5kVWCk1rFJT2VYG3byRy7rtGZmUwmq9+HwxIgXdRq9yC1pwCpWYVUXnvtNa666irWr19vqYG0fft2Tp8+zerVq1u0gaIDuezvkLgGjnwH4xZAyEBHt0h0ADVDbPblIFXoDZacHltDbH2CvfhyziV212hyd9bg4qSmsspIbokODzvqS72z4QQllVUMCvPhmsFdySqqZG9qPmuOZDArNtKyn3l4bXRPf6ZEB7MjOY9tSbk8OLG3XW3rrLKKK6nQ1wxFyhDbxc16iK39zGRrVg/ShAkTSExM5IYbbqCgoICCggJuvPFGjh49yn//+9+WbqPoKEIH19RF2iS9SMI+5h6kMzZ6kKoMRsvCs2bm3iMPZw3ebraDmdE9uxDRxcOu66tUKkuiuD0z2U5ml/DZTmUywtPTo1GrVZYK5ztP5VklmcZVD69Njg5mbO8AAHan5FFZZV9PVWdlzj8yr613tqCc3HZY6Twpq4QJb2xl47mWycfrjIxGk1USfnvqQWp2HaSuXbvyz3/+k++++47vvvuOl156ifz8fD7++OOWbJ/oaCY+rVTXTlgFZ/Y6ujWiAzAPg53JL69TGPDelXsY/fIGTmaXWLZZ8o983VosUbwpidr/+u04BqOJSVFBxPZSksC7d3Gnf6g3BqPJ0muUX6pjT2o+oARIvYM8CfRyobLKyP60gjrn1RuMJGYWt8j76ehSc5X8o6gQb3oGKoFue+xFWrkthXOFFezJbvZHaadXoqui9j/77HYUCMtPVbSswL4w+Fbl+aaXHNsW0SGE+riiUavQGYxWM1iSskrYkphNcUUVb66rmS3WUP5Rc9k71X9vah7rjmWiUatYON16osoVA5VepLVHlDykzYlZGIwmokK8CPd3R6VSMaY6oNqWVDcP6envDzP1ra2sOpR+we+nozPnH3Xv4s7gMB+g/eUh6Q1GVh9WflY5FTQpwV/UKCyzrn+WVSQBkriYTXwS1E5wciMkrHF0a0Q756RRW+oZ1U7MrV0gcPXhDA6dKQDgTCsESJaZbI0MsZmH1mYMD7NU7TYzB0i/n8ihpLKK9ceyAKX3yMwSIJ1XDykxs5hv9ykrEnxf/dVeu1Py+P1EdpOOae/MQ2wR/u4M6uYLwKF21oP0Z1KOJReu0qiSpWqa6fwCsdKDJC5ufpEw4i/K869nQeI6hzZHtH+1lxwBJS/hxwNKgGROxH69uuZQTQ2kC1sotzbzEFteA0Ns5TqDpXdo5sjwOq/3CfKkZ4AHOoORtUcy2JKoBC2T+9cOkJQ8pAOnCyitrMmt+vf6E5g7IH5PyrF6rSFluipmfbyLez7ZTXph+1rH6kKkVv8eRHRxZ3C39tmD9PMB68KgaU1cT1AozFP8XbVKONKecpCaNIvtxhtvbPD1goKCC2mLuJhMfQkKzyq5SF/eDjd/AtHXOLpVop1SAqRcS7HIvWn5nMkvx9PFiRWzRzL9nd/5/UQO25JyWmWIzZ4Fa9fHZ1KqMxDu72aZ1l+bSqVi6oAQlm45yRvrEiiprCLIy8UyRARKvlW4vxun88rZlZLHZf2CiE8vYtXhdFQq8HN3Jq9Ux9bEbK4cFNpou/em5lOuVxK+tyXlMiOmW1PferuUVp2D1N3fg4gu7qhVyvp6WUUVBHm3XGDcXBV6A2urC4P6uWvJL9OTmlvG6F4OblgHZO5B6hXoydFzRZTpDJRUVuFpx2zS1takHiQfH58GHxEREcyaNau12io6EicXuGUlDLhRqbD99d1w+FtHt0q0U927mHuQlODnh+rhtSsGhtAn2IvbR3UH4F9rE2otM9LyOUg5DXTv/1Tdo3XdkLB6k8PNw2zp1QUjJ0UHo1Zb7zump9KLtL16mO2t6sVYrxoUyozhYQCsq1VcsiE7kmuG6rYnO2YZk8IyPUfOFrKneqhv3dEMNsRnoqtqXsXwogo9+dW9Ct27uOPh4kSv6sKk7SVRe0N8FqU6A2G+bkyJDgKaVsdL1CgoV/5TEurjhoezBmg/vUhNCtE++eST1mqHuBhptDDjI3ByhYOfw3f3gUEHQ293dMtEO9PNr2a5kcoqgyVR+YZhSsAw//I+fL3nDAdPF1iOsVUDqbnM0/zrS9LOL9WxOUEZMrt+WNd6zzM4zIdQH1dLgDSlf1Cdfcb07sJXe07zZ1IOh88Usu5YJioVPDq5D/llepb9fooN8ZnoDUa0mob/D7sjuWblgu1tvM6bwWjikz9P8ca6BKuaRWZPTOvHvMuaXu8prdYUf3MvwqBuPpzIKuHQmUIm1crpchRzsHzt0K54Ois/o9RWDpBMJhP3/3cvReV6Vv5lFK5aTater62Yh9h83bUEerlQmltGdnElPQLsK9PRmiQHSbQutQauW1Kdk2SCXx8DXWmjh4nOxTLVP6+MzQnZFJbrCfZ24ZKeSlJzoJcL947rYdlfpcLuQpD2MPcg1TfNf9XhdKqMJvqHetdJzq6tdk0kN63GknNUm7k0wLH0Il789RgA1w3pSu8gL4Z396OLhzNFFVXsOtXwsk1luipLwKhSKbWCmrJcy4VIyirmpqXbeGlVPBV6IwGezvQI8CA6tGZafu016JrCnKDdvVYVdPMw5ZF20INUWK63BMvXDe1qaWdr5yDll+mJO5bJzlN5fLHr4lkUvKh6iM3XTUuQl/Jvur30IEmAJFqfWg1XLQavrlBVAecOOLpFop0xJ2mnF1Xw9e7TAFw3NAxNreGpOeN74uOmBSDYy7XR3pWmsCRpl+psTtc29xg01HtkdvOIbjg7qZkRE2bzf/lBXq70DfbEZIJdKXmoVfDwpD4AaNQqy6y3dfUsfmu2NzWfKqOJMF83hoX7Aq3fi6Q3GFmyKYnp//6D/WkFeLo48cqNg9j998lsenwivz1yKf+9dzQAB08X1JmhZI/U6jXYahf6rD2TzdHT6dceyUBnMNIv2IuoEG8iLAFS6wantZPwl2xKqlNAtaMy9yD5uCk9SNB+qmlLgCTahkoF3WKU52f3OLYtot0J8HTGTavBZIINx5Xp8ebhNTMfNy0PTlSyYCMDGl9jrSnM0/x1BiPF580gO5Nfxu6UfFQquHZImK3DrQzo6sPBZ6fy/LX1L7VTu2fphmHdrBb/nTqgOkA6ltlgMLCzenhtdE9/y/laOw/pPxtO8PraBHQGI5f1C2TdY+O5bVR3q5ysMF83egZ4YDRZ50jZK81GD1L/UG80ahXZxZVkOrhOzk8Ha4bXoGapnPwyfbMCQntlFtUEDTklOlZuS221a7Ul8z0zD7GB9CCJzihshPL1jARIwppKpbJ80ABEhXgRHepdZ797x/XgpesH8sJ1LbvOn5uzBvfqBNHzp/r/VD2d+5IeXewe1nNz1lj1fp3PPMymUat4eJJ1ns7Y3gG4O2tIL6zgyNmies9hDj4u6dnFcr7tJ3NbtYdlywmlwOXjU/uy/J6R9SbKm5dV+dNGQczGWGogdakJkNycNfQJUoJIcz0sUGaTvbc5qc3yr7KKKiw1rK4dogRIni5OeGmVe24O7lpDRqESNJh/T5duOUlRResFZG3FnKTt4+4sAZLoxLpVB0hn9zm2HaJdMg+zAVw/zHZPjZNGzZ2XRNA3uP48oOay5CHVKhZpMpmaNLxmr8v6BXHryHBeuG5AnTXjXLUaxvcJBGDdMdvDbGW6Kg5WBwqX9OhCTIQfzho1GUUVpLTSh7TJZOJklrLkyxUDQxpc5mVcHyVA+uNE0wOktLy6ARJQUw+pOg+poEzHXR/v5LU1CTz9w+EmXUNXZeT3E9lUGZo20+7XQ+mYTDC8u68lbw4goDpuNg8PtoaM6h6ka4d0pU+QJ4Xlej76/VSzzrX8j1Pc8N6fVmugOYrtITYJkERnEzpUWaet6AwUN5xfITof8weOSqUkv7a1LuYFa2v1IMWnF5OYWYKzRs0VAxuvS2QvZyc1r84YzB2jI2y+bhlmO2o70XlfagF6g4muPq6E+7vhqtUwtLsv0Hp5SBlFFZRUVuGkVjW6EPAlPbugVkFyTqmlLIM9KqsMnKvOtenub32NQdWJ2ofOFHImv4yblm5nd4qy1l1aXlmTgp13N57gro93sfzPpgUYvxxSehOvG2odwAe4Kj1Iqa3ag1RT3mLBlL4AfPx7cqPL49jy5e409qcV8HszAtiWVjtJW3qQROfl4gmB0cpzGWYT5+lVPfsptmcXQn1abgq/vQLOKxZpMtVU8748KsiSIN4WLo8KQqNWkZBZTEpO3V6J2sNr5p6c2OoZf62Vh3QiU+k9igzwaDRB3sdNy+DqxOo/m/AhfCa/HJNJGUYKqE6cNzMnau9Ly+fG97aRlFVCqI8rWo0Kg9FkKa1gj40JSp5bUwIEvcFoqeZ9eZR1+YYAF3OA1Jo9SErQEOLtyrQBIQzo6k2pzsDSLSebfC5zr017qN1UUCsHKcgcILWT5UYkQBJtSxK1RT1uHhHOE9P68dpNgx1yffMQ25vrEhj1z/X0/cdvfLg1GWjZ4TV7+Lo7M7qHPwBxNopG1g6QzMZcQB6SwWjildXxXPXO7/X+7z2penitd62E8oZcah5ma0IeUu0E7fOH8KJCvHBSqyiuqCKruJKoEC++f3AM3aqHZs/k29dTVVSh59g5JbfrQFoBRqN99yo1t4wqowl3Z42lbpeZeYittYY3ATKrA8AQH1fUahWPT+0HwMptKWQVNW3Wlzkoac2Azh66KiNlOqUSfO0httySSgx2/lxakwRIom1Joraoh6tWw7zLels+8Npan2Dlgz+nREdWcSV6g/IHemCYN5dF1S342NqmVq/htua86f7lOoMl/2h0T3/L9qHdfXFxUpNTUklStv0ffLoqI498uZ8PtiZz9FyRzYAM4ER1gGS+T42pnahtfxBiXmKk7u+Aq1bDgOphttieXfj6gVhCfdysiozaY09KHubmFFdWkZRdYtdx5gCxV6BnneDNPMTWmkna5mn+5okCE/sFEhPhR2WVkeV/pth9ngq9wVLlvDWHBO1hnsGmUoGXq5YuHi6oVWA0WecCOorjFzsRnYs5UfvcfjAalEKSQrQDd4+JpF+INxqVCl93Lf4ezvi5O+Pm7Jjf0akDQnjh12PsTc3n0+0pzIqNBJQhJr3BRKiPq1Ug4eKkYUSkH38m5bLzVB7+9Zy3tjJdFXP/t8+ysC7A4bMFQPc6+5oTtHsH2RcgDe/uh5tWQ26pjuMZxfTvWndW4vlS60nQNnv9psHsOpXHzSO64eKk/FwsPUh2DhftPK8A577UfLuS/k9m1//+A6t7kDKKKqjQG5pV5XpLYjZ+7jVDk7WV6wwUVSjlJ4Kr16JTqVTcNqo7e1PzOXA63+7rmIfXgDYrLFqfwuoZbN6uWsusT38PF3JKKskurrQUjnQU6UESbSswCpw9QVcC2QmObo0QFi5OGib0DWRcnwAGhvnQ1dfNYcERKMm4T0yLAuD5X47x+wkliLGVf2RmzkOqvQRJfQrL9Nz18S62JGbjqlVzx2glKKpvvbMTWcWA/QGSs5Pa0sNl73R/yxBbPUngfYO9uPOSCEtwBDV1iE7bOcRmrh9l7nnal2ZfcHHS0oNUt23uTuDlqvQ3NCev51xBObM/2cXsT3bbHB41z2Bzd9bg7VrTr9GvOrBLzLSvFwxqptWDUpi1ssrQ5Pa2lNoz2MyC2tFMNgmQRNtSa6DrMOW55CEJ0aAHJvTkxuFhGIwmHvxsH0lZJbUCpLp9ROZ6SLtS8mloVKuoQs/MD7ezNzUfb1cnPrtvNA9MUIpwJmQU1/nQzC2pJL9Mj0qFZeFYe4yrHmb73c4AydKDZGOIrT7hlhykxgOT0soqSwB4//ieAOxLK7DrOkkN9CCpVDVttpVU35hDZwqqh5V0NhOULcNr3q5WQXHvIE9UKqUCfEMLLddWWKsHyWSqWSDaEWoXiTRrTzPZJEASbS9suPJV8pCEaJBKpeKVGwcxIsKP4ooq7lu5mwPV66+N7tGlzv6Du/ni7qwhv0xPegPxwte7T3M8o5hALxe+fiCWmAh/uvm54euuRW8wkZhh3SNhzj8K93Nv0vCRuR7SrlO5jfZUGI2memsgNcSSg2THB/2+tHwM1cuzXDVIKduQlFXSaAXs2jWg6utBu5AlR46lF1ue28oLMlfRNg+vmbk5ayzDrImZxXWOs6XgvPea1oq1mxpjqwdJAiTRuYVJwUgh7OXipGHpXTGE+bqRkluG3mAixNvVZhCh1agZEan0LJ0oqr+Q46rD6QDMv6w3USFKbpBKpbLUGjp/mC2piflHZv2CvQjwdKFCb2RvasNDWVnFleiqjGjUqnordNtirp+VWdz4cJFleZYe/nTxdLHcQ3PQWZ+MogpKdQY0alWd+kxm3auH+lKaMTMsPr2mYvopGz1Q5iraoTYqufepXjw5McO+AKl2DxK0bmJ5Y8yBqa0hNgmQROdkTtTOOgo6x04zFaIjCPB04eN7RuBRnRN1SU//eitZj60eZjuSZ/v1cwXl7E8rQKWCKweGWL02sJEAqU8TAySVSsW43kp7GstDMs9gC/N1a9JCxF08atbxO1fQ8HT3naeU4UlzbtTw7n6AkqjdEPP7j+jijrOT7bZ1rw62mjMzrHaAZGvqvblIZLCNAKlfiPIzScyyLw/p/N6yVAcmahfIEFvjlixZQmRkJK6urowePZpdu3bVu++KFStQqVRWD1dX61+a8183P15//XXLPpGRkXVef/XVV1vtPYpavLuCV1cwGeHcAUe3RogOISrEmw/uGsHoHv7MHtuj3v2uGhyKSgUnitQ2P/xWV/cejYzwJ+i8IRtzD9KRegKkXk0MkKBmun9jy440NoOtPrXX8WtoVlaF3sDB08r7Mg9PDq+uPt5YorY9NaDMPUhNDZCKKvRWNZxs1VIyJ2mHeNcNkMwz8OztQTInabtqlY//C+lB2puax20f7uB4Rv1rBjaksHqpExliq8dXX33FggULWLRoEfv27WPIkCFMmzaNrKyseo/x9vYmPT3d8khNtV7VuPZr6enpLF++HJVKxYwZM6z2e+GFF6z2e+ihh1rlPQobpGCkEE02rk8AX/1fLEPCfevdp5ufO5dW99p8u/dsndfNAdL0QSF1XjMHSAkZxZZaOVAzg62pPUjmNgMcOltIVnH9PTy1i0Q2lXmqf0O1kPanFaAzGAnyqhlaG1bdg3TgdMMFIxua4m9mzkE6W1COvgnLnhxPtw5sbPYgmatoNzTElllsV4FQcw/SgK7Kz/pCepBeX5vA9uRc3lyX2KzjLUnabjVV081T+9tDNW2HB0iLFy9mzpw5zJ49m/79+7N06VLc3d1Zvnx5vceoVCpCQkIsj+DgYKvXa78WEhLCTz/9xGWXXUbPnj2t9vPy8rLaz8Oj4fWFRAuSgpFCtJpbYroB8N2+s1Yf1ucKytlnHl4bVHdtuW5+bvi4adEZjJak36IKPZnVH9DN6UEK9XFjeHdfTCb4ctfpevdrbg8SQLgdido1w2s15RGiQrxw02oorqiyBEG21C4SWZ8gLxdctWoMRhNn7Sw5ADXDa90ts+DK6gQ6GbVmsZ2vZ6AHGrWKouoK440xJ0abg+HTeWV2F/KsLbOowlJTatPxrGb1+JiH2HxsDLE1tTp4a3BooUidTsfevXtZuHChZZtarWby5Mls37693uNKSkqIiIjAaDQyfPhwXn75ZQYMGGBz38zMTFatWsXKlSvrvPbqq6/y4osv0r17d26//XYee+wxnJxs35LKykoqK2t+AYqKlF9qvV6PXt/wDIimMJ+rJc/ZHqlChuIEmM7socpB77Wz3Ov2Qu532xnXyxdPrYnsEh1xR9KZ0l+pBP7rQaVHKaa7L/5uGps/iwFdvdh2Mo/9qXn0C3Ln+LkCAIK9XHDTNO/nd/uocPalFfDZzlTmjO2Ok40co9QcJQgJ83Fp8jW6+igfqqdzS+s9dmd1eYSY7j5W+wwK82ZXSj67T+UQ6W+7MKE5QIr0d61zfvP3VVVVdPdzJzGrhOSsIsJ8nOucx5ajZwsAmBIdyPJtqZRUVpFZUGpZPLnKYLQEH13c6/7MNECEvxvJOWUcPZuPv1tAg9fLr15rsF+wElhVVhk5m19iM/hqyE/7z2CO46qMJr7bm8a9YyObdA5zWzy1asv78nVVfjdKdQYKSsrxcKn5TG6pvyH2Hu/QACknJweDwVCnByg4OJjjx4/bPKZfv34sX76cwYMHU1hYyBtvvMGYMWM4evQo3bp1q7P/ypUr8fLy4sYbb7Ta/vDDDzN8+HD8/f3Ztm0bCxcuJD09ncWLF9u87iuvvMLzzz9fZ/u6detwd2/5pRHi4uJa/JzticZQwVWoUBWfY+NPn1Gh9XNYWy72e93eyP1uG6MD1Ww4p+Ld3/ahT1F6kb44ogFUdFflsnr1apvHuZWrATW/7TiCd/YhdmSpAA0+6vJ6j2mMygieThoyiyp57fO1DO1i3WORVQ5HziltOxu/l9UpTTt/Zq7SxiMp6axefabO61VG2JOinL8y7TCrcw5bXvPSKe/3521H8Mg8VOfYsirIKVE+KpP2/8mZursAyu+1i1451+rfd1N8wr5emR3HlXYZspPx1arJ16n4ctUGelQX9y6oBKPJCTUmdv++EbWN3HsvY/V72Lyb4sSGr5uWoVzvVPwhfLVqcitVfL16I70bL3Ru5bPDynm6eZg4U6pixZYEQgqOUc/cAZvSc5RzxB/agz5F2WYygbNag86o4rtV6yzr3NV2oX9DysrsG1bscEuNxMbGEhsba/l+zJgxREdH88EHH/Diiy/W2X/58uXccccddRK5FyxYYHk+ePBgnJ2d+b//+z9eeeUVXFxc6pxn4cKFVscUFRURHh7O1KlT8fZu4m9WA/R6PXFxcUyZMgWttu1WD3eIjLch6xiT+vliipre5pfvVPe6HZD73Xb0ej1ZP8ex4Zya44Vqho2dCMCp7VsBeOzmy+rtMVAdyWDDV4cocvJl+vRLOLQmAU6mMjo6kunTo5rdpiSXE7y/9RTH9AE8PX2k1WvzvziA0ZTFhD4BzJ4xvMnnjjhXxPLEHRSbnJk+/bI6r+9NzUe/czd+7lpmz5hiNQPQOT6LDZ8fIBcvpk8fW+fY/WkFsHsXwd4u3HjN1Dqv1/69PqRO5vCfqXiF9mT6lf0abXeVwcjfdm8EjNx+5XgSf4lnW3IeXfsOZXr1AskHThfAvl0Eebty9VUTbJ7nhEsSBzcnow3ozvTptkdTzN44/juUljP50liO6JPYdjKPrn2HMH14WKPtNUvLKyN1+x+oVbD8vnFcs2Q7GeVGug0Zy5BuPnaf57mDmwA9V1x2qdVyL4sTfyctr5z+MbGMiKj5z3NL/Q0xjwA1xqEBUkBAABqNhsxM68URMzMzCQmpm0Boi1arZdiwYSQlJdV57ffffychIYGvvvqq0fOMHj2aqqoqUlJS6Nev7i+2i4uLzcBJq9W2yh/71jpvu9JtBGQdw2nX+9BjLHgGOqQZneJetyNyv9tGkBuMivRjV0o+Px7MsAxVjIz0I7xL/WuPDe2uJHgnZpZgUmk4lavkv/QL8b6gn9udY3rwwe+n2Hkqn1N5FZYPxL2peaw9loVaBU9f1b9Z1+gRpPwnNa9Uj96kwt3Z+qNt72nlA3F0jy44O1sPfY3sqQxJJWWXUm5Q1gWrLSVPyYXpE+TVYNu0Wi2Rgcp7Op1fbtf7SM0vprLKiJtWQ69gHyIDPdmWnMfpggrL8bllyhpsIT5u9Z4zqjrh+kRWaaPXNSdG+3u5ERngybaTeZwrrGzSfV9zTFn2ZkyvAPqG+nLlwBB+PHCO7w+kM6JHw0N8ZiaTybK+XBcvd6vrB3m5kpZXTn65wWa7LvRviL3HOjRJ29nZmZiYGDZs2GDZZjQa2bBhg1UvUUMMBgOHDx8mNLRuwuHHH39MTEwMQ4YMafQ8Bw4cQK1WExTU9qt2d1ox94DWHU7vgA/Gw+n6yzsIIZrulhFK2sFXu0/z66FzAEy3kZxdW7i/daJ2U9dgq0+YrxuTo5V0iv9uV2Yem0wm/rkqvrqt4fQLaXzRWFt83LSWtdDO2EiQNicTj7axPEuApwvd/d0xmeCAjWVHGlpi5HyRTayFZK6g3S/EC41aZTm+9lT/jEIlQLNVJNLMvCZbUlZJgzPZDEYTxZVKUOLrrrXMvGtqaYJfDiq/S9cMUX6XbhkRrmw/cI5ynX1ru5VUVmGoTg6vXQcJ2s9Uf4fPYluwYAHLli1j5cqVxMfHM3fuXEpLS5k9ezYAs2bNskrifuGFF1i3bh3Jycns27ePO++8k9TUVO677z6r8xYVFfHNN9/U2Q6wfft23n77bQ4ePEhycjKfffYZjz32GHfeeSd+fo7Lhel0wmJgziYI6AvF5+CTK2HHUrBjqqoQonHT+gfh7erE2erikABXDmw4QFKpVAwMU3pkdqfkWQKO5kzxP9/dYyIB+H7fGYor9Kw5ksG+tALctBoWTOl7Qec2r8l2fi2kKoORvSlKgDSqR90ACWBYdT2k/TYCpJNNqAEVUV1lO9XOmWHmGWzRocr9jqxepLf2VH/zFP/zlxmpLTLAA61GRUllFecK65/9VVyht/x59XHTWmbONWWqf2JmMcczitFqVFwxQPlduqRnF7r5uVFcWcXaoxlW+6cXlvPv9Sfq/FzMs+lcnNR1lq+pWbDWsTPZHB4gzZw5kzfeeINnn32WoUOHcuDAAdasWWNJ3E5LSyM9Pd2yf35+PnPmzCE6Oprp06dTVFTEtm3b6N+/v9V5v/zyS0wmE7fddluda7q4uPDll18yYcIEBgwYwD//+U8ee+wxPvzww9Z9s6KuoCiYsxEG3ADGKljzJPw4V4IkIVqAq1bDjcNrJq+MiPCzWUvnfOaK2j8dOIfJBH7uWsusqgsxplcXegV6UKoz8PWeM7y6RpmMM2d8zzpFK5uqvmKRB88UUKoz4OOmtSyrcj5LRW0bBSPNPUi9AhsvA9PV1xUntQpdlZFMOz7czQFS/1ClBygyQLnGqZxSS0+QZYp/Az83rUZNz4DqitoNFIw0ByUezhq0GrWl+ndaE5ZHMfceTegbaJmer1aruKm6tMTXe2pKOexJyeOa//zJW+sT+feGE1bnsbXMiJn0INUyf/58UlNTqaysZOfOnYwePdry2ubNm1mxYoXl+7feesuyb0ZGBqtWrWLYsGF1znn//fdTVlaGj0/dhLHhw4ezY8cOCgoKKC8v59ixYyxcuNBmjpFoAy5ecNMncMW/QO0EB7+QddqEaCEzR4Zbnjc2vGZmrpFjXqPMXIzwQqlUKu66JAKAV3+LJzW3jABPF/5vfM9GjmycuQfp/CG2TceVfJlL+wSgsTUFjJoAaX/1YrZmFXqDJeCyZ4jNSaO2LJ5ra021853fg2Tu0SmuqCK/OphpqIp2bX2CqwOkBhattRRmdFfysCKqe6zyy/QUVTQ+9d1kMvGzZXitq9VrN8V0Q6WCbSdzOZ1Xxhe70rht2Q5yqgs+nl+dvdDGMiNmEiAJUZtKBZc8AP2uVL4/udGx7RHiIhEd6s3U/sEEernU+VCrjzlAMmtOgcj63BjTDXdnDXqDEogsmNLXqtZNc5kDk/OraW9KUFZluKxf/fmlUaFe+LhpKaqo4qcDNdXHU3JLMZrA29WJQDt70My9VOZlTeqTV6qzFOCMqg6QXLUaS66RedHbzAaqaNdmTnpPaCBAKjiv18bTxYkuHkqwZM+SI4fPFpKaW4arVm3JJzPr5ufOmOp1AO9evouF3x9GbzAxrnqpmZPZJVbV2c29WbWraJuZq2nbU/iyNUmAJNqXXpcrXyVAEqLFfHBXDLv/PtnyP/PGdPd3x9u1JmhpifwjM29XLTcMU6aU9w7ytCSSX6hwf3MOUk0PUlZRBUfPKb00E/rVP0tWq1HzwIReALy5LpEKvZJoXHsNuvoWBz7fiEilN2pval6D+9WuoO1ZK0CsnYdkMplIb6CKdm3mAOlEZv0VwQtsrH1mvm9pduQh/XxA6T2aHB1sM6g1J2sn55SiUsET0/rx6V9G4eXihN5gsqpWbu5B8pYhNiHsZA6QzuyCSvsWXxRCNMzeD/fa+w+s1Yt0oTPYzrdgSl/ujo3g3duH2ayq3RzmD/oztXqQNicqw2tDuvkQ0EgP0OyxkYR4u3K2oJz/7VBm2dmzSO35YiLMAVJ+gzPKaobXrIcvIwNqlhwpKq+iQq/0ujTeg6S08URWcb0J4kU2hrUi7Jx5ZzSa+PWQkg9cX0/ktAEhhPq44uXixMd3j2DeZb1Rq1VEVb/H2ovamhfNbWiILbdUZzXk2dYkQBLti18k+PVQErZT/nB0a4TotGoPs5nzW1pKF08Xnr9uYL1J080R5qsMsRVVVFl6JzZXD69NaGB4zcy11ky6dzclUViurwmQmhAgDujqg6tWTX6ZnpPZ9echHbMkaFsPZ0bU6kEy5x/5umvrzPQ6X0QXD5yd1FTojfUu2msZ1qodINnZg7T1RDYZRRV4uToxsZ7eOFethnWPjeePpy7n8qiaITjzz7n2wryFliG2ugFSFw9nVCqlLEFe9XIkjiABkmh/ZJhNCIcz9yB5ujg1eZ0uR/ColU9zOq8MvcHI74k5AFzWwPBabTcOD6NPkCcFZXqWbjlpCXCaEiA5O6kZ0s0XUGZx1Se+Olio04NU3aNzKrfM7uE1AI1aZenpSqxnmK3AxrBW9+qALC2v4aTyldtSACUZ28Wp/mDNy1VbZ2aaOQndHBRCw7PYnDRqy8/SkcNsEiCJ9kcCJCEcbnzfQPqHenPbqPAmD9E5Srdaw2z7UvMprqzC38OZwdUBS2OcNGr+doWynMryP05ZcmZ6NWGIDWrykPak1i0bAKCrMpKUZQ6QrHvRzFP9U3NLyazuQWqoBlJtfRuZyWaZOVYrMbq7HcUiU3JKLcOVs2Ij7WpLbTVDbDXtstWbVZt5SDS7xHEBUodbi010Aj0uBZUGcpMgPxX8IhzdIiE6HR83LasfudTRzWiSbn5uHDxdwJn8cg5UzyKb0Dew3un9tkyODmJkpB+7U5TgxtlJbclvsteICH/gJHvrCZBOZpegN5jwcnWyzL4zMwcsBWV6S0DRUBXt2vpWVyKvL0AyByW1e23MOUjnCsrRVRlxdqrbb/Lp9lRMJpjYL5AeAY3Xgzpfv2AvVCqlNyinpJIATxdLDpKPe91ZbAA3jwinsFxvGTp1BOlBEu2Pqw90q17MMnmTY9sihOgwalfTNucf1ZcvUx+VSsVTV9YsytszwKNJARYodZVUKqUWkq0homPVM+uiQ7zr9M65OzsR7K30nuxMVobo7O5BCjIHSLaH2AptJEYHebngqlVjNClB0vlKK6v4prr4o7kSelN5uDhZcp3MeUiF5cqSJ7aG2ADuHdeDBVP6tvgEgaaQAEm0TzLMJoRoInM17V0p+RzPKEalgvF9mr4IdkyEP1P7K0nGzfmA9nHXWoIVW71I9c1gMzNP9Y+vnvVlT/VzqJnqfzK7hCqDsc7rNUNsNUGJSqVqcMmRH/afpbiyih4BHkxoxr00Mydqm997YXXJAVtJ2u2FBEiifep1mfI1eTMY7Vv8UAjRuZl7kMwfwsPCffHzsD2E05iXrh/IXZdE8NDlfZp1fEw99ZCMRhMbjyu9WwPD6q70ADUBkrlKgL0BUjc/N9y0GnRVRpuz0sxDbOfXHjIHSOcvOWIymfh0ewoAd10SgbqJPWm1mXOtzEFfQQOVtNsLCZBE+9R1OLj4QEUhnNvv6NYIITqA8/N5Gqqe3Zggb1devH4g/UKat8zKiAjbidobj2eRnFOKl6sTV9az9EtEgHXOk72zCNVqlSWnyGaAVE9Q0t28yO55idrbT+aSmFmCu7OGmy6woKc5UTs+vRhdlZEynfIf3/qG2NoDCZBE+6Rxgp7jlecyzCaEsEOYnxu1U3oui2p+gHShRkb6A8oaZObK3AAf/ZEMwO2jultV0K7N3INk1pQyC93MeVjnrUlXoTdYlvrwPS8x2hxUJddaJBdgRfXU/huHh+HtemGBTP/qHqSkrGJyS5W8LJVKKQvQXkmAJNovSx6SJGoLIRrn4qQhuHodrwBPF8uHsiN083MjyMsFvcHEwepFf4+cLWRHch5OahX3jI2s99jaAZKLk7pJw1DmPKwz5/UgmYfXNGoVHs7WdYzMAdLG41mM+9cmnvv5KL8cPMf6+EwA7m7G1P7zhfm64Vm95Mj+tAJAWXamqQnwbUkCJNF+1V52pKK6wFhJFuxaBptehrN7awbphRCCmmG2if0CLyhn5kKpVKo69ZA++l3pPbpqcCihPvVPXzcHLKDkHzWlDpVlJt951bQtS3u4aeuc75KeXbh2SFdctWrOFpSzYlsKD32xH6MJxvbuQp/g5g0z1qZWq4iqHq7cfjIXaN/DayB1kER75hcJ/j0hLxk2vgg5J+DUFjBVz87Y8i9lWZKBM2DQTRAU7dDmCiEc77KoIA6cLrAsnOpIIyL8WX04g72p+aQXllvWMrtvXM8Gj/NwcSLQy4Xs4kq7p/ib2Vq0F2qW9vCx0RvlqtXwzm3DKNcZ+CMph7hjGWyIz6KgXM+8ib2bdP2GRIV6sSc1nx3JSoDUnhO0QQIk0d71vEwJkHZ9WLOt63DwDYcTcZB/Cn5/Q3lc+leY9Kzj2iqEcLgHJ/bivkt7NLgcRlux9CCl5PHJnylUGU2M7uHPoG62Z6/VFtnFneziSruLRJqZh9jq9iDVv7SHmZuzhin9g5nSPxiD0USZrqpFc4TMM9lOVK9xJz1IQlyIYXfAwS/ANwIGzVB6i/yr//elK4WE3+DwN5C4Bra9C2MfBVfH5R0IIRxLpVK1i+AIlIDATauhqKKKT/48BcCcSxvuPTLrEeDB7pR8u6f4m5mTtAvK9BRX6C0BTkOLw9qiUataPIH6/MWJJUAS4kKExcDT58DWGLyzhzK0NnAGvDsSck9AwmoYcmvbt1MIIc6j1agZGu7L9uRc9AYTPQM8uNzOmXWzYiMpKq/i5pimDRV6ujjh564lv0zP6bxy+netDpAsU/ybVxeqJZxfMqG9D7FJkrZo/xpLUFSplEAJ4PC3rd8eIYSw08jqYTaAv4zrYXfi+MAwH5beFdOsSt7htRbtNbOsfebAXhtPFyerBPT23oMkAZK4OAycoXxN3gSluY5tixBCVBvdswug9JbMGH5hxRbtFW6jFpKthWodIapWL5Kvm+N6s+whAZK4OAT0gZDBYKyC+J8c3RohhABgTK8uvHjdAD6+eyRuzm2TG9XNnKhdqxZSYTtZ2iO6Vm0qWzPq2hMJkMTFw9yLdOR7x7ZDCCGqqVQq7oqNJCbCr/GdW4i5B6n2EFuhHbPY2kLtRG1Ht6UxEiCJi8fAG5WvKX9A0TnHtkUIIRzEVi0k8xCbo3uQalc3t3dGnaNIgCQuHr7dIXw0YIKjPzq6NUII4RDmauKn88ssa6u1lx6kbn5ueLkqE+gDvFwc2pbGSIAkLi4Dq2ezHZHZbEKIzinMVwmQynQG8kqV2WsFZeZZbI5NjFarVbw9cyh/nx5Nr8Cmz9BrSxIgiYvLgOtBpVbWacs75ejWCCFEm3PVagj2VnpnTueXYzCaKKqoAhw/xAYwKTqYOePtK5jpSBIgiYuLZxD0GK88P/KdY9sihBAOYpnqn1dGcYXest3RQ2wdiQRI4uIjs9mEEJ1cTbHIckuCtoezBq1GPvbtJXdKXHyirwG1FrKOQla8o1sjhBBtLrxWonZ7WGakI5IASVx83Pygx6XK85Q/HNsWIYRwgG7+NUNsBdUBkrcMrzWJBEji4hQySPmafdyx7RBCCAcwT/VXhtiUGWztve5QeyMBkrg4BUYrX7MkQBJCdD7mJO2ztXKQ2sMMto5EAiRxcQrsp3zNlhwkIUTnE+rjikatQmcwkpBZDMgMtqZqFwHSkiVLiIyMxNXVldGjR7Nr1656912xYgUqlcrq4erqarXPPffcU2efK664wmqfvLw87rjjDry9vfH19eXee++lpKSkVd6fcABzgFSWC6U5jm2LEEK0MSeNmq6+ymfj0bOFQPtfHLa9cXiA9NVXX7FgwQIWLVrEvn37GDJkCNOmTSMrK6veY7y9vUlPT7c8UlNT6+xzxRVXWO3zxRdfWL1+xx13cPToUeLi4vj111/ZunUr999/f4u/P+Egzh7gG6E8l5lsQohOyDzMFp+h9CD5OriKdkfj8ABp8eLFzJkzh9mzZ9O/f3+WLl2Ku7s7y5cvr/cYlUpFSEiI5REcHFxnHxcXF6t9/PxqVlKOj49nzZo1fPTRR4wePZpx48bxn//8hy+//JJz52SR04tGUHUekiRqCyE6IXOApKsyAjLE1lROjry4Tqdj7969LFy40LJNrVYzefJktm/fXu9xJSUlREREYDQaGT58OC+//DIDBgyw2mfz5s0EBQXh5+fH5ZdfzksvvUSXLl0A2L59O76+vowYMcKy/+TJk1Gr1ezcuZMbbrihzjUrKyuprKy0fF9UVASAXq9Hr9fX2b+5zOdqyXN2VuoufdGwBkPGUYw27qfc67Yl97vtyL1uO+35Xnf1sV4M1tNZ1S7baa+Wutf2Hu/QACknJweDwVCnByg4OJjjx23/r79fv34sX76cwYMHU1hYyBtvvMGYMWM4evQo3bp1A5ThtRtvvJEePXpw8uRJnn76aa688kq2b9+ORqMhIyODoKAgq/M6OTnh7+9PRkaGzeu+8sorPP/883W2r1u3Dnd39+a8/QbFxcW1+Dk7m/DcCoYD+Ynb+NO0ut795F63LbnfbUfuddtpj/c6J1sFaCzfJxzejynN5LgGtZALvddlZWV27efQAKk5YmNjiY2NtXw/ZswYoqOj+eCDD3jxxRcBuPXWWy2vDxo0iMGDB9OrVy82b97MpEmTmnXdhQsXsmDBAsv3RUVFhIeHM3XqVLy9vZv5burS6/XExcUxZcoUtFrpDr0g6V1h+Yd0MeYwffr0Oi/LvW5bcr/bjtzrttOe73VIWgH/TaqZ9DRlwlgGdG25z6u21lL32jwC1BiHBkgBAQFoNBoyMzOttmdmZhISEmLXObRaLcOGDSMpKanefXr27ElAQABJSUlMmjSJkJCQOkngVVVV5OXl1XtdFxcXXFxc6mzXarWt8o+itc7bqYQMAFSoynLRVhaAZ6DN3eRety25321H7nXbaY/3ukegl9X3Ad5u7a6NzXGh99reYx2apO3s7ExMTAwbNmywbDMajWzYsMGql6ghBoOBw4cPExoaWu8+Z86cITc317JPbGwsBQUF7N2717LPxo0bMRqNjB49upnvRrQ7zu7gVz2TTeohCSE6mUAvF1ycaj7mJUm7aRw+i23BggUsW7aMlStXEh8fz9y5cyktLWX27NkAzJo1yyqJ+4UXXmDdunUkJyezb98+7rzzTlJTU7nvvvsAJYH7iSeeYMeOHaSkpLBhwwauu+46evfuzbRp0wCIjo7miiuuYM6cOezatYs///yT+fPnc+utt9K1a9e2vwmi9UhFbSFEJ6VSqSxLjmjUKjxdOlxWjUM5/G7NnDmT7Oxsnn32WTIyMhg6dChr1qyxJG6npaWhVtfEcfn5+cyZM4eMjAz8/PyIiYlh27Zt9O/fHwCNRsOhQ4dYuXIlBQUFdO3alalTp/Liiy9aDZF99tlnzJ8/n0mTJqFWq5kxYwbvvPNO27550fqCoiDxN+lBEkJ0SuH+7pzMLsXXTYtKpXJ0czoUhwdIAPPnz2f+/Pk2X9u8ebPV92+99RZvvfVWvedyc3Nj7dq1jV7T39+fzz//vEntFB1QYJTyNTvBse0QQggHMNdCkuG1pnP4EJsQrcocIGXFg6njT28VQoimMA+xyTIjTScBkri4BfQFVFCeB6XZjm6NEEK0qZgIZRWJ6NCOO73fUdrFEJsQrcbZHfwiIf+U0ovkGdToIUIIcbEYEenPn09dTrBX3TI1omHSgyQufrImmxCiEwvzdcNJIx/3TSV3TFz8auch1WbQ41uaLLlJQggh6pAASVz8bM1kM5nQ/Hg/ExKfQ7X/U8e0SwghRLslAZK4+AWZA6RaM9kOfYX6+C8AaPatcEy7hBBCtFsSIImLX0BfUKmhPB9KsqDwLKz+m+VlVeZhyDzqwAYKIYRobyRAEhc/rZsykw0g6xj8PB8qCzF2HU66zzBl+wEpGiqEEKKGBEiiczCvybb+OTi5EZxcMVyzhDT/8cr2Q1+DocphzRNCCNG+SIAkOofAfsrX9APK18nPQUAfMr2HYHLvAqVZkLzJ9rEVRW3RQiGEEO2IBEiiczDXQgKIvBRG/R8AJrUTxgEzlO22htn+fAdeDYf/zYCC023QUBuqdI65rhBCdGISIInOoWt1rpGzF1y3BNQ1v/rGQTOVJ8dXQXlBzTGp22D9IuV50np4Lxb2LAejsW3abDTCqr/CP0MgaUPbXFMIIQQgAZLoLAL6wO3fwL1rwS/C+rWQwUqOkqESjv6gbCvNhW/vBZMRoq6GbqNAVwy/PgafXgt5p1q3vSYTrHkKdn8EJgMc+Kx1ryeEEMKKBEii8+g7FYIH1N2uUsHQ25TnB79QgpMf50LxOejSG274AP6yBq54FZzcIOV3eH+sMiTXGlW4TSYlmXzXBzXbkjZIErkQQrQhCZCEABg8U6mVdHonrH4CTqwFjQvcvAJcPEGtgUvmwoPbIGIc6EuVIOr7+6GyuGXbsvV1+PNt5fn0N8DNDyoK4Mzulr2OEEKIekmAJASAVwj0ulx5vnuZ8vWKlyFkkPV+/j3h7p/h8n+ASgOHv4YPxsO5/S3Tjj/fgU3/VJ5PewVGzYFek5TvT6xtmWsIIYRolARIQpgNua3mef/rYMS9tvdTa2D8EzB7NXh3g7xk+GgK7Fp2Ydc/+iPEPaM8v/wfEPug8rzvNOXribgLO78QQgi7SYAkhFnUVUrOUVB/uOYdJTepId0vgQd+V5K4jXpY/Tj89iQYDbb3N1TVn7OUdRx+mqc8v2SeEoCZ9Z4MqCDzCBSeafLbEkII0XQSIAlhpnWD+XvggT/Azde+Y9z9Yeb/YFJ1OYCdS+GLW63zknKS4JdH4OWu8Ol1UJxpfY6KIvjqDtCVKDWaprxQ9xrdRirPpRdJCCHahARIQtSmUilDaE095tIFcPNKcHKFE+vg42mQuBa+ugveHQF7VyhlBE5tUXKWUrcrx5pnzOUmgXcY3PQJaJzqXqPvVOXriXUX9PaEEELYRwIkIVrKgOvhntXgEQRZR+HzWyD+Z8AEfa9QZsQFRkFJBqy4Cra9C38shuO/gsYZbvkUPANtn7tPdYCUvBn0FW3zfoQQohOz8V9VIUSzdYuBORvhy9uUvKLBt8CYh2qWOuk9BX59FA5/A+v+XnPcla9BtxH1nzdkMHiFQnE6pP4JvSe16tsQQojOTnqQhGhpvuFw/xZ4MgWuf896HTgXT7hxmVLfSK1Vtg27E2LuaficKhX0maI8b61hNn05rHsGTm1tnfMLIUQHIgGSEK1BrVGCIVtUKqW+0f2b4KrFyqOxGXNQM8zWWgHSnuWw7R344YG2W29OCCHaKQmQhHCUkEEw8l5wcrFv/54TlV6nvGRlZlxLMplg36fK86KzkLatZc8vhBAdjARIQnQULl4QMUZ53tJVtU/vguzjNd8f+rplzy+EEB2MBEhCdCSWqtotPMxm7j0K6Kt8PfYTVFW27DVE28k+jsooixsLcSEkQBKiI+lTHSCd+r3l1n+rKIKj3yvPr35bmS1XUQBJ61vm/KJtHf0R7YfjiMr43tEtEaJDkwBJiI4koDf0vx5MBvhuDujKLvycR74FfRkE9FOG8AbOULbLMFvHVB3YBhcedHBDhOjYJEASoqO5+i3wDIHcExD37IWfb+9K5evwWcpsukE3K98nrlF6l0THkq4ERl4VZ5XAVwjRLBIgCdHRuPsr9ZUAdi+7sPXZ0g9C+gFldtyQ25RtoUOUXKSqCoj/peHjS3Ph5EY4+JXyXDhWVSVkxQOgxogq84iDGyRExyUBkhAdUe9JMPoB5flP86A0p3nnMSdnR18NHl2U5yoVDLpFeX74vGE2gx52vA9f3AaLB8DrPeG/N8AP98PiaPhxHpw70Ly2iAuXdQyMesu3qpbKUxOiE2oXAdKSJUuIjIzE1dWV0aNHs2vXrnr3XbFiBSqVyurh6upqeV2v1/Pkk08yaND/t3ff4VFV6QPHvzNpJIEQQkijhiKE3kMAK0GKuzbsUbOsgiKowLqurCKKq9gWXcsPREXdFYXFtSCuSAgCq4QAoUgJTTSUNFp6z9zfH2futExCyiQT4vt5njwzc++dO3cOQ+bNOe95zwD8/f2JiIjg3nvvJT093e483bp1q3KeF198sdHeoxAuF/uMeW23LPj6UVXLqC7KiuCn1er+0Hj7fQNuUbe/bIH8THW/8JwKhtY9AYf/C3mn1Pag7hDSTy3Gu+djWHYlvDdeKnK7Q4Z93pEhQwIkIerL7QHSqlWrmDt3LgsWLGDXrl0MGjSICRMmkJ2dXe1zAgICyMjIsPykpaVZ9hUVFbFr1y7mz5/Prl27+Pzzzzl8+DDXX399lfMsXLjQ7jwPP/xwo7xHIRqFl69atsTopRa8/WlV3Z5/8CsozYXArhB5pf2+oEjoNBI0E+z/HDL3w7tXwa//A+/WEPssTP0WnjgJj+yGGT/CfRtU/pLRC05thxW3QfEFl71dUQvm3jstpB8Ahow97rsWIRri1x8hbSuU5LrtEtweIC1evJhp06YxdepU+vbty9KlS/Hz82P58uXVPsdgMBAWFmb5CQ0Ntexr27YtCQkJ3HbbbfTu3ZtRo0bx1ltvkZKSwokTJ+zO06ZNG7vz+Pv7N9r7FKJRhA+Eq55Q9zf+rfa1i8qKIOltdX/oPWB08qtgoHmYLelteH885JyAdpFw/wYYO1vNeGsVoI4xGKDzCJjyHsw5YM5hKobUtQ16e6KOzD1IpiGqR9Bw7phbv2CEqLeE+fDBJLf2RHu67ZWBsrIyUlJSmDdvnmWb0WgkNjaWpKSkap9XUFBA165dMZlMDB06lBdeeIF+/fpVe3xubi4Gg4HAwEC77S+++CLPPfccXbp04a677mLOnDl4ejpvktLSUkpLrV8+eXlqdk95eTnl5eVOn1Mf+rlceU7hXItp6xEP4LnjPQy5J6nc/j6mEdNqPt5UgcdnUzFm7UNrFUjFgDvBWRtc9js8v/0LBvNQmqn71VTeuAx82zk/XtcqCGP/W/HY9DymfZ9ROeAOoGp7G3b/C0PRWUyjZ9duLbpLWUku5J6C0Op/TzVYZTmeWQcwAGVdxmLyDsa/7CwVJ3eidbui8V73N67F/B5pZjwvpGEAylt3tPy+cVVb1/b5bg2Qzp49S2VlpV0PEEBoaCiHDh1y+pzevXuzfPlyBg4cSG5uLq+++iqjR4/mwIEDdOrUqcrxJSUl/OUvf+HOO+8kICDAsv2RRx5h6NChBAUFsXXrVubNm0dGRgaLFy92+rqLFi3i2WefrbJ9/fr1+Pn51eVt10pCQgNmJok6aQlt3TVwAoPzP6R844tsyAym0qOa9d00jUEnP6Tbue+pNHixtfNMzm9Jqfa8w9sOo2POdo6GTCI14Da076v/w8WWX2kg4wHDL5tJ/OpTSr3aWvYlJCTgX5JJbOrjAOz55SyngsbU+r1eikYcf4OI3J1suWw+F/x7NcprBBSd4OrKUsqNvqzfcYThft3xLzvL4e9XcSy0oFFeU1i1hN8jzYVHZSm/K1ITT9ZvP0SF50m7/Q1t66Ki2pW/MGhaXTM7XSc9PZ2OHTuydetWYmJiLNsff/xxNm/eTHJy8kXPUV5eTlRUFHfeeSfPPfdclX1Tpkzh1KlTbNq0yS5AcrR8+XIeeOABCgoK8PGp+uXirAepc+fOnD17tsbz1lV5eTkJCQmMHz8eLy8vl51XVNWi2rqyDM+lMRhy0qi8+mlMox9xepjxf6/gseUlNIORyps/QOtzXc3nrShRSeCBXet8SR4fXIsxfReVE17CNPw+u/b2SXwKj5T3AdD8O1Dx4DZo1fYiZ2xkmga5J6BtF9f2aJkq8Hy1B4byQiqvehLTmDmuO7cNw95P8Fz7CKauYyi5/TN++Xg2/dJXYepzPZVTqk9ZEA3Ton6PNBdnDuO1bAxaq7ZU/Olny2ZXtXVeXh7BwcHk5ubW+P3t1h6k4OBgPDw8yMrKstuelZVFWFhYrc7h5eXFkCFDOHbMfnXz8vJybrvtNtLS0ti4ceNFg5jo6GgqKir49ddf6d27d5X9Pj4+TgMnLy+vRvlP0VjnFVW1iLb28oKr5sGXD+KR9AYe0fdXDThSPoItLwFgmPwKngNurN15fdvU75oG3ALpu/A4+AUeMQ9aT1lRiMdPn6oHvu0wFJ7B638vw+SX6/c6rrJtKaz7C0x6GaIfcN15M1KhvBAAj+wDeDTWZy1b1TwyRgzBy8uLHL9I9ThjD8ZL/fN9CWgRv0eai4LTABgCuzht04a2dW2f69YkbW9vb4YNG0ZiYqJlm8lkIjEx0a5HqSaVlZXs27eP8PBwyzY9ODp69CgbNmygffv2Fz3Pnj17MBqNhISE1P2NCNEcDLxNLRdSkgNJ/2fdXlEKW16Bteaei8sfgxH3N/719LsJMMDJbZBj7SI37vmXqvAc2h9uMfds7HgXMn5q/GuqjqZB8lJ1f/u7dS+ZUJNTO6z3M/e57ryO9Cn+4YMALAESuSfqXydLCHfIMU+oqkfPtSu5fRbb3Llzeffdd/noo49ITU1lxowZFBYWMnXqVADuvfdeuyTuhQsXsn79eo4fP86uXbu4++67SUtL4/771S/88vJybrnlFnbu3MmKFSuorKwkMzOTzMxMysrKAEhKSuL1119n7969HD9+nBUrVjBnzhzuvvtu2rVr1/SNIIQrGD3g6r+q+0lvQ9F5tajt0rFqhptWCUPugWueaprrCYiArubcIvNiuAatEuPO99S2UTOgxzUqkNJM8N/HwGRqmmtzdGoHXPhF3T93FDJdGKyd2mm9f/44lDZCPpCp0hp8mQOkCg9ftPY91TYpGNl0Dn6lCqY2xr9z8YXfRsX6HHPpnt96gHT77bfz6quv8vTTTzN48GD27NnDunXrLInbJ06cICMjw3L8hQsXmDZtGlFRUUyePJm8vDy2bt1K3759ATh9+jRr1qzh1KlTDB48mPDwcMvP1q1bATVctnLlSq688kr69evH888/z5w5c1i2bFnTN4AQrhR1PYQNhLJ8NTX/o9/B2SPg3wFufg+uf7NpZ4wNMC98u+8zAMJzdmLIO62up7+5GOW1z4OXP5xMhr2fNN212dr7qf3jfatdd27bHiQ0Ve26vk4kw7KrIc0hWf7sUdUr5+UPelAEaOFD1J3Tu+r/mqL2KstVT+2ej2HXR64/99Ir4P9Gtfz6Yhf0AKmLWy/D7QESwKxZs0hLS6O0tJTk5GSio6Mt+zZt2sSHH35oefzaa69Zjs3MzOSbb75hyJAhlv3dunVD0zSnP1dddRUAQ4cOZdu2beTk5FBcXMzBgweZN2+e0xwjIS4pRiNcM1/dP3cMMKjhtFk7YeCtTT+dPuoGMHqqHplzR+lx5ju1ffh94GWugN+2o7WWU8LTKgg4tkFV+d62VAVXDR3yOrYB/nkjnD1WdV9FqSqGCdahx/2fu6Y3q+i86pEC6Dhc3TakdyrpTUjfpSqnmyqt2/XhtbABqifRTIsw/25MlwDpovausi7cXF/HN0GRuYdnz6c1HlpnGT+Zh0uzYY+b/pBoKpYhNgmQhBCu1Gs8DPuDqo59/wa47u/gG+iea/FvD92vBsBjw9MEFR5D8/CGEffZHzdqhlo2pegcLL8WPp4Cn9+vkqb/c5+lB6reNjwDx7+HtbOrBltHvlN5W23CYfxz4NMW8k7DidqVNKjRaXMJhfY9IdJci6i+eUgmkxoyBTh72L5N9IrZ5uE1nRY+WN1J3+3avKqWpvAsfPEAfP0InPv54sdXx7bnMWufa3PObD+PO95z33B0U9CH2Nr9xofYhBAuZjDA7/8B8Wug03B3Xw30V8NsxmOqdonWbwq0dpgM4eGlhv/ahEObCAgdoAKKzqPU/vVPQkle/V7/7FHrF9Wv/1PryNnSl2gZeBt4+0HU79VjVwyz6cNrnUao3h2o/5dm1j4VyOk2vaCGXcDagxQx2O4pWmh/MHioUg159utRVlFR9tvIb3Hm1x8AcwB5dH39zlFeBIe+UfeDeqhbV/Yi2QZI54/D8Y2uO3dzUpJnHUKUHiQhRIvW5zqwKVxZObKaKfSdR8KfDsGfUmHGDxD/tQrygnqoL/hN9VxMWh8+8/BWt+vnq2AA1BDYEfOw30BV8duyUO/BL63H1ZclQBqucsMAsg7aD4/V1vHN6rbb5SqH68KvsPtj1ZOgzwB06EHCyw9CVH7mRYfZ1jwMi/tUWfD2N+HXH6z3j9avCKHh6HooK1Bf6tf+TW3c92+orGj49WkanNim7utDtdvfc35sZTmUFzf8NV1J01QvXfoetfzQtqWQ+rXzY3PNM159g8CnniVGXEQCJCFE42oVAJddC8CZ1lFqen9tefpY6yMlL4WsA3V7bU2D/f9R9ye8AP4hcP5nNUQBap+pXAUvoeZAIvIKdVzxBTUsV18mE5wyD7F1GqEWAPbyV2vU1WcYR1+TqvckuPxP6v6WV+BMqkrK92ylyjw46qjnIdUwk620QM00rCxTuTi/Nb/+z+b+D1BWWOdTGA+YP2f9b1HD3H7BUHgGfk6s+Ym1cf44FJ1Vf2j8/h9q25F11mRmXWk+vDcOFkep4L85OLENXuoGr/SAZVfCqjg1dL7qbtW766iZJGiDBEhCiKZwzXxMva9jX6e76/7cnrFqdp5WCd/8qW65NNkHVb6Oh7caQtNLHGx+SX2B7F2pHg+6w/ocowf0v1ndb8gw29kjUJpr7sXpp86rB2F1TdSuKFMrm4MK4IZNhYCOKlfqG3OwFNoPPJzU/o2oxUy245tUcARwaG3d2rjofL0Cimaj4AycMS9t5R8ClaXWXK9a8qooxKAHQgNuVUPGA25Vj12RUK0Pr3UcCmH9oftVgAY7bSqkaxp8NVP1ABZfsH5e3O3AF9ah4dah0HGYCh7B+WeymSRogwRIQoim0KE3lbd8RL5v5/o9f+IiFWicSLLmDIEKHA6vg5QPnQ9l6MNrPceryuJD7lY9WCU58OVDcHqnytHRSw7o9C+3Q9/U/8tfH16LGGoNXOqbh5S+S1Xj9muvgi2vVnClWsvO8uWpJ2Q7ihhqPkcNidpHv7Pez0m7eE+dpsHPG+GTO+Dl7vD+hJqDquY47KPTe49C+1vzz+qYhxSeuxNDZZkaztSD4MF3qtvD/734tPzKCvj8AdhQdb1PwPpv3MWck6cvSL3rn1Beou5vfVPVYNI1l9pXelmL69+Cx47AtI3WP0Cc/aGgB0huTtAGCZCEEJeCtp2sAcH6p+DIepUz82ov+PR2Ne19yyv2z9E0S4FKyy9ko4c1P+TIt+q2xzXQxn7BbDoOg3bdVOLt4W/rd82ntqtb20T5+gZI+vBat8tVKQeAwXHQLtJ6jGP+kS6krxqaKclxPqShaao9QfWggDXZ2FFFKSQvg7dGwL9uMrehphLI8zOcP8dkgmVXwdsjG6d4YkPp+UfdLodeaiiYYwl16kXrdN4cwAywCbTDBqpgtrLMGqhX5+Q2+Gkl/LDY+fCrnn/UxbzCxGUTIaATFJ9XPTTHN8OGBWpfZ3OZnOYSIGWZA6TQftZtlv8HzgKk5lEkEiRAEkJcKkbNhPa9VF7HJ7eqv55LclQyJ8D//m79ZQxq6vv54+Dpq75QdD2utn9sO7ymMxisvUr1LTGgV9DuNMK6zZKovb9u59ITtPVSAaCGca6yrjJQbYDk6Q3dxqr7P62suj9jLxRkqvyoq83nO7TW+bm+ng3f/lnVdvJuAyMfsH6RZVbzni78ot5vzomqMwibA70HqdtYiLxcBZM5J9QQaW3kZxBckKrum2dsAuozpPci7XXS7rb0ABiqDskVnDHXNENNZADVIznij+r+j/+Az/6oqtEPulP1tkLzKO1QcEblTmFQZTx0tn8oOF5jjuQgCSFE3Xh6w+9eA6MXtAqEoffCvWvgz8eg92SVbL1mlnWGmP5X+2UTwKe1/bnGP6e+CP2C1XOd0YfZjiVAfmbdrrUkD7LNX5q2AVJIFGBQs/Lys5w+tYqyImtvVPerHK7xFrhskqp5VVPy+7B4dbv7Y2tpAJ0+nNTjalXY02BUf9nrQx26cz9bA6wJL6jZhpNftr6/6oI+294yV1Yod4X8LHMgZICuo8Hb3xpM1nKYzZj6FQY0TB1HqF5HWwNuVe15arvzIqU62wBp76f2sxxPJqvbkL7ga7MU1tB4lVt3JlUFIWED1P+P0P7q/0jx+ar/hk0t2zxUGxSpSmjoOkSpoe3iCyqPztaF5rEOG0iAJIS4lERervIYHjuq6iZ1v1INm133d/AJUIUZk5eah9e+UM/Rh9dsdbgMZvyo8iFsf3HbCumjhitMFSq/oy7SdwGa+ivYdvjO22YpkCyHYbb8LBXAVJTabz+5TQ3TBHSEoO72+4wecNdKVQ7BWYK2rvdkNXxWkKVmP9nSH/e6VhX27DJaPT7k0Nuz9Q3VS9FzPMTMtE7B1odOqg2QbIZRjiU2r4Vz08zDa2H9wc/cE6kPs9UyQDKYZ0lq/aZU3dkmDHqMU/cdl7PRlRVaexs9fVXA8Mtm6349/0gfOtP5B5sXhEb9wXD7x+Dlq2Z+6nlQ7h5m0/9I0EtN6LxaWXuUbAPo4hw1sQEgsJ75ii4kAZIQ4tLiF6R6k2wFRMC1z6n7ic+pnorck+Dd2vqF5yi418UTQa/4s7rdubxuX+wnbQpEOnKWh2Qywcq71CyktXPsj9d7FyKvrP9SMR5eMCRO3U/50Lq9INs6k0hvpz7XqVvbYba8DOvQz+Vz7c+tv5/qErtt36dWqepLNRf6bLVul1u39RqvbtOS1LT5mpz7GWPGbkwYMUVd7/wYfZjtp387H/I6sU31frbtbP03sh1mc8w/snXNU9DvZrhrlX3vlT5zUa+w7i76Z8IxQALn/w/0Hi+/YPXHhJtJgCSEaBmGxqsvuopi+OJBta33ZPVXdX31jFVfNuVFkPRW7Z93qo4B0u5/qRl1AHtW2OesWAIkm/yj+hh6r7o9lmitNXM0AdBU/lJAuNrWxzzkmLbVWktn29uqF6vzKDUUZUvvQTp71Dqjypb+Pi0Vyv/TsPfhSrYJ2rr2PVRPnancmvtVneObADjXunfV6vC6yyap/K7cE9aeIlu2CfiDzQFS6tdQkquGV/UgR5/BZiuwC9z6QdV9ljX43N2DpCdo1xQg2fQwNpMlRnQSIAkhWgZ9iRVPX9VTAc6H1+p6zivMs+e2v1u74nuaZhMgjay63/LFYB6SKjqv1ooD61T9tXNVwFGcY/2Sa2iAFNTdWj9n1z/VNn16v23SertuaqkXrVJVGS++ADs/UPsce49ALQ/jG6SO1+sJ6QrOmGe3GeCap9Xtia2Qc7J+7+Hczypp3hXrkOVlmBcSNkBXh96Z2g6zmXtIcvy6V3+Mt5816DzgZDabniQeeYUKbDpEQUWJyqE7naKGeNtE1C1p2TZAcleitskE2ebPQ117kJpBgjZIgCSEaEna94BrnlT3W7VVU/gbqvckFTCUFaj8pprknISUD1SCrIeP9UvAlr7t3FHVQ5D4rDo+pB/ct159UZYXwuo/qCrMmknlLbXt2PD3MuwP6nb3x+q1fzZXCu81wf4422G27e+p9x7Sz/lwpcFQfR6S3jvQvofK++o6Rj3eX89epM+nq8WLt79Tv+fbSvtR3YYPtE9+Busw29GLTPc395Dk+Xaq+bX6mQP1A1/YB3cluTYB8OXmmW93qcd7PlH5Z6B6iOoyvNohylzaIVfNInSHnDT1Ofbwsa5NZ0v/f3DhV3WdYBMgSQ+SEEK43qiH1Cyr2/6pElYbymCAKx5T97cttf4yB/XlmboWVk+FxX3h9f7WHKKOw6rmSoGqJuzfQQU+uz+GlI/U9uteVdd787sqByNrv5pWDw3vPdL1vk69dkGm6rUqzVOP9R4HnR4gHUuE5CXq/tg51X9J6zPoHPOQ9ABJL28wwJzIvL8epROKL6geFYDvF6neqYawHdpy1HWs6onMT7cOEznSNEtZibyLFUDtOQ582qreNNtFZ9O2qs9BUA9V6wtUxXeDh5r5pi/74iz/qCae3irxHFw3zGYywca/2eew1URP0O5wmfMJBH5BqpYTWD83zWiZEZAASQjR0hg91CwrxynxDRF1vZp1U5qrCiWCGhr48HdqbakDn6vZRwYPFWxEz1Cz7JwxGKx/PX/3V0BT9Wv03J42YXCz+TVK89Rt5JWueR+e3tY8F70Xpte11uKTurAB0LaLyucqOqeG3fQZU87oX8ZVepD2Wc8H0PdGMHqq7dkOw3FpSTXXnDqxDTD35pTmqp632igrUr1xn/3RfjjHWf6RzquVNSjVFzN2lHsKSnPRjJ7k+0TUfA2ePhD1O3XftvdMTxKPtLmGNmEq9w3MQ4BAF4cZbLXh6jykX7eoYqxr59Su7EV2DQnaOsdhNulBEkKIS4zRaJ3Rtu1tVbn7nSvUNHHPVjD6EYj/GuadhOmbYNKLENyz+vPpPS6mctWzMH6h/f6e41SPjc7Zl3h96cnauuqGzfS8GVDvr6YyAvoQW+Z++yGpDHMPUri5B8kvyPrlr/ciVZSp6ugfTFTDZ3ovkSM9oNG/+Hd/XP2xttY/pYa29v8Hlo5Vi6Qe3aAWLTYYq+Yf6Xqap+frQ3GO9J6l9r3QjDW0jU4fZjv4lXVZnOoS8PXZbKAKcob0o870fLb0PVX3/foDbHqpbsu/6MGrZlIz8i5GL9paqwDpJ/W5aUbLjIAESEIIUTv9blK5QMUX1DCDZlJferN2qhIDkVfUfmqyPuQEaqq2sxlQVz+peqLGL1T1iVylfQ9rj5TRUxWIdKbvDeq2dai116k6HaJUsFF83tq7UFZorQBt+34tFcpXq0rnyyfY15lyrL+k05OZY2bBwNsBDf7755oTtg99AzvfV/d7jgcMaobYCvNQX/gglavmjF61+tRO569h7i3TQqKqf31b3a9UyexFZ9V7KTxnrYXlGABfNtGaF9V5RM3BaXUsPUh77K+/NB9W3QObXlDtVxsVZZC6xvp476cXT/6urgaSLdsepOILUGYuq9D2IjldTUQCJCGEqA2jB4xbAJiHyKZ+q6ZY16egXbexaup351Ew/I/Oj/HwUj1RYx5t0GU7Ff2AutUX8XWm62i4c6XqGfNqVfP5vFqpZWDAOsyWdQDQoHWYfQDYe5JaePjCr/B/o1VRzVaBMPhutd/ZkFZxjrU3qusYFTR6t1Y9SNUVYMxLV3WlAEY/DHd/Bg8lmXtyzLlU3asJDkH18nn6quVs9EDPlrmHRKtt746HF/Q110ra/x9rkcqQvlUDZE8ftbAyVF/H62I69FG9m2X5KhDVJb+jAllQ5SUclzZx5udElXvn30ElXWcfVEvUVKeizDo86GyKv04PkLJTrWvQtQ5tWGkOF5IASQghaqvv9fD4cZi+pWo9oLoICIfHDqvgoz69Aw3V5zq4PxFuWlLzcb0nQYfetTun40w2S4K2w0w+n9bW5V0qilWQ+OAPKugxGFWvimMZgBNJgKZ68ALCVZ6OvnjxhgX2ifOglur4fLrqlQgfZC4xgFrq5dYPVKA0+VVr8r0zHl7WXhi9bIMtc2Kx1qGWPUhgHWZL/RqObVD3qxs+HbcA/vANjJxe+/Pb8vC09tzpeUgludbeOn1G4dq51Rf51Ol5UwNuVZ8JqD4wBRUcmSrU8HFADbMv23VTFfAry6wlFZpJ/hFIgCSEEHXjF1Q1qbk+fNo4n+XWVDoNrzq9vSH0RG29vpOeeBs+sOqxl89VOTJXPqGCgMDOahhRrxt11KEXSc8/0r/UQQ0/6osXr56q6gbpdaq2vqGGsbz8YMr7Vds5JApGTrv4kGin4erWMUCqKLX0kGihdcgP6jZWLflSkgN7zAFGdTMUPbzU8UaP2p/fkWOi9ral6rWDe8O9X6llUCqK4d/x1VcNLyuyDnv2n2ItQ7BvddV1/XSW/KOomssT2E5YOPSNum0mM9hAAiQhhBCu4DjVP6OaHiRQvU0PbIar59n3oF1mrsfkOMzmbMaZpzdMegkwqCGgz6bCy93hnSvVdHSASS+rJWXqS6+E7hggnT1i7SFpc5EZbLaMHtDvRnXfVK6uvduYmp7RMLYBUvEFSHpbPb7qCRWA3fyu6uE5dxTWPOI8r+jIOlXPKLCrKl3RY5wK8orOmSuxO5FtEyBdjGWYzfy5aSYJ2iABkhBCCFfQA6SzR1SCtv4lGeakB6k6+vDN8c3qHKDyj/ThOsdgouc4+ON3MGqmORlYU0tzmCpUSQE9j6e+9AAp+6B9D4veQxLar+7r4+nDbKCG/1zZi+coYrC6zdirhtZKc1U79b1RbfdvD7d+qJL1D3wO25dVPYc+vNZ/inqvHp6qVhPA3mryl7Jt2udiHANo6UESQgjRogREqGRrrVIVz6woUYnU7SJrf44OfdQXZGWpdR20E9usxRQDnPTWdImGiS+ovKI/HYab3lEzA294q/6L+1reU7haRFYz2dcT0vOsakpArk7naGteTmQ1+UeuEnyZGmYsL4Qf/6G2XTXPfoi480hrmYl18+DwOuu+klxrbtCAW6zbB5kX4D28zvnyO7WZ4q+TAEkIIUSLZjBYe5H0noXQ/nXL1zIYrOvCHTF/UevT+7uNvfjz24TBoDtUzSqfNrV/3Zo4y0OqSw+JI6NRDXEF9YAh9178+IYweqheKlC9amEDrYsG2xr1kJpFqFWqoponze81da1KoO7Qxz7YCeuvlt8xlVddNqYkTy3MC7UbYuvQR/Vg6SRJWwghRIujJ2rrvT/OErQvxhIgfafq91jyj2oRIDUGfZjtpE2ApOdZ1aeAI6hinY/sUstwNDbbZWSu/qvzXjWDAX7/uir7UFEMn9ymFkt2HF6zNdjci+Q4m01fsLhNuJrQcDGePipIUhfSbGoggQRIQgghXMXSo2JO9nWWoH0xeo2ogkzVe5RpU//IHfSZdad2qCTmovNqTTWoXQ+Ju+nlKDoOswafznh4wW0fQcRQVSfpXzfB8U1qX/8pVY8fcKtaWud0iv2yMXVJ0Nbpn5M24a5ZP9FFJEASQgjhGvoQm64uCdo6Tx9rde8Nz5jzj7pD2xrq6TSm8IHg4a0qYF/41dp7FNgFWgW455rqos/v4I5P4K5/Xzwny9sf4lar9s49qYbcwger6uuOWodAr/Hq/geTYMd7agmVuuQf6fQAqRnNYAMJkIQQQrhKhz6q2COovJL69rDos9nSd6lbdw2vgQrY9EDv1A6b/KP+1T+nOTEYVGFQ/+DaHe8fDHd/rqpmg+opqs61z6tlZorPwzd/UusT/rxR7atLgNR/ilpcetSM2j+nCbihhKsQQogWydtPJR+fO2pe6qKewyWOy2u4crHe+ug0Ak7vVAFSRanaVpcA4FITFKnKJxxZByPur/644J6qCnrKB6r2VLZNRe66zPBrE6YKVzYz0oMkhBDCdfRE7frkH+lah6icGZ278o90nW0KRupDbPWZwXYpad8DYmZePMj18FRVyR/ZrZZFMXio9f0sideXLulBEkII4TpD7lEzvhpapPGySSoBuF2k+/KPdPpMtsx9Kh8JWn6AVFd+QTD5FbUwMDSbBWcboln0IL399tt069aNVq1aER0dzfbt26s99sMPP8RgMNj9tGplv9K0pmk8/fTThIeH4+vrS2xsLEePHrU75vz588TFxREQEEBgYCD33XcfBQUFjfL+hBDiN6PnOJh7oOF5Q8PiVV7KVfNcclkN0razWmXeVAHlRWpF+yAnictCJa83o2KPDeH2AGnVqlXMnTuXBQsWsGvXLgYNGsSECRPIzs6u9jkBAQFkZGRYftLS0uz2v/zyy7zxxhssXbqU5ORk/P39mTBhAiUlJZZj4uLiOHDgAAkJCaxdu5YtW7YwfXo9V00WQgjhWq1DVF7KoNvdfSUq0VnvRQLo0Nt+DTnRIrk9QFq8eDHTpk1j6tSp9O3bl6VLl+Ln58fy5curfY7BYCAsLMzyExoaatmnaRqvv/46Tz31FDfccAMDBw7kn//8J+np6Xz55ZcApKamsm7dOt577z2io6MZO3Ysb775JitXriQ9Pb2x37IQQohLjW2AdKnMYBMN4tYAqaysjJSUFGJjYy3bjEYjsbGxJCUlVfu8goICunbtSufOnbnhhhs4cMCaOf/LL7+QmZlpd862bdsSHR1tOWdSUhKBgYEMHz7cckxsbCxGo5Hk5GRXvkUhhBAtgV2A1IJnsAkLt/YRnj17lsrKSrseIIDQ0FAOHTrk9Dm9e/dm+fLlDBw4kNzcXF599VVGjx7NgQMH6NSpE5mZmZZzOJ5T35eZmUlISIjdfk9PT4KCgizHOCotLaW0tNTyOC8vD4Dy8nLKy8vr8K5rpp/LlecUzklbNy1p76Yjbd0IOvTD0+CBQaukon0fNIc2lrZufK5q69o+/5IbRI2JiSEmJsbyePTo0URFRfHOO+/w3HPPNdrrLlq0iGeffbbK9vXr1+Pn5+fy10tISHD5OYVz0tZNS9q76Uhbu1a/4FgCi39l28ELVB76r90+aeum09C2LioqqtVxbg2QgoOD8fDwICsry257VlYWYWFhtTqHl5cXQ4YM4dixYwCW52VlZREeHm53zsGDB1uOcUwCr6io4Pz589W+7rx585g7d67lcV5eHp07d+baa68lIMB15ebLy8tJSEhg/PjxeHl5uey8oipp66Yl7d10pK0by2QAJthskbZuOq5qa30E6GLcGiB5e3szbNgwEhMTufHGGwEwmUwkJiYya9asWp2jsrKSffv2MXmy+uBGRkYSFhZGYmKiJSDKy8sjOTmZGTNUGfOYmBhycnJISUlh2DBVjGzjxo2YTCaio6Odvo6Pjw8+PlULZnl5eTXKf4rGOq+oStq6aUl7Nx1p66Yjbd10GtrWtX2u24fY5s6dS3x8PMOHD2fkyJG8/vrrFBYWMnXqVADuvfdeOnbsyKJFiwBYuHAho0aNomfPnuTk5PDKK6+QlpbG/fercugGg4HZs2fzt7/9jV69ehEZGcn8+fOJiIiwBGFRUVFMnDiRadOmsXTpUsrLy5k1axZ33HEHERERbmkHIYQQQjQfbg+Qbr/9ds6cOcPTTz9NZmYmgwcPZt26dZYk6xMnTmA0WifbXbhwgWnTppGZmUm7du0YNmwYW7dupW9f66yCxx9/nMLCQqZPn05OTg5jx45l3bp1dgUlV6xYwaxZsxg3bhxGo5EpU6bwxhtvNN0bF0IIIUSz5fYACWDWrFnVDqlt2rTJ7vFrr73Ga6+9VuP5DAYDCxcuZOHChdUeExQUxCeffFLnaxVCCCFEy+f2QpFCCCGEEM2NBEhCCCGEEA4kQBJCCCGEcCABkhBCCCGEAwmQhBBCCCEcSIAkhBBCCOFAAiQhhBBCCAcSIAkhhBBCOJAASQghhBDCgQRIQgghhBAOmsVSI5ciTdMAyMvLc+l5y8vLKSoqIi8vT1aGbmTS1k1L2rvpSFs3HWnrpuOqtta/t/Xv8epIgFRP+fn5AHTu3NnNVyKEEEKIusrPz6dt27bV7jdoFwuhhFMmk4n09HTatGmDwWBw2Xnz8vLo3LkzJ0+eJCAgwGXnFVVJWzctae+mI23ddKStm46r2lrTNPLz84mIiMBorD7TSHqQ6sloNNKpU6dGO39AQID8Z2si0tZNS9q76UhbNx1p66bjirauqedIJ0naQgghhBAOJEASQgghhHAgAVIz4+Pjw4IFC/Dx8XH3pbR40tZNS9q76UhbNx1p66bT1G0tSdpCCCGEEA6kB0kIIYQQwoEESEIIIYQQDiRAEkIIIYRwIAGSEEIIIYQDCZCambfffptu3brRqlUroqOj2b59u7sv6ZK3aNEiRowYQZs2bQgJCeHGG2/k8OHDdseUlJQwc+ZM2rdvT+vWrZkyZQpZWVluuuKW4cUXX8RgMDB79mzLNmln1zp9+jR333037du3x9fXlwEDBrBz507Lfk3TePrppwkPD8fX15fY2FiOHj3qxiu+NFVWVjJ//nwiIyPx9fWlR48ePPfcc3ZreUlb18+WLVv4/e9/T0REBAaDgS+//NJuf23a9fz588TFxREQEEBgYCD33XcfBQUFDb42CZCakVWrVjF37lwWLFjArl27GDRoEBMmTCA7O9vdl3ZJ27x5MzNnzmTbtm0kJCRQXl7OtddeS2FhoeWYOXPm8PXXX7N69Wo2b95Meno6N998sxuv+tK2Y8cO3nnnHQYOHGi3XdrZdS5cuMCYMWPw8vLi22+/5eDBg/z973+nXbt2lmNefvll3njjDZYuXUpycjL+/v5MmDCBkpISN175peell15iyZIlvPXWW6SmpvLSSy/x8ssv8+abb1qOkbaun8LCQgYNGsTbb7/tdH9t2jUuLo4DBw6QkJDA2rVr2bJlC9OnT2/4xWmi2Rg5cqQ2c+ZMy+PKykotIiJCW7RokRuvquXJzs7WAG3z5s2apmlaTk6O5uXlpa1evdpyTGpqqgZoSUlJ7rrMS1Z+fr7Wq1cvLSEhQbvyyiu1Rx99VNM0aWdX+8tf/qKNHTu22v0mk0kLCwvTXnnlFcu2nJwczcfHR/v000+b4hJbjOuuu0774x//aLft5ptv1uLi4jRNk7Z2FUD74osvLI9r064HDx7UAG3Hjh2WY7799lvNYDBop0+fbtD1SA9SM1FWVkZKSgqxsbGWbUajkdjYWJKSktx4ZS1Pbm4uAEFBQQCkpKRQXl5u1/Z9+vShS5cu0vb1MHPmTK677jq79gRpZ1dbs2YNw4cP59ZbbyUkJIQhQ4bw7rvvWvb/8ssvZGZm2rV327ZtiY6Olvauo9GjR5OYmMiRI0cA2Lt3Lz/88AOTJk0CpK0bS23aNSkpicDAQIYPH245JjY2FqPRSHJycoNeXxarbSbOnj1LZWUloaGhdttDQ0M5dOiQm66q5TGZTMyePZsxY8bQv39/ADIzM/H29iYwMNDu2NDQUDIzM91wlZeulStXsmvXLnbs2FFln7Szax0/fpwlS5Ywd+5c/vrXv7Jjxw4eeeQRvL29iY+Pt7Sps98p0t5188QTT5CXl0efPn3w8PCgsrKS559/nri4OABp60ZSm3bNzMwkJCTEbr+npydBQUENbnsJkMRvysyZM9m/fz8//PCDuy+lxTl58iSPPvooCQkJtGrVyt2X0+KZTCaGDx/OCy+8AMCQIUPYv38/S5cuJT4+3s1X17L8+9//ZsWKFXzyySf069ePPXv2MHv2bCIiIqStWzAZYmsmgoOD8fDwqDKjJysri7CwMDddVcsya9Ys1q5dy/fff0+nTp0s28PCwigrKyMnJ8fueGn7uklJSSE7O5uhQ4fi6emJp6cnmzdv5o033sDT05PQ0FBpZxcKDw+nb9++dtuioqI4ceIEgKVN5XdKw/35z3/miSee4I477mDAgAHcc889zJkzh0WLFgHS1o2lNu0aFhZWZSJTRUUF58+fb3DbS4DUTHh7ezNs2DASExMt20wmE4mJicTExLjxyi59mqYxa9YsvvjiCzZu3EhkZKTd/mHDhuHl5WXX9ocPH+bEiRPS9nUwbtw49u3bx549eyw/w4cPJy4uznJf2tl1xowZU6VcxZEjR+jatSsAkZGRhIWF2bV3Xl4eycnJ0t51VFRUhNFo/3Xp4eGByWQCpK0bS23aNSYmhpycHFJSUizHbNy4EZPJRHR0dMMuoEEp3sKlVq5cqfn4+GgffvihdvDgQW369OlaYGCglpmZ6e5Lu6TNmDFDa9u2rbZp0yYtIyPD8lNUVGQ55sEHH9S6dOmibdy4Udu5c6cWExOjxcTEuPGqWwbbWWyaJu3sStu3b9c8PT21559/Xjt69Ki2YsUKzc/PT/v4448tx7z44otaYGCg9tVXX2k//fSTdsMNN2iRkZFacXGxG6/80hMfH6917NhRW7t2rfbLL79on3/+uRYcHKw9/vjjlmOkresnPz9f2717t7Z7924N0BYvXqzt3r1bS0tL0zStdu06ceJEbciQIVpycrL2ww8/aL169dLuvPPOBl+bBEjNzJtvvql16dJF8/b21kaOHKlt27bN3Zd0yQOc/nzwwQeWY4qLi7WHHnpIa9eunebn56fddNNNWkZGhvsuuoVwDJCknV3r66+/1vr376/5+Phoffr00ZYtW2a332QyafPnz9dCQ0M1Hx8fbdy4cdrhw4fddLWXrry8PO3RRx/VunTporVq1Urr3r279uSTT2qlpaWWY6St6+f77793+vs5Pj5e07Tateu5c+e0O++8U2vdurUWEBCgTZ06VcvPz2/wtRk0zaYUqBBCCCGEkBwkIYQQQghHEiAJIYQQQjiQAEkIIYQQwoEESEIIIYQQDiRAEkIIIYRwIAGSEEIIIYQDCZCEEEIIIRxIgCSEEC5iMBj48ssv3X0ZQggXkABJCNEi/OEPf8BgMFT5mThxorsvTQhxCfJ09wUIIYSrTJw4kQ8++MBum4+Pj5uuRghxKZMeJCFEi+Hj40NYWJjdT7t27QA1/LVkyRImTZqEr68v3bt357PPPrN7/r59+7jmmmvw9fWlffv2TJ8+nYKCArtjli9fTr9+/fDx8SE8PJxZs2bZ7T979iw33XQTfn5+9OrVizVr1jTumxZCNAoJkIQQvxnz589nypQp7N27l7i4OO644w5SU1MBKCwsZMKECbRr144dO3awevVqNmzYYBcALVmyhJkzZzJ9+nT27dvHmjVr6Nmzp91rPPvss9x222389NNPTJ48mbi4OM6fP9+k71MI4QINXu5WCCGagfj4eM3Dw0Pz9/e3+3n++ec1TdM0QHvwwQftnhMdHa3NmDFD0zRNW7ZsmdauXTutoKDAsv+bb77RjEajlpmZqWmapkVERGhPPvlktdcAaE899ZTlcUFBgQZo3377rcvepxCiaUgOkhCixbj66qtZsmSJ3bagoCDL/ZiYGLt9MTEx7NmzB4DU1FQGDRqEv7+/Zf+YMWMwmUwcPnwYg8FAeno648aNq/EaBg4caLnv7+9PQEAA2dnZ9X1LQgg3kQBJCNFi+Pv7VxnychVfX99aHefl5WX32GAwYDKZGuOShBCNSHKQhBC/Gdu2bavyOCoqCoCoqCj27t1LYWGhZf+PP/6I0Wikd+/etGnThm7dupGYmNik1yyEcA/pQRJCtBilpaVkZmbabfP09CQ4OBiA1atXM3z4cMaOHcuKFSvYvn0777//PgBxcXEsWLCA+Ph4nnnmGc6cOcPDDz/MPffcQ2hoKADPPPMMDz74ICEhIUyaNIn8/Hx+/PFHHn744aZ9o0KIRicBkhCixVi3bh3h4eF223r37s2hQ4cANcNs5cqVPPTQQ4SHh/Ppp5/St29fAPz8/Pjuu+949NFHGTFiBH5+fkyZMoXFixdbzhUfH09JSQmvvfYajz32GMHBwdxyyy1N9waFEE3GoGma5u6LEEKIxmYwGPjiiy+48cYb3X0pQohLgOQgCSGEEEI4kABJCCGEEMKB5CAJIX4TJJtACFEX0oMkhBBCCOFAAiQhhBBCCAcSIAkhhBBCOJAASQghhBDCgQRIQgghhBAOJEASQgghhHAgAZIQQgghhAMJkIQQQgghHEiAJIQQQgjh4P8BAxbo2uNzWagAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = sorted(train_loss_dict.keys())\n",
    "\n",
    "train_loss = [train_loss_dict[e] for e in epochs]\n",
    "val_loss = [val_loss_dict[e] for e in epochs]\n",
    "train_auc = [train_auc_dict[e] for e in epochs]\n",
    "val_auc = [val_auc_dict[e] for e in epochs]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# --- Loss ---\n",
    "plt.figure()\n",
    "plt.plot(epochs, train_loss, label=\"Train Loss\")\n",
    "plt.plot(epochs, val_loss, label=\"Val Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title(\"Loss vs Epoch\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# --- AUC ---\n",
    "plt.figure()\n",
    "plt.plot(epochs, train_auc, label=\"Train AUC\")\n",
    "plt.plot(epochs, val_auc, label=\"Val AUC\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"AUC\")\n",
    "plt.title(\"AUC vs Epoch\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e99278c",
   "metadata": {},
   "source": [
    "### Check transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ed00674",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "from AIAH_utility.viewer import BasicViewer\n",
    "from monai.transforms import Compose, LoadImaged, RandRotate90d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4b18c90",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/dataset/PI-RADS_data_updated.json\",\n",
    ") as f:\n",
    "    data = json.load(f)\n",
    "train_data = data[\"train\"]\n",
    "t2_dir = \"/sc-projects/sc-proj-cc06-ag-ki-radiologie/prostate-foundation/processed/t2_registered\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d55df876",
   "metadata": {},
   "outputs": [],
   "source": [
    "from monai.networks.nets import resnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2f8bdefe",
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "Please set n_input_channels to 1and feed_forward to False in order to use MedicalNet pretrained weights",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m net \u001b[38;5;241m=\u001b[39m \u001b[43mresnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresnet50\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43mspatial_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_input_channels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_classes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgroup\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnum_groups\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpretrained\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m      7\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/monai/networks/nets/resnet.py:581\u001b[0m, in \u001b[0;36mresnet50\u001b[0;34m(pretrained, progress, **kwargs)\u001b[0m\n\u001b[1;32m    572\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mresnet50\u001b[39m(pretrained: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, progress: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResNet:\n\u001b[1;32m    573\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"ResNet-50 with optional pretrained support when `spatial_dims` is 3.\u001b[39;00m\n\u001b[1;32m    574\u001b[0m \n\u001b[1;32m    575\u001b[0m \u001b[38;5;124;03m    Pretraining from `Med3D: Transfer Learning for 3D Medical Image Analysis <https://arxiv.org/pdf/1904.00625.pdf>`_.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    579\u001b[0m \u001b[38;5;124;03m        progress (bool): If True, displays a progress bar of the download to stderr\u001b[39;00m\n\u001b[1;32m    580\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 581\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_resnet\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresnet50\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mResNetBottleneck\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mget_inplanes\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpretrained\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/monai/networks/nets/resnet.py:525\u001b[0m, in \u001b[0;36m_resnet\u001b[0;34m(arch, block, layers, block_inplanes, pretrained, progress, **kwargs)\u001b[0m\n\u001b[1;32m    520\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m    521\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease set shortcut_type to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mshortcut_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and bias_downsample to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbias_downsample\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    522\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhen using pretrained MedicalNet resnet\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresnet_depth\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    523\u001b[0m             )\n\u001b[1;32m    524\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 525\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m    526\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease set n_input_channels to 1\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    527\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mand feed_forward to False in order to use MedicalNet pretrained weights\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    528\u001b[0m         )\n\u001b[1;32m    529\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    530\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMedicalNet pretrained weights are only avalaible for 3D models\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: Please set n_input_channels to 1and feed_forward to False in order to use MedicalNet pretrained weights"
     ]
    }
   ],
   "source": [
    "net = resnet.resnet50(\n",
    "    spatial_dims=3,\n",
    "    n_input_channels=1,\n",
    "    num_classes=5,\n",
    "    norm=(\"group\", {\"num_groups\": 8}),\n",
    "    pretrained=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c451d986",
   "metadata": {},
   "outputs": [],
   "source": [
    "og_transforms = Compose(\n",
    "    [\n",
    "        # 1. Flip Left-Right (Axis 0) - Very safe, medically common\n",
    "        LoadImaged(\n",
    "            keys=[\"image\"],\n",
    "            reader=\"ITKReader\",\n",
    "            ensure_channel_first=True,\n",
    "            dtype=np.float32,\n",
    "        ),\n",
    "        # RandFlipd(keys=[\"image\"], spatial_axis=0, prob=0.5),\n",
    "        # RandRotate90d(keys=[\"image\"], spatial_axes=(0, 1), prob=0.5),\n",
    "    ]\n",
    ")\n",
    "rand_transforms = Compose(\n",
    "    [\n",
    "        # 1. Flip Left-Right (Axis 0) - Very safe, medically common\n",
    "        LoadImaged(\n",
    "            keys=[\"image\"],\n",
    "            reader=\"ITKReader\",\n",
    "            ensure_channel_first=True,\n",
    "            dtype=np.float32,\n",
    "        ),\n",
    "        # RandFlipd(keys=[\"image\"], spatial_axis=0, prob=0.9),\n",
    "        RandRotate90d(keys=[\"image\"], spatial_axes=(0, 1), prob=0.9),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be1231fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "og = og_transforms({\"image\": os.path.join(t2_dir, train_data[0][\"image\"])})\n",
    "rand = rand_transforms({\"image\": os.path.join(t2_dir, train_data[0][\"image\"])})\n",
    "BasicViewer(og[\"image\"][0]).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d07ed418",
   "metadata": {},
   "outputs": [],
   "source": [
    "BasicViewer(rand[\"image\"][0]).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a689d93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2f0ce1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/sc-projects/sc-proj-cc06-ag-ki-radiologie/pirad_model_test_PICAI/marksheet.csv\")\n",
    "with open(\n",
    "    \"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/dataset/PICAI_cspca_updated_with_psa.json\",\n",
    ") as f:\n",
    "    json_data = json.load(f)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37e217d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = json_data[\"train\"][9]\n",
    "print(i[\"label\"])\n",
    "p_id = i[\"image\"].split(\"_\")[0]\n",
    "st_id = i[\"image\"].split(\"_\")[1].split(\".\")[0]\n",
    "row = df[(df[\"patient_id\"] == int(p_id)) & (df[\"study_id\"] == int(st_id))]\n",
    "row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "068ce2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "from monai.data import load_decathlon_datalist\n",
    "from monai.transforms import (\n",
    "    Compose,\n",
    "    ConcatItemsd,\n",
    "    LoadImaged,\n",
    "    RandRotate90d,\n",
    ")\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from src.data.custom_transforms import (\n",
    "    ClipMaskIntensityPercentilesd,\n",
    "    ElementwiseProductd,\n",
    "    NormalizeIntensity_customd,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6cc7589",
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = Compose(\n",
    "    [\n",
    "        LoadImaged(\n",
    "            keys=[\"image\", \"mask\", \"dwi\", \"adc\", \"heatmap\", \"smooth_mask\"],\n",
    "            reader=\"ITKReader\",\n",
    "            ensure_channel_first=True,\n",
    "            dtype=np.float32,\n",
    "        ),\n",
    "        ClipMaskIntensityPercentilesd(keys=[\"image\"], lower=0, upper=99.5, mask_key=\"mask\"),\n",
    "        ClipMaskIntensityPercentilesd(keys=[\"dwi\"], lower=0, upper=99.5, mask_key=\"mask\"),\n",
    "        NormalizeIntensity_customd(keys=[\"image\"], mask_key=\"mask\"),\n",
    "        NormalizeIntensity_customd(keys=[\"dwi\"], mask_key=\"mask\"),\n",
    "        ConcatItemsd(keys=[\"image\", \"dwi\", \"adc\"], name=\"image\", dim=0),  # stacks to (3, H, W)\n",
    "        ElementwiseProductd(keys=[\"heatmap\", \"smooth_mask\"], output_key=\"final_heatmap\"),\n",
    "        RandRotate90d(\n",
    "            keys=[\"image\", \"final_heatmap\", \"smooth_mask\"], prob=0.9, spatial_axes=(1, 2), max_k=3\n",
    "        ),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd510912",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list = load_decathlon_datalist(\n",
    "    data_list_file_path=\"/sc-scratch/sc-scratch-cc06-ag-ki-radiologie/prostate_foundation/WSAttention-Prostate/dataset/PI-RADS_data_updated.json\",\n",
    "    data_list_key=\"test\",\n",
    "    base_dir=\"/sc-projects/sc-proj-cc06-ag-ki-radiologie/prostate-foundation/processed/t2_registered\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f01753a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "864ad54c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "057ce049",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = transform(data_list[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42883554",
   "metadata": {},
   "outputs": [],
   "source": [
    "a[\"image\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84119dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(a[\"image\"][0, :, :, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5cb1342",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(a[\"image\"][0, :, :, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0c93a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(a[\"image\"][0, :, :, 11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7956298",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(a[\"image\"][0, :, :, 11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "547d636a",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(a[\"image\"][0, :, :, 12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0074f700",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "foundation",
   "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": "3.9.21"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}