CLIP_ade20k_v0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sE_ECMJf2G0J"
   },
   "source": [
    "### Set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "vqhgDvoB3HVB",
    "outputId": "9ae9fbbc-a1b5-4b10-bd3c-6f78ecc21646"
   },
   "outputs": [],
   "source": [
    "# prevent autoscroll in Colab when working in multiple tabs\n",
    "# %%javascript\n",
    "# IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "#     return false;\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "itzQf5X48Bv3",
    "outputId": "ce802f7a-e750-4e9c-c75b-703751f73b5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ftfy in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (6.1.3)\n",
      "Requirement already satisfied: regex in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (2023.10.3)\n",
      "Requirement already satisfied: tqdm in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (4.66.1)\n",
      "Requirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ftfy) (0.2.12)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Collecting git+https://github.com/openai/CLIP.git\n",
      "  Cloning https://github.com/openai/CLIP.git to /tmp/pip-req-build-2bfys0ko\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/openai/CLIP.git /tmp/pip-req-build-2bfys0ko\n",
      "  Resolved https://github.com/openai/CLIP.git to commit a1d071733d7111c9c014f024669f959182114e33\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: ftfy in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from clip==1.0) (6.1.3)\n",
      "Requirement already satisfied: regex in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from clip==1.0) (2023.10.3)\n",
      "Requirement already satisfied: tqdm in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from clip==1.0) (4.66.1)\n",
      "Requirement already satisfied: torch in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from clip==1.0) (2.1.1)\n",
      "Requirement already satisfied: torchvision in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from clip==1.0) (0.16.1)\n",
      "Requirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from ftfy->clip==1.0) (0.2.12)\n",
      "Requirement already satisfied: jinja2 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (3.1.2)\n",
      "Requirement already satisfied: fsspec in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (2023.10.0)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (11.4.5.107)\n",
      "Requirement already satisfied: networkx in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (3.2.1)\n",
      "Requirement already satisfied: sympy in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (1.12)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (8.9.2.26)\n",
      "Requirement already satisfied: filelock in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (3.13.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (2.18.1)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (12.1.105)\n",
      "Requirement already satisfied: triton==2.1.0 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (2.1.0)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (12.1.105)\n",
      "Requirement already satisfied: typing-extensions in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torch->clip==1.0) (4.8.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->clip==1.0) (12.3.101)\n",
      "Requirement already satisfied: requests in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torchvision->clip==1.0) (2.31.0)\n",
      "Requirement already satisfied: numpy in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torchvision->clip==1.0) (1.24.2)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from torchvision->clip==1.0) (10.1.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from jinja2->torch->clip==1.0) (2.1.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from requests->torchvision->clip==1.0) (2023.7.22)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from requests->torchvision->clip==1.0) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from requests->torchvision->clip==1.0) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from requests->torchvision->clip==1.0) (2.0.7)\n",
      "Requirement already satisfied: mpmath>=0.19 in /home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages (from sympy->torch->clip==1.0) (1.3.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "! pip install ftfy regex tqdm\n",
    "! pip install git+https://github.com/openai/CLIP.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "OMSS4YTBuMtX"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "from time import time\n",
    "import pandas as pd\n",
    "import numpy as np # for numerical operations, super useful for dealing with arrays/matrices\n",
    "from tqdm.notebook import tqdm\n",
    "import pickle # for storing information into pickle files\n",
    "\n",
    "# for visualization\n",
    "from plotly import graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# for the scores and confusion matrices\n",
    "from sklearn.metrics import fbeta_score, confusion_matrix\n",
    "\n",
    "# for visualization\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import cv2\n",
    "\n",
    "# PyTorch imports: useful for anything to do with our model\n",
    "# (i.e., the model itself, loading the data into the model, etc,)\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torchvision import transforms as T, models\n",
    "from torch.optim import Adam\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "!pip install -q torchsummary --user\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1L1CBpsY5Nbz"
   },
   "source": [
    "### Load CLIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JCMR5gdt8MwM",
    "outputId": "32a1b190-c032-49cd-89ec-5d957008efb0"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import clip\n",
    "import torch\n",
    "from torchvision.datasets import CIFAR100\n",
    "\n",
    "# Load the model\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "clip_encoder, preprocess = clip.load('ViT-B/32', device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "7i8tBg6UXfc7"
   },
   "outputs": [],
   "source": [
    "# # Calculate features\n",
    "# with torch.no_grad(): # was I supposed to do this...\n",
    "#     image_features = model.encode_image(image_input) # image_input is of dims (batch, num channels, height, width) and the features are of size (batch, 512)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ipRh0dzv98m2"
   },
   "source": [
    "### ADE20K Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yWFqtw2B-Jle",
    "outputId": "3ec9478c-4fdc-4dec-b38a-de0481486440"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/gpfs/ryzhu/cos514/ADE20K/ADEChallengeData2016\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning:\n",
      "\n",
      "using dhist requires you to install the `pickleshare` library.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# from google.colab import drive\n",
    "\n",
    "# # This will prompt you to click on a link and get an authorization code\n",
    "# drive.mount('/content/drive', force_remount=True)\n",
    "\n",
    "# %ls\n",
    "# !sh download_and_convert_ade20k.sh\n",
    "%cd /{PUT DIR LOCATION HERE}/ADE20K/ADEChallengeData2016/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "pvf9aM_3JcYa"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# checking how much training and validation data we have\n",
    "train_paths = [\"images/training\", ]\n",
    "val_paths = [\"images/validation\", ]\n",
    "# num_train = sum(\"_train_\" in s for s in df_classes['filename'].tolist())\n",
    "# num_val = sum(\"_val_\" in s for s in df_classes['filename'].tolist())\n",
    "# print(\n",
    "#     f\"train files: {num_train}, \"\n",
    "#     f\"validation files: {num_val}\"\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 729
    },
    "id": "WTN_z-4DLmEp",
    "outputId": "254840a6-8438-4135-fccd-d502f1a96583"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8809\n",
      "           image_name              tags           list_tags\n",
      "0  ADE_train_00000001  airport_terminal  [airport_terminal]\n",
      "1  ADE_train_00000002  airport_terminal  [airport_terminal]\n",
      "5  ADE_train_00000006          bathroom          [bathroom]\n",
      "6  ADE_train_00000007          bathroom          [bathroom]\n",
      "7  ADE_train_00000008          bathroom          [bathroom]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read the text file into a DataFrame\n",
    "df_classes = pd.read_csv('sceneCategories.txt', sep=' ', header=None, names=['filename', 'label'])\n",
    "\n",
    "# Count the frequency of each label\n",
    "label_counts = df_classes['label'].value_counts()\n",
    "\n",
    "# Exclude the label \"misc\"\n",
    "label_counts = label_counts[label_counts.index != 'misc']\n",
    "\n",
    "# Apply a cutoff to include only labels with frequencies above 97\n",
    "cutoff = 97\n",
    "filtered_label_counts = label_counts[label_counts > cutoff]\n",
    "df_classes = df_classes[df_classes['label'].isin(filtered_label_counts.keys())]\n",
    "\n",
    "# Sort labels based on frequency in descending order\n",
    "sorted_labels = filtered_label_counts.sort_values().index\n",
    "\n",
    "# for loss calculation function. Take anything that occurs less than 1/10th the most common frequency\n",
    "rare_label_counts = filtered_label_counts[filtered_label_counts < filtered_label_counts.iloc[0]/10]\n",
    "RARE_CLASSES = rare_label_counts.index.tolist()\n",
    "\n",
    "# Create a horizontal bar plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(sorted_labels, label_counts[sorted_labels], color='skyblue')\n",
    "plt.xlabel('Frequency')\n",
    "plt.ylabel('Labels')\n",
    "plt.title('Frequency of each label with cutoff at N=97 (descending order)')\n",
    "plt.tight_layout()\n",
    "\n",
    "print(df_classes.shape[0])\n",
    "df_classes = df_classes.rename(columns={\"filename\": \"image_name\",  \"label\": \"tags\"})\n",
    "df_classes[\"list_tags\"] = df_classes.tags.str.split(\" \")\n",
    "print(df_classes.head())\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "YFTfUuQT2kuR"
   },
   "outputs": [],
   "source": [
    "# another helper function for loading data information\n",
    "def load_split(paths, df_class):\n",
    "    if isinstance(paths, str):\n",
    "        paths = [paths]\n",
    "    elif not isinstance(paths, (list, tuple, set)):\n",
    "        raise ValueError('Expected type in {list, tuple, set, str} but got ' + str(type(paths)))\n",
    "    image_names = list()\n",
    "    for path in paths:\n",
    "        image_names += list(map(lambda x: x.split('.', 1)[0], os.listdir(path)))\n",
    "    return df_class[df_class.image_name.map(lambda x: x in image_names)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "HYpvGvOU7eoT"
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, Dataset\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "class ADE20kDataset(Dataset):\n",
    "    # basically defines what information we store for a dataset\n",
    "    def __init__(\n",
    "        self,\n",
    "        df,\n",
    "        transform,\n",
    "        paths,\n",
    "        is_train=True,\n",
    "        idx_tta=None,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.df = df\n",
    "        self.ohe_tags = list(map(transform_targets, self.df.list_tags.values))\n",
    "        self.transform = transform\n",
    "        if isinstance(paths, str):\n",
    "            self.paths = [paths]\n",
    "        elif isinstance(paths, (list, tuple)):\n",
    "            self.paths = paths\n",
    "        else:\n",
    "            raise ValueError('expected path to be a string or list or tuple')\n",
    "        self.is_train = is_train\n",
    "        if not is_train:\n",
    "            if not idx_tta in list(range(6)):\n",
    "                raise ValueError(\n",
    "                    f\"In test mode, 'idx_tta' must be an int belonging to [0, 5], got: {repr(idx_tta)}\"\n",
    "                )\n",
    "            self.idx_tta = idx_tta\n",
    "\n",
    "    # returns how many examples are in the data\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "    # get the example corresponding to the parameter idx from the dataset\n",
    "    def __getitem__(self, idx):\n",
    "        filename = self.df.iloc[idx].image_name + \".jpg\"\n",
    "        files = list()\n",
    "        for path in self.paths:\n",
    "            file_path = os.path.join(path, filename)\n",
    "            if os.path.isfile(file_path):\n",
    "                files.append(file_path)\n",
    "        if len(files) == 0:\n",
    "            raise FileNotFoundError(filename + ' not found in ' + ' '.join(self.paths))\n",
    "        elif len(files) > 1:\n",
    "            raise ValueError(f'Found {len(files)} values for {filename} as {files}')\n",
    "        else:\n",
    "            file_path = files[0]\n",
    "        img = cv2.imread(file_path)\n",
    "        if img is None:\n",
    "            raise ValueError('Problem reading image from %s' % file_path)\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        label = self.ohe_tags[idx]\n",
    "        return img, label\n",
    "\n",
    "    # returns images and labels in a batch with augmentations applied\n",
    "    def collate_fn(self, batch):\n",
    "        imgs, labels = [], []\n",
    "        for (img, label) in batch:\n",
    "            img = Image.fromarray(img)\n",
    "            # img = self.custom_augment(img)\n",
    "            # img = torch.tensor(img)\n",
    "            # img = img.permute(2, 0, 1)\n",
    "            # img = self.transform(img) # looks like it's already taken care of with the preprocess transforms from clip\n",
    "            image_input = preprocess(img).unsqueeze(0).to(device)\n",
    "            with torch.no_grad():\n",
    "              img_embed = clip_encoder.encode_image(image_input)\n",
    "            imgs.append(img_embed)\n",
    "            labels.append(label)\n",
    "        imgs = torch.cat(imgs).float().to(device)\n",
    "        labels = torch.tensor(labels).float().to(device)\n",
    "        return imgs, labels\n",
    "\n",
    "    # loads and displays the example corresponding to the parameter idx\n",
    "    def load_img(self, idx, ax=None):\n",
    "        img, ohe_label = self[idx]\n",
    "        label = self.df.iloc[idx].tags\n",
    "        title = f\"{label} - {ohe_label}\"\n",
    "        if ax is None:\n",
    "            plt.imshow(img)\n",
    "            plt.title(title)\n",
    "        else:\n",
    "            ax.imshow(img)\n",
    "            ax.set_title(title)\n",
    "\n",
    "    # applies data augmentations to the parameter img, not used so far\n",
    "    def custom_augment(self, img):\n",
    "        \"\"\"\n",
    "        If we want data augmentations\n",
    "        \"\"\"\n",
    "        return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "ts7z1IdSEtO2"
   },
   "outputs": [],
   "source": [
    "def get_transforms():\n",
    "    transform_train = T.Compose([\n",
    "      T.ToPILImage(),\n",
    "      T.Resize(224),\n",
    "      T.ToTensor(),\n",
    "      T.Normalize(\n",
    "          mean=[0.485, 0.456, 0.406],  # Set by previously known values\n",
    "          std=[0.229, 0.224, 0.225],   # Set by previously known values\n",
    "      )\n",
    "    ])\n",
    "    transform_val = T.Compose([\n",
    "      T.ToPILImage(),\n",
    "      T.Resize(224),\n",
    "      T.ToTensor(),\n",
    "      T.Normalize(\n",
    "        mean=[0.485, 0.456, 0.406],  # Set by previously known values\n",
    "        std=[0.229, 0.224, 0.225],   # Set by previously known values\n",
    "      )\n",
    "    ])\n",
    "    return transform_train, transform_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "CNRItR6TE_Ho"
   },
   "outputs": [],
   "source": [
    "# the possible labels for our dataset\n",
    "INVERSE_LABEL_IDXS = dict(enumerate(df_classes['tags'].unique()))\n",
    "\n",
    "LABEL_IDXS = {v: k for k, v in INVERSE_LABEL_IDXS.items()}\n",
    "\n",
    "# converts the parameter tag_list (list of words, subset of the keys in LABEL_IDXS) to a one-hot encoding\n",
    "def transform_targets(tag_list):\n",
    "    targets = np.zeros(len(LABEL_IDXS), dtype=np.int8)\n",
    "    mask = [LABEL_IDXS[tag] for tag in tag_list]\n",
    "    targets[mask] = 1\n",
    "    # returns list of ones and zeros\n",
    "    # (e.g. tag_list = ['primary', 'haze'] -> [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0]\n",
    "    return targets.tolist()\n",
    "\n",
    "# converts the parameter targets (one-hot encoding) back into a list of tags (subset of words from LABEL_IDXS)\n",
    "def inverse_transform_targets(targets):\n",
    "    idxs = [idx for idx, target in enumerate(targets) if target]\n",
    "    tag_list = [INVERSE_LABEL_IDXS[ix] for ix in idxs]\n",
    "    return list(sorted(tag_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6xrpF8c5F-qC",
    "outputId": "fa8e0d01-c91c-4fd9-b346-c7aa3be8693b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'airport_terminal': 0, 'bathroom': 1, 'bedroom': 2, 'conference_room': 3, 'corridor': 4, 'kitchen': 5, 'living_room': 6, 'mountain_snowy': 7, 'office': 8, 'poolroom_home': 9, 'skyscraper': 10, 'art_studio': 11, 'building_facade': 12, 'dining_room': 13, 'game_room': 14, 'highway': 15, 'home_office': 16, 'hotel_room': 17, 'street': 18, 'waiting_room': 19}\n"
     ]
    }
   ],
   "source": [
    "print(LABEL_IDXS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "uzGgoRljCoet"
   },
   "outputs": [],
   "source": [
    "df_train = load_split(train_paths, df_classes)\n",
    "df_val = load_split(val_paths, df_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "VsUucBagD2uk"
   },
   "outputs": [],
   "source": [
    "transform_train, transform_val = get_transforms()\n",
    "ds_train = ADE20kDataset(df_train, transform_train, paths=train_paths)\n",
    "ds_val = ADE20kDataset(df_val, transform_val, paths=val_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "U4KpeSZQ_5Nr"
   },
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# # Read the contents of the text file\n",
    "# with open('sceneCategories.txt', 'r') as file:\n",
    "#     lines = file.readlines()\n",
    "\n",
    "# # Extract labels from each line\n",
    "# fns, labels = [], []\n",
    "# for line in lines:\n",
    "#   e = line.split()\n",
    "#   fns.append(e[0]), labels.append(e[1])\n",
    "\n",
    "\n",
    "# # Count the frequency of each label\n",
    "# label_counts = {}\n",
    "# for label in list(set(labels)):\n",
    "#   if labels.count(label) > 97 and label != \"misc\":\n",
    "#     label_counts[label] = labels.count(label)\n",
    "\n",
    "# sorted_labels = sorted(label_counts.keys(), key=lambda x: label_counts[x])\n",
    "# print(len(sorted_labels))\n",
    "\n",
    "# # Create a bar plot\n",
    "# plt.figure(figsize=(10, 6))\n",
    "# plt.barh(sorted_labels, [label_counts[label] for label in sorted_labels], color='skyblue')\n",
    "# plt.xlabel('Frequency')\n",
    "# plt.ylabel('Labels')\n",
    "# plt.title('Frequency of each label with cutoff at N=97 (descending order)')\n",
    "# plt.tight_layout()\n",
    "\n",
    "# # Show the plot\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pswof8zV5PnZ"
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "OOtgSAG2bX3w"
   },
   "outputs": [],
   "source": [
    "from torchvision import transforms as T, models\n",
    "import torch.nn as nn\n",
    "\n",
    "def get_model(weights=False, dropout_rate=0.2):\n",
    "    model = nn.Sequential(\n",
    "      nn.Linear(512, 128),\n",
    "      nn.ReLU(inplace=True),\n",
    "      nn.Dropout(dropout_rate),\n",
    "      ######### FILL IN HERE #########\n",
    "      nn.Linear(128, 20),\n",
    "      ######### END #########\n",
    "      nn.Sigmoid()\n",
    "    )\n",
    "\n",
    "    return model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "7_hLcbxZfjLD"
   },
   "outputs": [],
   "source": [
    "def train_batch(X, Y, model, loss_fn, optimizer, loss_scale=1):\n",
    "    model.train()\n",
    "    ######### FILL IN HERE #########\n",
    "    optimizer.zero_grad()\n",
    "    Y_hat = model(X)\n",
    "    bath_loss = 0\n",
    "\n",
    "    # scale loss for classes with fewer images\n",
    "    if loss_scale != 1:\n",
    "      loss_per_item = loss_fn(Y_hat, Y)\n",
    "      # RARE_CLASSES is a str list of rare class labels that should be defined globally earlier in this notebook, manually\n",
    "      rare_classes_enc = torch.Tensor(transform_targets(RARE_CLASSES)).unsqueeze(0).to(device='cuda')\n",
    "      rare_classes_enc = torch.tile(rare_classes_enc, dims=(Y.shape[0],1))\n",
    "      mask = torch.tile((torch.sum(torch.logical_and(Y, rare_classes_enc), axis=1) > 0).unsqueeze(1), dims=(1,Y.shape[1]))\n",
    "      scaled_loss = torch.where(mask, loss_per_item * loss_scale, loss_per_item)\n",
    "      batch_loss = torch.mean(scaled_loss)\n",
    "    # don't scale loss\n",
    "    else:\n",
    "      batch_loss = loss_fn(Y_hat, Y)\n",
    "    \n",
    "    batch_loss.backward()\n",
    "    optimizer.step()\n",
    "    ######### END #########\n",
    "    Y_hat = Y_hat.detach().float().cpu().numpy()  # detach predictions from gradients for logging\n",
    "\n",
    "    return Y_hat, batch_loss.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "29sngwavfknP"
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def compute_val_loss(X, Y, model, loss_fn):\n",
    "    model.eval()\n",
    "    ######### FILL IN HERE #########\n",
    "    Y_hat = model(X)\n",
    "    batch_loss = loss_fn(Y_hat, Y)\n",
    "    batch_loss = torch.mean(batch_loss)\n",
    "    ######### END #########\n",
    "    Y_hat = Y_hat.float().cpu().numpy()\n",
    "\n",
    "    return Y_hat, batch_loss.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "xStAEF84f8zF"
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import StepLR\n",
    "from tqdm.notebook import tqdm\n",
    "import numpy as np\n",
    "from sklearn.metrics import fbeta_score, confusion_matrix\n",
    "\n",
    "def train_model(model, optimizer, loss_fn, dl_train, dl_val, epochs, filename, loss_scale=1):\n",
    "    lr_scheduler = StepLR(optimizer, step_size=7, gamma=0.1)\n",
    "\n",
    "    loss_train, loss_val = [], []\n",
    "    score_train, score_val = [], []\n",
    "\n",
    "    Y_hat_val = None\n",
    "    best_loss_val = np.inf\n",
    "\n",
    "    for idx in range(epochs):\n",
    "        loss_train_epoch, loss_val_epoch = [], []\n",
    "        Y_hat_train_epoch, Y_hat_val_epoch = [], []\n",
    "        Y_train_epoch, Y_val_epoch = [], []\n",
    "\n",
    "        for X, Y in tqdm(dl_train, leave=False):\n",
    "            Y_hat, batch_loss = train_batch(X, Y, model, loss_fn, optimizer, loss_scale=loss_scale)\n",
    "            loss_train_epoch.append(batch_loss)\n",
    "            Y_hat_train_epoch.extend(Y_hat)\n",
    "            Y_train_epoch.extend(Y.detach().float().cpu().numpy())\n",
    "\n",
    "        for X, Y in tqdm(dl_val, leave=False):\n",
    "            Y_hat, batch_loss = compute_val_loss(X, Y, model, loss_fn)\n",
    "            loss_val_epoch.append(batch_loss)\n",
    "            Y_hat_val_epoch.extend(Y_hat)\n",
    "            Y_val_epoch.extend(Y.detach().float().cpu().numpy())\n",
    "\n",
    "        avg_loss_train = np.mean(loss_train_epoch)\n",
    "        avg_loss_val = np.mean(loss_val_epoch)\n",
    "\n",
    "        Y_hat_train_epoch = np.array(Y_hat_train_epoch)\n",
    "        Y_hat_val_epoch = np.array(Y_hat_val_epoch)\n",
    "        Y_thresh_train_epoch = (Y_hat_train_epoch > .2).astype(float)\n",
    "        Y_thresh_val_epoch = (Y_hat_val_epoch > .2).astype(float)\n",
    "        Y_train_epoch = np.array(Y_train_epoch)\n",
    "        Y_val_epoch = np.array(Y_val_epoch)\n",
    "\n",
    "        score_train_epoch = fbeta_score(Y_train_epoch, Y_thresh_train_epoch, beta=2, average=\"samples\")\n",
    "        score_val_epoch = fbeta_score(Y_val_epoch, Y_thresh_val_epoch, beta=2, average=\"samples\")\n",
    "\n",
    "        # saving values for debugging\n",
    "        if avg_loss_val < best_loss_val:\n",
    "            best_loss_val = avg_loss_val\n",
    "            Y_hat_val = Y_hat_val_epoch\n",
    "            Y_thresh_val = Y_thresh_val_epoch\n",
    "            Y_val = Y_val_epoch\n",
    "\n",
    "        loss_train.append(avg_loss_train)\n",
    "        loss_val.append(avg_loss_val)\n",
    "        score_train.append(score_train_epoch)\n",
    "        score_val.append(score_val_epoch)\n",
    "\n",
    "        print(\n",
    "            f\"epoch: {idx}/{epochs} -- train loss: {avg_loss_train}, \" \\\n",
    "            f\"val loss: {avg_loss_val}\" \\\n",
    "            f\" -- train fbeta_score: {score_train_epoch}, \" \\\n",
    "            f\"val fbeta_score: {score_val_epoch}\"\n",
    "        )\n",
    "        \n",
    "        train_results = {\n",
    "            \"loss_train\": loss_train,\n",
    "            \"loss_val\": loss_val,\n",
    "            \"score_train\": score_train,\n",
    "            \"score_val\": score_val,\n",
    "            \"Y_hat_val\": Y_hat_val,\n",
    "            \"Y_thresh_val\": Y_thresh_val,\n",
    "            \"Y_val\": Y_val,\n",
    "        }\n",
    "\n",
    "    \n",
    "        torch.save(model, filename + '.pt')\n",
    "        pickle.dump(train_results, open(filename+'.results', \"wb\"))\n",
    "        print(f'Saved model to {filename}.pt')\n",
    "        print(f'Saved results to {filename}.results')\n",
    "        \n",
    "        lr_scheduler.step()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "9d33bfdfe0be4299aba8a638fb343418",
      "c9046f7a76cf400f89334cada5426dde",
      "a3aa5a7a699143c79f22e316792f04ea",
      "6f0aeea5f7a04fc7b5cb9b136b04e827",
      "fd615312758c409d8963ae3017285160",
      "eabc168a5d374df8bc3b995d316cc5d5",
      "56ad8734c4c1408595c5043df7edc893",
      "cc0ffbd45dac439fbc8a7b7f959ca2dc",
      "9214534ee6304c768f592943c5dc0123",
      "bcad5cfceeb948019c37ec886fdae4a7",
      "bfb3cc2690bf4cc7a7acb8fc0e2c8aa2"
     ]
    },
    "id": "RYq_5eGzgCHV",
    "outputId": "e1eb3e21-ac76-49b8-d95d-59eb79a1b328"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
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    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0/10 -- train loss: 0.7959169411943072, val loss: 0.2623865558550908 -- train fbeta_score: 0.2227072062195287, val fbeta_score: 0.3290371565938139\n",
      "Saved model to ade20k_clip_loss_reweight_5.pt\n",
      "Saved results to ade20k_clip_loss_reweight_5.results\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1/10 -- train loss: 0.3217814089286895, val loss: 0.15184599275772387 -- train fbeta_score: 0.39139155771946316, val fbeta_score: 0.43096623520563676\n",
      "Saved model to ade20k_clip_loss_reweight_5.pt\n",
      "Saved results to ade20k_clip_loss_reweight_5.results\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 2/10 -- train loss: 0.2291667994762224, val loss: 0.12267947426209083 -- train fbeta_score: 0.49617526771892656, val fbeta_score: 0.5661961168507303\n",
      "Saved model to ade20k_clip_loss_reweight_5.pt\n",
      "Saved results to ade20k_clip_loss_reweight_5.results\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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     },
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    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 3/10 -- train loss: 0.19139061483835418, val loss: 0.10388574147453675 -- train fbeta_score: 0.5952473088572752, val fbeta_score: 0.6645365752285952\n",
      "Saved model to ade20k_clip_loss_reweight_5.pt\n",
      "Saved results to ade20k_clip_loss_reweight_5.results\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
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    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 4/10 -- train loss: 0.16190312950620575, val loss: 0.08858964133721131 -- train fbeta_score: 0.6720176393274933, val fbeta_score: 0.7090977912361952\n",
      "Saved model to ade20k_clip_loss_reweight_5.pt\n",
      "Saved results to ade20k_clip_loss_reweight_5.results\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 5/10 -- train loss: 0.13808415250645745, val loss: 0.07628697128250049 -- train fbeta_score: 0.724195198249151, val fbeta_score: 0.7559078494240589\n",
      "Saved model to ade20k_clip_loss_reweight_5.pt\n",
      "Saved results to ade20k_clip_loss_reweight_5.results\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 6/10 -- train loss: 0.11913079094319116, val loss: 0.0668487842552937 -- train fbeta_score: 0.7686424180429426, val fbeta_score: 0.7882080513003206\n",
      "Saved model to ade20k_clip_loss_reweight_5.pt\n",
      "Saved results to ade20k_clip_loss_reweight_5.results\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 7/10 -- train loss: 0.1109098292414158, val loss: 0.06605237987465583 -- train fbeta_score: 0.78986318518915, val fbeta_score: 0.7890393064956657\n",
      "Saved model to ade20k_clip_loss_reweight_5.pt\n",
      "Saved results to ade20k_clip_loss_reweight_5.results\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 8/10 -- train loss: 0.11000247027665849, val loss: 0.0652778256827822 -- train fbeta_score: 0.7933561224587827, val fbeta_score: 0.7904346277164234\n",
      "Saved model to ade20k_clip_loss_reweight_5.pt\n",
      "Saved results to ade20k_clip_loss_reweight_5.results\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 9/10 -- train loss: 0.10840769407768099, val loss: 0.06451372866733716 -- train fbeta_score: 0.7927163434375875, val fbeta_score: 0.7964612278826744\n",
      "Saved model to ade20k_clip_loss_reweight_5.pt\n",
      "Saved results to ade20k_clip_loss_reweight_5.results\n"
     ]
    },
    {
     "data": {
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   ],
   "source": [
    "from torch.optim import Adam\n",
    "\n",
    "# Set hyeprparameters for batch size, epochs, learning rate\n",
    "BATCH_SIZE = 64\n",
    "LR = 1e-4\n",
    "EPOCHS = 10\n",
    "LOSS_SCALES = [5, 10, 50, 100]\n",
    "\n",
    "\n",
    "for loss_scale in LOSS_SCALES:\n",
    "    filename = f'ade20k_clip_loss_reweight_{loss_scale}'\n",
    "\n",
    "    # Load dataset in DataLoader\n",
    "    dl_train = DataLoader(\n",
    "        ds_train,\n",
    "        batch_size=BATCH_SIZE,\n",
    "        shuffle=True,\n",
    "        collate_fn=ds_train.collate_fn\n",
    "    )\n",
    "    dl_val = DataLoader(\n",
    "        ds_val,\n",
    "        batch_size=BATCH_SIZE,\n",
    "        shuffle=False,\n",
    "        collate_fn=ds_val.collate_fn\n",
    "    )\n",
    "    \n",
    "    # Initialize model\n",
    "    ######### FILL IN HERE #########\n",
    "    # What needs to change to use the pretrained model?\n",
    "    model = get_model(False)\n",
    "    ######### END #########\n",
    "    optimizer = Adam(model.parameters(), lr=LR)\n",
    "    loss_fn = nn.BCELoss()\n",
    "    \n",
    "    # Checkpoint file to save models + results\n",
    "    # filename = f'/content/drive/MyDrive/pretrained_full_data_resnet18__batch_size={BATCH_SIZE}__lr={LR}__epochs={EPOCHS}'\n",
    "    # Train\n",
    "    train_model(\n",
    "        ######### FILL IN HERE #########\n",
    "        dl_train=dl_train,\n",
    "        dl_val=dl_val,\n",
    "        epochs=EPOCHS,\n",
    "        model=model,\n",
    "        optimizer=optimizer,\n",
    "        loss_fn=loss_fn,\n",
    "        filename=filename,\n",
    "        loss_scale=loss_scale\n",
    "        ######### END #########\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "id": "EYHupnaB8g8s"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/gpfs/ryzhu/cos514\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ryzhu/.pyenv/versions/3.10.12/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning:\n",
      "\n",
      "using dhist requires you to install the `pickleshare` library.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%cd {PUT DIR LOCATION HERE}/\n",
    "filename = 'ade20k_clip_loss_reweight_10.0'\n",
    "model = torch.load(filename + '.pt')  # load best model\n",
    "train_results = pickle.load(open(filename + '.results', \"rb\"))  # load best model results"
   ]
  },
  {
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       "                            \n",
       "var gd = document.getElementById('1cc2960b-90ec-4a00-9716-397b9b3401bf');\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Track loss and performance over time (for train and val)\n",
    "loss_train = train_results[\"loss_train\"]\n",
    "loss_val = train_results[\"loss_val\"]\n",
    "score_train = train_results[\"score_train\"]\n",
    "score_val = train_results[\"score_val\"]\n",
    "\n",
    "fig = make_subplots(rows=1, cols=2, subplot_titles=(\"Loss\", \"Fbeta scores\"))\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x=list(range(len(loss_train))),\n",
    "        y=loss_train,\n",
    "        name=\"loss_train\",\n",
    "    ),\n",
    "    row=1, col=1\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x=list(range(len(loss_val))),\n",
    "        y=loss_val,\n",
    "        name=\"loss_val\",\n",
    "    ),\n",
    "    row=1, col=1\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x=list(range(len(score_train))),\n",
    "        y=score_train,\n",
    "        name=\"score_train\",\n",
    "    ),\n",
    "    row=1, col=2\n",
    ")\n",
    "fig.add_trace(\n",
    "    go.Scatter(\n",
    "        x=list(range(len(score_val))),\n",
    "        y=score_val,\n",
    "        name=\"score_val\",\n",
    "    ),\n",
    "    row=1, col=2\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
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",
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    }
   ],
   "source": [
    "Y_hat_val = np.array(train_results[\"Y_hat_val\"])\n",
    "Y_val = np.array(train_results[\"Y_val\"])\n",
    "\n",
    "pos_probas, neg_probas = [], []\n",
    "for class_, idx in LABEL_IDXS.items():\n",
    "    pos_probas.append(Y_hat_val[np.where(Y_val[:, idx] != 0), idx].mean())\n",
    "    neg_probas.append(Y_hat_val[np.where(Y_val[:, idx] == 0), idx].mean())\n",
    "go.Figure([\n",
    "    go.Bar(x=list(LABEL_IDXS), y=pos_probas, name=\"Y_hat proba | Y = 1\"),\n",
    "    go.Bar(x=list(LABEL_IDXS), y=neg_probas, name=\"Y_hat proba | Y = 0\")\n",
    "]).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_thresholds(Y_hat, Y):\n",
    "    \"\"\"\n",
    "    This is just a simple solution, but there are more rigorous ways to choose this threshold.\n",
    "    \"\"\"\n",
    "    N_tags = Y.shape[1]\n",
    "    best_threshs = [0.2] * N_tags\n",
    "    resolution = 100\n",
    "    for jdx in tqdm(range(N_tags)):\n",
    "        best_score = 0\n",
    "        threshs = best_threshs.copy()\n",
    "        for kdx in range(resolution):\n",
    "            kdx /= resolution\n",
    "            threshs[jdx] = kdx\n",
    "            Y_hat_thresh = (Y_hat > threshs).astype(float)\n",
    "            score = fbeta_score(Y, Y_hat_thresh, beta=2, average=\"samples\")\n",
    "            if score > best_score:\n",
    "                best_score = score\n",
    "                best_threshs[jdx] = kdx\n",
    "\n",
    "    global_best_score = fbeta_score(Y, (Y_hat > best_threshs).astype(float), beta=2, average=\"samples\")\n",
    "    print(f\"threshs: {best_threshs} -- best score: {global_best_score}\")\n",
    "\n",
    "    return best_threshs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "f2e68e0aeff945c58bb2ec09f3a17658",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshs: [0.06, 0.23, 0.2, 0.11, 0.04, 0.17, 0.13, 0.14, 0.11, 0.04, 0.12, 0.05, 0.1, 0.18, 0.08, 0.16, 0.1, 0.06, 0.35, 0.04] -- best score: 0.8658166784725388\n"
     ]
    }
   ],
   "source": [
    "threshs = find_best_thresholds(Y_hat_val, Y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "dict_keys(['airport_terminal', 'bathroom', 'bedroom', 'conference_room', 'corridor', 'kitchen', 'living_room', 'mountain_snowy', 'office', 'poolroom_home', 'skyscraper', 'art_studio', 'building_facade', 'dining_room', 'game_room', 'highway', 'home_office', 'hotel_room', 'street', 'waiting_room'])\n"
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7YIFeu/VgHXfbuED30odWOpKrQ0Tld7IbevMPP+vY26eUbJZDNyUG+IDODXVRMwK9UccR+OPi1UAF+oEjv2nZ7vegU1cg/RPojbxG4Pa9h+8J9HpKlksoF4dAb9l9rD54VN4WSKlikTuShM+R+O8hJtBVbHyJke8qbf7hIDoPnIqurWoii48QLz7zZk6fxnm4bGB9FOgAZAYSIAESIAESIAESIAESIAESIAESIAESIAESIAFLBCjQTeCTQzwLVWmPRAniYN3cwYioDt10lV6+fKXDiTTrIiFcLmDvmom+Qrj8ff02StTsbCiEi1GBfujoGTToMAxjVAz1EgVzOi/LP4Hu87qfPX+hdkZPxu4Dx7F//WS1UzvaewJdQtdICBu/4WvkYYCEo0kUP06gIVz+S4FeqlYXHVZmwYQevrrIlUDPU64V8qv46cN6NPWV17+d9JLJbwgXiWMuccu/yJJePSDwcjkuTpz+EzVbDjR8CG1gfWRi6DIrCZAACZAACZAACZAACZAACZAACZAACZAACZBAEAhQoJuEJnHAJR546SK51OGgjRHJh0QXmSxhOvb8fEIf+rl83U4MGLtA7+qW3d2OJD+T38mu8swqfIok/0K4GBXoDilfo0Jh9O5QV9cpO9dnL9mMCbNX6VjrEnNddmnvOXgCpQvn0iFcHElicIswdsT79rsD/c/L11BeHYBZrngeX6LZEZbEq1k1NKrp+xBRv7HD/0uBXqfNEFy5dgvb1YGljgcdchBqu14TcOnvG752oEu7okaN7Ct+unAxI9Alv1e/Kdi++zCWTunt7Ff5ueMQUYmbX1I9OHmhHrDIgwgR/I4kY+fAL78hzxeZDPeRyaHL7CRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiYJUKCbBPby1Wt9EObOfb/qAzILq9jh8eLEwt37D5UAPa2lbXEVmmVs/9aQnegiwE+fv4QKJfIhzUdJ8MuJc9i1/xgqlvpaCfhGzk+3KtClIvmso6fOo2ShL3W87wMq5vi9B//o2OAOge7YkS4yt0i+7IgTOwb+unoLy5TQz6vk7aQh7fQ1+RXo8rMhExZj8eodWvJ+lSMDRNqv3rQHqVImVvK5rzM2un/i+b8U6Gu27EWv4bP1Ya+5sn2GW3cf4AfVZ+HChtHhanyGcHG065uS+ZAqRWI8+ucpOjavZlqgS99Xb94fz9WO/gqqrhRJE6jY8Fe1VO/ephaqlC2Anw6fQisVXiZSpIgoUzQ3UiRJAOmTH/cfhRxEunXJCP3vApXa6WsPqI9MDl1mJwESIAESIAESIAESIAESIAESIAESIAESIAESMEmAAt0kMMkuu4U3qfjhIml/P39ZC+qY0aPpXcelCn+JUoVy6RAukmTH98Q5a7REva9kdtLE8VCpdH40rFHaV1iX4BDoN27fw0C1u/3I8bOIED48iuXPgepqR3rlxn2cAl12pS9f96PeKS8xtl+rfydLHB9li+VBvWolEFmJXf8EurR70aodWL7+R1xR0l1CvYjgbde4CmLHiu4kGRoEulzMvBVbsWjldtx/+BifpEqGOlWK65A6fmOgP1J9NHjcQuxVO/NfvHyJ3OoBwcRBbU0LdPlMkeiTVH/Lgan/qHGRXAnygnmyol7VEs4d5zJmhNHhY2f0+Eiowt9kTJcK35T8GgVUKBmjfRSEocsiJEACJEACJEACJEACJEACJEACJEACJEACJEACJghQoJuAxawkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAL2IUCBbp++ZktJgARIgARIgARIgARIgARIgARIgARIgARIgARIgARMEKBANwGLWUmABEiABEiABEiABEiABEiABEiABEiABEiABEiABOxDgALdPn3NlpIACZAACZAACZAACZAACZAACZAACZAACZAACZAACZggQIFuAhazkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJ2IcABbp9+potJQESIAESIAESIAESIAESIAESIAESIAESIAESIAESMEGAAt0ELGYlARIgARIgARIgARIgARIgARIgARIgARIgARIgARKwDwEKdPv0NVtKAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRgggAFuglYzEoCJEACJEACJEACJEACJEACJEACJEACJEACJEACJGAfAhTo9ulrtpQESIAESIAESIAESIAESIAESIAESIAESIAESIAESMAEAQp0E7CYlQRIgARIgARIgARIgARIgARIgARIgARIgARIgARIwD4EKNDt09dsKQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgAkCFOgmYDErCZAACZAACZAACZAACZAACZAACZAACZAACZAACZCAfQhQoNunr9lSEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEwQo0E3AYlYSIAESIAESIAESIAESIAESIAESIAESIAESIAESIAH7EKBAt09fs6UkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAImCFCgm4DFrCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAvYhQIFun75mS0mABEiABEiABEiABEiABEiABEiABEiABEiABEiABEwQoEA3AYtZSYAESIAESIAESIAESIAESIAESIAESIAESIAESIAE7EOAAt0+fc2WkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJmCBAgW4CFrOSAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAnYhwAFun36mi0lARIgARIgARIgARIgARIgARIgARIgARIgARIgARIwQYAC3QQsZiUBEiABEiABEiABEiABEiABEiABEiABEiABEiABErAPAQp0+/Q1W0oCJEACJEACJEACJEACJEACJEACJEACJEACJEACJGCCAAW6CVjMSgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkYB8CFOj26Wu2lARIgARIgARIgARIgARIgARIgARIgARIgARIgARIwAQBCnQTsJiVBEiABEiABEiABEiABEiABEiABEiABEiABEiABEjAPgQo0O3T12wpCZAACZAACZAACZAACZAACZAACZAACZAACZAACZCACQIU6CZgMSsJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIB9CFCg26ev2VISIAESIAESIAESIAESIAESIAESIAESIAESIAESIAETBCjQTcBiVhIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAfsQoEC3T1+7VUuv3riD4jU6oVHN0vBqVi3UXPuVa7ewfttPqFquEBLGjx2k61q9eQ8iRoyAskVzB6l8SBXqMXQm1qm2/bp9JiKp6wvutPfgSTTvOhqrZg1A+k9SBnf1rI8ESOADExg5ZRnWbN2L/esnW76SN2/eYur8tciTMxOyZUrrq77g/BzLF8oKSIAESCAUEPgv5sUF323D8MlLQ2ydGAow8hJIgARIwBeBk79f0PPemT8u658P7NIIpQrnIiUSIAGbEqBAt2nHh/ZmP/znCWYv2YScWT/D17kyh5rLPXDkNzTuNBLLp/dFpnSpg3RdtVsPRozoUTF1WIcglQ+pQtt2HcJvZy+hbePKCB8uXLB/DAV6sCNlhSQQqggEp8B59fo1shRphE7Nq6NBjVIU6KGqp3kxJEACoY1AcM6//rWNAj209TqvhwRIICQJvHz5CkWrd0SKpAlR45vCCBsmLNKmSYa0qZOH5MeybhIggVBMgAI9FHcOLy30EfBkgR7StCnQQ5ow6yeBD0sgOAUOBfqH7Ut+OgmQgHsRCM75lwLdvfqeV0sCJBAyBM5f/BvfNOiFeeO6qU196UPmQ1grCZCAWxGgQHer7vKMixUJPWbGd7h+8y4eP3mKOLFj4Mtsn6Fdo8pImji+buS/L14ie/Em6NqqJupWLaF/5vhysGB8D4ybuRIHfvlNh1HZsngEqjbth1gxoyF75k+xbus+3Lh1D8mTJoBX02oo8nV2X+AWrdqBpWt/wNXrt/VnlyqUC23UZ0eJHFHne/rsOXKWao5urb/F69dvdN5rN++gfrWSmLNsy3udsHBiD/25RpLsPj966ryvrF9lz4DZY7ron506exETZ6/GryfPI1w49ZQ7dTK0ql8RX+XI4CwjbU2mOH1TMh+mqBAHZ/+4gvIl8qJ7m2/1dTepVRb/PH6KHXuO4JH638zpU6Nfpwa4+Nd1zF+xTe0yv4jYsaKjStmCaFmvgrPeMdNXYPn6H3Fw01RfHKS+5/++gOxQf/TPU2RMlwp9verh41TJnGW7DJyGY7/9gTv3HiJ8+HD6Sf23FYuicpn8zjwU6EZGCPOQgPsScMzRU4d5YfS05Th55iJiqrdtZC5oVqecs2GB3QMc8twviTYNK6F53fLOe8GMkZ0wac4aHD52Rs//VcsVRPM65REmTJhA7xmSYf+RU7r873/8pcNW5fkiow4ZljxJAudHy/1A7jfbdh3Gw0eP9dxWu3IxVK9Q2Jln1/5jaNVjHAZ3a4wf9v6Cn3/9Xf+uUJ6s6N62lron/YRVm3bjirrnSN3yGYXzZnPfjuaVkwAJhDoCRudfo2tNWYdPnLMaG7bvV2v1Z2rNlxSxYkTX86Yj1J9/62WZC8sXz4u/rt7EKHUvOKjmRNnJKevHNg0rqzW/bxFlZC6WtW+8ODF12e827NLfIZLJfKrW+UkTx8OMRRvU94LTkNn/61yfo49ap0aPFiXU9RMviARIwBoBCakic5N875T13ifq+2j54nmc6zIj845jvhzYuRHmrdiivn9fQhz13djnOvL46T/xbcuBvi42VYrE2LRwmP7Z5h8OKi+wGX9evqbXuhJu0KtZVaRMlkj/PrD5Ub4zy/py94FjePL0OVIkU7vc1dpS/nOsYx3znqxPZd6TdWQKNe91aFoVhfP59huBcTHyeUZ7JiAnI3UE5locfLi+Nkqc+UITAQr00NQbNrkWEbt7D55ApvRpEFcJ7Ft3HiixuxWRIkXEmtkDtTj2T6DLhBwxYngUVGLi0zQpIHFyRczIDeb0uUsoXuALfQMVGTJv+Vbs/OlXrJs3BGlSJtF0ZaKepULDiADJmSU9Ll65jukLN+DzDGkwa1RnfcNy3PCiRomM+HFjoWShL3V9mZSIvqBukhIHbUDnhkidMrGuU67D6CL93IW/IbI5WtTI6NjcO7a7hHORV8FkIdDIa4QS2wWQ78vP1bVAS5u1Kqbw0ql9nCFjpK2XrtxQYVbComyx3OohQhwkThBXPygQgR42bBjU/KYoyqnFxGMl0LsNmaGFeswY0dC2USX1BSY19vx8XLOYOLidU+T4J9CFiSwoKihJ//z5CwydtBhvFfe1cwfrz5LUsf8UzUHkklz3z7+exsqNuzGqTwtnnDgKdJv8gbOZtiUgC+r5KkauzIciT0Rq/HTolBYuA7s0RKXS3g/UjNwDRIrXbz9MzWVFULqId6zJJIniI0nCuFqgL1i5DZHVPaNMkdxIpebin9UDVZljRvZu4cwv+fy7Z0jelt3HIP9XWVCuWB795UXuDTL/r1b3IRE18gBVrkF2IDWrXU5/uflZCRp5qNq6YUW0qOv9ANIh0GUul4eS8kBYwmH1Gz0XEcKHx6cfp9BiP1GCOJi5eJPKfxTbl43S9xcmEiABEggOAkbnX6NrzZbdx+q1evXyhbX4lrX6hh379WYMvwLd73o5V/bPkFTN15Ub99HrfAnDJfP1mi179fpwplpv51LzpCQjc7Hkc6zzJaxj/WqlED16FH0vOHbqD0SIEA4NqpfSQunKtdvoOWyWXkvLJhwmEiABzyEga8MmKpSqOAT5birfp+U77b5DJ7Hzu7G4efu+oXlH5o55yj2kVOs62aiW5qMkei6aMHuVcx0p68JNP/yM/qPnoYfaDPFZ2uX87sIAACAASURBVI/0PJbh01R6rTtt/jo0qV1W/1seMorLuHbjrvIOg/U62D+fIPNjmpRJ1ZzWV3+ubGiT9eMJJewnz1uLjmqTRZ0qxXWnOeY92Ugn817MGFH1ZjjxG5uVyHdsPAyMy8NHTwx9ntGREtD62ohr4fraKGnmC40EKNBDY6/Y8Jp2/nQUbXqO1+IinZIN/gn0JUpcLJjQQ++q9pnkBpNIieRJQ9o5fyx1FKrcHiXVQR99OtTVu6OLVPXSO9od8loyi6T26jdZxyQXmeK44ZUp8hUGdW2kD/x0pJAM4VKjxQB8kSWdjvnrM1Vp0le3t2/H+s6b6YuXL7XwTxDv3UGmjutu36SK3oXuSCLGZy/djH3rJqqn6zGcPy9QqZ2S7jk0G0n+CXS/9cluJJHyWxYPdz5ldzVk5YtTsiTxMWFgW/1rCnQb/mGzybYiIAvqFWqHzNYlI7SAlvT27VvUVHPb02f/Yv38If7y8HsPCCyEy/L1O/X9wrHTRz5H5rTcOTJieK9m+nPkevy7Z8gruZHVW0fL1MNJR7px+x5KfttFP2CVeVhEf/s+kzBtuJfe0ehIg8YtxCp1GPSuleP0zneHQF8ypTeyZPjYmU/m9H/V2zsrZw7QD4YliXwqW7c7RvdtqR/OMpEACZBAcBAwOv8aWWvKg8JGHUcoaVQbtSoVdV6e3xjoAa2Xh0xYhNVqnty+bLSW6JJk00vFRmrujRhRnyUkychcLPlknS87ROXNI0dyrCvH9GuFEgVzOn/eacBU9XbmX9iwYGhwoGUdJEACoYSAfLeUt1lWzxno67yuazfuaJlsdN6R+XKlejNwz5oJepOcI8kaUNZxjnWkYxf63LHdnG/OiIwuVKU9xg1orb2BI8lb33nLt8Lwns31Ro6A5kf5zn1IPQxYMrm3czOa1DNCbdLbrR4IOHa5u5r3ZK0qPqO/ertcHhRKCoyL0c8z2s3+ra+Nuhaur42SZr7QSIACPTT2iodfkyyg123bp+WEyIR/Hj/D46fP9A1x4cSeKhxK2gBDuOxfP/k9Qo6wJnIz85madBqlwo/8q+uVECRe/aZg2TRvIe1I8hQ0W/HG+mlv5xY1nDc8v18cJH9ICXS5GedRN105vDPM/3d1O67v1avXkKfVs0d7h3nxr62OG7V/X3gOb5mOqFEiOdstYl52iDoEt38C3W99R46fRb12QzF/fHct/CXJ63KLVn2vdtGfx+27D/RuTvlPnpg7rpsC3cP/sNk82xPwLwbv1AXrMHnuWhzdMUvtyA6nJUpg94DABPoa9WaO33tBnTZDVAipsJAvOpL8u557D/7B19+00Q9SG9Yo7avfGnQYpueuFdP7YeDYBfo6j2yd4SuPYw6cMrQDCuTO4hToa+cO8nWwlDwUvnnnvq7LkaTuL0s3VyG3amlRz0QCJEACwUHAyPz7VM0/Rtaajrd8flFzn89NJP4JdFfr5fL1emihJQ8gfaYpatfm5LlrcGjzNL3WNzIX+7f2PaMkuYgjx1zs+ByRULLb/cDGKcGBlnWQAAmEAgL3H/6DfBXawO/GLp+XZmTekV3r/s2XsgaU5FhHuhLo36tQfe16T1RvvoR/j4q4jLYqLKy8He/f93IpVLFhL/xx6araXBHOVx1v3rzR/z7xwxz9v66+88uGkc+LNESLet/otx6NcDH6eUa72T9+Rl0L19dGSTNfaCRAgR4ae8XDr6nr4OnYuOOADrdSSMWBTZIwnnrl8hZ6j5ijRLd3PPGAYqCbEeiye/DchSvYvGg4lq/biQFKiOxQr847XnlyoM5fsa3eYShxGwO64YWUQHfsSpSbbjHFxW+Kol4ZS5Ionr83U/mFf9e9ePX3+on84S3TlECP7KxadiElUCEEJIyLJKMC3e9i4uyfV1Cr1UD1Gl0ULYQklIvEWB86cTGiqc9zxHenQPfwP2w2z/YE/FtQL1nzAwaPX4hdq8bpt2aM3AOCItCbdh6l7x3ycE+Sf9dzQT24Lad2gct8L2dJ+EwSjuqEiq8p9wmf/99nHr/lHTvQ/Qp0uf9cVbuivpvRz1nc1b3N9gOHAEiABCwTMDL/SpgBeQMmsLWmvGV46OjvOiSCz2RGoPtcV/usw+da/Lmar43MxVLelUhyHPA3eUh7HdrRkfyuZy3DZQUkQAIfnIDju7KrtZvj4ozMO+IA/Jsv/a4jXQn0Feq8sP5j5qtQVJ2QWDkMv0nelJE3vgPyCfLGZGYVhsbr/+Fc/dbhCD3r36a5rMUao8m3ZdCqQUXnm40BcTH6eUY72T9+Rl0L19dGSTNfaCRAgR4ae8WDr0le7ZFJXEKMyBNkR/r15DnI7sHgFug1mvdXT4gj6HqdT0XVK/uZP0vj/GznDvTKagd6y4B3oDtea5VXTzOpWOJBSXXaSGy0qDpkjCM5dkTKbkif4WVc1W92B3pIC3RZRGxUcTF3qNd0RZw7ksRzl0SBHpRRwjIk4H4E/FtQj5+1SoWR2qQe4k3X5zEYuQfIvCw7bCSUisTP9ZmMfvEJbAe6HObZqKbvHegS81x2iYv0lh0ya9Wh1L9s870D3RGf3SFtKNDdb6zyiknA0wgYmX9lbpMd34GtNUdOVeEN1Dk2jkPlHazMCHTvnaDx1A70jr5QT1ExfiXOr88d6IHNxVIBBbqnjVi2hwTMEXB8Vw58B3rA805AO9CNCHSHT/Ab3s9vawIS6DI/ylvh8lZ8QMmIQDfKxcjnGe2RQHegB+JauL42Spr5QiMBCvTQ2CsefE2yG694jU46tnjFUl87W/rT4VOQm1ZwCnQ5sFNeWWrXuDKaqgPgRN4XrtpBxXMs5utgITkgRA729BsD3dUrqXIwXLVm/Zx5g9JVcjDT3XuPnPEfHXXItd64dQ8b1aEgjvjB8jsJdyC7bCQ2vH9fIuTnH2oHeo+hM3Uct++Xj/aFo1arQfqwFQr0oIwSliEB9yPgakH9UoWg0iJFvUEjc4HRe4C0Xg5Frl6h0HvnQlgV6FJ3hQY9EVE9XF2hHobKQcmSJIZm6dpdUVuF8xJxv333EXToO8nXYcuSr9+oeVirQrvsXjXeVwx07kB3vzHLKyYBTyFgZP6VthpZa278/gC6DpqONXMGqbcKkzsRzVm2GaOnrXjvEFFX62V562jVJomB/u7AZHkwKp8fJXIk5xrYyFzs39qXO9A9ZfSyHSRgjED5+j11xjXqDBzH2TLyb4kLnjhBXP22o5F5x+g60tUOdPEJRat54XMVK33euO6+YpjLWz4SUiVF0oQB7kAfMmExFq/eoc8zy/1FRl+N//38ZX1gqX/znvzc5w50+XdgXIx+nrFe8P8NT6Ouhetro6SZLzQSoEAPjb3iwdckcbsqqMPbJA5uz3Z1EEudJi2hPabMX6t3/VkR6Ndu3kG1coXw8UdJcf3WXX1KtTxlXjmzvz7dWpLjQM2a3xRBzqzp9WtPMxdvVDfBNJgzpqsWKQE9MZaD8IooCZ9CndrdqGYZvZtS4nwnT5LAcK/NWLRBnfK9Gm0aVkLqlEkgB4KWLZobB9Xrss3UQ4R4cWKhXrUSSJY4gY4tvkbtgPxSXWuv9nUCvJl+KIG+9cdDOtSB7JwvmCcbbqmYv7OWbNLx4r/KrmKgK2kmiSFcDA8RZiQBtyQgX0gWrtquw6Jky+R9lsVaFYf2jArzJOdQyNkTRu8BAqBxp5E4fe6SPptC4vAmiBtbH+Jk9IuPf/mk7t0HjqNVj3HImzMTyhXPgyfqS88sddiyHPoph5PGV+GtRPbIeQ8SBkzempIDS/cfOaV3Zraqr2JPqv8kcQe6Ww5XXjQJeBQBI/OvNNjIWlMefJZRDxNlDd1Jzb8x1RpahIfM7xLj99ftM/XBewGtl0VoVWrUG7FjRtdvEUWJFEkdvrwbcobEzJGd9drZ6Fws+bgD3aOGKxtDAkEi4Fi7Zc34CSqXya8fxsmctvfgCb2Ry+i8Y3Qd6Uqgy4XLd3l5u1LWurLmlQPlT525qOV97w519aHGAc2PIpqrN+uvZXvdqiWQMV0qPHj0GDvUPCtn56ybOzjA7/x+BXpgXIx+ntFOCWh9bcS1cH1tlDTzhUYCFOihsVc8/JpECkss8qMnzyNSpAjIle0zZFU3IDn0x4pAl0V9vLgxcVLFr5UkMc27tKyJRAniOImKvFm4cjuWqXjoV6/fQZzYMVCy0Jc6HqTjgM2AbnhSkdyk5VolbntsFeNs+oiOzt3hRrpOJPyAsfOxe/8xJc9foUzRrzCgc0NdVHa4T1u4Hr+cOAs57ClB/DjIrthIbHFH2JnQFsJFrnve8q2a6z21EEipHi6UL54XO/f9yh3oRgYE85CAhxCQBbXszC6SLzv2/HxCfxmQXTQiwOVwaEcycg+QvJf/vom+o+bqOT2iOqxJRI58YTL6xSegBb7Uv+/QSR1KQHb7iAzKnSOjfhAoO4ccSXYTjZ+1UsujBw8fI3nSBPotpm8rFnHmoUD3kAHMZpCAGxMwOv8aXWvKBhNZqx777U/EVWtd2SUpQn3Rqh2GBLp8zqUrNzB6+goc/PU0RMpnUPcD2TzikOcO3EbmYgp0Nx6cvHQSCEYCEk5VDqeXDRay8U3O3pK32mV9aHTeMbqO9E+gy+dIKBeZD+UwY3lbPJmKrZ5HbcpoUL2Udg+B+QSR2rIG3fvzcdy++1CHQZW3zaUtpQrn0m0xEsLFgTYwLkY+z2g3BbS+NuJa5HO4vjZKm/lCGwEK9NDWI7yeIBHw7wYTpMpYiARIgARIgARIgARIgARIgARIgARIgARIgARIgAQUAQp0DgOPIPAhBbqEKmnTa3ygHKNGiYw9ayYEmo8ZSIAESIAESIAESIAESIAESIAESIAESCAkCdBlhCRd1u1pBCjQPa1HbdqeDynQbYqczSYBEiABEiABEiABEiABEiABEiABEiABEiABjydAge7xXcwGkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJBIUABXpQqLEMCZAACZAACZAACZAACZAACZAACZAACZAACZAACZCAxxOgQPf4LmYDSYAESIAESIAESIAESIAESIAESIAESIAESIAESIAEgkKAAj0o1FiGBEiABEiABEiABEiABEiABEiABEiABEiABEiABEjA4wlQoFvs4mIRalisgcVtRyBMGNs1mQ22RmDz08WIED6stUo8oPTT569QKUNXD2gJm/BfEnh95ep/+XH8LDcn0GVpBxSrksvNWxE8lz9x+k6sWn0oeCpjLbYgEOnweVu0k40MPgJrrk1H1Mjhg69CN63p5as3KBOjrptePS/7gxF48+aDfTQ/2P0I5K70Ffovbet+Fx6KrpgC3WJnUKBbBGjH4hTodux1S22mQPfGR4FuaRjZtjAFum27PkgNp0B/h40CPUhDyNaFKNBt3f1BajwFujc2CvQgDR8WokDnGDBBgALdBCx/slKgW2RIgW4RoB2LU6DbsdcttZkCnQLd0gCyeWEKdJsPAJPNp0CnQDc5ZJjdBwEKdA4HswQo0CnQzY4Z5vdBgAKdw8EEAQp0E7Ao0K3DclUDBXrIcPXoWinQPbp7Q6JxFOgU6CExruxSJwW6XXo6eNpJgU6BHjwjyZ61UKDbs9+ttJoCnQLdyvixfVkKdNsPATMAKNDN0HKdlzvQLTKkQLcI0I7FKdDt2OuW2kyBToFuaQDZvDAFus0HgMnmU6BToJscMszugwAFOoeDWQIU6BToZscM8/sgQIHO4WCCAAW6CVj+ZKVAt8iQAt0iQDsWp0C3Y69bajMFOgW6pQFk88IU6DYfACabT4FOgW5yyDA7BTrHgAUCFOgU6BaGD4tSoHMMmCBAgW4CFgW6dViuaqBADxmuHl0rBbpHd29INI4CnQI9JMaVXeqkQLdLTwdPOynQKdCDZyTZsxbuQLdnv1tpNQU6BbqV8WP7shToth8CZgBQoJuh5Tovd6BbZEiBbhGgHYtToNux1y21mQKdAt3SALJ5YQp0mw8Ak82nQKdANzlkmN0HAQp0DgezBCjQKdDNjhnm90GAAp3DwQQBCnQTsPzJSoFukSEFukWAdixOgW7HXrfUZgp0CnRLA8jmhSnQbT4ATDafAp0C3eSQYXYKdI4BCwQo0CnQLQwfFqVA5xgwQYAC3QQsCnTrsFzVQIEeMlw9ulYKdI/u3pBoHAU6BXpIjCu71EmBbpeeDp52UqBToAfPSLJnLdyBbs9+t9JqCnQKdCvjx/ZlKdBtPwTMAKBAN0PLdV7uQLfIkALdIkA7FqdAt2OvW2ozBToFuqUBZPPCFOg2HwAmm0+BToFucsgwuw8CFOgcDmYJUKBToJsdM8zvgwAFOoeDCQIU6CZg+ZOVAt0iQwp0iwDtWJwC3Y69bqnNFOgU6JYGkM0LU6DbfACYbD4FOgW6ySHD7BToHAMWCFCgU6BbGD4sSoHOMWCCAAW6CVgU6NZhuaqBAj1kuHp0rRToHt29IdE4CnQK9JAYV3apkwLdLj0dPO2kQKdAD56RZM9auAPdnv1updUU6BToVsaP7ctSoNt+CJgBQIFuhpbrvNyBbpEhBbpFgHYsToFux1631GYKdAp0SwPI5oUp0G0+AEw2nwKdAt3kkGF2HwQo0DkczBKgQKdANztmmN8HAQp0DgcTBCjQTcDyJysFukWG7izQSzUsjLwVvkCvCiMsUnBdvFjt/KjWqRyaZO1sqH7/8tfrWxWFqudBghTxce/6fayasBlrJ201VGeozOTmAr1yu9Ko0bk8osWKiiF1JmLf2sPBgjlc+HBoM6EBXr18jUnt5gZYZ5X2ZVC8bgEk/TgRXjx7gZ83/YqJqsyzx89RsFpu9FzU9r3y2xfswcjGU4PlWv/rSijQPUOgL9rfB/2bzcH5k38H6xBKljoBWvariNTpkuDN6zfYv+M3zByyDi9fvHb5OZUbF0Cxyl8iyUfx8OL5SxzceRqT+67Gsyf/IkLE8NhwZvh75dpXmoAzxy4H63X/V5W5s0BPmCIeui9si09zpMHl03+jZa7uwYJN6ms/pQlSZUqBm5duY4rXfBzedsxl3QWr5UHPxa7m1N0Y2WgqviieBUM3+b6ul/++ROnodYLlWv/rSijQ3xGfOH0nVq0+9F93QZA+L1nSOJg1tSFKVRgdpPKBFYoeLRLGDK+JxcsPYPfes76yx48XHY0bFECunB8jZozIGDhsPXbtOeOyyiiRI6B5k8Io8HU6vFbz9fdq/p01bzdeqrWPI2VInxQN6n6NjJ8lQ/jwYfFt/Wm4c/dxYJcYKn7v7gI9W4HP0HpkbST+KAHWz9qJ6T2XWeaao3BG1O5SAR+pfg0bNgzOHbuEiR0X4sr5Gy7rjqzGWoshNZGjcCaECxcG549fxqTOi3Hr77uIkzAmlp0Z+165m1fuom6WLpav9UNUQIHuTf3lqzcoE6Puh+iCEPnMJX9MQN9qY3H+14vBWn/d3pVRSH3XS5A8Hu7deIDVE7dg7ZTt/n5GTDU/d5zWFNmLZMLTR8+wWjmE5aM2BOs1fdDK3Figy3f/TjObIXe5HAij/Ej9DB1w/+bDYMEpnmLEtp5YNmId9gayjuk8uwUy502HeGod8eDWI2yc9QOWDlurr6PdpEYo06TIe9c0qvE0bF+4J1iu9b+shALdOm0KdIsMKdDfBxgvSRyM3tkXseLHwJ2r9wIV6IHlbzK0Fg5tParlQdrsadD3Oy/0Kj8cx3b9ZrH3PlBxNxbosqhfe3s2GquHItcv3NJfBF6+eGUZZO6yOdBqbH3EShAD2+bvDlSgizyX8XDtwk3ESRQLfZd7Yeu8H/HdmI2Qm7Fcp8/UdmJDXL94C/P6rrB8rR+iAgp0b+pPn79CpQxdP0QXBMtnhpRAn761M7Z9dwirZ+9GVCVvBsxqjKP7zmLxxB0ur7tY5Zz4648buHZZfRlX83TvqfWxbcVBrJy5yynQmxYfgbs+FrEi19+8eRssHP7rStxZoDcdVgsx4kbHhDZzlEgJi+dP/7WML3yEcFhwbgLWT9uODdN24Ksy2dFWfUGo+2lbPLzzz3v1u5xTVX65B8zru1wLdPmC0Txnt3dl377FE/VF1R0TBfq7XqNA92bRqF5+lCyeGXHjRMOAoet8CfTo0SNj1pQGWphv3HIM/6gH+VBT5UN/xn+3TmWUZI+CoaM2IlrUSOjX8xucOHUFU2bs1J+V4bOkGNK/CmbN3Y2Dhy9oqXD/wRNfgj00/125u0Cf+ENvbJi9E9+rByUyV8pDZqtJBLqaEnHxtyt6jdp0YHVEUevU3jXGu6y61YhaiJMgJoY3n4nX6sFKE5X/Y/Wws0uFkTp/tJhRfJUrXOUrFKqSC16lh1m91A9SngLdGzsFurHh13hwDRzadhx//X4VabOlRp9l7dC74igc233aZQU9F7bWf3cT1EarhGoz3qA1nTCqyXRdh0ckNxboX5bKisaDa8Kr8AD8qzbEvVJO4a1MlhZT/X7V1Ea7/NoRyGa/gAS63GMrtS2lxsMxPFDfez7OmgpDNnRD+wJ9ce6XC4ioHnpHiBTBeUVh1Vp80k+DMK7VLBzdecrilf73xSnQrTOnQLfI0F0FeuLUCTHn5Gh9Q5HF4etXr/FN/Ibos7wDnjx8qnZ7x0P6L9Pi4onLGFpvEm79dUeT+izXJ5iwb5CW4pfUQjCglKt0djRWOyiM7kA3mn/Mzn7Yt07JovGbLfbeByruxgJ92qGh+sbyXMk02e1dLUVzrLo+A+umbkeecl8gcaoE2L/hF4xtMROyA1GS7Fav6lUWlZM0DRR40+G11I0qYqAC3VGRCPyUaodWn2UdML71bBx3sXhKljaxvtHJU21XcijQiwoFGSjQvTvBnQV6+6HVUKLal3jxr1ocKhG9eMJ2HNr1O8aubIvvlDgpWS2XXqStnrVb/ftH56gbNK+p/nsb1Gq+y5EofwMbz45At9rTcOLgnzpPk+7lEEOJnjFdAt45J2VTfJIIvafUw4Req3Di5z+cAr12ngG4cyN4doF86D8hdxXo+Sp+iV5L2mnxIl8qZvdaqsdOgSq5ce3PG/hKPXh8fP+xnvt8LuI3P16I6V0WYd2UbS7RZymQQT2I7ojKiRo7v6hMPjAE66dvx7Z5uwLtrmRpk2DSfjWnftZez6ki0NtMaIh66dsFWtYdMlCgv+sldxLoi+c2g+xCf/Z/4dmx21KkU2O1UP70uHrtPvJ8lVbL7bETt+GXo5ecjdy+sTOmqjl4zfpfAh2e0yfVxxI/O9Ab1vsaiRPFxpARge9olDl36/pOaNV+Ac7/cVN/Xs4cqdGrW3lUqOotUyeOro0t209g87YTgV5PaMzgzgK9+ZAa+KZZUbV+faXf5hIhPWxNR70TPU/pbEiUMj4ObDmKce3n6zySqrcvjSqtS6DqJ8bmv7iJY6Pt6Do4p3blLhm90WUXjtzQBSfUQ/CFw9fp3+cq/jlEqtfN+v4GAvkeN/vQYLVDfRGO/OB+MkfaR4HuPQw8SaB3mNoYJesV0I5B1i2LhqxRm+GOYdyuvlihNjuVrF8QkdT3vVUTtuh/O9KQ9V3098uB304wPL2N3tEbP60/rHaiv/92urxVufbWTLTN3xd/nvhL19lEuYk4CWNhhNpB7BHJTQV6bPWQcPGfExEuQnj9NrmMj0WDVmHcnv5YMXqDGiOFECmKGiPjN6l/+xgjSm7rMVJzXKDdN/nAYCwbGfgOdEdFMl6yFsqo39CUNz5deQMZu6UbFUbbr/sE+vmhMQMFuvVeoUC3yNBdBbo021UIFxHoST9OjNk9l+Lv89fRSD0VjBw1ojPMS8r0ydB6fAOMbjodNy/fDpCeUSHuqMRIfplIl1ycjEHfjnfLp366rW4s0OMni4ulin+xiDWdfS8C/diu0+p1uPV6N3qvxe2wda7aDT7W+2YnIVUKVP4K/au//8qp3wFkVqDveLFUf+aENrPVZ+5yOR57L22PK+euue3uc2kUBbp317qzQJfr97sD/aNPE2Pqpo5YOuUH/LjuVyRJGQ+9lMzuVH2SM8xLg86l9RcQ/3aTS72NupVFcbWrfMHYrTj962V0n1Abw9otwoXfrwU4R2+9MFqHeZnUZ6Xage4dpsERwuW+EqPPnrzAxTPXMH/0FrVj3Vv2uGNyV4EurL2mN9Vvci0YsFKjr9CyBOr0VrtTeyzRb2EVUDsPq3qVw7epWzp3Sg5WXy42zfhePcw84rK75FXUUg0Ko3Wens7fd1/QGrf/vqfrDSz1Xqbm1LPX9e5zSSLQB63rgge3H+HJg6f6uub0XqYfxrtjokB/12vuJNBdhXCpWD4H6tfOh+mzf8TR43+hoJLpNdRO3aq1J+PF/9+eGz6oGtZvOoqfDpwPdLi6EugzJzfAvftPkDxZHCRQb/SIGB81fisuqtBIflNUtZ7evMYLDZrNdv4+oZIIKxa1RJmK3mukjas74Mc9vyPr5ykh4V4OHrmAkWO24EkwvH0SaAODIYM7C3Rp/txfhmJs23k48ZN3mJ7v/hiP43vPYIUSfSLNe85pjq2L9mLlJO8HlAUqfYn8KhzmwHpTAqUnYvxzFSZg8/w9mOC1wN+dlnnKZEPXaU2wbuYP2LFsP5oNqo59anPK1oV73/uMckrkFFIh2dx197k0iALdu1s9SaBLe/yGcEmVITmmHRqCJerB0I/L9yOJ2szXe2k7eBUd6Azz0nBAdbWWeaGFu5EkXmDx+QkYrHYZH/3x/TfTk6uHqHNOjES5uA31DmdJpRoUUv8V1FLdI5KbCnRhX6RmPuWjCqFTsYG6K/QYOTIMS1T4FOcYUWtOr8L9342RgTW8x8jg1YF2nxmB/nn+zzBKPYy5d/0B+lYZjbNHvDcl+UzyHWne6bEYpTyYO+4+l7ZQoAc6bALNQIEeKKKAM3iiQD/10xmsVgtFSR/piWw4SkerbfqVGiNC3CddI/k7zWqu4lPFRffSQyz23Acs7oECP+yPJQAAIABJREFUvUvJwfhTxWiUVL55ceT7JifkZ2aTWYEur119lCEZBq7pguldF2HfGt+xWtNmT43hW3qijgpN4K4yRxhuebYE4VUcTLunFy/foHw6Y2cqhEZWrgT6mO/aoHKWdyKz56S6uKCk9dJJ3xtuQqacqdG8T0VcPncdeUt8jl9UKIGRnZaqcB/eXxb8S/L3kzJtIhXypRFmDNmAn7ae0OEC0mdNiTvqNcaoKjRBxQZfI3u+dGhcdJjePe+OydMEevYimdG38ihnV3x3dTr6VxuDU/8XPoH1USV1jkXe8jnRsUh/Z9YOKj6o7HKf2HZOgMX1nLq1F+qkbeOcU2PGi6FikcbFIxWjWd5ek7Br927cx8Aage8OCuxaP8Tv+63ugrzlsn+Ijw51nzljzm6949odkn8CPUe2VOjVf5WzCWuWt0WfAatx8jfzZ1G4EujrV7bHmnVHsHrdL3ihwm00VLHLJb55rQbTXYZdmTS2Dm6rh02yE/6V2uVcSH1p79yhlBboCVV86znTGqF7n+9wXO2WjKbCfPTsWh4PVAiX/upcC3dInijQu34zChdUmB1JIqzzls2ObipkRFCS7EBvP64ebqhNSFO6un5gGV+9SdFzdnP8pe7pOYtmxmP1MLJf7YkqbOEtXx8p8nDer8MwpNE0nNx/LiiXEyrKbLg5ExEjhA0V1/IhL+LV67due3aIK26uBPrYnX1QMfG7N5J7qfNVLpy8rIRp0Oa3TjOaQkLBdi/3/tk9ck0fZ/kIU38ejOJRajsvsXCNPKipziRokt19Q0L64u1hAn3srn6omLCxs4nyJqYeI0O9Y5KbSWYEutQrgjxHsc8hMdFbfNndGYHB8ZlyDpyEPexcfJCZywhVeQvVyIvu81uFqmtyt4uhQLfYY54u0CX26uqbs1AhbgM8/cdcPFMjQtwn/sDyNx9ZB/LqeaeiA9w2tqpur4cLdAk7UKtbRbQIwmF3ZgW6Y/zImxJJVPiYQbV8v/I3bHMPnNhzWj/JdufEHejeveeJO9D9CnQ5DFQO0p0xeL2hIRsjdlTM390TrcqNwfW/7iJGrCjoPrGumq+f+xv2xW/FDbuUQeIUcTGkzcL3PlNCDqz9bRh61J2GU4eD9yAoQw0MhkyeLtBnHB2JuWrH94GNgYegEJyyA71EXbX76uveTrqyA/3O1fuY2X1xgMSHbZE59Xf1Rcb/3WEZcn+KMeoclJJRagVD7/33VXRd6oWiVb787z84FH7iJBVOauWqg6Hwyt6/JKMCfe70Rpip4ovvVyGrzCZXAn3dd+3Qb9AavcNdUqRI4bFlbUc0azPPGabF5+fIgaNdvEojS+aUeKLCc51S57lkz/oRylYeh9QfxcfcGY1RqOQwHbpJkoR46d+rIkpXHGP2cj9Ifk8X6PnUYXc1O5ZFq4LvHkCaBf2FOsyw85RGqJ6ug8ui47b1ULvUd2P7kp8g8Xbr96yIotXzoF62rr7OHarRoQyyfJ0e3SuFzMG5ZtsV1Pzcge5Nzg470P0K9FZj6uo17/SuAa89XI2tZirsZxb1ALJTicH6cFBXybEDvUys+s6/HdmBXlrtem7jpiE43munhwv0VmPreY8RFZrQbDIr0B31T9g7QB8QulG9zelIUdSmogVnx6Of2p3+2wH3fWCZR0UF6LekrVmUzO+DAAW6xeHgzgK9RL2CyK9e/+5Z7t2hMxLCxecO9E+/SINhm3qgkoqVajYFJsT91udfflk8ymvsyVRomV4Vhru3PJdGe7hAl3jnGXKlNRSyxe8YCKpAl0NCI0WJhJGNpzqrzJI/A3qpVwNl97nEkHbnRIHu3XvuLtAX7O2Fwa3n4+xx751sEsLFr0AfMLsxjuw+g/UL9hkashm/SI1Bc5ugYuYezvyFKmRHPa9SqF/A2FsgrQdUVn8/ETC68/sx0yNJXPYTQ9Ci9Ci3DePiyQJd4t+uVGG0vAr1w8X/75AMbODoGOgrvHzd16ccHKq+KOzAZnV4nn9JyvVSIbFk93lAc2qOop+jmxLyVZMGfu5FYNf6IX7PEC7vqLtTCJfE6rCw+TOboET5dzuDJYSLzx3ochDvuhVt0abjYpchVgIbb64E+tQJ9bBDxZ6WHeiSIqvDxrau74jqdabg5q1HgVWJ2jXzIHWq+Bg4dL0O2bJRhXhp3GKO8/q++vJjtG9dHDXqvlvfBFrpB8zg6QK9apuSSK++GxkJ2eJfN+RXb2k27l8VdbN0cZll080ZaF9iCM4fu6R/Hy9JbCz5bTRqf95Zh9qSFD2Wenh+bDh6VBmDs7+458NtR+Mp0L1JeJpAX6Rk48Ba41UojAu6fRKew69AlwM9D6szH+QsLaNJewEVYz1pmkToVWmUv/Jc6tMx0G/PQpt8fdQuZu+HnE2HfYu46tyKYQ0CD7tk9Jo+aD4PF+iD1nZRY+S4v+f6BMQ+qAJ91rGROg67SHRHqqvCJ4oX61VhxAftbqsfzhAuVgkqladOurV+1K3163DbGtxZoGcrnAld57ZyxkGVOKsi0P9RsRxlN1tk9aSt08zmOHPoD2dc1OCIgV68bgEdM3VIbd+7hV0JdLlJDtnYTcd2HaUO+5CYV5LkQBJHLDO3GzweKNBn91qG3St/hoyPPipW2ZjmM3D4/6ebB0cM9Baj6uC+CimxbOR6yI5YGadrJm3FZXUCeyYVT7LrnJYYUGOs8zNlTEzYNxC7vzugDh9x08NmfQxsCnRvGO4u0EevaI0jKrzK2rl7EVHtUoyt4uVOWNtexzy/fvkuchZMj1ZKZku4lAd3Hus2BxYDPWqMyHoH+tLJ32P17N36zIru41WIABXDb1KfVToky5AFzbBq1i4t5uXvp9eU+lg7b68S4jeQUS0GO4+qqXery+9zF82IhOqsA7nOF+og4PodSynRnwRtyo81HcYrtMzNnibQi9cpoN62GY9n6q2wGuo15Ay506Ftvl5O3IHFQJcvlAv/mKhCtW3WsdK/LJUNXjOaoV66dir0ygN9UHSz4bUxvP5k3L1+31mvHCC+e6WaU8dt8tW11TqVw42Lt3H653OQcC6ybji57wymdnR98G1oGRf+XQcF+jsy7iTQI6pxLfHFO3Vfhj9UqIvXr16jpDp8sWSxTDr8yVMV0qpWjdzIqMK+tWy3wNlIqzHQy5bOioZ1vkaXnsvxt3qLo3GDAvhUHc7ctpP3jsqSxTIjp3rQKYJcUhQVdiOKemAZQT38yps7Lep8mxdtOy7Clf+L0d7qQNF4apd6/8FrEUbN1/16fINjSvzMmf9+/OvQ+LfkiQJ9zoBV2LP2MFKmS4pec1voQ0QPf39S4zcSA10OAJWY6r8f/lPL8M5TGmP/pl8h9UoqVjMvvlDfyYY2ma7/PUxJxUf3HmN06zn6+4/seC+oYq03zfPuraFGfauo60mCvt9ODI3DwNQ1UaB74/I0gT72hz44vOO4+r62DRHVw0E5uHPivgE65vn1CzeRs0RWtBlXHw2zdMKD/z9sDCwGuniBwes646V4gWYznGe/OLyArHmHbeqGleM2a+kqqY/aTPVGeYNJ7eepMLBxMHR9V4xTh6/vV+cKeETyMIE+UR1SLzHPr6v7eM4Sckh9AzTMLGPkoe6uhhZjoOsxsrm7GiObtDfImCcd8qpzLHYu/UmvgUs3LozKbUurz+yo3YOkWOr72vwz49C52CCcP+reDywp0K3/1VOgW2TozgJdJpAei9ogd7kvVOzSf9QhZK20mJRDPSTOeHj1ZWSXOuRjaqcF6uCcl5rUZ7k+UWJyEJpk7YxLv3nvovSbEiSPpw+AkFOVo6j4jY9V7Mbv1YE7Uo+k+v2ro4Q6mbtmqpb63wHlj6Z2WKy9835M1itnr6FhJi+LvfeBinugQP/rzFWk+fwj1ddPsVxJ7vXT3u0kqNG5vDrkriwqJ/F/N6JI9jbqcNpIUSNp4ff8yXOMazUbe1d7vzou8dBuqgO5himhI6nj9GbIWjAD4qq4dzcu3sLSEevw/eJ3Xy7zlP8CbSc0RB0lhBxj9wP1drB8LAW6N0Z3F+hZcn8CrxE1EDdBDEwbuE6FRLmASes74NyJK/gkU3JcU2N8cr81OHXIe7eOpEHzmurdviK4/UvpsqRAk+7lkTxNQi29f9p2EnNHbtIxy+XLxubzIzGu+wpsXe7999RhWHXItcRV8XZvXLmL5VN/wA9rvL9IpFehBJr0KKd3x0s6qa5lav81uKXEkLsmTxPoVTqU1fOaxBs/pUT1GPUl8vbfd53ds/nxQv2q67op3ofcuUpyL283qTE+yphCHwg+vfNCZwgYebg+Ylsv1EvfDtf+9D48Vs+pExvpN3r8zqnyNlvl9mWQRI0/OUR0z6qf9UHk7vqQmwL93YhxJ4EuV11NHaZY99s8Wjy367QEmTMmR3UVjueFOiw5YcIYOHHyCkaO26JikP/jbOT2jZ0xdcZOrFnvv0xp27IYihTKgOhqTfuvmldfKjnfoNks3Lv3RNdTo2ouVFZfwKOoB5hH1G7g8ZO34776W5DUqF5+JfIzo2ot7/VLOvVActKY2qqelziurkfCyVy6fMd5PSLY27Qoinx5PlUx1F9h646TWp6/VvHS3SF5okCXWORp1FwpZ+ksH78FG3y8qVO9fWlUaV0CVT9p52/3fNOsKErVya9DDYoY36k2nCxUMZ9f/v8g23oqREtxJdFrZeqk64ir3qZoPrgGMqsxINvczvxyAdN6LMOt/8/z8vu5R4aiQ8mhuODPdzF3GCuOa6RA9ybhaQI9q3prraOKUS7jdWrnRfqclsn7B+KcmiM/UWdTXFWbOCZ7zVcP3L0P7JU0ZH0XPHv8HAO/9b3JzvF78QJrbsx4b3hfUX+jjbJ01mverY8XYGyLWdgyb5fOF1utdTuqc16yFcqow9HKbvfFQYinHWr/pjxMoE9WMevPqTnvk2ypvceIevAhmzKcY2RDN+8xUtP/c3Yk7EshFfZKxousReWMn6Yq5r0Icj1Gni7C2OYzsWXuj0ikQqe1HF0P6XJ+jKgxouAP9eaPrKF9HiIqG/kSpogfpLfrQ9u4oUC33iMU6BYZurNAd9V0vyFcLOJhcVcE3Figu2rOKhU+wOchouz04CdAge7N1N0Fut+R4SqES/CPHtbozgLdb+9VaFkCfg8RZQ8HLwEK9Hc83U2g+x0JfkO4BO9IYW2uCLi7QPfbpu/+GA+fh4iy14OfAAW6N1NPE+h+R4qrEC7BP5psWKMbC3SXY8TPIaI27NEQbTIFunW8FOgWGVKgWwRox+IU6HbsdUttpkD3xkeBbmkY2bYwBbptuz5IDadAp0AP0sBhIU2AAp0DwSwBCnQKdLNjhvl9EKBA53AwQYAC3QQsf7JSoFtkSIFuEaAdi1Og27HXLbWZAp0C3dIAsnlhCnSbDwCTzadAp0A3OWSY3QcBCnQOB7MEKNAp0M2OGeanQOcYCBoBCvSgcfNZigLdIkNPE+gWcbC4EQIeJtCNNJl5rBGgQPdMgW5tVLC0UQKeJNCNtpn5gk6AAt1zBHrQRwFLBpWApwn0oHJgOeMEKNDtIdCNjwjmNEXAg3agm2o3MweJAAV6kLD5KkSBbpEhBbpFgHYsToFux1631GYKdAp0SwPI5oUp0G0+AEw2nwKdAt3kkGF2HwQo0DkczBKgQKdANztmmN8HAQp0DgcTBCjQTcDyJysFukWGFOgWAdqxOAW6HXvdUpsp0CnQLQ0gmxemQLf5ADDZfAp0CnSTQ4bZKdA5BiwQoECnQLcwfFiUAp1jwAQBCnQTsCjQrcNyVQMFeshw9ehaKdA9untDonEU6BToITGu7FInBbpdejp42kmBToEePCPJnrVwB7o9+91KqynQKdCtjB/bl6VAt/0QMAOAAt0MLdd5uQPdIkMKdIsA7VicAt2OvW6pzRToFOiWBpDNC1Og23wAmGw+BToFuskhw+w+CFCgcziYJUCBToFudswwvw8CFOgcDiYIUKCbgOVPVgp0iwwp0C0CtGNxCnQ79rqlNlOgU6BbGkA2L0yBbvMBYLL5FOgU6CaHDLNToHMMWCBAgU6BbmH4sCgFOseACQIU6CZgUaBbh+WqBgr0kOHq0bVSoHt094ZE4yjQKdBDYlzZpU4KdLv0dPC0kwKdAj14RpI9a+EOdHv2u5VWU6BToFsZP7YvS4Fu+yFgBgAFuhlarvNyB7pFhhToFgHasTgFuh173VKbKdAp0C0NIJsXpkC3+QAw2XwKdAp0k0OG2X0QoEDncDBLgAKdAt3smGF+HwQo0DkcTBCgQDcBy5+sFOgWGVKgWwRox+IU6HbsdUttpkCnQLc0gGxemALd5gPAZPMp0CnQTQ4ZZqdA5xiwQIACnQLdwvBhUQp0jgETBCjQTcCiQLcOy1UNFOghw9Wja6VA9+juDYnGUaBToIfEuLJLnRTodunp4GknBToFevCMJHvWwh3o9ux3K62mQKdAtzJ+bF+WAt32Q8AMAAp0M7Rc5+UOdIsMKdAtArRjcQp0O/a6pTZToHvje/bva1T8rIsllixsPwIU6Pbrcyst7r68IwpXymmlCo8pO2Xmj1ix8qDHtIcNCXkCFOghz9jTPmHt9RmIEimcpzXLdHtevnqDMjHqmi7HAjYnQIFu8wFgrvn5quRGn8VtzBVibl8EKNAtDohiYatarIHF7UZg27Xjdmsy22uRwJsEZxA+XFiLtbh/8X9fvEbZyDXcvyFswX9KgHPuf4rb7T/sRbRpiByjsNu3IzgaMLPHEqwYtiY4qmIdNiHA+dYmHR2MzXwZ93dEikiB/ur1G5SKUD0YybIqOxDgnGuHXg6+Nr6OWA4R4o4OvgptWBMFusVOp0C3CNCGxXmjs2GnW2zy6/hnECE8BfrT569QIWpNizRZ3G4EOOfarcettfdRhMmIHa+YtUo8pPSULouwZtQ6D2kNm/FfEOB8+19Q9qzPeB77NKJGDu9ZjQpCa2QHeumIFOhBQGfrIpxzbd39phv/LEwZREs01nQ5FnhHgALd4migQLcI0IbFeaOzYadbbDIFujdACnSLA8mmxTnn2rTjg9hsCvR34CjQgziIbFyM862NOz+ITadA9wZHgR7EAWTzYpxzbT4ATDafAt0kMBfZKdAtMqRAtwjQhsV5o7Nhp1tsMgU6BbrFIWTr4pxzbd39phtPgU6BbnrQsICTAOdbDgazBCjQKdDNjhnmf0eAcy5HgxkCFOhmaLnOS4FukSEFukWANizOG50NO91ikynQKdAtDiFbF+eca+vuN914CnQKdNODhgUo0DkGgkyAAp0CPciDhwXBNS4HgRkCFOhmaFGgW6flogYK9BDB6tGV8kbn0d0bIo2jQKdAD5GBZZNKOefapKODqZkU6BTowTSUbFkN51tbdrulRlOgU6BbGkA2L8w51+YDwGTzKdBNAnORnTvQLTKkQLcI0IbFeaOzYadbbDIFOgW6xSFk6+Kcc23d/aYbT4FOgW560LCAkwDnWw4GswQo0CnQzY4Z5n9HgHMuR4MZAhToZmi5zkuBbpEhBbpFgDYszhudDTvdYpMp0CnQLQ4hWxfnnGvr7jfdeAp0CnTTg4YFKNA5BoJMgAKdAj3Ig4cFGcKFY8AUAQp0U7hcZqZAt8iQAt0iQBsWp8yxYadbbDIFOgW6xSFk6+Kcc23d/aYbT4FOgW560LAABTrHQJAJUKBToAd58LAgBTrHgCkCFOimcFGgW8f1fg0U6CFB1bPrpMzx7P4NidZRoFOgh8S4skudnHPt0tPB004KdAr04BlJ9qyF8609+91KqynQKdCtjB+7l+Wca/cRYK79FOjmeLnKzR3oFhlSoFsEaMPivNHZsNMtNpkCnQLd4hCydXHOubbuftONp0CnQDc9aFjASYDzLQeDWQIU6BToZscM878jwDmXo8EMAQp0M7Rc56VAt8iQAt0iQBsW543Ohp1usckU6BToFoeQrYtzzrV195tuPAU6BbrpQcMCFOgcA0EmQIFOgR7kwcOCDOHCMWCKAAW6KVwuM1OgW2RIgW4RoA2LU+bYsNMtNpkCnQLd4hCydXHOubbuftONp0CnQDc9aFiAAp1jIMgEKNAp0IM8eFiQAp1jwBQBCnRTuCjQreN6vwYK9JCg6tl1UuZ4dv+GROso0CnQQ2Jc2aVOzrl26engaScFOgV68Iwke9bC+dae/W6l1RToFOhWxo/dy3LOtfsIMNd+CnRzvFzl5g50iwzdXaBX7lAWNbp+g2ixomLIt+Oxb81Bi0SAmPFioOPsFshRLAuePnqK1eM2Ydnwtf7WW69/dRSqkRcJUsTDvesPsGrcRqyduMWZf8KBIfgsV1pf5Wd3XxxgnZYbEYIVuNONbsuP0XDg16gY0PF2sBNZszU61myLicdPwyJbhudoXvseEsR7oz+nUZck+Pt6xPc+c/6Yv5E4wev3fr77YBSs2BhLlQmPyJGAPDmeooWqL6KPKr7fF019XgxcvhoByRK9wvSh14O9TSFVIQW6Zwr0bEUyo+3kxkicOiHWT96GqV7zgmUIFatTAPUGVEfcxLFx5tAfGNVwCq79ecPfus3mD5aL/A8rcac51y+Wb9smQ78Ot/Fp6hfBSuz6rfCYPD8OTp2LjLixX6F62UcoUeCJ8zNkvpy/MhbuPwqPdKmfw6vJXSRL/P7c6ygQ0HwerBf+H1RGgf4O8pQui7Bm1Lr/gHrIfERIrHHDhAmDpiProESDQogQMbxaNx/C2KbT8eK567/RL4pnQZ2+1fBRhuQIGy4szh35E+NbzMCVs9cQJ1FsrLg+873G37x8G7VTtwwZKCFcqzvPtyG15h0/Jy42/xjjPfIdm9xB8fxP8Ox5GExdFAe/nIyCN2oZnDbVC7Sqdw+J4ruec83mD+Eut1w9Bbo3wpev3qB0xOqWeYaWChKmiI/uS9rh0xxpcPn032j5RddguTSpr/30ZkiVKSVuXrqFKe3n4fDWo/7WbTZ/sFzkf1iJO825w6bGwycfvUSV0o+ClZDMiTOXxsG+w1ERLtxbFMr9BA2qPkCECO8+5vc/ImLh6tg4fT4SXqmpdd6oa4gf1/91rX/5b9wOj0nz4uCPyxGVwwinnMJL1K38AHm/eBasbQqpyijQrZOlQLfI0J0FeuRokbH2/jw0zuSF6xduImzYMHj54pVFIkCvZR0QLnw4jG85EwlTxsfgTT0wsv4kHNri+ubWZHhtHNp8VN1cryBtjo/Rd1Un9Co7FMd+PKWvRQT61jk7sXvFfue1vXj2Iliu1XJjg1CBO93oQurLxPY90bB4bSwM7HgL8eIomb0kLi6oG9Gkgd6S7+mzMPpLhCPJzarzkMRYNO5vRIv69j3qG76PjpgxXiNj2hd49Dgshk5OgHw5n6BelYc676otMbBpZ3Ql6e9rEfXseVgkSWh9rAeh+4NUhALdG9vT569QIWrNIDEMjYUmHxqG9VO2YcfC3QgfIby/AsbMtYukmaTqHVBlNM4cPI/afaogY550aJ2ru8tqzOY3cy2hJa87zbl+mYWEQH/5EmjWIwmK5H2CKqUe4fyliOg3LiFE5uTO/hyX/g6Ptn2ToFfb2/js4xdYpObq0+ciYuKAmy67NLD5PLSMA6PXQYH+jpQ7C/SQWuOWa14clb3KoXe5oXj2+Dl6Lu2A0wfOYmbXRS6HmAj0t2rZcuHEZbU2Dovmo+shcvTIep0rSTaw+EyFv/0ahb/Nhw5f9zY6ZENVPneeb0NqzfvvvyJHwzj76c2bMGjTNzHaNbyH7JmeY5J6mPngYTh0aX4H4cMDM5bEwYW/ImBEj1su+9Zs/lA1QFxcDAW6NxRPE+hNR9RBjLjRMaHVLCU1w+L5U/WHYDHJWnnBn5P02nnD1G34qmwOtJ3SBHU/bo2Hd96XsmbzW7y8D1LcnebckBLoI6fHxeMn4dCp6V08UQ5h8MT4yJTuXzSr9UD3yenzap07NqGW6l98/lTdk8MgTqzXvgS7z84LKP+Va+Fx/mJEZPj0X0SJ9Bbb90bHojUxsWbGFe3SQnuiQLfeQxToFhm6s0Cf9utIfJw1FZ4/ea6/BHQpOgDjfhqEhf2/wzetSyFa7KhYNXYjFg9a5aQku9WrdiqPygkauiQnu3HWPVyANl/1wJ/HL+k8cgONnSgWRtSbZIj2mN0D9E542bkuSQT6uklb8MPivYbKh/ZM7nKjkx2KTbolxWv1cDZihLdq8QOsnn4FA8fHR6yYb3DrTji9ezFFkpfo3uo2kiZ69xS3bMOUaFLjHioUf+yyO7oNS4icWZ6hcql/9O9FmFdpkRyTlUBPnULZHT9puHpiLU+JG1X3vhEGlmTn5MUr6mapdm4+eQp829a77uRJ3Eea+2wjBbo3DU8S6C3G1kfFtqXx8t+XePP6DdorWVKrZ2U8uP0IiT5KgEz50uNvtUNxUI2x+gGnJHkTR+bDJpm9cOm3Ky7/DOqqnY4yr/etOEL/PlrMqFh9dy7qfdoGNy6+/2XcbP7A/vZC4+/dZc71y27MrLjYtju6epPmLWRNXuubB3re9BqYBLUrPsD6HTH0GzyVSj5Sv3v3xbHniITqTZw36N3ujsvuOHU2EvqMSYiVU/9yLvbnrIiNq+oNHimzcHUsJW8iom977zePnjyV+TkF5oy85vLBo9n5PDSOEZ/XRIH+joY7C3S/a9xqSZpg1e05+G7UeuSvmhvJP02KI9uOYWit8c4NGUbm2NE/9sdPaw9h9XjvNWqu0tnRdmoT1PqoRaBDO16SODrvucN/YvHgd2trR0HZfDL3zHgtnOTa3DG563wbkmtev/24dVc0bNkVA+P7eW8a6Tw4ET7/7DnqVPLe9PHz0SiYsiAOFoy95nIImM0f2scRBbp3D3mSQM9XKZfeUPdWPT18pTbnze6xBG/fvMXXlb/CX2eu6v+VzXDyNo7PDXabny3B9E4LsG7yVpfDNkuBjOi7uhMqx2+o65akN6Op8qUjAAAgAElEQVQomb5t7o/vlTGbP7T/rbi6PneZc3fsjQZZ14pkDq92iWdRc5684V6lRTJULf0P9hyKqt8k/yLzc3Rreccpt2UnePv+STBtyFXlCN7/Hi8b7so3Tomxva8jbWpvh3DkRGSIrF859ar+d8dBiVAs32OULPjuTcuA+tpofnEM8jb9qbPq87q5fuAZ2sYUBbr1HqFAt8jQnQV6/GRxsfTKdDjakCpjCsw4MVpL8y2zd3rvHt/YHfXTtXUKnILV86KA+uLRv8ool+TkC4ks/stGq4V/1Y1RUunGRVCqcVEl1V3vgPRZUaQoEbHkr2laGh394aT+lQij5J8mwbN/nuOGelVrxcj1OLjpF4s99+GKu8uNTgi52o0jAv2SCoMiMjtNypeYujCO2hX+Ru2cueuE2mtkApQu/FiFUnH9OlPLXolRQr22WqG4t0CXVM8rqXpSfP+9MhevRNA3vvmjryFGdB/b0gPowp4jEqib6AvUr/oQ+3+JjHGz4yP9x//ipLrByU27bJF/nLvTP9xIMP7JFOjerJ79+wrlo3jODvT55ydiTONpOL77N92+Pt911K+kzlI7GWW3YsvxDfD4wRPnw8eU6ZOh9cRGGN14KuQVf1epx5L2uH3ljq/dkMv+nq7KTHP5iqvZ/MZHbejJ6U5zrl9qfnegX7wSHs17JNXSvGTBx7h1Nzz6jE6I/7F3HvBRFV0bfwi9994URMWCIAgK0nvvvffee++99957782CKFgQFEFQ0BdFeq8Cob9zZt1Nstkke/cmJLv3mffH5weZmXvnPyfnzn3umTMLx110fMQUMTxWzBdKZHe9RVZ84qQFKdSLxQXH5SRly6Y9iTBrxGWMmpkcKdUHy5Z1Az5Yyn10aX4TeT/wDzaxRvx51LGKkO/kQexZSJi0hDfcaoTf45zeK7FxfMgp+CL8BkxcwHmNK12JgH768P+wfNgGtaZ8hOHb+2DF8A0O4cUdH7v20nxMbD7LIfqkfyMNlvwxPci619Vti/Ceo/A72DX/C0xpM88h/gSuW6ldaRSt673R5zIWb/a3EbXmDTzHsgOoac/06Nbipo4+l3LwcFyMn5sClUreR8mCDzB3RRK1i/IhyhZ1LfYYrW/i1+iVNH2c9DcVyalC7y1efElAl6nsNr8Nbly4hWVD1+mZrdy+DJqOqItF/Vdp/1m8fkGUa1kiyMdH2bW+c+7n+G7bYZfWUL5VSZRtXizIrsq+KzrhurrOgj7BdwEZre+NJuhNPtdVBLoI6G+pd/b6Ve8ibpwXGKwCPOpXuetIK3juUkz9QVHWoK7SuIqIXa11ZiWwX3IE4UmQX8OuGVTg3zk9pdVaZ0ShfA9w/Pc48H8cDR/l8EdX5YNd7WqXoBF36kuwyYrNSfBWFn+M6HFd7YR3T6OIbBt7HL0C4qacFNm34dXXp4Bucvp8TUCfcnAEqiRt7KAiYvjsrktCTL/ijE8iHyXqJzAX2Y5ar181lSqma5i0eyxqh+TpkqFvmRGOutLng7sPIXknJdKn5bgG6JS/P878cjbM/qJiBW960IX0MiHboqqWsYnfX32nIlz3JAxxe7+rOVi8LrEStuOrCPFrSqh5htN/xcbQqSn1C4Wz6D54kk0Mb6AerO4USSewZEMSJQRdQpJEL7H1s4Q693m/DjfwWvonOgf6QCU4SX8ViruOkHfnOq+yzosUp9S2Xr9Xeckoea0nT5+jfOw6UfLePLkpVwL6iYOnHJGNcjaE5PANKf2Kq2sO3dJL+8ZlQ2wvLFJE3Fmkon++2fB9sCZG63syzshu400+15mVKwG9+4i0ejeQvTTrmVZ/fMyXM7i47Yr9nXt+aNYzHepVvodKJe7h/gM//RIgflgE9CGTU6qPo09UTscAn9u0Rzr1QfI2CucL/lHUiD+PbFtw5/ovEs1DjHhF3Knq83UWDViD1aOCR0p7w8BDEtBlt6V9h2SXOa30+jKk9CuuxrnlzlIMrjIOx/bbPnzKmlU+UtZI1dxlCoHAfUgEetd5rXFZ7Qaa2WlRkO4lgGTZmZkYqQJIjn/zmzcgdnmP3uxvI2rNGxiUpBQ8dDSeSs8SkBLr+k0/9eEyJTKme6oiJ+MiftwXegdl+jSud00arR/VjelZ8t/VR1+1zdXi5ZnKgV7Wh3KguxLQPyyRw7FDUs7pkQ+SgYPuwjKBal3Ko0CVvOheZLCjatd5bXSU+/QOC4I1N1o/rOtHxZ97k88NSUAf2+c6sma2BV5OWSiBeS+DBHGExb3rsNR6p3pHdXZE9Ogv8M2h+JiyKIUW0CXQpI1KWyjR7jne9le7Kv0wbo7spn+O/kobcC4SuOdufdlBv35nIhz6JR5mDLusouvDutMo8PM4leCXxHUgbBS4O6+4BQroJqfJ1wX0mYfHqhQuG/DdVtdfgp3x2SPQy8Wp69gSKxHo8oU5LBFI8kJ+UORd9Cg6BA/U4aMhlTF7B+DXA78HSS1jchpfaXNvetC58zIhW00lZcrskSEfUugMWNLCzFqeDF99H1//6MP3Hqktq/EwdfAVxwNU/v2kyrs7WIk5SydeVA/TsKfpmx/jqoM9kmN076uqH9s2rq2fJcCBH+NjwoCAlxWJ0PznQkwM7Rb+h6OGfZfGa7xIqQR0lT/Q6uWxEtArWEhAz1c+t4rWqYM2uXq6PfUSUS6HKsl2WXsRcWdSyzkuP4Qare/2jUShit7kc52xuSOgdxiYBnUr3zV0gNHxU0osVxE9F67EUgfVPUXyJC/0TiJJ2yIR6BLl06xW0Aj0zs1uuhTp3fXnUcgkQr2VpwnmIHaCYt5yuxF6n/OVH1k3ZnOEXiOiOndHQG81vpHOSy6BIu4WEXzkXJ8jnx3TTdyNQLf3n6d0TvRe2gE107QIcsm6fasiZ9H30LvUcHdvJUrW82Z/G1FrXvtEidjSuFt65Wevqfy8AYfOdhmaBuWK3tcHioo/XboxCfRBzmrtG/gQPHs/RutHSUMJdFNPlYAemwK6OthQCegxfecQ0bAEdDmnYvv95TpwTz5kulMkorx0kyI6kM5eJAL9xsVbLj+EGq3vzj1EtTre5HPdEdDnrUqC5+qciLbq3DJ3y41b0TF5QXIcVykK5QPku9ke4+hvcbBxznl1ro8I4umxe+lZR9pCSfEyYnpKbJkfPB2mbaen+/XljAtJITN/7GVkUh9Bo3p5EasiYiSbGNVvM0rfHwV0k9NDAT0oQJ0D/d5ydMjbR6cgkNJ6QiPIV+bRDaa5pO2nhEF5yKZ/Qx1apg5VCk08lw6mfjsSX6z4Rh8e4o3Fmx50Es39zaF4GNEzQGiWFC6BI9A9EdCd5+3E6Vg6+mbFlItBvt72UKlb8uZ8iFoVAlK9hDTnckjoqq2J1Taqa0HyqP94LA4mzkuOlVMv6sOZpMxfnQR379sOG/GGwhQutlnypRzoMp6wItA9EdAbDamFLDkyY0i18ZqZzoF+azGavd0ZF/8X/COX0fre8PvifI/e5HOd771hl/To3/G6SkFlE1tkYe8cge6JgO58nb5jU+mUAeVV6q1lG+UMiZhK5LFF5sh21uptMqo0MZdCjIgM3F9I/txbbIc50ANmyptzoEeUgD5x/1Ac3HQIm6ft0qDkEDuJZK+TobVbJi5pEFuqs4EavN7OUT9BkvhY/tdMvfvy1I//c6ufqFrJm/1tRK95xbf+8VesIGtqyeNcvmkmTBl8VR9yL0XEoPqdM2D55AtIlSLgfCH5mdH6UdVOAt8Xc6DbaFghhUvgCHRPBHQJtBu8oQeqpWjqMKFZR8Zix5zPsGvBl8HM3Wh9b/h9cb5Hb/K54+cmR2a1Gzzwe72kcAkcge6JgO7MZNXWRFo479f+Jh7521KyyA5L+zlrh36Ji5nq8GZX50wYrS+7Omu3z4ilky64TDET1WyKOdDNzwgFdJMMrSagh5UDXXDKg+2FOtFhujoEKbnKsz5mzwBMbj3XEcXedlIT3L56B2vGboGI56N29cMT/6eY0GyW/q9eIKr2kkNdDtOTnGn7Vh/UX5eL1M6P5qPqoakSg+Tv3li86UH384k4mKDE56mDL2vUKZO/0IeIhiWgh5UDXfq6dDW6ynX2Up2MHRtzViTV0Y5F8wdEINiFb4nAiRMn6ExLfTk9u3ZFW47fFZsTY59KJSPRk6mSB7xsyEF6z55FQ/Ne6VRqmIdoUuOOiriMif7jU6F324D8k1Hdjiig22bI6gK6O/l5RTyXcyMkxYDk+m0wsAZyFnvPEcUuKbFaq6jLsY2m4+bl21psD61+VP/dcOf+vMnnOo+n2/DUyKNyNVYpdQ9P1OPx9t3oYQroYeVAl2vcuuOndrUAd9SHxM0qBZcc0jR96BUd7Xjmn5joMiwNBne+pnI7PsHKLYlxTOWNtO8y+t/ZmFiwJil6tr6J5Elt/jYsf+7OPEWVOhTQA2bCagK6Oz62SoeyqNKxLPqrgA//f/3RXx2U9+fPfzmi2Es1LgKJMh9Vb4oGKedWHP/6N/z2/WmIqN9raUd9COnCvisdoFuMaYBM2dNjUOWxUeXXwOP78GZ/G5Fr3jv3oqFJ9wwY1/cK3swSNEpRDmKWM366t7ihD41evS0x9qsdmhLRKMV5zRtWfY8nL5IaUkC3gaeADoSVA10C9Zb/PQubpuzUudLzlsuFbgvaovEbHXDryh04r3HDqh9JJh+ul/Umn7tkfWK1xoylDwn1f+yn15BhCehh5UDX74dqd48I38+eR8P3P8XTPnRC/ysqLZYtDdboWSlw63Z0lc71utKogFEzUqiDm5+o89DuqL+/RL9xqVGt7D3HOT+h1Zed7S9fRlNr80cq6O+lCspLptfmkwdd1umGo3qhgG5+hiigm2RoNQG9Tu8qqNmjEqqnbBYiuSSpEqPHwrbIVfx9PFQHf26ZvitIupXJB4arFAPXMabhNMRPHA9bbi8N1tf505fQLHtnJEyWQPXVDtk/zoa4CePi7InzmNdzmU7h4q3Fmx508lCRh4ikV0mkFvcSxe2OgF6hWSa0rHNLHRIaco7xaq0zqMViNGRVB5HWrngHn3wYkL9XImzaD0yLUuowpSqlg0efi6iUKsUz9FEiuJS2/dPgr3Oxg5nE6unnkUylJrh4JQamLU6GU2di6Yd1fZXuoPin7m0XjAp2RgHdNgtWF9Cz58umxe6W73fD2ZPBtx3abbVMs2JoOLgmkqZWea2ViD6x+Wxc+OOS/rH45XGfD0LjbB1x6YwtIj20+lHB/s3egzf5XOexHj0ZG5PmJ8fte2pLaf1bePdN/zAF9P7jUkE+Hg7sHDy3o71/Obdi/uqk6pyI5/99XLwb5JDmPfvjY8WWJPql4K0sj9FVHd5kfxERkanv2NRYNP6Ciki3Ceih+XOz8/eq21NADyBuNQHdHR/rp5KctpnUGCUbFUYMlXbiu21HMLnVXPg/sK1hmgyvo1IMFEXdjLaI9KqdyqGsSmWYNktq3Lt5H/tWHtBnVDxVOXulyA5N2Y3UucAAx87NV23z4Xk9b/a3Ebnmna0CP66rg+0G/bezJzDzm7f9MGdlUpxQB92L/vK28rmtVfqC1P9FnzuvecOqH57z+Sr6ooBuo0wBHdj1aBXm9liGrTP3hGh64qc7z26FzO9mwNV/bmBu96X4fvsRXd/VGje0+q/CviP6Gt7kc+X8huHTUuKMemcvmPeheo+/EaaALgEeXYamVYeEXlQR5K7PhTitdvZ0G54GsWO9UHnOH+sze17LEFBXBHbxwd//HA8xY7zUhzU3qnZH70yXtFnlmmRGl2Y3HAc3h1b/p1/j6MASiXCXnOe53vXX6/PkSb3jEFEK6OZ/Iymgm2TozQK6yaGzuYcEvOlB5+EQ2SycCVBAtwH1NQE9nM2E3YVAgD6XpmGEAAX0AFreLKAbmXPWDT8C9Lfhx9IqPVFAt820rwnoVrHfyB4nfW5kz4B3XZ8Cuvn5ooBukiEFdJMALdicDzoLTrrJIVNAp4Bu0oQs3Zw+19LTb3jwFNApoBs2GjZwEKC/pTEYJUABnQK6UZth/QAC9Lm0BiMEKKAboeW6LgV0kwwpoJsEaMHmfNBZcNJNDpkCOgV0kyZk6eb0uZaefsODp4BOAd2w0bABBXTagMcEKKBTQPfYeNgQXOPSCIwQoIBuhBYFdPO0XPRAAT1CsPp0p3zQ+fT0RsjgKKBTQI8Qw7JIp/S5FpnocBomBXQK6OFkSpbshv7WktNuatAU0CmgmzIgizemz7W4ARgcPgV0g8BcVGcEukmGFNBNArRgcz7oLDjpJodMAZ0CukkTsnRz+lxLT7/hwVNAp4Bu2GjYwEGA/pbGYJQABXQK6EZthvUDCNDn0hqMEKCAboSW67oU0E0ypIBuEqAFm/NBZ8FJNzlkCugU0E2akKWb0+daevoND54COgV0w0bDBhTQaQMeE6CATgHdY+NhQ6ZwoQ0YIkAB3RAul5UpoJtkSAHdJEALNqeYY8FJNzlkCugU0E2akKWb0+daevoND54COgV0w0bDBhTQaQMeE6CATgHdY+NhQwrotAFDBCigG8JFAd08ruA9UECPCKq+3SfFHN+e34gYHQV0CugRYVdW6ZM+1yozHT7jpIBOAT18LMmavdDfWnPezYyaAjoFdDP2Y/W29LlWtwBj46eAboyXq9qMQDfJkAK6SYAWbM4HnQUn3eSQKaBTQDdpQpZuTp9r6ek3PHgK6BTQDRsNGzgI0N/SGIwSoIBOAd2ozbB+AAH6XFqDEQIU0I3Qcl2XArpJhhTQTQK0YHM+6Cw46SaHTAGdArpJE7J0c/pcS0+/4cFTQKeAbtho2IACOm3AYwIU0Cmge2w8bMgULrQBQwQooBvC5bIyBXSTDCmgmwRoweYUcyw46SaHTAGdArpJE7J0c/pcS0+/4cFTQKeAbtho2IACOm3AYwIU0Cmge2w8bEgBnTZgiAAFdEO4KKCbxxW8BwroEUHVt/ukmOPb8xsRo6OATgE9IuzKKn3S51plpsNnnBTQKaCHjyVZsxf6W2vOu5lRU0CngG7Gfqzelj7X6hZgbPwU0I3xclWbEegmGVJANwnQgs35oLPgpJscMgV0CugmTcjSzelzLT39hgdPAZ0CumGjYQMHAfpbGoNRAhTQKaAbtRnWDyBAn0trMEKAAroRWq7rUkA3yZACukmAFmzOB50FJ93kkCmgU0A3aUKWbk6fa+npNzx4CugU0A0bDRtQQKcNeEyAAjoFdI+Nhw2ZwoU2YIgABXRDuFxWpoBukuFD/2cme/C95n5+0SB/Xrx4qf+wBBCIFg2Ipv4PuQS3iujKZp7TXlz+usSJHQMKj+XLs+cv8eTpc8tzcAYQI7qfcizA8+cv8JIuNwge+hXXvy7iTuQ5TZ8bnI9wiRMrOv2MIuD/5DnXK06WIOu46MrnvlTO9rl6JrEEJUCf69oixK+IzfAZHZxPrJjRESM6F7nyCuT/mLqCs4WIT4kmuoLyty/4CxQED/1KyE/g6Mqn8Bntmk+8ODG4dDFBgAK6CXhsSgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk4LsEKKD77txyZCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiYIUEA3AY9NSYAESIAESIAESIAESIAESIAESIAESIAESIAESIAEfJcABXTfnVuOjARIgARIgARIgARIgARIgARIgARIgARIgARIgARIwAQBCugm4LEpCZAACZAACZAACZAACZAACZAACZAACZAACZAACZCA7xKggO67c8uRkQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJmCBAAd0EPDYlARIgARIgARIgARIgARIgARIgARIgARIgARIgARLwXQIU0H13bjkyEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEwQooJuAx6YkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAK+S4ACuu/OLUdGAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRgggAFdBPw2JQESIAESIAESIAESIAESIAESIAESIAESIAESIAESMB3CVBA99255chIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARMEKCAbgIem5IACZAACZAACZAACZAACZAACZAACZAACZAACZAACfguAQrovju3HBkJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIAJAhTQTcBjUxIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAd8lQAHdd+eWIyMBEiABEiABEiABEiABEiABEiABEiABEiABEiABEjBBgAK6CXhsSgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk4LsEKKD77txyZCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiYIUEA3AY9NSYAESIAESIAESIAESIAESIAESIAESIAESIAESIAEfJcABXTfnVuOjARIgARIgARIgARIgARIgARIgARIgARIgARIgARIwAQBCugm4LEpCZAACZAACZAACZAACZAACZAACZAACZAACZAACZCA7xKggO67c8uRkQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJmCBAAd0EPDYlARIgARIgARIgARIgARIgARIgARIgARIgARIgARLwXQIU0H13bjkyEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEwQooJuAx6YkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAK+S4ACuu/OLUdGAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRgggAFdBPw2JQESIAESIAESIAESIAESIAESIAESIAESIAESIAESMB3CVBA99255chIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARMEKCAbgIem5IACZAACZAACZAACZAACZAACZAACZAACZAACZAACfguAQrovju3HBkJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIAJAhTQTcBjUxIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAd8lQAHdd+eWIyMBEiABEiABEiABEiABEiABEiABEiABEiABEiABEjBBgAK6CXhsSgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk4LsEKKD77txyZCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiYIUEA3AY9NSYAESIAESIAESIAESIAESIAESIAESIAESIAESIAEfJcABXTfnVuOjARIgARIgARIgARIgARIgARIgARIgARIgARIgARIwAQBCugm4LEpCZAACZAACZAACZAACZAACZAACZAACZAACZAACZCA7xKggO67c8uRkQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJmCBAAd0EPDYlARIgARIgARIgARIgARIgARIgARIgARIgARIgARLwXQIU0H13bjkyEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEwQooJuAx6YkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAK+S4ACuu/OLUdGAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRgggAFdBPw2JQEzBAoUbs7UiRLjDWzB4XaTb7ybVG7UlF0a13LzOXYlgRIgASiBIE5y7Zh5pLN+HXf4hDv5+KVGyhVpwea1y3n8H2bdn2DWLFiokKJTwyN4+Ejf3xUtg36dWqA+tVKGGrLyiRAAiQQGQQmzV2Htdu+wqGds/XlXflEd+5r8+4DGDB2IeaM7Y6C+d53pwnrkAAJkAAJhCOBFy9eYvbSLcj/0XvI9V42wz176v8NX4gNSIAEwiRAAT1MRKxAAhFDYMGqnYgfLw7qVike6gUooEcMf/ZKAiQQOQTcEdDv3n+AhcpHfpQzu0P0adBhJBImiIfZY7oaunEK6IZwsTIJkEAUIOAsoLvyie7c5snTZ7F3/4+oVq4QXsuYxp0mrEMCJEACJBCOBJ49f44PijdHjza10bROWcM9e+r/DV+IDUiABMIkQAE9TESsQAKRS4ACeuTy59VJgATCl4A7ArqrK1JAD995YG8kQAJRl4CzgB5175R3RgIkQAIkEBoBswI66ZIACUQdAhTQo85c8E4igcCvv/+F6Ys24ZeT/0O0aNHwxmvpUalUftSuXEzfzbmLVzFhzloc+vl3PH36DO++9Ro6NquOvLnedtzt+FlrsHnPASyb2g9T5m/A9z+dRKoUSbB75TiE9rM6bYchpUrhMn1kZ0dfP//6BybP24ATp/9G4oTxUUBt9dq97xAaVC8ZJIXLd0dOYMaizfj9f+cQW6U0yJ/nXf3zDGlTOvqq2WoI0qdJgSplPsUstW3s9P/Oo1LpAhjeq1kkkOYlSYAESMBGwJWA/u3hE2jfdzKqly+MgV0b4fGTp/iwVEv0bl8XjWqWhojnR0/8GQThxx++g4WTeul/u3bjDqYt3IhvfjiGfx88Qsb0qVDkk5xo3bCi/rmkcOnZtg78Hz+BpIK5dece3nnzNQzu1hhZld8PXHZ9eQiL1uzCmX8uIZGKeJfttt1a10Sm9Kl1tf3f/YL2/aZgdL+W+v//7shJ+KnnR8F8OTCgS0MdJc9CAiRAAmYIOAvozj5x5NTlypcdwNebpiJB/LiOS4mPK1ilE2pWKIxeyn8eOPQr2vSeiI0LhuHtNzLBviOnZf0K2h9KdPq9+w/1+taVP/ziwE/Kt27CP+evIKVa28q6dMOOrz1KCRPamlgGsGLj51i95UtcvHwdSZMkRNmi+dCxeXXEjRPLMT65f1lr791/GHfv/YuM6VLpNbJ93R7YR4/s0wJfqvv/Qa3hpRTNnxN9O9XH1j3fYuPOrwNODzYAACAASURBVHFeXUfWzbJ+LlYgl5npYlsSIIFXRED7EZWaanD3Jlis1mqnzpzX78ziA2pVLIJ5K7bj82+OaL8m67yhPZsiS6a0hnzI9s++Q59R8/Dl+klIkzKZo62zX3bHn9rFc2c8HZtVQ5tGlbBk7R7tj67dvKO1hlQpkqJ0kY/QrkkV/Y4vxdn/y7/Je37ypIm0BrB++37tzzIqf9a1VU0U+/RDw7MhaWaWbdir2V64dB2JEsZDttczoHGtMtrvuzNW+0XD8tPPn79A/krtUbn0pyq9Yn3dTK5fuFonFMmfK4hWUahqJ1RU2oys4au3GISYMWMES3/7w8+/oXm3ceq51E2vxVlIICIJUECPSLrsO0oTOPzLKbTsMR7vvZ0FNdUDV9KpiPhy8MdfsW/9ZFy9fls76mRqES/breLEjqUfKuKk50/oiXy5suvxyYNcFv2xYsVQTj8n3sySUT8ERLgJ7WfOArqI+I07jcbr6iFfrVxBfb1jv53Bts++RdPaZR0CurwMtes7CYU+/gAVS+bHg4f+kHQw8rDatHC4fpjaH6xn1QtPjOh+qFDyE/1AlkWAPIRYSIAESCCyCDgL6D//+qf2xeVLfIyhPZrqj5nOLwt//HUBvYbP0X66exvbeRAiVMvi/ubte6ilXiReqv81r1tefzg8cepvrNz0OVbOGqj8XlItoEv+dHmJKlM0Lx4/fooV6uepkifBlsUj4ecXTfe5dP1ezFm6FS0bVNAvXiLGL123B5eu3MTWJSO1UGUX0OVeWjWoqF9e/jp3GcMnL0O5Yvn0Sx0LCZAACZghEJaAfkoFUMgaVXxmDSWW24td+Nm2dBSyZk4XooAuflbWvpVVYIW//xOMnrESL9XaNbA/XL9jP4ZMWIJShfPoNefDR4/xxYEj+PHoKY8F9JDWyyKKy1pWxPCPPngbf5+/jLnLtyPHO1mwQK255X5FdGnSZQz+/PsCWivfKx9Kf/jpNy26d2hWFW0bVdYY7D5anhHtGldWQS/ZIalshkxcjJgxYuDNrBnRpmElpFbPhvkrd6r6R/HZmgn6XCIWEiCBqE1A3q2XqHWZpKTq1Lya+m9aLUCv3PSFFpxLFsrj+KA2ZOISxFKC64b5Q/Wg3PUhRgX0sPypaA7iuyRta7ni+fS9pE2dAmlTJcOsJVvw0P+xDuKTdeWZs5eUX9qhg97ko6aUkAT03/44i49zv4MmtcpqwXvpur3Y9+3P2LV8DNKptbCRMnvZVsxfsUMFClbTPvK6EvT3fPWjus/k+j7sAnpYY3WXsQSiSPDL+nlD9G1KEGHDjqOQJHECfLNpGqIr/cKe+33W6K4o/MkHsD+TZD6zZ8vsGF6XQTNw+sw57FoxVj8rWEggIglQQI9Iuuw7ShOQFw/50rtp0XAlMkd33OsldXidPHRGTVuhIxU/WzNRi+hSRBiv2nwA4sSKhbVzB+t/kwf5KrV4XzatH95/+/UgYw7tZ84CesOOI3H56k1sXzYmSLSNcwqXKk3V9VU0TuDDR69cv4Uy9XrpFw/JryZFvkw/efpUv3ikVCIRCwmQAAlEBQKBBfTf//xHv1QUV9EyEi1oX/i6elkIKYXL2JmrsXrzF9ipFs4intvL3XsPVKSKzbeLgF5LHcY8sEsjh1guosuIKcvVbqGxOrpc6het0QVThnXQYpG93Pv3IQqoSJmx/dvoFx+7OLNKifMfvJPVUW/guEXqA+xxfLVhSlTAzHsgARLwYgJhCegytFqth2hBeOXMAY6RNu8+zvaBcEZ//W8hRaB3aVkDEoVuL3bByO4P7yu/Jwc5F1aBIWP6tXLUswv3nhxKGtKa+Matuyhes5vebWT/QCoXlCjzbkNm6nMvxCdLVKkIJc5RhuLHN6r1+n7lexMnih+ij5Z192MVdb9h/jAtzkj5W338rNCoLyYObqc/rrKQAAlEbQLiRzYowfzA5mk6MEKKfNzLV74NalcqpncC2ss6dRDz0ElL8Y2qKwFm7voQowJ6WP7UaAoXWdduUUF73++YpYcSkoCeVInN88b3cIxX9ADxpc4fVt2Z0dqth+qdP+JfAxcJJJHgEbuAHtZY3WUsASsT1S7/H3bMRry4sbWeckqJ4D8e/R0LJvbSgYqyI7T3yDn4fvssfQ+P1MdeWadLhL6MUYqI8CVqd9MR6g1rlHJnqKxDAqYIUEA3hY+NvZXA7bv38WnljnB+CAQeT6XG/bSQ7vwgmaWiE2cu3owfd9miIe1bUr/bNjMYjtB+FlhAl4eTCOUStd5JbVcNXAIL6Lfu3FdbczvqF4xmdcoFqde06xgdjb5u7hD97/YULiIGsZAACZBAVCFgF9C3LhmFRiraRCK4x/Rv7RC2Q3pZCElAr9SkP1KrHTbzJwS8RAQea0iHiNq3fC6d2hd5PnhLRVb+hM4Dp+vtoc5FPraKbxYfbRfQtyweoSPg7cX+bDi5f0lUQc37IAES8FIC7gjoa7fuwzC182WnijaUaMzL126hZO3uevt71bIF9chDEtD7dWqA+tVKOOgcOXYajTuPht0f2v3ckil91GHOAWkLzQrokvLQeb0saWS6DZmFNXMGBwlEkUjGXKVaaFFExBHZ5bN170Ec2TMvyKza790epRiSj+7Yfyqu3rjtWCdLJ7JuzluuDfp2rK+DUFhIgASiNoGQ3q3lfVl244ivsJev1O6SDv2m6h3ab6moand9iFEBPSx/GpqALhqARM9LelaJuH6g/v7vw0c6oOT4l4v0UEIS0CVoJPB7/suXL5GjeDO0bVxF774xUiRlzR6VNrZ5vfI6ZUv2bK8FS58lwShhjdVdxvZniaRilJSMZev3QucWNSAfPbK+lg79OzfEmBmrcFTtUrUHLcp4Rk9fqXccSLCK7DKSlLZL1u12/N3ImFmXBDwhQAHdE2ps4/UE7BEnEvEoOcJdFcm5JXm0pE7gYn9h+Vxt9xSBPTwE9POXrukI8mE9m6kcwIWCXC+wgC5pAiqqSBlX99196CwcVznd5b6kUED3ejPlAEjAJwmIgC5nT8j2eUmVNX5gW8eWVvuAjUSgF67WGZ/mfT+Yr7b3FZKALimy6rUbjsWT++hzLeyRSiLEp0mVPBh7ifRJmjhhiAK6bLmVNAQU0H3SbDkoEnilBNwR0EV4Ef8nArMEhEjuX0mD8rXa/m7PG+6ugO7sD2UHpuyqkfN8MqlUKfYSEQK687o6MOjAa3Hnda69nvPaOCQBXaLXRaCypwyQ9q6eNa90onkxEiABQwRCeu+WnNpVyxREz3YBArqz/3PXh5gV0J39aUgCukTOy04i8Uu11S7JD99/U6WSSqQjr9du24df9y3WbNwV0KVuzpIt0FKJ4O2bVjXE9e79BzoiXNK2yIdF2aXzSe539blEclaEu2tpdxmL2F+gcgf9/CpZMA9qKg7fbp2uPpJ+q1PYfLluEuq1H4E8Od4KsjNJ0tOWb9hH506XvPcScV+8YG4MUvfJQgKvggAF9FdBmdeIcgTskdxhR6An13keAxfJVTZT/QnPCHR5gH5UtrXjwLzA13MVgS4HHjWvGzQCXdIgyAPP/mJAAT3KmR1viARIQBGwC+jt1QFJ/zt7UeVrPIp543oEOZzZ1cuCpLlKED+e3s4fuMhuoTQqj2TgbayBf+7uot8eBRnWIUQhiTMU0GneJEAC4UXAHQFdrtV31Hx1Ns9JfLF2Eio27ot8KpLPnjdXfu6pgG6P3JQ1pZwHYS8RIaA7ItBnD8L72bM4ruWIQK+uItCVKCaRjVv2HMRPe4NGoNvzC88c1UWfRUQBPbyskP2QQNQjYEZAd9eH7Pjie/QeMVefjRA4NWBIh4g6R2U7C+jiyyQyXNKsyrlq9rJbRXz3GDZbr18l6ttenM8KehUCuv3acq9/nbuEw7+cxozFm/TZbrITyd21tLuM5Xqy6/ORyv+eW4nkx38/A/Hhknu9aI2uejeUpCSbPsIW0Bi4yL9fV6lb5KBVEey3qTOKsqoc8iwk8CoIUEB/FZR5jShJQLb9S9mstnXZcyHK3yV/mBy2OXKqyqm4U3KgBxwsJA+Vqs0GqMie2EFyoLvakip9uZvCReqWrd9bPaQyYOrwjg5eknNdBPS6VYo5DhGt3LS/OhAlptqCOtiRL1jytpdr0BsN1FfcwDnQnbd2RcmJ4E2RAAlYikDgF4MnT56ihTpA9PSZ8/ocCdliK8XVy0K7vpNx89a9IFs5pa7kv5V8mM6HJkkOXz8/P+UnbTnQw3rBkTy8JWp1U4fWZVUvC32DpJSRSE9J/ZUxXSpGoFvKWjlYEogcAu4K6D8d/wONOo1Cm0aV9MdJZ8HbUwH9wuXrKF23p9pSX10flmwv9tRXnuZAd7VeFt9brGZXlVKmpA4ksZedX/6gD4+250D/7Osj6Dp4BqaP7IxiBXI56slBp1tUapevN04NkgPdOc0WI9Ajx5Z5VRIITwJmBHR3fYgclCypUWVdmjvHm47bl/Xm9s+/w6Gds/W/uSsqS11Zh9auXNTxni7/tlnlOR8wdmEwoV52My5cvfOVRqBLZLekAgtc5N4OqZzksrvd3bG6y1iuI6lrpi7YoM8hkpRi9tRjkrIxRozo+mBRyX8uKXMDF3vKRTnjLUumtFg0uXd4mhj7IoFQCVBAp4FYlsDX3x+DnACd8903dNoUEcXlIXHg0HEVyTNRC+nVmg9EkkQJ9NfiuLFjq0OKvobkWpw/vqc+9VpKeKRwkX5ErB88YbHaclUFhfJ9oLdzzV2+DRLtI9HmEnUuxX7f8qW6Yqn8OlfagtW79MFIkuMtRbLEuh4j0C1r2hw4CURpAs6RNbJttKFaLMt/V6uDOSU1lisBXdITTFu4CR2bVcPrasEshyRXKPGJzvtbo+UgxFaHScnZEBKNfurPc1i/Yz8Wq6iZNCpVjDsCukCTa0xdsBG53sum03vJgXQnTv2t/bNsY5WDixiBHqXNizdHAj5BwF0BXQYr29n/uXBVf4DcuGBYkPF7KqBLJxLZ98U3P6ngjJL64+HJ02e1eCRnQswd112nzjJSQlsvy3gXqrVs3SrFdc51SbUou3pyvJMFiyb11gEjEsQiedr/+Ou8PgBVRBfJG7xhx9eQHU0SjSiFEehGZoV1ScC7CJgR0N31IbIzvEy9njoXd482dXSUtKT5k496cpilJwK6BIv89sdZnaNdDj9NmSyJSo2SQh9iXLzgh2hRrwKePnumDg89iDXqfAsJ/niVKVwkFcp7b7+OkoXyIFnShMoHX1Fr7o2oVbGoTqHiroDuLmOxujNqF6oENEog44HN0/WaW4ocMDpOHaQqO5LWqJ1JzkWuIYdci1YzbXgnzY+FBF4VAQror4o0rxMlCfzw02+YvWyrfqDJ4ly2KcnXT3secvkaO1Et6g/9/Jt6qD3HO9kya/HGLp7LoMJLQJe+5GVhxcbPVSqWR8iaOT3qVi2OUdNWoI7K8WUX0KXewR9/1Wlkfv/zHy0aSY4yebjJC469UECPkibHmyIByxNwFtAFiIjg9doNQ7y4cbByhtrlEzc2PizVMkhaK3mhGTZ5Kb7+7hclnj9D+RIf63MjpJy7eE3nH5dUBo/8n+icvSVUTsTmdcurn750W0CXviSdgPhh+Xgpu4BkJ09+9cGyae2yOm87BXTLmzABkECEEzAioIvwLPUHdGmoBejAxYyALrt4Rk1biS8P/oRnag2c6/1sKFfsYwwavwgrZw7QAShGSmjrZcmHu3zDZ1o4unj5BpImSYgyRfPqw5vjqeeBvchuIIlYlCjHO3f/RYZ0KXXkej21XrYXCuhGZoV1ScC7CJgR0GWk7vgQqSdpWEZNXaFTDaZNnVwLy7fu3NM5wj0R0OUjpwTK/arOK4ulDqvvoYR00RvknX7C7LU4e+EKUiRNhKJqd80zJRBvUEEgr1JAl7MoJHWNaB+SElbWvlXLFUTjmmW0wO2ugG6EsdSVcy6yvZ4BcpiovVy+ehMl1IHYgQMIna2025CZ+uy3vavGB8kk4F3WzLv1RgIU0L1x1njPJEACJEACJEACJEACJEACJPAKCchunGGTluKgOuwtYYJ4r/DKvBQJkAAJkAAJADrlohLYO6iDUluoA1NZSOBVEqCA/ipp81okQAIkQAIkQAIkQAIkQAIkEMUJSLoC2aGZSe1uTJggvorEvKB355RXqbPkoNLvj5xExwFTwxyF7Cz6ZvO0MOuxAgmQAAmQQPgQkDOG8ldq71Zny6f3R3a1y95byvRFm7BozW58tX4ykiRO4C23zfv0EQIU0H1kIjkMEiABEiABEiABEiABEiABEggPAnv3H8YMJVRcvnZT5T1/rs+nqFjyE7RUh4rGVAe8sZAACZAACZDAqyQgZyRJvvYi+XNiRO/mr/LSvBYJaAIU0GkIJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJOCCAAV0mgUJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJUECnDZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZCAewQYge4eJ9YiARIgARIgARIgARIgARIgARIgARIgARIgARIgARKwGAEK6CYn/PTJiyZ78L3mMf2iIXqMaHjx/CWeqD8sAQRiKDZ+6uClp89fEIsTgdgxo+PJs+d4SZMJZhtZ30yD6Mp2rF6ePHuBs/+7ZnUMwcYfO5YfxDoeP32pfn/4CxQYkLB57P+UfsXJavyiRUPM6NHwWP1OsQQlkDxNEqRIHp9YFIHLV+/j/p0HZBGIgDyLY8bw0z7lsTpYkyWAgCxT5HBRcgluFWIzL168xHP1hyUogdeypkIsxcfqRWzjzO+XrI4h2PjFNqIp83imNIXn1BWC8IkZ3Q/PX74gFxe/NXFiRYf/Ez6jndHESRAHmV9LQT9jggAFdBPwpGnJWHVN9sDmViMQPXVKqw2Z4zVJYPvfU/QLu9XLQ/9nqJa9p9UxcPwGCTy/dMVgC1a3MoFeK7uiZI28VkbgGPucMTuxddHXZEEC7hOgPuw+K9bUBDb+Ogrx4sSwPI2n6oN2+fgNLM+BAIwRePn0qbEGrG1pAp/UyI9h67pamoHZwVNAN0mQArpJgBZsTgHdgpNucsgU0G0AKaCbNCSLNqeAbtGJ93DYFNADwFFA99CIrNyMArqVZ9+jsVNAt2GjgO6R+Vi+EQV0y5uAIQAU0A3hclmZArpJhhTQTQK0YHMK6BacdJNDpoBOAd2kCVm6OQV0S0+/4cFTQKeAbtho2CCAAAV0WoNBAhTQKaAbNBlWD0SAAjrNwQgBCuhGaLmuSwHdJEMK6CYBWrA5BXQLTrrJIVNAp4Bu0oQs3ZwCuqWn3/DgKaBTQDdsNGxAAZ024DEBCugU0D02HjYEBXQagRECFNCN0KKAbp6Wix4ooEcIVp/ulAK6T09vhAyOAjoF9AgxLIt0SgHdIhMdTsOkgE4BPZxMyZrdMALdmvNuYtQU0CmgmzAfyzelgG55EzAEgAK6IVwuKzMC3SRDCugmAVqwOQV0C066ySFTQKeAbtKELN2cArqlp9/w4CmgU0A3bDRsEECAAjqtwSABCugU0A2aDKsHIkABneZghAAFdCO0XNelgG6SIQV0kwAt2JwCugUn3eSQKaBTQDdpQpZuTgHd0tNvePAU0CmgGzYaNqCAThvwmAAFdAroHhsPGzKFC23AEAEK6IZwuaxMAd0kQwroJgFasDkFdAtOuskhU0CngG7ShCzdnAK6paff8OApoFNAN2w0bEABnTbgMQEK6BTQPTYeNqSAThswRIACuiFcFNDN4wreAwX0iKDq231SQPft+Y2I0VFAp4AeEXZllT4poFtlpsNnnBTQKaCHjyVZtBemcLHoxHs+bAroFNA9tx62ZAoX2oARAhTQjdByXZcR6CYZUkA3CdCCzSmgW3DSTQ6ZAjoFdJMmZOnmFNAtPf2GB08BnQK6YaNhgwACFNBpDQYJUECngG7QZFg9EAEK6DQHIwQooBuhRQHdPC0XPVBAjxCsPt0pBXSfnt4IGRwFdAroEWJYFumUArpFJjqchkkBnQJ6OJmSNbuhgG7NeTcxagroFNBNmI/lm1JAt7wJGAJAAd0QLpeVGYFukiEFdJMALdicAroFJ93kkCmgU0A3aUKWbk4B3dLTb3jwFNApoBs2GjYIIEABndZgkAAFdAroBk2G1QMRoIBOczBCgAK6EVqu61JAN8mQArpJgBZsTgHdgpNucsgU0CmgmzQhSzengG7p6Tc8eAroFNANGw0bUECnDXhMgAI6BXSPjYcNeYgobcAQAQrohnC5rEwB3SRDCugmAVqwOQV0C066ySFTQKeAbtKELN2cArqlp9/w4CmgU0A3bDRsQAGdNuAxAQroFNA9Nh42pIBOGzBEgAK6IVwU0M3jCt6DNwvoZZsVRYFKH2FAlXERgQYlGxREre6V0DJXz1D7z1MyBxoOrI7M2TPAL7of/vjpL0ztsBDnT19C0tSJse78nGDtr/5zAw2ydYyQ+47oTr1dQK/WsihqtSuB+AnjYkzHpfh29zFTyJKmTIjVP48MPscXbqHxJ0Nc9l29VTGUrJkPaV9LgSf+T3HoixOYOWA9Hj147KhfvPpHqNq8CDJlS4NLZ2+gTcnRpu4zMhtTQPc9AT1d5hSYtasnqrzbO9xNK0/h7Gjaqzzkd8v/wRNsWrgfO1Z8G+p14ieKi7Er22HNrC9w0OTvdLgPyGSHviSg5yr2HjpNbYY0r6fCttl7MbvncpN0AppnfCsdZh8ajXpZO+Dezfsh9hszdkzsurcs2M87FRyI33/8X7jdT2R1RAE9gPycMTuxddHXkTUV4Xrd0jXzIn+JdzG49eJw7TdOvFho078Schd8C35+0fDnyYuYNXQzrl26E+J1kqdOhCZdyyBP4beRMHE8jO2+Cgd2Hw/X+4q0zrw8hUvO/G+g/ZCqSJMhGbav+A7zRm03jVLbyIBANnIibBuxX7RZz3Ko2aoIymbr5biP9Grt23ZQFbz+Vhq8ePES339xEvNHb8fTJ89N32tkdEAB3Ub96bMXKB+/QWRMQbhcs/eSdjhz7B9smLwzXPoL3En0GNHRYWoTPH/6HDO6LAm1/xpdyqNUo0JImyU1njx6gh92HcWMzovx6F9/FKn5CfqtCK4ffLb8G0xoEVxvCPeBRECH3pzCRea1x6J2yF/5I0SLFg2NlbZz+2rIz0538b2ZOwu6zG2N197LhKtnr2GWspnDe46G2jy+ehaP/3IwVo/ejAMbf3DUfe3djGg/rRlefz8TYsSKgZPfnsb09gtwRfXrjYUCuvlZYwS6SYYU0IMDTJ42KSZ+OQiJUyTEjYu33RLQX758ib9+PQdxpG3GNUCcBHEwoLJN2BeHFrgUq1MAxeoWQNciQ0zOXuQ092YBXV4CNp4ch9bFR+HyuRvqZdFPLdifmQYp4l3gUrRKbhStkgfdq01x2beI5+f+vKKE8etKJEyEgfOaY+/aH7Bhzpe6voj85RoUwJwhG/Hn8fOIGz82rpy7afo+I6sDCug28g/9n6Fa9tA/yEXWHBm9bkQJ6MnVR8eF+/qhd/2ZOP3LOaR/PSUmb+yM4W0X49dDZ1zeZuPu5VCyRl4kVT57dKdlFNCNTuYrrD/zu5HYNuczfL7yAGLEjK4/IIZHGburH15XLxry0bp6ulZuCejNc/bAzUu3HZd/dP+RFnO8vVBAD5hBCuhhW3M7JWQmSZEA43uswfNnz9GiTwVkeTsd+jSa67JxArXembm1C75RgvmedYfw791HeKn+d+/2w7Av5g01vNwFTN3UCTtWfocvt/yMGDH88OSx+TVuu8HKRpI72Uh2ZSMNXduIfZqrNi2IQuU+wNs5MwUR0Ofs6o6963/E5sUHEE+9Lw2b3xQ/f/snVs34whssJNg9UkC3IaGA7tp8P67wIdpPaoLEKijks6Vfhymgl2pYCP/8fhGX/rqq1zSD13TFnqX7sX7SDq0zxFHvhIFLp+nNcPmva1gyZJ1X/v54s4Cer9yHaD6mProVGoTH6mPHM6UpiCZkpsSIGQPLzszAtll7sV0FmnxcITc6zWqJRio45O6Ney67bjK8Dko3KartZWTdKUEE9JxF30OWHJnxw46f1Jr7CdpObqLqJUG3woPM3GaktaWAbh49BXSTDL1VQJfotUXHJ+gHibyAy6K/SsrmGLSmCx6oxXzKjMnxdt438Pev/2B045m4psRSKdnVv007OFyL4mdPXgiVXr5yudBiZL0wBfTAnYj43mlGc/xx5AxWqi+AzkXud/GJSZjWaRGOfGYu8tnk1Hvc3JsF9Jl7eiHruxng//CJjvau+2F/rDs+GtuXHMAnpd9HahWx88PnJzCl92o8/e+lQ6LVa7Qpjlo5+rrFLLp6YVmwf4COKD+y//dQ20i0V0YVYT5wbnNM67sWx7//U79MrDwyHB3KjsPFv6+7dc2oXokCum2GfElAX/RVf6RTUWTyuySlb4PZyPZ+BhQqnwuX/rmOfMXfU8LKQ8wYuAFHv/3DYaLbT4/H/JFbsW3ZQZdmm+PjNzB8UUvU+nAAHv8nrk7f1g1blx7AFxsPh2rqUm/t7C8poEdRh9B2QiNU7VBG+9YXz1+gS9EhqN+nCh7cU8/sDIGe2U2cntkH/ntm/xb6M1teKrffWuK2gF43S3v1kfxWFKXl+W1RQA9g5ysCepqMyTBvdw9EV7scRRB9rn5/auQehP7TG+LBfX+kTJsYb3+QCX+fuoxxPVY7osfl3yav74A25Sfinz+vujSqcSva4Lj6OLli+uf653mLZke7gVXQpJjrXW+NupRG6vRJMb7nGs+NNCq3NKd9ROrIWqudBJUbF9CR3OJje9SdhVFLWulI9E9KvGNb4355ElP7b3QEj9RqXRTVmxdG7bxDQrz3cSv/s5FpgWxEfXhpUjTknZHFKn+IMrXyYtrAjZi//UtbqAAAIABJREFUt6dDQJd17/bfRqNv43nK7v7S15SPNomSxMOkPt4pAFJAt5mONwvosuu829xW+iO6RIn/sv8kBlWbgA2X5qrdcp/jk0q5kSZzSnyvRMjJbReodYzt43/tnpVQs2t51EjXOszf/VZKaI0VJ2aYArq9I/ldyfR2egxU2sa0jotw7Ovfgl0jvXqHnK50jabvdlPiasg778K8uUis4K0CehIVALfq3BxEl2AQJZ7/uPsolg9dj6kqUGTduK0o07wYYseNhQ3qw8e68VsdhEfv7q93EwyrOdEl9Q8Kq51mm3qgeopmDjF+5o9j9K7NvYu/CnWmZh4eizVjgkagOzcQQX3IZrWDOEnjSJx1zy9NAd1zdvaWFNBNMvRWAV2G7SqFiwjo6bKmwcIBq3Hhz8toPqIu4sSL7UjzkklF1XSY0hQTW8/DVSXyhFaMCugTvxiEHIWyY9eCfZiitsa4+gJZqU0pFK2T32ujz4WXNwvoKdIkwYrDw1AmYyfH1IuAfuy7P7F+9hda2Ok3uyn2rPkeG+fu03UKV/oQhSrkwvBWC936bavYuCCKVM4dYvR54E72nJ+mX2Jm9F+HvWts260+Lvkeuo6vh1NHz+L9fG/g6dNn2KHExuUTd7l1/ahYacffU3QklNWLv3qprfJWD5/A4CoCvVKjT1G/cxksGrsdx77/HwqW/wA1WhZDw/xDlehje9kYvrgVdq36Dt+rD1WuikQlj13VXvntWFg+ebcSAV6qdC4V0K3G1CApjly1pYAe9U1r6W9TMKnNPBz7xvYiOGi1ema/4fTMjque2VVtO7j0M3uyemarNmE9s40K6Lev3tUvMbJ7bPGQtTinIr58oQxY1x2FK+fxhaGYHsOCCbuxcV7oL5ymL/KKOnCVwkUE9HSZkmPxxN3qg/sNNO1RFrGVQGNP85Ixayothk/pvx5X1Y5KVyV/yXfRc3xdbFNpsr7Y/BNa9a2Ig3t/1RHCrsqMLZ1x6/p9SBoOWVOd+e2iFknP/nHlFZGI4Mt4sYAuZBZ92RtT+q13iNNrfxyCYz/8Dxvm79frzb5TG+i53bjwGw2ysHpOFyybAyM6hJxOS9vIBGUjy/+zkX7KRvaEbCMfqdQ+DbuUUhHq81SKn7hYsr9vkAj05r3UrjGVpnDZlL34/eg/6DO5PsZ2W4W/fr8UwZMbMd1vPjkacWJFj5jOvajXZyqFSzkfS+EiAvov+3/DuokqxZB6RxywshN2L/nKkeZFUqoUqp4Pw+q43nEcePqMCuifPV6lf2enK/F8z5L9Li1hwKrOuPDHZa+NPpdBeauALvdevH5BlGtRAt2LDtbzI+lS5v4yAatGbcK+VQd0Gp5BG3qoCPWBOsWvlOaj6ulo9RXDN7ic0/KtSqKsEt875AsI3Ou7ohOuq9SwC/qsCNUjuCOg1+5VGR9XzIOuKm2hN5ZCdT7FQGX3LJ4ToIDuOTvd0hcF9BMqt9Om6bv1+DK/kwFzDo9BuQQNDW+pMSqgy/UkAr3r7Ja4rPJKzXTKcSZfIZednoqR9afh+IHQI5NNTmuENvdFAb1PnRn4S70EShEBPH+ZHOhbd6ZhjvLiuvjbwRjdfgl+VS8sYRXJl5bpzTQYtrg15g3frPOxV2paCFWaFVF9LMbZ05eRWUUXDFvaBiun7MHO5a6jdsO6TmT/nAK6bQasIKDn+vQtDA30sWnN4eEq/coinDzyt9tmWLHhpyqSPSceK9H9Q9Xf0gm7VGR52Fu7KaC7jTjSKroS0E98F+iZrc4S0c/shMaf2e4K6OJ3ZYfazUu3EE+dhVG1Y1nkKZEDTd/vFm4pZSINsLowBfQA+lYQ0MW3bllqWxtkeiO1Tq9S6b1+bq95U6gt332n1sf5M9eQp9DbKqL9EYa2Xap2Etl2bjqXdUqQ3aYimrep9YhEOjfsVAqflnkfzUuMVR/8vTOHdZAx+qCALtHednG6Qv38KFDqPR0B7m5JkcaFjbRxbSMZsqTEgBmNdP+31YcW2a3gLKC/m+d1tB1YSX90KVDqffx04DQm9Frr2NHm7n1FlXoU0G0z8UzteigXz7dyoIuA3rvMKJw5/o8eY6U2JVFA5bvuXXaUYfMzKqDLWiVz9vQYpqKF5/VZiYObg37UzJbrdYzZ3Q+N3uqsdt97bwotXxPQpxwcgSpJA6K7B67rhr9Ubv2VIze6ZTPVVA78AlXyonsRmygvpeu8Njo9zPQOC0LtIywBXQT+yWpX56DKY/Grl2pRhWoXwEAVfMPiOQEK6J6z0y19XUBPmCwBNl2Zj8rJm+GheikwUjwR0KX/PKU+QO9FbVEzQ5sgl6vbuwpyFnnHo4eukfuO6Lq+LqAXKPsB6nYqrVOoGC11OpTCB/mzoW89Y+J7s76VkEalHRrVbjEqNSmkIndzomfNaY7LN+1TEZnfTIshzdx/4TF67xFZnylcbHR9KYVLSBHozgL6HJU2acmEnfhBHRLmTsld6C20G1IdrUuPxTMlxmTMmhqjl7fBOnU+QEhpX+z9UkB3h3Dk1glLQNfP7Mvqma22rhp9ZrsroDsT0CkFbi9F73KjcOLbU5ELKByuzhQuARB9JYWLjCikCPTAAnpClQZDBO7quQbiYaBDyUMzq8nrOmCX2gH3+aYj8FMpYhqrFC3F1TkuTVUKF1eC+NpDQzCy03Kd9kWKBA5s+mUEOlWbpqPRvb74uIBeQKUrrNtORTdWnur2VEkaIG0jG/+zka6BbMTp4E+JPh80u3GgDzjREFNFZ0sk7TD1Yea0OtdnyVd99PUvq7N9EqgI9b5T6uPhv48xsmP4HSrt9uDCoSJTuNggenMKF7l/V4eIOgvon1bNi3oq9Vy7fP0MW45RAd1+geYj6uiD1yUAL3AZvbOvDshbPWaL4XuJSg18XUCXAzwlLdCc7kvdwi4R6KWbFEGn/P0d9SUCXVIOzu/teQR6xrfS6UNGF/VfrXLx73frXqJiJaZwMT8rFNBNMvRmAb1048Jq29TH6F9prIOCpHAJHIEupxiPUQ+YamlaGiblqYBeuMbHaDm6Phqok5jtJUGS+Fj+x1T0LT8Gpw6HHZls+GZfYQNfF9Al33n2D19zO2WLY45lm+q3Q9C/wSx1+KEtUsHd0mFkLZUnLSYmdluJPCr/aPeJDdAg70CV2/+F7qJF/8pIpISlSd1XuttllKpHAd02Hb4koEsu1Xmf90Hl7L0ctiYpXAIL6HIewNqfRqBHrel6N4U7pW6Hksie6zUMaj7fUb3L6NpK2ImGSb1Cz7lLAd0dwpFbJywBXT+zd6hndlrjz2xPBXTZHbb1xiK0ytPbJ9K4UEAPsHFfEtBLVsujU20MarnIMUBJ4RJYQH9TnUMxYmEL1Aoln7WzB9iu0k90qz0Tf56wnTGQPHUirDgwAI0Kj8L1y3eCOYwp6zti37afdToPKbJ22XJsJBoXGeXIvR65Xsbk1X1cQK/RorA+1DO0lC3BbETlLO9Wy8lGDiobKeTaRgK3d45Afze3Wl8vbI5qOQPSBxStlAuSW79psTEmJy9ymlNAt3H3dgG958K2+Oe38ypdyw6HITkL6DW7VdDnqbmTssXZGj0V0DtOa4rYKh3thBZzHF1KylhJ3yLR5/5ufiyNnN+OsK/q6wL6CLWmPbLnF2yZYcuOEFb5oIjKga7SvlRL0dRRddaRsdgx5zOVJvjLUJuHFIGePV82lVe9J+Z0W4r9a23Pbm8tFNDNzxwFdJMMvVlAz1XsPRXp3Q4dCgzQFOTLnAjo928/wOJBaxFHHcbYY15rLVgv6Lda1wmPHOilGhVGnpI5MKrhdN1nh6lNcVzlc/3thz+RIl0y9FLR599uO4KF6gufvbQYVVcfBCIHknh78UUBfdHobTiw8xe9/bn/3GaY0ms1jnxlS7Pjbg705v0qIaPK5esqSrz14GpqK+s9rJv1BSTacYA6NHTLoq9x7s8rePejLOg5pSFGtFmoryk5oBd+PRDf7jmGpeN2IIOKwB2xvC3GdVqGowdPe6X5UEC3TZsvCeixYquow19Ho1+jOTr90TN1kHOpGnlRonpejO64VEc/1m5bHO/kfh1dqgbkhgwrB/p76vdh5NLW6N94Lk4c/gup5LC6NR2wcMx2fKN+R7O+kx4t1O/aePWx6da1oKfRU0CP+u7BlYB+/06gZ/bc/57Z/z0/wyMHeqmG6pldKuCZnb9ibqTKlBKHP/sFT9VBtU2G1sbraltru4/dT3sRlUlTQA+YHV8S0HPmfwM9xtZBlxq2tecNlcNfBPR/7z7Ckkm7EVcdott1VE0d4btovO3MFHdyoI9Wh0zeU7+DcoCjHFBap20xFKmQE63L2Q44E+E+96dvYozKUS2lbO18Om3LgBYLcfHsdTTpVhZvKL/cs/7sqPxr4f69+aCAvlilQTuw+7i2B7GZqf034Mg3tvWkOznQRy/9z0Z6/2cjKoJd20jZQDZSUNlIV5uNBC7OAno89W4mEeirZ32JzYsP6PNOJAe6fKyZOWSz+/MUhWpSQLdNhrcL6E2G1MIbOV/DqEbTIR/kbymbFAF94YA1+GbjIduBnqs7q0NE5+Pw3mN6zOGRA73N+Ia4fe0u1o7fpt8RB67pii0z9+AfdS7LewXeQi8l7A+vO8VxTbnutAPD8PWGH7Bxqveej2X/FfY1AX2GOvBTcp5fOnMVecvmQseZLdD07c64o+ZYSlg50GPGioHlf8/Cpik7sXPu58hbLhe6LWiLxm90wK0rdyCpfcZ+NhDrVV7+w3uOBvGErgT0PKVzovfSDpjUcg6O7gs4f+qJ/xN92LS3FQro5meMArpJht4soIsD6be8Iz5RL8P3bt5HvSwdtICe9vXUSJ4uKWIoB7R/3XeY3WO547Rs+Wo8TZ1W3TJXT5w9aYu2cS4pMyRXOVhHq1OVY+gXkn/Vi8UXKw/ofqQ0GVoLpZWIXvf19vrvVTuUUQeaFlMHRaRS9/Ev9q0+iGXDNjhOuE+mDlla+vsUdC48CH8dP2dyxiK/uS8K6CJkZ1EvgA/uPdIi9/alBxyga7UrAYlKr5Uj4DAP51lIlioRFh0YhK5VJuFvF4cgTdzUBVfV4R8igkuRQ0Il1Yu0u3L+JtbO+Bxfbjrs6DbdaynRaUxtvJUzM26ql+RVKv/5vs1HIn/yPbwDCug2cL4koMt4qrcogrodS8FP+eKe6hwByWtavWVR7ftSpk2CX5UAPqXPWtwIFMW4/fR4zB+5NdR0LMWr5kEtJb4nShpf/05uVh+bdq78TjPMVeBNjF7RFs2KjHTk6G03pBqKqMN+46t81o+VIPpMHbzbpvQ4ddBdUIHdQ/ON9GbPL/nI4XyKpCsBXQ5ZkvNDHM/snk7PbJWvUT+zf3P9zJYJmn1oNFKpNFiJkifE/Vv/4tJfVx0f1+WlWD+zs9ie2bIOaDW2AV5TZ6RIOX7gFGZ2W4Jr51znfI50AzB4AxTQA4D5koAua97ek+rh4+Lv4P6dh2hYaKQWQ9NmTKbXEjHVmnW/+sg4b5Q67E75YClvf5AJkn6jTfmJ+OfPqy4tKWnKhGitPkq+/9HrKu0GcPrYOcwdtc0RTS6RwSWr50HDgiMd7SWKubLacSRr5J8P/oFZw7bgjlr/+kTxQQH9vBJyXlcHMkt++3Vz92PHf89Tma9arYuievPCqB3KrgVtI/2dbGRkIBtRKV3kQ0tgG7Hbgqsc6G/myIiWfSpA8qXLR5tv1aG1S9RBuPL/e2OhgG6bNW8X0FOkT4ZBa7si6weZcWDTIYxpPFML6OdOXUKWHJnwQPndtRO2Yduczx1mWrtnJdTsWh410rUO0XRFZO8wtYmOIhc/7v/AH1M7LFTXsOU0n/zVYFz55zrGNpml/95tbivkLPwOkql10ZW/r2H1uK34clXAGVj5K+VBRxW81+jtLg59wxt/b+z37GsCukSLnz5yBtk+zIKLf17GjI4Lg+QbH727vz7AflhN2wdIV0UixjvPboXM72bAVXUeyVyV/uX77TYdQFKt7X26VgviuxfaItIlTUyxup8ifuJ4+oBSyZfeUp3rI4J728lNUK1z+WCXGak+ynhjNDoFdPO/7RTQTTL0ZgHd1dCdU7iYxMPmLgh4s4DuakLXHR+NwIeIctLDnwAFdBtTXxPQnS3FOYVL+FuSNXv0JQHdeQYHqYOAAh8ias0ZDt9RU0AP4OlLArorK3FO4RK+lmTR3rxcQHeetbUqJ37gQ0QtOqsROmwK6Da83i6guzIS5xQuEWpIFu3cmwV05ymTQzqdDxG16LRG2LApoJtHSwHdJEMK6CYBWrA5BXQLTrrJIVNAtwGkgG7SkCzanAK6RSfew2FTQKeA7qHpsJkQoIBOOzBIgAI6BXSDJsPqgQhQQKc5GCFAAd0ILdd1KaCbZEgB3SRACzangG7BSTc5ZAroFNBNmpClm1NAt/T0Gx48BXQK6IaNhg0CCFBApzUYJEABnQK6QZNhdQrotAEPCVBA9xBcoGYU0E0y9DUB3SQONneDgK8J6G4MmVVMEqCAbg0B3aSZsHkIBHxZQOekhz8BCujWEdDD33rYo69FoHNGI54ABXTfFdAj3np4BV+KQOdsRjwBCujmGVNAN8mQArpJgBZsTgHdgpNucsgU0CmgmzQhSzengG7p6Tc8eAroFNANGw0bBBDwsQh0Tm3EE6CATgE94q3Md69AAd135zYiRkYB3TxVCugmGVJANwnQgs0poFtw0k0OmQI6BXSTJmTp5hTQLT39hgdPAZ0CumGjYQMK6LQBjwlQQKeA7rHxsCEooNMIjBCggG6Eluu6FNBNMqSAbhKgBZtTQLfgpJscMgV0CugmTcjSzSmgW3r6DQ+eAjoFdMNGwwYU0GkDHhOggE4B3WPjYUMK6LQBQwQooBvC5bIyBXSTDCmgmwRoweYU0C046SaHTAGdArpJE7J0cwrolp5+w4OngE4B3bDRsAEFdNqAxwQooFNA99h42JACOm3AEAEK6IZwUUA3jyt4DxTQI4Kqb/dJAd235zciRkcBnQJ6RNiVVfqkgG6VmQ6fcVJAp4AePpZk0V6YA92iE+/5sCmgU0D33HrYkilcaANGCFBAN0LLdV1GoJtkSAHdJEALNqeAbsFJNzlkCugU0E2akKWbU0C39PQbHjwFdAroho2GDQIIUECnNRgkQAGdArpBk2H1QAQooNMcjBCggG6EFgV087Rc9EABPUKw+nSnFNB9enojZHAU0CmgR4hhWaRTCugWmehwGiYFdAro4WRK1uyGAro1593EqCmgU0A3YT6Wb0oB3fImYAgABXRDuFxWZgS6SYYU0E0CtGBzCugWnHSTQ6aATgHdpAlZujkFdEtPv+HBU0CngG7YaNgggAAFdFqDQQIU0CmgGzQZVg9EgAI6zcEIAQroRmi5rksB3SRDCugmAVqwOQV0C066ySFTQKeAbtKELN2cArqlp9/w4CmgU0A3bDRsQAGdNuAxAQroFNA9Nh425CGitAFDBCigG8LlsjIFdJMMS/rVNNkDm1uNwN5Lx6w2ZI7XJIEXKU8jRvRoJnvx/uZPnr5A+di1vX8gHMErJUCf+0pxe/3Fnieci5jxi3r9OMJjAAv7r8aa0ZvCoyv2YREC9LcWmehwHOaz5KcQK6ZfOPbonV09e/4SZWPW8s6b511HGgH63EhD75UXfhm7IqInneiV9x5VbpoCusmZoIBuEqAFm/NBZ8FJNznk5ylOIWYMvlw89H+GyvHqmqTJ5lYjQJ9rtRk3N977sWYhcbIS5jrxkdaze63EpglbfGQ0HMarIEB/+yoo+9Y1/JP8hnhxYvjWoDwYzdNnL1AuFoNEPEBn6Sb0uZaefsOD9/crj3ipJhtuxwYBBCigm7QGCugmAVqwOR90Fpx0k0OmgG4DSAHdpCFZtDl9rkUn3sNh34s5E0mSl/SwtW81m9VrBTZP2Opbg+JoIpQA/W2E4vXJzimg26aVArpPmneED4o+N8IR+9QFHkUrj/ipKaCbmVQK6GboqbYU0E0CtGBzPugsOOkmh0wBnQK6SROydHP6XEtPv+HBU0APQEYB3bD5WL4B/a3lTcAwAAroFNANGw0bOAjQ59IYjBCggG6Eluu6FNBNMqSAbhKgBZvzQWfBSTc5ZAroFNBNmpClm9PnWnr6DQ+eAjoFdMNGwwYUc2gDHhOggE4B3WPjYUNwjUsjMEKAAroRWhTQzdNy0QMF9AjB6tOd8kHn09MbIYOjgE4BPUIMyyKd0udaZKLDaZgU0Cmgh5MpWbIb+ltLTrupQVNAp4BuyoAs3pg+1+IGYHD4FNANAnNRnRHoJhlSQDcJ0ILN+aCz4KSbHDIFdAroJk3I0s3pcy09/YYHTwGdArpho2EDBwH6WxqDUQIU0CmgG7UZ1g8gQJ9LazBCgAK6EVqu61JAN8mQArpJgBZszgedBSfd5JApoFNAN2lClm5On2vp6Tc8eAroFNANGw0bUECnDXhMgAI6BXSPjYcNmcKFNmCIAAV0Q7hcVqaAbpIhBXSTAC3YnGKOBSfd5JApoFNAN2lClm5On2vp6Tc8eAroFNANGw0bUECnDXhMgAI6BXSPjYcNKaDTBgwRoIBuCBcFdPO4gvdAAT0iqPp2nxRzfHt+I2J0FNApoEeEXVmlT/pcq8x0+IyTAjoF9PCxJGv2Qn9rzXk3M2oK6BTQzdiP1dvS51rdAoyNnwK6MV6uajMC3SRDCugmAVqwOR90Fpx0k0OmgE4B3aQJWbo5fa6lp9/w4CmgU0A3bDRs4CBAf0tjMEqAAjoFdKM2w/oBBOhzaQ1GCFBAN0LLdV0K6CYZUkA3CdCCzfmgs+CkmxwyBXQK6CZNyNLN6XMtPf2GB08BnQK6YaNhAwrotAGPCVBAp4DusfGwIVO40AYMEaCAbgiXy8oU0E0ypIBuEqAFm1PMseCkmxwyBXQK6CZNyNLN6XMtPf2GB08BnQK6YaNhAwrotAGPCVBAp4DusfGwIQV02oAhAhTQDeGigG4eV/AeKKBHBFXf7pNijm/Pb0SMjgI6BfSIsCur9Emfa5WZDp9xUkCngB4+lmTNXuhvrTnvZkZNAZ0Cuhn7sXpb+lyrW4Cx8VNAN8bLVW1GoJtkSAHdJEALNueDzoKTbnLIFNApoJs0IUs3p8+19PQbHjwFdAroho2GDRwE6G9pDEYJUECngG7UZlg/gAB9Lq3BCAEK6EZoua5LAd0kQ28X0Kt3rYA6vasgfuJ4GFVvKg5uPmSSCJAoeUJ0X9gWuUt+gIf3HmLTlJ1YM3ZLiP3W6FYRpZoUQbqsafDk0RP8sOMnTO+wAI/+9UeR2gXQf3WXYG0/W7of45vONH2vkdGBNz/oxsxOjjcyP0WNcvfCFd0j/2iYvzopDh6Oh+jRX6LoJw/QtOYdxIxpu8zde36YvDA5fjoRB/HivEC1MvdRu2LI93D5WgzMXJoUJ/6Ig2RJnqF2hXsoXfhBuN7zq+yMArpvCOipMqZA31Wd8WbuLPjntwtol6d3uJiR9Ndlbmu89l4mXD17DbO6LMHhPUdd9h07biz0XNIB73zypvbV536/gPm9VuDovl91/Tylc2L07v5B2j59/BTl4tYLl3uNjE68yedGlo/94+9YmLooGc5eiIXUKZ6iTYPbyPuBf6jT9e+DaOg9JrXyxXdRKO8jR90nT4CKzTMHaztl8GVkf0P9MIoXCugBEzRL+YbNE7ZG8RkL+fYiYo0bLVo0tBrfEKWbFkXMWDHUuvlHTG41F0/8Q7ftkg0Lo1avymj5frcgN9x4aG0UrVMAKTMmx63Ld7Bxyg5smb7ba5l7k7+9eCU62g1Ih60Lzoc77x+PxcGS9Ulx644f4sZ5qdat91CxxL/6On+fj4HZy5Op/8bCs+fR8G42f3RochtpUj5zeR/im3d9lTDYz7q3vIFShR7A29e8FNBtU/v02QuUi1U73G0xsjqMCmteGbuRNXJksTJzXW/yuc7jjKw1b2j+2dVcdB6SBqfOxA7yo2a1boeqRZiZ04hsSwHdPF0K6CYZerOAHid+HGy5vQQt3uuGy39dhZ9fNDx94nrxZgTTgDVdET1GdExtNx+pMqXAyJ39ML7JDPy427WgU6pxES0mXTpzBUlTJ8HgjT2wZ9E+rJ+wTfcTJ35Qh9VpVkt9v0sGrjFyW1GmLh90wadi/Nxk+PdBdPRodRMPHkXDyOkp8N5bj9G6/h1deeSMFHjxHOoF4xau3YyBgRNSoXurG8iXM7jA8/Qp0LpfWhQv8AA1yt7Dn2djYcgUVV+9aHzyYeiCUJQxEqcboYBuA/LQ/xkqx6sbVacpzPtqNa4hEiZLgGntF6gPRX7wf/g4zDZhVYgRMwaWnZmBbbP2Yvvsvfi4Qm6Ij2yUtQPu3gj+kUmu32BADchHyJuXbmkRqL76e530rfDw/iMtoHdW7dt82DPg0i+BB+pjqLcWb/K5EfUyEZqPFZ/ZpEd6VCx+HxXUn0NH42L60uRYMvECkiRSk++iLFmfGJ8dSIDbd/3Qt/0NlwL6vDEXkTyJctz/lXhxX6p1RtS3IgroAXPkzQJ6RK1xK7Ypheoq8GNgxdE60KP/6q747fvTmN97hUvjTp42KSZ+PQyJUyTEjYu3ggnoLcc2wI+7jqp18Hlky51Vr4EHVBiNX746EfV/WVzcoTf524gS0G/cio7mvdJhTJ+r+qPhhcsx0GVYagzqfAM53n6Moydj46wSz/PmfIhYMV9i7spk2pdOHHjN5Zw/VkuFp8+iOX724kU0dBycBp2b3cL7b/l7/ZqXArptan1NQI8Ka16ja2RvdLre5HOd+UbGmjcs/+zKBkRAL13oXxTKFxCMFzvWS0egnzfZDQV087NFAd0kQ28W0Of8PB5Zc74G/wf++iWgV4lhmPLtCCwfuh5VOpRF/CTxsHHyDqwcsdFBSaLVa/aohOopm7kkJ9E4W+8uQ8eP++HMsbO6jjxAk6RdH/htAAAgAElEQVROjHGNZ4RK20+9WWfKnh6DNvTA1LbzcGz/yWD102dLixmHRqPJm51cikMmp/OVNPfWB93nB+Jj0oJk+kNLDBUl/kF2fwzrfh012qZHzXL38c2P8fRLQp73/dGn3Q3HQ+X3/8VCl6FpMWfURbyeMfgHmhcvgEotMmHywMvI9rpSclQ5cjwO5KG6YfZFiLhTtXUmTB18BVkz2yK85q9Ool42oqNXm5vB5uzE6dgYNCmVantO36uUReuS4KK6t4Hq5cUbCwV026x5s4D+abV8kI+LL1++xDP1oXJhv1V4+eIlClb/GOdOXdT/lR044vsCf2zc9WgV5vZYhq0z97g03Q8Kv4vBm3qgeopmum8pM38cg21KTN+7+Kswzd1PCfl7nqxB29y9cOaXs1pA7zijORpn6xhmW2+p4C0+11Mfu3Z7IqzflVD7S1clLB977PfYGDY1JdbPOu/wmR0HpdFielg7dzoMTINaIUSgr5hyHimTKwfvZYUCesCEebOA7rzGrZW2JTZeX6SDMwrV/AQZ3kyHI3t/wej6Ux3BI9nzZcO070dpkfvsSddRyRO/Gopvt/yITVN3alD5yn2ITrNbon7mtqFaer7yudFiTP1gArpzo0lKbJfdoLJ70xuLt/hbYdu0RzpcuhoTcWLb/NTo3lfxp9qN8+2R+MiQ9qneFSkiSYcmN4MEbFRolgkt69xC5VK2iHLnIj5VAj3WzTyPOHFsPxVfWbnUfZQsGHw3pAjqw6amwuZ57kXC79kfH7v3J8TUIVfgC2teCug2G/ElAT2qrHnNrpG9wQd7k88NzDMy17xG/LPcswjolUrcQ/FPvTeYyM6eArr532oK6CYZerOAniJ9Mqw+Pxf2Mbz2bkbMOz5Ri+a7F+6zRY/v6Ismb3XSEd9SJKVKYfXiMbTGBJfk5IVk8ampqBC/Ph4rMUhKuRbFUbZFCSWq9w2V9ucv1uuXmGkqcl0i0F2Vgeu64fzpS14bfS5j8tYHndy7qy/FIqC/9foT1K96V21TfYHBSryuX+WuQ3g5dykmZi1Lii7Nb6rtqQHRiPb5faCeRdVaZ1YC+yUlsNsE9Gs3oqNh1wzYNPec2v4aHS16p8fW+f84XkR2f5VAvTzEx7ShNrsMXL77KY4S+lMoMemC45+/OBgfm/YkwqwRl03+xkdO8xcpT6mPFl4QuhnBeB4/fY4KsetE8FUirvtu89vgxoVbWDZ0nb5I5fZl0HREXSzqv0qL5sXrF0S5liWCCDGyg2fn3M/x3bbDLm+sfKuSKNu8GDrkC/CvfVd0wnV1nQV9XEdEBu7o7bxvYPy+IaitxCV7BPqI7X1w5/o9PLjzQEVBntT39+Cu9y4avcnneuJj938fDwfUB8yQPhCG5WP3/xAfIshMHxbgT8fMToEUSZ+hRR3bLqCQSmgCepJEz3TqgiwZn6CxSsmVOb35HW4R99sZ0PPTBHMQO0GxV3GpKH+N+epD37oxm6P8fbq6Qec1rtQRAf304f9h+bANeKR23AxXvm7F8A2Oj42Z3k6PDtObY2KL2bj6z3WX4157aT4mNp/l+NCZ/o00WPLH9CDrXlcN3RHQJcXWqnNzMKLOZBz90pZWy9uKN/lbVxHoWz9LgKUbk6JJjdv4SKWx2vddPC1Wr5gS8IFywPiUKFfsX+TPHZC6KvA8SeCHpLfyf+yHBlVvqx1n0bBYBXJMGngFshPHuazdnhCHfomnfh58TetcV/pu2jM9urW4iQ/f84cvrHmfJv8dsWNG9zZTD/f7ffb8BcrG9J0ULlFhzWt2jRzukxwBHXqTz3UefmSseWXXjxH/LPcsAvqFKzFUGtmXKs3hM9Qofw8f53Lt/yNgisO1yxexKiJGsonh2qfVOqOAbnLGfU1An3JwBKokbeygImL47K5LQky/4oxPItol6icwl2L1CqJev2oqVUzXUGlLXsnM72TQLzQScXlwU9B87Nk+zIKxnw9EwyztKeaYtFtPm4f0oBvb57ojOnzKwqSIH+8lWtYNXXgJfA9d1dbWFMmeo2PjW+pF4wW+ORQfUxal0AK65HZsPzAd9iw7C7ERKfu+jYfV2xJj/tjggvgdlS+9Wc90qFf5nv5afP+BH1ZsToLTf8X2XgH9/+ydB3hUxRbHD0kIIbTQOygCT0RBkKKgSJHee++999577xB6771XwQKIUhRFRLEgvfcaIMA7Z9bdbJJNdm9uQvbu/c/3+b3vkZm5M785O3fmf8+cScECug8E9OcsoJf3MAE97xe5aHDVccquk6UJIBFn7D9AOvutVutSngpXKUDdiw62Ze06t43ycpe7JCJL8RP60dTvRtLXqw/RqtEWkUzioqfMkJwe3H6oYvK25BNEd67epeG1Jjlritv+3Uibi9iYY/ccSEiHj/vThAEh4s3kBcnUSaOOHJc3suRIQH/FJyskVmSKpC9VSK7NexPTT7/Gp/ljL1O80BHZ3NJmXiWeSz7+Rd2ybW+6UQsHrKZVo0JOIb7p5+t5XkQCupy2tJ6Q7DK7lVpPRhR+xdHzN99bQoOrjLOdkkyeLhmtvjSHaqRqHunJSFcE9B4L25HU17fMCD1dj9WyRppvIxLQfz4dnwZ3sXxAuX3Xi+p1yhjKicMVwFu/TKjWsuLBLvf3NKlxj+pUCh9WTeKhdx+RhoZ0vanCuzhLG3ax2H7Cn8b1s8zXnrDmDWYB3RcCOgVzDPSyHhQD3ZGA/qbXvHrWyM5+i+7ydyPNuWGZxcaaVzQKV+dna3v/PhdXaRty0vfYL/40b3UATRl0jbK+ZXH8M1Tyq0ReAY4dYQ3Vj1hsLAR0nfA9XUCfeWwsh3BZT4e3OPZ+DIvP6oFezq+u7UiseKCLV6W9h2Rk2JuPrk9p306lPHDs05g9A+jkt6dp5aiNOkctdot7+otu7soAesnxGdvyJXSuJolHNnl+cjrJ4VcSxH/FFypxjMjTfrRh9kUOCxNXeaBvX3jeFhZGPNB3fh3aY9L+WSf/YLGcvd4vXbNciJc84BW/+Ng7/r8Nkavtcpd8COFiGYmnz4KpUnzjxkB3tpmQmL3bHi5THzFd9fgW75rSfAlzp0IhF3+KB7rE2o1MFBLxXLzbL/91VXlbRpTkslEJKVDG17ie/0aac13ZTET3HCuX033J8cyncJgsaxIP9OR8AbOzD6GOBPSwtmQJIZORRve6QR+4IBDF9rz7OF4gJUr6RWw3wy2eP7v3CtowPuJL4N2ikRE0whUBvdX4RnzXjpdyFHE1yUdOudfn+N5fVJHo8kBvM7Ex5S6ak3oUG4I7J1wdDJ35XBHQ5ZL7Ki0z8Xr0AiVM4PhOiLDNkDCEgXxJ6Bw+WRk3LpGcxOw7NhXVKn8/VNiXC5d9qM/Y1Epcl8tAnaUn/EGycbf0vJa9wfcEhVxaa/Q177Okpyl+PB9n3ff4v3tSCBcZLHdY80Z1jWwkYzPSGjcs19hY8/7IDh2uzM+R2YDM5+/z/RP1q4T/KOrutvPMuwLFT2lcpyh34AsBXecoQEAPDVDFQH+wjDoU6ENnT55Xf2w9oZHyrBzdYJpLtDvNbEHx/OPR+KYzbfklhtkADt8i3ucSs93IycgvuvFzkvMx/OdUq8JD2xBICBd7D/SoiDthx3PllsR07lJc6tf+toqBXqVVJpo25Cp7uVu+9M5ZwTHQH/DGo61rMc3lRfdp/idUno/cGjFBQLeMmpFjoMfUZkIEl8F8b0S1FE1tph14fCxtn72Xds7f79DcA1IloVE7+9Fv352hmZ0XRvqT+KhkburDgnzN1M2N+NNRbTbSnBsbc+zPp+PRiOmWGOjWUz7tOV5vBZ4vyxaLfM50RUAP4ld21dYZOUzXVUOEcUEM9JCfupFjoMeUgD7xm6HqhOSmaTsVKLm4WTzZ62RoHekcGZEHutxDIUJT+qxp1eWhRr6w2Wjz7bWbPtSyT1ratiAk9riEcLH3QI+KgL5ySxL6g+//kXuCrElO9cjln91bWu7ukfuB5O6J1vXuUtFPXAuRtnRDEvrzrC+N6Ok4vJD1WUZb8yIGumXkIKA7X2ZqXfNqze+8Be6Xw0hr3LD0YmPN68r87GyUuwxNTSUKP6aKXxhPV0AMdGej6/zvENCdM4o0h9kEdGcx0AWWiDmv2OVsevv5lJzjrI/ZPYAmt55j82JvO6kJ3b1+j1aP3cwXlnnxpaHd1Ubk/OlL9P6n71LvJR1oWM1JdGz3CRt7udTp27WHVXx2oycjv+gWr0tC/5z3VZeESmzH5HxE35mA7iwGuoyneNXIJiX4ZRz6/kdLeJYJ/a9RxnSWmLnDp6ZgYYcvYWpyh8Rbvf/41NSp6W0qnM8Sf2z28qSUNMlLql3R8iX4zj0vDj/AR1sfetOm3YnURmX60GuGvC1b+gMB3fKrN6OA7iwGuny0XPZvoLpwTmKlFyiXh7rNb0uNs3agO9fuqYuiW7OX5dhG0+k2h2KRuy3kEry9S76hdRO32abTly+C1amhWj0r07V/b9Dp78+ocC4SUuDXg79r8tB0tznaSHNuVOZYZzHQnc2x8pGyEXs1Vi39QH1kPPpzfJq8IDktmnCZ5/hX/D5/Tf3GpaZqZR9QAY4JbJ8cCeiHf4zP91jwhdK5nvKx/Ne0eH2A+iA6Y9hV2yWl7mYj9u2BgB5Cw2wCuisx0Kt0KEtVOpal/ix2Bz0Kov58OfRfP521zZGlGhdVlzGPqjcllJk7EtBFPJePmc+DXtCEZoHqfyW95jW09R4hd/6tOGqbkebbZxwxRT7uje59Q93V4MNO0HsPJHAqoDuLgf4rn4LsPz4VjexpOXVzne/16TkytbpTokjBJ3SMPdQnzElBnZvdojw5Q8K2yHzpzWtXacPxX/2UE4k13XsQh5p0z0Dj+l6j7FlChw0w+poXArpllCGgkzoZGdm9P1rXvM7yG21+NfqcG7b9sbHmdTY/h13zXrvpTdv2JaKiHz9W4Wa/5XuDFq0LUGEJUya3XEBtpAQBXf9oQUDXydBsAnqd3lWoZo9KVD1lswjJiXdjjwVtKU+JD/hSuiDaPH0nh4EJiaE5+eBwun7uJo1paPFI785iz4fF3qdkaQOUcCOxePctP2Crv1Dl/CRe6Q3f6UAvnhkw1lQYUkbaXIQd5Ju3vWj4tJT0z4V49FmBJ8oD3JmALuJ1l6Fp2fvwMl8S6vgSuTPsUdNteBqOFflKxYBsUvMuvZUhJO/d+14qxMtPv/mpCzwql3oQ6thUt+GpKRVf6tGnrWWzsZFF83mrklJA4pd8ydMTPh57nxIlNN5LzsofArqFhBkF9J1PV6o7IbbM3B3hnJujYDbqPKsVZc6ZgS++u0Vzui+h77cdV/llHh735SBqnK0jXfnnGsl8OnRTr3B1bWfxfWrbuRwOphhV71aB0mZJrS4RPbD+B1rQd4VhxRzpqJHm3KjMsWu2JaZ1OxPxxckhl9yFHWBnc6z6yLg4OQvdPhz26iWHbrnDc6dFLH/Jdz+Xa5KZurDYU7aYJczATA6R9TVfXvr4iRfP28TC0ysOV3BVCe5Sl8y/IppLkjm9bcM7ql4jJAjoIaNkNgFd5lJx2Gj5QTc691uIV7K93YrjR5tJjalko8/Jh+M2H956nCa3mmM7HdlkeB01j9bNaPFIlzslZp8YT96cNz6H6XrE8+q+ZQdoVrfFlCCJP22+uyTcz+LimSvULEdnI/xcwrXRSPOtNH49z53ikfj6dRwa3+8a/fZnPKcCeoVmmahlnTuhwrGEBbHvkD+t2ZaEHjzy4tAvr9QHygolLPPnLHb62LwncTh2fdvdVN7oIirt5bBaK6eFzOlS5iYL8YO6hD95afQ1LwR0iylAQCeK7jWvcI1sjWzISTZMo40259o3P7bWvJHNz2HXvA8esgbBTiWn//JlZz8v1ieeUwu+582VOyvc0b4goOsfFQjoOhkaWUDX2XUUjyIBI7/oothlFNNJAAK6BaDRBXSdZoDiUSSAOTeK4ExaDAJ6yMAbWUA3qfnGercx38b6EBiuARDQPVNAN5whGrTBmHMNOnCx1GwI6PrBQ0DXyRACuk6AJiyOF50JB11nlyGgQ0DXaUKmLo4519TDr7nzENAhoGs2GhSwEcB8C2PQSgACOgR0rTaD/CEEMOfCGrQQgICuhZbjvBDQdTKEgK4ToAmL40VnwkHX2WUI6BDQdZqQqYtjzjX18GvuPAR0COiajQYFIKDDBqJMAAI6BPQoGw8KGipMIYYr9glAQNc/BhDQdTKEgK4ToAmLQ8wx4aDr7DIEdAjoOk3I1MUx55p6+DV3HgI6BHTNRoMCENBhA1EmAAEdAnqUjQcFIaDDBjQRgICuCZfDzBDQdTKEgK4ToAmLQ8wx4aDr7DIEdAjoOk3I1MUx55p6+DV3HgI6BHTNRoMCENBhA1EmAAEdAnqUjQcFIaDDBjQRgICuCRcEdP24wtcAAT0mqHp2nRBzPHt8Y6J3ENAhoMeEXZmlTsy5Zhnp6OknBHQI6NFjSeasBfOtOcddT68hoENA12M/Zi+LOdfsFqCt/xDQtfFylBse6DoZQkDXCdCExfGiM+Gg6+wyBHQI6DpNyNTFMeeaevg1dx4COgR0zUaDAjYCmG9hDFoJQECHgK7VZpA/hADmXFiDFgIQ0LXQcpwXArpOhhDQdQI0YXG86Ew46Dq7DAEdArpOEzJ1ccy5ph5+zZ2HgA4BXbPRoAAEdNhAlAlAQIeAHmXjQUGEcIENaCIAAV0TLoeZIaDrZAgBXSdAExaHmGPCQdfZZQjoENB1mpCpi2PONfXwa+48BHQI6JqNBgUgoMMGokwAAjoE9CgbDwpCQIcNaCIAAV0TLgjo+nGFrwECekxQ9ew6IeZ49vjGRO8goENAjwm7MkudmHPNMtLR008I6BDQo8eSzFkL5ltzjrueXkNAh4Cux37MXhZzrtktQFv/IaBr4+UoNzzQdTKEgK4ToAmL40VnwkHX2WUI6BDQdZqQqYtjzjX18GvuPAR0COiajQYFbAQw38IYtBKAgA4BXavNIH8IAcy5sAYtBCCga6HlOC8EdJ0MIaDrBGjC4njRmXDQdXYZAjoEdJ0mZOrimHNNPfyaOw8BHQK6ZqNBAQjosIEoE4CADgE9ysaDggjhAhvQRAACuiZcDjNDQNfJEAK6ToAmLA4xx4SDrrPLENAhoOs0IVMXx5xr6uHX3HkI6BDQNRsNCkBAhw1EmQAEdAjoUTYeFISADhvQRAACuiZcEND14wpfw817QTFRraHr9PP1pvjxvOnZ81f05FmwofsS3Y2PF9eLvL296EkQuIRlmziBLz168pxevY5u6savL2mieOTjHcf4HdHZg6DnL+nhkxc6a/G84okTxCVvrzj04PELeokfUKgBTuwflx7zfAsuoe1e5pP48Xzwe3IwHfj4eFHShL6eN1FEoUd3Hz2n4OBXUSjpuUV8eA2XyN+HXjCXR0+xlrMfaXkP+fthXnFk/QmYi9jMc/yewuFJxO9p2TuaPQW/fE13Hz4zO4Zw/Zc5RfbPspZ7/gLvI3tAwib4Jc8r4BLObgJ4HXeP1zBI4QmkDPADFh0E4IGuAx6KggAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIeC4BCOieO7boGQiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgA4CENB1wENREAABEAABEAABEAABEAABEAABEAABEAABEAABEAABzyUAAd1zxxY9AwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQ0EEAAroOeCgKAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiDguQQgoHvu2KJnIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACOghAQNcBD0VBAARAAARAAARAAARAAARAAARAAARAAARAAARAAAQ8lwAEdM8dW/QMBEAABEAABEAABEAABEAABEAABEAABEAABEAABEBABwEI6DrgoSgIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgIDnEoCA7rlji56BAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjoIAABXQc8FAUBEAABEAABEAABEAABEAABEAABEAABEAABEAABEPBcAhDQPXds0TMQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEdBCCg64CHoiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAp5LAAK6544tegYCIAACIAACIAACIAACIAACIAACIAACIAACIAACIKCDAAR0HfBQFARAAARAAARAAARAAARAAARAAARAAARAAARAAARAwHMJQED33LFFz0AABEAABEAABEAABEAABEAABEAABEAABEAABEAABHQQgICuAx6KggAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIeC4BCOieO7boGQiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgA4CENB1wENREAABEAABEAABEAABEAABEAABEAABEAABEAABEAABzyUAAd1zxxY9AwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQ0EEAAroOeCgKAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiDguQQgoHvu2KJnIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACOghAQNcBD0VBAARAAARAAARAAARAAARAAARAAARAAARAAARAAAQ8lwAEdM8dW/QMBEAABEAABEAABEAABEAABEAABEAABEAABEAABEBABwEI6DrgoSgIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgIDnEoCA7rlji56BAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjoIAABXQc8FAUBEAABEAABEAABEAABEAABEAABEAABEAABEAABEPBcAhDQPXds0TMQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEdBCCg64CHoiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAp5LAAK6544tegYCIAACIAACIAACIAACIAACIAACIAACIAACIAACIKCDAAR0HfBQFARAAARAAARAAARAAARAAARAAARAAARAAARAAARAwHMJQED33LFFz0AABEAABEAABEAABEAABEAABEAABEAABEAABEAABHQQgICuAx6KggAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIeC4BCOieO7boGQiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgA4CENB1wENREAABEAABEAABEAABEAABEAABEAABEAABEAABEAABzyUAAd1zxxY9AwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQ0EEAAroOeCgKAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiDguQQgoHvu2KJnIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACOghAQNcBD0VBAARAAARAAARAAARAAARAAARAAARAAARAAARAAAQ8lwAEdM8dW/QMBEAABEAABEAABEAABEAABEAABEAABEAABEAABEBABwEI6DrgoSgIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgIDnEoCA7rlji56BAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjoIAABXQc8FAUBEAABEAABEAABEAABEAABEAABEAABEAABEAABEPBcAhDQPXds0bM3TODly1c0Zd562rH/e7p95wFlfTs9bZg/jH79/SyNnbmK/vj7vGrR8F7N6eCRk7Rlz3f00955FM837htuKR4HAiAAAjFH4NnzF5S3VEvq3b4uNapZOuYeFIs112w1hNKnSUFThnWIxVbg0SAAAiBgIfDTr39Sw46jaNn0fpT3g+zAAgIgAAIg4AYEXr16TbOWbKZC+d+nPO9ni5EWXbxyg7ayrlCzYjFKlSIg2p7xx98XqHqLQTR7bHf6rOAHqt4vanenFMmS0OpZg6LtOagIBIxEAAK6kUYLbXVrAss3fEnjAldRx2bV6K2MaSgo6DmVKVZAvWgypktFdaoUJ684XpQtS3o6e/4K/XbmHHVqUZ18vL3dul9oHAiAAAhoIQABXQst5AUBEAAB/QQgoOtniBpAAARAILoJBL98SblLNKcebWpT0zplo7t6Vd/3x3+jFj3G05o5g+n9/70dbc9wJKDPX7mDEvj7Ud0qJaLtOagIBIxEAAK6kUYLbXVrAl0GzaB7Dx7R4il9bO38699LVKXpAPVv+T98163bj8aBAAiAQHQQgIAeHRRRBwiAAAi4TgACuuuskBMEQAAE3hQBTxPQ3xQ3PAcE3JUABHR3HRm0y60IHD5+imYs3ES/81EmCblSKF9O6ta6FmVIm1K1s1XPCfTdsVOh2tyhaTWasWhjqH8Tz/Qdy8bQpDlrac3Wr+nIjlm2v794EUxzlm2j7fu+p2s371DqFEkpX+7/UasGFSlzhtQq3879R2jh6p30D3uwJ07or46CdWtdkzKlt/wdCQRAAATsCYwPXE0bdx6g3h3q0YqN+0g+6iVJlIBqVy5O7RpXDgXL2TxnzSynbVZt3k+Xr96kpAGJqGyxgtSxeXWK7+ersjgS0K0hT6qU+ZQC+Sjrmb8vUqXShTmkVTNVxlmdkufC5es0YfYaOvLT7yTzZc7/vcUnfqpTgTwhHyelv5t2HaTB3ZvQIp4r//jnoq2/tSoWpbnLt9GXB47Tg4dP6L3sb9HQnk0pS6a0mo3G2p9Sn+e3zckZOKRL11Y1qfineTVzlfqSJ02s+rJu2zd09fptSs/vl26talG6NMlVu7//8TTF4Zo/K5iLBnVrTAkTxLc959ad+yqE2Lff/0yPnwRRxvR86ru+yEgAACAASURBVInHWP6LE0dKIYEACBiZwL37j9Tc+e33v9DN2/codcqkPAe+TZ2aV1NrQEcCuswjDTqMpJR8pH/BxF40feFGWstrz683TFHzojXJnFG0emeeL0pQ9za1SEIOLF2/R82ll67cpMSJ/Cnb2xmoca0yVJjDEFjT9i+/p8Vrd9vWpDKnNuawXR9/9B6puXj3QVo6tZ+am77/8TcVWmDXinHUa/hs+vm3v0nmLR8fb3VKs17VL6h6+SK2urW8u06d+ZemL9jIDP4ib28+6ckhFNs3qaraIenJ0yDKX7YN9eH3oIRblPfXleu3aGSfFlSpVGEjmwXaDgIgEMMExLt70tx1al326PETte4tkCcHdeZ1bzpe91mTo3VuhZKf0Obdh8K1UE6rt2lUyeWWRzb/X756S3mfh00SzkvaV6JmNxrTrxVVLFXIluXgkV+pTe+JKtTsu1kz2f59w44DtGjNLrrE6/u0qZLTB+++zaFpfwgVwqVO22GUkkO4TB/Z2VbO1f2Dyx1GRhBwYwIQ0N14cNA09yAgL5l2fSdRkY9zU8WShZQ4IceXZEG+ccFwJXr8/e9lGjBugRJ1+nduoBqeigXww/zSHTpxMfXrVJ9yZMtMfvF8lWgTVkB//fo1v8gm0dGf/6BmfLzrfX5hXbl2m1Zu2keNapRSYteSdXto9pIt1LJBBVXHo8dPaQlvXCTflsUjQ4kp7kEOrQABEIhtAiJCiMAhC+TWDSupuN1ff3eCZi3dQkN6NKGaFYqqJroyz0k+EUJk/mtQvSTlz/0u/Xvxqvrwl+u9LDR/Qk8l1kYkoJ+7eI1DVnmRbChkfkyTMpla0LtS5/Wbd1UcxmS8cZEjsDKXirjzw0+naR4/tyBvZiRZ+ysfK0VYeitjWtqw41v18UA+fpYskk/Np5KG8NzsG9eH1s8bqnmYZKP097nL6kNm1bKfUSL+X7nXQkT+nfyR1LqpcpWr1Hf6z3MqxmSTWmUpYcL4qi8/n/qb4sb1pqa1yyph/iKLWf3HzKcaFT5XMeYl3X/wmGq2GkxZMqflfy+q2nLy9D80c/Fm6s4fehvyOwQJBEDA2ASadR2rRI22/OFT5s9zPPeu5Y9trRtUonIlCoYT0EWclpjo8qFt4aReal44f+k6lWvQm3rx3CFCtzXJ/Dh6+grau2q8mrvk/TBv+XYVkjD7OxmVYL/766OUNnVyGswf7yTNW7Fdzd2V+UNo0UJ5KOjZM9q697AS5icObqfmL/kw6uvrw3//kLJnyaiE+dYNK1L3oYHq/4twLt/3ZB5fv/1bmjCoLZUtXjDUXO7s3SVCfPNu49Sc+GmBXKq+Pd8cY9HqIK3iGL0SzsAqoPvH91OxeyW8orwPCubNEWMxiY1tbWg9CICAlYA4XcjdZe+/m0WtQW/cuqf23/FkHco6gHy0kyTrOEfr3DSpklGTLmNUyBOZqyWlTZ2CBepkLkOObP6XdaOsh+W+tWE9m9HbmdKoemWOffTkqcsC+uI1u2n8rNX0+Se51Zz+7Nlz+ubwz2p+to+BHlZAd3Wd63JnkREE3JwABHQ3HyA0L/YJSAgWP/astL8sQzzEy9TrpUQkiWkmSbzQRThaMrWvrdG/sIhRr91wWjS5TygvybAC+lcsaHXsP5XGDWxD5Ut8bCsvx77u3H2oFvrFanRRF9aJkG9NDx49ocKV2tPY/m1sL+XYJ4YWgAAIuAsBqxffd1tnkpdXiCeyeJ6I18q2paNVU12Z50SQEU8WuRhUvBStScSKbkNm0qwxXdX8FJGA/vzFCyWyp0wecsGRq3WOmrZcedLvXT1RbWAkiRhTtTnPz76+Ku6jJOnvehbMD26axsKN5YLmJ0+fUcHybah2peI0oEtDW7vFE3PopCV0gPPKh1AtSTZKItQsnNTb9vHyHxbUKzXpT0N7NFVijqtcJZ/UlzRJQpo7voetGVYPoUlD2lPpovlt/95j2Cz24L9gGzt5n8jH15UzB4Ya43G8mfr2h1/UqSckEAAB4xKQOSx/2dZq3m1Wp5ytI+J8IX+TeLT2HujvvJWemnQerT5oyvozSeIQb3PxVLxy7ZaaF6ynUyo17keZ+aPj9BGdVN21Ww9VXpazx3YLBU0cN0SQv3PvIRWv2ZWqlP5UfYi1T1K3iPAyF69kT++l0/opL0ZnST6Qpk+bgqYNt7TB1XeXiDlyWtO6Frc+p0bLweq5chrJKqDL+npE7+a2d4OzNuHvIAACIOCIgHXfLo50/+OPjNZ1nKN1rt4QLq7M/xHFQBe9whUP9IesJ8icXjj/B0prsCZHMdDDCuiu7B9gRSDgSQQgoHvSaKIv0U5ANgmfVekYbtMiD2radYzyRl87Z4h6rh4BfcSUZbSBxaGf9sx1eNx+38EfqfPA6eyJ6BOuj+L13omPkYlXDxIIgAAI2BOwHqM/zAK6fRLvwanzN9CJL+eTLJxdmef2fHOUhfJAWj3bIkxYkxyJz1OqhfJ07tm2TqQhXOwX5lLe1TpF4BFRJqygE8incmYu2kRHd85WIlJE/S1Yvq0StaV91vT14RPUod9UdZLIugFy1XqsR3Xt+yMccpVoRu2bVlXhcbS8PxzVZ924BI7uqjyCrEmEcfE2+n57oPqnqs0GKG947zAXUr969Ur9/eT+ha52C/lAAATckIAI5SX5QvqX/JtuWb8ifZQrO2Vlkdzq+ShNtgroc8Z1V6dPRDSW+3eSJrF8cLSm/Qd/ok4Dp6mQLhLi5MiJ30m8G63/X/L1GTWXdn91hJrXK69CtuTI9pYtRJdl3rZ8NF0xcwB9mDOrQ2IRzcWSWU7qLN+wj8O4/KW822UtLf9Je6Qdklx5dz3hMoXYicSH5744dh+IpXxw8EvlYS71WQX0fp0aUP1qX7jhCKNJIAAC7kpAnDW27Dmkwv/9e+Eqr5mfKs9u2X8vm96f8n6QTTXd0TpOzUU6LxF1Zf7XK6CLp3n7flNCvQek7c4EdC3rXHcdX7QLBLQSgICulRjym4rAWX5RVmzUV8VJlNi99kmOoJ78/Sx9uXqC+mc9Arp4FIq3urWusJCtnpLzJvSgNByTLGwSz8WwmyRTDRQ6CwIg4JBARCKEHNkXr+5vOBbuQ/YqdGWeW7PlKxo2eamap+zjPsqDi1TtpGJzy1wZWQz0sAK6q3Xa12/f0bDlI+qviCxVy3xGPduFCOgRxYB0xZQi2ih9WLIFtWTRSUR0Le8PR/VZL6GeOaqLCoFgTWFPMH1erTN/0MhC3exOBdj3ISox3l1hgDwgAAJvjoDMBxNmraEf+C4EEWQkHImEw+rVrq4St60CuoTGEq9DufNBvK/DJvnQV6pODxUqcOrwjtR18AwVhtB6Gkny33/4mCbyfRMStkWEbRHqP/koJw3s2kjd/WNdkzp6F1ifF9FcfIbvpajffjh/8IyvTnFKmIEAXsNKCJkE3KcFHG5GkivvLvGIr8BrdHEiKfl5vnB9jc8hFiTsDAT0N2eneBIIeBqB3iPnkNz3UIrnmGKF86jY4Bev3KCB4xaygN6PBfTsqssxJaBL3c7mf70CupzwlP7IHRWZ+A4da3ImoGtZ53qaXaA/5iUAAd28Y4+eu0DA+mVVLgxtXjfk2KwUlXhmsrFYN3eIqkmPgD6cRSnxKPwxAg90q5emeF+KSIUEAiAAAq4QiEiEmDh7LS3lexWO7ZrNnjRBygPd2Txn8xbnuLIf5Mhie7zNA706e6CzQK1FQHe1TosHenIVh9E+BbKnpXhbOvNAjw0BXcv7Q4+ALmz848dTJwOQQAAEPJtAEMelFRFa7rKQk0Ttm1ShdvyfVUCXeLgf582pYtl2aVmDPdYrhAMi91bMXLyJPcgHKjFbPLPlwuGwSeb2sxeu0LGfz9CMRRuV2C1e7dZ5e2XgQMr93jsOgUf07pGwWdu/PMwfYicq4dyaJI65JGcCuqN3l4S1sQ8rFrZBENA9+zeB3oFATBGQMIPipCDzqMyn1uTo0uaIBHTr6UQJMyV3+OhJEc3/8mG1efdxKpyh3PtgTRKvXULASugqua/HmsI6kMjl1O36Tg53qagzAV3LOldPv1EWBNyJAAR0dxoNtMUtCVRu2p8vmovLoVoG28KrSIxHuYipAYcsiI4Y6NbjsOMHtg0Vy1yOjcnLW2IXf1GrG1/U9w5vXvqGinMrHjh37z9UlzEhgQAIgIA9AUcihgjc5er3prf50kmJSS7JlXlO5iKJkVi/WknbBZZSdsf+H6jX8NlOY6DLBaZhPdBdrXPkVA5ztUNioE9Ql8BJkk2JhC+J7xcvVAz0TXx5XNiQNbEhoLvKVfLpEdBHTVvBl6R+qcbyk3w5Q/0Afv/rvLrAGgkEQMC4BEQAFocN+/sjpDel6/ZU8b/l5E9YQUeE5oWrd4YTTqTc7bsPOC5uVxawEynvbDmJJB7t1iQX4clFzPZpwNgFKtyLeJ1L2JUSvCatXq5IOC938X4XL/iIBPR+o+epOxv2rZkYqv767Ueoy6EjE9AdvbvkHXDtxh3azjHd7e+ykPWzeG1KeC4I6Ma1fbQcBGKTwGXe78uJnbAC9HfHTinHOVc80KX9+cvyPTyVi4W7q8GVvrky//925hzVaj3Etg631iunlfKWaqkun27bqLLtcVbdYcP8YSQXNV/lObRUne7Up0M9tca3pl//+JfqtBka6SWiruwfXOkn8oCAUQhAQDfKSKGdsUZAvspKXDCJA1mxVCF6zIL1/FU71e3UEjvXKubo8UAXIahRp1F0msWOprXLKMFDNgTilV67UjF+6Ranucu3qZjFed7PpsLJyKVQp/jFJqKSHKu1v2Qu1mDhwSAAAm5FQESMZRv2UrWyRejD97OqC+c28CWb/5y/Qstn9Ld5qrg6z0n4kAU8/9WtUoLyf/iuigcpXpC53suiLtSUS+m0eKALLFfqFFGmWvOBFJA4ofLgiR8vHt8b8S0d/+UMzRvfU8XOleROIVykPa5y1SOgy0cIufRPPqTKBa85//cW3XvwiL789jhdv3WXtiwa6VY2icaAAAhoIyAxw+WiNrnHoWCe99QlmEdOnKbFa3aThHiSOxLCCugSN7fPyLm06+sjNGNk51AX0MvTew6fRTv3H1FhVPp2rB+qQXLpnIR4KVkkHyVLmojn+Ws0bcEGDgtTzObpbZ23K5UqzHXnIvGMlBjBIsRPGNQ2wrlYwsJICETxGC9aKA/d4Dlq/sodJCEIPs7LMdDtQri48u4SUb81C1nJkyahxrVKU/o0KVWM9U27D1EBfkfJxdEQ0LXZG3KDAAhYCMg8Wpnn3rg+3tS/c0NKksifxHs7cMlm9VHTVQFdLm8+/ec5dQ+PzN8pkwVQgTzvuoTZlflf1vbyUTQjh19pXre8uttI1sUScqttn8kcy/w8je7bihIl9Kf9h36kRfzueM7ONFYBXRoioWoOsOYhJ5Kyvp2eTpz6m2Yv3aI+uMrpTzndJCnsJaKurnNd6iwygYABCEBAN8AgoYmxT+DQ0V9VmADx5ovHLz6JBSmLf3uvbz0CuvRQFvjTF25i0eOY8jpPmSIpFWJvwhYcT9f6HDk2u3zDl+pSD/GuEY/OQizsN61dllKnTBr7oNACEAABtyIggvKarV9Ric8+ou+OnlIXH0m8bJm/wl7+5so8J5uJZev30mqOh3756i1KGpCIyhQroGLQShgRSVoFdFfqlHrFK3IiC/hHfjpNL/iCuPf4Q2PHZtVs4rnkcTcBXdrkClc9Aro8Q94Z8o46+MMv7B16X4VGEM9LObJbtnhBt7JJNAYEQEAbgadBz2kWCzbi9Xjp6k1V+J3M6ZRQUuKzvOr/OwopIPNkm14T+bLOv2nR5N7qFKM1rdq8n+QC+53Lx1LmDKlDNUjulti+73s154pIJGvNquU+o8Y1y4S6uFTqkHfBec6XOFECypXjHSViy8fVyC4RFeFf3iN3+KOfxNsVEf6rQz+F80B39d0l3pezl22lH0+eIblYVNbPednZRD4OSLgxCOja7A25QQAEQgiIgC33/5z49S+KFy8uf8TMwQ4p2UgudHdVQD9/6ToNnrCIfuW703zj+lAPFtKrly/iEmZX5n+p6OCRk6pNEp9dThfJhdKyDpQwLsM4dNbRn3+nRAn8WbjPQe9lz0xjZqwMJaDLXC93I8mHUB/+YCB7hSIf51b/FpmA7uo616XOIhMIGIAABHQDDBKaCAIgAAIgAAJRIRCZiBGV+lAGBEAABEDA+ASqtxikTlCKyOKOCe8udxwVtAkEQAAEQAAEzE0AArq5xx+9BwEQAAEQ8GACECFcG1wJH9BxwFSnmSU8wYFN05zmQwYQAAEQcFcCP/ApHrm0c9aYruFCu7hLm/HucpeRQDtAAASimwDWnNFNFPWBwJsjAAH9zbHGk0AABEAABEDgjRKACPFGceNhIAACIOD2BNr1nUxnz1+lXSvGqnsr3DHh3eWOo4I2gQAIgAAIgIC5CUBAN/f4o/cgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIREICADtMAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAAQcEIKDDLEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAAAnr028CL4FfRX6nBa1TRFFVMxdf0+rXBO4PmvzECFotBckTAx8eL3DNK6Zsdr1dsIC9fYs4NS90aw/Y1Jtw3a5B4mkcSkN+TjzdmXBnc4JeyjsOb2fGcizWuR04A6NQbJ+Dt7UVemHLVHigYukI4+7Nc0xAH76I3/ss09gOhK0Q8fnFZV0CKOgF4oEednSpZ0reuzhpQ3HQEXr40XZfRYX0Edj5bTXjZET0JCqbKiRvog4nSpiMQxwsLRdMNuo4O91zehUrWKKCjBs8pGjhwHW2evstzOoSexDyBZ89i/hl4gkcR2Hx3Cfn7+XhUn6LSGXHKKxevTlSKooyZCfh4m7n36LtGAp9U+5iGreqssRSy2xOAgK7THiCg6wRoxuIQ0M046rr6DAHdgg8Cui4zMm1hCOimHfoodRwCegg2COhRMiFzF4KAbu7xj0LvIaBboEFAj4LxoAjxkTlQAAGXCUBAdxlVhBkhoOtkCAFdJ0AzFoeAbsZR19VnCOgQ0HUZkMkLQ0A3uQFo7D4EdAjoGk0G2e0JQECHPWgkAAEdArpGk0F2ewIQ0GEPGghAQNcAK4KsENB1MoSArhOgGYtDQDfjqOvqMwR0COi6DMjkhSGgm9wANHYfAjoEdI0mg+wQ0GEDOghAQIeArsN8UBQCOmxAAwEI6BpgQUDXD8tRDRDQY4arR9cKAd2jhzcmOgcBHQJ6TNiVWeqEgG6WkY6efkJAh4AePZZk0lrggW7SgY96tyGgQ0CPuvWgJEK4wAa0EICAroWW47zwQNfJEAK6ToBmLA4B3YyjrqvPENAhoOsyIJMXhoBucgPQ2H0I6BDQNZoMstsTgIAOe9BIAAI6BHSNJoPs9gTggQ570EAAAroGWBFkhYCukyEEdJ0AzVgcAroZR11XnyGgQ0DXZUAmLwwB3eQGoLH7ENAhoGs0GWSHgA4b0EEAAjoEdB3mg6IQ0GEDGghAQNcACwK6fliOaoCAHjNcPbpWCOgePbwx0TkI6BDQY8KuzFInBHSzjHT09BMCOgT06LEkk9YCD3STDnzUuw0BHQJ61K0HJRHCBTaghQAEdC20HOeFB7pOhhDQdQI0Y3EI6GYcdV19hoAOAV2XAZm8MAR0kxuAxu5DQIeArtFkkN2eAAR02INGAhDQIaBrNBlktycAD3TYgwYCENA1wIogKwR0nQwhoOsEaMbiENDNOOq6+gwBHQK6LgMyeWEI6CY3AI3dh4AOAV2jySA7BHTYgA4CENAhoOswHxSFgA4b0EAAAroGWBDQ9cNyVAME9Jjh6tG1QkD36OGNic5BQIeAHhN2ZZY6IaCbZaSjp58Q0CGgR48lmbQWeKCbdOCj3m0I6BDQo249KIkQLrABLQQgoGuh5TgvPNB1MoSArhOgGYtDQDfjqOvqMwR0COi6DMjkhSGgm9wANHYfAjoEdI0mg+z2BCCgwx40EoCADgFdo8kguz0BeKDDHjQQgICuAVYEWSGg62QIAV0nQDMWh4BuxlHX1WcI6BDQdRmQyQtDQDe5AWjsPgR0COgaTQbZIaDDBnQQgIAOAV2H+aAoBHTYgAYCENA1wIKArh+WoxogoMcMV4+uFQK6Rw9vTHQOAjoE9JiwK7PUCQHdLCMdPf2EgA4BPXosyaS1wAPdpAMf9W5DQIeAHnXrQUmEcIENaCEAAV0LLcd54YGuk6GRBfSyzYpR4Ur5aUCVcTopOC5essFnVKt7JWqZp2ek9ecrmYsaDqxOmXNkIC9vL/rzx7M0tcMCunjmCiVNnYTWXpwdrvz187eoQbaOMdLuGK/UgwR0b/7q3WNBWypUOT/FiROHGmfvRHev39ONMPtHWajL7Fb01vuZ6Pq5GxTYdTEd2/1zhPVOOzySchTMFurvC/qtpNVjN+tuiztUAAHdMwT0Vf8G0uDqE+jPn85Gu1nJb7HjtGYU/CKYZnRe5LT+iPK/lTMjtZ/chN7m356Prw/9dvgMTe+0gK6du+m0TnfNYGQBPWXG5NRvSQfKljcLnf/9ErX/pH+0YU6QxJ/G7e5Pq8dtoYObjkZYb9Gan1C/ZeHft3uXHaAJLWdTvlK5aNTWPqHKv3j2gsonaRxtbX2TFUFAD6EdOHAdbZ6+603ij7ZnpXs7Fc0+NJQqpW8bbXVKRWkyp6AOExpS1lyZKFFAArr8z3VaPHIjHd5xIsLn1OhYhio2L0bx4vvSzct3aN6gtXTy0BmVf8GxUZQxW5pwZRvm6knXL9yO1ra/kcoMLqDnKfY+dZzahNK8lYq2zt5Ls3stjzZsX9T/lGp1q0itPuodaZ1+CeJR2wmNKN8Xuchb9kUn/qUZvA6+ceGW2hetORcYrvx1/lvD/3WOtra+yYogoFtovwh+ReXi1XmT6KP1WX2WdqR/fjlH6yZui9Z6EydPRN3nt6WPWC948uAJbZy6M8L9XdLUAbT2ytzwv4/zN6lBlvbq3z1uz2hgD3SlI8xrTYUq5rPoCO91YR3hfrTYj6xxx+8ZQKtkjbvxiNM6S9b/jKp2LKv0qMv/XKNWeXupMm+9lyH8vqjzQsPuiyCgOzUFpxkgoDtFFHkGCOjh+SRPm5Qm7h9ESVIkoluX77okoL9+/ZrO/nqBZCJtM64B+SX0owGVLcK+TID2qXidwlS8bmHqWnSIztGLpeIeJKAXLJeXmo+uR90+H0zPnj6n4OfBJGOpJ/nE9aGlf0+nrbP20LZZe+njCh9Rp5ktqFHWjnT/1gOHVctiaPeir+nbtYdtf3/O7XnB7fGEBAHdMopPgoKpcuIGhh3SmBLQP+HfiIjeSVImpj1LvnEqoEeW/8OiOSnLB5noh50/0fOgF2oTLxuSbsUNOt+ytRhZQG/J82uipAlpOn8UESEl6MmzaLH/JkNqUamGRZQYM6rh9EgFdHkvi6Bjnzrxx5qr/96gxUPWKgG909Rm1PbjfrYs8h548uBptLT1TVcCAT2EOAT08NaXIWsayp7nLfrtyF/09NEzKt3gU2rQqxJVydje4frns8r5qNngGtStzCi6e+MBfVrpI+oZ2JxqZ+/Cv+fnFJ/Xu17ecWwPSpM5JU3c3pvqvd/DmL8hgwvoMw4Np61zvqR9Kw+RT1xv9R7Um5KlDaCJX/K+KHlCunXlrlMBvcPkxhSQKgmNbTaLXvJH8VZj6lOWXJmpV5mRjvdFtQtR8dq8LyoxVG9TY6U8BHQLdgjojs1vwKouSh+Y2n4+pcqUgkZu70vjm86ko7scf7QMpxvU+5R1g0+pa5FB6gEet2c0sIBesGweaj6yLu8xhkabjiBjLGvc0o0+V2vckbLGdSKgV+9cjiq0+IICeyyhv376V+lQ13iNKyncvmi87IuSUDeDzrcQ0PW/5iCg62RoVAE9DXvmLDw5Qb2QZHH4MvglVUnZnAat7kKP7z8l8Xp7t0BW+vfX8zS68Uzl9SApB//bNF5cilf5ud8uRUqvYLk81GJkPacCun0lIr53mtGc/jz+D60YvSlc/dLeRacm0bROC+n43l90jl4sFfcQAT2AxbqV52eRt2wwWKw+uutnWjZsHU39bgStHb+FyjQrrryt1k/ezv9/qw326J39eNMZRMNqTXI4ALk/f48Gb+hB1dkerWL8zCOjlSfQHhbJHSVZDG2ZuZv2rzgYS4Mas4+FgG7ha2QBvRufqCjTtJiab1+/ek3LRqyno3yqYuqBYbR2wlb+23+/lyn8e7Hz3hnNGwX1e6kz2amRtRrbgHz94joV0K0VuZJfFo5D1vegKimaOn2+u2YwqoD+aZUC1H95R54HSX2cXDhwNb3i//N59Y/pCnu9flw+Lz2691i9D098dcqGf8f9JTSnzwr+CLnX6ZDM5LlzNc/XkXmgh60kPQuI03kd0PT9bvxR86ES0DtMbkpNcnZ1+jwjZICAHjJKRhbQF/00htJnSUVBjy0fnXpXmUA1OpSmx/xhJ2WGZJSDT7qd5XXsmFZz6cZFi6f3u/my0LR9A6jVJwPp3O+XnZqrfyI/qta2FH1QKLuq31Fq2Kcy5Sr8P+pZ0eIUIoL55oszqWGuXrbn2pfrPacl3bp6lxYMWe/0+W6ZwcACetvxDalK+9L04lkwvXr5SgnSY3f0U+vPTyp+xKcPUtL323+iySzkySkbSbV7VKSaXSpQjQytnQ6HEotG1HEqoE/YO4B+OfA7rxM2qDqlXHsW1Ru92yXcM2RfJPs5+ch6/MuTTtvgjhkgoFtGxcgCekkWK7vPa0OveH0rH31+/vo3Glh5LG24sYD3Z3v4pHI+darj8NbjNLn1HNvvp07vKlSze0Wqnqq5Q9OMyycht9xbQh359J14t0uStWsAO3aMazLDn6+VDQAAIABJREFUqTkr3eD3KTSNT7Yf32M5yexxe0aDCuhKR/hnBusIPhYdgfdEsjdS+yLeB5VpYjm1tX7qjtD7om19LPuiulOcjv/M72WNuzVSAd0/UXxazad62n7cly7/dc1pnWpftK4762bNnOZ1xwwQ0PWPCgR0nQyNKqBLtx2FcBEBPd07aWjBgFV06a+rvNCrS37+8WxhXjK9m446TGlKE1vPpet8HCqypFVAn7hvEOUqkoN2zv+KpvDi1JEnc6U2pahYnULG9T4XYB4ioEtXSvBxp3LNS1D3/7xTJfzDnBPjaeWojfTVqkOUNktqGsQvmW6fD1KheSQ1548qz4Ke0/LhjjeH5Vt9wbZZnDrYeTP2XdaJbl66TfP7rnBocrIYypA9LT19GMRHqm4oQfLIjp90/rrdpzgEdMtYGFlAl/aH9UBXv5fjY2nlmE301ervKC1/2By0pht1KzbEFuZF5mA53bF8pGUjHVlyRRC3L+9K/to9KqlTIF2LDnb2eLf9u1EFdAHadRaLaRzywSqkVGKxrmH/6uod/fO3p6lItYJUs2sFqp+1g81TcsSWXrRj3n4Wen50OiZREdAHrOxMl/68qrzPJYmAPnxjT7p/8yE9uv+Yfv7mNC0avIY/xj9x+nx3zNBrRVf6onp+d2zaG2/TLA7hssmDQrgMXNKO0vG6ZOHQ9XTp72vUfEgNtUEfWHuqYpuR1xEdxtWnSZ0WOQ2fIsK4/PcHr23615hMD+8+djg+Ekpm0q4+dProP7Rmyk4qWCY3pWUhdmzreeHyv/Veepq0sy81/rA3PeSPY4ZMBhbQhffi3ybRpLbz6CQL2JLWX5rDc+1vtHbSdiX6DVjeiXYt/obWT9mh/i4hrmQedkXMcVVAL1wpH/Ve2I628EfQvcsPqJO5hzjMljw3bKrUuiQVq8X7IoN6Q0p/IKBbRtXIArq031EIFxHQRUxfwx/q1e9ndVfavfArW5iXonx64vMan9DQmhMdTncZsqdTAniFhA3UWlhSuRYlqCzvPTt+EnLqLaK5slK70qwb8OmM/7zPJZ/H7RkNKqDLWJTgkwHleM/fveQwNYQSLsWyL9pMX635b1/ENiMe6tbwl82H17HoCBw6zVlyRUCXU7ni5PTH0b/pg89yKIeVbXwKaQk7BTpKal/EDixdea9mxFSoOv/eVnYyYtPdps0Q0HUOhScK6Ke+O0Mb/9swZeaJbPaxMVQuYUPNoTm0CugyFOKBLoLBVRZBZ3ZZHGp0ZJOz9MxUGll/Gp08aFnYGjJ5uIA+5eBwqpKsiW1oBrIgePbkOVrhwotOClXrUp4Kc0z17nYvpq5zWqsX2vSOCxwO+TsfvqXEGomfJnbXkr0TOhUeQP/8fM6QJhK20buer+Y7Yrw8oi96OvH8xSsqn6CenipitawjAX3KN0NDeTEM5KOqZ0+ed3gCx1njXRHE7etwll8E/slfD6FB1cbTr4f+cPZ4t/27pwnoeYu/T0NqhpzgWXthFg2tM0XFq9eatAro2fK8TWP4FFGjdzvbBHKJT5oyfTJ6cOche/Ymp5aj6tHta/doRD2LKGm0NHh9dyrMGyokjs89bCOtYeHQiMlRDHQR0E/98BdtmvWl6lJmdgqZdXAolU/VSvMaV8qLN3nNTmXokzIfUvuiQ5X3ZdgkYUC6z2jGd0p4U4asaSnd2ylpWKNA+tHu1Ii1zLBVnehPXrcsHxtyas9w7D1QQO9dbhT9w+9lSSJYF2Zv2t7lRmseGlcF9BQ8n/bneycu8F1QBUrl5g+TT3jOn6ji7dsn2RctOT2ZRjWazvsi476jtz9YRr5xscYNfvmayvrW1mxX7lIgIgG9V8nhNu9xEbTldF2vUsNdarbs72b/OI5Ketey5S/OIVnq9a1GLT7oFmkdSjf4ewaNZE/lkwdO2/J63J7RwwR0tS+yO5EwcCXvizgigqPIBM6MyBUBvQrbZNUOZWlkg2n0728XOQZ6ehq5pbdyXNo2d1+oR4jAr/ZFfJ+VUfdFEgq5L9+thBR1AhDQo85OlfR0AT1RsoS08do8qpy8GT15qC2eaVQEdGGajxeLvRe2pZoZ2oQanbp8zOvDou9R77KjdI5aLBc3mYDenmPjynG+2T2WugRePNBLNy6qBHBrEg908cKc18e1y5zG7B7AL7bfacV/x19derAbZ4IHumVwPNEDPayA3p5P+KjfS89lmi3SmSAetsLI8mf8Xzq+fGcgLRy0mvYu/VZzW9ypgKcL6HN/HMvjtIZ+iMKpG60CuoQUkg/YqyK5oPm9j7ORnCgryx/ejZgQwiVk1IwcwsUVAT1R0gS04d/pHL+8Ha9xg6JkrhI2a9vV2dSi4AC6yCczwqZmg6tT8jQBNL6txQHg47If0uBl7VlwH0ZnT120ZX+PQyQOW92JGuXuFeW2RKkD0V3IwwX0T6vkp3q8H2kXhQudXRXQp7KAtGPBfpKLmr3YeaLJ4JokF9w1ytEl1N0+dXtVJgl72Ke8djE/uoddT33wQLfQ81QPdHsB/VM+rVG/XzVqmy/yi3St9mT1QC8Xv57N9sUDXf6zP6nsyP7q9q2q4lf3Lj0iUvM0/J7RwwV0uePp5YuXfKGz9n2RKwJ6ZT7ZWYRDI3b/wuIFL0m83MWJVIRya1L7ItYXZL29d5lx90UI4aLnbWUpCwFdJ0MjC+ilG3+uJoz+lcbaKEgIF3sP9OwcI3LMjr5ULU1LzaSiKqB/XuNjajm6PjXI1tH2zIQBCWjZn1Opb/kx9MexvzW3xa0KmExAH8GxyiTu3OYZu10ahty82BnMYV+q2cUWC+RTENv5ONXO+ftdqmPqoRG0b8UBdQmpJyQI6JZRNLqAvoI9YSSW+Rm+40GSeHiHFdBHsNeD+r0E7tFsutEloMtdF/IbFBH/G7uLeTU3yE0KeLKALvE9112azZcZDeN7SULEOFfRaxHQJcTagBWdlfe5Na60o+d89MUH1HtRe6qVMfRHcFfbFNv5IKCHjICRBfQ0mVPQvB9GUMW0IXYY1gNdLgMdvbE7VX87ZL2p1f4CUiSitX9P5ZjmPR2GfRm7uQcd23+K1k8PWQNJfPYV47bSvtUhF59P2NGbju49SWun7tLaBPfK7+ECes2u5fk+qGwuhWwJOzCuCug7Hyylzhw2TS6zk5Q8XVJaxbGC62fncIb/xeuXfdHS3ydTP97D/XHMsqYwaoKAbhk5owvovfi9f+70xVD3XkkIF3sBvSaHv3ivYLYIQ7aEtWEVA/3+UupQsK86nSmpNV/imIw/So5uOC1Ck1e6Af9m+pYdqUJzRJYMv2f0cAF9xOZe6t67qOyLXBHQ85fOTT3ntaW6WdqrOwEltWIdKjG/2ye0nK3+v9oXre1m2Ret+96oU61qNwR0/cMHAV0nQyML6Hn4GLjE2Ovwn6evePiKgC5xHBfx1zW5gbjH3NZKsJ7fb5UiFR0x0EvxRSP5SuaiUXwrsqQOU5uqo1Wn+VhtinTJqBd7n3/Hl4ws6G95pqQWo+rys9NzKAHHlzTpHMY3W9zDBfQZfOGnxDyXS+4K8MVHHflS2KbsNXPvxn3F2VkMdFksLTs7kzbypSE75uyjAhySpRtfTNOYP6jc4bAAEqZl7J4BtG7SNjrGF46k5liilfn4lcSQFhuWeHpyo7c8U/6/JyQI6JZRNLqAPpm9ykQclxBZ4rWYlC9BmsHx+yXm+ZWz/HvhMAAdpzVTlzPafi86Y6Cr38uu/rSOL/M99t8FStbfhCPB3XICqB1NajOXTnwdcjGlXH4ql6oZMXmagF6qQREayRtHufOhds9KlPPj7NTJLr6n3hjo7+TOrDYPY5sH0p2r92xDPo0vdvp2/Q+0YdrOUGZQq1sFvnviJp0+8hcl5lNr3XndcOq7P2hWD+3eQu5gXxDQQ0bByAK6zLGbLwZSn6oTlKd3MHuw9QxszhfvPqFFwzeoNW736U3pzI//0vwhllinrsRAr9SyBHl5xaFj+35Vc2LrkXUoaarE1LnkSFXHO7kyUathtWhsm/lqzVKPL5ksXrMg9a02iW7ymiQPn6QctKQ9tS0ymK6dv6XK5OePTj24beJ9bo3z6w6/hSi1wQMF9AV8gfOBjUfUHmggf0Sc3G4+HWNBR1J0xEAv1bAIffRFLhrd2HIp4liOg//g9iOa0GqOutuiXp8q6jkt8/ayDUkLvoxU7YtqOI4dHaWxi6VCENAt4I0uoDcZVoeyykdJDrXqlyAe3ebLkEVAX9BvJX3LomOmHOzRyyLkpFaz1f5N/X6cxECXPOLQIeGxpneYT8k5vNEYXtNO5jXq4S3HVB1tJzamu7zHXG13Mq4Fr2EycSiOQVUslzdbk0fuGT1MQFf7Io55rvZFpWVf1JSaftA9ZF+kMwa62hdxKMJ1U2Rf9AtJmLVFpybTd2xPcn+PnHoYzc5/o/mS2hMcak3tixa0VXdjeMK+CAK6/hcdBHSdDI0soMsE0o9j7MnN8g9uP6R6WTooAT3t26mVt4MPC5nifSibYOtt8/IFbtqh4dQyT0/2drvkkJ7EQJ19bLS6VTk+v0Af8UVI+1YctG2mmwytRaVZRK/7dntVvmqHMurSyLRZUqkFo1w+uXTYettRLfnKvIQvEOnMouzZkxd0jpgbFPdwAV28xcXDNlveLHyb9VWa0Wkh/WoXs340v7TU7dm1QmL4hh2VHOyd0DmwJWXOmYEvq71Fczj8y/fbjqtscpx1D8cElwXYrgVfkYQZ6jG/LeXgsAHx+Sbtc7xRnsvHvOyf6QajrqsJENAt+IwuoMtR0h78MUjmtFndl9KvLDIG/jCKBZyzJLGl1e+ly6JQcfUkZIb6vbDnekSpKF8g1pE/RMbjC59lXg96HKQuYj7IG371e3m6Ugniu/jiJkmR5ZeNSLWOZcM9SmIDGtUb3dME9Bqdy/P78YWKN37q0BkWc+byJcshHwt33F9Cc/qsoK2RnMBpP6kxFeONa4Ik/kqwkzsmWn3UWwl+8nFdNhdNcnZVH0IlFaqYT9mYCiHAF4HZJ/koXqNzOUrDlyU+ZnHywKYjtHDAasMKgRDQQ0bXyAK69KJGh9JKwBbBu3uFsVSvewVK+1ZKdd+OxCT/ZsNRms0OIi+eBatOv5svC03bN4BafTKQzv1+2eGUm5cFcLk8NDOLl3LZ/U/f/Eaz+qyiO9ctTgIikIvXeZM8fejKvzcsITgGVKXifLpS1tXXL9yiBUPW00m+b8iaAr8dTHtXfkeb2WnA8MkDBfQLf1ymLPxhROa3NRO30VY+EWlNtdm+anapQDUytI5w6FJmSEazfhithBoRFuUjzr6VBzkkgSUsoYRoERG9XlbLSQhZI8jFobk+zaFsTByZZnHeG2w71r8vPjWJuvDH97O/Gn9fBAHdYjpGF9Aldv/g9T1IYowf5I/t4iEuAvoFnkuz8Id50QPWjNtCW+1OWNbhcEg1u1ek6nYxr8P+kAJSJVH7vDwl3lfhrTbP2BUqROdk/rh/nT/ij+G7AKy/jyV/TqPOnw60ea1b6/TIPaOHCeih9kV82Xe4fRGL22pfxLHtI0oS9qV4mDWufICUNa7aFz1ZYdkXLfpaVZE+axrqPKMFvZv/Hbp95S4tH7WR9rMeJanthEYR74sM6I0OAV3/KgsCuk6GRhbQHXU9bAgXnXhQ3BEBDxLQw3ZPhaQIc4kojEA/AQjoFoZGF9Ad/l7CXCKq31pQQ1gCRhbQw/alEsdqDHuJKEY8eglAQA/haXQBPaxlhA3hEr2Wg9oUAYML6GFHcf2lOXxhaMglohjl6CcAAd3C1OgCuiPLCBvCJfqtBzWSgQX0cPsijjse9hJRjHD0EoCArp8nBHSdDCGg6wRoxuIQ0M046rr6DAHdgg8Cui4zMm1hCOimHfoodRwCegg2COhRMiFzF4KAbu7xj0LvIaBDQI+C2aCIlQAEdNiCBgIQ0DXAiiArBHSdDCGg6wRoxuIQ0M046rr6DAEdArouAzJ5YQjoJjcAjd2HgA4BXaPJILs9AQjosAeNBCCgQ0DXaDLIbk8AAjrsQQMBCOgaYEFA1w/LUQ2eJqDHDCXUGoqABwvoGOmYIQAB3TMF9JixFtQaloAnCegY3ZgnAAHdcwX0mLcePMHTQrhgRGOeAAR0zxXQY9568ARPCuGC0Yx5AhDQ9TOGB7pOhhDQdQI0Y3EI6GYcdV19hoAOAV2XAZm8MAR0kxuAxu5DQIeArtFkkN2egId5oGNwY54ABHQI6DFvZR78BA/yQPfgUXKbrkFA1z8UENB1MoSArhOgGYtDQDfjqOvqMwR0COi6DMjkhSGgm9wANHYfAjoEdI0mg+wQ0GEDOghAQIeArsN8UBQCOmxAAwEI6BpgRZAVArpOhhDQdQI0Y3EI6GYcdV19hoAOAV2XAZm8MAR0kxuAxu5DQIeArtFkkB0COmxABwEI6BDQdZgPikJAhw1oIAABXQMsCOj6YTmqAQJ6zHD16FohoHv08MZE5yCgQ0CPCbsyS50Q0M0y0tHTTwjoENCjx5JMWgtCuJh04KPebQjoENCjbj0oiRjosAEtBCCga6HlOC880HUyhICuE6AZi0NAN+Oo6+ozBHQI6LoMyOSFIaCb3AA0dh8COgR0jSaD7PYEIKDDHjQSgIAOAV2jySC7PQF4oMMeNBCAgK4BVgRZIaDrZAgBXSdAMxaHgG7GUdfVZwjoENB1GZDJC0NAN7kBaOw+BHQI6BpNBtkhoMMGdBCAgA4BXYf5oCgEdNiABgIQ0DXAgoCuH5ajGiCgxwxXj64VArpHD29MdA4COgT0mLArs9QJAd0sIx09/YSADgE9eizJpLXAA92kAx/1bkNAh4AedetBSYRwgQ1oIQABXQstx3nhga6TIQR0nQDNWBwCuhlHXVefIaBDQNdlQCYvDAHd5AagsfsQ0CGgazQZZLcnAAEd9qCRAAR0COgaTQbZ7QnAAx32oIEABHQNsCLICgFdJ0MI6DoBmrE4BHQzjrquPkNAh4Cuy4BMXhgCuskNQGP3IaBDQNdoMsgOAR02oIMABHQI6DrMB0UhoMMGNBCAgK4BFgR0/bAc1QABPWa4enStENA9enhjonO7nq0mHx+vmKjaUHUGPX9JFRPWN1Sb0djYJwABPfbHwEgt6L+mO31e+SMjNTnG2jp3yAZaP2VHjNWPij2QADzQPXBQY7ZLW+8vJT9f75h9iAFqDw5+RWXj1TFAS9FEtyIAAd2thsPdG1OkViEauKyjuzfTrdsHD3Sdw1PSq6bOGlDcbAT2XPnFbF1Gf3USeJXiDwjozFAJ6H7YXOg0J9MVx5xruiHX1eEg/9nkn7i4rjo8pfDcvitp3dhNntId9OMNEMB8+wYge9gjnif7HQI6j2nwSxbQ49b2sNFFd2KaAObcmCbsWfUHx61AvskneVan3nBvIKDrBA4BXSdAExbHi86Eg66zyy9ZQI8LD3R6EhRMlf3r6qSJ4mYjgDnXbCOur78P4s6kgOQl9VXiIaUDey2nTRO2eEhv0I03QQDz7Zug7FnPCAo4Tf5+Pp7VqSj05gV7oJfzhYAeBXSmLoI519TDr7nzT+OUpwSpJ2suhwIhBCCg67QGCOg6AZqwOF50Jhx0nV2GgG4BCAFdpyGZtDjmXJMOfBS7DQE9BBwE9CgakYmLYb418eBHsesQ0C3gIKBH0YBMXgxzrskNQGP3IaBrBOYgOwR0nQwhoOsEaMLieNGZcNB1dhkCOgR0nSZk6uKYc009/Jo7DwEdArpmo0EBGwHMtzAGrQQgoENA12ozyB9CAHMurEELAQjoWmg5zgsBXSdDCOg6AZqwOF50Jhx0nV2GgA4BXacJmbo45lxTD7/mzkNAh4Cu2WhQAAI6bCDKBCCgQ0CPsvGgIGGNCyPQQgACuhZaEND103JQAwT0GMHq0ZXiRefRwxsjnYOADgE9RgzLJJVizjXJQEdTNyGgQ0CPJlMyZTWYb0057Lo6DQEdArouAzJ5Ycy5JjcAjd2HgK4RmIPs8EDXyRACuk6AJiyOF50JB11nlyGgQ0DXaUKmLo4519TDr7nzENAhoGs2GhSwEcB8C2PQSgACOgR0rTaD/CEEMOfCGrQQgICuhZbjvBDQdTKEgK4ToAmL40VnwkHX2WUI6BDQdZqQqYtjzjX18GvuPAR0COiajQYFIKDDBqJMAAI6BPQoGw8KIoQLbEATAQjomnA5zAwBXSdDCOg6AZqwOMQcEw66zi5DQIeArtOETF0cc66ph19z5yGgQ0DXbDQoAAEdNhBlAhDQIaBH2XhQEAI6bEATAQjomnBBQNePK3wNENBjgqpn1wkxx7PHNyZ6BwEdAnpM2JVZ6sSca5aRjp5+QkCHgB49lmTOWjDfmnPc9fQaAjoEdD32Y/aymHPNbgHa+g8BXRsvR7nhga6TIQR0nQBNWBwvOhMOus4uQ0CHgK7ThExdHHOuqYdfc+choENA12w0KGAjgPkWxqCVAAR0COhabQb5QwhgzoU1aCEAAV0LLcd5IaDrZAgBXSdAExbHi86Eg66zyxDQIaDrNCFTF8eca+rh19x5COgQ0DUbDQpAQIcNRJkABHQI6FE2HhRECBfYgCYCENA14XKYGQK6ToYQ0HUCNGFxiDkmHHSdXYaADgFdpwmZujjmXFMPv+bOQ0CHgK7ZaFAAAjpsIMoEIKBDQI+y8aAgBHTYgCYCENA14YKArh9X+BogoMcEVc+uE2KOZ49vTPQOAjoE9JiwK7PUiTnXLCMdPf2EgA4BPXosyZy1YL4157jr6TUEdAjoeuzH7GUx55rdArT1HwK6Nl6OcsMDXSdDowvo1btWoDq9q1CCJP40qt5UOrTpiE4iRImTJ6LuC9rSRyVz05MHT2jjlB20euzmCOttPLQ2FatTmFJmTE53rt6jDVO20+bpu2z5p30/inIUzBaq/IK+KyKtU3cnYrACI73odn2dgL7/yZ+Gdb8Z7UQ27U5Im/YkpkdPvCjPe0HUpsEdSpn8lXpO815p6dJV33DPXDLpEqVJ+TLcvz8NikOzlielH3+NT6+4imxvPaf2je9Q6hSWvEd/8aPF65LSnXteFN/vNVUr84AqfvEo2vsUUxVCQLeQfRIUTJX968YU5hivN1XGFNR3ZWfK/lEWOn/6ErXL1ztanin1dZnTmt56PxNdP3eDArsspmO7T0Rat8z54/cPplWjN9HBDT/Y8hap8QnV6VOFMmRPR8+ePON3wlGubxG9ePYiWtoaG5UYac4dMys5Zc38gmqUexCtqGSOnLcqKR065k/e3q+p2CePqWnNexQ3LtE33/vT6MCU4Z73xacPqWfrOw7b8ee/vjR1YTI6d8mX59kXPH/fpQK5g0LlvXDZh9oPSkvLJ1+mJIktc7sREgT0kFEK7LWcNk3YYoRhc6mNMbHmjRMnDrUa35BKNy1GcX191Jw5udUceh703GGbtOZ3qWNulMlI8+3la97UbkA62jL/YrQT/OmUH81fHUCXr/nQ2xlfUMu6dyln9vA28cOJ+DR0Sgoa0+cG5c7xzGE7/r3oQ7OWJaN/L/pS8Ms4lDNbEHVocpfXw8G2/L//7UvLNgbQ6b/icR6ixROuUIpk4dfL0d7RaKgQAroF4ovgV1TOt3Y0EI2dKrx9vKnHwnZUqHJ+knmucbaOdPf6Pd2N0aor1OhWkUo1KUrp3klDz58+px+2/0jTO8ynp4+CqPOsVlShdclwbRrfdCbtXfKN7rbGRgVGmnNjY40re/+6HTOGG5pUyV/QsilXHA7ZkvVJ6NsjCejGbW9KHvCSqpZ+SFX4P2t6zlP5qq1J6KvDCejmHW8qV+wRdWh8NzaGX/MzIaBrRhauAAR0nQyNLKD7JfCjzXcXU4v3u9HVs9fJyysOvXgeshiLKpoBq7uSvESntptHqTKloJE7+tH4JjPo6C7Hgk7LsQ3o6M4TLChdpGwfvUODN/SgARVG089fn1JNEAF998Kv6Nu1h0MmLn4hRkdbo9pHPeWM9KKLKQF974EEtGJzEhre/QYlTxpMc1Ymo7PnfWnG8GsK7ZOncZQQbk3XbvpQz1FpaPmUS5TA/3U4/DOWJKV7972pV5tb5ONDNHdlUjp7IS6N63eDbvGLrXmvdLw5uU45sj5nYd6HugxLTYM636Jc7zrerOgZ35goCwHdQtXoAnqrcQ0pUbKENK39fBYxvSiIBWq9ySeuDy39ZwZtDdxD22btoY8rfESdAltSo3c60P1bjkXYJsPrUOkmxShp6iQ0su6UUAJ6xbal6f7NB/Tb4TP8MTQhyXx+YP0PtGTwGr1NjbXyRppzY2pzMX5OMnr0mDe3rW7TY55fR05PQe//7xm1rn+Pgvm1H/QsTqjxmb4kOaVN+YKa1Lwfbtxe8LeUJj3SU8USD6kC/3eEBSDJv3jiJQpIbJmf+4xJqcSeew98aO3MixDQY8369T3YkwT0mFrzVmxTiqqzYDOw4mgl0PRf1ZVOf3+G5vVe7hC+1vz6RvDNlzbSfBtTAvrFKz7UcXBa6t/xJuV+N4j2f5eQZq8IoIXjr/CaN2Rxe+pMPJq+OBndZoFnYKdbEQroJ36LR+d4Pi3w4RPyjfua5qxIRnfve9HEgTfUAJ/+y5eGTE6lPormy/WEXr+OQ0mTvFQfSI2QIKBbRsnoAnrBcnmp+Zj61K3IIHrG+/Rg1hRevw6/Z9Nqk1p1hVKNiyonlSv/XON1boDSFURHWDdhK/n6+VLceLxR/C958Vp85tExNLn1HDqx/1etTXOL/Eaac2NjjSs2+Jid9ezT198nIPlv0sDrDsdw3qoAnm+fUub0L0gcRoZPS0nDut2gPDkt+7Yhk1OqtXOjGvcpTYpgesaCutUJ0C2MIpJGQEDXP0IQ0HUyNLKAPvun8fTOh29R0OMgtejv9cUwmvLdCFo2dB1V6VCWEgT404bJ22nFiA02SuKtXrNHJaqesplDcuJ9s+X+Uur4cT9IOByNAAAgAElEQVT655dzKo+IRgEs1IxrPMMl2pO+HaY84cVzXZII6Ftm7KL9Kw66VN7dMxnlRXf1hg+17JOOXrIDiyzYvb2JNs65SMOnplBiyI1b3nTqTz/KmPYF9W1/k9KlDvF0qdAsE7Wsc4cql3Ls5d1nTCrKn/spVS9r+ZorgnmNthloJgvo4qkTNo1lr0zxpGle27EnQ8+RqSlXjiBqWM0i9ohHT+DSpLR08hX65fd4NHBCKiXi+PlZau4wMA237SGV/Oyxu5uLah8EdMswGVlA/7RaQSVGy0JONhUL+q2k169e02fVP6YLf1xW/yueMlPbzg31sXHn05U0p8dS2jJzt0Nbzf15Thq8sQdVT9HMtlGRzcBWFtP3LPo6UvueeWwsrR4T2gM9bAE5IZQld2YaXGWcIX4rjhpplDn3y4O8mJ+fTH3M9mEv8dw8p8npnxpt01PNcg/pwFF/9QEw3wdB1KfdLZs4smZbYlq3MxGtn3XZ4RjJx8hKLTLR5IFXKdvblvn1+Ek/ko2MozLyjE5D0rDYc9kmiNtXLHPqsKkpaV3gRdVWSR0HpVFieunPQ+ZU8Xqv0jITBHTD/nKIPElAj4k1rwztxK+H0nebj9LGqZY1q4hInWa1pPqZ2zocea35jWY+RplvhWvTHunoyvW45BfPImqP7n2d/mKx5LvjCSgDr23lxE4839fs7X2bCn4YcsLG2Rp35ZYkdOYfXxraLeT0ZpehadTJH1l7ShKv8pHTU9LwHjep16hU1KP17QgF9LA2IIL6sKmpaNNci+d89xGpqeSnj6hMUWOsacP2BwK6hYiRBfSAlIlp5YXZ5B3XW61lxWlO9ARnusLoXf2VBjGs5kSHU50eXcHLy4sy5UhPg9b3UGvrX775LdwzyjQrTuVafkGdPulntKnW1l6jzLlRXePK6ZouQ9PS7FGXWSMI7+ipdY0rwrc41nVocofy5wp9cjIiI+g+PBUVzv+UT7A/pBO/+akTmPPGXDHMR0r7fkFA1/9Th4Cuk6GRBfQU6ZPRqotzyNqHt3JmpLknJyrRfNeCryze49v7UpP/dVIe6pKK1i5Mn9f8hIbWmOCQnBz7X/THVKqQoL76+iypXIsSVLbFFyyq93VKO158X/UCHlFnsu1LsAjoGbKnpacPg+gahydYO34rHdnxo9O63DWDUV50ws+RB7oI6Ocux1VidpZML/hYaVL2Cn/F3t+3bcgHjE9J5Yo/okIfPXU4DO0GpKHSRUI2EpKpcbd07A15N1yZfy/GVZuDJROvUKKEjsMAHDoWn8bPSUGVSlpE8TnLA+jT/E+obLHHJN6SvcekZg9LL2pQ9S5/CIhDi9YG8Ffna+QfX79nxJuws1cp/mDP+tBfz9/Ec93tGUHPX1JFvzru1iyX29NtXhu6dekOLR26VpWp3L4MNR1Rlxb2X6k2GyXqf6YW8vbCi5zg2THnSzq89ZjD55RvVZLKNi9OHQqGzK99l3eim/yc+X0ce0BaK3JFQB+1sx/9+eNZWjxwtcv9dLeMRppzHXnniID+v7efU/2q9zkE1SsaPCkV1a9y3yZWSwiWgyyuD+RTNY7S4ydE1Vpn5s3HFdsHSvkA2rBrBv4oeiHcqZ4R01Io8ciR97nUv+OrhLT7mwQ0fViI586YWSkoBZ8malEn5COnUQX0IP/Z5J+4uLuZcay0Z27flbRu7KZYeXZ0PzQm1rzSxjVX5tHE5oG2D5/ps6ahxX9OD7UOtu+L1vzRzSGm6zPSfOvIA33L3oS0ZENSalLjLjt6BPERfX/a9U0iPgEZ8oHS2Rp3Nq9Bb972CTUnT5yXnBLEf6XCXV3n+XfA+NTqQ+g7mZ9Twy7pNAnoa7YloiM/+yvvycdP4vD8npGKFHxMJ3/3U6eJRBTq2uK2wxObMT3+Uan/ebLfyc+XvXRMnoI5hEtZA4dwUWtY3u93LzZYjaQrukLzUfWUXrB8+HqHo69HV/jy1Tp1Wn0an4gXD/SwScT5xX9NpwnNAg3rfS59MtKcG5U17oUrcZVTXJfmtx2GcdW6xt36ZUL65oeIvc/D2kkQa+wNuqanfu1vU973g0jm91Nn/DhM0WvWQ3xViJdmte9SkQKONQ93m9aC41Yg3+ST3K1ZhmoPBHSdw+VpAvqUQyOoStLGNioihs/qujjC8Cth8YlHu3j52HMpXu8zqtevGoeK6eqUtsROS54uGfUtM8KWV+p8fP+Jiqcmnj0txzWgToX60z8/n3NanztmMNKLLiIBXY7+V+WvsJK+5s3Fxt2JQokpzrgvWpuEDv+YgIZ0vUEpkwXTmbPxaCh7NHbjBX9Y0X3wpJTsNfmcxe/woQSsz7l524tGzUxJGdO9YM/K+GqTMqTrTUqfxvKlWl6WBziWmXgS/chxKZvUuEd1KkVvjGFnfdbzdwjoFnrPX7yk8vE8S0DP+0UuGlzV4t2dLE2AEmPsP0A6s5tqXcpT4SoFqHtRy4ZFUte5bZSXu8R8jCw5E9DlGGyzkXWp9Yc9IwwH46x97vB3I825EW0uxva5qYQWSVMWyEfL1xxT1/XYol05bJWc4unId0N4e79S8+GUhSnCCeh/no1LfcfJB8vLlDCB4w+MMt8fPu5PEwaECOiTFyRTXvMdOS6vNRlVQH+VeC75+Bd1B9ON9TYsHLCaVo0KOYUY6w3S0QBHArreNa80Z/O9JeqEjtW7Udawqy/NoRqpmjucN7Xm19HlWClqpPk2IgH959PxaXAXi/f47bteVK9TRtoy77ztFKMzsMdPxlNH/gd3uUUf/C+Irt6MS5PmJaN333muYqG351OQ7RrdtYUR1CKgi+d69xFp1BpXwhCKk0mbfmnVaaVcHC5GQhWMmy0nRV9S/w6OP6o6a/+b/ntw8t/5pCsEdE8U0PXOsXp0BdENMr+XgYZv66NOch7aGPqeN7kT45OK+ahH8SFv2uSj9XlGmnNje40rYriEIOzHc6OrYVwnzOU7+u5508ie15UWJY6EcheFfAwVxxE5qTRxfnKaOvgar9MNcF+UXyXyCnDsCButhunBlUFA1zm4ni6gi8CyYsR6OrzFsfdjWHzWL8Xl/OraYpSLB7p4Vdp7SDrC3mZiY8pdNCf1KDaEHvPloxGlMXsG0K8Hfw8VWkbnML7R4kZ60bkioEu4FLlsY9ZIS/xyV5KEhQnkC5Ek/pikvO8/5bAr/v+9fEIuWfrtT18azHHGRMxJ4B9xzXI0tlyxh1SKvdql7iUbAmjfoQSq3C/skSPPmsPelxIPUr5k9x2bimqVvx9hiBlX+vAm80BAt9D2RA90ewFdYvRue7hMfcSUj4auJPFAL82XJclHRWsSD/Rbl+9EGIPXmi8yAV1Cykgs9d4lh9HZk+ddaYrb5jHSnOvK5mLuygB6+SoOteXFu6tJ7oKYzAv8kxxzVz4w5sz2jE6c9qMNsy+qDYHNdnhu/IBFmHqVI/7AKB7oXx5MSFN4s2BN4oGePCA4lKhvVAEdHughVuXpHuhhxR2ta14hJR895Z6f43t/UeBc8UDXkt/V37i75DPSfOuKgG6dxzbMvhDhR0VH7LfvT8CXzAXQg0delJ2dQO498KIKfDKzLF82V7VVBnUa0ppevIijPmzW4FBdzWpF/GFULmbuMza1cgKR9a4kEdTb9EtPu5acs4XUkhBdIzg8zOZ50X85akzYGTzQLVTNIKBrnWP16ApWW20+uj6lfTuVOt1uTfET+tGyszPVx0+588fIyUhzbmyvcVdtSaxCu45hpxRXknibn/zDj8b3u2470SMCeuYML6hR9RDHPtEhPivw2Baa1pW6YysPPND1k4eArpMhBPTQAFWssgfLqEOBPjbRpfWERsqzcnSDaQ5pywUeEtogfda06vLQyMRzqWDqdyNp3/ID6sI8IyYjvejkss8DR/xpRM+QF428OOw90KMioIcdt1NnfJUHuRyR5ZB1ttSDQ7fIpUm1KoTcfB22rMSULt80E4s519UmRZKIRfU7Z6Blky/RPr686Q+OnybeOdYk3pKvWIDq3jIk7Iw72xJioFtGx8gx0KX9jkK46BXQ5aPjYI7vWC1FU5sJBx4fS9tn76Wd8/dHatYRCegiytcfUJ36lxtF/5664M4/DZfaZqQ5d/yc5Hxp0fNQc56EcLH3QI+KgB4W1EreRJy7FFcdSbUm2VRITN4lky5zqJiIw1v9fDqeEmckBrpVfBePSqs4ZK3PqAL6g7gzKSB5SZdsy9MzeVIMdFc80LWKOzL+E78ZqjwbN03bqcxBLnLuMrsV1cnQ2qF5aM1vNBsz0nwrF9S37JOWti0IEZolhIu9B3pUBXT7cZMwK03Z63HigGt8UjJ8DF9XPNAlDrDcPdG63l0q+knIB3Zpn4RwCRxx1Rai68jP8f/P3nmAR1V0ffxAAgm99ypFQVREUQRREKX33nvvEHrvvdfQe++9ShFBBXkFBRFUEOm990B4z5l1N8lmk927d0Ny9/7ne3zeD5iZe+c3Z+fO/d8z59C0hZY8QEYoiIFumSUjx0CX+3cUwkXvR0p3dAV7m+8wrRn5xfejMY2n2f6pwcAa9M4nOahP2eFG+IlEeo9GWnOjc4/78FEsatglA3uS36DcOUKc9RzBFWe8CXNTkOSDk+ShcurTWuasSEpXr4cN0SV5gKxhZGO6QSEGuv4ZgoCuk6HZBHRnMdAFp4g5wZzRYUrbOZSC46yP3NFXZbe2erG3Ht+I7l6/RytGbSARzyXG7otnQSoGmfyvlNfcXmKipcmSSsUJ3rv8oPKoLFqzEEm8tMa5Oqo/G7EY6UH3C4c7kaNLkwZcVaglw7QrArqz+JDS15XrPkqkOfWXH8cTS6a8br4qFPJScORXfxrH1xYvcmvyT+t8S/1kSV5RzfIWL0lJSirx0bs0u0VxOUzL8k1JaD97t88edZVOnPajPmNSqwfm+3zcVWJPStJRidX7ZQHXvHyj284goFtmwIwCurMY6PJysfif6SrpssRK/7RMPgqY05oa5mhHd67dU4miW45pQKMaTKHbV8N6LDsS0Ov3r07F6hTmsDJj6MaFkOPfkmzaqMVIa+6C1Uno7L9xVWxcyduQItkrlUQ0MgHdWQx09dvhRM0itMix0x//F1+tkWP7hBVzOnLiUImja03ubJ3vYE5225vDulQp/YA+5ZjAkleiQUAGqlzyAZVlj8ojLNbIi8b8sZf5fkPyVEBAN+ovJuS+zS6gu7LnrdSuNFVqX5r6sAPIM06G14eTRf/1yzkV/lBK6D2v/NlZfaNbjZHW2+fPiSqz+Dyixw3KlukF55ohEscRZwK6K3tcyTPh7/eaLl3zpbksuGTO8JI6NnH83mIvoMs9HD3hb/vA+TN7lI/lPD8dm9yifHn4pv8rceO8Zs91Tn46PSXduevDYQlu8vsX0fCpKemD3C+oIXuqG6FAQLfMkhkFdGcx0IVLZLqCfMQftasfrR63mX7ecYydsGJz0tAu6oPmv6cu0XuFc1GPhe04Sel49e9SkqRMTIvOTlUn3mWtNnox0prrzh7XWQx0V/e4Inxf5FPooZM7S1v7Pa6I533HpuawUq9VaFnRFaTEotdKjzh/yZfa9U9P3VveooIfPeGk0/FpKsdon8OaQ9LEjnO1xSQbg4CufzYgoOtkaDYBvVaPSlS9awWqmqpJhOSSpk5CXee2pnxfv09POPHnhinbwoRbmfD9ELp+/iaNrD+ZEiSJTxvuLgzX18UzV6hJ7o6UKHlC7qsN5f4sJ8VLFI/On7xIs7otUiFcjFqM9KCTh4pszCW8SmIWqJdOuuySgF6uSWZqXutOpCFSqrTMyJvFWJSdE5HWLH+PH0IhAp14lbftl45KcELQSiXDe58HDElDqVO+pJ6tLd6TEqNyxtJk/yX1IMqV7Tm15PAGaVLyU5DLtwfj08rNSdRR2oQJgpXwU+5ry/FXIxQI6JZZMqOAvu3pMhW7ceO0HRGaau4COaljYAvKkicjXf/3Fs3sspB+3HxU1Zd1ePTu/tQwZ3u6ctYScqPt5CZUrHZhtf7Kh0qJl978/QAluM84Noay580a7lo10jVXHz6NWIy05ko+B4mde/aCHx8HfcJr3C2nAvrKzYlp9bZEtCYwJMmd/TydOReXAoak5TwQwSruY6PqdylrxhBPyENH2WNxUXJawCJ43LhhW8vLRJlGWagTizeSmFmKeENOWZBCvUjIOtu89h3OXxGyhkui6Bu3fejhI1/+uPmK0qUK0pQnIzrtDB7oIfTNLqC7sucVwabV+IZUvEER8uU4zj9sOkoTWswk60fH0HteIeusfnTavieubaT1Vsa7htfOZRuT0OvXsfiY/jX6/U8/pwK6K3vcQRNTcaJPf0qX+iWVKiIfJu/bQqzYc7YX0EVk2sVhspZNtqzpgew0smFn4nDT06vNTeWNLh9Ipc6Pv8SnOL6vqTjvnRtUuac+CBihQEC3zJIZBfQR2/vQU/7wOLj6uAhNNTJdQRzxdgatpPHNZ9D2uZZTl13YieTDr96j5OmS0rV/btDyEevVyXVraT2hEaXOnJIGVfWOONBGWnPd2ePKfrPToHQ0Y/hlPmUT/gSPzKuzPa7oBE26ZeDEy+HjlNvvcR89jkVVW2UOZ48Z072guaMtDoXyUXP2smTslOfL9/RC5bSwnoKP6WsuBHT9MwQBXSdDIwvoOoeO5m4SMNKDzs0hopmHCUBAtwA1uoDuYbNAdy4SwJrrIihUUwQgoIcYgjcJ6DDvN0MA6+2b4exNV4GAbplNowvo3mSTRhoL1lwjzVb03ysEdP1zAAFdJ0MI6DoBmrA5HnQmnHSdQ4aADgFdpwmZujnWXFNPv+bBQ0CHgK7ZaNDARgDrLYxBKwEI6BDQtdoM6ocQwJoLa9BCAAK6FlqO60JA18kQArpOgCZsjgedCSdd55AhoENA12lCpm6ONdfU06958BDQIaBrNho0gIAOG3CbAAR0COhuGw8aEva4MAItBCCga6EFAV0/LQc9QECPEqxe3SkedF49vVEyOAjoENCjxLBM0inWXJNMtIeGCQEdArqHTMmU3WC9NeW06xo0BHQI6LoMyOSNseaa3AA0Dh8CukZgDqrDA10nQwjoOgGasDkedCacdJ1DhoAOAV2nCZm6OdZcU0+/5sFDQIeArtlo0MBGAOstjEErAQjoENC12gzqhxDAmgtr0EIAAroWWo7rQkDXyRACuk6AJmyOB50JJ13nkCGgQ0DXaUKmbo4119TTr3nwENAhoGs2GjSAgA4bcJsABHQI6G4bDxoihAtsQBMBCOiacDmsDAFdJ0MI6DoBmrA5xBwTTrrOIUNAh4Cu04RM3RxrrqmnX/PgIaBDQNdsNGgAAR024DYBCOgQ0N02HjSEgA4b0EQAAromXBDQ9eMK3wME9Kig6t19Qszx7vmNitFBQIeAHhV2ZZY+seaaZaY9M04I6BDQPWNJ5uwF6605513PqCGgQ0DXYz9mb4s11+wWoG38ENC18XJUGx7oOhlCQNcJ0ITN8aAz4aTrHDIEdAjoOk3I1M2x5pp6+jUPHgI6BHTNRoMGNgJYb2EMWglAQIeArtVmUD+EANZcWIMWAhDQtdByXBcCuk6GENB1AjRhczzoTDjpOocMAR0Cuk4TMnVzrLmmnn7Ng4eADgFds9GgAQR02IDbBCCgQ0B323jQECFcYAOaCEBA14TLYWUI6DoZQkDXCdCEzSHmmHDSdQ4ZAjoEdJ0mZOrmWHNNPf2aBw8BHQK6ZqNBAwjosAG3CUBAh4DutvGgIQR02IAmAhDQNeGCgK4fV/geIKBHBVXv7hNijnfPb1SMDgI6BPSosCuz9Ik11ywz7ZlxQkCHgO4ZSzJnL1hvzTnvekYNAR0Cuh77MXtbrLlmtwBt44eAro2Xo9rwQNfJEAK6ToAmbI4HnQknXeeQIaBDQNdpQqZujjXX1NOvefAQ0CGgazYaNLARwHoLY9BKAAI6BHStNoP6IQSw5sIatBCAgK6FluO6ENB1MhzTarbOHryveRzf2BQ3TmwKevmaXgS98r4B6hiRL7NpO+QKPQ8K1tGLdzaN7+dDT1+8otevvXN8ekbln7wfye/K7OXJs5c0rdN8s2MIN/74fr4Ui83j6bNXFIwfUBg+8ZhN474XmAvMJjQBn1ix1HNa1lyUsASCfKtR8uR5gIUJ7F79Ex3fcwIsQhHwiR2L/Hm/EvzqNX4/dpYRm9l0HH4NXBz8Yvzi+NCrV8H0Eg+jcHR8Eveh+P6+pl9ngl4G08R2c03PwR6AX1wf8vWJRc9f8O+Hf0MoIQSETfMBl5gLNrn2dpGA15TH/N6IYrfHjfUuJU9VHVh0EICArgOeNJWHHUpYArHkj/xyTvQaYiiMAwQ8QEA+vKjflcmLvHfKCyiK3Zqr1ltecSGewzRAQDeBWPx7kpd1FFIv5VhXwluC2Aj2uPiFgIBnCPj4xCb+9mL6IhLoS+gK4ezAssWNhWeR6X8h2gBYntIojgjAKU+fXUBA18cPrUEABEAABEAABEAABEAABEAABEAABEAABEAABEAABLyUAAR0L51YDAsEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQEAfAQjo+vihNQiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgJcSgIDupROLYYEACIAACIAACIAACIAACIAACIAACIAACIAACIAACOgjAAFdHz+0BgEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQ8FICENC9dGIxLBAAARAAARAAARAAARAAARAAARAAARAAARAAARAAAX0EIKDr44fWIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACXkoAArqXTiyGBQIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgoI8ABHR9/NAaBEAABEAABEAABEAABEAABEAABEAABEAABEAABEDASwlAQPfSicWwQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAE9BGAgK6PH1qDAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAh4KQEI6F46sRgWCIAACIAACIAACIAACIAACIAACIAACIAACIAACICAPgIQ0PXxQ2sQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEvJQAB3UsnFsMCARAAARAAARAAARAAARAAARAAARAAARAAARAAARDQRwACuj5+aA0CIAACIAACIAACIAACIAACIAACIAACIAACIAACIOClBCCge+nEYlggAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAL6CEBA18cPrUEABEAABEAABEAABEAABEAABEAABEAABEAABEAABLyUAAR0L51YDAsEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQEAfAQjo+vihNQiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgJcSgIDupROLYYEACIAACIAACIAACIAACIAACIAACIAACIAACIAACOgjAAFdHz+0BgEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQ8FICENC9dGIxLBAAARAAARAAARAAARAAARAAARAAARAAARAAARAAAX0EIKDr44fWIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACXkoAArqXTiyGBQIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgoI8ABHR9/NAaBEAABEAABEAABEAABEAABEAABEAABEAABEAABEDASwlAQPfSicWwQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAE9BGAgK6PH1qDAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAh4KQEI6F46sRgWCIAACIAACIAACIAACIAACIAACIAACIAACIAACICAPgIQ0PXxQ2sQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEvJQAB3UsnFsMCARAAARAAARAAARAAARAAARAAARAAARAAARAAARDQRwACuj5+aA0CIAACIAACIAACIAACIAACIAACIAACIAACIAACIOClBCCge+nEYlggAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAL6CEBA18cPrUEABEAABEAABEAABEAABEAABEAABEAABEAABEAABLyUAAR0L51YDAsEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQEAfAQjo+vihNQiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgJcSgIDupROLYYEACIAACIAACIAACIAACIAACIAACIAACIAACIAACOgjAAFdHz+0BgEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQ8FICENC9dGIxLBAAARAAARAAARAAARAAARAAARAAARAAARAAARAAAX0EIKDr44fWIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACXkoAArqXTiyGBQIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgoI8ABHR9/NAaBEAABEAABEAABEAABEAABEAABEAABEAABEAABEDASwlAQPfSicWwQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAE9BGAgK6PH1rrIDBm+gpav+N7+mHTNB29hDTtPWI2bdx5iH7ZNZv84sah5y+C6KMSzalH29rUoHrJCK9x+u8LVLVZf5oxqgt9UeB9Ve+bml0oZfIktCKwv0fuTU8nt+7cp+GTl9BPv5yiJ0+eUY0KX1HvDvX0dOmRto64eaRjdAICIBCjCRQo25pq8joU0LKGuk/7tdfVm7987RaVqNWVmtYuY+vL1baoBwIgAAIgAAIgAAIgAALRScBVPWP99u+p76i5YfQGV+77ydNn9EnpVurdv26Vb1xpgrCU+/4AACAASURBVDogAAJRSAACehTCRdeRE3D1geMqx537j9DvZ85Th2ZVydfHR5eAPmfZVkoQ359qV/ra1ctHWb0O/SbTiT/OUeuGlShp4oSUMIE/Fcr/XpRdz9WOIaC7Sgr1QMC7CNgL6PZrr6ujvf/wMc3ltfaTD3PbPl662hb1QAAEQAAEQAAEQAAEQCA6CbiqZ4hGIfvlKmW+pKyZ0rp8yxDQXUaFiiDwRghAQH8jmHERRwRcfeC4S0+PB7q714yKdoUqtKV6VYpTm0aVoqJ7t/uEgO42OjQEAUMTsBfQDT0Y3DwIgAAIgAAIgAAIgAAIuEEgqvUMCOhuTAqagEAUEoCAHoVw0XXkBKwPnMCRATRuxko6cfofSpwwPtWp/A21rF/e1rjf6Hn08/HTtGPZ6DAd1mo9mFJxmJUpwzqqvx8/cxWt3LSPDm8NVH+OSEBfu/UAzV+5nS5dvUnpUqeg93O9RVv3/BTmSJV93/t/OE5te0+kEb2bk/z/Pxz9nWLHisVekx9Q3071KRHft7Xcf/CYRk1bRt9+/z969SqY8ud9h+7ce0g+PrE1h4T5sHgzCgp6GWbc362bRH+du0TjZ62mq9dv06PHTyhZ0kT0ab7c1LFpVUqfNmWY+lt2/0gLVu2gs/9eUXzffTsrNeSQNp99/C79yONwtR9XuMmFt+05TPNWbLNdL997OTk8Q3XKnCENfhIgAAIGI/DLiT9pwqw1dPLMP5QkUQL6/JP3aPvew1SvanFb2BX7tdfV9dLRGm19Lgzp1pTXre183fOULElCql6+KLWqX4Fi8bprLX/9c0mFtzr++1mK5xeXihX+SK27NbiuNbyMq7it97xwUi81vh3sJfTo0VPatnQUZeA19YejJ2nqvPX0B4f8khBhhfLnUdfImC5VmEssWbublm/YQ5f5+SLrcumvClB7Xpfj+cdV9awvQs3rlqOHj57Q7gNH6QH/rzyHBnZtTP9cuEoLV+3k01T/UFIed7VyRalNw4quDgP1QAAEvISAK+ubK3s4T6w5Ekpw4uw19N2Px+kxhxLMlCE11apYTP0Xek12BX31FgPVmlqpVGGavnADnfn7IlUo+TkN6d5ENXe2hlrXUbmfnft/pvsPHlGm9KnVM6km34+1qGcJh0wY0KURzec96emzF9UzTOrIM2LWks2W9ffhE7UvHtStMWXLnM6VIaAOCIAACIRdazgk7awxXdU+UTSLJIkThNu3fn/4BLXqMY7WzhlMuXJkVu1FJ5BT7ys27qE7dx9SzmwZKV2aFLT34C+2kLTWNbxb61r07PkLWrftAOsKD9S6NSCgIWXPmkH11brnBLp24zatnzfUdm+d+k8lcXgLraF0GTSdLl65QatmDlT1ug+Zwfvov0nWeV9fH7WeihZTteyX6t/lXtr3neww9Ez99sNZpwiiFTMGwCJAwDQEIKCbZqpj3kBlc7tw9U4OSRKPKpT4nIXfFHToyEklVMhGWo44SfGkgL5g5Q4aE7iCihTMS0UL5aPn/CAS4UTii4eOgR6RgC5hXVrUK6/Ek3MsdAyZsIjKFCugNuhS5MFWuUlf9YJRv1oJSpMqGZ3hTfv6bd9T5oxpNAvox07+RY07jaRyxQsxjy/UNd7PnZ3v+Rh9f/g3ei9XNkrOIs2NW/dYdNlBfiwirZ87RIn1UmYv3aJeeiryy4mM99nz57Rp1w/qJWLcgDbq5cGVflzlJvM5Y+FGal6vnHqwP3r8VN3XlWu3aeOCYWquUUAABIxBQDbUDTuMoLdYVJD1x5/Xl19PneU15BA1rlnaqYDubL2MSECXD36ZWaBp26gyZcuSjteoEzR57loa0681lfm6gIL3J39ErNlqEOXgF4fKpeXe4vBLyxnavPsHalIr5N5cJW0V0OPH8+e1KwsV/DgPveCXgnpVS6jQYG16jacvP8tL5XktlvVdXnjkpWYdr7cpkiVWl5G1Vv5ehJxP8uaify5epZmLN9MH72ajOWO7KaHJ+iIUO3YsDhH2DZUvUYiF+ifUc/gsJagn5rW5Q9MqlOedt+jAT7+qPuUjcbHP87k6FNQDARAwOAFX1zdX9nB61xxxCqneYoBai+WDnjiM/MbPgWkLNlAX/ogoe10tRQT08xevcajF2Ly3LUipUyajtKmSq7XQlTVUBKdGvC+WDwwteT8uYv5P/zulPly2a1KZWjewfHCUdwx5lkioBFlTs2ZKR2u3fkdL132rPoIW/zK/TXAfOG4BxY3jS2tmD9IyFNQFARAAAbXWLFqzU+2Ry35dkLJmTstr0u9q7xp63+pIQBfxWj4ESmzzXDkz0/Wbd2nDjoNqjbTmdLOu4XF53ZKPfKW++pT1iyBasm43pU6RlDbMH0ayp5T1bmzgSvppy3T1vi177MIV2/G+83kY0V7yvJUs+gmJIC9FBPW3s2VSwrn4qIgmsmbLdzS2f2sqzRqHrLkla3dl0T8LTR1ucVqUImtwpcZ9aWTvFmr9RgEBsxCAgG6WmY6B45QHzqrN+9VXUasA8fr1a6rNnuWy2G9aOFzdtacEdBEnilXvzB6U79PEwe1sRByFIolIQF82vR/lfTe7ra3c28Ejv9G+NRPV301n8XjGoo20acHwMPHN2veZRDf5y647SUnFC715nbLUtnHlSGdx76FjJNcRQeed7JmU17uMt1LJwuzZ2ChM2yucvM/eU91awb4fV7nJS9ZX1ToptiI0WYt4V37OYWhG9WllE79ioDnilkAABOwI1G8/TJ1y2bxopM2DWqrYh3CJyAPd2XoZkYC+hkWOA+snK5HDWkrV6a7W3lF9W6q/atx5JHva3FFrbRwWPqzF3fAyVgHdUZImeUHwZw/y0Ov3tZt3SO5JxPKurWoqz52vqweohNVdWlmSq0qRF6OAgdMocGRntS5aX4Q6Na9G4oVuLcJw7vJtdHDjFPa4T2T7+yJVOtLXX3xM/Ts3gH2CAAiYhICe9c1+D6d3zZG16Qh7VC6b1k+JNNYyetpy+o4/8m1dPFLTrIiALh8n5aNiKhZ/rMXVNVQ+GohX5YxRAeoUqLUMnbiY1rJn5n7ej4v3p7xjyLPke36WiPAkRd4tCpRtxUmwi6nTo9ayik+vDhq/UD13rO8jmgaFyiAAAqYlIGvNyk171fu39bS16BmyfxNnDOu+1V5AP/rrGWrYcUQYp0GBuIid0Ubx+movoNeo8BX169TAtg7LR0NZ97bzSUm57h9//UvVmg+gmaO7UOFP31cOggPGzleOdl8X/lh9YJR1Vu7LuieNaNKqNutPGdKlpMlDOqgqcmJnyrx1tGvFOD69n1z93WB2Itz93c+0d/WEMPtw0xoCBm4aAhDQTTPVMW+gEcUMC2QBetr8DXRs9xyKw0eJPCWgWwWSueO6q/Al1qJFQN8wfyjlfCujra0I5tPmr6ff9y9QfycvBuJ1uWBizzDAPS2gBwe/po07DyoPcjny/5BDDTx68lSFe1k8pQ999H5Om3CzdFpf+jBPDocG4Eo/rnKT0Akd+01x+BCV++rAYQxCh+aJeRaJOwIBELASkNMjIkbLb1Z+u6GLqwK6s/UyshAuP2yaFuaaIihJmT+hpzrZIvfQlvNC2OeG0Cug29+zfIj8olJ7JYo3qVUm3D2JN7ocg5XEUAEDp6tjrBKOxVrEcydfiWbKS1O8fSKKZWl9Yfp5+0yKH8/P1l5ehuR0lvUlBhYKAiDg3QS0rG+u7OH0rjlyqvLv85f5ZKNPGPDBwcHqz7/tmadpQqwhXEI7skgHrq6hcvJT9r9Hd8wKc12rGDV9RGd1yjSidwx5RlQrV8TmfSmd7ONTne16hzigaBoQKoMACJiaQERrjYQ38fWNrfatUuwFdGknIav+t2sWn8gJWV8jEtDtHTzEU7xpwGiS0IMSLlaeB+JxXrvy19S+SRWln4iDSaoUSWjHviO0kT3VJRxLx/5T2Es9UOkVUi5cvs738S2HcfmLbt6+p05Zyn+ilYhmIuX23QfsJNKZmtQuo94J5N+LVu2o9rb27wimNgYM3hQEIKCbYppj5iAjeuAsW7+Hhk1aTPvXTlTeKZ4S0CVmmPS1feloFR7AWvQI6NYQKVYBXTwQ83/4DntbW7wkrcXTAnqPYTNJYpuXKJKfvuKj/RLLXeKZyfgWT+nNAvrbZPWo2b1ibITe5q704yo36/Vmj+1Kafl+7IvEMQ7tWRkzrRJ3BQIgIARkPREP68HdmtjiIFrJuCug26+XWgT0Ft3GquOo8qKg5d5cnU3rh0J7AV1CdZVv0IuG9WymYvaGLnLs9bc/zpGssSs37rV44zhYb7+s3EF5SkofEYlZElZA4rn/vH0GC+iWlxop9qehXB0P6oEACBiTgJb1zZU9nN41R7wV3+dwgQGhTtaEJqs1bnhEArqra2jodTf0fdiv1RG9YxTiE5GVS31B3dpYwhdIcRRawZjWg7sGARB40wQiWmtC71sdrTMSuu/IsT+UB3fo4qqALiEV67QZogT6T/PlUl106DdZ5e+Zw8J3kSodaHTfVhwmKylVaNSHti0ZpeKnH2bh3RqzXMLM1m07hMX0eOpEpYRykfw7I6YspQS8F5073iKgS5FwM4f5fr9dNZ5WcwSBkVOX8p53nApXiwICZiIAAd1Msx3DxhrRA2fSnLV8lH0rCwkz1RF+OX506OeT9O3KcWFGoDWJ6Hc//spxbCeEiQMmHXpSQBdvQXmQTBveKcy9elJAtx6/kuP/EgbAWiTZn3zttgroVm8e+zAK1vqu9uMqN+v17I/VxjCzw+2AAAi4QECOun9SuiX1aFtbhSUJXaJbQLclVGIBpFGNUpHemwtDVVUiEtCtHuiSMLQpe96ELhKHV7xwVs8K5YEe2J/zVGSzVbN5oHMsdRFs9IpZro4H9UAABIxJwNX1zdU9nN41p0LD3upUjKeSxDn1QHeyhooHusQI/t/OsB7okrhP1mTZfxct9GGEHugQ0I35u8Bdg0BMJeCugC5hsFZzrHFxnAhd9AjoSzku+oRZqzlES4A6FX5gw2Tl3V6OHUHECeTgkRPqg6g11KCErtrCuYNECBfh3FrEs11KaAH9lxN/sc4wTMVGD+QT+Dn4RP74gW1i6rTgvkAgyghAQI8ytOjYGQFHD5ygl69INuvpOQO1ddGWECkSG1aSYljjGMoxpSpN+3HCi1QqwZoU+zi89t6NVzlebolaXahnuzqcrKO47fZOnP6HanEyOleSiNp7J9p7VPYZOYf28PEo+ZpsPYYv99okYJRKMOqJGOiXOX55iVpdaWiPpip5nrXIRwb52m0V0OUY1tc1AqgqJ2O1Jjm11pX4vSLsuNKPq9zkZe4bvt4HHKd4wcReYWJlypHku/cfqgQlKCAAAsYgULpuD/ZGyUiThrS33bCsZyKg165UzGkSUWfrpbse6HIzxXkNzMKJmSWOrrWImC0eN3Uqf2O7N1dJRySgS/uKjftwgrk4HKplgEoEKkXySJSp14Pq8fFVawx0yTkhzxb56GAtW/f8pLx27GOg2x/FhQe6qzOFeiDg/QRcWd9c3QvqFdCHT17KiTd3q7W2YP48YeBLzN3cObNompCIBHTZQ7qyhu767ih1HjA1XHLlgWMX0AYO7fLd2km2GOjrd3xP9uHAIKBrmi5UBgEQcELAXQFdPgSKbrBkah/K915O21Uk1viMRZvCxUC33zc68kC3JvYUj3Q5nT68V3PVrzgninh+npPbjx/Yjk9Fvq/+vveI2SrHhb2TYt22Q1VS1NACutQX7UUcS0RjWDS5N338wduwDxAwHQEI6Kab8pgzYHngLF67S30RlQeHiCkbtn9Pp/k4kcTxtsaRtQrcEmdLBGN5aZi1eBPJ3xfj8CWuCugycjnueoA90eUhlOOtDHTs5N8q6afE9vKEgP73P5epctO+9G7OrFShZCEVm3wXJ9iQI1IiLC/nJKRai30SUUlMUpGT2kl8+D4d61OSRPHV8dPpCzcob0irgC7XsSamq1Dic05g94ES8SVuuoQIGNOvlcv9uMpNkozIQ1rmU+ZVEjmd5Hlau5XD53ASPMn6jQICIGAMAvK7lRNAbRtXoi8L5FVr70xee+XUjnhji1e2dZ1ZyUnYDm8NVH+OSIz2VAgXuYb1uL+sKQXy5aZLV2+puLiyljfjpMudW1TXBDkyAV1O4bTtPZETUL9H5UsUosf8QXAOf9R9zuupJI1KmTyJjYN87K1d6Wv65MNcKj+FjPmDd7PRvPE9lPiuV8zSNChUBgEQMCQBV9Y3V/eCetccEbZrthyknCDkNFKed7LSvQePOHncUbp+666Kq6ulRCSgh96zRraGivOHJN7789xFlYhZkuf9cPQkrWFPztB5MRDCRcusoC4IgIC7BNwV0CU/WOm63UmcByV8ipy6//F/p+gAJ2eWcmzXbOU4GNEa7khAl3aSt0dEbtFHRCeRYk0w6uMTWzkkWkMFSmx0CYslHulFC+WjG7ymz1m2lX48+jt99hHHQA8VwkX6WcWhWwaNW0DvZM+k9r8oIGBGAhDQzTjrMWTM8sARb5GvC3/ED4vf1IZcPFkk0ZokwQxdJGbXrCVbWBy5T9kyp6dq5YtwjO/9lJbDpWgR0EVgljizIiL7sgAtx5i+/Cyv+jtPCOhyz5KgQ45PXbh8Qwkr1csXZaH+L5XcQ+KDay32Arq0l4QfEm/3GB+n8vOLowSkD1m0luNgoQV0qStZuldwfN5/L16jxIkS0Ae5s1PDGiWVwONqP65yk+tJKBdJiiIim4w5Q9qUVIiFp8Y1SyNOmtbJR30QiGYCIgDL7/kxJynOniWDSk4k62WtitHrgS7i0czFm9X6du/+I3orczpq3bAi9R01l1rUK6eEFS0lMgFd+hHPnWkLNqiXEHnJKfhxHvXCEfpUjdzT4jW71Hp7mQX9ZEkTUamvPlUJlqwnkvSKWVrGhLogAALGJODq+ubKHs4Ta46I6LL+fc/Czs3b99VRfxFQxKmldLECmiBHJqC7sobKxeRU46Q5a9hB5aha/zPyaVQ5/VOHn0/WAgFd07SgMgiAgJsE3BXQ5XJnOUHzkImLOYHn3xSPPb6LsSYi2sHCVTvo+Ldz1R1pFdAlof2Bn47ToU3T1H7VWkrW7qb2pfan4Res3KH2rnf4I6nkiBOnO9EyHHmgy/1KPHVH+ZHcxIdmIGA4AhDQDTdluGGjEXj56hWV4VAIJdhTUo76o4AACIAACHiegHy0FG8ea7gUz18BPYIACIBA9BDA+hY93HFVEAABEHiTBOTU9/kL12glhw2MaUWSi27e9QPtXTNBCewoIGBGAhDQzTjrGHOUEpB4t7lyZqZUyZPSCz6etZmTc5w8fY6POg1VX3Ybdx5JJ/445/QeOjWvro50oYAACIAACIQl8Oe5S7Rs3bcqFFfypInV6SSJIy4nctbOGUzBfMxfYt26UiRkmNY4vq70izogAAIg4A4BZ+ubJIWLKUWO+rfvO8np7UjIgAPrJzuthwogAAIgYBYCU+etVznD0nHuN1nXDx/7g9ZzOFtJ1Kn1dE9UM5OTP19V60w1K34Fh8Coho3+YzQBCOgxenpwc0YkIIk85Zj//YePKZ6/H8cDz0Htm1RVcSNRQAAEQAAE9BOQ+OK9hs+i85euq+OtyZIk4qRIH6jY5ymSJdZ/AfQAAiAAAtFEAOtbNIHHZUEABEDgDRKQvGGbdx2iW5y/Jzbnycn5VkYOs1qKynytLTTWm7hlCfUydsZK2rl8jArPigICZiUAAd2sM49xgwAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIREoAAjoMBARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAAQcEICADrMAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAAQjosAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQcI0APNBd4xRhrSVjt+jswfuax/GJRXF8Y9PLV6/pxctg7xugjhEJm9g+selFELjYY4zn50PPXryi1691APbSpjU7leHs7LG8dHSuD0vsY23gbtcbmKSmf1wfih07Fj17/oqC8QMKM+vC5sWzIHCx+y2Ivchz+jn/plDCEshfKh+98256YGECh/f9Tn8dPQcWoQj48G/HL25sesV73OfYy4WxDUZDfnF86GkQ1hX7H40fr7evgl/TS/4PJSyBah3KkDyrzV7kvXnl1J1mxxBu/HF5TZF3oOe8rsi6ixJCQNi8evYc64qdUcgbc7y4vvTkxUuYix2BdNnT0teVPwEXHQQgoOuAJ02L+9bU2QOam41ALB9sEs0253rHu/XxEiV2mb08efaSKqVtYXYMGL9WAs+fa22B+iYm0G1JRype9VMTEwgZ+vTuS2j92I1gAQKuE4iFj/2uw0JNIbDx8TKK7+9rehhB7HRWNmUz03MAAG0EXj98pK0BapuaQMFqBWnwys6mZqB38BDQdRKEgK4ToAmbQ0A34aTrHDIEdAtACOg6DcmszSGgm3Xm3Ro3BHQI6G4ZDhpZCEBAhyVoJAAB3QIMArpGw0F1RQACOgxBCwEI6FpoOa4LAV0nQwjoOgGasDkEdBNOus4hQ0C3AISArtOQzNocArpZZ96tcUNAD8EGD3S3TMjcjSCgm3v+3Rg9BHQI6G6YDZr8RwACOkxBCwEI6FpoQUDXT8tBDxDQowSrV3cKAd2rpzdKBgcBHQJ6lBiWWTqFgG6WmfbIOCGgQ0D3iCGZtRMI6GadebfHDQEdArrbxoOG8ECHDWgiAAFdEy6HleGBrpMhBHSdAE3YHAK6CSdd55AhoENA12lC5m4OAd3c869x9BDQIaBrNBlUD00AAjrsQSMBCOgQ0DWaDKqHIgAPdJiDFgIQ0LXQclwXArpOhhDQdQI0YXMI6CacdJ1DhoAOAV2nCZm7OQR0c8+/xtFDQIeArtFkUB0COmxABwEI6BDQdZiP6ZtCQDe9CWgCAAFdEy6HlSGg62QIAV0nQBM2h4BuwknXOWQI6BDQdZqQuZtDQDf3/GscPQR0COgaTQbVIaDDBnQQgIAOAV2H+Zi+KQR005uAJgAQ0DXhgoCuH1f4HiCgRwVV7+4TArp3z29UjA4COgT0qLAr0/QJAd00U+2JgUJAh4DuCTsybR8I4WLaqXd34BDQIaC7aztoR4iBDiPQRAACuiZcEND144KAHhUMzdYnBHSzzbj+8UJAh4Cu34pM3AMEdBNPvvahQ0CHgK7datDCRgACOoxBIwEI6BDQNZoMqociAA90mIMWAhDQtdByXBchXHQyhAe6ToAmbA4B3YSTrnPIENAhoOs0IXM3h4Bu7vnXOHoI6BDQNZoMqocmAAEd9qCRAAR0COgaTQbVIaDDBtwkAAHdTXChmkFA18kQArpOgCZsDgHdhJOuc8gQ0CGg6zQhczeHgG7u+dc4egjoENA1mgyqQ0CHDeggAAEdAroO8zF9U3igm94ENAGAgK4Jl8PKENB1MoSArhOgCZtDQDfhpOscMgR0COg6TcjczSGgm3v+NY4eAjoEdI0mg+oQ0GEDOghAQIeArsN8TN8UArrpTUATAAjomnBBQNePK3wPENCjgqp39wkB3bvnNypGBwEdAnpU2JVp+oSAbpqp9sRAIaBDQPeEHZm2D4RwMe3UuztwCOgQ0N21HbRDElHYgDYCENC18XJUGx7oOhkaWUAv3aQYfV7xE+pbcZROCo6bF6/3JdXoVoGa5+0aaf/zfp9Amd5JH65Ovezt6Pq/N6np8DpUq3vFMP9+dOev1Kvs8Ci576ju1OgCetWOZahm1/KUIEl8Gt5gKh3a8LNuZA36V6OvahSiVJlS0J2rd2ndlO20YdpOp/3G8YtDI7b0VPW6Fh9iq5+v2HvUfEQdypAjLf1z8iLN6rGETv30l9P+YmoFCOiWmXny7CVVStsipk6T0/tadmocDagzhf46ft5pXXcqxPHzpRFru1h+D+Wcr+sR1a/c6huq3LoEJeTf+PEDf9D0nsvo1pW77txSzGhjYAFd1sTeC9pRzo/eon//uERtC/XVzdQvXlzqNrsV5S6QkxKnSEgXTl+m2b2X0/H9vzvtO22WVDRx/0DaOncvLR66VtX3T+BHLXi9LVzpUwp+GUx7Vx6i+QNWUdCLl077i4kVIKCHzMr07kto/diNMXGanN5T+uxpaebxMVQ+UX2ndbVWKF6/CDUcXJOSp01Kp4/8TWObTKcrZ69F2o3scwN/GUN1MreiB7cf2up+Wa0g1epZiTK+nZ6eP3lOB9cfoemd5lPQ8yCttxUz6htcQM/39fvUYWpTSvtWatrE+9DALgt1c43FTFqMrk8lGxelOHF91RxPaDmLXjx74bDv/CXyUv3+1SnLuxkptk9s+vPoWZrUZjZdPHOFkqVJSquuzArXTt6X6mVrq/teo6MDCOgW6kH8/Cybsll0TIFHrtljVnM6e+IirZmywyP9WTup1q4klahTmNK9lYp/M0H0047jNLXbUnr66Fm46yRLnZhW/jkx/O/jwm2q/0G3MH+fNktKmrirD22dv58WjzTmc04GZGQPdB9fH+o6tzUVYj1K1smGb3egu9fv6baftz/ORp1mtKCs72Wm6+dv0PTOC+hnthtHRe2J57eldwu+zXviRHSB99qzWTc4tvekqi5aw7YnS8M17VCoD/1x2HjaAgR03eZFENB1MoSAHh5ginTJaNy+gZQkZSK6dfmOUwE9XkJ/tUG0Ftm0jts7kOpmbU2PHzxVAnqK9MloWsf5tjqvgl7RM37RMGIxsoAuQsn6G3Ooeb7udPXcDYodO5ZHRJJmPMdHdhzjh9ZlJRT1X9mZ+lUaE6mgI9fut7wTpciQnF48fWET0DO+nY6m/jCUhtaZRL9+9wd9U7cwtRpTnxrnCaA71/Q/lKPD5iCgW6hDQI/Y+tTvYWFbXiuT8u8hyKmAHlH9EnU+WUElKAAAIABJREFUp7rdK1Df6hPoNv9eWg6rRTk+yExtiw6ODtP3zDUNLKA357UxUfIENKXjAvLh56QnnnuJkiekuizY7V7yPd3mD5YlGhRRf66drR09efg0QuZJUyWmUdt60+vXr+nQpqM2Ab3r7JaUOFlCGtN8BsVPHI/6LulAJw6doVk9w79weGZCo7YXCOghfCGgh7c1ETWnHhlJg6uNo9P88lyPHQDyFHqH2hXoFaFhjtrVj956P7MSP6umahJGQC/fuiTdv/mAfv/hjPqg1XdFZzqw5idaOGBl1Bp6VPVucAF92uERtClwJ+1efIB84/hGKHJrwVe+VQmq2rkc9aswUol+fZZ1olM//kmzey5x2I0I6LLOnvvtAvn4xqZWYxuQP78r9S0/UtUXB5bQpRiLi8VqF6bOX/bXclsxpi4EdMtUQEB3bJKyL/339BW68s8NSpY6CQ1Y3JZ28P5l9WTHQn2CJPHC/j6qfUbFqn9GnUuNsP19UtYoRm3sZtnPbPkFAno0rQYFynxETdkBI6DIAHrO7/Iv2fFC5kRPkXV70d9T1Dq+OXAXfVbuY+owrRk1yNGe7t96EK5r2RPX61OVdi36jm5fucMfOr+iuvznWhlbqj2xVUBv+l6A+ndrefrwGQUHB+u51WhpCwFdP3YI6DoZGlVAF5F63snxvDHzUV9zX718RZVSNGbhMoBF6yeUKmMKysXeaf/89i+NaDCFbly4pUiJx9rkQ0OVKH7+94uR0itQ9iMSYdSZB7p9Jz0WtlPC+9zey9Q/iYAuYvz4FjN1zlbMaG5kAT3wyAjKnjcLPXv8XL0E1MzcmtZcmckPqd1UsMLHJN6JP275H01oPcfmPVWTTyFU71yWqqVv6fIEjNvTnw5t/JnWTd4eYZvOgc3p4d3H6kuxCEBWD/Q6vSpRrvw5qH/Vsba2kw4Mor0rfqCN0517tbt8k2+wIgR0C2wjC+idJzeiUvW/UOvt6+DXtGT0Jjqy6zfl/bJq0nb1b+IFsZY93uTP1jJ8bYD6rQ1pOD1Si+s8qRE9vMe/B/ZQE08dZx7oEdUftaErHdn9G9/HLnU9+cC59p8p1KbIQDp/6vIbtHoPXsqgAnrhSp9Qn8Xt+WWC1EvFvP4rebP+mopULUBXzl2nz/jF4xHP+eQO8+nYPounjJSt9xbQTD41sGmGZQ6dFfmAvf3hImpTsA+d/fVfh9XFDkZv701LR25gT/NP+HTYLSWgy4eYzXfmU8eiA+nv/05WfPzNB9RrQVuqxi8fRiwQ0ENmzcgC+oI/p6hTaM8eW7wUu/MptepdytPj+7zH5ZMdlj3uBRpRb1LYPe6Pw6n5+wER7nEbDKhB2T/MSgMqj1b9Jkgcn9bdns+ec+3pGgs8ERX/BP60+eHicAK6ff2Gg2pSNt5nDahk6d9wxcACeuvxjahyh9Jq/xr8Kpg6sSA9mj9+bOTncqGK+Slt1tT0A388nNBypm2PW6tHJWVXVVM3jXCqxCno0MYjtG7SNlVHRKMO05uxo1Abp9MrTkkdpjdnL/S/aemwdeHqy3vc/D8m0uR2c+noTscelk4vEs0VIKBbJsDIAnrx2p9TwJRGao8iTm7Hvz9N/WtNojXnJtOm2XupYJl8lDZzSvpx+zGawA4BQc8tJ9RqdipD1TuUomrZOji1QtlvZOaTPP0WtaXJAYvoV76GsyK/j3lHh9OULovp6B7LPkntZzZ1o6VjNlPh8h/TddY44IHujKTn/12cMpb9G0g+cViLYvH8yPbjtHjwaprEOtOqMRupFEdKkPeiNRO28J832W5gBDtyyHvR4BrjHd5U3iLv0oC1XflZ29QmxqsPo7wn3jl/n9OByJ54x/Pl1Dp/DzrL+1qrgF6bT4+JPmX0AgFd/wxCQNfJ0KgCugzbUQgXEdDT8wvH3D7L6NKfV1m8rk3+8fxsYV4y58pA7SY1pnEsZstxwciKOwL6W+9lovH7B1GDnO2VOCpFBPRKbUuplx451rN3xSFaPW6zzpmLvuZGFtBTsrf3snNTqYRfHRtAEdCP7z9Fq3hOZEPUd2kH2r5gHz/wtqo6RasXpC9Z8BlcK/yROkezIA/LpWen0LB6U2zHp+zrNeaj03IqYWyzGVSi/pdhBPSWo+tRGt6khb5el1ktlf3M6LY4+iZex5UhoFvgGVlAl/u3D+GSNXcGmnFwEC0bt4X2rf6J0mVNpV4MAkqPtIV5aTKgqvIoF8E9otK4f1VKwaEExraZy+L5504F9MjqT/9uAO1cepA2ztpju9yi46MosPcK+nHbMR1WHI1NDSqgCzH5UCgbdmu4lArsyVi/TxWa23cFHf/ulFpbq3cqS3X5mSkfZ6QM3dCdts7Zoz5mulJyfZKdRu/oQ7WytnXogS4hB4Zt7E57lh+ineyhIx7nVgE9fqJ4tIFPJbXI39MmOIo4ufTPyVQpTTN6wqfIjFYgoIfMmJEFdEchXPqv7mLZ4/aSPe4V9nyrS/7x49o8e9Ued0pTGtcsMMI9bm/2Hr558ZY64m0tKy7N5DYz+Ih4xGukqwL6cBYH/vzfOVrQb4XRfjqW+zWwgC63v5DXrvF8muZXXl+lrL0xl47v+51WsqAjwrqcENgxb6/tPaRozUJUhMPwDKo+LsL5Wnl5lrKpIyweSpEPOwvOTKZyCespr8uIigjvH7AYtG32tzSx9WyHnpkV2pSkr2p9bljvcxk7BHSLBRhZQJf7dxTCRQT04wdOs2PINnViue/81rR98fe2MC9Fq3xKX/JH+cENpjld73bdm6f6EDF8B/fhSqnQrBh9Va2Azftc7WdWd6Y9q3+knUsOUtfpTSGguwIyiup8XfcLKtP0a+pSbKC6QtY8mWjmsTG0bPg62rv8IKXLlobkuR1QpL96LkppOqwOPefwV0uGrHF4V2VbfKM0rnaf9bb9e6/FHejmpds0p5fzk5G5Ps1BY/YMoJrpW4TxQBcNSoR7ORk0n5/P4sBnxAIBXf+sQUDXydAbBfSTh06z56/FS0KOqs7432gqE7+u5iM17gjog9d3p79+OUeLQy2K6bOnUZ7ysskU7+dOgS2UF4YczTFi8UYBvUep4XSWTytIqdCquIqt36O0ezHqRZwRj5te5SxHVe3LN/ywFdFoYPXxykPIXkD/+Jv3VQgY+fcT3/9B6dh+usxsqeKUGlVA3/ZkCfmGCnNkRLv3xD0/Z6+W8qmae6KraOnDkYA+YUdvqpwlJG5p3wVt6BzH7V821rWPhN/UKqRePgbWnWL5PTgR0J3Vb9yvCn1e7iMaUHsy3WDh9h0OqTRoWQca23YeBPRosBpHAvpHnONhYCjPm1XswTOo9gQOAfGn5jsUT6yJHHJtP79MLh/tOAZol5kt6CJ/UJePpFJCC+jy5wl7ByiRfzKHWRPPM/GQl/s2qoDejx0JvuATVSjEsfGX0aqR6w2JIiIB/eRB3uNOsnzgV3tcflkv41/H5T3uIP5AJV5piwausnERb/d5zOrAmh8jZOWKgF6iYVFqMqw2tfywm8Oj5oaYCC8U0OX0wtlfz1v2uCxYS76H7iVC8u44m5cNdxfwiYUx9Ot/eSZSpE9OKy7OoGr8kdFRSIHQ/cl+uDPvYa/y6YZpHeeFuZQ4nCz6eyoNqz2RfjtgEfyNWLY8XU5+cX2MeOseveeXvIcrk8K7YqCLgN6j4hgVG139fljQlj1mj0ohp4RdhSgxsrPkSk+DV3SkWf1W0sFNkTsJyO9jITuADG8yg37jsHJSukxtQhf/5v3MRMtJTwjortKPmnqOBPSJ3w+hSskb2S4oe7Jzv513eALH0V1VYacS0SG6fDXQ9s+yhspJzint50Y6ENkTTzo4lPZxLp/lIyx7H7E7ObF2m/e5EqawcocyJGG2Gufu5JEQX1FDNuJei9YqTH3Y2RHFfQIQ0N1np1p6u4AucaHWsfdFxWSNIo2N6gijVgFdkjcM4ReT+hyjKrI4rLV7ViZJEqll86pzmj3a3NsF9MKVP6U6HE+3TYGQL7+uAhTvcTl6JeFYIvJcbMYvl3LEVsU14CJHreQ/eTBW5QSTEiO4bPOv+R4qq3iif/3yjwoBtGX2HlrPyUmNWOCBbpm1p89fUcU03i2gtx1dl16yCDmzj2veh80GVqfKbYrz78HCKLZPrP9+D6+o6lvt+PcQ1rvNWf0XfIpE7qEYx4yU8j8ODVKwdD7q8A0LCP+9ABnuN+RlHuj2AvqsoyNpHift/GnrL5qmRl4Uhq7vRpc5+eH4VrMjbDv5u0Hq47W1+HB8SVl//+CPkp2LDVKngbpwsqb3C+dSJ30kYfOHRd+lKumMmfC35/LOVKxSfk0svbXyDI5jv3b0BkMOzxUBXe1xb82nikkauLzHFQ90SUpmDTMocMQDXbyWrR7GjoA5E9C/qPqZCtXRo/hgFgsch1IyxER4uYBeuEoBqtu7ijre72oRD/QxjafR0V2/qiaueqBb+89f8kPqwWGxqqcLu/+p3asyr7V5qEfJoa7eSoyst+nJcornBwHdWz3QQwvohfnjdJ0u5Tgs4CC3bbHpwGocMjQlDWs8I9I+ageUpbxf5qaeocT6yd/2peycjyJkP8N2J/uZo+eoc0n3HL/cHoiHGho5iagrAnrbSU3YOeMlzei6yCVi4oFekj9Gd/i8r62+eKCLo0dEeSekouyJh23pRZf/ukrj+HkeUYkdOzaHY1uk1l35IG+0UrhGIRrAOeRQ3CcAAd19dqqlkQV0WVy+ZJGkz39JaWQ8EsIltAf62/mz00g+Tlolkth+ESHUKqDLUcXD236hVWMjDlUg12oytJY6gjvUxZAgOqfY4829XUCvHlCOcvPxJ1dDtghgEcADZjQneeHty3E/tRz7t/dAt58w+Vq84NQECmChR8ISGbFAQLfMmtFDuCw5MUbFMj/DH3WkSAgXew/0oas60c/fnggTQkWLzTrzQLfvy1n9PAVyUB8+clvvva4qtqUhixcL6HI6a/XFQArgDxzO8pKEnrukqRPTMP5g/TsnspvexbWXEmt7ew90e5uo3aMiZX03E41oONWQ5oIQLiHTZuQQLhKves7vE6hcgrq2AclR8NAe6GqPu6MvVUnZ2GVbbTCwBmX7IAsNrDJGtVEx0DkPQJNcHeny39ci7CcyAb1si+JUt29V6lNmOP1z8oLL9xIjK3q5gF69awV6l70RIwvZYj8v4/iUz8H1R2j9f6d7JaldJz6lUytTK5emsAiHQmw+qh7VyxZyWi1h0gS0+OxU6lV6mDphaeSCEC6W2TO6gN4tsKlK9hk6j4+9B7rEO8/N664rIVsisun24+qr2NgStjCikjBpfFp0fDT1rjqeTv8X+sNRXXigR+/K4YqAPnRzT5XfYcNUx0lj7UeQlz8qDuBnfRVO2G0t038eSVtm7qZtHN7QUUnKyWmHb+1Fv/NJhWmd5kcKRWxv472F1IJPihkxjAtCuOi3eQjoOhkaWUDP9/X71GN+W2pX0OIpLF/mREB/eO8Rzef4qpLxvevsVnT65785ZpQloacnYqBLwsf8JfPS8LqTbfQ/KfUhdZvbhr3P24WLB9h+ShM6sPYw/XvqEuXIl5V681fEsfxl8AdOMmnE4o0CusTjlTkS++i3vCMnEZ1NP++0eNo4i4Eu4vmwTT0oiOP3juXY+i84rpkUSbRojQ3Zakx9unvjPsegDP9xxZGALjF4JdFpxpzpOJFtbX7AXaZJnGDJqAUCumXmjC6gT9jRi8Xxk7R+xm6K6xeHkrGIOWVvPxXz/Cp7NH7C4Yfaj61PTT7pTfduWjLFuxIDPbRd2wvicvRw5PoutGbqTiXM2xdHArrEYn/Kv593P81OrUfUoXmD1tA+/n0btniZgF6i3hc0rP4USyJnFnPyfJaTOhQZYJseZzHQU3OOiLE7+9CuJRyHdKIllIUUCb8i8UXF27wFz/uopoF05+q9cNNuL6CL147858uxRQuxMFSHk+oFfDOYLrEXjxELBPSQWTOygB7Xn19y7y+kniWGqvAbcrKnO3vxSn6d+ZznR+1xed8p4uOcnpZ45q7EQBfxfDInGpUkn2d4f1yvXzX6kE9FtsrXTfUhCUZbjmlAoxpModtX79pgRiSg1+9fnYpx4mcJ8XGDk9lZizX5qeF+Q14ooMtpg+84zFXm3Bmp/6oAGt9C4t1bEna6EgO9UrtSVKldaeWw9IzX7T7s/SenIwMDFqg+JHSPhAQYXneS+nO7yU1USJZT/IFTcg91X9CODm04EubUQzOO35+ZP8L3N2qy2VCGDQHdAsPoAnqjvpUpB6+Pw5vN5NwSfnTn2j2VRHQu7yEPbPiZMr/NCUAXtqEJHRbY9qPOYqBL4lDJDbRh5rdKnH+vYE7qHthMOaNY97Sthtfid8QHtHKiJfysFDltmfmddNSfwxFGViCgR+8TxpGAPpUTfkrM8ytnr9OnfAK2/dSmKlzKPdYBpDiLgS5x7hefm6ZCtW1lu/mUE9gGsJ7VkHMFiU3Ke9GonX1p9fjNah2XPbE4ce5atJ9zW2yxARGvd9kTF6qQn1JnSaXqilbRaEgtkpx9bT7p6XLot+ilHPbqEND1zwYEdJ0MjSygywLSm2MgFSyfnx7cfkh1OBu8COjpsqXmI9nJ1cvwfo4BFcjeaZI4R0pu9rqYzNmRm+ftGqG3W6qMKThu+ijOquxL8RL40aN7j+nbJQdUP1IacQJI8X6vnaW1jX4gfxncufA7/roYPsRG6/ENefH6hJJzkjxJ3CTJKbfM2q1z5qKvuTcK6Bd4U5Ptg8z0+N4TWsknCDaxQGgtNbtVoOqdy1K19C0dQk+QJD6t5yR09uXiGU7w9UFX9dcT9g2ga5y0dlSj6eHqORLQB64OUA/dq+eu044F+5XNvP4v5Ev0zbz7V4aAbmFndAH9wy9zcfzFppQ8TRIK7LWcTvLL8bT9A+jPY/9QDhYtL/NmcVr3pXQiVCzr4WsDlFAqLwuuFHtBXGWTvz1HvbBsX3Qg/O/HQcz0deenctZ5XxWLfTlvJn/aYfkYZtjiZQJ6tU5lVMLmVBmT84mxMzShzWxOjnTHNj1b7y2gmT2X8Tq8y+GUFSr/MQ1kEci+SOLRSe3nUb6v3qNR23pRozwBdIXXUPtiL6C//XE2FQf9OYfPOsHHWef1X0n/8kdLoxYI6CEzZ2QBXUZRLaC88uyOxSJMl6IDqG6fqiopmYQdUntcTkofGLAw7B6XxfHm7wdEeqKjFCcoqz+gOiVLk1SJ6OP4Y5MkJZUizimjd/dXL+tXODySlEDOJSQv6YlTJKKHdx6pv29XoJf6N4nBnj1v1nA/lxocrkOSlhmueKGALk4Y2fgZLe8zKzlXxKbpITmYavEHw+pdylPVSE7qypH/VuMaUHF2IPKN40M/bDpKE1rOYkePZ2p6Gw2uRSUb8XtRZotHusTYLd20mLJVeT/by4m9Fw1apcQcKfI+JMlOOxbuZ+xwP/8ZNwR0CwijC+gpeV3tv7itCpHy/cajNLL5LCWgXzhzlbKx4Cgh3kTk3jRnr21Zq8n7GfFKr5Yt4pjMAVMa04df5FJ2L++Cy8dvpT0rQ/JNiHPKNf74OKqFJRSd7LEX/DKSOnFIFtnHRlYgoEfvE8aRgC7e4meOnqWcH2VT4VSmdpincppZywiOjCDvRYND5QKyH4XoVR05JFqWPBlV0vuZHP7lx81HVTV5L9r5YoX6ELp97l4qxPHSB62zfAAPXcRjfRLvr6WvFuzIJwlOpfx24A+VjyL0B+/opajt6hDQtfFyVBsCuk6GRhbQHQ3dPoSLTjxo7oCAkQV0RxO65spMCp1EFJPueQIQ0C1MjS6g21uGoxAunrce9EgGFtDtZ69CqxJkHwMdM+xZAhDQQ3gaXUC3twz7EC6etRz0pggYXEC3n8W1nAcqdBJRzLLnCUBAtzA1uoDu8B3RLomo560HPRo5Bnq49yIWqe2TiGKGPUsAArp+nhDQdTKEgK4ToAmbQ0A34aTrHDIEdAtACOg6DcmszSGgm3Xm3Ro3BPQQbBDQ3TIhczeCgG7u+Xdj9BDQIaC7YTZo8h8BCOgwBS0EIKBroeW4LgR0nQwhoOsEaMLmENBNOOk6hwwBHQK6ThMyd3MI6Oaef42jh4AOAV2jyaB6aAIQ0GEPGglAQIeArtFkUD0UAQjoMActBCCga6EFAV0/LQc9eJuAHiWQ0GkYAt4moGN6o54ABHTvFNCj3nJwBUXAiwR0zGjUE4CA7r0CetRbD67gbSFcMKNRTwACuvcK6FFvPbiCNwnomM2oJwABXT9jeKDrZAgBXSdAEzaHgG7CSdc5ZAjoENB1mpC5m0NAN/f8axw9BHQI6BpNBtVDE/AyD3RMbtQTgIAOAT3qrcx7rwAB3XvnNipGBgFdP1UI6DoZQkDXCdCEzSGgm3DSdQ4ZAjoEdJ0mZO7mENDNPf8aRw8BHQK6RpNBdQjosAEdBCCgQ0DXYT6mbwoB3fQmoAkABHRNuBxWhoCukyEEdJ0ATdgcAroJJ13nkCGgQ0DXaULmbg4B3dzzr3H0ENAhoGs0GVSHgA4b0EEAAjoEdB3mY/qmENBNbwKaAEBA14QLArp+XOF7gIAeFVS9u08I6N49v1ExOgjoENCjwq5M0ycEdNNMtScGCgEdAron7Mi0fSCEi2mn3t2BQ0CHgO6u7aAdEQR0WIEWAhDQtdByXBce6DoZQkDXCdCEzSGgm3DSdQ4ZAjoEdJ0mZO7mENDNPf8aRw8BHQK6RpNB9dAEIKDDHjQSgIAOAV2jyaB6KAIQ0GEOWghAQNdCCwK6floOeoCAHiVYvbpTCOhePb1RMjgI6BDQo8SwzNIpBHSzzLRHxgkBHQK6RwzJrJ1AQDfrzLs9bgjoENDdNh40hAc6bEATAQjomnA5rAwPdJ0MIaDrBGjC5hDQTTjpOocMAR0Cuk4TMndzCOjmnn+No4eADgFdo8mgemgCENBhDxoJQECHgK7RZFA9FAF4oMMctBCAgK6FluO6ENB1MoSArhOgCZtDQDfhpOscMgR0COg6TcjczSGgm3v+NY4eAjoEdI0mg+oQ0GEDOghAQIeArsN8TN8UArrpTUATAAjomnA5rAwBXSdDCOg6AZqwOQR0E066ziFDQIeArtOEzN0cArq551/j6CGgQ0DXaDKoDgEdNqCDAAR0COg6zMf0TSGgm94ENAGAgK4JFwR0/bjC9wABPSqoenefENC9e36jYnQQ0CGgR4VdmaZPCOimmWpPDLT7kk70TdVPPNGV4fsI7L6U1o3dYPhxYABvkABCuLxB2N5xKQjoENC9w5KjZxQQ0KOHu1GvWqhaIRq0spNRbz9G3Dc80HVOQ/HY1XX2gOZmI7Dzyq9mGzLGq5NAcKrT5OsTW2cvxm/+IiiYyvrVNP5AMII3SgBr7hvFbfiLvUo0g+IkKGb4cXhiAHP7raQVo9Z7oiv0YRICOy/8YpKRYpieIvAyxWmKGwd73JevXlPpODU8hRX9mIQA9rgmmWgPDfO1X3nySTbOQ72ZsxsI6DrnHQK6ToAmbI4HnQknXeeQX6U8TXF88XLx5NlLqhi/tk6aaG42AlhzzTbj+sb7IM40SpqiuL5OvKT19J7LaP34TV4yGgzjTRCAgP4mKHvXNZ4lPUXx/X29a1BujCboZTCViQsnETfQmboJ9rimnn7Ng38aqywlSDNBczs0CCEAAV2nNUBA1wnQhM3xoDPhpOscMgR0C0AI6DoNyaTNseaadOLdHDYE9BBwENDdNCITN4OAbuLJd3PoENAt4CCgu2lAJm+GPa7JDUDj8CGgawTmoDoEdJ0MIaDrBGjC5njQmXDSdQ4ZAjoEdJ0mZOrmWHNNPf2aBw8BHQK6ZqNBAxsBCOgwBq0EIKBDQNdqM6gfQgB7XFiDFgIQ0LXQclwXArpOhhDQdQI0YXM86Ew46TqHDAEdArpOEzJ1c6y5pp5+zYOHgA4BXbPRoAEEdNiA2wQgoENAd9t40JCwx4URaCEAAV0LLQjo+mk56AECepRg9epO8aDz6umNksFBQIeAHiWGZZJOseaaZKI9NEwI6BDQPWRKpuwGHuimnHZdg4aADgFdlwGZvDH2uCY3AI3Dh4CuEZiD6vBA18kQArpOgCZsjgedCSdd55AhoENA12lCpm6ONdfU06958BDQIaBrNho0sBGAgA5j0EoAAjoEdK02g/ohBLDHhTVoIQABXQstx3UhoOtkCAFdJ0ATNseDzoSTrnPIENAhoOs0IVM3x5pr6unXPHgI6BDQNRsNGkBAhw24TQACOgR0t40HDRHCBTagiQAEdE24HFaGgK6TIQR0nQBN2BxijgknXeeQIaBDQNdpQqZujjXX1NOvefAQ0CGgazYaNICADhtwmwAEdAjobhsPGkJAhw1oIgABXRMuCOj6cYXvAQJ6VFD17j4h5nj3/EbF6CCgQ0CPCrsyS59Yc80y054ZJwR0COiesSRz9oIQLuacdz2jhoAOAV2P/Zi9Lfa4ZrcAbeOHgK6Nl6Pa8EDXyRACuk6AJmyOB50JJ13nkCGgQ0DXaUKmbo4119TTr3nwENAhoGs2GjSwEYCADmPQSgACOgR0rTaD+iEEsMeFNWghAAFdCy3HdSGg62QIAV0nQBM2x4POhJOuc8gQ0CGg6zQhUzfHmmvq6dc8eAjoENA1Gw0aQECHDbhNAAI6BHS3jQcNEcIFNqCJAAR0TbgcVoaArpMhBHSdAE3YHGKOCSdd55AhoENA12lCpm6ONdfU06958BDQIaBrNho0gIAOG3CbAAR0COhuGw8aQkCHDWgiAAFdEy4I6Ppxhe8BAnpUUPXuPiHmePf8RsXoIKBDQI8KuzJLn1hzzTLTnhknBHQI6J6xJHP2ghAu5px3PaOGgA4BXY/9mL0t9rhmtwBt44eAro2Xo9rwQNfJ0OgCetXO5ahWj0qUIEl8Gl5nEh1cf1gnEUtzH18faj+1Kb0HlzWDAAAgAElEQVQMekVT28912mdE9f3ixaVuC9rRuwXfpsQpEtGFPy7R7O5L6NjeE077jKkVjPSg274vAf34S3wa3OWmx3Gu35GQ1u9MTI+exKZ87z6jVvXuUKoUweGuM2dFUlq9NQntXPxvpPdw644PzV+dhI7+Fp8ePIpFvdrcpi8LPAnT5tpNX+o0KA2VLfaI6le57/ExRVWHENC9Q0BPnSkl9VrWkd7+OBv9e+oStcnfwyMmI/11mtmSsr6Xma6fv0HTOy2gn3ccc9h30ZqfU5/lncL9266F+2lM42mUv+SHNGJ7nzD/HvQ8iMrEq+ORe42OToy05o4MTEE5sgRRtTIPPIrq6bNYNHt5Mjr4c3zy8XlNXxV8TI2r36M4cYiePSMaOzsl/fG3H6+dsSlz+iBqVuse5cvD/+CgOKs/aV5y2rYvUbiWXZrfohJfPvbouKKiMwjoIVSn91xG68dvigrMb6TPqh3LUq1uFSx73PpT6OCGIx67bvF6X1KNLuWpeb5uLvUZUf2GA6rTVzUKUSp+Pty5epfWTt5GG6btcKnPmFjJSAL65Ws+1KZveto456LHUf5y0p9k/3r5mi+9lSmImte+S3nefmG7zpptiWnztwnp+YtYlCr5S2pW+x7lzf3c4X3I3nXqgmT0979xec/sQxnSBFGDqvfo8/xPVf1LV31p+uLk9M/FOBQ7NlHBj55Qyzp31fpuhAIB3TJLQS+DqUzcmkaYMpfuMSbseeVGteyRXRpYDKuEPS5RZHtc63T98XdcWrwuKZ36y49eviJaMPYKpUzO/4+Dcv9BbJowNwX9j9fx+P7BVKXUQ6pZPuy+/MJlX2rbPx0tmXCZkiQOr1/EMDOx3Q4EdP0zAwFdJ0MjC+j+Cfxpw90F1Oy9ALp67jpvumJR0IuXOonwxq18fmo7uQklSZWYds7f51RAj6x+ouQJqV7faiTizu0rd6hk46+oLv+5VoYW9OShZeNotGKkB11UCei7DiSgpRuS0JAuNyhFspc0c1lyOscvBlOHXAsznWu3J6IDhxPQ6bN+kQroD1kwb9M3nRLMSxd9RAkTvKJY/H+hH2j3+GHYc2Qa7v+1eumAgG60Xw7Rk2cvqWL82sa78f/uuMXo+iRr2uS2c1jEjE3Pnjh+WdYyQN84vrTo7FTaNH0nbQ7cSZ+V+5g6TG9ODbK3o/u3wouw8rHSP4FfmEtIfXkGLOi3QgnoHfnPrT4KJQq9Jnr8IOzHKC33GN11jbTmRpWAPmZmcnr02Ie6trhNj5/GomFTUtJ77zynlnXv0YOHsWnZxiRU/IvHaj3e+V0C9edlky9RgvjhZ89Z/eds1kEvY9kaBgfHovYD0lLHJnfoo/cci/LRbSOhrw8BPYSGkQV0Wec23JxHzT7syuvbDY/tcVOkS0bj9gygJCkT0a3Ld5wK6M7qNx9Rl45sP0b/soNIzo+y0YBVAdS34ig6vv/3mPSzcPleIKATXbziy2teOurT/iblzfWM9hxKSDOWJqV5Y67wGhtMB47Eo/mr2I76XqPkSYPpe/6wOZbX6JXTLpO/Hz9w7Yr099c/cendt59TPP73Xd8npCXrE9P6WReVXbfomY5KFnlEVUs/pMdPYlH/8an5A+hTqlfZsx9iXTYCjRUhoFuAeZuAHhP2vFr3yBpNN0ZUxx6XKLI9rkzSqb/i0sAJqZXjSP4PntDr17EoWZJXEX5kHDY1JQWztt6u0R26cduX+o1NTV1a3KICH1r2sD1HpuIPlnHp3gNfWjXtIgT0GPFLeHM3AQFdJ2sjC+gzfhlD2T/MSs8eP6Onj55R928G08RDQ2nxoNVUqV1pSpA0Pq2dsIWWDl1royTe6tW7VqCqqZo4JddiTAOK6x/HqYBu7ciV+rFZdNrxYgW1/rg7nT1+3uk9xMQKRnnQXb3hS817pqdX/ACJG+c1C35E62ZepCGTUqoHxY1bPnTyT3/KlC6IerW9SenThHzFLdckMzWvdYcqlnjkcAp6jkxNn+R9qjb7Up6woFOtdUaaxgK6eOpI+fZgfBZyElH7Rrf5PjJEKqAvXJOUrt30oR6tbzu8nvTfY0Rqql3xPh06Gp/SpnoFAT0m/jic3JORBfTCVQpQ3xWdedP2ml7yh8q5vZfR6+DX9EXVz+jC6cvqf188fUGTWs9SYoq1bHu6jGZ2XUQbI/BIzFskDw1Y15Wqpmyi+pYy7chI2sRiunzAdFYy5ExHUw+PoEZvd1CCuwjocnqoYc72zpoa5t+Nsubu/j4BjZ+TXAkivuwlnjf3M3X6p1rrDFS9zEMWXeIrT8P87z+jnm1u2Tb+KzcnptXbEtGawMsO5ySYHWMqNMtME/pdpZxvWdbXo7/5k4j1jtrIml+mUWZej69SjqyW+pEVZ/V37E9A2/cnokkDw34gddZvdP07BPQQ8kYW0GfwOmjZ4z5Xe9wamVrS2quzafX4LfQlr7cZ305HR3f9SiMaTLE5j+T+NAdNPjhUieLnf4/cK7lAmY+o2bDaTgV0K01X64/fO1B5yq9jT3QjFiMJ6I27pqcr1+OwaG3xHhzR47oSqg8dTUAZeW8rJ3b84r5mEeW2TTiRes72uPIB8szZuDQoIOT0ZqdBadXJn4olHrIXZBI6cdqfRve+rq4re9TKLTLRIvZkTJPSsUek1RYe87dsOb158gyv4T1vkKzvZRtn5v//us2DfdaypHyayPLB1AgFArpllrxJQI8pe169e2Qj/H68fY8rnuOdBqWjGcMvs0YQ3tHTlT1ul6FpqHjhR1SqqPNTkEG87a3cMjNNGnCNsmexnBqavTwp3b3vQ91bhayp4vVeqXlmCOhG+JF4+B4hoOsEamQBPWWG5LT84kyyjiFrnkw067dxSjTfPncvpc6ckoZt6UWN3umgvBOlyPH/ItUL0qBqY52Sc0UQD92JK/Vz8cvNGH65qJmuOTzQnc6A/gqOPNBFQD9/OQ41rXmPsmUOosDFydhLMTjMQ6XvmFRUhsOkFPrY8SmBNn3TUkk+yi8vEtbSMCA9e0PeVW0OH/enxWuT0qhe1+nh49jUMCBjpAK69JecvyRf5hehmxzKJQc/8MTbUcR4eRD25S/HxQo9Zg+dx+orNQR0/bYRHT0YWUAXXgGzW9GtS3do0aBVCl/FtqWo8dDaNK/PMiWaf133CyrT/Buqm6W1De+wrb1p68zd9MOmnx0iL9uiOJVuWozaFehl+/deSzrQTb7OnJ5LnE5TP/Z2vHjmivI+lyIC+tDNPenezQf0+N5jOr7vd3V/j+/DA90pTA9UcOSBLgL6O2+9oLqV71M8Pko6gL0L61a6r9YzKft/jE/fs7jer+Mth3cggkuVlln45eOK7QOlfACt3zkjfxS9wOt3WI9HeVnpMSINLZ9y0aEHuv1FIqsv62/jbhkooNltQ3ify9ggoIfMsJEFdLXH/Wc6FY9byzYgEdDPHD1Li9kxRET1Ieu705Lha2nngv2qTuZcGajdxMY0ruVMuv5v5KHrXBXErRd3pb6ELVx2bhoNrTuJQxWe9MCK8ua7MJKA7iiEy8ZdCWnh2mTUqNpddvR4Rnt/iK8+AC6ZGPKB0tked8aSpHRTvBZDrcnjZqegBPGCOVzhXQ7rwuL2sLT0bo7nVL3cAzp8LB47gfhG6ARinUUR3pesT0rvZHtGQ7vepMSJLMK/iDu72Su9QdW7lDvHCxo5PZX6yGoVf968FWi7IgR0Cy9vEtBlPDFhz6t3j6zNkqOntlEEdKHjzh73wpU4NH1RMurU9LZ6f7cvzva4Ur9Ky0x8Sv0x/faHPz17Hos++eAZdeZ9qf3+V+rKiZ9mPTLQxtn/kr+/5Wrb9yXk50ACmjzIoodJgYAePfYeE64KAV3nLHibgD6RPW8qJWtoozL/9CQK7LwgjEekq8hcEcRD9+WsfryE/jTp0DDat+IgLR+x3tXbiHH1jPSgi0hAl6P/lTkemJR9/HKxbkcimjI45KHiDPr8VUnoh/8loIGdb6jYj2fO+dGgSamUyJIxbRANnZJKeQLJUVfxLHcmoIv3eoVvHlLF4g/ZK/M1LVqbhA6yB9H8MZdp8oIU3OcLjl1muV8jCujBKU+Try8HtjR5ecE5Fcr6hYghRsPh6GXio28+oAGVR6uhJE+blFZemU3lEtSl5+yN7kqp0qksfV7pU+pSdICteudZrZSX+5R2cyLtQsIFjNrdj+pna2sTyCXXRKqMKejB7YcckzcFNeewMxKXd0iN8a7cToysY6Q1N6KXi1E9b9rEkIlz5aPla46pe89l3p0Hp1GxHts3vMOniTiEAIfGmjgvZTgBXTwhA4akpSL8olG7ovPj/87qSxiuw8fi2zwtXb7haKwYnHgW+cYvGo13EHMuPa//Slo+0pj7rYgE9O6lhtLZXy05VTpxuCr5ODi711LN0F0RxEN36kr9rvyRNUX65NSr7HDN9xNTGniDgH78VDwa0MnyAeX23dhUp0OmMGKKM9ZHf/OjIZNTcR+36P13ntHVm3Fo/OzklCv7CyWgy4fF8XNScBzeWOpU0ZXrLLZ3uMWhBZyHuJI1d/XWxOxoEp+mDr6qYp6fOO1HgUuSUdaMQeqUZf73n1LXlrf5g2v4cDDO7j06/v1lij/4pCsfczV5eckx0Et7UQz0mLDn1bNHNoo5Yo9LFNkeV0KwtOqdTp3o/IBDaj3m3GujZ8hp+lfUp114x5O/z8ehtv3S045F5ylWLEs4wr2H4tPyTUlo9qirNrMwqoBO/hUodlLnjrBGsf/ouE8I6Dqpe7uAPu3nURzCZQ39sNGx92Nk+JwJ4vZtI6sv4rl4Yl7+6yqNaxaoc9ait7mRHnSuCOg/sefMwjVJKHCY68fz5ci/JDza92MCNRkfvfeUfmKRRY5L3eKXlUETU6kwBlIkKsVLjqcrwnj/jjfoU/YIsi/iodm3/S368F1LTGlJclepRWaaMugqTV2Ygs5eiGtr8pJPf8nzMFf25zShv+uif3RaDQR0C31vF9AlL8Xmh4vVR0xXPb7Fu6Zko6LUoVBI4k/xQJfYvLN7RO6BPnJnX/rtu1O0bPi6CM1bEjiP/24wlQrlxRmdvwV3rm2kNdcVAV2O57/iuOKtWYhxtUiS5Qks2Px2xk95QebJ+ZyOnfKntTMu2l4QRJjpNzYVpU/7Un3MtL44RHQNZ/Xl3xsGZGAR6QbHW3ftg5Cr44nKehDQQ+h6u4DeYlQ9lfQ+sMtCzSbliiAeulNn9VuNqU95v3yXuhYfzDknjJnjR8brbQK6VSRZO+MC59dxXZDesicBCy4SSiU2vc0niCQPTzk+mSnOJ/NWJWVh3oe6scgt5Yf/+SvBfepgCRngPGyW5JmQsFwi5iRN9IoadsnAIbeuqFCKD/l6w6elVInvIjqVpNnYo7gBBHQLYLMJ6G9iz6tnjxzFZu+x7rHHJYpsj3v+UhwW0DPQ9oXnbdqChDEUZ70Ns8OHarN6oG+Z968tVKJ4oG/blyCMsyAEdI+ZsOE6goCuc8ogoEcM0FMCetLUSWj4tt70+6EzNK3jPJ0zFv3NjfSgk2SfBw7Hp6HdQo4ySwiX0B7o7gjo9rNw8kxc3vCnUkdkxZsmdHHFA73DgDT09edPbCFh5HhWRX65WDThUrh4kkb0QH/FHuhx4IFu+CSizrxx3HmZyFuUY6Cv6UpVUja2/WymHx1FW2bsom1z9kS44ElcyL4cvkW8zyUPRkTl4+J5qScL8tXTNI3+xdPNOzDSmjtmZgrKkuEF1SgXEt5KPhCG9kB3R0C3R7dsY2KSl4rebS0Czt37sVWoqzycpK51vTtOxXNX6stJoD/PxQ3z/HBzCt9oM4RwCcHtjSFcQnugxwQBXXL7BMxoQRlypFXJQ40snovlGElAl7ApzTkB5+a5ISKKhHAJ7YHuroAeetGSxJ6Nu2ZQSUMzpX/JCehSK2/zamVCTvlIPHYJzfVNYecxekWMr9k2Ey0cf4mFI1/qw2ETN865ZLukeEsu5DCIC8dfeaNrp7sXQwgXCzkzhHAJferyTex53d0ju2vL0dEOe9zw1EPvcWUNlxAu04detYUxPHw8Hk1bmIx1gvBrpJwQEie8yQOv2j5ozuQk0Hc5YWjP1iEe60YV0J/GKksJ0kyIDlP1mmtCQNc5lWYT0D0RA731+EZ09/o9WjFqQxj6jgR3icM+bt8g2rVwP60et9lW/1XQS1vSJ51T+MabG+lB98tJfxo7KwV7hluOLKVKEaySiDoT0J3Fh5S+rlz3UcdLT/3lRzP46GmTGvfoq0Lh4yw7EtClvmTPrlne8vKxbW9CWsSxIYex0J+BQ8DMX52U/v4nDo3rdyPc/EJAf+Mm77ELemMMdGcvE85ioMeJ60uLOc7vuv+zdx7wURVdG3+AkIReQ2+CqKiIiDRfUAHpvffee++9hd5771V6B0U6SFEQRERFkd57Cf2dc9fdtE12b+4mZPc+8/38fIWZuTP/OTt35rlnzozfrMVKz1MqJzrNbon677bBnWv3tEv0mqsLnUeoi/Juq1As1jTxkD/2rDyo3XkRNFXrWh7X/rmB3w6dhYRz6TK3FU7tO6OF8nLX5E5z7vxvE+Hcv95a/NqAZzFVGKtX2iWi4QnojmKgy7iJN7gs9iVkwKGfLEdRR/e2iDnXVTz0bv7qgqWCj9XFzoGCjlxkGjs21CV1b9BrZEpUUn8nJ4Ac5Zfn3XsQAw06p8PIntfwXmbHHpXRybYooAeOhtkEdFfEQC9W7yt8rj48+tedGMys7Xmgi3jur+6ceB7wAqObTlf/tpzUkAumnQ3jFZ1+O9IWdxLQxZO7ohJWhnW/gczpn6tQeYA4jjgS0J1Z48o9E74+b3DpmhfmLE+MDGlfanfzSJJLRncdjKfWrNeQIvlryFpbvCFF4Enl91Jrw7FTvrYPnCLqv3kTQ4nuT5WTyRsV8zypdqHduH5X1dyu7glSHug1y91HpRIPtPfGMOWB7pdMhexqYHledE8U0C0jRAEd2mnz8O790bvmdZQ/uv82nGmfp69xHcVAd7TGlb8fNjU57qhTP73a3NQuXvafnByfZHuO+lXuhVrjSn7ROuS0ehs1h4p3e+9RKdGu4W387/PA02EU0J2xTs/MQwHd4LiaTUCv0b0CqnYph8p+jcIkJyJ728mN4RPXR5t8Ah4/w/gWM7Fv9Y9amXH7BuP6+ZsY/t/mIrz8X5TPjYHqkqeQaZMSiia0nGlw9N5OcXd60YlwIi8dCa+SMP5rLJlw2SkBvUyjDGha447yCH8UJuRKzdOpxWIMZFEXkVYvew/5P7PvBWtPQO80OKXadCgvnpaBt2FLTMh1OxIokSimFhKmdb07SmS3XLAUNFFAfzt274qnmlFA3/J0KWZ0WYj1U7aFiTBb3qxoP60ZMn6UTl18dwszVDiCQxuPaflzFsmOkd/1Q/2sbXHlnCXMksyr7aY0Qd0sbfDiWXBxs3iDQqjcqQxSZ06pXSK6d9WPmKPiA7urmCP9dac59+btmNpR/nMXfFAwzxPN28WRgL5iY0J8uyUBVk0LvOQupLGcVZ7gEtvcx/u1igH5DA2q3lXxclU8K5UOHIuDQRNShLKvUoUeaoKPhNwq1SAjOjS6hZKFHjvMLxVJPN6bSkDqp2IAu1uigB44YmYT0LOpi+onqruAmubsivOnQx/tFjJyR8T0o8MRK7YX4sTzwSM1T36/ZB+mqXlaUoOB1VFcieg132ml/Xd4+eMliot1N0OfrJSLnRtl7+RuPx2tve4koEt7V6m5UwRtEahH9bqG03/4OBTQnVnjSijCwyd8kTrFS5RQFz5XLnnfFj5A5lTxEBcRXfkDaRfjNap+V5ubJcmH1B3qUtClEy1z+k9KTF+yLpF2akhOaeb8KAAtat/R7gmS9Ps5b8xSob0uXfNWscTfKJHnCRqqOd7Hxz1MiAK6ZZwooCuHqEhY84a3RnaPX0j4rfT0Na5cVN9hYGpM97+sPMgt69aQKbw1ruQVJxJZlx76Oa460f1GcxipV+me9tE05BpX8sspSwl7+PNpXxUO643SMx6oE0KBDiat+qTCjduxVMgsLySI/wqp/V7ougvubdodPdCN06eAbpChOwvoBrvO4hEk4E4vugh2kcVcTIAhXCxA3V1Ad7FZsDonCXDOdRIUs2kEKKAHGoI7C+g057dDwN0E9LdDiU8NSoACuoWGpwnotPKoIcA1btRw9pSnUEA3PpIU0A0ypIBuEKAJi/NFZ8JBN9hlCugU0A2akKmLc8419fDr7jwFdArouo2GBWwEKKDTGPQSoIBOAV2vzTB/IAGucWkNeghQQNdDy35eCugGGVJANwjQhMX5ojPhoBvsMgV0CugGTcjUxTnnmnr4dXeeAjoFdN1GwwIU0GkDESZAAZ0CeoSNhwXdKkwhh+vtE6CAbnwMKKAbZEgB3SBAExanmGPCQTfYZQroFNANmpCpi3PONfXw6+48BXQK6LqNhgUooNMGIkyAAjoF9AgbDwtSQKcN6CJAAV0XLruZKaAbZEgB3SBAExanmGPCQTfYZQroFNANmpCpi3PONfXw6+48BXQK6LqNhgUooNMGIkyAAjoF9AgbDwtSQKcN6CJAAV0XLgroxnGFroECemRQ9ew6KeZ49vhGRu8ooFNAjwy7MkudnHPNMtKu6ScFdArorrEkc9bCGOjmHHcjvaaATgHdiP2YvSzXuGa3AH39p4Cuj5e93PRAN8iQArpBgCYszhedCQfdYJcpoFNAN2hCpi7OOdfUw6+78xTQKaDrNhoWsBGggE5j0EuAAjoFdL02w/yBBLjGpTXoIUABXQ8t+3kpoBtkSAHdIEATFueLzoSDbrDLFNApoBs0IVMX55xr6uHX3XkK6BTQdRsNC1BApw1EmAAFdAroETYeFmQIF9qALgIU0HXhspuZArpBhhTQDQI0YXGKOSYcdINdpoBOAd2gCZm6OOdcUw+/7s5TQKeArttoWIACOm0gwgQooFNAj7DxsCAFdNqALgIU0HXhooBuHFfoGiigRwZVz66TYo5nj29k9I4COgX0yLArs9TJOdcsI+2aflJAp4DuGksyZy0M4WLOcTfSawroFNCN2I/Zy3KNa3YL0Nd/Cuj6eNnLTQ90gwwpoBsEaMLifNGZcNANdpkCOgV0gyZk6uKcc009/Lo7TwGdArpuo2EBGwEK6DQGvQQooFNA12szzB9IgGtcWoMeAhTQ9dCyn5cCukGGFNANAjRhcb7oTDjoBrtMAZ0CukETMnVxzrmmHn7dnaeATgFdt9GwAAV02kCECVBAp4AeYeNhQYZwoQ3oIkABXRcuu5kpoBtkSAHdIEATFqeYY8JBN9hlCugU0A2akKmLc8419fDr7jwFdArouo2GBSig0wYiTIACOgX0CBsPC1JApw3oIkABXRcuCujGcYWugQJ6ZFD17Dop5nj2+EZG7yigU0CPDLsyS52cc80y0q7pJwV0CuiusSRz1sIQLuYcdyO9poBOAd2I/Zi9LNe4ZrcAff2ngK6Pl73c9EA3yPDeo+cGa/C84rFjxYSXVwy8evUGz1++9rwOGuiRV6wYiBEjBl6QSyiKvt6x8OzFK7x5YwCwhxZNEDc2YsWM4aG9c75bMp88CXjpfAGT5JTfjppWEPD8tfr98AcUdNg5r9j/EcRUBhPbK6Y25zIFJxBLrWESxPEiFkXg4dOXai3HdVxQY5B3sXfsmNpaJeA5fz9B2ch7yCd2LHKxM3uIzci+6NVrvqND4onr6wVv9T4yexLbePjkhdkxhOq/zCkxlXnI3vml+g0xBRKQ343YDeeV0FYRxycWnj7jO9re7yVxfG/+jAwQoIBuAB6LkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJeC4BCuieO7bsGQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgAECFNANwGNREiABEiABEiABEiABEiABEiABEiABEiABEiABEiABzyVAAd1zx5Y9IwESIAESIAESIAESIAESIAESIAESIAESIAESIAESMECAAroBeCxKAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTguQQooHvu2LJnJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACBghQQDcAj0VJgARIgARIgARIgARIgARIgARIgARIgARIgARIgAQ8lwAFdM8dW/aMBEiABEiABEiABEiABEiABEiABEiABEiABEiABEjAAAEK6AbgsSgJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIDnEqCA7rljy56RAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkYIEAB3QA8FiUBEiABEiABEiABEiABEiABEiABEiABEiABEiABEvBcAhTQPXds2TMSIAESIAESIAESIAESIAESIAESIAESIAESIAESIAEDBCigG4DHoiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAp5LgAK6544te0YCJEACJEACJEACJEACJEACJEACJEACJEACJEACJGCAAAV0A/BYlARIgARIgARIgARIgARIgARIgARIgARIgARIgARIwHMJUED33LFlz0iABEiABEiABEiABEiABEiABEiABEiABEiABEiABAwQoIBuAB6LkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJeC4BCuieO7bsGQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgAECFNANwGNREiABEiABEiABEiABEiABEiABEiABEiABEiABEiABzyVAAd1zx5Y9IwESIAESIAESIAESIAESIAESIAESIAESIAESIAESMECAAroBeCxKAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTguQQooHvu2LJnJEACJEACJEACJEACJEACJEACJEACJHEYQlcAACAASURBVEACJEACJEACBghQQDcAj0VJgARIgARIgARIgARIgARIgARIgARIgARIgARIgAQ8lwAFdM8dW/aMBEiABEiABEiABEiABEiABEiABEiABEiABEiABEjAAAEK6AbgsSgJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIDnEqCA7rljy56RAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkYIEAB3QA8FiUBEiABEiABEiABEiABEiABEiABEiABEiABEiABEvBcAhTQPXds2TMSIAESIAESIAESIAESIAESIAESIAESIAESIAESIAEDBCigG4DHoiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAp5LgAK6544te0YCJEACJEACJEACJEACJEACJEACJEACJEACJEACJGCAAAV0A/BYlARIgARIgARIgARIgARIgARIgARIgARIgARIgARIwHMJUED33LFlz0iABEiABEiABEiABEiABEiABEiABEiABEiABEiABAwQoIBuAB6LkgAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJeC4BCuieO7bsGQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgAECFNANwGNREiABEiABEiABEiABEiABEiABEiABEiABEiABEiABzyVAAd1zx5Y9IwESIAESIAESIAESIAESIAESIAESIAESIAESIAESMECAAroBeCxKAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTguQQooHvu2LJnJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACBghQQDcAj0VJgARIgARIgARIgARIgARIgARIgARIgARIgARIgAQ8lwAFdM8dW/aMBEiABEiABEiABEiABEiABEiABEiABEiABEiABEjAAAEK6AbgsSgJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkIDnEqCA7rljy56RAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkYIEAB3QA8FiUBEiABEiABEiABEiABEiABEiABEiABEiABEiABEvBcAhTQPXdsPbZno6Yux9pt+3BwwxSP7aOjjr169RrjZ63C5p2HcPvOA7z7Tlqsnj0It+7ch//Exfjx59/w5EkAqpUrhGxZM6LPiDmYPqIzCubN7qhq/j0JkAAJhEnA1fPvmi174e0dG2W+yR8h6jVaDoJf0kSYNLR9hMqzEAmQAAmQQGgCF6/cwIbtB1C1bCGkSJ44yhG9fv0G0xaswxe5P0bOj7NG+fP5QBIgARJwhsDla7dQrEYXNK5ZCp2aV9OKyD785G/n0KxO2WBVrN26j3tyZ6AyDwlEYwIU0KPx4LBp9gm4WsBxR86LV3+HkVOXoW2jSsiUPhUCAp6jbLEv0K7vRJw68zda1q+AxAnjI348XyRKEB/bdx9BpVJfanmZSIAESCCiBFw9/9ZpMxQJ4sfFtOEdI9QkCugRwsZCJEACJBAugUPHTqNJl1FYMaM/Pn7/nSin9fLVK+Qo0hhdWlRHwxolo/z5fCAJkAAJOEPg/sPHmLN0M3J/ms3mqDZh9mosWrUdx7bNDFbF6bPnuSd3BirzkEA0JkABPRoPDptGAT0sG+jQbzLuPXiE+eN7BMvyRbnWqFOpKFo1qEDzIQESIAGXE6CA7nKkrJAESIAEoh0BCujRbkjYIBIgATchEJaA7ibNZzNJgATCIUABnebhdgSsAs7MUV0wee5aHD3xOxIljKeOmX6NFnXLIUaMGLY+Xbh8HaOnr8Dhn8/gxYuX+Oj9TMpruzLy5PzAlkerTx2p6t+5AeYt34Lfz11UXtvxUL18YVRTdc5cvBHf7T2GBw+f4MP3MmFg14bInCG1rfyLl68wS+XZsOMgrt28g5TJk6Bwgc/QWonY8ePF0c3XUZubdR2NA0d/DVbvqL4t0Wv4LK2PQdOeNRNw5s8LaNF9jBbi5YN3M2h/LflmLNqITd8fsrX58xzva0fNMqZLqeXZsvMw5ioe5/69goTKQ1SO0HZqXhUZ0lr+nokESMB8BFw5/4r3+fFf/wwGMd9nH2LO2G7an0lIKglVtefQCTxWIanSp02BGmpeln+s83xEPdB3HzyB1r3GY8GEntj6w2FsU6d0Hj16ii1LRiBtquQ4eOxX7f1y5q8L8FEhZr74/CPtaG661H7B2iungZat24nLV28iSeIEKFkoL9o2row4vt5avidPA5C7ZAs0rV0GDx89sbxL1L+zf/AOBnRpiH8uXMWCldtx+uw/SJwoPqqU+Rqt6pc3n2GxxyRAAlFK4N79R5iqQqTsOfQLbt6+h5R+SdQa+R20a1xJzWe3NO/zkGnRpF74LPt7qNpsgDZPVihRQKvj7F8XUa74/zC4WyM4uyb+Vc15k+aswc+n/kSsWDGRVYUibN2gIvLl+hBW7/OQz5dTly3qlYtSTnwYCZCA+xOQ+aZ684GYMLgtvimYS+vQn/9cQoWGfTC8VzPtFLekv9WarGy9npg6rCO+yp8D8iFx7MxvcfX6bTx6/ERb5+XJmQ3t1TovjZoDJT17/gKfFWuK7q1rol7V4hDxXLSDoClmzBg49cM87Dt8KtiePOgaMeDZc807XfQG0Sv6d6qPLJnSBqvn+30/YaKaN/+9eA1+KrTW/1SIq1Wb9kQoTKt1Pb9wQi9trX3op9NauK6tS0Zqz3S0vrWucaXs9t1HcV859qVPkwJ1KhfVNBRrsq63h/Zogp2q/T8qTUZSoS8+Rc92tbF+2wGs3rwHF9U6WtbYstYu/L+c7m907IHHEqCA7rFD67kdkwl/oToW5evjjdJF8iNThlT4UU368lISIblUkbxa56/fvIvKTfohqXrZyfFPyS9CucQlmzW6K/KqF6AkqW/+ym1aeBPZOGRKn1qbyJes+V4TTop++bntRTBgzHx4x/bCqlkDbYDb9p6A8+pF1kjFPkubyg+Xrt7AlHnr8KF6+U0a0k7XQDjT5r/+uYw+I+doInjv9nW0+t9Rgr60oWGH4ShT9AsVrqWg9ufZs2VRbH4L9rJ+8+aN+u+xOKI+PDRSXD5WQs6Va7exdO33qFelmNbXBd9ux/QF69G0Thnto8Gjx0+VyLNNy7d+/tAIfRjQBYKZSYAEoiUBV86/f/x9Cd0GT0e8uL7o3MISN1LCuWR9J51aiD9WIk1/ZM6YWhOV5c8lnuSU+evQWS2u66q5SpJRAT1uHF81x2VE/lwf4fmLF2rhX0yJ2efRqudYfJkvB8qq+VTE+9nqeK5sdNbMGYxkSRJqz5ZNg/y5bBZy5/gA/1y8qn2Y/OTDzJit3jEi8ls3R7J5qlnhG22T9kgJ6D38Z2qCekL1sVbeOyJc7f3xF61OiefOzUO0NH82igQ8hkCjjiPUevWmCvlXXokmSdQa8ipWbtyN5nXKaWEIZL08YsoyDOraSK0xLeH/3sucXlv/iYAua04vJXyXKZpfK5/KL6k2vzmzJj5x+i807jRSze1foUCeT9RcCU2AWafuN1o2rZ8WMkacYxqoNW3NCkVs6/rUKZMjdYqkHjMG7AgJkEDUEJA7FeSUdmUVzrRrqxraQ6cv3IBJc9doTm/W/brMe/1GzcWhjVO1uU6cHvYdPqn2ypk1PeHGrXvafthHNAW1HpSPfyEFdBHbReQWMXz2mK7/dTCG+viYNUwBXdaL4ghYXn2IlLCswyYvwRvV5nXzhkLWj5K+3bQbA0bPR7GvPtfWp0+ePsP3+47hyPHfIyygi0ju7e2Fr5WYLfO7cGpet6xT61u5j03maPkQ0Vw54ImTi2gO4lTSplFFtKxncQaxCuiyjhcHEfkAIevsAWPmIbaXF97Lkl5zgJSPuLOWbFb5j2PH8tFIru43YiKB6EiAAnp0HBW2KVwCIuCs2PCDJmRYvaFFFP6qUntNBBnRp7lWXi7TlAvqdiwfo730JMmLoWLjPvD19tbiOkqS+lYpwXzf2onaZXaS5KWUt3QLVC9XGH061LW1Z+WGXRg4dgH2qrwiooho317FHd/57VgkSWR5hqRdavJv02sCxANczwvA2TaLF7q8sMV7Mmj6tGgTNK1VGq0bVrT9cciv3T8cOK5tcEb2baE+QOSz5ROPnzt3H2ofDQpV6YDxg9poL2hrEq/J/6nFx4jeLWybGZoqCZCAuQi4ev4NKwb62BkrtY98S6f0tW0ehPRIJejsUULz5kXDNfBGBfRe7eqgdqVvgg2ieCT5Kg/y5UrIsSY5XVSiVjdNLJeYvOIdX6RqJ83byCr+S14RgToNmKLFdLdscCwe6B2aVtG80K1J+jdn2RbsXz8p2LtD3mNFlHdUv471zGVY7C0JkECUEZA1bu6SzbW5q1GNUrbnylpa/k4+aoYXwkUEdPngKB8K/ZIFXjDq7JpY5m059ShzadBUpWl/7XSOnAhlDPQoMwc+iARMQUD25bfvPcCyqX21/so8JvqAfKw7sGGKdnJwoHKU++2Pf20agT0w1n206BDvK/E3pIAuZcIK4RKWB3rINeJGdapdHC22qlORonWIw4VcVPqVErrFY96aflenJMVZcPqIzrb4684Opqznlyqxe+HEXtq8a03Orm/l44KElJ0+opN69ie28kPGL8Jqpb/sXjVeixBgFdCXKu45PsxiyyfvgWfK637VrEHahwhJciqzjDoBMKZ/K5QolMfZrjAfCUQpAQroUYqbD3MFgbBi8NZt6w8vr5iYN84SF7xc/V7a8SqZ2IOmqcqzesq8tTiyxeL5GFZ9eUu31Lxjura0fKmWZBXGrS9N/4lLlKf6d4itvNKDJbUJkWOsiyb11r44O5ucbbMRAd36Yvt5+8xg4W6sbZTjYe37TgrdJ5VBvN7bqWNr8nWaiQRIwHwEXD3/hiWgV2zUB3+dv6wW1bGCQX79+rX23yd3ztX+bVRAXzdviObxbk137j1EwQptQwlL8vcNOw7XvNFXzhigeRZ1GjAVy6dbBB9rEo+cnMWaaB7y8u6wCughhfqF6pSPeHce3ToDceP42MqLgJQmVTJMHKzv9JL5LJE9JgESiCgBEcqLVu+MV2o+bVq7LHJ98h7eVaECrCKG1OtIQJcQLuJoETQ5sybOkjGN5gnqpeb2GP95VlrreKnWzXk/y4Y5Y7pRQI/o4LIcCZCAXQKy7hLnhcNq/3/3/kNtDpRwJRUa9oZ/z2aaZ7eI0flVyD7rxz1xvFu/fb/miS7i7kMV6u/Rk6fafti6x3eFgB5yjXjsl7Oo336Y5ignHxutIrTcfZb708AwtEYF9LXq1M9B9fEgaHJ2fTt43EKNTciLUq1tt4bBsbY95HpbnPmu37qrramtSdbYeUq1QM+2tTWHFSYSiI4EKKBHx1Fhm8IlEJaAE1JU/rJiO+2LqMTcCppWrP8Bg9Sk/506HiQCe1j1yQK/YomCtqNeUkfIL8ddBk3TvlzPC3GZp/V5qVMks8XCdWZYnW2zEQFd2vyLCoUg/beXrF72s0Z3QSrV/pApiYrTG9Tb3pl+MQ8JkIBnEHD1/BuWgC6e2NnVkdlO/4V2CUnPeg+FqwV0a/xLeW9IfN+gqfPAqTh55m9t7gz5HgmaL+g8HpaALiHC5MTR0a3TlYDuayse0f54hnWxFyRAAlFFQI7dj562QjtyL97eMg9JOJZurWpq69aICOjOrImv3biteRiKM0ZRJViFTHFUaITUKZNRQI8qQ+BzSMAkBM6qO84qNe6reVzL/96y80csntxbOzUoH/QGqnBVcvp8in8Hm0d196EzsOm7Q5q4XkjF5ZZ9/cUrN9B35FwloFvuhIgMAV326bVaDdacAuXeNjlRL88UwT+DCpViTZEhoDu7vg26Jg5qQiHX0WEJ6OK9fvnaLXw7c4CtuD2WJjFPdtONCFBAd6PBYlMtBJwVcCze3Mm0Y01B01QVQ1fi6DryQHdGQJevrytUWJcjW6YFE0EiOlbOttmIgC5tlhhvP4XhgW798hzySFZE+8RyJEACnkPA1fNv3bZyp0JcLeRJ0CRzoXhmi4d3eCmignNYC3qrB7pcYtRY3WsRNEmsR/GOkcW+zUNHhXnJni2zLZvNA13FUpc4mxTQPcf22RMS8EQCcnGdiEm7VHi/WUs2qYs8K6CV+keE9cadR2qhDCQmedBkvUQ0pAe6M2ti6xwroWOChr8KyVbm0k+KNNI8QeUeIyYSIAESMEJATt4UUCcMG1Qrod2HJk528r/lInm542zcwNbaHWES/1xOqEsoE3HmkPB7EmLFmn4+9Qfk1Ht4AvrEOashHu8hvbPDCuES0gM9pIBuPQEv60+5m8yaIkNAd3Z9K/P9um37NT0haLLeXyEfIiS2OgV0I1bLstGRAAX06DgqbFO4BJwVcIZOUDG4NksM9MCLKGRBLqEB4vj6BIuBbu8IkzMCujXcSTN1eUb7JpWDtVuOeokXjVxe6mxyts1GBHRrjN6gF65K++SYmiwW5LKSb6p1UhfhZcH88T2DxR+Wy0Tl2Jvcss1EAiRgPgKunn9b9RyH23cehIo3aQ0FIDF25Tht0HTmz3+RLWtG7Y9cLaBLneXVcV7v2LHVsdL+tjBXV5SXTKk63VFHhWaxxkAvXLWjip9eFN1b17Q1b7PyaJKLUUPGQA+5OaIHuvl+O+wxCUQXAvJhTz4GBo1fLm0rXrOrFi5ATuDIJW/Vmg+wzWVB2x6WgO7smljW4ddu3MEmdZeF9VJm6zpUPOMlrrAkuT+ievlCoWKlRxeObAcJkIB7ERCv59t376tL6f/GFhVfXEJRyXz4v/Jttfjc8kHRev+NeEdL3PEh3RujYsmCto4eOPorZB8enoA+f8U2jJ6+IpSDXUQFdLnwWeZn0RpEc7Am+RAgFzJHNAa6Pf1DtABn1rc79hxDx/6TQ118LxedrlOhXfasnhAsBnrIEC70QHev3w5bG0iAAjqtwe0IOCvgyKVvclQrccL4mvdKHB8fdanFHkhsrlmjuiJfrg+1vhsJ4SKic4vuYyAv07LFvrAd+ZKjr5u+O4hdq8frCnfibJuNCOjyEaFeO3/8pkSohtVLaEKUbGTEK716uUJqs1IYMxdv1C5AyflxVi2MgVwC8uvv/2gfJPqqy+2Kf53b7eyGDSYBEjBOwNXzr8w1E+esQdtGlfBOhtTaxXRlvsmvfcyr3nyg9sFOLur86P1MuPfgEb5TC3aJmbh+3lCtM5EhoO859Ata9xqP/+X+WJvXH6sPh7PVhZ9y2ZHcf2G9GNp6EWjNCkW0mJTy0VQ8OD/5MDPmju2uie/0QDduc6yBBEjAtQQuXL6u4v720e75yZvzQ3iry+MPH/8NIvqI1+BX+eUC5GfqouSOSK/CBTSuWVq7xE7WzelS+2mX79mLge7smvjw8TNorgSoZEkSoX614qouP0ib1ipvxjxqLu3Toa7W4SZdRqkL/c5r90lIG/2SJtbCGTCRAAmQQEQILFOXZspdYB+8mwGrZw+yVSEXjIqXt5w8lBOIksRjvbyaJ2N7xULv9nWRKEFcLZTr1AXrtA+Q4Qnox3/9ExKiUNazEqpKxPj6ai0bUQFd2iMhU77f+5Ny5CiqObLJR86NSmuQeOwzRnZGgTzZdSEJaz0vlTizvhU9QeK0//H3Rc1LXy47PXjsV6zatMd2kknqoge6rmFhZjcgQAHdDQaJTQxOwFkBR0qdv3gNY+TCEPWFVi71/FCJxSLUWMVzyWNEQJfy8uKav3Kbeokd0jYA4nGeKX0qFCnwGRpUL6m9ePUkZ9psRECXtoioM2nuWiVGHdWEKr/kSfCF8vJsUqu0zbtcjnAtXv0d5HiYbIpks/SFEpQaqj6l9Euip0vMSwIk4CEEXD3/ikgzaNwC7Dl4QonnL1H6m3wYpOJQSpK5ScJt7fvxF9y8fR+J1f0L4pkonkAlC+fV8kSGgC717j9ySnu2eLv7KOEmf66PtHADQU/fyOZq0aodWK7u1bh89RaSJE6AEoXyaLF9rReDUkD3EMNnN0jAgwg8DXiOaUoEEucP8WyUJJd7ilBepOBntp7uO3wSI9VlxxLzN3GiBJpII3NwWAK6njWxiD/TF23ATyfP4ol4w6t16GfKaUMujrOGxfr30nX0Hz0Pp9TdE96xvdBFCemVS3/pQSPBrpAACUQlgXP/XoGECBQtoEW9crZHr99+AL2GzQrlyS37erk37fipP+HjE1t9cMyGT9U8JfNieAK6VCwOFUvXfo+76nJ6WTtuXDjMkIAuHzHldObO/T9BLlzOmT0rShXOh36j5mLJlD749KN3daEMT0B3Zn0rD5OT6RNmr4J4o9+7/wjp0vhpJzNrVSxiawsFdF3DwsxuQIACuhsMEptIAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAnIyfNDYBdi/fhISxI9LICRAAlFAgAJ6FEDmI8xL4PnzF5BY6s6kRZN62+L6OpOfeUiABEiABCwEONfSEkiABEiABEiABEiABDyRgNyxIyGtMihv9gTx4+Gv85e0k+KlVZiY/p3qQ8LHtu0zwWHX48bxxd61Ex3mYwYSIAH7BCig0zJIgARIgARIgARIgARIgARIgARIgARIgARIIJoR2L77KCbPXYOrN26r8LGvkEaFVi1bND+aqktF9YaLjWZdY3NIwK0IUEB3q+FiY0mABEiABEiABEiABEiABEiABEiABEiABEiABEiABKKKAAX0qCLN55AACZAACZAACZAACZAACZAACZAACZAACZAACZAACbgVAQrobjVcbCwJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEBUEaCAbpD0k4CXBmvwvOIxY8aA/PP69RvtH6ZAAjFiADHU/yOX0FYRK1YMvHpFe7H3e/H18YL6SZk+vVT28VzF/WMKTsArVkw1sUD9fl7jDX9CweDEUj+cV3wPhfrJyHQi72myCT2bCBdf71icZhSBgOevuF4JYQmyjoul5tw3arLlmsXOWo7zit25Q+YVsRm+o0Pj8Y4dC15qD2D2JEuVgGfUFULagazjYoiuoPYAr/kDCoaH80rYs4bMKbJvZApNIK6vF7EYIEAB3QA8KVo0ZlWDNbA4CZAACYRPYMvzFeqCGCWSmjzJB8sKieuZnAK7r5dAzOTJ9BZhfhMT6Dy1EYqWy2liAoFdH7VqL5bu+YksSMBpAn4LfnE6LzOSgBBYd2M2KOgAL16+RimfGjQKEtBFIEaC+LryM7O5CeQvnxsDF7Q0NwSDvaeAbhAgBXSDAFmcBEjAIQEK6BZEFNAdmgoz2CFAAZ1moYcABXQK6HrshXmDE6CATovQS4ACuoUYBXS9lsP8QoACOu1ADwEK6Hpo2c9LAd0gQwroBgGyOAmQgEMCFNAtiCigOzQVZrBDgAI6zUIPAQrogbToga7HcphXCFBApx3oJUAB3UKMArpey2F+IUABnXaghwAFdD207OelgG6QIQV0gwBZnARIwCEBCugWRBTQHZoKM9ghQAGdZqGHAAX0QFoU0PVYDvMKAQrotAO9BCigW4hRQNdrOcwvBCig0w70EKCAroeW/bwU0A0ypIBuECCLkwAJOCRAAd2CiAK6Q1NhBjsEKKDTLPQQoIAeSIsCuh7LYV4hQAGddqCXAAV0CzEK6Hoth/mFAAV02oEeAhTQ9dCyn5cCukGGFNANAmRxEiABhwQooFsQUUB3aCrMYIcABXSahR4CFNADaVFA12M5zCsEKKDTDvQSoIBuIUYBXa/lML8QoIBOO9BDgAK6Hlr281JAN8iQArpBgCxOAiTgkAAFdAsiCugOTYUZ7BCggE6z0EOAAnogLQroeiyHeYUABXTagV4CFNAtxCig67Uc5hcCFNBpB3oIUEDXQ8t+XgroBhlSQDcIkMVJgAQcEqCAbkFEAd2hqTCDHQIU0GkWeghQQA+kRQFdj+UwrxCggE470EuAArqFGAV0vZbD/EKAAjrtQA8BCuh6aNnPSwHdIEMK6AYBsjgJkIBDAhTQLYgooDs0FWawQ4ACOs1CDwEK6IG0KKDrsRzmFQIU0GkHeglQQLcQo4Cu13KYXwhQQKcd6CFAAV0PLft5KaAbZEgB3SBAFicBEnBIgAK6BREFdIemwgx2CFBAp1noIUABPZAWBXQ9lsO8QoACOu1ALwEK6BZiFND1Wg7zCwEK6LQDPQQooOuhZT8vBXSDDCmgGwTI4iRAAg4JUEC3IKKA7tBUmMEOAQroNAs9BCigB9KigK7HcphXCFBApx3oJUAB3UKMArpey2F+IUABnXaghwAFdD207OelgG6QIQV0gwBZnARIwCEBCugWRBTQHZoKM9ghQAGdZqGHAAX0QFoU0PVYDvMKAQrotAO9BCigW4hRQNdrOcwvBCig0w70EKCAroeW/bwU0A0ypIBuECCLkwAJOCRAAd2CiAK6Q1NhBjsEKKDTLPQQoIAeSIsCuh7LYV4hQAGddqCXAAV0CzEK6Hoth/mFAAV02oEeAhTQ9dCyn5cCukGGFNANAmRxEiABhwQooFsQUUB3aCrMYIcABXSahR4CFNADaVFA12M5zCsEKKDTDvQSoIBuIUYBXa/lML8QoIBOO9BDgAK6Hlr281JAN8jQnQX0ko2L4H8VcqNP2eEGKQQvHiNGDDQbVRfFGxZCbG8v7F97BOOazcDzgOd2n/Nllfyo0aMC0r2XBs+ePNPyT+0wDy+evdDyNx5WGzW6VwhW9tj2E+hZcqhL283KnCNQuWMZbTziJYoL/1oT1Hgddq5gOLkSJkuAznNaIlfRHHjy4AnWjN+M5SPWhVtvLK9YaDu5MV6+eIXJbefY8vrE8UbX+W3wYf73IPVeOHMJs7otxvEfThlu59uqgAK6hby7C+hLz01G/6pj8OfP/7jclOT30GZ8A7yS30PH+WHWnyRlIqz4d1qov7/+7y3Ufb8dYvvEaNPfhgAAIABJREFUxub7C0L9fbsv++H3I3+5vN1RUaE7C+h+aZKgx5QGyJo9PS78eQ1tSo50GbIiVfKgYuOvkSFrKlw5fxMtvhkWZt2fF/oQDXuURRK/hAh4/AxrZu3CpoX7bPmTpUqEBt3LIrfKlyBxPIxoMx97Nx13WVujsiIK6IG03UlAT588MVb0rIMvOk92qbn4xvbCoLrF8ck7aZAoni/+uXYH49ftxZE/LmrP+SJbJkxpVTHYM5+/fIW8HSeG244yebKhwTe5UcV/YbB8aZMlQo9qhZEzcxrcevAY8747ivU/nnZpnyKzMncX0HN+/SHajqmHVJn8sGHm95jec5lhXF+qvVb1TqWR7t1UePb0OQ5s/AlTuy+17XPsPSBZ6sRo2K8K8hT7BAmSxMOwxjOwV+2PJNXrXRGFKueDX7qkuHPtHtZM3YF1078z3M63VQEFdAt5dxfQeyxsi3O/nMe3Yza61JS0PeJs2SN+YtkjTtgS5h4xScrEWHllpp017k3Uydxa+/OJB4ciW96swfLM6bXU4b7TpZ1yYWXuLKDL3qXLlEbIXyonlHyEBp/1wN0bD1xGJ1XG5Bi/ozc2z9uNRcPX261X0w2mNUa23FmQMGl8XDh7FbP6r8SJPWdC5c9bPAcGLGmD7hVG4+T+sy5rZ1RWRAHdOG0K6AYZUkAPDbBsi2Ko3Kks+pYdhqePAtB7WUf8dugsZnVfbJd22ZbFcf/mA5w+eFYJnvHRZ3lH7F31Ixb0X6HlFwE9mRIRprSbaysvIlGAEtuZopaAr9o8rrs7H00+7oSrf19HzJgx8OL5S8ONkDGXl+iEVrOQIkNyDN3cC6MaTMaRrfYFmPxlP0friY2QSIk52+ftCiagJ1Avvzp9qmDHgt24feWO9iGntvrvGmmb4cnDp4bb+jYqoIBuoU4B3b715Sv9GVqPbaB+DwmU3e8JV0CXGuTjV9BUuMb/ULj6F+hYeKBNQG/yaVfcvnrXlu2p+u28fv3mbZi/4We6s4DepE8FJUjHxeTeK9V8G1MTX1yRKjUthFJ1C2B6/9X48+QFxInng2sXbtutWsTxOXv7oXv1iTh7/F+kfccP4zZ0xuCms3Hqx78QP1EcTNneA/uUYL51yQE8uv8Ub968wYO7j13R1CivgwJ6IHIK6ECiuL5oWiIvNh7+DTfuP0L5fB+p/86HYn1m4rFyDBEBvVf1wqg5YokNnNj/ozCcRvwSxcPs9tWQJH4c3Lj3KJiAHlutg77tWRebj57Bwp0/4YP0KTCheXn0W7Qde3/9O8p/CxF5oLsL6JN398eG2T/g+2UH4BU7lnL+sTjzGEllmxTG/VsPcfrwn5pA03t+K+xddxQLh661W218NedP2z9ICeZHsUWtZR/dfWKZU+880vI3GVQNR3acxIXfLyNrzkzot7gt+lYdhxN7Qws+RtodVWUpoFtIU0C3b3F9lnWw7BFbz7bsETf1xKiGU8LcI4Za49YqgMI1C6CjcgSRJAL6NrV33LPyoO2Bz9XayhX72aj6zQR9jjsL6PKBsMmAquhUapi2vn35/JU217kiJU6eACPWd9XqO7Dp5zAFdPlAWbtrWXyn5vzbV++hWJ0CqN2lLGpm66R0gwBbUz7KlxXtx9ZDUrUmHlRvCgV0VwySm9ZBAd3gwLmrgJ7qnRSY+9t47YUknuGvXr5GhST10e/bznh8/wn80ifDB+rr7D9qYz2szgTcuHBLIyVfbCce8kfT7J1w/rTF+yZkGrNrIA6sO6K+EG/W/ipvqc/QblpT1M7Y0ina9QdWR+YcGdG/gsXTTgT0RGoSHNt0ulPlmSnyCEz/eRSyfJpJeSAGaB9HqqVuitU352L9lG34onxu5bGTAgc3HNVOHFhPEIi3etUu5VDZr5HdhskphfX3F6Jtvl6a54KkZiPrIrHylB1ZP3xPsmaj6sHbN3YwAT3kQ2LGioltz5ejZa5uOHfCUr+7JQrolhFzZwG9o5oDSzT4WtuMv1FC9OKhq3Fk2wmM3zMQK5W3TokGhSBeEKvVvLly7Cabifpv6IGn6vc2uOZ4h2bbbHhteCsP8vA80ENWIu+AuSfHYFKHuTimNuRWD/RaWdrg1uU7Dp/pDhncVUD/X6kc6DW1kbb4l5M284Zv0Gzny7KfaR7jeYt+rInVk3uuwPEgnjAbz43DrMFrsWH+XrvDEze+L5b8NARtSozA5X9uOhzCT/JnxeCFLVAtew88+09MmrSlK9bP3YPvVx1Bva6lkTJdMoxqH9yT1mHF0TQDBfTAgXEnAX19v4bI4JcYT59bBM8Wk1YjW4aUKPbZe7h48x6+/DgzHj59Bv8VO3H47AVbJw+Pa4exa/dixd4TTllkLOU4cHR8B9QcuRhnL93UBPQe1Qqh3MB5TpW3Zir40TtoX75gMAH9U+V1PqlFBXzZfar63Vtyti1XABlTJEGX2a716tTVWB2Z3VlAbzm8Fiq0LKrWry/x+tVrdCw+FCM2dFOe6DuRv3ROpFLi3aEtxzGu3XzbGrd6x9Ko2r4kqmRq4zQl8SDPkj0D+teYYLdM/T6VtGeNaBbak9ZegTHbemle7WumbHe6DdEpIwV0y2i4s4BetN5X6DyrheZo8erFS5zYdRp9y4/A6htz1B5xu9ojfv7fHvEYxjUPsUfsXBaVUzS2a5LaHvHeArTN3ztwjziijtojJsZI5WjlKMkad96Z8ZjYZg7k9LokEdBl37pzSeApOkf1ROe/d1cBXQTuJafHIJZ8qFTi+ZHvTmHxiPWax/jKCVtRom5By75I2Y/8tzX5r+6kaRCD608Nc1jiqHXuyA1dsWTURhQomwvXlY4Vlgd6yEpEN9h6axZafTUQ55QOJilTtrTou6AVelcbj1HqnTCq1WwK6NH5RxHJbaOAbhCwuwro0m17IVxEQE+jjhjO6bkUl/64oonXvnG9bWFeMnyQFm0mNcaYJtNw/V/7G+8VV2ZhTOOpti/DaVV98/+YhDLxajvlPee/pRf++OlvzO+7XBsdaUOFNiU0Yf/u9fv4Ydl+fDt6g8GRY/GIEEieNimWXZyBoHYvAvqJXb9ixcj12oaiz4pO2DZnp+0I39fV/4evqubHwCqj7T5SQvfM+31CMPso1aQISjb5RonqPcNtpjMC+gd53sWoHwaguhL76YEekVGPPmWePnuF8onqRp8G6WxJyBAumT5Mh+lHh2OpCle0a/lBpFYfNvsu74BORQbawrw0GlxDW1gu9l/j8GkREdDLtSiKQtUs3ueSrAK6zLWyQP3n1wuYp44yiqebuyZ3FdCFd4dRtXBLecQsHrtFw1+uwZeo3akk5vpvwC8H/0DBMjlRpUUR1M3Tz+YpOXhhS2xZvB+HdtgPW5WvaHZ0HF0Lvx8/j+x538ULtdndtHA/Fo22fPQOmcQLc8TKdtpaYNHoLWqD/FqFcymHThXGqo87zzB5W3ftyK14pidXYQfOnb6ECd2X4/zvV9zSZLrPaIJCpXO4Zdtd3eixa/Zh0a5jrq42UuqzF8Kl+pefonnJfJi4YT+OqpArRXNmRb0in6Nkv9l4puxe0qSWFbF6/0nsPnXOqXZ9nDEVZrWrim96z7B5oE9oUR531SkdEeiP/XkRkzce0P53eMmegP5V9swYWKc4vu4eGGZLQr3UKZQLNUbYP8XpVKOjMJM7C+iCaf6JkRjbZq4SR37XqK06P1nz7F45fotljbugNbYu3INVE7dpf/915bz4smJuDKrjWMyzDsPQ1Z3xp3LomD94td2RmbpvIO6od3DaLCnhp9bdf/3yL8a3n4/zv10KlV8EpiVnxmJog6k4vvu3KBxp1z1q/c05iOMTy3UVumlN7iygC3J7IVxEQBcxfcWo//aI6sTxtrk/BNkjfoGvVCjXgSq8ob2k7RGVAF4mfh2bhqDtEVUY2rb5ezkc6XKtiqOQOmVp9T6XAiKgp3svNZ4q7+Jr529gpdIUDm/+2WFd0TWDuwrowrNItfwoWe9LdCkzQsMrQvX0/QOxdMwm7Pr2R6RWobT6LmyNTiWHa3OmpEb9K6t90QssHmlfC5KPLkO/7Yid3x7CdrUW7jK1sS4B/YNcmTFyYzfUeL+D5oGeQjmV+q/qiGFNZuDcqYtYdHKUWwvoBSrmQb95LaKrObtFuyigGxwmTxTQf1WLRqv3eEYReI6PQinfWk4fqVmnvhSL9/gvuy0xG5OlSYrll2agivq6fP9W+HGtitX/Go2G1kRzFT7AmjdNllTKU95ydD1LjkzoMKM5lgxZhQ1T3dPTwqDJvdXiYQno3b4ZZPMMkMVKgYp50a3oIKfaKh7t4tke9LdUuFZB1OpVSYWK6RhuHY4EdPkCPeHAUCVO7seyYfaPyjrVyLecaeuLFfBSX8TNnp6po31lE3qWgD5u1wBUTNnENrR9lrbH3yf/xdLh4d8BYM8W9ArosvFeoDYm/nUn4eQ+y9FvucNCPjrdUuGP4iWIg4ptSiLXN9nR6JPOLjnK/jZs2NME9JwF38fAxrNsKJef8NfCqZw+6lyIh3INv0IFFft8WKt5OH/2CjKqGOiDlOi+ZNxWbF603+4Qla1fUPN8Fw/0z778AAtGbsSKyZaYu9/+OgIb5u3RPNLlCHTdLqVRsPSnaFRgkFseie49uykKlvjkbZhqtHvmhPX7Mf/7o9GuXfYaFJaAnu/9DOg4K3CjvdO/uYqnuxEn/tb/gSeuOuEzv1MNbP/pLObssMSjTqxC26VMnAD3Hj9FqiQJ0KHCl7h5/zG6zQ08SWSvvfYEdAnrIp70s7cfxvI9J5BQhY+RDwDZM6WmgB5FVmhPQO9edqQSTiyeiOWaqvujlEdj93IRu4+iWO0CWmzzFl/0xf3bD+32avX5KViv4q9LXHOZU+v1qgiJo97g026h5lQRh5KlToKeFe07qUQRNkOP2XhrDny8KaC/VKceSnrXMMTybRYOS0DvVnRw8D1ihTzoVmywU03V9og/jUTRWNVs+QurkCy1eqo9ojoNH16SNe7CvyZjqDrBeXJv4MclqVOc8mS9m1fF3m6qPNrb/a+P255S9jQBfZw6UVMxoyVevaQ+KuTV379exNLRzp3C6jy5ES7+dVV99LR4resR0EU3GL+9F3avPoxlYzdDvNGn7umv3Vlx8oAl5rm7C+hfVcmL3rObO/X7Yyb7BCigG7QMTxfQJZ70mlvzlNdnPae9d8UDXeJXH9vxi0bXWQ/0gupCnHZTm6K7El5FQAor1exZETmLZIeItkxRS8AZAb1Apbyo3buyFjLFmWT1QC/lW9O2MRDvglJNv0GbvBH3QJeXoMRSv/znVe3EhDsnhnCxjJ47h3CR9tvzQA8poLceWx8v1QV0M9TFt3qTXgG9ZrfyyPHVh+hROuzLI+Wegw135qs8/vj1v8Wj3na97fyeLqBP/74n5o/YhB/V8Vdnknixi+d61yqB4QPEozzj+6kwoGHokAG5vsqGVkOqonnhoVoomfTvpsSwZW2wcsp3WpiYlaeGY0jzOTh58E/t8T4qrNbaP8agXamR+OvX0B6TzrTxbeZhCJdA+u4UwsVZAV1ijE/edAB7Tjn3wclKQ8Rz8Va/cOMuBi4N+8LGHO+kxuwO1ZC7vf3wHNb67Ano8nefZUmL7lULI1PKJLhy+4F2kegj5c0e9CPA2/x9OHq2J3qgBxXQC5T7HLVUvNxWBfs7QhHq7wuqMBZtx9VHDyW+iyAUVhKv98F1J+OXfRYveBEC11+dgTZfDdC80a2puX9N5Cj4AbqUHq4uV3TPO36kLwzhYhlRT/VADyqga3tE5SDV8vPuTv1+rB7opeLUCr5HVPvENir0Z3hJ9IJPv/4I3YsPCTff8G19cGr/GeWcZ/9EiFMNfYuZPF1Abz2ytrb2nNHbEpnAUZr4fR8tRJY1SYgYiYl25tjfKiyXf5jFRTcYsrIDLp+7jrFtLSHZ5OTl2gtTVEivwLjssX28tPasmrwNcwe6n83wElFHFuT47ymgO2YUbg53FtCLq5i7X6rQGr2VMGJNEsIlqAf6e59ngbxYKiVv6DSpMbsHYv+aw1g70XLkPF+ZXOgwvRlqpAv7a1fpZkXVRY+V0buUvxYyILzUaGgtLczMkOpjnW4TM7qGgDMCusQ7/zDfe2GGbAnZEi2+3YNFaJOnh+3DSfPRcklHYhV/f2K4DQ/LAz1xikSQUECnleA3pX3g5bOuoRD1tVBAtzB3dwF98R8TMbjWBJw9ZgkVICFcQgroQ9Z1w1H18XF9BE7Y6BHQ4yeOh4W/j0evssPx+9GwQxfIxn2dOl7d/PMebhvGxZMFdDmdteKX4ehSebzTIVM+L/QhOo+tgzq5+2j3n0iSy0oTJo2HsZ0CL0K0znQ12xVHtlzvoF/9wHtIOoysqXnmjO28BBM2dsHONUc1L3RJmtjz5xjUy9sPNy4HXkQb9TNnxJ5IAT2QmzsJ6GmSJcTq3vWRv9MkWwckhEtQD3QvdRHvD8NboNH4lfjriuVuH2dS0gTqIl8lnovX+shVu8Itku+DjPCvXxKFe4Z/b09YAnrIyqe1roTvT/yJ1Qec+0DmTH8iM4+nC+gS7zxb7iy6QrYI79INv0atbuXQu/JYu6FYgo7JxB/6YeeKg1g/43vtj0XE2XBtJup81Bk3Lt7W5t5OkxoijQrx0kddHurO4rn0jwK6ZfTdXUDvNq+1su2LWDkq8MSPhHAJKqBre0R1n1pYIVtCzk3We7LEocrqXNdc3X+l7RHrhr1HlDXuonOT0bPkUPx+5K9wp7wJ+4fg+yV7sXHajsicGiOtbk8X0EXUPvr9KXUqZ2eEGDrjgZ7YL6EW9kUuehZv8/CSu3ugU0CPkBkFK0QB3SBDdxbQxYu7+4I2Ni9fuTBOBPSHdx9jXu+l8FVf4rrMaaW9eGb3sHhDOhMDvYI68l+hbUn0LjMMASqGbm8V7+zPn//GtI7ztTokTMvnxT+Ffy3LpXh1+1WFHMfqX3GU7bJS+XO5qFJS28lNsHfVIfyrYv+9m/Md9FrSHqNVjPWD693jWLFBE4tWxcMS0Of0XII9KtZYhmzpNBuSC1+Pbjuutd1RDHTJ039VFy2u7iR1w3oyFe9RPtrIJTPWMW45toGKf38Py1Ws6KDJnoAuN7TLRbY7Fuy2xdiTMnKpjbvesE4B3TLq7i6gj/uhvyaOr1VeC3L5bRL1oWfSgSFazPOrf99A7uI50HZCQxUupQvu3biv9dloDHQ5ojp8c0+sUpeTHt1uORUkqYkKlSXzeb/KwY9951cfPFOq35C087mK99pwQDVk+ig9WqsLnOQyS3dMniagf1M1rxZ+5Yl6v1ZvUwwfKnG7Q7nA+KGOYqBLTPM5e/viwLaTWDBiI9IpEWbI4lYY2XaBdhlplo/SoUnfCupS0EVaHN6P82TB0CXq8qTaU/HrkXNIkTYJRq1qjzlD12PvpuMoWfsL1O1cGn3qTNUuJW3YvSzezZ5eE/XdMVFADxw1dxLQfWJ7Yf+o1mg5ZbV2uaeEQyiX7yOUzatO2czbrMUrb1QsD3K8kwb1xiyzddJRDPTUKizLrPZVsfHwb1i48ydbuZdqzfJCnRaq/83nylP8Pn7556oWzmVA7eI4fu4yRq3ereUtp56fX1002nO+xanEmsIS0JMpsV7aLqJ9ra9z4hPV3jqjl2rPcofkiQL6nP7fYu+6o8jwfmoVj7cNxrWdi6P/nfhxJgZ6nR7lUVg5LA2oNRE3Lt22DWOAukNCkoR1yaX2ZMMaWU5LllIXjstFo70rjVHekNe0kC/vqrATnUv4a+L5UHWJ3gsVTmt0qznae1qSXDAtoS7dMVFAt4yauwvoDQbVUPv0TBhWeyJ84/ng9tW72iWic3otDdwjruyEsc1kj2i50PPr6uHHQJc8/dW+Ui4nndTmvz3i1t4Y12Jm4B5xTH11D8v9YHvEJur+tAwqnnY/FVI2aEqZ0Q/lVajRH5YfgOge8vzGaj3cMFsH7b/dMXmagD7ph75azPOrKj59bhVCsu3oumiUuxfu3bSEAXYUAz3kGIYU0LV90drOyoN8uybMS4zz0Srm+Y5lB7Q/s6ZXysvcnm5AAd0dfyWubTMFdIM83VlAlwmkl4q3m79cbjxQcfhqZWihiZ+pM6dUccuTwEt5Bu9WL5hpnRbYbpvPpr4aTzzkj6Yq7tj50/aPH8ZUHj4tVBgCuZFbNuoHN6gbt5vNsAniDdSleOL9XjO9xSNdYqxLbPOQqZq69FFE05bjGqjbu3NrX5tvKs+LVWM3YtOMsI/PGhxSFg+HQFgC+oUzl5E5R0Y8uvcYK5TIHTQ+fY3uFSAeB5X9GoVZs3iMd5nTUgvNIxd2rJu0JdhRunH7BuP6+ZsY/p+3gYjybSc3hk9cHxXDTj62PMN4tZjat/pHzVYGrg0dPkZsZkLL0OEJ3GHAKaBbRsndBXQ5StpZ3eGQNFUiTOu6SDvtM+XQUO3SZPk4ePmva5iiPjSe+u/yMumz/4Ye6qJGddu8iuEYVvpabczbjG/w3+8hhjbXTmgzB/vWHtE229seL8a4lrOwdZ7Fc1Lm0vmnx6HD1/3x939xXa11S/zz5sNrQ+6/kCRtmaLeATfUDfbumjxNQK/cvLC2qPdT7+lTh89hfNel2kWj1rTx3DjMGrxWC68SVkqjLvxsN7wG3v80I24rkXzp+G34QXmRS8pZ4H0MW95GxTAfiCvnLeNepHJuVGtdDAmTxMPjB0+wdvbuYPHS5SJTiaseR22af9r7O6b2+Rb3btmP7xvd7YgCeuAIuZOALq2uWzgXmpbIq8W2bTJhJT7NnBb11J89V+KzxCf/+dwlDFLhV67fe2Tr5OFx7TB27V6s2GsRdEKmQp9kUU4B5UL9+Sp18ejQFTs1kb5uoc+QLnlidXFoAL47/icmqUtLA/67pLR1mS+UiP4Rive13FuQMnF8LO9RV7vXJI53bO2y0c1HfsPoNZYTHLVVXR1VHPU7D59g18m/MGXTQTx4YnEocYfkiQL6BXVXROaPM2hxk1eM24wNswI9Iat3LA3xSq+SqU2YwzPtwKBgIQWsGau/214T/hr0rayJ6LU+CLz3R+qsoC75jqucmX764VdM7rJYE5DiJYqLtRenhnrWRRWusHGu8MMeRlf7oYBuGRl3F9BljygOURJjfN+qHzUPcRHQg+0RR64PvUfsXBaV1T1pYSVtjzhb9ogfW/aIk7cG3yPuHWTZI9aznD6SNe4CdeKzfYG+oULCSmhaqStbvqyIo+75Oa9CKc3stgin/rsHKLr+RsJrl6cJ6FN298cfx//Bu0pXkHAqU7otwamDf9gQ+KsPiE+VA8ng+qHnQXucQgro2r7o9myMazdfXQi9F1+UzokBS9qGKrp53m5M6Lgw1J9TQHfHX4lr20wB3SBPdxbQ7XU9ZAgXg3hY3AQEVt+cq8WjP/fLeRP09u10kQK6hbu7C+ghrcdeCJe3Y2Ge/VR3FtBDjozELw95iahnj17U944CeiBzdxPQQ1pLyBAuUW9N5nuiuwvoIUdM4pEHjYFuvhGN/B5TQLcwdncB3Z6lhAzhEvnWZL4nuLOAHmpfpE4NhLxE1HwjGrk9ZggX43wpoBtkSAHdIEAWd3sCFNAjfwgpoFsYU0CPfFvzxCdQQPfEUY28PlFAp4Aeedbl+TVTQPf8MXZ1DymgU0B3tU2ZqT4K6GYabeN9pYBunCEFdIMMKaAbBMjibk+AAnrkDyEFdArokW9lnvsECuieO7aR0TMK6BTQI8OuzFInBXSzjLTr+kkBnQK666zJfDVRQDffmBvpMQV0I/QsZSmgG2ToaQK6QRwsTgIkEAkEKKB7poAeCabCKu0Q8CQBnQMc+QQooHuOgB751sInhCTgaQI6RzjyCVBA91wBPfKth0/wJAGdoxn5BCigG2dMAd0gQwroBgGyOAmQgEMCFNApoDs0EmYIkwAFdBqHHgIU0Cmg67EX5g1OgAI6LUIvAQroFND12gzzBxKggE5r0EOAAroeWvbzUkA3yJACukGALE4CJOCQAAV0CugOjYQZKKDTBlxCgAI6BXSXGJJJK6GAbtKBN9BtCugU0A2Yj+mLUkA3vQnoAkABXRcuu5kpoBtkSAHdIEAWJwEScEiAAjoFdIdGwgwU0GkDLiFAAZ0CuksMyaSVUEA36cAb6DYFdAroBszH9EUpoJveBHQBoICuCxcFdOO4QtdAAT0yqLJOEiCBoAQooFNA5y8i4gQYwiXi7MxYkgI6BXQz2r2r+kwB3VUkzVMPBXQK6Oaxdtf3lAK665l6co0U0I2PLj3QDTKkgG4QIIuTAAk4JEABnQK6QyNhhjAJUECnceghQAGdAroee2He4AQooNMi9BKggE4BXa/NMH8gAQrotAY9BCig66FlPy8FdIMMKaAbBMjiJEACDglQQKeA7tBImIECOm3AJQQooFNAd4khmbQSCugmHXgD3aaATgHdgPmYvigFdNObgC4AFNB14bKbmQK6QYYU0A0CZHESIAGHBCigU0B3aCTMQAGdNuASAhTQKaC7xJBMWgkFdJMOvIFuU0CngG7AfExflAK66U1AFwAK6LpwUUA3jit0DRTQI4Mq6yQBEghKgAI6BXT+IiJOgCFcIs7OjCUpoFNAN6Pdu6rPFNBdRdI89VBAp4BuHmt3fU8poLueqSfXSAHd+OjSA90gQwroBgGyOAmQgEMCFNApoDs0EmYIkwAFdBqHHgIU0Cmg67EX5g1OgAI6LUIvAQroFND12gzzBxKggE5r0EOAAroeWvbzUkA3yJACukGALE4CJOCQwNYXK+AVK6bDfJ6e4dmLVyiboK6nd5P9czEBCuguBurh1fWe3RQFS3zi4b10rnsT1u/H/O+POpeZuUhAEaCATjPQS2DjrTnw8Y6lt5jH5X/56jVKetfwuH6xQ5FLgAJ65PL1tNq/qpIXvWc397RuRWl/KKAbxF00Nl90BhGarvhUTttGAAAgAElEQVT2iz+brs/ssDECr5L/jtheFNCfPnuFcnE45xqzJvOV3n7lF/N1mj2OMIEnvlMRP/E3ES7vSQWn91iC1SPXeVKX2JdIJsD5NpIBe2D1z5KcQRwfCugvXr5GKe/qHjjC7FJkEuCcG5l0Pa/u57HKwNdvrOd1LAp7RAHdIGwK6AYBmrA4BXQTDrrBLlNAtwB8EvAS5ePWNEiTxc1GgJsLs424sf4+iD0FiZMVNVaJh5Se2m0x1o5e7yG9YTeiggDn26ig7FnPCEj8G+L6enlWpyLQGwroEYDGIuCcSyPQQ+BpjNKIl3KcniLMG4IABXSDJkEB3SBAExangG7CQTfYZQroFNANmpCpi3NzYerh1915CuiByCig6zYf0xfgfGt6E9ANgAK6BRkFdN2mwwKKAOdcmoEeAhTQ9dCyn5cCukGGFNANAjRhcQroJhx0g12mgE4B3aAJmbo4NxemHn7dnaeATgFdt9GwgI0A51sag14CFNApoOu1GeYPJMA5l9aghwAFdD20KKAbp2WnBgrokYLVoyulgO7RwxspnaOATgE9UgzLJJVyc2GSgXZRNymgU0B3kSmZshrOt6YcdkOdpoBOAd2QAZm8MOdckxuAzu5TQNcJzE52eqAbZEgB3SBAExangG7CQTfYZQroFNANmpCpi3NzYerh1915CugU0HUbDQvYCHC+pTHoJUABnQK6Xpth/kACnHNpDXoIUEDXQ8t+XgroBhlSQDcI0ITFKaCbcNANdpkCOgV0gyZk6uLcXJh6+HV3ngI6BXTdRsMCFNBpAxEmQAGdAnqEjYcFGQOdNqCLAAV0XbjsZqaAbpAhBXSDAE1YnAK6CQfdYJcpoFNAN2hCpi5OAd3Uw6+78xTQKaDrNhoWoIBOG4gwAQroFNAjbDwsSAGdNqCLAAV0XbgooBvHFboGCuiRQdWz66SA7tnjGxm9o4BOAT0y7MosdVJAN8tIu6afFNApoLvGksxZC+dbc467kV5TQKeAbsR+zF6Wc67ZLUBf/ymg6+NlLzc90A0ypIBuEKAJi1NAN+GgG+wyBXQK6AZNyNTFubkw9fDr7jwFdArouo2GBWwEON/SGPQSoIBOAV2vzTB/IAHOubQGPQQooOuhZT8vBXSDDCmgGwRowuIU0E046Aa7TAGdArpBEzJ1cW4uTD38ujtPAZ0Cum6jYQEK6LSBCBOggE4BPcLGw4IM4UIb0EWAArouXHYzU0A3yJACukGAJixOAd2Eg26wyxTQKaAbNCFTF6eAburh1915CugU0HUbDQtQQKcNRJgABXQK6BE2HhakgE4b0EWAArouXBTQjeMKXQMF9Mig6tl1UkD37PGNjN5RQKeAHhl2ZZY6KaCbZaRd008K6BTQXWNJ5qyF8605x91IrymgU0A3Yj9mL8s51+wWoK//FND18bKXmx7oBhlSQDcI0ITFKaCbcNANdpkCOgV0gyZk6uLcXJh6+HV3ngI6BXTdRsMCNgKcb2kMeglQQKeArtdmmD+QAOdcWoMeAhTQ9dCyn5cCukGGniSgx/KKhS6zW+CLcp8jRowYqP9Be9y9ft8gIUtxqbvtxEZ4+eIlJrefF26dCZMlQOdZzZHrm0/w5MFTrJm4BctHrndJO6JDJe4koG/dFQ+Hfo6LQZ1vuhzd2m3xsXZ7Qjx6EhM5PwxAizp34JfstfacpwExMG1xEvx0Kg5eqz/Kmuk5Wte/g5TJX4XZjlt3YmHet4lw7GRcPHgUAz1b3caXeZ/gzZs3GD0zOX4964Pb97yQOOErlCr0EDXL3dfs3B0SBXTLKD0JeInycWu6w5DZbWOK9MnRc2l7vJcrM/797RJafd7dJX2R+jrMaI5MH2fA9fM3MLXDfBzddtxu3T5xvNF1fht8mP89yFx74cwlzOq2GMd/OKXl/7z4pxi2tXewsi+evUCpOLVc0ta3UYk7bS6GT0uGdzO+QJVSD1yKSubUWcuSYP/RuIgV6w0K5X+MhlXvIXbswMc8ehwD3YenRPWy9/FlnqcOnx9W/ufPgbKNM4YqP77/VWR7V/1lNE8U0AMHaKqaG9aOdt/1V+WOZVCjewXESxQX/rUmYP/aw4atT9YNzUbVRfGGhRDb20vVeQTjms3A84Dwbbto3a9QrVt5NM3eKVgb6g+sjkI1/ge/9Mlw5+o9rB6/CesmbTXczrdVgTvNt5evxUKrPmmwfvZFl+LafSguhk31C1XnNwUeomvzO/jnohemLUqq/u2Nl69i4KOsAWjT4C5S+b0Msx3f74+HBasS4e4DL7z/TgA6Nb2NtKle4c69mKjZNn2ocimSvcCi8Vdc2q/IqowCuoXsi5evUcq7emRhjvJ6o8OaVzo98ZA/suXNGqz/c3ouwfIR66KcSWQ80J3m3Le5xhX2sj7tPSqlNgyjel8PczjmrEiMlZsSBfv7z7M/xdBuN2x/duGyF1r3S43F4y4jUUKLfuEOiQK68VGigG6QoScJ6HlL5kRj/1roVGgAnj19jpfPX2rio9GUv0wutB7fAIn8EmL7/N0OBfQ+SmASwX1CmzlIkSEZhm7ogVGNpuLIthNGmxItylNAB3bsjYcl6xJhcOcbSJbkJWYsTYq///XG5MHXtDGavCAJ7t2PhW4tbsHLC5i5NAn+vhAbI3sFvriCDuZDJZi36pNaE8xLfv0I8eO9Qgz1f/JCe/36jSbU584RgCRKPP9LPaf3KD+M63cd72eO/mKO9JMCumW03V1AbzayLhIkjY+JrWcrETMmAp48MzwnecX2wsJzk7Fh6nZsnLYd+dR8225qU9TL0gb3b4UWYeX5dfpUwY4Fu3H7yh1NBKqt/rtG2mZ48vCpJqC3V+VbfNY1sG3qNfD4wRPDbX1bFXBzoTYKM5Li0WP1kbzZbTx+GgNDJyXHx+8/Q/Pa97Rhma8+Pu7YFx9378dEz9a3HAro4eW3Cugzh19GssSBHz3jxnmDmDHflhU4/1wK6IGs3FlA943ni3V356PJx51w9e/ryvZi4IVa1xpNZVsUQ+VOZdG37DA8fRSA3ss64rdDZzGr+2K7VSdLnQRj9gxCouQJcOvynVACetMRdXBky3H1UfUisubKgv6ru6BPmWE4setXo019K+Xdab6NLAH9pTKzgGfBHTQmLUiG1H4v0KDqfRw/7YPzSjzP8+kTeMd+gxlLkmpz75i+9te45y95oV3/1OjT7iayZXmOxWr9/Nsf3pg06Lq2T3usHFGCpl2H4kH+Gds3bIHorRhHGA+lgG4B42kCenRY8wpXEdC3zf0Be1YetFngc6VzuOJ9EB1+R+4050aWgO5ojSvjJA55QyYmx627seDj7VhAv63ytap7xzbEokf4+lh0sR7D/bQPoPfUB82VUy5SQI8OP4QobAMFdIOwPUVAT6zE7aX/TEEsJcbIS+WI8l5cNHg1xu8dpP69ChVaFUe8xPGwesJmLBm6xkZt2Oae2gZiUPVxDkk2U5sEb9/Y4Qro4s2z/s48tP1fH5z75V+tzmbDayNxykQY2XCqw2e4QwZ3EdCv3vBC0x5p8ErpH7LAjxULWDPjIgZPSK69KG7cioVf//BF+tQvlOByE2lSBgolZRplQNMad1C+2CO7Q9JjeAolaD9F5ZIPtb9/ogSdKi3TYYoS0N9J/wJdh6bEJ9kCULeS5QTEj8fjYOrCJFg4zr43zYJViXHtZix0b3k7XBN48QI4/psvJsxNpp51RXmjG/9AFBU2RwHdQtmdBfQClfKiz/KO2mZXPk7O6bUUb9THnYKV8+HC75e1f8vcO6HlTBzZGug9vuXpUszoshDrp2yza2o5vvoI/dd0QeXkjWwfPKccGY4NSkzfPm+XQ/OMqYT8bc+Xo2Wubjh34rwmoLed3Bj1s7Z1WNZdMrjL5uK7fUrwmJ1UE/q8lJd4DjUHyumfKi3Tomqph9h7JC4uXfXC59kD0KPVLZv3+IqNCfHtlgRYNe2y3SGRTUO5Jhkwru9VZH1HTYIqHTvpC9nIhCzTpm8qVHPSA13qsZffKqAvHn/RdqrIXWxF2kkBPXC03FlAn/7zKGT5NBMCHgdo69RqqZti9c25+Hb0BnxZNT/SvZcGx7afwLDaE2xCingpitgiXuLnT9v3Sh6zayAOrDuCNWo9LClvqc/QblpT1M7YMlwzz1s6F5qo9WxID/SQhcYqsV085deMt9Tvbsld5lvh2rBLGly5HluJIhbvwWHdr+PPf7xx4Fg8pFNrWzmx4+P9RnmH30beTwNsQ+FojRtyzGTebjcgFeaOumx33SmC+qAJKbB2pn2bW7QmkXIi8Ub/DpbToI+fyJo5varvClKnCP5RSMT7xt3SqDbfQe5PAtscne2IArpldDxJQI9Oa16Z09dP3oqdS/ZF559BhNvmLnNuRNe4Z/7yRoeBqTHd/7LSCEJ/BHd2jTtuTlLEj/saGdO+wHfKYcSRB/r9hzHRqUmggB5ygORkZ4WmGSigR9hy3bcgBXSDY+cpArpgKFKrAEo1LozORQZpVDJ9lB4zj49Ux0m3YOu8HyBHsYZu6I4GH3bUvHkkNR5SE8/UsdXFQ1Y7JOmMgJ7uvdSYd3ocyiSsp3nBS5I2lWxUWBPVPSG5i4AurO2FcBEB/fzl2Ghc/R4yZ3ihjqEmQTz1QurWIlC87qM8vEsVfoQvctkPA9CqTyoU//KxEtgtArqk+p3SKG/Iu1qZ/UfjKI/J5ChX9CGKFnyMGYsTo0Bu5V1e6LFdE5D6kiZ6hctqI3RThXJ5N+NztG90RxPjremXMz7o5q/yJX6pbUI+UF487pJeJ/9deeK7getmJAMNeP4KZX1rRPJTIq/6TrNa4NalO1g4cKX2kPKtS6ChmkPn9l6qieZFahdEqabfBBNihm7uhc0zvsPBDUftNqx0s6IoqebINnl72v6+5+J2uKmeM7uHfY/IoBV9kOddjPphAKorccnqgT5kYw/cu/kAj+89Vl6Qp7X2Pb5PD/TIs4zAmu1554iA/v47z1G74n3E8X2N/mNToHaF+yj+lWU+lHAB+5S43rf9LbtNfKyGrlLzjGrzccU2J8oH0Lod06mPohfU/B34IdGVAnrihC9Ve98gc/rnqK/CxWRMa9z7NyrGICDudMRNWDgqHhXtnzGz51J8O2JttG+nvQYmT5sUyy7OQNGYVW1/LQL62aN/YdGgVXiqTtwMVnPdYuUkYv3YmOGDtGgzqTHGNJmG6//aD1234sosjGmsTkX+96Ez7bupMP+PSSgTr7Zt3WqvPc4I6BJia+mF6RhSYxyO77SE1XK35C5ijnC154G+fkd8LFidBA2q3NVOLv5wMC627k6AxeMDP1A6WuOGHDPxehRBXrzP7aUVGxPg8Im4YXqM+09JBr+kr9C0puXEkKRa7dKiQ+PbyKPaGDRt+C4+dv/oPt7n0vbnSc/A11t56Zg8vVQhXEp6UAiX6LLmFQFd9IWnDwNwTYU5XDlqAw5v/sljrM2d5tyIrHEvXImtOdHJfJfKL3QoV2fWuHNXJoJ4lMspTBHPnRHQN3yXQNM3JPRrofxPULV08FO97iqgv4xdBt7JxnqM/b+NjlBAN0jd0wX08XsGooLybLQmEbendV4QoXAqzgjo4i00/ehwBOVauGYB1OpRAU1ydDE4WtGjuCcI6HL0v2IJi/i9S20u1mxLoB0ldTbNUy+ygz/Fw4CON9Sm4CXO/u2DgRP81Jfe25qAfvN2TPhP8UP6NC+Up2QcxIvzWuW9qeI92hdgxHu93DcPUV4J7rGVt/zC1YmwX3kQzVPePkFj/IoH+rFTvlpM9KlDroYbU93ZvkRFPgroFsrPX7xCaR/PEtA/U3c99K84Uutf0lSJIeKMIyEmqM1V6lAa/6uQB52/7m/7444zW2he7pP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       "                            \n",
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       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizes the confusion matrices\n",
    "fig = make_subplots(cols=4, rows=5, subplot_titles=list(LABEL_IDXS.keys()))\n",
    "accuracies = []\n",
    "for jdx in range(Y_val.shape[1]):\n",
    "    y_val = Y_val[:, jdx].ravel()\n",
    "    y_hat_val = (Y_hat_val[:, jdx].ravel() > threshs[jdx]).astype(float)\n",
    "    tn, fp, fn, tp = confusion_matrix(y_val, y_hat_val).ravel()\n",
    "    accuracies.append((tn+fn)/ttl)\n",
    "    mat = np.array([[fn, tn], [tp, fp]])\n",
    "    ttl = np.sum(mat)\n",
    "    col = jdx % 4+1\n",
    "    row = jdx // 4+1\n",
    "    fig.add_trace(\n",
    "        go.Heatmap(\n",
    "            z=mat, text=[[f\"fn: {np.around(fn/ttl*100,2)}\", f\"tn: {np.around(tn/ttl*100,2)}\"], [f\"tp: {np.around(tp/ttl*100,2)}\", f\"fp: {np.around(fp/ttl*100,2)}\"]],\n",
    "            texttemplate=\"%{text}\", colorscale='Viridis', name=list(LABEL_IDXS.keys())[jdx],\n",
    "            hovertemplate=list(LABEL_IDXS.keys())[jdx],\n",
    "            showscale=False\n",
    "        ),\n",
    "        col=col, row=row,\n",
    "    )\n",
    "\n",
    "    fig.update_yaxes(showticklabels=False, row=row, col=col)\n",
    "    fig.update_xaxes(showticklabels=False, row=row, col=col)\n",
    "\n",
    "lbl = '10'\n",
    "print(accuracies)\n",
    "print(LABEL_IDXS.keys())\n",
    "df[f\"{lbl}\"] = accuracies\n",
    "df[f\"{lbl}-keys\"] = LABEL_IDXS.keys()\n",
    "\n",
    "\n",
    "fig.update_layout(\n",
    "    width=1200, height=800, title=\"Confusion matrices\",\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/gpfs/ryzhu/cos514\n"
     ]
    }
   ],
   "source": [
    "df.to_csv(\"COS514Results.csv\")\n",
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['conference_room', 'hotel_room', 'office', 'mountain_snowy', 'corridor', 'airport_terminal', 'game_room', 'waiting_room', 'poolroom_home', 'home_office', 'art_studio']\n"
     ]
    }
   ],
   "source": [
    "df.head()\n",
    "# df = df.drop(columns=[\"1-keys\"])\n",
    "print(RARE_CLASSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "gpuType": "T4",
   "provenance": []
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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