DeeplearningModels_Jaro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9cb9d8a4-9cec-4168-be8c-7689693b1846",
   "metadata": {},
   "source": [
    "## Het U-net "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267c1510-0710-45f8-9545-388cc0f2eb54",
   "metadata": {},
   "source": [
    "**The installations of necessary packages on the virtual machine.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "204833ec-0b01-4e16-b27d-2b3b021aea82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install nibabel\n",
    "# !pip install medpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab2abca-6305-4dae-b82e-7c46370c003c",
   "metadata": {},
   "source": [
    "**The necessary imports of the packages that are used during the program.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ad3b1525-2161-47f5-87c2-755e8ba13c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import monai\n",
    "from PIL import Image\n",
    "import torch\n",
    "import nibabel as nib\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "from skimage.restoration import denoise_tv_chambolle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d1eee9-3ccb-40f6-a987-1310c8f226a0",
   "metadata": {},
   "source": [
    "**The definition of the location of the database of ACDC and verification of the datapath location.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ecc87eba-7eab-451f-b2a3-e9ca34449438",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Congrats! You selected the correct folder :)\n"
     ]
    }
   ],
   "source": [
    "data_path = \"./preprocessed_ROI_TV005_v3/\"\n",
    "if not os.path.exists(data_path):\n",
    "    print(\"Please update your data path to an existing folder.\")\n",
    "elif not set([\"training\", \"testing\"]).issubset(set(os.listdir(data_path))):\n",
    "    print(\"Please update your data path to the correct folder (should contain training and testing folders).\")\n",
    "else:\n",
    "    print(\"Congrats! You selected the correct folder :)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d2764d-fadf-48cc-b468-d7f706206c87",
   "metadata": {},
   "source": [
    "**A modification of the build dictonary function of the tutorials to make sure the ACDC database can be allocated on the right place.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a1f31258-e559-4be6-a500-4d5385c3773b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:05<00:00,  9.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1076\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# print(img.header.structarr['pixdim'])\n",
    "\n",
    "def build_dict_ACDC(data_path, mode='training', heart_mode='Off'):\n",
    "    \"\"\"\n",
    "    This functioprintn returns a list of dictionaries, each dictionary containing the keys 'img' and 'mask' \n",
    "    that returns the path to the corresponding image.\n",
    "    \n",
    "    Args:\n",
    "        data_path (str): path to the root folder of the data set.\n",
    "        mode (str): subset used. Must correspond to 'training', 'val' or 'testing'.\n",
    "        ----heart_mode-----\n",
    "        Off: 1 dictionary of ED and ES together\n",
    "        On: 2 separate ED and ES dictionaries\n",
    "        secret: 1 dictionary of secret test set\n",
    "        \n",
    "    Returns:\n",
    "        (List[Dict[str, str]]) list of the dictionaries containing the paths of X-ray images and masks.\n",
    "    \"\"\"\n",
    "    # test if mode is correct\n",
    "    if mode not in [\"training\", \"val\", \"testing\", \"secret\"]:\n",
    "        raise ValueError(f\"Please choose a mode in ['training', 'val', 'testing', 'secret']. Current mode is {mode}.\")\n",
    "    \n",
    "    # define empty dictionary\n",
    "    dicts = []\n",
    "    dicts2 = []\n",
    "    paths_mri = []\n",
    "    iBegin = 1\n",
    "    iEnd = 101\n",
    "    \n",
    "    if (mode=='testing'):\n",
    "        iBegin = 101\n",
    "        iEnd = 151\n",
    "        \n",
    "    if (mode=='secret'):\n",
    "        iBegin = 151\n",
    "        iEnd = 176\n",
    "        \n",
    "    for i in tqdm(range(iBegin,iEnd)):\n",
    "    # list all .png files in directory, including the path\n",
    "        paths_mri.append(glob.glob(os.path.join(data_path, mode, 'patient{:03}'.format(i), '*[!gt].png')))\n",
    "        \n",
    "        # make a corresponding list for all the mask files\n",
    "        for mri_path in paths_mri[0]:\n",
    "            if (heart_mode=='secret'):\n",
    "                dicts.append({'img': mri_path})\n",
    "            mask_path = os.path.join(mri_path[0:-4]+'_gt'+ '.png')\n",
    "            \n",
    "            if os.path.exists(mask_path):\n",
    "                if (heart_mode=='Off'):\n",
    "                    dicts.append({'img': mri_path, 'mask': mask_path})\n",
    "                else:\n",
    "                    if 'ED' in mri_path:\n",
    "                        dicts.append({'img': mri_path, 'mask': mask_path})\n",
    "                    else:\n",
    "                        dicts2.append({'img': mri_path, 'mask': mask_path})\n",
    "            paths_mri.clear()\n",
    "    if (heart_mode=='Off' or heart_mode=='secret'):\n",
    "        return sorted(dicts, key=lambda d: d['img'])\n",
    "    else:\n",
    "        return sorted(dicts, key=lambda d: d['img']), sorted(dicts2, key=lambda d: d['img'])\n",
    "\n",
    "dicts = build_dict_ACDC(data_path, mode='testing', heart_mode=\"Off\")\n",
    "print(f'{len(dicts)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861472bf-f951-46d1-a486-5265e6546909",
   "metadata": {},
   "source": [
    "**A modification of the LoadTranform function to make sure that only the image data is used in the DL model.**\n",
    "\n",
    "Some things are commented out for adaptation purposes later in the project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1fcae8ff-968a-4441-89c8-3fbe83fc504a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LoadMriData(monai.transforms.Transform):\n",
    "    \"\"\"\n",
    "    This custom Monai transform loads the data from the rib segmentation dataset.\n",
    "    Defining a custom transform is simple; just overwrite the __init__ function and __call__ function.\n",
    "    \"\"\"\n",
    "    def __init__(self, keys=None):\n",
    "        pass\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        image = Image.open(sample['img']).convert('L') # import as grayscale image\n",
    "        # image = nib.load(sample['img']).get_fdata()\n",
    "        image = np.array(image, dtype=np.uint8)\n",
    "        mask = Image.open(sample['mask']).convert('L') # import as grayscale image\n",
    "        \n",
    "        # mask = nib.load(sample['mask']).get_fdata()\n",
    "        mask = np.array(mask, dtype=np.uint8)\n",
    "        \n",
    "        # mask has value 255, 170 and 85 on the different classes, put them to multi-binary\n",
    "        # mask[np.logical_and(mask>170,mask<255)]=1\n",
    "        # mask[np.logical_and(mask>0,mask<85)]=2\n",
    "        # mask[np.logical_and(mask>85,mask<170)]=3\n",
    "        \n",
    "        mask[np.where(mask==255)] = 1\n",
    "        mask[np.where(mask==85)] = 2\n",
    "        mask[np.where(mask==170)] = 3\n",
    "        # mask[np.where(mask>0 & mask <255)] = 0.5\n",
    "        return {'img': image, 'mask': mask, 'img_meta_dict': {'affine': np.eye(2)},\n",
    "                'mask_meta_dict': {'affine': np.eye(2)}}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba7cba7f-cf8d-47c3-b63a-4ee60285097f",
   "metadata": {},
   "source": [
    "**A function, modified from tutorials, to visualize the images together with their masks.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f088f962-89a4-4c3d-872f-d37a36ebf0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_mri_sample(sample, title=None):\n",
    "# Visualize the x-ray and overlay the mask, using the dictionary as input\n",
    "    image = np.squeeze(sample['img'])\n",
    "    mask = np.squeeze(sample['mask'])\n",
    "    \n",
    "    plt.figure(figsize=[10,7])\n",
    "\n",
    "    mask1 = np.where(mask==1,1,0)\n",
    "    mask2 = np.where(mask==2,1,0)\n",
    "    mask3= np.where(mask==3,1,0)\n",
    "    overlay_mask1 = np.ma.masked_where(mask1 == 0, mask1 == 1)\n",
    "    overlay_mask2 = np.ma.masked_where(mask2 == 0, mask2 == 1)\n",
    "    overlay_mask3 = np.ma.masked_where(mask3 == 0, mask3 == 1)\n",
    "    plt.imshow(image, 'gray')\n",
    "    plt.imshow(overlay_mask1, 'Reds', alpha = 0.7, clim=[0,1], interpolation='nearest')\n",
    "    plt.imshow(overlay_mask2, 'Blues', alpha = 0.7, clim=[0,1], interpolation='nearest')\n",
    "    plt.imshow(overlay_mask3, 'Purples', alpha = 0.7, clim=[0,1], interpolation='nearest')\n",
    "\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.show()\n",
    "    \n",
    "    plt.figure(figsize=[10,7])\n",
    "    plt.imshow(image, 'gray')\n",
    "    if title is not None:\n",
    "        plt.title(f'{title} without masks')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "176daea7-e580-497d-a2ab-55b4e8e54980",
   "metadata": {},
   "outputs": [],
   "source": [
    "# RandScaleIntensityd(keys=\"image\", factors=0.1, prob=1.0),\n",
    "# RandShiftIntensityd(keys=\"image\", offsets=0.1, prob=1.0),\n",
    "\n",
    "basic_transform = monai.transforms.Compose([LoadMriData(),\n",
    "                                                monai.transforms.AddChanneld(keys=['img', 'mask']),\n",
    "                                                monai.transforms.ScaleIntensityd(keys=['img'],minv=0, maxv=1),\n",
    "                                                monai.transforms.RandFlipd(keys=['img', 'mask'], prob=1/2, spatial_axis=1),\n",
    "                                                ])\n",
    "\n",
    "\n",
    "composed_transform = monai.transforms.Compose([LoadMriData(),\n",
    "                                               monai.transforms.AddChanneld(keys=['img', 'mask']),\n",
    "                                               monai.transforms.NormalizeIntensityd(keys='img'),\n",
    "                                               monai.transforms.RandFlipd(keys=['img', 'mask'], prob=1/2, spatial_axis=1),\n",
    "                                               monai.transforms.RandRotated(keys=['img', 'mask'], prob=1/2, range_x=np.pi/6, mode=['bilinear','nearest']),\n",
    "                                               monai.transforms.RandScaleIntensityd(keys='img', factors=0.1, prob=1.0),\n",
    "                                               monai.transforms.RandShiftIntensityd(keys='img', offsets=0.1, prob=1.0),\n",
    "                                                ])\n",
    "test_transform = monai.transforms.Compose([\n",
    "                                           LoadMriData(),\n",
    "                                           monai.transforms.AddChanneld(keys=['img', 'mask']),\n",
    "                                           monai.transforms.NormalizeIntensityd(keys=['img']),\n",
    "                                           # monai.transforms.Zoomd(keys=['img', 'mask'], zoom=0.25, keep_size=False, mode=['bilinear', 'nearest']),\n",
    "                                           # monai.transforms.Resized(keys = ['img','mask'],spatial_size=(1024,1024), mode=['bilinear','nearest'])\n",
    "                                            ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cf2179a8-c816-4c1c-8e14-b393c68d77e9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def from_compose_to_list(transform_compose):\n",
    "    \"\"\"\n",
    "    Transform an object monai.transforms.Compose in a list fully describing the transform.\n",
    "    /!\\ Random seed is not saved, then reproducibility is not enabled.\n",
    "    \"\"\"\n",
    "    from copy import deepcopy\n",
    "        \n",
    "    if not isinstance(transform_compose, monai.transforms.Compose):\n",
    "        raise TypeError(\"transform_compose should be a monai.transforms.Compose object.\")\n",
    "    \n",
    "    output_list = list()\n",
    "    for transform in transform_compose.transforms:\n",
    "        kwargs = deepcopy(vars(transform))\n",
    "        \n",
    "        # Remove attributes which are not arguments\n",
    "        args = list(transform.__init__.__code__.co_varnames[1: transform.__init__.__code__.co_argcount])\n",
    "        for key, obj in vars(transform).items():\n",
    "            if key not in args:\n",
    "                del kwargs[key]\n",
    "\n",
    "        output_list.append({\"class\": transform.__class__, \"kwargs\": kwargs})\n",
    "    return output_list\n",
    "\n",
    "def from_list_to_compose(transform_list):\n",
    "    \"\"\"\n",
    "    Transform a list in the corresponding monai.transforms.Compose object.\n",
    "    \"\"\"\n",
    "    \n",
    "    if not isinstance(transform_list, list):\n",
    "        raise TypeError(\"transform_list should be a list.\")\n",
    "    \n",
    "    pre_compose_list = list()\n",
    "    \n",
    "    for transform_dict in transform_list:\n",
    "        if not isinstance(transform_dict, dict) or 'class' not in transform_dict or 'kwargs' not in transform_dict:\n",
    "            raise TypeError(\"transform_list should only contains dicts with keys ['class', 'kwargs']\")\n",
    "        \n",
    "        try:\n",
    "            transform = transform_dict['class'](**transform_dict['kwargs'])\n",
    "        except TypeError: # Classes have been converted to str after saving\n",
    "            transform = eval(transform_dict['class'].replace(\"__main__.\", \"\"))(**transform_dict['kwargs'])\n",
    "            \n",
    "        pre_compose_list.append(transform)\n",
    "        \n",
    "    return monai.transforms.Compose(pre_compose_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7f4933-cb87-4c47-bfb7-a250d9f7e18c",
   "metadata": {},
   "source": [
    "**Testing the function whether they perform as intended.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3446979f-bf25-4fc6-80ca-4e67d08bf768",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:04<00:00, 20.84it/s]\n",
      "Loading dataset: 100%|██████████| 714/714 [00:06<00:00, 103.93it/s]\n",
      "100%|██████████| 50/50 [00:00<00:00, 884.57it/s]\n",
      "Loading dataset: 100%|██████████| 1076/1076 [00:06<00:00, 160.68it/s]\n"
     ]
    }
   ],
   "source": [
    "# data_path = \"./preprocessed_ROI_v3/\"\n",
    "train_dict_list = build_dict_ACDC(data_path)\n",
    "train_dataset = monai.data.CacheDataset(train_dict_list, transform=composed_transform)\n",
    "\n",
    "val_dict_list = build_dict_ACDC(data_path, mode='testing')\n",
    "val_dataset = monai.data.CacheDataset(val_dict_list, transform=test_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8268b041-e8d5-4392-a0f1-a348f407213c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1. 2. 3.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1. 2. 3.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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I6pe4Hy3jSm5vIKpv4piv6Xsu+BzWM7FRKo9pfk58ZbJxahLNRVpKpcOyypTxpxDHH/oyJYQQQgiRAk2mhBBCCCFSoMmUEEIIIUQKjhvNVAiWDoH9clhDxboPIKqZiquh8m2zktVamiDA1lUVQ1PFXkxnnnmmE7PPEgC8+uqrTsxtzL5TnGwZAMaOHevErG/yaY36w/oeINrmXC/2hGKdkc/njDV7XE++BveRzw+LkxDzWLI0VYCthWM/MR7TnPjYdw6PR985/bE8t4DSJFvNIgGzVaaP49WrqlrrnRVpx0/I+ZXgK1Uqj6hKGk/6MiWEEEIIkQJNpoQQQgghUqDJlBBCCCFECqpaM1VTU3PM9eCQtVTrGNZt+PKtsQ6G4xCvKta5cMxaGSv/GmB7VVlkoQ3h9jvxxBMjx6xbt86Je3p6nJjbb+XKlZEy2HuK26OxsdGJLX8nINp+fAzr6VgDxPnrgKjWjXVZPDa431kf5TuG8yPyOT6fM0tTxtfg9vQ9F1wm9wlrt7j9fPn+uA9CdFUWljYkiS4rCy1XWq+qStKS9KdScy6KdKgP9WVKCCGEECIVmkwJIYQQQqRAkykhhBBCiBRUtWZqxIgReS0Ga2tC9BRZ5AezdFWsrWFNFWB7VXGZId5Vlq4qSb4/xtJtsG7Gp5nq7Ox04qVLlzoxa2k6OjoiZbBXFeuGWPPj0w0xlj5nz549BWPud8D2kWJvJvbU4hx5QNTby8oF6dPX+XITFiqD8ZW5a9cuJ2YNGXtsccztCdg+cNxHPKZ9HluM1c++d4LlsVMMTzfl/6t8suj3UvhKJdGxZTF2qt1XitGXKSGEEEKIFGgyJYQQQgiRAk2mhBBCCCFSoMmUEEIIIUQKqlqAPmbMmLw4loW3PgEri9SZYgjiOOEti8uBqJCWhbZ8Dt+HLwEutwcLlVlUzOJcnyA9iUi9Pz5zyMmTJzvx2rVrnZgF6D6DyPXr1zvxuHHjnJjbh00+uW0A+wcMVp/46sntx8JPa79PKM7H8HjjHzxkYfDKpp2+8bd9+3Yn5h8asCCdYzZvBaLPNN8bP0eWeSsQvVfuR66Hz4zV19f9SfJeKUUS51Jco5rqkZZi/NCgUqnWPiom+jIlhBBCCJECTaaEEEIIIVKgyZQQQgghRAqqWjM1atSovNlfSPJf1sawPoJ1MJYZXwgh57CZI8esDbE0VUDU2NMy+uS2YRNFIKqrimsW52uLMWPGOPGZZ57pxHzvrMUBgN7eXidevXq1E7OJJ+vFWGMFRLVHPFZYy7Vz504nZtNKIKqtYb0O9yvfl888k+tpGVn6ngsu1+rHEDPMhoYGJ2a9F8dWImTfOTzuuS1YCxaiOePngsefrwzexvVIkpA5rvFnFhqWStEuVUo9yoHVz779aU06Q9q3HCad1dbv+jIlhBBCCJECTaaEEEIIIVKgyZQQQgghRAqqWjM1evTovLaHdR8+fQlvY10Qa1rY18bnJ1MKXRVrqFiTwVobIFpX1vxYmqqxY8dGyozrR8T47tNKhtze3u7EPi0S64Q2btzoxKwH43v13QePFWtssB+RT8fG/cTaBUs7xzEQ1fjwmOaxYiU1BqIJlX3+YP3xaTZYA8VlJkkGbD0n3L58POvcfNflMlgz5XuvWGXw+MzinWHVISQhc9wyk5SRBZWqoapWX6kkGqlKafNKRl+mhBBCCCFSoMmUEEIIIUQKNJkSQgghhEhBVWumamtr8xoRy7fm6PH9YR2Mpbvy6WBYO+PTL/UnC01VyHq25T/E9856nFGjRkXKZH1OMeB7mTp1qhP7dGvr1q1zYu6nLVu2OPFrr73mxD7NFGvGWHPG9WRdUYhmhWPuA9Zh+TRTfK88Zlnz44PL5THM/e7TDTHcpnwO58ljrZfv+bXgPrLGvO8Ybi/2iPLpx7iuVo7FkD5hLI1ZEg2adQ0fpfC7SluHUtUjC+L6SiXp13JozorlyVjJ6MuUEEIIIUQKNJkSQgghhEiBJlNCCCGEECnQZEoIIYQQIgVVLUCvqanJi+s4MWpIQlIWjrLwlvf7hLcsnGVBMAvUQ8SnWSSE5G18XctIMCT5ryW8DbkPLpPFkiwEnz59eqQMvhcWpLNZ48qVK53Yl9SZ74XryfD4Y6NQwBaxM1bC65BzWEDtuw/LqJL3c3uNHDkyUqaVFJufRTZS9T2/Vpk8HkOSFFvPJ1/D92MFK/Gz1Qe+RMhWcuS4gnTfMUmwxmwlCNRD6lEtlKLPsjB4rXbxeBboy5QQQgghRAo0mRJCCCGESIEmU0IIIYQQKThuNFMhsKaCtQ0cWyafvjL5HNbOsEbDt81nztifYhiise6IDSOBqI7KSoDL+306EK6XpRWpr6+PbDvllFOcmDUpW7dudWJu3zVr1kTK5DLYZJLL4HHIYwmItgdfwzJ79LWNpaPi9uV+BqLjure314nr6uqceMyYMU7sM9hkfZL1nHLb+DRnVhtzEmzWqHHsO4evwef4+sBX1/5Y+kXfs8ljwXouQkirdyrVNY4X/U0WBptZXLdS27NS65UUfZkSQgghhEiBJlNCCCGEECnQZEoIIYQQIgVVrZkqRJK1Z9Z+sO7D51NjJUPmMn36Ek4kyxoqji3PIyD+enSIp5Gl5fJ58FiwFsSqt6/9JkyY4MTWvXR0dDgx62Z89WBdEY8FvqZvrPCYZJ2QNWZDPI4YHiu+scP3xr5cHPsSfjP8XPjaoxC+ccAaKdZyMeyH5UvUzcew/mnnzp1O7EtkzueMHj3aiXfs2OHE3N6+9rT0dEk0VJaWJotExxaluEbodQpds1g+XXETGychrq/UOzEpcTHQlykhhBBCiBRoMiWEEEIIkQJNpoQQQgghUlDVmqkjR47ktQWWR4+PuBoVX24+1oKwroP1Oj6vKt7GuiDWerCGxeefY2kq+N753nz36ttWCNZ9sH8REG0vXosP0WGx9qi1tdWJuT1ff/11J960aVOkTG7Tbdu2OTFrbbi9fZozS2dlaah8uqO4uisf3OaWP1OIfxP3NevaWFfE+O6Dt3Fs+T35fNF4bHC/jho1yol5HADR55f7nsvgXIY+7y+Gx1eIB1lcQnRDleBVFULaepTKIyruOdXs01Wp9coKfZkSQgghhEiBJlNCCCGEECnQZEoIIYQQIgVVrZnq7u7O+76wLsGnj4irqwpZn+brcGxpqgA7B6DlXeXzz2EdC5dh5ULjXHRAtP14DZz9sNrb25149erVkTKZyZMnO/HEiROdOKRPWPfS3NzsxOwV1NXVFSmDdWnd3d0Fy2DNis+Ti/stbm5Dn37M8m/iPgrxKGP43rgtOAai3l08FlpaWpyYNVXch4CtueD24vb2nc/3xs8SP68+zSOPlc7OzoLX4D7weafxvfB44jKtawDF0SLF9SzKQlNVKZTCdyoLyuErdbzro3zoy5QQQgghRAo0mRJCCCGESIEmU0IIIYQQKdBkSgghhBAiBVUtQF+1alVevMlGjb4kqCNGjHBiSzwegiUgDLkGb2PRqyVQ9wlYWcDLgmkWqLKRoGWq6KvH2rVrnfill15yYp85Jt/r1q1bnfi9732vE59wwgmRMiyzRhZpT5o0yYl9ffjyyy87MQuqfULk/lhJoX3XtRJrJ0l0HIJV1yQiWW4vFmVzzGNj7NixkTKbmpqcOCQReX98z55lGstxSFtYiclZLO6rN9eLYy6Df2DiS8jM/Wzda4iIOIsyKhFfP8cdC1kY6IrqQV+mhBBCCCFSoMmUEEIIIUQKNJkSQgghhEhBVWumXnnllbwOYvPmzc4+n7aGzRtZF8SGfSGaFYuQdXPWQ1haENZI+RIQc/JU1m3wftaT+XQ0rMPgNucEwmvWrHFiXzJW1h5xglzWEPjKYKNPvheG23P8+PGRY1ifw/Wy9CchmgvW8LDOLWS8Wcl/Q7CMPZPoOLgMbq+Ojo6C8caNGyNlcntwH7FOkp8rnw6Ly+QkxdzvO3fujJTBpp2WRorxaab4XcSaRoZ1Wj4jVTbytYw+Q0xOmbSmnlkRkrS50PGlMhe13hvl0q3JpDM++jIlhBBCCJECTaaEEEIIIVKgyZQQQgghRAqqWjO1bdu2vOahp6fH2bd9+/bI8ZxclXVVrJ1hTZUv+WoxdFUcx9VUAbYOxvIWYg0GEPWAYi8m1r2EeC1ZyafZf4g1LEBU/3Xaaac5MbcPX8OnOeOxwb5IfK9JdAo8nnxJsLPGdw2uK/eblWTXl1TX8jWz2sfn48X9xgmqeb+lMwKi/nQ8Vlgj5UuKzfcaV5/je35Z32XpADnRu69Mvi7rw/g58umjrATLcTVVQDIvrzTHJz2HKYUWSVQP+jIlhBBCCJGCzCdTCxYswDnnnINRo0Zh/Pjx+NSnPoUVK1Y4x+zfvx9z585FY2MjRo4cicsuuyzyf/pCCCGEENVA5pOpZ555BnPnzsXzzz+PhQsX4tChQ/iLv/gLZ9nouuuuw+9//3s8+uijeOaZZ7BlyxZ8+tOfzroqQgghhBBFpyZX5EXc7du3Y/z48XjmmWfwkY98BN3d3Rg3bhwefvhh/NVf/RUA4K233sIpp5yCxYsX4wMf+IBZZk9PD+rq6jBu3LhYGiXWULDOYOLEiU7MuhmfT42lq+L6ZZFLLYn3COteWC/BWpAtW7ZEyuTce2+//bYTsy4mRJdgaaZC9CWshTvrrLOc+N3vfnfBMlgrAkS9g1555RUn5q+tls4IiN4rj0f2D+PjfbnlLG0c46sXl8F6JW4LLsOnb7J0Vha+MW558FjjLYvchr77SJujjT2lAGDChAlOzGOF6839zho/3zb2neLnwPdc8HuDsTRUIe8ubq+474iQMi3/Ol89454TUoa1PwufqbjnF+ucauTofXZ3d5v5aouumTpqHtfQ0AAAWLZsGQ4dOoRZs2blj5k2bRra2tqwePHiYldHCCGEECJTivprvr6+PsyfPx/nnnsupk+fDgBob2/HkCFDUF9f7xzb1NSE9vZ2bzkHDhxwfjHDv9wTQgghhCgXRf0yNXfuXLz22mt45JFHUpWzYMEC1NXV5f9NmjQpoxoKIYQQQqSjaF+m5s2bh8cffxzPPvuso0Vqbm7GwYMH0dXV5Xyd6ujoiOTOO8pNN92E66+/Ph/39PQkmlCx9sOK2VfJd03WVR1dzjwKr7OyLgaIr6sKyd/Eege+N9ZPrF+/3olXrVoVKZPzfVkaqWJ4ufg0P9xPnCOwsbHRiTmnm6+erKsaM2ZMwXqFaIIsPQnXgzVSIWOHy2SNi29sxfV84tinrbEohk6D9/O9+s63NGalyNHmq9e+ffuc2MrHyWX4/MR4G2u1uC3YPwsAhg0bVvC6llbO9/z6tICF9ltj3rfNikM0fnHHbMjYyUJ79E7RL1U6mX+ZyuVymDdvHn7zm9/gqaeewpQpU5z9Z511FgYPHoxFixblt61YsQIbNmzAzJkzvWXW1tZi9OjRzj8hhBBCiEog8y9Tc+fOxcMPP4zf/va3GDVqVF4HVVdXh2HDhqGurg5XXXUVrr/+ejQ0NGD06NG45pprMHPmzKBf8gkhhBBCVBKZT6buvfdeAMB5553nbH/wwQfxhS98AQDwz//8zxgwYAAuu+wyHDhwABdccAF+/OMfZ10VIYQQQoiik/lkKjQn2T333IN77rkn68sLIYQQQpSUqk50XFNTkxf5ZSHC4yS6nOyXRdxANBHv5MmTnZgF6nV1dZEyWAPGQlEWYLKw0ScA3rFjhxOzCScLznm/L6GwJZhOIta1hNuWOBWIikc5yTWnKho3bpwTh9SbBb8sSGdbjxChrZWAOSShtWVwaJk7+mCxMwvOfcl+LeKOjSTPcxYJb+PGWdTLd688fliQzoQkkubxx9fg/b7kynwvLGK3xk7IvXLMQnjeHzKmLYPXkB8rWGMhiWlnXMPNcgnWJXK3UaJjIYQQQogUaDIlhBBCCJECTaaEEEIIIVJQ1Zqp/hTDII31PD6tCGuLWFdlaaoAoK2tzYk5ATObTrKWa9u2bZEyWRO1efNmJ2YdESc9zSJ5aNxEtD6SrNXzvbCpJ3uf+bQhlgli3MSpPthQ00qSnaRPkiTW5nN4PLIeL4lpZ4jxrHVOFnqnuJqoJO3JYyFEx8Yps1h7xGX4DF2terBGytoPRLWD/Oywhoq1hb6xwttYH8ZmwayZ8vWJ9RxYOquQ57ccOiJplyoXfZkSQgghhEiBJlNCCCGEECnQZEoIIYQQIgXHjWYqhCQ6DQtee9+1a5cTs4aKtRBAVFc1YcIEJ2YdAuu01q5dGymTdVWsQ/D5IPXH1zZJ9CJZE+L/wve2ceNGJ+Y+890Xaya431jnwWX6NBlWEuK4Pja+Y+LuDzln5MiRBWPW8wDFSQrLx1h+bFnosLIgSZmW9xLfK/eBz5PMen4tHzRfGdzGVvJznw6Lj2H9F8ecbNk3/qxnjc+x3oc+iuEzZR2fBPlKlYby/3UUQgghhKhiNJkSQgghhEiBJlNCCCGEECl4R2mmmCQ5s+LC2pqdO3dGjmFdFed5Y6zcVUCYT0p/QrQjlm9UMXymLI8e33Wsc1hzxrkRgaj2gzVnrKFiTUZI+/PY4H5lT58kZDGG+V5Yw+cj7bPl62duD9bOMJauLQkhukErh2W5sHykrBygQLTfWHvEbW7p3AA77+WkSZOcmJ9fn/8fe1PxMfy88tjwPb9x9U7FeIeKykVfpoQQQgghUqDJlBBCCCFECjSZEkIIIYRIQVVrpmpqao6pR8hirbkY+f58sObJp4GqROJqopL4JGUBa9JY18H6CSCa3481GNxHXEaItsbqd9YE+drK59tTbNj3x6ehYo2ZNVZCcgpyjkC+d26/EI8jJgudn6U9CtHFWLo/LpM9oULy1VltHqJPZK2RleOT808CUR0V3wvvr6+vLxgD0Xyc/A5gfWIx/JxCyoyru8qiTFEc9GVKCCGEECIFmkwJIYQQQqRAkykhhBBCiBRoMiWEEEIIkYKqFqAXolTi8VIYfxaDJMLatILzYonNrXJZMM0Gfo2NjZFzWEhrJY5mfKZ/LOjlY1gky8Jun0mlZURZjD5gk8WQhLiWiWmIuSP3I5eZJFmtVY8s2stKwOzrQ0sMzm3u6wML60cSIcazVkJmxtee/Kzxc2D9EMNXJv9YgZ9x/sEI/3jBd+/lMF+t1L8fIoq+TAkhhBBCpECTKSGEEEKIFGgyJYQQQgiRguNWMxVCKfROpdJuZU0xDDZD7tO6hm+/dQ7rODhJ8ebNmyPnbNu2zYk5QbXP6NPCMmK0NFQ+PYqlx+G28SVPtgwhuUxLL+Yrw9Ln8PG+e2WtG+uEkiSejUsSM0zLUNOnmeJtrBfjmPG1n5WAOeT5ZMNbS1dlmXoC0WTJ/Hxyv48bN86JfaadrDdsa2tzYjYP5Xj79u2RMrleWYwnqw+S9JEoD/oyJYQQQgiRAk2mhBBCCCFSoMmUEEIIIUQKqlozNWjQoLz+gNfuQzxSLKrZqyrten6INiluvZLUKSRhMMMaFdaXsGdUR0dHpAzexrqNJLAuY+TIkU7M98paEp8OJq6/EJfpqxfrqlgjZSWzBaJaLssPKwS+f37Gi5H0OST5L1+XPbK4fa1EyL5tVj24bXztzV5KVp/4xkpcL68QTy3WH/L44njXrl1O3NLSEimzubnZifkdMGHCBCfmZ9GnSeN+6+3tdWJur3Lpm6SzKg/6MiWEEEIIkQJNpoQQQgghUqDJlBBCCCFECqpaMzVs2LD8OnaIHqAcuqpSeVWV4hp8L5Y/UTF8WEL0OawjGj16tBPz2GD/GMD2eIqbe84Haz34GqwN8V2DfX+sNveVwc+OpXPha/rgelh+WCHEHU9ZaPSs+wCiueM4Lxzrb/gavrbgbXFzG/rqycdwv/P4C+kjPsYaOyHvZS6DtUgc+9qG73/s2LFOzLpA9qryaRFHjBjhxBs2bHDiHTt2ODG3r48svNGkiaoM9GVKCCGEECIFmkwJIYQQQqRAkykhhBBCiBRoMiWEEEIIkYKqFqAPHjw4LzRkwaHPXI7N4SzBZamMPyuRYtQ7iTlhSMJcFouymJSTnjK+seITpafFEh43NjY6MQteOdkyEJZ0uD8hSXUtc0zGJ4DlPrF+/JEkCbY1VpgkSYr5HJ8wmQXobABp4bt3Kykx91nIDzWse7GS7PqOsd6ZXM8Qsb0VMz5DXX7Xcz0mTZrkxPwjAU6mDETNV/n53LhxY8EYiBqQZpHYWCadlYG+TAkhhBBCpECTKSGEEEKIFGgyJYQQQgiRgqrWTA0aNCivlbIMJY8e3x9LQxWiHcnCdM2iHGviIfWOa9oZYrhpmQ/6zAhZy1BXV3eMGv8Z7kdOWArE79cQ/Q6PJzYGZe0Nl+lLvhpXM5Vk7FharxAtkgX3SRb6piQmslZC4ZAy2NSUEx9z+4UkD7beRawJCjF4tcaC71njMuJq34rx7vKZyPKzxfol7ldOjMzGq0BUi8kxa+X4vQQAmzZtcmI25uV+zsK0Uzqs0qAvU0IIIYQQKdBkSgghhBAiBZpMCSGEEEKkoKo1U/3hNV6fH4yl9bA0U761ecuDx/KL8dUrrl9OMdazs9BMhehNWJfB5/B+nw6B9Q2WJw/34549eyJl8jlcD2t/iGbPSmxsXTPkmCTeab66FyrD96xZ17GuYe0POSdEh5WFRoq9g6zkvlnoUax+TlImt5dPM2UlrE7izRe37iH3xu3DzxbD9+XTJ/K7h8/hxOXsQwX4n5X+dHd3OzHrekXloi9TQgghhBAp0GRKCCGEECIFmkwJIYQQQqSgqjVT/X2mrNxfgK2r4jVw3m+td4ecw/oKILounoUGKq4Oiwk53vKIsvQoIcfwNdizx3eMlXORvZl87W3pb+L6YwFRHxrfvfTH0t6EEHJO3PEWomcK6ftCdQjJLcdYY9a333oHMCHtuX//fie2PKJ87V+OnJ4h/ZrEuytrstCDce7NtWvXOrFPHzt58mQnZk1UiL6Tc/6xHxbXk7VevvFn9UEx8v9Z13gnoi9TQgghhBAp0GRKCCGEECIFmkwJIYQQQqSgqjVTAwcOzGseQvJdWXoHS1Pl00zxNl4T53VzXiMHgK1btzox66qSeLfE9aoKOT9tLjRLjwLYnjO+fj1w4IATW3nKLF8gwL5XS0Pl00tYubss7yBfn1i50kK0hGnx9SvX1cpPZ3kxAenHm887yOrXEJ84xsqbx2SRfy3keOs6STR5Fvx+9GmRLA1PKfL5dXZ2mufweGpqanJi1lD53rGNjY1OzGOS83PyvbMPFRCW21EUH32ZEkIIIYRIgSZTQgghhBAp0GRKCCGEECIFmkwJIYQQQqSgqgXogwYNOqYAPSSpblxTRJ/QlpPs1tXVOTGLEn0i2J07dzoxC6STJH2Na64XkuA1rkFfknoz3CdsuOmDRa8sCLZMPX1Y4mbLxBOIirCt9rGE9L5tlgFsEjGvNZZCxM5xE1r7nkVLDG6J3mtra80ymSSmp3GTPGfxo4CQfi2FGbD17Pl+xMNicGt8ZZEo2ho7vb29kXM2bNhQ8Lr846PRo0dHyjjppJMK1quhocGJub1WrlwZOaerq6tgmUzI38q4bZpFn1QKSe9FX6aEEEIIIVKgyZQQQgghRAo0mRJCCCGESEFVa6aGDx+eX1PmdXefORyv31u6DcuY0beNDSR5/XXHjh2x65UFcRPPhpTBlGvdnHVUluEmE5IU28IaSyHXZX0O6598ejEeb5YZZhaEjB3WevA5rGeyjgds7ZulY8siQW6IQWJco8+Qey0HvjrE1XuFPEdJElQXG999cXLkjo4OJ2Y9LOtnAeDEE090Yh6TrJni58RXrzVr1jgxG3vymE2ieaxmDVSp0JcpIYQQQogUaDIlhBBCCJECTaaEEEIIIVJQ1ZqpyZMn59epOVGlz3vD0lVZSU592gbWsbDOhTUre/bsiZTBa9qsH8nCR4UJ8ZVKSxZlJrn3uN5KSeppaaJY6wBEx9v+/fudmMcO66F8OsBiaKLi4mvftJofX5/wc8G+UTymQ3y6Qo6pRLLQsCS5V75uMTyz4pLFeyZJsmnWUHHCek5q7Dtn/PjxTsyJjkP0n/z3Y/Xq1U7Mf3NC+qgY3l6VSlZ/+/RlSgghhBAiBZpMCSGEEEKkQJMpIYQQQogUVLVmavz48XndxMiRI5199fX1keNZV2X5cfg0KgyvHbPOJcSXhrHylIX4/FSCd0u1rKuH+KyEnNMf1kMBtmcRj50s9FBJ/MRC/NX642srLiPkWeqPr325HkOHDo1VZohWhNvLl0suSbmFjg95FqtF22Xp1nxjOq2msRi5+XzPDR/DGkfW6XIuPwBobW11Ys7fx7leW1pazHrxdblemzdvLnh8MUiiQSsFxfy7py9TQgghhBApKPpk6nvf+x5qamowf/78/Lb9+/dj7ty5aGxsxMiRI3HZZZdF3GSFEEIIIaqBok6mli5din/5l3/Be9/7Xmf7ddddh9///vd49NFH8cwzz2DLli349Kc/XcyqCCGEEEIUhaJppnbv3o3Zs2fjgQcewG233Zbf3t3djX/913/Fww8/jI9//OMAgAcffBCnnHIKnn/+eXzgAx8IvkZtbW1eN8GeM8OHD48cP2LECCfm3EmsoWJ/Dp/ug7UxSdZkLa+WkDxvTDnyWSWpQynWzS3PlGL4YfnGCmsZ4upeQnQcPFb4HM4f5iNE59KfkPuImx/M1yd8b/zs8TuAjw/RbfG9hozPuLq0JOOP77VS9IhZaDOt9ggZG2mvkUU+VH6+t2zZEjmG8+g1Nzc7MWumeEyzLxUANDU1OfGECROcmMfO3r17nZjrDcTXsVXKeGSyGCuh91a0L1Nz587FRRddhFmzZjnbly1bhkOHDjnbp02bhra2NixevLhY1RFCCCGEKApF+TL1yCOPYPny5Vi6dGlkX3t7O4YMGRL5tV1TUxPa29u95R04cMD5pRO7yAohhBBClIvMv0xt3LgRX/va1/DQQw/F/unysViwYAHq6ury/yZNmpRJuUIIIYQQacl8MrVs2TJs27YNZ555JgYNGoRBgwbhmWeewV133YVBgwahqakJBw8ejHhddHR0RNaPj3LTTTehu7s7/2/jxo1ZV1sIIYQQIhGZL/Odf/75ePXVV51tX/ziFzFt2jTceOONmDRpEgYPHoxFixbhsssuAwCsWLECGzZswMyZM71l1tbWRoR4DAsIOWEkEDX5Y6NPNlDbtWuXE/f29kbK5IS2fExcs0IfpRCTZ2HumIS495bEyDILcalFFm3B9QwxkORtVjJg3w8zuO4sUGUBJovJfca0vM26txC4Hlzv3bt3F9zvu6bVbyxi943XuOJbLsPXr1wm14Pbt1TPIh/DP2jgmMeSD35Hxr03X70twbklQM/ixzO+e1+/fr0TczJkbj8Wl/vKZNE6i9S5PVkms3PnzkiZ1r1lITivFhF7KJlPpkaNGoXp06c720aMGIHGxsb89quuugrXX389GhoaMHr0aFxzzTWYOXNmrF/yCSGEEEJUAmVJJ/PP//zPGDBgAC677DIcOHAAF1xwAX784x+XoypCCCGEEKkoyWTqD3/4gxMPHToU99xzD+65555SXF4IIYQQomhUdaLjzs7OvCaE15598Lo4m3iypsrSowDRdd99+/Y5cTFMAJPoI7LQDVkGkZamJYtrZlFGFsaq1v6Qe+f24zhk/FmaldZXVznxYE8ZPEY3nzbViS3DTdYNArZWMIlGJS48Hn3mhBYhuivG0rpxe/l0bFwG6ze5TE6S7YPbg/s9RB/GOlN+h1raVX4/AtGxwsdYGj4f1niyNHwh+jpL8+OrJ+uTVq1yn09uT197MePGjStYBo8v/vEXaw19161UE89i/H1IihIdCyGEEEKkQJMpIYQQQogUaDIlhBBCCJGCqtZMrVy5Mq8/4HVjX0LIMWPGOLG1Ts6+Uz4/HU6GbCUkzWItOYn+qRReVcXwbkmiWcmCtNdJkpSY9U6Wpsq3LaL7G8h6nWii45oaV7MyfvkbTsx9wpoqHz4dVRze6p0Y2WY9OdazFvLstQ1Y4cQ8/nw+Z1a/Mqx34tRaQFTnMnGi2x6se+H3jq+evM26N9998PiydJPsc8Z6HiCq92K9Dj9LrKEK0SdmodHLQsvK+rCtW7c6MXtG8d8c39+1hoYGJ+ZEx/wsbt++3Yk7OjoiZfK2LPwSLUrxN6qY6MuUEEIIIUQKNJkSQgghhEiBJlNCCCGEECmoas3Uhg0b8mv27MPS2dkZOZ7Xmzmxcl1dnROH+Pywdw3HpfDbqCSvjUJl+toi7nVDcqOVIw8X49NXWOOJ45B68raGF15xyyAdlu8+jpDWw7rV1ldcb5wt77U1VG/tnuTE00ZWZrLyDUfenbqMttxKJ7a0Sr73CutcWKvEvnp8jZAcltZz4dMAWb55lseWb/yxBorzm3JbbNu2zYlZt+qrRzHeCVaZIX3A+jDWKrFPF+t4Q46ZPHmyE2/ZssWJN23aFCmT8/exdiuu71TIOaUgSb+Goi9TQgghhBAp0GRKCCGEECIFmkwJIYQQQqSgqjVThw8fzq/Dsu9Kd3d35HjOi8TnsJcLa6h8PlO8zVp7z2LdOItccxYhuani5qMr1np13DxbIfWO208hXkPsI2Xda5J+5mqzHgrw+A95xnUcWEMF2Doq1lBF8ejrzCMSYPQz7w0ZnRv6Ti64/8SBa5zYp/lh7QzrivjdxH5OvvHH497yKOPx6jvG907sD2umfPkRWSNl1YPLCMlLyITk4ouLpcMCbG8v7ne+d/ZKBKJ/69h3isfK1Knus7lxY1S/yPXgMVoM/8RqR1+mhBBCCCFSoMmUEEIIIUQKNJkSQgghhEiBJlNCCCGEECmoagF6LpfLC99CDNLY2JMF6Lt373biE044wYl912CBJScoZfOzEKFeXGFyEizBpe8a1jlWgtckhCQPHTlypBNzn3AZLJr1iWg5safV5iFJiYvxwwEeT0f63DFaQ7eWGxAdf3YCYTd+6sSP2xXbbR9SkADTv9iJjQOePbMtPNsiNeXr0L2sO/Qud/fh6L1Oq3FFwVYCcE6WzIJ0IPr88jFsjslCZiAqZrZMO/nZ8gnQWWTNAnR+pzJsfAlE37tMEgG69WMi7pOQd6j1buKkxD6xOJtPt7S0ODGPjRNPPNGJ2QQVANavX1/wGF+bWxTjx1hxrxlyjEw7hRBCCCHKgCZTQgghhBAp0GRKCCGEECIFVa2Z6o+V7BawdVWbN292YjaT8xmmsTaGdQe837dOnCRppEVcE7qQa3CZIW2eFtZT+LQgra2tTsw6BNY/cb9yDNjr+5ZBXxITwCTnRDRlVM9Dh1wNRk2N5xmgbuN7fWrKxwru9xEZCTHHRpLnJIkGoyiqjQyeg5V72twNe90ypx/ZSpd097NOBohqCa3n1TceLeNZy9jXl9SZdUKW9og1kj7Nj88ItRBZ6GaSJIq2YLNWn2Zq/PjxTjxlyhQnZp0btx8fD0SNPTkZMidLjntflURWf7f0ZUoIIYQQIgWaTAkhhBBCpECTKSGEEEKIFBw3mikmiZ8Te6CEeESNGjXKiVlTMGLECCfev39/pAyf90ocknikxN2fxTlZ6Ig4GTUAjB492olZg8FtzloGn2Yqrv4mi0SpSeAkw5aLVJ/vvthHakqAj5RBJEGw4b0Ukjg1dnJVPr7w0dlh3GsWvNblegmdVtPuxL73DPtG+RIZ94f1OoD9vksC60wZfs/w882aISCa1D6uLiuEJDor1tBaWiOuJ3shAsDWra5+jvVM7DvFPl7sUwUAJ5/sJut+8803nZh1alzPJH9/k2ge4/ZbMXS9R9GXKSGEEEKIFGgyJYQQQgiRAk2mhBBCCCFSUNWaqZqammOugfrWX+P6qvA6sC8fEWsGeP2+sbHRiXkt37eN19GL4UNVDLLQDfG9sEZj7NixkXMs/YOVZ8+XR4/PYSxfqUw8o4xrAp6xQUOB9U9BuoRi5MziMRo3r94xtlUCEX1YEcq0+uSVziazzDMOuzoX1mqypso3HocNG+bE7L1n5avz5cHkevCzZz2LrJkEonlV2Z8piU41i/cbtwf7blnPgU9j1d7u6uXYE+rUU091Yvad8rXf5MmTnbitzfU9W7t2rRN3dnZGyohLuf6OFXqvxHnn6MuUEEIIIUQKNJkSQgghhEiBJlNCCCGEECnQZEoIIYQQIgVVLUAfNGhQXjzMwj5LzAvEFxH7yjxw4EDBa3BiXp+Ams3xrCSdpTA381EMY0rL+I7F4T7hKIs4WbDKBoaWqWcIWRjOWUx6Y615zCLLYDODemUx3oqRpLhSKIZsthjtsXyb+2OYATvc5/ncEzqc2Cf85meNn0/ez8+r7x3K5sj8PrQE6D7jUBZZs8mkVaaPYrxDQ/5O9cc3Lvj9xT9o4phNOn3tN2HCBCeeNGmSE/MPD7gPfT80qIRnPG4dJEAXQgghhCgRmkwJIYQQQqRAkykhhBBCiBRUtWZqwIAB+TXoJGvRfI6l3/FphnhtuKurq+B+n8aKzUCzMOlMu75fqsS91r1aeifANr6zkpyyrg2I9pM1NpL0SYgmKmtKpVtIe51i1NPXQ0VpjQy0NXGf3yTtxc/Bjh07nNinJWStESfN5ZiP973/+J3JmlF+vkPahp9pNv+1dKlJ4HpxvYH4Wq2Qe2WNGRtocvuyjm3EiBGRMtmclTVT48aNc2I2RX0noi9TQgghhBAp0GRKCCGEECIFmkwJIYQQQqSgqjVT/RMdh+ibGPZI8SW87Y9Ph8Xbent7nZjX5n1r5laiXob1EZWS6Jjh+/L1iaVt4/X93bt3R46xdBusCfDprrKmGH3CSYtDSKKlKYcfTBINmllPI7kykMAjKqBe1rvH0moCQA3XLGZFfW0Tec/Q/hV73GS2NXujFz39iJtUl3VB7GHE1/TpsFirxRogvsbIkSOd2Kd55DbmY3g/JxD29Unc926pniO+F26/7du3OzG/U7k9geg7k/uVNVPDhw8veA2gNN585URfpoQQQgghUqDJlBBCCCFECjSZEkIIIYRIwXGjmWJ8ugXexmvxPl+Q/vjWeFl/Y+lx4uZi8lGpefYsktw7t+fWrVsjx3CeKCtfGOsjfG1RDM2T6SuVQEJQzFxTlU5sDUaRnpsBrPeK6V/n1UyVQAfJz2PI2OBjOCebpU3yaaZY48O+SFbO1MGDB5v1tDS1/E4I0ZxZ71BfGVa/JtEV8THsW7hr1y4nZt1pY6Obs9EHa6bGjx/vxNwHSfSxlUL/9lRuPiGEEEKIEqHJlBBCCCFECjSZEkIIIYRIQVVrpgrhW5tm7Qyv71vr2b41X15r5/idTJJ8iUlgHQZ7pFhauJB8WZlozIzl96dOdH2kIuv1CfQTieAyLL+mEvmcxdWbMEnaJsS/LknuuLjXLQYDB7jawhwNUF97vdbV4sTnDOtyYtbr8DMf8n7k6/LzvXPnTidmXzkgquFhHSX3YxJtEpNFftgk70geK/w+4z4JyQXLZfI7lTVTDQ0NTsw6LaByNVNZ6Uj1ZUoIIYQQIgWaTAkhhBBCpECTKSGEEEKIFGgyJYQQQgiRguNWgB5CEtO1aqUcJp3FqINPLM7bLJO/JKRNRu3DFJwHYJ6TpEyjjCyk0UkSG1vnWKLiLH4cUgyD10hSY+8xhSnGmyrETJTbmA03WZzvaz/+IRCXyc/3gQMHCl4DiBr5hhj19ieJWDruOyLpdawyOMkwtxebdvreqUOGDHFiFvSzAL2+vt6JWfAPRA2Yy5H4OO4PDWTaKYQQQghRIjSZEkIIIYRIgSZTQgghhBApeEdppnjNls3LLHNH3/r28aSrqgZ8OoQ9e/Y4MSfu5PV/33p+1vi1ELYpYiGyGGtZjFYuo8ZXr7Q6Is/5J68b5cTcj5YGw6eZemtyT2RbfziJcTFgs0wgqqPKxCeVPWAzGA3LtxVOkntWU6cTs/kjENUvjRrl9rNlBLpjx45ImWz0WQwTVEvvlCTZbxbPuKWh4vb0YbXX2LFjnZgTIfuST3M9KkUjlRX6MiWEEEIIkQJNpoQQQgghUqDJlBBCCCFECo4bzRSvTyfx+OA13ZBkmJWa2LgSfKWK4SMSUoblKZOFZorrwbFv/C2a8vHItjjXOMZBbhjrCtnguybrqGqM5zPEdyqtt5fv/GnrRxc8ZuWJriePD8tfyBorWTwXSTRBVj1D+sC6LmtnfFoahnVVVjJgnw6pp8fVwrEe1rrXLJJiZ+EhlaQerA1mHyl+P/LxQNT7i9+Z3EfsM8U6VSCqbS0GRfHqC6T8f3GFEEIIIaoYTaaEEEIIIVKgyZQQQgghRAqOG81UEs2AtaadxZp3MagEPRRQHO+WJNew/MKsXH1Z3EeI3iRuGZ4DoptiX6VExMyjx7CnVMg5Fj6tnKUBOuXtOif2vRMsrypL11apXnW+McxtaPXJ85vcfjzvXdE8cFwm66qGDRvmxOwh5YN1Qda7PEQLZvVTpfQj3yvn5gvR+fI7lMtkTdTIkSOdmDVXPsqRm6+YVMZfZSGEEEKIKkWTKSGEEEKIFGgyJYQQQgiRguNGM8XrwFaePR9Z+L9k4fdyvBKiQ0jSFrxeb+lzWJPh81lhuJ6W102IvieJRqpasHQwrEWKtIXHjiitX9MAj2ZqsPGeYI8en/bm3Wvd8WdqqDKgFJ5tXp+pgGMKXYP1OwAwfPhwJ+axwvtZIxmiobLulcdriKdgOTRUITpeyx8x5D747ye3D/tKDR061Il9mimuVxYejeXIb3os9GVKCCGEECIFmkwJIYQQQqSgKJOpzZs344orrkBjYyOGDRuG0047DS+++GJ+fy6Xwy233IKWlhYMGzYMs2bNwqpVq4pRFSGEEEKIopL5ZGrXrl0499xzMXjwYDzxxBN444038E//9E8YM2ZM/pg77rgDd911F+677z4sWbIEI0aMwAUXXBC09i2EEEIIUUlkLkC//fbbMWnSJDz44IP5bVOmTMn/dy6Xw5133olvfOMbuPTSSwEAP/vZz9DU1ITHHnsMl19+edZVOiYhCTP74xOvWQLMLAwii2HSmUQwzWQhHo8rYPUl0ORtnITTEir7fqzAwmOLuGPJSwUkLU6CzwyTBainbx3nnjOisODX1/7cpixg5ZjrNWhQVNU+aJB7TKUY9RZDKJuFWPcwJxGnNraeZ04mD0QF5vwu4v3d3d1O7Bt/IT8q6U9JTHc9lGO8hbz/+P4ts1Y2VvUJ0KvdlNMi87/Sv/vd73D22WfjM5/5DMaPH48zzjgDDzzwQH7/unXr0N7ejlmzZuW31dXVYcaMGVi8eLG3zAMHDqCnp8f5J4QQQghRCWQ+mVq7di3uvfdeTJ06Ff/xH/+Bv/3bv8W1116Ln/70pwCA9vZ2AEBTU5NzXlNTU34fs2DBAtTV1eX/TZo0KetqCyGEEEIkIvPJVF9fH84880x897vfxRlnnIGrr74aX/7yl3HfffclLvOmm25Cd3d3/t/GjRszrLEQQgghRHIy10y1tLTgPe95j7PtlFNOwa9+9SsAQHNzMwCgo6MDLS0t+WM6Ojrwvve9z1tmbW3tMddgj67DhiRNjKvPycK0M4k2KW5S2BCDOYskZnBWPUJ0B1mso9fVuQaQbPrn02n0J66+IoR1B0+Kbqyhey2CRioL7YcFl3nqpobIMaNHuQluhwx1dW2WCaoPHtM1Ne54ZF0H6zh845f7/hDFScbntPWjC5axYkpv7DLjUip9Stz329Kt9ZFtn2iMp7vi59s3diyjyuMZy6SYNVK+9rZMr1mXytfgGIg+n9xvIX/DK8mkk8n8y9S5556LFStWONtWrlyJE044AcCfxejNzc1YtGhRfn9PTw+WLFmCmTNnZl0dIYQQQoiikvmXqeuuuw4f/OAH8d3vfhef/exn8cILL+D+++/H/fffD+DPs8/58+fjtttuw9SpUzFlyhTcfPPNaG1txac+9amsqyOEEEIIUVQyn0ydc845+M1vfoObbroJt956K6ZMmYI777wTs2fPzh9zww03YM+ePbj66qvR1dWFD33oQ3jyyScj+X2EEEIIISqdoiQ6/uQnP4lPfvKTx9xfU1ODW2+9Fbfeemtm1wxZG7USQBYjeailoQLspJIcc5nF0Pz4sHQtVvsmgcvkpMZAdP2ez2EfKk62msSnxtL8eO+8DD4rIX0QV1c1nTRStUOj3l/WWEjir8P1ZI+o2trhFLs6S69vEvXzkQTPUojWoxClSFocWEjsU7Lwq1u0wj1n1rTCPkhR7ZwnIXPMMR15fjNozyw8pJLUg8e0pWfy9Rlv45jvLaTMJBopJu2zVkyUm08IIYQQIgWaTAkhhBBCpECTKSGEEEKIFBRFM1Uq4vpM8do7r+vyWnzImncWvj6s2WEhPms/6uvrnZg1QACwefPmgtcshh9RFhoB7hO+99GjXQ8f3zkM53nj9g7xoCmGX5NFpegBLN8zX79zyifWVAzkZzGDnJWsjavhegbooXz6OQseP3H7zTe2StL3ZdBq+Y6fMcEdK4MGjXFifh9yH/l85OI+4zyWkuhQk7z/rPdwknHA7cXPRYi+yecT1R/+W8rX8PlUWT5TIVjtUU79ob5MCSGEEEKkQJMpIYQQQogUaDIlhBBCCJGCqtZM9fX15deYrTVcwF4HZvbt2+fEIfn+LG2NT5PBa9xjxriaAfZRmjZtmhPv378/Uubu3budeNeuXQXrlUUewiQ6LD6G+20U5XjjtgBsDQC3r6W1AaI6DMvrK9J+AanAKkUTZXEy5ZoDNZdP+8DbWNc3gHRsli4GsLUfHEfys3nGI18niRddXD3dyevcMZ1Frr6K8d/hywY0Ddedn3H2ltu6dasT83saiO9/lUQfWwx9ThZ+bPzu4vbkZ833d9EaT1b+vyz0sz7i/s2p6tx8QgghhBDvJDSZEkIIIYRIgSZTQgghhBAp0GRKCCGEECIFVS1A72/ayYK4YcOGRY63zAb5HBa1+4SOcc3dfIK4urq6gnFTU5MTsyDTl/x30qRJTrxz586C9UqSoDSugVoS48uQRNHWjwD4HBa1+8q0kk1zH/HYmLj7zUiZm3BKZFslYPYT9zO3t2fsDKJt3PMsWOVnj0Wyfz7HFaAPGVJYcB4RzfoMOY1z+McdxTB4DTm+YoxAjbrmSIFeE6BAZxNiNublsXLKKe5ztHLlykiZLFJnkhjRMtYxvvY2E6Rn8EOghgY3ETkL0DkePtxNEO4r02ovfi58zy//PWWhfMi9l8K0Myn6MiWEEEIIkQJNpoQQQgghUqDJlBBCCCFECqpaM9WfEM0PH2MZfFkmgEDUjNDSUPkSQDJcL15/Zh2Hb+1+7969TpwkuW9a4hqaAtF19O3btzux715ZY8H6G753Nn/0mXZyv/JYsK7J7Q8AuSOVadJp9UvkOeE+8BlsUntZybs5DjEStGJu7VrPO4Lryc84P88hmr245o6+d1cxnk+rfYISH7Pmh41Rqd6DBrrvu0GDo+8/Hhscsz6RNaK+dyqXwe+RPXv2RM6xsPokYhIb0IdZmFtyv/K7iNuPNWpJ9LKWiXEI1jnVYmp8FH2ZEkIIIYRIgSZTQgghhBAp0GRKCCGEECIFx41miteeD1EiVSCqw4iblNiXfJXLYL8hXjdnTRAAtLe3OzGv5/N+XgP3lckaAStRZaXC/bpjxw7zGNbfcHuxz0rIej/r1LiPeGz5fFZq9lZIMloiMjaoPSydX59PG0LtYSUpTpIU2zyeYl9rH+GEuPSMhyRwjXgFGfV6c5KbdLzmUPx7TzJ2SjHeIm0x0L3m/+exWrN8kHjs8LN16qmnRspkXdArr7zixGvWrHHiJNolSyMV4jNlxSFjnttrzJgxTsxtwT57/L4MwdIS+t4ZSZKIl4Ksrlsdf1GFEEIIISoUTaaEEEIIIVKgyZQQQgghRAqqWjPVPzcf64Z8a7a8NswaKMuHyud9w+fwWnLIOjrrcdijiO9l27ZtBet5rOukJa4fUQiWdiukzN7eXifu6upyYvaE4nHQ2NgYKXPLli1OzO3J1+Q+8t1X24AVTvz2kXdHjikHkTam+K3JPU48bb3bnj4/HfbpsjRmWWBpMHz15GfvINU7Cx0Mw95Mfbn0XkOZkMQrKFJE4RxurNcBohof9kniMlkPy88iAHR2djoxjzd+Xnkc+PqQdUFWXj2fDivuezlE68p/x1gjxe83fv/56mTl3uN74+N9Ot64erBSaaiy0CMC+jIlhBBCCJEKTaaEEEIIIVKgyZQQQgghRAo0mRJCCCGESEFVC9ALwYJCH2x2xmZwIckcWczHSThZiOdLhGwZxlnJk31YhqNJxOKMVUaIMWgW9eD22bx5sxOPHTvWibnPWKAOREX+3I9szsr36kueHBFdFz/XdBCW4DIk2S8TEaBSP1vPjW/M8zksimXRO78DfO8EFjNnISrmY1jAnztcGWaFFkmMVDn+8BS3zVlsDkSfFe4T7tcNGzY48csvvxwpkwXojDWmfT9WsATo1thJQkiyeH5/NTQ0ODGL/q2/cyH1sEyyffW02qdSn4NQ9GVKCCGEECIFmkwJIYQQQqRAkykhhBBCiBRUtWaqr68vvzYbsr7POg7rHGutGbCNyNiAc/fu3WY9syBkrT3O/nKRpF7d3d1OvGuXm1iWNQW+fmVTP58JXX94HISY1lULXO832rqc2NdHJ69znx02w9xFxqo9Pa6uyNcnlgaPNVGs4/DpYCyTyZDxZ2o/KqTbLXPCLN4B557gvu+amiY5sS+pLmtpWI/IBrp//OMfnZg1kr4yWK/IxqCW+bKvnkk0UmmNK33Hs96Q32+sqWKNVIhpp/WcsCkv69x8ZGWWWSnoy5QQQgghRAo0mRJCCCGESIEmU0IIIYQQKahqzVR/QvxgeI2W13V5P6818zq7D9basGaK42PVNS3W+nMW+ogQH6m41yyGdxUnPuZEoL6ku8OHD3di1uNk4ZlSqZoBS6cR1QRF683eSgxrqvgau0mDAQADY443xje2eDxF7o3OWTUlmlT3iKGlYc1UpH09dbVGQhZjpxhec/zO5NinW2O93KZNm5z49ddfd+JVq1Y5sU/fZPkiWYl6Q5LHW3GSBPSW1pXfXUA0kTFrffldxro13zvVqgf/HeO/pb4+8fV9JdL/XuM8V/oyJYQQQgiRAk2mhBBCCCFSoMmUEEIIIUQKjhvNFONb6+Q1W8tLiNfyeS0asNefOReVz2eK15+z0FCl9ZSJq4cqFcXwnfLl0WOvFh4L7GEUsrbOmrsTD61xYh5/G3PTzDIrAe+dG7qgFR7tUX/evXZkZNsRQ8/E1+CxstJzzRrWylApNQOo1ASyj1wCo6li6OksHQy3X5/nmlyP08a00xETnYjHND83ALB9+3YnfvHFF52YfaZ8ZVj1tOIkuUtL4c3H9WJPKcD2lWLPNv67F3If/LeTz2GfKZ/PnvU3pVI1pKFU5l9MIYQQQogqQZMpIYQQQogUaDIlhBBCCJGCd5RmyvIeYVjL5FsHtnI88fo1a6gA28OoUimGTw2ThXaLdWqs0WCfFiCqhePYytUXosPiMtlfB256sbKRhWdZpARDw7fyRDuHZdx8k6yH8tWD9U01fQFlcJExNVK+o+PqRZLoTSwNlavW8ZfL+huO+dnbsWNHpMzVq1c78bZt25w4RCNl1ZPhd2xILtMschtaZXDMf1/4HeLbxroq9pnyvZssWDPF3n3sK+XzlLJ8pqpNI8Xoy5QQQgghRAo0mRJCCCGESIEmU0IIIYQQKdBkSgghhBAiBcetAN2HlQzZEhCyUBwA6urqnNhKmOlLVMkmkj5jz7hYInauZ0iiTyau6DUk0WzIOXHha3A/7ty5M3IOC2njCuF9iT65T1gYymNp4r43nXgTTolVh6rCMORMVmT2yX+TGHCWg6AfBSR4Xs9q6nTiMWPGOjE/J/xsrV+/PlLmxo0bndj37PQnSb/GFaQn+eFLkjaPiP7JpJPF4r4fy/DfFP7RUxLBOcPtwz8K2LfP/bVMSPJpa3+1CdL1ZUoIIYQQIgWaTAkhhBBCpECTKSGEEEKIFLyjNVPWmiyv+7KJp+8YK/Exr2cDQEtLixOzhsCn1bKIqwcLMa2zKIWJZxKSmMVxv7KeiTUDPDZ8moGtW7cWrBePHdZCbO6Otk0l6ApCDHLNMvh8331lYJoYua6RkDkL7VY5CNL8cBtHDEyjDB482InHjnU1U/wcsEEuG3IC0Wcl7nvahzU2rGv4NKdWm2YxHlkzxSbP/B4Con9TWIvJ75UksEkxm3byft8719LCMZXwbouDvkwJIYQQQqRAkykhhBBCiBRoMiWEEEIIkYLjRjOVhU8Ir9HyGq9Pu8Tbhg4dWvAavnpxokr2nUqimWJ4DZt9lHh9P6T9TE+eDDQESbDW2vneOZmoD9aGsHbhzTddTyhfImQeo6w7GDFihBNHE/V6qHJvlmPhu4uIjiqmhipE2xU3IXOxSOvhFuLpZumEpte7Gj8A2L/ffXbeeustJ+Zxz8m7Qzz0LD1TKTRUvvaL+z4LGW8csyaN9VA+ze2oUaOcmDVSSdrL8ilkbRz/jfJppvhe+RpJ+qCS0JcpIYQQQogUaDIlhBBCCJECTaaEEEIIIVJQ1ZqpAQMGJNJGHQtLM9XT0xM5Z8+ePU7MWpoQjQVreJqbm52YdQZZaKiS5OWKqxfJIj9YyPHW+j7D+idfvkS+DusS2BuMdW8vvfRSpEweP+xVxf3M42LKkDWRMg8cOODElZK/L8n4SnARLjSDIuP7TtWAdVeGxidgzKfVhvjOtzQp/C7l8QdE34ms79yxY4cT8/isZM1Lf7LwTgsttz8jR46MFQNRzSdrpiwNVch98d85/hvEcUhuUouQsVJJ40lfpoQQQgghUqDJlBBCCCFECjSZEkIIIYRIQVVrprLGWjv2rfn29vY6Ma8Vs29IiOaH/YZYh8XXCFmLLobeqRRYefUAe9183LhxTsz6Jp/PVFxfGu6zpqamSJlW/j6+V8uDxldGy8HXnHjLgFMLlllNWOoIvrNMvINC2ity4cKHl0vlYflIsUbK9+zxGN63b58TW++mLDyiygXXIwu9Lufi4/cIazV97yr2NmSNlJWn1de+1nuXtXA8DkJ8ppLoKpN4yZUKfZkSQgghhEiBJlNCCCGEECnQZEoIIYQQIgWaTAkhhBBCpEAC9JSw8SIn+vQZ3zFWwlEWJbLo3ZdUl8tkoSMLGVnE6DNdCxGDFyLESDCuKNtXBt8r3xuXwWJKICou5T6xzAgnTpxo1vPtt992Yh5LfPzw4cMjZfK9dnZ2Ro6pRIqSBDtmImTfda1nMYmZbRaJei1CWs9KNMvict/z3t3d7cT8HPBzE5KAuRTtE5ck/ZxkDA8ZMsSJ+V3Pse8dwIJz629OyA+WuO/ZUJj73fo7CFRGvxYTfZkSQgghhEhB5pOpI0eO4Oabb8aUKVMwbNgwnHTSSfj2t7/tzEpzuRxuueUWtLS0YNiwYZg1axZWrVqVdVWEEEIIIYpO5pOp22+/Hffeey9+9KMf4c0338Ttt9+OO+64A3fffXf+mDvuuAN33XUX7rvvPixZsgQjRozABRdckEnOOSGEEEKIUpK5ZuqPf/wjLr30Ulx00UUAgMmTJ+PnP/85XnjhBQB//ip155134hvf+AYuvfRSAMDPfvYzNDU14bHHHsPll1+edZWOSdw18BD9k6V78cHr0zypZB0CG7nxejYQ1R5ZOiG+N9YZAdF1cF57t0z/fOvoXE/WbYRoa1g3xMdwMlauF2sOfGVwe3DCUW4Ln6GfpX/genIf+RJtsyEpazAG7VjhxD4t3PFi7MlPWk1AIuS4JoDFSoCblj42lPTU6YxxO2iLO6b5vdPV1RUpg9813B5pdZW+MuPu95FFH8XVxoXorjjmdwK/6/n59pXBzzjH/L70YY17fk/zu70SnolSk/mXqQ9+8INYtGgRVq5cCQD405/+hOeeew4XXnghAGDdunVob2/HrFmz8ufU1dVhxowZWLx4sbfMAwcOoKenx/knhBBCCFEJZP5l6utf/zp6enowbdo0DBw4EEeOHMF3vvMdzJ49GwDQ3t4OIJpuo6mpKb+PWbBgAb71rW9lXVUhhBBCiNRk/mXql7/8JR566CE8/PDDWL58OX7605/iBz/4AX76058mLvOmm25Cd3d3/t/GjRszrLEQQgghRHIy/zL1d3/3d/j617+e1z6ddtppePvtt7FgwQLMmTMHzc3NAICOjg60tLTkz+vo6MD73vc+b5m1tbVeXUvW8Dov6158a82snWFYQ+DTFLBWgZcxfVqj/vj0Tay38Wll+rNnz56CMRDVFlkaIG4vn7Zrxw5Xx8HaoxCfKcsLiL1xuB6+Mrnv2ZeL9WGjR4924vr6+kiZ3E8nnHCCE/O98jjwjR0rsTbXM8RjphyE6GDi6jBMDdWfC41VjxCfpHLA9Tp5xIbIMYcPu88vj3HWe3Lsw/KF4zgk0TuTxIeqHIndQ8rgY/gdyu8Zjn2aKYbbM0TPyXDfs0aK/77w3yifd9/xTuZfpvbu3eudhBzt0ClTpqC5uRmLFi3K7+/p6cGSJUswc+bMrKsjhBBCCFFUMv8ydfHFF+M73/kO2tracOqpp+Kll17CD3/4Q3zpS18C8OeZ+fz583Hbbbdh6tSpmDJlCm6++Wa0trbiU5/6VNbVEUIIIYQoKplPpu6++27cfPPN+OpXv4pt27ahtbUVX/nKV3DLLbfkj7nhhhuwZ88eXH311ejq6sKHPvQhPPnkk97lKiGEEEKISibzydSoUaNw55134s477zzmMTU1Nbj11ltx6623Zn35gsRdF+flStajANH1e9Y/8Rq3T7u0b98+J+b1aj4nZM2bsXLNMb79XE9eF+f2Zd1QiE9XXP0EENVlWFqPEC8w7jfue64n67B82gbWR/Ax3F5cpm/s8C9gWcPHOjbfvU489Ka7gQ7ZXPOeyDnViE9p49VROQfE01QlIYleJ6LvpNj3ruLnhPWK/O7yjTdLv8TPhbU/pMxqIYknHvcTf1BgnarvvWLl0bNyk/qwtKs8NvidmuRvFFNt40C5+YQQQgghUqDJlBBCCCFECjSZEkIIIYRIQeaaqXKRie6A1nlD1n1ZZ8B+G+zP4dMyWOvPDGuP2IsEiK61cz2sNXEffA7Xu6Ojw4k515yvPVl3lcU6ueV9E9LP3MaWX5OVt9BXD9ZPsFcVZwng9gRsfR3X2zf+rHx0Ew+/WXB/qTRVSTygzDKN/aamyl+ReHVI4Jt0yqhNThwdn9Ey+V3Fzx5rIn2aKcs3Kq6myretFBqquHlZsygTiD7zrGlkfRM/rz7NlOWHyPUK8ZrjsWH9nbP2J6FSPd2Ohb5MCSGEEEKkQJMpIYQQQogUaDIlhBBCCJECTaaEEEIIIVJQ1QL0YcOG5cV2LIArBj7hHos0WfxnJYQMwRIR+5JAW8ewIJ0TGycxXbNE2SFJTi0xdIgo0RKCJkn8aSVcZmGo70cE3MZcDz6H+8yXPNkSl4aIT/nZsX5oELm3wjm0S0Y5BOoAECk1phGoD+tHEtZ48yUq52fe+tFEiEEux+UQkych7jvDd0wWInXuR37mOfa9q1i0zj9I4r9JSerJ7wgeSzz+QhLSW2OhUsZKKPoyJYQQQgiRAk2mhBBCCCFSoMmUEEIIIUQKqloz1dDQkNcTdXd3O/t8GirLmNIy8QzR64QYosWF17xDEgZz3fkcNovja/CaOGDrvZLcu5WAOWS9n8uw9BAhpp1W37MmjXVFvb29kTK5XqwzsAz9fJop1lxYmjPW8AFRfY1lzMhlTN23PlLmmgMnOnExnotKgZUdkbeEobXxvVdOb9zmxNHn0+0DTm7LMRB9J1p6Jt/7Mq4pZ4hOphy6qmLon0L2swaK9U6Waafv3c/n8DvBMgv21ZOfcX5HsMErjzdfH1rtlUQfW0noy5QQQgghRAo0mRJCCCGESIEmU0IIIYQQKahqzdT48ePz68PsrbFjx47I8ZaXEq81c5yF7iNJGZaXi09XxOvNfIylCfLpiHid3EpSHJI8mWFNQBJ9E2PpnXzJQ1mHwFoH1jtZbRMC9xHX05fQ2pe4uD/cJz5PMuveGO5Xn5ZuzAE3KTOf86ed453Yl1S3aqHxdvKIDU7MY9z3/Pb1Ffb52bbN1VSxZiVEbxJX7xRSRtz4WNuyJq4mKomGKqQM613EzziPFd+7ytJAJfEM5OeVx1dPT0/qa1gkGRdxdVlZoi9TQgghhBAp0GRKCCGEECIFmkwJIYQQQqSgqjVTDQ0Neb1HiHcG+/awToN1MlaePSCqL0myJmudw9dgDyiuN2D7k1iaqZDcSpbOhcvwHc/14rbg/SEeW3G1Wr7jrVxUDGsKfPW06m7lYPQR1y/HN9Z4/PBzwvXifvTlgbN8uD4wzPXhYt0V+8YB0TbeXPOeyDH9CdG9nDBwpRNv6DvZiacMXu3EvrHC17FypXEf+DzxeLx1dXU5MWv0rJyMvm1JxkraXHwh78di5FgsRhlWmUl8pqz9Ps1U3H4NydfJzzT75vF+Hq9ZaJOK4TMV0u9Jr6EvU0IIIYQQKdBkSgghhBAiBZpMCSGEEEKkQJMpIYQQQogUVLUAfejQoXnBXl1dnbMvRGhrCc14v8+YLEmyUAurDBbi+kwWua4sbBw5cmTBOvju1RLvWSaoPgE2ix+tc3x9Zv0IgK/Bx/tMJ+MmdQ4xyuNz+JgxY8Y48ahRo5zYZ+5omZqGmMRaPyzgZ4sF6rt2uQadALB9+/aCZVrjc/x419QTiLbH8O71Tsx9ZiX7/vN13efgrGGdTnzgwAgntgxNgeh7hscGi3lZXA5En3HLIJdJYm6bhJBExnH2+0iSUDitSWcxygSiY9BKbMyxr0zrx1dJ2o8F5vyDEMsAu5qxksUfi+OnBYQQQgghyoAmU0IIIYQQKdBkSgghhBAiBVWtmeoPa218RpaWZiWJ3onXq1lTEaJ1YD2JpXMJMf1j2OSPdRqjR492Yp9hJLeHlTyZ+8SXZNcyD+W24ASbvnpZOg4+PmRNPG4i3pBEx21tbU7c0tLixNwHvnERVw/hqxe3KZ/D9eJ6s5YJiOq/OPH41q1bndgaW0BU58f91tnp6p1CDF95TFqJaEPeCTxWuExub5/mLESbVegaIWPFevZ89xr33VSKJMYhxNURZVGGbwzzeLM0olbyc982q18ZnyGxZdoZott9p6EvU0IIIYQQKdBkSgghhBAiBZpMCSGEEEKkoKo1UwMGDDimvwUnFwXia6K4bN8aOK9Xs9eNlWAYsDVPWXhVWZ5ZvObtW5vnNuV4xAjXk4e1ND7NCt8ba7nYr4jX6oEwL6VChOg64vo3+XQKrI8YN25cwf0h2gfLU4u1Dzt37oyUwR4y3K9jx451Yu5Hvg/gz0nIC5UxefJkJ2YN1ebNmyNl8lixksTy8T7NHre5pS/xvVcszSO/E1i35tObWD5m3O9JfM74fWaV6aMYGqm4OsAkSZ3jxiGEnMNj0Pr7wPtD7jWu55Pv74ulmeK/WSFjpxRjo5waPX2ZEkIIIYRIgSZTQgghhBAp0GRKCCGEECIFVa2Zqqmpya+h8vq/z2eK15JZu8Brx6wb8q3HWt5VTEgZ1vpzyLown8Nr76z9YA8fX+4+yzeKvar4eJ82hPU67IfFGp8scmYlIe41+N6BaJvyvbO2JiS3IW/jNl6zZo0TsxYCiI775uZmJ7aeg5B8Yew7xWXwfh5LALBq1Son5ueGy+B7ZZ2Wr57s+cTjMSRnJb9XWF/C/e7T38XVfljaOd827nfLd89XRik0Klnk5rPiEH1i3DJ9+jp+pvnZ4v2WjvJY2wrBGj8e40DUe8rymSrHuPCRNK9eFujLlBBCCCFECjSZEkIIIYRIgSZTQgghhBApOG40U5aPEhDVl8T1XfFhHcMaFt/xaX1CfOv7vF7PehL2hOJ4+PDhkTJZI8VxfX29E/PavE+v09HR4cQhehKG2ytE+xH3GnHX3ln/BETbg4+xfJR8OiyGc+Bt2bLFiUPy08XV/fnaxtKgcD1YO1JXVxcpk8/hMXraaac5sc//iuF6vfbaa068YcMGJ/b5nHG/slca5+KLm+cRiD/+kuRxDKlXXE+3JN5VWXhCJdFExS3TukbIs8YxP/MhPlNWrlGrnr58nTyGWTPF48DyRQupVzH0TUm80pKiL1NCCCGEECnQZEoIIYQQIgWaTAkhhBBCpECTKSGEEEKIFBw3AnTfPsZKSmwJDJMI1VjU6TOujCsgZELE9ix0ZPEuG0r6TE9ZpB5XsM8iRgBob2934s7OzoJlhGDVI4sfFsT94QEQTdrM/coCfstk0XcMX4PHhk9kzNs46fD69eudmMdGY2NjpExuHxaD89jhtvAJ0Nva2iLb+jN16tSC1/CJp/leeYzyDyRY4A9ETTn5vWL9CKUYSWF956c1Ai3WORZxhd++Y6z9SUw7GR5vPtNOKzk37+fYN4b5RxFcT74Gj0c26ASiP5pgM1vfOWkpl+FmVsmT9WVKCCGEECIFmkwJIYQQQqRAkykhhBBCiBRUtWbq0KFD+bVu1kP54LVP1hWxdiSJtoY1F1YyZcA2wvOdU6gOQNQg0zJatMzkgPimf3xfPj0Um8NlQRZr7Vbi2CRGoNyPPFZ8bd4fn96J9TqWmZ6vXqyh4ITCrCsaNWpUwXoCwIQJE5x47NixBfdPnz7diX2msaeffroTs1aEtUrcvps2bYqU+corrzjxm2++WbCMkKTEIaamcfZnRTGeCyZJ0vG0SYmz0EwlMQK18I1h1lHxMRyHaLm4T/h59pmH9senZeV3NWukymWwyZQrobIPfZkSQgghhEiBJlNCCCGEECnQZEoIIYQQIgVVrZnau3dvXofCa80+DRWvP7MviKWZ8iWvZc0Kry3zOVkk1eX9Pi0NX5dj1nLxfta0ANFEuxxzvVjT4ksSWwq/Esby9QKi/WSNDS4zJFl1XM1USJJTPobHuG8MW35WrGtjzxnfvXKCZSvx9tKlS534rLPOipTJmilLk/fCCy84MScxBqK+Ufv27Ysc059K0mj0J65OKyuS6FysMiydUBb6prTJ5X3wGPf5TLGfH3v3sR6R9U78PPu2xdVI+XSr/CzxMx834XUSshhbpXxe9WVKCCGEECIFmkwJIYQQQqRAkykhhBBCiBRUtWZq3759x/Rg4rVpILqWzLoqXl9lryaf5oe1R5bXUhbwWrKvDSzvEY65DJ/mzMrNx7AeyqdHYd0Va9BCsHRC3Ae835dHL65fU4iOjeE237lzZ8F6+nxrrH7k2DcerXuxYt/4421cTysHHntbAcCLL77oxJzLcOPGjQXjYniaVSpZ5PvLQm8SonuJ6ytVDC+ruPlQgejzye8ynx/b6NGjnbihoaFgmXzvPj0UX5ff0/ws8nt427ZtkTJZM1UuTZ5FJemq9GVKCCGEECIFmkwJIYQQQqRAkykhhBBCiBRUtWbq8OHD+TVl1i7xOjJgr0dzzPodn+bH8pEKWWu2PE54f0iZIZqd/rA+jH2CAKCurs6JfW3cH663T0Mwfvx4J2ZNQE9PjxP77tXqV8tHKcSnK25uviTr8Dy+WLcQklePy0ii2StGbjmrTO4DHo9AVFfF53BbVIquoxRYGqAsykxyTognlOX/F/cax9qWNfz+Yz2U733H/mp8DpcZ0jZDhw51Yr53673CWk3fMZb/WgjFGKNxr1lM9GVKCCGEECIFmkwJIYQQQqRAkykhhBBCiBRoMiWEEEIIkYKqFqAfOXIkLyZmAbrPdJLFzZaA2pcUlrFE7CECuBBjtv6EiIrjCtAZn0Hp9u3bnZiNUbneXAefkSqLJ/ne+EcAPoNIq425Hlymr62S/JAgzn4fXA82mfQZmlptnoWImEkiOLcIGdMsgq1WgXlIva0+4H4PMVItRnvFHV++H9tYPyBJe82s4HpxYnI24GRTWSAqOOf3H/dryN8G/lvH7cN/x/g97jPt5B+AWO+/JOLyLN4zWZDVeNKXKSGEEEKIFGgyJYQQQgiRAk2mhBBCCCFSUNWaqb6+vrw2gA37fBoMXoPlxLFchmUeB0TXW3lNO8TsMW5iz5C1ZNaQZZHktLu724lZ08Ptyfceopfg9X/LgNMH96OlkfLdaymMK+Pi00zx2LB0gMVIDJqFaWeS68alkpKixi3Tegfwc+S71yxMTUMMM/sTYjppaUSZLPQ4SeC6swFnY2Njwf1ANAkxvzOtpO0+LTDD7zdOKs4mnTt27IiUwZrZEP1w1oT0YSXpJvVlSgghhBAiBbEnU88++ywuvvhitLa2oqamBo899pizP5fL4ZZbbkFLSwuGDRuGWbNmYdWqVc4xnZ2dmD17NkaPHo36+npcddVV3l+PCSGEEEJUOrEnU3v27MHpp5+Oe+65x7v/jjvuwF133YX77rsPS5YswYgRI3DBBRc4SxSzZ8/G66+/joULF+Lxxx/Hs88+i6uvvjr5XQghhBBClInYmqkLL7wQF154oXdfLpfDnXfeiW984xu49NJLAQA/+9nP0NTUhMceewyXX3453nzzTTz55JNYunQpzj77bADA3Xffjb/8y7/ED37wA7S2tgbX5fDhw/l1VV4n3rt3b+R4TprLvkfs+cFeG6xDAqLr/ZZPSBL/F2sdnevtO4bvPUkC3F27djkxt19LS4sTWx5cQHQtnvsxiV8W91OSey0GaXVrIUli+RzWWPjGH2+z9DnVpmWIQ5J7j6uny0JjFjcOIa4eKuQcHn8+fZTlK5XF8xtXQ+U7nuvOGqmxY8c6sU8z5fPa6w+/y1ljNWzYsMg5Vvvxe5sThrP2FYjqM+OOp1K9IyrFqwrIWDO1bt06tLe3Y9asWfltdXV1mDFjBhYvXgwAWLx4Merr6/MTKQCYNWsWBgwYgCVLlnjLPXDgAHp6epx/QgghhBCVQKaTqfb2dgBAU1OTs72pqSm/r729HePHj3f2Dxo0CA0NDfljmAULFqCuri7/b9KkSVlWWwghhBAiMVXxa76bbroJ3d3d+X8bN24sd5WEEEIIIQBk7DPV3NwM4M9rsv31Mx0dHXjf+96XP4ZzAR0+fBidnZ3585na2tpIHqSj5x1dM2WdTMivAznXF6+Js56HfVoAe407bt4pIMybqj++deNRo0Y5MeeE6uzsdGLWmIXoFPiYEF8uqwz2QAnRrIT4RhXCV2batfhS+RHxNh5fIZopnxawP5aOKKRelYql6UmrHSkWXI8kORmzyNto+UiFaKay0Htlje89zbn3OGbNFL9zgagPHLcHl8F/T3zw3yXWSPHfW87Nx8cDUb0wk8T70CqDKYamqphjK9MvU1OmTEFzczMWLVqU39bT04MlS5Zg5syZAICZM2eiq6sLy5Ytyx/z1FNPoa+vDzNmzMiyOkIIIYQQRSf2l6ndu3dj9erV+XjdunV4+eWX0dDQgLa2NsyfPx+33XYbpk6diilTpuDmm29Ga2srPvWpTwEATjnlFHziE5/Al7/8Zdx33304dOgQ5s2bh8svvzzWL/mEEEIIISqB2JOpF198ER/72Mfy8fXXXw8AmDNnDn7yk5/ghhtuwJ49e3D11Vejq6sLH/rQh/Dkk086y2EPPfQQ5s2bh/PPPx8DBgzAZZddhrvuuiuD2xFCCCGEKC2xJ1PnnXdewXXHmpoa3Hrrrbj11luPeUxDQwMefvjhuJcWQgghhKg4qjrRcX/hHQvmfElhWVRoJf5MYhbHgvSQBKSMJeQOEdFZ12VhI4vxQ2DxZF1dnRNz+/mEzmwYZ5l0+u6d+5XrZfWr75pWguUkRpZMFmJIS4jM+Axe+TngH14k+QEEUwliUl89WfBbX1/vxOxpx+JdwBbwl4OQhOpxkxT7tnHMAmsWoIeYnpbCpNMyQvaZY7KgfNy4cU7MJp2+MviHVDze+L3Mz6uvbXgbv1PZpJMTG/t+rJX22SqWIWcpkp0nvUZVWCMIIYQQQlQqmkwJIYQQQqRAkykhhBBCiBRUvWbq6Poma358655sWBh3rT5Jol6+hs+0Li4h2gdLE8AJN1n30dvba5Zp6SWY4cOHR7Zxe7GmwGeUatWLy+A29xlXMnxdSxeTROdRDJ0B35vVvkBU22E9J0n0YXEJaYu47ed79njbxIkTnZj1J93d3ZEyKlEzlQTLgNO3jWPrHRBCORKTs7bLl6SYNVKsEbX0Tr5jWDPFz6f1zgWi72o26bRiX6Jjqw/KpREth9FnKPoyJYQQQgiRAk2mhBBCCCFSoMmUEEIIIUQKqloztX///lgapLR+Jr7juUz27LDWwAE7+WUx1om5TNbNhCSK5mTKrLUJ6Rsug3UJIfVgLL0Yx75+5bqzpoJ9zFib5NNlFUMLwvdqeWj5NC18r3yMVe8k91WOZLY+/R0neX3llVecmDUqIfrESkjUG1JPKym2T59jJTK2ErmH+CQVQ6/D5/AYZw+ppqamSBnjx493Yk50zO96frcBUY0Uv3ctj0Ff21i+Uhs3bnRi9pmyvOmSkOQ5qRZN1bHQlykhhBBCiBRoMiWEEEIIkQJNpoQQQgghUnDcaKZC1kKLoVlhnylr/d+nI2ItB6+b81q8ta5+rOvEwZdXiq9reUCFrImz5oJ1CFu2bHHiJJ4+1jp6SHta+f+4LXy5Ia2xUoz1fr6mb1zw+OK+D9GDMXF1LyFeVsXQO7Amj3N8cr/77r0SNFIhWJqpkDyiIV5UhfCNC8vXzCJEQ8XHsOcd66Gam5sjZbCOir362EOKdVhA1JvK954tBGv8AGDTpk1OvGbNGid+++23nZg1VSHPcyly74VcQ7n5hBBCCCGOUzSZEkIIIYRIgSZTQgghhBApqGrNVCHKpWNgfQnHPs2KpVXgMlhn5FsH5nNYI8D1YC0E6wF8x7BGwPIr8a3Ns7aItUfF8EBhkmgu4nr2ANH8kRxb2oUk9QzxVuO6ck6xuHkKfde1nscscn1loevgc5KMv2L451iE5OssBjxmrT4olf8av1P5XTV27FgnbmlpKRgDUV0VvyPZQ4r1n756WL5cPP58uSFXrFjhxG+88YYTs+40i/au1Bx5ys0nhBBCCFGlaDIlhBBCCJECTaaEEEIIIVKgyZQQQgghRAqqWoCey+WOKTgrhqgxC3yCVt7GokTL1NMnYrRE6nGT2QJRQSWLNPkaLDZlQ0QgKo7kJJwhpomlEtsWuqYlSAeiBn18jpU8OQkhbcOmnTw2WPSahcC6FMmBKyUBcTnqEZJoluF3gG8MxzXYLIUY31dP/hFFY2OjE0+YMMGJJ02a5MQ+AToLzDmRMRtycgxE38vWj0527tzpxK+++mrkmNdff92J169f78S9vb1OXK7xV05xeJZ1OBb6MiWEEEIIkQJNpoQQQgghUqDJlBBCCCFECqpaM9Wfakk2GgJrFyzjO9+9W0l1WVPAugPWzQBRzZSly+K1ek6wCQDt7e1OzLqquMmAQ7CM8nzXiTu+fBo0NkplI1WGNVS+Mi3jyhAtF/c1jw2OOTlwiBGjpZ0Jae+4RoFZmHZmQSm0Ilk8FyHtVw5DUobv1ZcsmA02J06cWDBmDRUnNQaizwknMmYNVYjmjGPWSXIS4z/96U+RMjmRMZsBV4qmsVKNPrOqh75MCSGEEEKkQJMpIYQQQogUaDIlhBBCCJGC40YzVam+UsUgJHkob2MNFSer5QScPo8U1gCw3xX3AXukbN26NVIm18PSMyVZE7fW/5MkEGa4zBBdFsPaJIb7EIifHNmnhWOPHa67L7lqf0rhEQVENWY83rh92J+tmnym4iYuDjneOiYkKXYxPN3ilsnaQ19SdtY88RhnzdSYMWOcmN9tQPT5ZK0Wa6Z8cJuyRnT79u1OzEmM2YcPiGpTWdPIz0UWGiqmVHqntNcppiehvkwJIYQQQqRAkykhhBBCiBRoMiWEEEIIkYKq10wdT/5SR7HuydLJhJTBHkasEeB8bUBUs8LX2LZtmxPz+j/7n/gohZ9OKXQfIbmpWJvEMWtDfLn6LK2gdQ0gOhY4BxnrSdasWePExXgGuQ4AcOKJJzoxa1gsfYnvuSmV3qvYZKGZCsFqr2LcO49Z1iaxpxQANDc3OzHn2mPPPC6Tnz0g6qvHYzREi8n6Q/bZ4zHb2dlZcD8QzW+6d+/egvUqBqXQO4Vcp5xeVfoyJYQQQgiRAk2mhBBCCCFSoMmUEEIIIUQKqloz1dfXVxZ9QqWRZJ2YNVKsh/J5Hlkanq6uroJxEpL4NTGsK+IyfbqjkGPiYo1Vy8fLp+MI8RzrD3vQAFE/MPbPYQ0Ga0d8/lfF8INhnQvfC9erUsnCK83aH6KZKkW9ksBl8ruIc+LxuACiminWRLGvHj9bvrHEflZWbk3fM8C59lhn2tPT48Scd2/9+vWRMllHVamei5WgdyqmfkxfpoQQQgghUqDJlBBCCCFECjSZEkIIIYRIgSZTQgghhBApqGoB+vFCFiadFizMY3Ezi4594koWnLPQkZMWFyOhpg/LLI+F3db+0GP6k0T0aV2DRbG+9uQ+serpK4MTpfIPB3j8+cxDs4bNCgHg6aefdmIWFbOQ/ngy9LUE5nHHq++cuPuTECKM53HPgvPGxkYnHjt2bKRMSyzOMV/Tl+iYk7/z+OKYDTmBqACdxeP8Y4/XX3/diTds2BAp83gZ55WS6DjpNfRlSgghhBAiBZpMCSGEEEKkQJMpIYQQQogUSDNVgVhrtknWdFlDwaZ1nLTTl+jYIiSRcVx4jdt372m1HyHr6HENN31YuqokOhjWehw6dCjWNYCo6eaePXsKxqwnCdGLhfSjBd+bT1dVjWRhlmnpAn3bSqWJsvbzGOZ3E2uVrETcQFRPx+ewKSfHfD4QNQ+1xj3ro4Co5ok1UqtXry54vM9019JuHU9UgvHnsdCXKSGEEEKIFGgyJYQQQgiRAk2mhBBCCCFSIM1UiQlZ0y1GUl3WBLB3C+sOQjRTvH5firX7ciW2tu4lC01QXN8p3zFJ2sdK8spxEt+zuDpA3/HF0BKWgyQaPe5nK85CMxXSnnE1Uj7dH79rWCPFiYw55uOB6PuN9U5DhgxxYh7jvuTJfC/8HGzevNmJfZ5QW7dudWLLV4q9qqpljJcLJToWQgghhKhSNJkSQgghhEiBJlNCCCGEECmQZqoCKIYWhL2A2DeFNVJWriogqhFgzVRPT0/BOvn0Elnow0pBXE1UqbRd3E/sGRUC60M49xn3K/ejT0P1TvK+sUjivcRtzM9zEp8pq0zuI9+zydusnJ8cs1YJsN9N7BHFGinOKwpENVKsy+KYva1YQwVE24dzka5Zs8aJWf8ERL3R+BzWVGWR71T8P3zPRVZtqi9TQgghhBAp0GRKCCGEECIFmkwJIYQQQqRAmqkiUwpfKd86sJVrKgtfKV7/Z71OiAajWrE0VKXKERW3TUeOHBnZZuUxY53L4cOHnbiaPaFKoYVLkteRn1+fhjEu/IzzNbie3M9AdLyxXs7SSLH/ExDVQPG7ifdzW/g0e729vU7M985jnO/Ld+/sCbVy5UonXrZsmROzHgoAurq6CpbB16iU5+R4Jit9q75MCSGEEEKkQJMpIYQQQogUaDIlhBBCCJECTaaEEEIIIVIgAXqJCREUxhUdsugTsEXFbGrHZRw6dChSJos6d+7c6cQSS1Y+bM4KAC0tLU5sGYEmSYBbLWOjGAJzK8Gwz8iSRdcssmaBNAuofaaT/MzzMbyfheAAsG/fPifesWOHE3M/sxkmv4eAqGns+PHjnbi5ublgmb6xtX37difmH89s2rTJifm56O7ujpTJ13n11VedmAXnXAcg2l7cntXynIgo+jIlhBBCCJECTaaEEEIIIVKgyZQQQgghRAqkmcoYa83bZ7IYd52cNRc+w002x2NdAusjGNZHAVGNlGXeeDyZdJaDJEagrH1jfUlTU1PkHNbX7dmzx4lZ5xGihzpetR8hmior+S8/e75nkfVMHPOzx7HP5JPHAseWeSYQ7Vd+97A2ifVgfA3fdXiMsmaKx6uvT1jnx+8z3r9t2zYnZnNNIGpS3N7e7sScpJiP99VD78jjB32ZEkIIIYRIgSZTQgghhBAp0GRKCCGEECIF0kylpBwJXVmDwRoCIKpDYO0CJ1dlDQFrH4B3lq+UL/lsIVj7kKRtrCSyvoSujJVo1ufzw33P2g/W32SVGDRrylUvHiusbxo2bJgTs6dR3LEGRPuVtUs+HRa/E8aNG+fErGfyJcW2Eqizvo7vzZfomH2luF6NjY1OzPfmaz/WkPG4Zz0TP1s+nylOQtzR0eHE/H7k44H474VSJUwX6dGXKSGEEEKIFMSeTD377LO4+OKL0draipqaGjz22GP5fYcOHcKNN96I0047DSNGjEBrayuuvPJKbNmyxSmjs7MTs2fPxujRo1FfX4+rrrrK+yVECCGEEKLSiT2Z2rNnD04//XTcc889kX179+7F8uXLcfPNN2P58uX49a9/jRUrVuCSSy5xjps9ezZef/11LFy4EI8//jieffZZXH311cnvQgghhBCiTMTWTF144YW48MILvfvq6uqwcOFCZ9uPfvQjvP/978eGDRvQ1taGN998E08++SSWLl2Ks88+GwBw99134y//8i/xgx/8AK2trQluo3LJQkvDa96swfDpEFgjwOcw/GWQtQ+A7Xt0PHmmsE6DcxVyTq0DBw6kvqalmQppX9aPsN7J55/Dfc9aEO7nJF5LVhkh17DOyeLZsvb7jmf9EuuILI2Pr1+57/neLJ0W+8oB0XcCv2tZu+TzqrI0UKyz4vvweeLxs2bpPUN8zvgYbmPWM7FOcN26dZEyedvmzZudmJ+jJP5rxdChWmVKU5UNRddMdXd3o6amJv8gL168GPX19fmJFADMmjULAwYMwJIlS4pdHSGEEEKITCnqr/n279+PG2+8EZ/73Ofy/wfT3t7u/T+ghoaGiKPsUQ4cOOD8n39PT0/xKi2EEEIIEYOifZk6dOgQPvvZzyKXy+Hee+9NVdaCBQtQV1eX/zdp0qSMaimEEEIIkY6iTKaOTqTefvttLFy40FlXb25ujuRBOnz4MDo7OyM5mI5y0003obu7O/9v48aNxai2EEIIIURsMl/mOzqRWrVqFZ5++umIuHDmzJno6urCsmXLcNZZZwEAnnrqKfT19WHGjBneMmtra73CxVITIg4shoCQhaAscPUlJOX2YpHh/v37nZiFyT6h8vEqOJ8wYUJk28knn+zEu3btcmIWo7JAvVxjxYINOoFoYmMW53I/swjZZ5oYd2yEiIqtc7I4no/he/OZYVoJhC0xtK9PLINW653gM/JlcTjHLL8IEcZzPfi9w8+Fb6ywiJ3bz3rv+NqKpSAsMF+9erUTr1y50onffvvtSJn8w4wkPxCpRKPjEAG/sIk9mdq9e7czENetW4eXX34ZDQ0NaGlpwV/91V9h+fLlePzxx3HkyJG8DqqhoQFDhgzBKaecgk984hP48pe/jPvuuw+HDh3CvHnzcPnllx93v+QTQgghxPFP7MnUiy++iI997GP5+PrrrwcAzJkzB//4j/+I3/3udwCA973vfc55Tz/9NM477zwAwEMPPYR58+bh/PPPx4ABA3DZZZfhrrvuSngLQgghhBDlI/Zk6rzzziv4qTLkM2ZDQwMefvjhuJcWQgghhKg4lOg4Y4ph0sk6DdYYcIJS3zkMG8yxJiiJ4Vy10tbWFvsc1nWw7sVn4sntZ+likiTAtc7x6Tr4HKuf+fgQg00miZFg3HNCDDctjRQbRvr0iVYScU66y2PDp5lirH61dEdA1LSTEy7z2PAZ/fKY5fZjDRXrsthcNKQeDGv6fEmJN2zY4MRvvPGGE69Zs8aJ2ZaHdYRAtB/LYcBZKmT0GR8lOhZCCCGESIEmU0IIIYQQKdBkSgghhBAiBdJMFaBafKVCPLgsnQHHvvs6XnylTjjhBCf2aUNYx8IaM9ZUDBw40IlZB+Irk72+LB2Cr/35HNbFhCTVtZI0W0l2k/g3WSTxvrE0Uj7dEfcb659Y82NpEYGotob7mWMf1r1aukn2jPJt42TI/F7x1YHHj08DVagMX5nWGGU95/bt252YPaMAYO3atU7MvlGcyJ2fgWr1jCoV0lRF0ZcpIYQQQogUaDIlhBBCCJECTaaEEEIIIVIgzVRKsvCVYt0Ge8SwDob1EiGaKdb47Ny504m53seTHoA1aCG+UpZGyvKI8sF6HKsfWXfg62dLF8RaOdabAMC+ffucmPs+if7ByrVXijL5ueIYiLYpa924j3xaGtbC8VhhDZWV69AHj51x48Y5cVNTU8H9QFQbyPfO7ROSc5GfLYbv3eep1dnZ6cT8buK8eqx/8iW+37x5sxPzGOfnt1zvuyyei0qkGLk1Kx19mRJCCCGESIEmU0IIIYQQKdBkSgghhBAiBdJM9cNa502ik2FCfFbYV4o1U0k0Ul1dXU7MmiDWQhwvnlJA1FeKvXF8vj+WZioJfF3u1+bm5oL7WX8CRPupo6PDifk+fBoX3sbX4fGZJGdgXL1TEu8qK/ZpprhP2EeK29c3VliPY/l2hcD14rHBcRLvOdbP8fuNfagAu18OHTrkxDz+fJo91jxxzGOa8+j5cvNZGimmGJ6CSXRY7yT/puPtXvVlSgghhBAiBZpMCSGEEEKkQJMpIYQQQogUaDIlhBBCCJECCdBTEldk6BPvWoJzNg5kAz8fLPxkI7zjSWDOsIjYMuns7e2NbLME+klgUTEnp+VxwGPFJyrmelqJj30CdB5fLPBlMS/jG9Nxn4ssxKaW4NxXTxYms6kk3we3N+D/YUAhuB6+ZME8NtiUc+zYsU7MAnTudyAqjOdxz/fhE21bJp38ntmwYYMTcwJiIGqwyUmIWWDe09PjxCE/zLDGY7WYFCdJAF6tVJvxp75MCSGEEEKkQJMpIYQQQogUaDIlhBBCCJGCd7RmqhwmnT7NwciRI504rgGfT8exa9cuJ+aEt3zvx5OGatKkSU7MfcAaIJ8hp6UTsvDp2ljHwv3O2hnL6BKI9qs13viaQFQnxBoq1sGw5ifEiLYUGiq+JmvnQuCxUIznhPvEp29qaWlxYn4ncMyJjVl/54NNOVlj5usDfi62bNnixKtXr3bilStXOjHro4CooTBruzjOIrl8MaiUZMlMJemKsqaS7l1fpoQQQgghUqDJlBBCCCFECqpyme/op720y3BZLPPF/czoK5PzWfGyC3/m5jJ8ucC4TOtnz8fTMp/Vfhzz8UC0/eK2j+/zslUvzvsWsswXd0nE9zNyrhffuzVWfG1jLcWU4vN8kjFt1TOL54Tb09cn1juBxwov94bkT+QykyzzWXkJrbEE2OPJ6hPfWIo73rJYoquU5UaRHUf7NMimIVeFI2DTpk0RXYwQQgghRNZs3LgREydOLHhMVU6m+vr6sGXLFuRyObS1tWHjxo0R0ayIT09PDyZNmqT2zBC1abaoPbNF7Zktas/sKWeb5nI59Pb2orW11fzaW5XLfAMGDMDEiRPzTrijR4/WwM0QtWf2qE2zRe2ZLWrPbFF7Zk+52pSzERwLCdCFEEIIIVKgyZQQQgghRAqqejJVW1uLb37zm6appQhD7Zk9atNsUXtmi9ozW9Se2VMtbVqVAnQhhBBCiEqhqr9MCSGEEEKUG02mhBBCCCFSoMmUEEIIIUQKNJkSQgghhEhBVU+m7rnnHkyePBlDhw7FjBkz8MILL5S7SlXBggULcM4552DUqFEYP348PvWpT2HFihXOMfv378fcuXPR2NiIkSNH4rLLLkNHR0eZalxdfO9730NNTQ3mz5+f36b2jMfmzZtxxRVXoLGxEcOGDcNpp52GF198Mb8/l8vhlltuQUtLC4YNG4ZZs2Zh1apVZaxx5XLkyBHcfPPNmDJlCoYNG4aTTjoJ3/72t518Y2rPwjz77LO4+OKL0draipqaGjz22GPO/pD26+zsxOzZszF69GjU19fjqquuwu7du0t4F5VDofY8dOgQbrzxRpx22mkYMWIEWltbceWVV2LLli1OGZXWnlU7mfrFL36B66+/Ht/85jexfPlynH766bjggguwbdu2clet4nnmmWcwd+5cPP/881i4cCEOHTqEv/iLv8CePXvyx1x33XX4/e9/j0cffRTPPPMMtmzZgk9/+tNlrHV1sHTpUvzLv/wL3vve9zrb1Z7h7Nq1C+eeey4GDx6MJ554Am+88Qb+6Z/+CWPGjMkfc8cdd+Cuu+7CfffdhyVLlmDEiBG44IILIgmABXD77bfj3nvvxY9+9CO8+eabuP3223HHHXfg7rvvzh+j9izMnj17cPrpp+Oee+7x7g9pv9mzZ+P111/HwoUL8fjjj+PZZ5/F1VdfXapbqCgKtefevXuxfPly3HzzzVi+fDl+/etfY8WKFbjkkkuc4yquPXNVyvvf//7c3Llz8/GRI0dyra2tuQULFpSxVtXJtm3bcgByzzzzTC6Xy+W6urpygwcPzj366KP5Y958880cgNzixYvLVc2Kp7e3Nzd16tTcwoULcx/96EdzX/va13K5nNozLjfeeGPuQx/60DH39/X15Zqbm3Pf//7389u6urpytbW1uZ///OelqGJVcdFFF+W+9KUvOds+/elP52bPnp3L5dSecQGQ+81vfpOPQ9rvjTfeyAHILV26NH/ME088kaupqclt3ry5ZHWvRLg9fbzwwgs5ALm33347l8tVZntW5ZepgwcPYtmyZZg1a1Z+24ABAzBr1iwsXry4jDWrTrq7uwEADQ0NAIBly5bh0KFDTvtOmzYNbW1tat8CzJ07FxdddJHTboDaMy6/+93vcPbZZ+Mzn/kMxo8fjzPOOAMPPPBAfv+6devQ3t7utGddXR1mzJih9vTwwQ9+EIsWLcLKlSsBAH/605/w3HPP4cILLwSg9kxLSPstXrwY9fX1OPvss/PHzJo1CwMGDMCSJUtKXudqo7u7GzU1NaivrwdQme1ZlYmOd+zYgSNHjqCpqcnZ3tTUhLfeeqtMtapO+vr6MH/+fJx77rmYPn06AKC9vR1DhgzJD9yjNDU1ob29vQy1rHweeeQRLF++HEuXLo3sU3vGY+3atbj33ntx/fXX4+///u+xdOlSXHvttRgyZAjmzJmTbzPf86/2jPL1r38dPT09mDZtGgYOHIgjR47gO9/5DmbPng0Aas+UhLRfe3s7xo8f7+wfNGgQGhoa1MYG+/fvx4033ojPfe5z+UTHldieVTmZEtkxd+5cvPbaa3juuefKXZWqZePGjfja176GhQsXYujQoeWuTtXT19eHs88+G9/97ncBAGeccQZee+013HfffZgzZ06Za1d9/PKXv8RDDz2Ehx9+GKeeeipefvllzJ8/H62trWpPUdEcOnQIn/3sZ5HL5XDvvfeWuzoFqcplvrFjx2LgwIGRX0N1dHSgubm5TLWqPubNm4fHH38cTz/9NCZOnJjf3tzcjIMHD6Krq8s5Xu3rZ9myZdi2bRvOPPNMDBo0CIMGDcIzzzyDu+66C4MGDUJTU5PaMwYtLS14z3ve42w75ZRTsGHDBgDIt5me/zD+7u/+Dl//+tdx+eWX47TTTsPnP/95XHfddViwYAEAtWdaQtqvubk58uOow4cPo7OzU218DI5OpN5++20sXLgw/1UKqMz2rMrJ1JAhQ3DWWWdh0aJF+W19fX1YtGgRZs6cWcaaVQe5XA7z5s3Db37zGzz11FOYMmWKs/+ss87C4MGDnfZdsWIFNmzYoPb1cP755+PVV1/Fyy+/nP939tlnY/bs2fn/VnuGc+6550asOlauXIkTTjgBADBlyhQ0Nzc77dnT04MlS5aoPT3s3bsXAwa4r/qBAweir68PgNozLSHtN3PmTHR1dWHZsmX5Y5566in09fVhxowZJa9zpXN0IrVq1Sr853/+JxobG539FdmeZZG9Z8AjjzySq62tzf3kJz/JvfHGG7mrr746V19fn2tvby931Sqev/3bv83V1dXl/vCHP+S2bt2a/7d37978MX/zN3+Ta2tryz311FO5F198MTdz5szczJkzy1jr6qL/r/lyObVnHF544YXcoEGDct/5zndyq1atyj300EO54cOH5/793/89f8z3vve9XH19fe63v/1t7pVXXsldeumluSlTpuT27dtXxppXJnPmzMlNmDAh9/jjj+fWrVuX+/Wvf50bO3Zs7oYbbsgfo/YsTG9vb+6ll17KvfTSSzkAuR/+8Ie5l156Kf/rspD2+8QnPpE744wzckuWLMk999xzualTp+Y+97nPleuWykqh9jx48GDukksuyU2cODH38ssvO3+jDhw4kC+j0tqzaidTuVwud/fdd+fa2tpyQ4YMyb3//e/PPf/88+WuUlUAwPvvwQcfzB+zb9++3Fe/+tXcmDFjcsOHD8/9l//yX3Jbt24tX6WrDJ5MqT3j8fvf/z43ffr0XG1tbW7atGm5+++/39nf19eXu/nmm3NNTU252tra3Pnnn59bsWJFmWpb2fT09OS+9rWv5dra2nJDhw7NnXjiibl/+Id/cP4wqT0L8/TTT3vfmXPmzMnlcmHtt3PnztznPve53MiRI3OjR4/OffGLX8z19vaW4W7KT6H2XLdu3TH/Rj399NP5MiqtPWtyuX42uEIIIYQQIhZVqZkSQgghhKgUNJkSQgghhEiBJlNCCCGEECnQZEoIIYQQIgWaTAkhhBBCpECTKSGEEEKIFGgyJYQQQgiRAk2mhBBCCCFSoMmUEEIIIUQKNJkSQgghhEiBJlNCCCGEECnQZEoIIYQQIgX/PxXfnPXNJ7/9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1. 2. 3.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZM2fiqquuwumnn4633noLjz76qO/bhXxy0UUX4Sc/+QkuuOAC/Md//Ae2bNmCuXPnYujQob66TKNGjcKECRMwZ84cbN++HaeddhqWLFmCVatWAfB/Y3LnnXfi+eefx+jRo/GVr3wFxxxzDBoaGvD666/jr3/9KxoaGg7bn7Vr12L8+PEoKirCv//7v+Pxxx/3/f2EE05IfUgfztChQ1FTU4OVK1f6AvJnn302brrpJgBwupkaNWoU5s2bh9tvvx1Dhw5Fnz59cO6551rfd0jv3r0xffp03Hbbbbjgggswfvx4rFy5Eg888ABOOeUUXHnllalpv/zlL+P3v/89LrjgAnzuc5/Dv/71LzzyyCMYMmSIb55DhgxBVVUV5s+fj/LycnTt2hWjR48OZNmS8pnPfAZ//OMfcdlll+Giiy7CmjVrMH/+fBxzzDHYtWuXr/8NDQ0499xz0a9fP6xduxb33XcfTjrppMOWn/jxj3+MpqYmTJ06FeXl5bjyyiuxatUqnHfeefjc5z6HY445BiUlJXjiiSdQX1+PiRMnZmQdRWLLzY8IRQrb3LlzPQDeqaeeGvjb3r17vW9961te3759vS5dunhnnHGGt3Tp0sBP3JMujfDqq6/6pj30k/itW7f6Xp88ebLXtWtX32sPPfSQN2zYMK+0tNQbPny4t2DBgtT729q9e7c3depUr0ePHl63bt28Sy+91Fu5cqUHwPcTe8/zvPr6em/q1Kle//79vY4dO3o1NTXeeeed5z344INp1/H555/3ABz2X9sSDOl89rOf9QB4v/3tb1Ov7d+/3ysrK/M6derk7dmzxze9qTRCXV2dd9FFF3nl5eUegNT+O9w2P9T3559/3vf6/fff7w0fPtzr2LGjV11d7X3961/3duzYEejzPffc4x111FFeaWmpd8YZZ3ivvfZa4LjxPM976qmnvGOOOcYrKSmxHkNhjgPP+6gUw7HHHptqt7a2enfccYc3cOBAr7S01Bs5cqT39NNPe5MnT/YGDhyYmu73v/+9d/7553t9+vTxOnXq5A0YMMD76le/6m3evDk1jWm7HTx40Lviiiu8kpIS78knn/S2bdvmTZ061Rs+fLjXtWtXr7Ky0hs9erT3u9/97rDrKJJrRZ4XISkpIvL/vfnmmxg5ciQeeeQRDacjIkckZaZExNmePXsCr82ZMwfFxcVOVbhFRNojZaZE8sD+/fvT5oiAj+r/tC2umQt33XUXli9fjk996lMoKSnBM888g2eeeQZTpkwJ1LMSETlS6DGfSB7429/+hk996lNpp1mwYIFxINxsWrRoEW677TasWLECu3btwoABA/CFL3wB3//+962VyUVE2ivdTInkgR07dmD58uVppzn22GPRt2/fLPVIRERc6WZKREREJIacBtDnzp2LQYMGoXPnzhg9ejReeeWVXHZHREREJLScfTP129/+Fl/84hcxf/58jB49GnPmzMHjjz+OlStXok+fPmnf29raik2bNqG8vDzrQyuIiIhI++d5Hnbu3Ina2lrrKAw5u5kaPXo0TjnlFNx///0APrpB6t+/P66//nrjOE1tbdiwQb8cEhERkYxbv349+vXrl3aanPz8Zv/+/Vi+fDmmT5+eeq24uBhjx44NjPcEfDSuU9sxmRTzKgxJ/LqrY8eOod8Tdhy3KLKxjCNZa2tr7PfwdcI0z7DLidIvCadQtnGhXgOi9Nv2Hv676YkRT9OpUydfmz8vOnfuHJgHv8ZtLh3DbdM8eZq2/Tpw4ACeeOIJlJeXB97HcnIztW3bNhw8eBDV1dW+16urq/Hee+8Fpp81axZuu+22bHXviJCNx6NJLCPKPApl3eTwMrHfTX8Puxzt98wrlG1cKP1k2Ti3XM41bvPNlukG7tCg24dr8w0Zt03/c86vmaZx2WYFcWs9ffp0NDU1pf7xKOoiIiIiuZKTb6Z69eqFDh06oL6+3vd6fX09ampqAtOXlpaitLQ0W90TkSME/99voTxikuw7kh7rZWKZtm+J+HFb165dA/Pgx222x3g8D9N9BPcj6jeOOTk6OnXqhFGjRmHx4sWp11pbW7F48WKMGTMmF10SERERiSRn4z9MmzYNkydPxsknn4xTTz0Vc+bMwe7du3HVVVflqksiIiIioeXsZurzn/88tm7diptvvhl1dXU46aST8OyzzwZC6SIiIiL5rCCHk2lubkZlZWWuu1HQsvFLFJdfZ9ioNIIcYssz2f5uutTxe+IuQ5KXL9u8UM/5TJRC4F/RcZkDzjIBCHxmd+vWzdfmPJQpM8Wv2TJT/PkRdlvs27cP8+fPR1NTEyoqKtJOW5hHh4iIiEie0M2UiIiISAy6mRIRERGJQTdTIiIiIjHk7Nd8IiJJykYBTlOANV8C0u1VLgqrFmrYHAjfd9MYqlzc0lZQk8PlprA2B8xtbdOPjzjozuua9HiwbccEtincI0ZEREQkD+hmSkRERCQG3UyJiIiIxKDM1BEiG0U6RTJJgxIL4JYJsh0bhZyJYrbiyJx/4rwTZ5WAYObJVlDTln8y9cOWdzLtIy4WastI8TI5c2Wapu0y9uzZk3b+bbWfI0pEREQkB3QzJSIiIhKDbqZEREREYlBmqp1SRipZ7SljIcnKxyxXpo7XfFg3F+3lfOX1MNVeKisr87WrqqrStrt37+5r84DDptc4I8Vtnp77BAQ/kziPtHv37sB7wuKsF2ekTIMnc+6qbT/D1K1qH0eciIiISI7oZkpEREQkBt1MiYiIiMSgzFQ7oHyUHIlsWSWXLJMtWxMlI9Sex5LLxPY6ktgyUJw14iySKd/EmagePXqkbXMNKB5XDwhmi3hsPq7N1LlzZ1/b5TOJx71ramrytXfs2BF4j+d5vrat7hSvm6lfvE/aTsPzT0ffTImIiIjEoJspERERkRh0MyUiIiISgzJTBShfM1LtpbYL0L7Wpa1MrFe+5GTysd4TkEy/CuV4zNd9kAm2seU4ZwQE6yDZakDx3035Jn6NM1G8TM5DmWpCcd/DZIdccc6Kl2nafo2NjWnnmS7/BJjXw1S765ADBw6kXZ5v2c5TioiIiEiAbqZEREREYtDNlIiIiEgMupkSERERiUEB9CzL1/D4kSRKmDcXAeAkwrvZ6HeuwtG27ePSL1uhz7DLdFEoYfIkFGog3bSPOKjMQe9evXr52n369AnMg6fp2bOnr20LnHOYHAgGyLmgJrc5KB/lPLG1XfByORzOgxQDwaKlu3btCjVPDr3bqGiniIiISJboZkpEREQkBt1MiYiIiMRwRGemlF8KJ1/zN/yeJPqZiXlmA+ch+BjngUKB8HmHsDmjqO+xyUS+KYl1k8PLl+3J/eACkVw8E7Bnompqanzt/v37B+bBmSjOXdnanH8CgrkePsej5J1s+yAT+8jlmsu5NdPAz2GWkeT0hfEJISIiIpKndDMlIiIiEoNupkRERERiKPjMlHJPychEJshlnmGzSZwJcpmHLQdjqiViG8SU2zwPl36apmmLsw+mvBMf/1xH5eDBg772hx9+GHo5tu0X5Ry0DUBqmqfLNG3t3bvX1zblPHh77N+/39fmgU737dsXmEdLS4uvzducKWOVXibydJw14vpNffv29bWPOuqowDwHDBjga3NGqkePHr62KXfFGR8+X201oVzyTXxMJ/EZabr2tJWremL5lGXNn56IiIiIFCDdTImIiIjEoJspERERkRgKPjPVHuTTc984ouSd+Hk+T8P1X7gNBHMHtvGUeHqu5QIEswu22iycn+jatWtgnvyaqWZMumWY1ovrrvA0nN/hHBEQzAGFzTuY9qstD2bLepm2jS1PwlmRPXv2+NqcbQKC22P37t2h2gCwbds2X3v9+vW+Nq9rlH3C2S1T9i0fZaKuFO9307HC9Zz4/OSMVL9+/Xzt2trawDy5rhRnpDiHZboG8PnKbNeZKDlUZss/mdhygKJvpkRERERi0c2UiIiISAy6mRIRERGJQTdTIiIiIjEogJ6w9hImN7EVx+Twc6dOnXxtDmgCwaAoT8MhT1NYnAOpHGbmfvA8eABT0zQc+N2yZYuvzWFTDrQCQEVFha/N/bYdO6bwKq8b4wCrKezM4WZmK1Bq6kPYdXH5oQEvh0Ox3Ob1Mq07h9I5YL5z505fe8eOHYF5NDY2+torVqzwtbdu3Rp4T7p+AkBzc7Ov3dDQ4Gs3NTWl7ScXHwXsxViZ6e9xr28ug9fyfuaAOZ+bfI0AgoHzgQMH+tocMOd58HUJCBbh5Gm436ZimWH3AYtSDJO3uUuY3GUwZPFrv5/8IiIiIlmgmykRERGRGHQzJSIiIhKDMlMhZCsPFXY5STxH51yMSz6Hcy22vBMXvQOCxfM4l8BZJB4o1NQvzlRwtoGXYZpnWVlZ4LW2ODvDuRlTDov7wdkuzli4DAJtO1Zcsg/8mm1QZz42XAZ1ztcByTk/wlmjXbt2+dqcTQKCx8LgwYN97bVr1/ranMsyFeDkLBcfX5yh4r+b+smFQDlDxkVOuX24vrbFx4It/wQEzzVu285n07nGmSmehq8BtmUA9swjM51rfLzZzl/+u8tA5bZ+RLkm2LhMH3aehZY/LqzeioiIiOQZ3UyJiIiIxKCbKREREZEYlJlKI4lntlHmEfY9pulttYA4i2TLO5new7h2C+cWqqurA+/p2bOnr81ZBV6mqf4QZ544/8DrxvOIkkXirBfnUUxcllsICiUP5YLPE9s+Mh1/fMzycX700Uf72vX19b62afBkzm5xfonrTHG2i+tUmabhtq3mFmCvScZ5OpcBrHmb8jlvqytlulbZBh221a4y7eewTOc354Y478TvcRno2FY3Kkqm1lYPi7Nb3AfTMvk9PA1/Rplyu/l8zczfnomIiIgUAN1MiYiIiMSgmykRERGRGJSZaiNX+aawY9zx3zmXAISvzcI1oHr37h2YJ9dm4UwF55/69++fdpmmftraptxWEvmGsHif2MbIAwp3fKt8zilkG597QPD442OB6xNxxs9UE4ozU7Z6WJzZM+WdOEdly0iZcoCmMf/asuVeTJmpsGPx2fJQpuXarqGZOMYzcb5noiaU6f28n/kY5WOFM32mbB1npmx5RNM4rDyNrdYXM+U7k9r3ukqKiIiIxKCbKREREZEYdDMlIiIiEsMRnZlKIu9km8aWhwLsdVY4Y8H5J9OzZVsmimvhcE0ozj8BwewHZyr42TvXYjLNk/MRnP9yGQdOJCqup2Mbey4KvgbwMW/KrNhygLbci2k9OMdiG6vPlHux1ZkKm/cEwo8TyvN0ycFkI/fnkkUKO44e70fT9rct11YzypTZ27JlS9o2jwXJmSlbtg4I7mc+L/jzxvQav8f2eWE6DtJNYxqf8nD0zZSIiIhIDLqZEhEREYlBN1MiIiIiMehmSkRERCSGdpPmzVVhQVtYkgNypgJzHMzm8LitzUXsXN7DgXQeLNQUarcFZznoyNObQrW5KKYn4dgGW7WFal3mGaXAoW1QWNsyXbi8x7b+HOy2DUzrwhawNp03fK7x+eiyrvlYeDZX1wjeFhwON4WwMzEose09HGJvaGjwtdeuXRt4z4YNG3xtHhQ7E8c0/5CA+wkEP1P4s6+srMzXdimmnC6g7zKA/SH6pBIRERGJQTdTIiIiIjHoZkpEREQkhoLOTBUXFxsLtiU5/7ZMg3RyBso2wDAPFgwEC2ZysUvON3HGivNOpn5w4U/bAMKm4phhC+HxvjFNz8/7+T22vI7pPWEL45kyB2HXlfMRUQb6tBV4NfWBswq2ZZiKJtoK2/H2sQ3Ca2Lb5i5ZEZcCfGGFzVmZprftA9vf80XYDFquRCmeHJZp3W1FT7OxX10GOuZBiDdu3Ohrc0Zqx44dgXnaCjKzsNdgE35PlKKxLsVCbfNsu+5h5qdvpkRERERi0M2UiIiISAy6mRIRERGJoaAzU0nj5+xch4VrNQHBPBPXveCBGU3z4MzUgAEDfG0elJjzTi71m7j+FQ8o7JJviitKBiPKQLRJDF4bNzeUrbwJL5czUZwzcslMcd95sE+XjJRpOenmwfvMlNnj99hyGknUsrL1wfSarR9JyFTNrPbCJWvZFueKVq5caV1GbW2tr83X9mxk+gCgubnZ1169erWv/a9//Sv0cvnzwjYYte36CASvw1FyVXGzWabzN90gzbaMVlv6ZkpEREQkhsRvpmbNmoVTTjkF5eXl6NOnDy699NLAXf7evXsxdepU9OzZE926dcOECRNQX1+fdFdEREREMi7xm6klS5Zg6tSpePnll7Fo0SIcOHAA559/vu/nmjfeeCP+/Oc/4/HHH8eSJUuwadMmXH755Ul3RURERCTjEs9MPfvss772ww8/jD59+mD58uU4++yz0dTUhIceeggLFy7EueeeCwBYsGABRowYgZdffhmnnXZa0l1KsdX14SwSZ5X69esXmCfXkeJ5cF0priFlmi9PYxrPry3Ts/mwY9y55DzCZgBcskth6zm5sGVl+Lm56bm4bXuErbHlMs8ksjbcL84uRKlVZeuHyz4LOw/TPomyzW3vt72H94kpcxE3m+VSO+hIyjtlgu0a4FJPqLGx0dfma72p3h+LO54k9wEA3nvvPV973bp11n60Zcon2phyujac4+V14/H+WlparPPkbBfjdYuS03KV8cxUU1MTgP850JYvX44DBw5g7NixqWmGDx+OAQMGYOnSpZnujoiIiEiiMvprvtbWVtxwww0444wzcNxxxwEA6urq0KlTp8A3OtXV1airqzPOZ9++fb6qpPzrBREREZFcyeg3U1OnTsXbb7+Nxx57LNZ8Zs2ahcrKytS//v37J9RDERERkXgy9s3Uddddh6effhovvPCCLxNUU1OD/fv3o7Gx0fftVH19faDe0iHTp0/HtGnTUu3m5ubADZVLboOfr3KeaeDAgb42L8NUI4rzTNzmOlS8DNM0tnVxyRnZMhecEbDVDQGCOSxbLssl5+FSsyjdMl3maRvzyaWfvG5Rnr2HHZ8uiZyMyzzDrovLeJhh818uYwjajvuwtXBMbDk/l3yTTb7mn5LYPixKJtJ2fIXNLwLBawLXFuLr9rBhwwLz4OuG7VoUZT/z9ty6dauvbap/tWnTprTzzEQu1caUH+Pahrz9bLk2ILh9bMcfH6+cyzLNI8r4fkAGvpnyPA/XXXcdnnjiCTz33HMYPHiw7++jRo1Cx44dsXjx4tRrK1euxLp16zBmzBjjPEtLS1FRUeH7JyIiIpIPEv9maurUqVi4cCGeeuoplJeXp3JQlZWV6NKlCyorK3HNNddg2rRp6NGjByoqKnD99ddjzJgxGf0ln4iIiEgmJH4zNW/ePADAOeec43t9wYIF+NKXvgQA+OlPf4ri4mJMmDAB+/btw7hx4/DAAw8k3RURERGRjEv8Zsolf9G5c2fMnTsXc+fOTXrxIiIiIllV0AMdd+rUKRVW5NAiFwgDgqE4DoNz6NCleCaH6rjkAw+G6VLYLawowVHWtvQEkEwAOIqwReyiTmMTd92iFGK0hbZNYcuwwe5MhE9N/Qpb9JTPX1MhQX7NNjh3lAGuowzGGvZ4s627y3ui9CHsDw1M28+2fWz70eX4C9tPU4FX7idPYzs2TMUgbQUiXYLxPM2uXbt8bQ6Tv//++7729u3brfMMW4Qzyg9wWGlpqa9t+vzlQp8ux31YtuuOKVzOn+uVlZWp/+bPxXQ00LGIiIhIDLqZEhEREYlBN1MiIiIiMRR0ZqqsrCz1LJczPm2fex7CNa+GDh3qaw8YMMDX5npWnI8Cgs9be/fu7WvzYJhJiJIJSqKwYNisR5QBcKMUTbSJkvXignG8rrZcR5TMlG3wVVM2hHGuwza4KGDfHrZ+h8kVHA4v06VgX9g8WBLnTRIDpdqyXqbXcpFHNB1vcTN6UQZQt+WfTDmYXAwUzcdGQ0NDYJr169f72pyR4oGMubioSw7Qli1kpnwTZ6Bsy+A8lC1fFlXY84DvC0z94s/wtsW5efun7VuonomIiIiIj26mRERERGLQzZSIiIhIDAWdmaqqqko9Q+U801FHHRWYnjNSnKHq2bOnr83PjU3PkXmw5ExkpGwyUVfJJfNje37tMoixLQ/hkn2w5TRsAzK7CFsTymUevO6cPeK2aRm2wXy5bcpH8LrY9luU+k02Lvs57HKzkTNKQhL9jJKxsh2PUfaz7TpiOrY408PT2HKT2Ro4mq9NnG/64IMPfO1Vq1YF5rFmzRpfm7eXKetrw5mxnTt3+tp8fnO+qaamJjBPW92obt26pZ1n2FpXJtkYkDlJhdVbERERkTyjmykRERGRGHQzJSIiIhJDQWemevXqlXo2y8+ajz766MD0nJHq06ePr83PfTmHZRqbj2tR2WSjRhQQfyy5KJkLWz9NeYkotZSYy7hutn7Y+mXLcbiM6WbLSO3du9fXdsk7cd0UWw7GtH05U5ZE3sEmGzmXbGVp4nLJJoWtb+Uy7lmU3F/ceZpqQuUC95P71dzcHHgPZ6JWrFiRtm2qM8XXVc7Y2jJTpmNl27Ztad/D+HPO9BnGn42cocrVOK35nKPK356JiIiIFADdTImIiIjEoJspERERkRgKOjPVt2/fVGaEx8TjfBQAVFdX+9pcK8NWW4OfNSclE9mOuPN0eb8tH+GSl+AsUpQaMvwc3Ta+ny0vAQTHZOI25514HqZ58nt4nryunNkrKysLzJNf49wVz8Mld8WZKdtYXy5jy4UdFy4J+ZKvsK2bKQ+Vj9eEKMuIsg9s73HJgzE+97hGFNd/4vwTALz77ru+dn19va/tsn35MyZs5nb37t2hpgeCOaxRo0b52pyPkmjy42ojIiIiUqB0MyUiIiISg26mRERERGLQzZSIiIhIDAUdQO/Vq1cqUFtbW+v7G7eBjwZGbovDuFy80Pb3KPKlkGDYIoCAPRxuG5DUVCyT32MLpJvYws7c3rVrl6/NQVIgWAiP32ML17e0tATmaSvOyOFU/sED/2ACCAbQubAsz8MlgM6hde4XT2+apy1EnK3itW2ZipHawsxRiteyTBTHDNsH0zSZGLw7CtvA5Hx88fSmH3ts3brV137//fd97ffee8/X5kGJN2zYkKbH0XEYvGfPnmmn5+uh6Rjmc5wLgZ544om+dvfu3a39zAY+3lx+WJDEDxwyJX96IiIiIlKAdDMlIiIiEoNupkRERERiKOjMVE1NTSrfUVNT4/sb56OA4LNkfv6cxICvhZyJasu0HmEH97VlqoDgwLtRBhDm5XAxzC1btvjamzZt8rXr6uoC8+SBTm0DtNryJyacPeLsA2eVTEU7+T22wbq5bXqNl8PnDeeyTFm4uIUXTe+PMpB2W1y4EQie85kYsDUTwuafXN4ThW0gbZeBy/k84H3ChSr53OSCmwDwzjvv+NqrV6/2tTlTlS2mczgd3hZ8TQCCWUrOC5vO+XxU6Bmq/LxSiIiIiBQI3UyJiIiIxKCbKREREZEYCjozddRRR6UyIr169fL9zVRLg583h81H5KI2DhA//+QiSr7JlsHgtimzwvkmzibZBgcGgO3bt/vaXDeKM1INDQ2+NteQAoLrmkStID6+OCsStt4TEKzBY8tImWpCcc6KM1E8GKstQ2WaJ/fDlk+Mkn2wHcO2TJVLv1xqVWUid2Wr2cbnjSmrxOcSbx8eJNvlGmAb8NuWiQSC68bv4UGJOePI5zcQzEnmIstq+gziulJ87PB5wseOKUdku05E+VzLh6ygy+ceb49cZqhyv8VERERECphupkRERERi0M2UiIiISAwFnZnq27dvKq/BYx5xZgOwZxuYy/PXsM9oTc+ns5GJsvXDVi8GCF9riefB9WGAYF6Ja8pwXoLzUQCwefNmX5vH1eOMFI+bZ8rShM2D2d4PBI8NWx7HZQy8JHJXnHnidlNTU9q/m2rncM6Ka+Fw2zb+H2A/t2zjOpqOXz5GbfvItP24r1Hq0zHbWI983uzcudPXNp1rO3bsSDsN72dTPtGWu7KNz2m61vF7eBmc5XLJYeUCfwYNHTo0ME2/fv18bdNxHpdtDNl8qs0Ul+2z0yUnma6eVZisXeFuRREREZE8oJspERERkRh0MyUiIiISg26mRERERGIo6AB6dXV1KsiajUGKXQZ0zddwX9jAuSmsawvz8Tw54GoKj3M4nEOyXHyP20Bw0FIO43Ko3RaSzRbbsWEbiBsIhtJtAXTTDzNshVH5RwC2gWlNy+VwLgfUOdRu6qdtXXl7uvyowhaotvXB1A9uc0iWty8fn0AwDG77IQYH0jm0DdgLbroU7o3yw4u2TNcQW4g9X/ExO2TIEF978ODBWemH7YcrSQxwnS+fYyzT124F0EVERESyRDdTIiIiIjHoZkpEREQkhoLOTJWVlaWeWycxMKpLkT8bW4YqW2y5IFtGyqWQJS+DcxucZeLimoA9E8Xz4IwVEMxI2bIgmZDE8cd4n5iWwfuR80u2AohAMMNjWxfOALkUneWsERf65GKYnLkyvca5K85Z2YpBAvbBffk8MG2bsJkVzhKaimOGHfDbtF9zwTYgs+mYL5SMFB/DAwcO9LUHDRqU+DJdtg0ff7bi1FFyvbnIAufqszMqfTMlIiIiEoNupkRERERi0M2UiIiISAwFnZnq1KmTse4L4JbjSDfAoev0tuVk4tmyS90QW/bDlpFymactI7V+/Xpfe9OmTYF51tfX+9qcmeJ6OqZ8ST48W89GpsBlPW2ZFdP2y0UNGdu5FyWbxNcCnocpV2SrncbzMA1Ma6t/5ZIHY7Z9HfbaBdjP8Xypv5YPTMffUUcd5WsPGDDA+p6w+PzkrJxpoG3OH4atseiyn/MlC5zP9M2UiIiISAy6mRIRERGJQTdTIiIiIjEUdGaqY8eOqXxClGe4YfMSUZaRxLNlW0bKVIskbEbKZXyslpYWX5vzTJyR+uCDD3xtU52puro6X5vHICuUGjT5yiXLFTbvlcQxbVuG6dzk1zJRT4z7xdkkUx6Fj1HORPE8bPWygGDNLF5XPm9cxuYTd3369Am81r9/f1/bNH5kWLxfTWOPttW9e3frPG2fF1E+1/J1bL58oi0kIiIiEoNupkRERERi0M2UiIiISAwFnZlqK8r4QjyOmUttqrB4mS7LsD3zdqkzFTYjxdObMhc7duzwtdeuXetrr1mzxtd2qTPF4+pJZpmOlVzUkMnXOjVR+sXbj3NVfK7x9JWVlYF5mnJUbXHWxpRHFHecY6upqbFOY/s8MR1LPJbmihUrfG2+Dh8ae/ZwbSB4fPEybMdnvo7NV2i0RURERERi0M2UiIiISAy6mRIRERGJQTdTIiIiIjG0mwB6EmzhU1Pozlb406UYZtjQK0/PgUPTNLY2z6O5uTkwTw65csCc2xs2bPC1d+/eHZhne6FA5pHBdL5zGJzPcVtx0aqqqsA8OYDORTn53OLgsoTDwW7TgMLMNqg473cAWLlypa/d0NDga/N1hAfRNn1+8ODIPA/uFwfpww6MbKLrn76ZEhEREYlFN1MiIiIiMehmSkRERCSGgs5MFRcXH/ZZbZTie1xQM0oxQ36mzc+rkxgs2WWevC62jBQXz6yvrw/Mc926db425za4KGd7zkjlgnIJ+cl2TnOeic+1VatWBd7Dg4Tz+cxZGwmHzyXOqHFWCQjuR1sO1WVgd1u/mCmHxccT95MHZC4rK/O1TevKuSqepmPHjmn7ma/Xqrj55HTyc41FRERECoRupkRERERi0M2UiIiISAwFnZmKy1YjirnUmeLMlMszV9s0UQY65te4X/ycfcuWLb4214wCggMbb9y4Me082zM+FqIMFhp2GVKYbOeiaVBx02vp5inh9OjRw9fu1auX9T18ref29u3bfW3OvQHB7FtRUZGvzec8559MOVTOv/KAy5x/4ppanKECgG7duvnanLtitgwVkJnrWSYyUG2nMdX1OhxdrUVERERi0M2UiIiISAy6mRIRERGJ4YjKTNmeefPz1Cg5GNs8k6h/FSWHxc/at23b5mtz/olrSJlea2pqsvajUPG+57G6TLVZ2jI9a7eN0ZaNjJRpGZyxsPUjidppR7Ikas1JON27d/e1BwwY4GtzrsgFZ0S5rhRft01s1/aWlpa0fweCY+vxtYmvO7Z6WSac7eI2c8lQsSh1HW2fry77IN1ywuSq9c2UiIiISAwZv5m68847UVRUhBtuuCH12t69ezF16lT07NkT3bp1w4QJE4wVt0VERETyXUZvpl599VX8/Oc/xwknnOB7/cYbb8Sf//xnPP7441iyZAk2bdqEyy+/PJNdEREREcmIjGWmdu3ahUmTJuEXv/gFbr/99tTrTU1NeOihh7Bw4UKce+65AIAFCxZgxIgRePnll3HaaadFWp5t7Dkg+PzU5XlqW6YsSZQaULZ5RMlIMX5OzmN5cf6Ja0iZ6kw1NjaG7kchMO1XrqtSWVmZdh4ux59pXK1s4+zX4V5LJ+x5A9gzFLy9ONdhmiZfc0TcL86kMdPxxzkY0/aQw+O6UYMHD/a1o2SkOHfK4+yFqUl0OJzR2bVrl69tqj/Gxwqfz1xXio83l3lyDstWZ88l25WEsJ+/pn6ly4jmRZ2pqVOn4qKLLsLYsWN9ry9fvhwHDhzwvT58+HAMGDAAS5cuzVR3RERERDIiI99MPfbYY3j99dfx6quvBv5WV1eHTp06oaqqyvd6dXX1YUfU3rdvn+//6pubmxPtr4iIiEhUiX8ztX79enzzm9/Eo48+GumrVJNZs2ahsrIy9a9///6JzFdEREQkrsRvppYvX44tW7bgE5/4BEpKSlBSUoIlS5bg3nvvRUlJCaqrq7F///5A/qa+vh41NTXGeU6fPh1NTU2pf6Y8j4iIiEguJP6Y77zzzsNbb73le+2qq67C8OHDcdNNN6F///7o2LEjFi9ejAkTJgAAVq5ciXXr1mHMmDHGeZaWlhpDsq2tralAma14JpBMsNvUhzDzzMQyTWHnHTt2+NqbNm3ytfmGlP/ORT1Nyy0UtrCk6dji0GbYZUQJDGeiiCcHv8OGzU24YJ/pG+g+ffr42hzC5mN2z549vrZp0GwOAPN78hVfd3i/8qCyLkwD3h4pePtVV1cHphk0aJCvHTb8bApl82Dwputu0vjYMS3T9uMNXnf+cY0pZM3XIts8mamose2aaCuqbWL7cQfPw/TjmXQFSMP8aCjxm6ny8nIcd9xxvte6du2Knj17pl6/5pprMG3aNPTo0QMVFRW4/vrrMWbMmMi/5BMRERHJlZwMJ/PTn/4UxcXFmDBhAvbt24dx48bhgQceyEVXRERERGLJys3U3/72N1+7c+fOmDt3LubOnZuNxYuIiIhkTEEPdNw2M8XPfU3PRuPmmUx/D1skLIl+cZvzUUCwoBwPZMwZKc4DuBQ3s2V8spWx4uXy83rbQL5JFJPjQZ9zlefJxLoxLmD6sY99LDANL5fPT94+3G9TjoMzF7aBo/OFrV+23AeQmf1YKDiT17dvX1+bBzEG7APxMj7e+HoImHNUbfE+ykSmKolB7l0Gbed14e3Jn2NhB0s3cck924pockbUpR/ppgmTfdVAxyIiIiIx6GZKREREJAbdTImIiIjEUNAP4vfs2RMrSxA2Y2GaPuygr1FyHfweHvzS9Hx/8+bNvjZnpOrr633tbNRMSYLLoMR8TER5fs/bg9tcBykTeZ0odadsNbWiDFLMeQgurhulThLvI85HmLIRnJHi3FW+ZqZYlH5yziVX+cRM4HXhDBTXLLMNmg3Yj3POP3FdPVMdL9s8eR+YcltRzj8b23WC15ULZpvONV5/vgYkcY1lPA/TfrZ9TvF5wm1TTTzeT23bykyJiIiIZIlupkRERERi0M2UiIiISAwFnZkKK4mMVNj3uIwvZJsHZ0W4rtTWrVsD81i7dm3adpSxvWzPxTNRd4qfzZvGzLPl5qIsl3MEvL2izDNursCl9lcS9V4Y16FxGd+vrKws7d85u+CSbbAdT3xeRDn3ssFU18eGcx18zEcZCzIXTDWhbOM4Mj43TZkfW00oPp9bWlrSTu8iiXONhR371eU9XBOP29kSZcxU27WIc1acpzV9fqTL4IXJEuubKREREZEYdDMlIiIiEoNupkRERERiaLeZqSTyTtw2PZt3GXsvLF4OP9PmcfZWr14dmAe/1tzcHKoPpuf/cWvZmLIinI/gaWxjRJmE3QemcfQ4c2Fbd5e8RJS6UTa2cQmTWAbXkeLcS1VVlbVfNi77mTNQvI84W8i1wHKFcx3l5eWh58HHdCbyOUngrFt1dbWvbcqsZAOf47bMaJTreBLj0YX9exLLyMRyk+i3KcfG1wlbhor/btrv6fKdtrEA28rPM1JERESkQOhmSkRERCQG3UyJiIiIxKCbKREREZEYCjqA3traGivoFjb8ZyoCmETQjkNuPJDx+vXrfe233nrL116xYkVgnjyYZRJsBSJt4UCX8Hg2cAjRVuDPRSbC5S4ysRwuYte7d29fu6KiwteOM9j4IRxcNq0XnyccKub9yNNzQB3IzADB3PfKykpfmwsJuuBrTzbOJZdBxTm8y8eGS4HXuEz95O1l+pFJ0lx+jBSlCGfcfiQ9fdT3MN4+LvPk9/A5zsVr+TPIVNyWg+5tj9kwhX/1zZSIiIhIDLqZEhEREYlBN1MiIiIiMRR0ZiqssM+r+XlppgqocYZnw4YNvvZ7773na7/zzju+dkNDQ+x+ueCMFGdBbPkdl6KnUbYxP0fn7EwSgxSH5TIocVim9yeRneF5cBFOHojWNAhx2GXYmAq8csHHHj16+Nq2/Woq4snHii0j4bJfOXNmG7g3Ct4H3G+XgY9tBQ9N+5lf48xUEgNtcwaPi5zy9jUNRrt9+/bQyw2LjwXOZYUp+Bh1mdmah+09UfJPYZfhMg2fB7ZMFRA87ttOo8yUiIiISJboZkpEREQkBt1MiYiIiMTQbjNTpmertnxO2HYUpnonPHDxqlWrfG2uI5WNjJSpPgxnpMJyGTyZp3Gp3cJZmGzUlMlFnZ9MDabM2Zna2lpfmzNTUfB+s20/07nGeRyuecQ1jvg4MOUlbNkil+1pq7cWBZ9/tgGtOctkyhHxNudlcBbJtB78mm3dud+m6wpn4Xr16hWYJh3T+c45SV43WxbGZaBjW12zJGQj3+myXFsmKom8UxJ4GbxPTOcF159re6yE6bO+mRIRERGJQTdTIiIiIjHoZkpEREQkhnaTmXJ5hsvPT5N4DmzrBz+P3bJlS+A9a9as8bW5rtS6detC9yMszi1wO1d4H5lqBWUjI8XCZoAAe/7GljeJwiXzY6srlQTbucTnpil/wnkHzr3wPrFtX9M0tsyeia3OFC+Xj5Xu3bsH5hk2d5WJa5cJ94vXlbNbLnkyzrqFPe5NYx1yDTJmy1maxuvk48+Uv8lHYT/3TK+FHcc2X7n0s22O0iU7d4i+mRIRERGJQTdTIiIiIjHoZkpEREQkBt1MiYiIiMRQ0AH0gwcPpsJ1LoG5sIHzKKF2bnOBTR7EGABWr17ta//rX//ytcOE4Fzla+CcQ8VNTU2+Ngf6gfDhxySC3Zlg65dpwNywQWVT0cSePXv62kkcC3wecKCXA7/cNu1nnicfK7ZlRCmqGOVY4fdw4Lxbt26+tmkf8n7iefB7XI4DPk9sg8Ka2ML1tu1l+rvtuhyleC2H2rmfvH35Ou1y/GWCreikqdio7T25CIfn6zU2k468NRYRERFJkG6mRERERGLQzZSIiIhIDAWdmTpw4ECowmm2Z962jJRpWfwMmwfYrK+v97V5EGPTa5koQslZh1xkpEzP7m0ZqUxsiyiDAecC7zNT3ilsRoULdALhB5ZlpvOK9xufF1w00WWwah6U2DaIKbdNOZgk8Da25ShdMiyckSorK/O1o2SXbJkpzi6Zsppxi4O65E553aLksHh72bYPHxt8HQLsgyO7sH2m2I4VlwKb+SBKsdtC177WRkRERCTLdDMlIiIiEoNupkRERERiKOjMVDouz2xtA0C6DL7KWZBt27b52lwz6t133w3Mg2ucJIEzF7nISPH2MmVWOCuTqVxLOqZjJRfP87mOVGVlpa9tqiXE25j7zTWNamtrA/PgLJZtnvx3U76JX+MMyq5du9K2TfO0ZaCyUQfI5biw1b/i7Jfp3LQNENypU6e005vwNJxNijIAri3T6HId5v1qyze51J3ideU2Hyu2vx/utbZc8k1xj9l8zEdFVSjZVVeF3XsRERGRHNPNlIiIiEgMupkSERERieGIykzZxjmyZaRM+YAtW7b42v/85z997ffee8/X3rx582F6nCyus8IZC85UsSjjAfI253yAS+2gfMkEZON5Ps+TM1J9+/b1tXkfAsFtyv0uLy9PuwwgeB60tLSkbTPOO5le48xUc3Ozr825IlNNt7DHiss+i1LDKCxbPSzTevD5yf1wyUiFxcs0LcOWV+J15Uypab9ydov3sy072KVLl8A8bfj4cxkb0jYGni1za5qHtB/6ZkpEREQkBt1MiYiIiMSgmykRERGRGAo6M9Xa2pp6Bu2Sb7JlFWzjJm3fvj0wz7Vr1/raK1euTPv3JMZ3YqYx27i+UFimTJUtR8X7wFZfB8jM9shXnC/h+kI8Rl6fPn3Svh+w5/w4Z5VEBmjHjh2+tqlOGmdSOEPFWZp8qRGVifny3215KCCYC+JcUa7YrpmNjY2+Nh8rpv3MxyjnlXj7RMlIMb42uYwNyf2KO06h+BV63anC6q2IiIhIntHNlIiIiEgMupkSERERiUE3UyIiIiIxFHQAfd++falgpq0wHhAMGTKeBxcrNBXc5IGM161bF2qZUbgMYmwrymnjUrTTVqST26Z9ciThoG1VVZWvzQF0DtqainbyNuX9Fvc4AILhcQ6c8+DeQPC458BvoYpSyNI0QHUhMF0DOJjNxwJfI/kaagoVd+7c2dfm7cU/VuBjyXT94+XweWIrImsKoOcicH4khdoLLXDOCrv3IiIiIjmmmykRERGRGHQzJSIiIhJDYT7M//8OHjyYyjlFeZ7NmR5+Ns+DGHMeCgA2btzoa3ORuiRw7oUHrzVlaTIxECrnDsIW6Szk5/9hC8qZ/s4ZKB7AlfcrZ0FM2Rsu2GrLsbkUSeX9xnkSzsmYjvlsFOEMy6U4Jp9r/HfTPMJmPXjbmIrZ2jKgtkKgUXBGinNFQDAfxxkpzsq5nDf8Gl9HOL/EOSw+b4Dg9Y+P0a1bt/ravK6ma1UhX78k8/TNlIiIiEgMupkSERERiUE3UyIiIiIxFHRmqu1Ax1FwnoQHMl6zZo2vzYMWA8FcVSbq6ZSVlaVtu+SjkshU8Dw4x8HtI2kQY+aSz+GsG2eqeD+7sGUHTfuEszKcSeEsIedi8jEfBQT3gWmwYK5xZMs/JZGb4YyUqRad7VhIYlBYvv5xP0z1w+rq6tK+x8Yli2TL/XH+yXRt43ON+71p0yZf21RXSjKr0OtKsfa1NiIiIiJZppspERERkRh0MyUiIiISQ0FnptpyqTPFr3G+iTNT/FzdlJlKoq4UP/O3ZWlyhbM1nP0w1ctpK1t1WnLxLJ63jWldbdmiJPptGyfOhHNUfF7w2HycqcoXvK58HrnUYzPlqtoy7UPbtcfW5kwaADQ2NvranO3ia4ZLJpKXy7WVOCPFedDD9TUu3m98LvHxaMtuAsH9xPUAefu64H5GuZ6FfU8Sy8yF9paHcnHkrbGIiIhIgnQzJSIiIhKDbqZEREREYijozNSHH36YejbOz9lN2QbOfvAYYzxeE2cIwtZUAdyeHfM0nJHiv2ejrhRvTyCYleHaLJy9ydXz/SRq8MRdhqmeky1jZhvLMFM5BM6kcDtfM1KMz5tu3br52jyOIRA8lzhXxdvcdF3hOkh8XoStowQEr03cd25z1st0/nLeiZfB7Uzko0yZNL5W2erT8T4wXZc5E8XrJvEciZkoG20RERERkRh0MyUiIiISQ0ZupjZu3Igrr7wSPXv2RJcuXXD88cfjtddeS/3d8zzcfPPN6Nu3L7p06YKxY8di9erVmeiKiIiISEYlfjO1Y8cOnHHGGejYsSOeeeYZrFixAvfccw+6d++emuauu+7Cvffei/nz52PZsmXo2rUrxo0bl5Fx7UREREQyKfEA+uzZs9G/f38sWLAg9drgwYNT/+15HubMmYMf/OAHuOSSSwAAv/rVr1BdXY0nn3wSEydOdF7WwYMHU2FFDiW6FMLjonTcjhK8jRLM4zApzyNKmJwDqGHnYRr4k1/L1wFuWT4E0oFgcUE+Rm3blwdvNeH3cL9MwWReLgd683UQ2K5du/raPXv2TPt3U/Fb22DILj/24P8J5O3H4WfbDw1M8+SCwhyUr6iosM6T+9Xc3OxrZyJw7oKPSW7bAummYsHZCJxHKagZtwin6bqSjR/6HEmB87brarpeHvZ9SXfkT3/6E04++WR89rOfRZ8+fTBy5Ej84he/SP19zZo1qKurw9ixY1OvVVZWYvTo0Vi6dKlxnvv27UNzc7Pvn4iIiEg+SPxm6v3338e8efMwbNgw/OUvf8HXv/51fOMb38Avf/lLAEBdXR0AoLq62ve+6urq1N/YrFmzUFlZmfrXv3//pLstIiIiEkniN1Otra34xCc+gTvuuAMjR47ElClT8JWvfAXz58+PPM/p06ejqakp9W/9+vUJ9lhEREQkusQzU3379sUxxxzje23EiBH4wx/+AACoqakBANTX16Nv376paerr63HSSScZ51laWmosuHfgwIHU83LOdZgGsgxblJMzLkk8mzY9e+bXOBsTtwAnYH/2y+tqKiSYL0U548pFhgoI5pl4G9syVKZzwLZcl2f+vO85K5gvPwzhnFBlZaWvzbkhHhzYtP04R2XLWZmOFd5PnNfhfcLXHZfsIRcc5gwV54Zc+sn7NRPns20QY5fl2jJUPGBzFEkMKJyrQYkzsdx8yEjlQx/CSLy3Z5xxBlauXOl7bdWqVRg4cCCAj8LoNTU1WLx4cervzc3NWLZsGcaMGZN0d0REREQyKvFvpm688UacfvrpuOOOO/C5z30Or7zyCh588EE8+OCDAD76luWGG27A7bffjmHDhmHw4MGYMWMGamtrcemllybdHREREZGMSvxm6pRTTsETTzyB6dOnY+bMmRg8eDDmzJmDSZMmpab57ne/i927d2PKlClobGzEmWeeiWeffTbwtbyIiIhIvsvIQMef+cxn8JnPfOawfy8qKsLMmTMxc+bMWMvZtWtX6vmwbRBP4KOCom3xs3ZbZiWJZ9GmWkG2jJRLrZuwOCfD+QlT7RZbHqdQM1TZwtuHjy9um3JrLGyuwCWzYtr3+aC8vDxtm/9njPNP3AaCOSvOYdlqwAHB7cU5K8748HXGZXtzroozVC45QNvA2ixKTSN+j0vNPF43vt7xe7jfLudJLrhsv/aad3JRKP101b7WRkRERCTLdDMlIiIiEoNupkRERERiyEhmKluam5tT2R+uEWWqM8XD0PA0/PcomakomYFsPDvmjBTnDvjvpto3LmOKFSLTemRin3B2hrcnH2/8dx43Dkimn7bx/HLBtF6cLbTViKqqqvK1OQ8FBDNTPA+ubeVS882UzWqLs5t87QLs+4CPDe6XqZ+289clv8P7wJbndDmWeBpbBioT9f9YpsbAs23jXNWqyoZ8yEglkTE97LzDdkZERERE/odupkRERERi0M2UiIiISAwFn5k6lAPg2i2mzNSWLVt8bR6rj7MMLuOc2TIULs9oeTmcQ+CsDf/dlG/i9/A0YTNUpnlKPLbxEF3G5jPVLUvHJfOTDzkNlz7wunNWietQcYbKNA3n0ngZnKECgtu0rKzM1+brxrp169K2XbiM5xf2PS717fr06ZN2Hlz/isd5NOF9bdv3uRorMheZnygZqlz0Mx+uGUBuc1n6ZkpEREQkBt1MiYiIiMSgmykRERGRGHQzJSIiIhJDQQfQd+3alQrxcvBx27Ztgek5gM7TRBkw0xZ4cwmxhy2aGKUQnq1gJK+7y0DH+RI6LFS8320DHZuOTx7c18alaGw+FNczsQWoud8c2Ocin6bX+D0cJjeFsm3nGv84hueZRIHIKIF0myFDhgResw3inIlBhwt1QHWX8yjs54dpelth6CTOZ9s2d9lHSXyusSTWLd08VLRTREREJEt0MyUiIiISg26mRERERGIo6MxUc3NzKuPA+afNmzcHpq+rq/O1OcsQha2oGj9zNWUbbJkVbrsUarQV4eTCd5x9KJRcQiHjY4H3CWeoTPko0+C96ZgyP1yIko+vfBl8NWxmj7MjpvOGM1K8PcMWRQWC+9GWicyXjNqwYcN87W7dulnfE2X72Niumfz3TA1KbBMlqxR2X7scK2EHn2amXFDYc81lelu2MBOyeW7lx1ksIiIiUqB0MyUiIiISg26mRERERGIo6MxUXV1damBSzkitX78+MH1TU1Os5bk8B+ZntFHqv4TNUJnwcm25gyi1qyQeW4aAszemwaeZbSBjl9wQZ6jyJTNlw1kRWxuwr2uUzAXvp507d/ranIXLlSgZKdbQ0OBr29bNlJOx5W1ycbyZ9rstE+Vy7Nim4WPUJS8bNiPF+8D0GcWvFco1IJf0zZSIiIhIDLqZEhEREYlBN1MiIiIiMRR0Zmrt2rWp58X19fW+v23fvj32/KM8F07iWXLYcZCSmKfkni1fZ8pLcI6DM0Auy+D5coaK/56JceBc8HK5lhr/nbMhprxO2HExTVpaWnztHTt2+NrNzc2+dibGr3MxaNAgX7tr166h58Hrtm7dOl+b82JR8k9JXKvi1hcyZQ9t9df474fyvGHmEaWmoA1/XvC6mTJXtnM+SqYq7LGQjTpUSdI3UyIiIiIx6GZKREREJAbdTImIiIjEoJspERERkRgKOoC+bt26VPAtiUGL80WUgLlEly8DzdqYAsNlZWVp3+MS5uVAKi+H2zxPDoJnCge3uRgmF+XlfpsGirYVPOQgrmldOWC+ZcsWX9tW2NKluKNtP3I/a2trA9PwDwtsOGwOAB988IGvzevSXn7o4jKgMAfM+ccfLsFuW5FdFrZAp4lLqN1W2JM/o1yKQNtC7C7nWtjjK+5naZjlFcaniIiIiEie0s2UiIiISAy6mRIRERGJoaAzUy0tLaGfOR8p2kt24UjGuQ1TZspUGDAsHuCWczA84LKtWCaQmeOPl8PFMrdu3eprc17CVCyTs5aNjY2+NueMTINNc3Zr27ZtafvFyzDlc2zFV/k9VVVV1nny9uN+87bgweOB4LHQXq8zplwRn2s8DeeZTOdmEpkn2zyTGKzbxlaA06VALrf53DJtP56vrR8uRXmTyijrmykRERGRGHQzJSIiIhKDbqZEREREYijozFR7obpS2VUodaU4N9OlS5fANPyabd1MtYb4PZwz4KwRZ5VMWaRs1J7i/A5nkTgfYeon54a4bpdLbSbOmHGdKW5zNsSU+7Rla2yDFLtkmXjduZ/Zqh+WD3gfmDJT/Jqt7ZKPsmV+XQY+zsX1zFYHzaVPtnU3rastd8WZqigDMLv+jRXGp4qIiIhIntLNlIiIiEgMupkSERERiUGZqTTCjo8VFT87ztcMVba2R3tlq//CGQHOQ5nG4eN52OoT2f4OBOsN2frBGSrAXu/FxmVsNNsYgTxmnqkPvK5c28Yl+8EZqF27dvnapqxWWLZ6Yi7jJXK/eOy9sPsoV5K4XtoyUqa8UxLZpLA1oQol38lM/eZtztvC5fizjSto+4wyfWal28bKTImIiIhkiW6mRERERGLQzZSIiIhIDMpMhRDl+XWuckXKN/2PQskdcIagc+fOvrYpN5PEunEOgZfDmSmuceSSmQr79yj1dGzjcJmWyX2PUisoE2z5EsbrzvkoAGhqaorfsTwUZXxW3s9cT8yWzTEtl9um89VlvvIR0361bWPOz7l87vF1pe0yTGMMHnY+zlOKiIiISIBupkRERERi0M2UiIiISAy6mRIRERGJQWm4EFzCg3GDt4A9UJkvRT3zNeSeD4Fzl6J1NrkKP3NhTy7SWVlZ6Wtz0UoTPndc3hOWLXBuOj55XXOxzV2OC9sAr1x8lNuZkq/XAMbHH+93bpuu9bZCny7zOFKYjgP+3AoT7j4c248AXD4L0p3zYa7Zuf/UERERESlgupkSERERiUE3UyIiIiIxHLkPdR3Yihma8HNgHnDU9Aw3bM4gXwdGzkZ+Ih/yUEkJW4TSlDPigprM5Zk/H+dclJP3I2cMTNkQ7tfOnTt9bS4quXfvXl87E5kqk7jnHmA//+L+HbAX5SyUQYqT4HINsA0AztdyPoZNy+DCni7nwZHK5bzKxedWJjOR7eeTSURERCQHdDMlIiIiEoNupkRERERi0EPeNvg5OT8j5yyJyZ49e3xtl1o3Nrb3uOQ4clEPpj3lm2yirCu/h9v79u3ztTl/BwT3Ix+zLmx5Ec4Z8DJMua2qqipfm+seNTY2+tqcqeI2EDy3TNujLV4PU6aFa2hFyb3YcpLMJcfG5y9nynKVR7QtN8p1xrbcKH+3HcO8n10GlrbVMDqSrne8X22DjAPJ1JUKK5t1446cvS8iIiKSAbqZEhEREYlBN1MiIiIiMRzRmSlbRqpXr16+dufOnQPz4GfF/FyYcy9RRHkWz/3KhwxVe2LbJ6ZcjC1jwfusqanJ1962bVtgnlw/h/NLSWSobGOSmZZhOw94fL8dO3b42s3NzYF52nJVttpUpn0WNlPB2SUgmJHKxLkUdp75kt9JIkuYxHJtbdsYb0c6W0aKz3dTPioTY/Plk/w440REREQKlG6mRERERGLQzZSIiIhIDO02M+Xy3J3zJn369EnbNuHaN1w/x0Um8g2ca+Fn3u3teXWmhc1IudS+4X3CuSI+ljZs2BCYJ2ekuL4T55mijGtmy+uYajNxpoL7wW3bWGmmefK5F2V8Ott7eJ/kagw83iecP8lW5jEfslgu9Z1sx5MyUemFzUjZ/g5kZiy+fNqPuT8zRERERAqYbqZEREREYtDNlIiIiEgMupkSERERiaGgA+jFxcWpAJpLKJEL9PXu3dvXHjBggK/NRTpNBfuyEVCNMqCmbYBlFen8H1FCtUkMesr7gItOtrS0+Nrbt28PzKN79+6+dnV1ta/drVs3az/CDiTLYV7TMc8BYFv4lIP0/OMQIBhatxXc5GWa+sD7wDZIcb6wFVLN1PmdjeKhtoKavK6mHyvwsWELKvP0pmMr7Dlu+ns+XndNxzy/ZisCna0fREQplpwt+mZKREREJIbEb6YOHjyIGTNmYPDgwejSpQuGDBmCH/7wh747V8/zcPPNN6Nv377o0qULxo4di9WrVyfdFREREZGMS/xmavbs2Zg3bx7uv/9+vPvuu5g9ezbuuusu3Hfffalp7rrrLtx7772YP38+li1bhq5du2LcuHHGx2giIiIi+SzxzNT//b//F5dccgkuuugiAMCgQYPwm9/8Bq+88gqAj76VmjNnDn7wgx/gkksuAQD86le/QnV1NZ588klMnDjReVllZWWHfYZqep3zJcOHD/e1y8vLfW1+7mvKXHCuhZ8t87N3lwwBP/fl7BbP09Qv7oct23UkFfGMsg+iZKLCshVWNWUbeIDgXbt2+dp8zLsMfMz9iLItbPNgvK6mzAqfB127dk07Dy7qacpxZCPDkomBe20ZSJeBtm1M28Y2D35PlHPNNpC2KSMVdhksicxUJgriZgJfR0zXFc5v2jJRucqC2fJ1LtcVzmu2va6EyUAn/olw+umnY/HixVi1ahUA4O9//zteeuklXHjhhQCANWvWoK6uDmPHjk29p7KyEqNHj8bSpUuN89y3bx+am5t9/0RERETyQeLfTH3ve99Dc3Mzhg8fjg4dOuDgwYP40Y9+hEmTJgEA6urqAAR/dVRdXZ36G5s1axZuu+22pLsqIiIiElvi30z97ne/w6OPPoqFCxfi9ddfxy9/+Uvcfffd+OUvfxl5ntOnT0dTU1Pq3/r16xPssYiIiEh0iX8z9Z3vfAff+973Utmn448/HmvXrsWsWbMwefJk1NTUAADq6+vRt2/f1Pvq6+tx0kknGedZWlpqzHqUlJQc9jk1f/MFACNGjPC1D/XlEH7uu3v3bl/bVOfHFpqPkpeorKz0tbnmDj/3NT3X5YxUodTPyYQotUmykZFitsFFTbkE3s9NTU2+Ng987FJ/LRM4c2FbN1M/uWaWLRfokpnKBFvG0QVnPXj7Rck45iIz5bJfeftwjTLb9ouyLWwZKZeByguFbZ+YMre28zVXwmby+Fzk2pIA0KNHD1+7bYYqzOdm4kdHS0tLYAU7dOiQ2giDBw9GTU0NFi9enPp7c3Mzli1bhjFjxiTdHREREZGMSvybqYsvvhg/+tGPMGDAABx77LF444038JOf/ARXX301gI++Bbjhhhtw++23Y9iwYRg8eDBmzJiB2tpaXHrppUl3R0RERCSjEr+Zuu+++zBjxgxce+212LJlC2pra/HVr34VN998c2qa7373u9i9ezemTJmCxsZGnHnmmXj22WcDX8mJiIiI5LvEb6bKy8sxZ84czJkz57DTFBUVYebMmZg5c2asZRUXF6ceKQ4ePNj3txNPPDEwfa9evdLOj2trRMkZRakjwnVTOCNlu8nkjAEQfOZdqM/7owibkcqXbWMbq8+lTlJjY6OvzbWYTLi+mq2OmUuWy1aXxjbenyknw+eFbTy6bGWkOMuVRMaH1423Z5RaS2H74ZJv4nPJtu6ZGDvNZVtEGUevvbBlpEzHQT6OIWjC+40zUf369fO1OUMKBD9f2x5PnMNM2xfnKUVEREQkQDdTIiIiIjHoZkpEREQkhsQzU9l04oknpvJCRx99tO9vffr0CT0/fv7K9Z569uwZeA9Xbec6P1GexSeRh+Bsgm3cN8l/phwD5/q2bduWdh5cOw0IHuc8VhVn8mx5J9M03Lbln0y47zwuIefFouQ+bOdrWVlZ4LWwdaR4epdzMYmsEWdlotS/4v2UiQxUWC41oTKRiYoyz3zIIvHxlqsxK21M25dzz0OGDPG129auBNzGdUx37bHVkWxL30yJiIiIxKCbKREREZEYdDMlIiIiEoNupkRERERiKOgA+pAhQ1IBM5fq6bawJAdtuW0q+skF+6LIRDiSQ4btOXBeqEU6bVyKUHJAktdt586d1uVwsJsDmRzi5MC6qRAeB8wZ95MLgfIPOwBg/fr1vvauXbt87UyEZm2DjCfBNM9MnK9hw+KmfuVj4DzKDyDyIWCdLWEHp84W7hd/vg4cODDwHi7OzQWHkxbms6IwPlVERERE8pRupkRERERi0M2UiIiISAwFnZmqqqpKZaX42abpeX/c5+aZytpwXiQT87Qto1AyBYWSd4oiSs6DX+P8CBfgNB0H/NqePXvS9pNzWKZ8lC0ztX37dl979erVvvY///lPaz8zwZYXy5ZMZLNs8iEPZWLLSNmONZd5RpGv10jG/XQZqDwTON90zDHHpG1n4hzgwrVJar+fTCIiIiJZoJspERERkRh0MyUiIiISQ0Fnpnr37p3KhNjq1gDBQWEPHDiQ9j38LNk06GGYgRCzidctiXxYLjICR1JGirlkWHifdO3aNXQ/eHBuHkCYcV0q03pwPoKXsXnzZl+bM1RR6ixFOVY4l8EZMxeZzGEkqVAyUba/c0YqE4NAu8jX+k28XM5AZqNfPAAxAIwaNcrXNtWnaysT/TQdK7zv294HhMlptt9PKhEREZEs0M2UiIiISAy6mRIRERGJoaAzU926dUNZWRmA4PNVzgy54HlwbsM0T9vYaC7PfePmgkzPdblfSTx/zkRGoD1noljYbAgz5TrC1mLZt29f4DXOQNmyg5ypsmWsgOC5lItjxVQzyja2l61GD9C+x72MK4nzOxvXCM7SFEoODggeoy0tLb52lM/CsPr37+9rDx8+PDBNRUVFqHlmIrdr2q9J1a87cj7JRERERDJAN1MiIiIiMehmSkRERCQG3UyJiIiIxFDQAfQOHTqkArguQVxbCNYWNnUZJJa5hCd54E4bDtFx4BAIrms2ApVHUpjcxrQtbAMZcwiWjwuX4oQcJjcFzuNKV+Qun3ABU5eCnLZ9ZNqvXAw4HwLpuSrQmYnAue08yQSX4o75go8/bttECXpzwc0ePXpY55mL7ZfNa5U+/URERERi0M2UiIiISAy6mRIRERGJoaAzUyUlJalcCT/j7dixY2B6W1FO/jsPqGnKQmRiMEZ+rst5MB640pSLydccS3sRJVvDOJdhm4cpc8CvRTke+Ti3ZTCycWy5bD+e5lAB30M6d+4cux987pmuK/wa59aykaEq5IwU46xg2ExpPotb+NiUh+LPgyT6xK/xMd69e3dfm8+9XEmiOHBU+mZKREREJAbdTImIiIjEoJspERERkRgK+mF0cXFx6tmuywDDYetIuWRWMvFMdteuXWnbzPQcXZmpeMJmoqLUwkkid8Xa82DTnJ3hWjdR2M55zkxFqR+WD3WnosjWcWHLSOUqD5YLts8xUz42E/WbuB9cR6pXr15p22EHNc4nbQdED7NtC+MqKiIiIpKndDMlIiIiEoNupkRERERiKOjMVNux+VyebcatX5KtOlNh5UMfCplLDoaPFa67EiXvFDaTYqpxxHkSWy7QdMznQ10pZupn7969077HVmNm7969gffYarrZjgPTcvJVPmTjTNvvSM5I2XANqUyci6bjgjNS/fr187UHDhzoayeRX3Tpl+0Y5pp5tnpZtnmGuUfI/dklIiIiUsB0MyUiIiISg26mRERERGIo6MxUUVHRYZ93ujx3t+VebPmTfOFSU+tIEjZzYTqG+Fk5Z2lsz9J5eiA4flW3bt3S/p3Xw1RjhvNNnB3kceJMDhw44GvnQ0aquro6MA3nIfgYt2W/eD2BYM7Ktu5xx0FLiu0Yz1Y+KmxWMEoOJhcyUbvJRUtLi69tyvnFxceO6VrGGShum7JHYfFybTXeoswjrjDzy7+jWERERKSA6GZKREREJAbdTImIiIjEoJspERERkRgKOoDedqBjDqO6BMdsBb448GsKp9oCmNwvU9jPFrSzhclNwVrGocNcBSxzwbaPTPukS5cuvnZlZaWvPWjQIF+bB/rk9wNA586dfe22A2oCwX3E4dPGxsbAPLdv3+5rb9u2zdfmY9YUYs/FjxV4H3DgvLy8PPAe7qftuOeweJTzhAPqLqHYTMhE4DyJgbXjLhPIjyKd2RgsGLD/qCnKjz9s13b+O7d79uwZmGf37t19bb5GRrlmmK6JYeXjjxUOyd+eiYiIiBQA3UyJiIiIxKCbKREREZEYCjoz1VaUZ6m2wosuA75yhsJWCJRzMy7L5TYXYszXYqLZEjZzwfudi2cCwMc//nFfe+jQob52TU2Nr815AM5DAcGMHrdteZ2mpqbAPDdu3Bh4rS3OSJkyU9nA27xv376+Ng+sajqfbUU4d+7c6WtHWddcZKKiZIYycb3LhCiFGLMhV5lRW16Tt49Lzo/ZMlI8YDhfywCgoqLC1+brme1YSiIfVWj0zZSIiIhIDLqZEhEREYlBN1MiIiIiMbSbzJSLsINwci0N03N2zkDxs2TOUJmeNdtqj/Bz8yRyL4Vad8olX2LLJXAe4OSTTw7M47TTTvO1a2tr0y6DmbIh3A9b7oCPCx5s1ITrTm3ZssX6nkzg475Pnz5p25wfM+UAOUPGdaSam5vT9sklZ2Q7vrgPQPgcUCYyUrmoGeUiFzktoHCuZ7yfkqi5xeeeLZ9ouq5wjpSn4UHZ8+V4yyVtAREREZEYdDMlIiIiEoNupkRERERiaDeZKZdntrbx+zj7wNObxiPi+hv8vJqzICacD7HVlcoEl2f12cghJJEZ4P3K47yNGDHC1+Z8FAAcc8wxvrZtbCqXfF1YLjkYHkOL2127dvW1TbmiuGPzcQYNCNau4bELbfXWTLlAfo3HLrQxraetzk++iDL2aC7kol+Fko8y4Ws/H9Mu10M+hrmOFJ9r/JlkGgeTM1LZqBvlcg3lzKKtrmM25ecZKSIiIlIgdDMlIiIiEoNupkRERERiKOjMVElJyWHrmLjkQHga29hoJqYx2Nri/pnq53COhdtxMy1JSSLPlAn8nJyf7/O4eqNGjfK1hwwZEpinLdNjy0yZ9nPYbIfLPPn44hoynKHatm1bYB5hx3bkGlH9+/e3TmM7T/hcMx1rjY2NvnaUccuYbRwzF9z3bOSu8iVDZavplq/XjFzh/cbHtI3p2OJ8E+8Tvu5w27SPwu43PgdMOV+uC8fXBJdj2JZrjlJvjfvVdozPMLlMfTMlIiIiEoNupkRERERi0M2UiIiISAy6mRIRERGJoaAD6B06dDhs2NMlQGcrEsbBXFMBTg4qcxEx7t+uXbsC82hpaUm7XEmPQ4b9+vXztXkg4+OOO87XNg30GXYQYhe2wDTPk4ORpkKWXOCVQ50cSOcinqb5cj94UFMOnA8aNCgwz7AhbA6w8nqZpglbtNMkGwHpbCzD5XjMREg9bNHTfPkxTa7EPWZNP+Tg44u3MZ/ffB6ZPm/4WmW7HnKQftOmTYFpeLlcyJd/OGQ6b/gY5n6arpFttQ2XH7JhwwZfu+2PdML8yEXfTImIiIjEoJspERERkRh0MyUiIiISQ0FnpoqLi2PlAFwGMm6rW7dugdf4OS8/0+bnxKZCoJnISPF2aS9ZBdP+5n0wfPhwX3vkyJG+Ng9izDk3E352zpkelwKvPA8ubFdfX+9ruxRv5SwDZzJ43UyDmvLxx8vhXCDPwyUfFfY8NU3P/UqiaGcm8LFgy5skIYkini75J57GtpwoxZPbC9N62TI9SeB9wp9J3DblEznby9cIPsbr6up87S1btlj7yRnlvn37+tqm67Lt85RzptyvNWvWBOa5efNmX7vturpc1w/RN1MiIiIiMYS+mXrhhRdw8cUXo7a2FkVFRXjyySd9f/c8DzfffDP69u2LLl26YOzYsVi9erVvmoaGBkyaNAkVFRWoqqrCNddcY/yVm4iIiEi+C30ztXv3bpx44omYO3eu8e933XUX7r33XsyfPx/Lli1D165dMW7cON/jh0mTJuGdd97BokWL8PTTT+OFF17AlClToq+FiIiISI6Efph/4YUX4sILLzT+zfM8zJkzBz/4wQ9wySWXAAB+9atfobq6Gk8++SQmTpyId999F88++yxeffXVVP2f++67D5/+9Kdx9913o7a21rkv6TJTptdt2RB+PsrPlnnwVpfl8jPyHTt2BN4T5rnskc60Xz/+8Y/72jyQ8fHHH+9r2wYxNrHlOmy1XQBg69atvvY///lPX5vrnfDzf9O6c90ozrTwPExsmR7OLrhkXPg9tgFIXXKDmchIZWNQYsbrbqpfF7bOmcv243nyPgqbh3LB8zDlc9pLvpP73dDQEJgm7GDntmUAwe3H+5Xr6PF+Nh07XPuQ25znNNVvYtx3HnS9srIybdtlnpyR4musqf5VUhLNTK1ZswZ1dXUYO3Zs6rXKykqMHj0aS5cuBQAsXboUVVVVvkKKY8eORXFxMZYtW2ac7759+9Dc3Oz7JyIiIpIPEr2ZOnRXWF1d7Xu9uro69be6urrANzwlJSXo0aNH4K7ykFmzZqGysjL1jyswi4iIiORKQfyab/r06Whqakr9W79+fa67JCIiIgIg4TpTNTU1AD6ql9O2ZkR9fT1OOumk1DRcg+LDDz9EQ0ND6v2stLTUOCZRhw4dDpstcHm2zFklW30dU7bBtlweXyhX4+4VSi7BltMw5dZOOeUUX3v06NG+tst+Y5wB4Of7nEXiLJypzsrGjRt97XXr1vna/PjaJUvHmR9eV5csF+N9wOcYZ5dMxzTX/rJlgFxqMZnyNvmI14XHNmSmY96W5eK/u9RKc1muuONzq6mpKePLNGWu+Hjr2bNn2r/z+Wo6fznvxddDvo7YMpFA8JjlfvH4fqZrAh/nfF3mayzPM5MSPZsGDx6MmpoaLF68OPVac3Mzli1bhjFjxgAAxowZg8bGRixfvjw1zXPPPYfW1tbAh6CIiIhIvgv9zdSuXbt8Cfk1a9bgzTffRI8ePTBgwADccMMNuP322zFs2DAMHjwYM2bMQG1tLS699FIAwIgRI3DBBRfgK1/5CubPn48DBw7guuuuw8SJE0P9kk9EREQkH4S+mXrttdfwqU99KtWeNm0aAGDy5Ml4+OGH8d3vfhe7d+/GlClT0NjYiDPPPBPPPvus75HZo48+iuuuuw7nnXceiouLMWHCBNx7770JrI6IiIhIdoW+mTrnnHPS1sooKirCzJkzMXPmzMNO06NHDyxcuDDsokVERETyTkEPdFxUVBQIeB9iCsDxa/xeW4guSmCTg3qmALAtlJ6J8Hi+BNJt25QD1aZHwUcffbSvzcFFXjceYPjtt98OzJN/McoBTA5hc1E7U/CRX+N5csA6yj7JxKC6fHzyYMqmY5qLiTI+9/h/0Ew/OIkbmM5U4Jq3eZSisLlgO76S2F75+kOXKHhd+DzIBtPnXUVFha8d9scIa9euDbzGBYb5esf94PPVdP7brglcGomLGAPBH+XwNZOvRdkcDF0/5xARERGJQTdTIiIiIjHoZkpEREQkhoLOTKVjerbM2QZuc1ExW67DBRd35LwOEHwWn4s8k0s+wtbPJPA+4efspqKdnKviQm7bt2/3td977z1f+x//+EdgnvX19b425yP4WOBckanAJD/v5zbP83B5wHR4Hi4D+YY9vvgYNh3T5eXlvratcCqvK78fCBYCDSuJ49W0PcPO15bdzJSwg3W7FD6Ou0zXaTItiWt9JnC/evfuHZjGZUDgtjibtGbNmsA0fD2z7XeXQqB8TeR58nXEpWhxlAG/M0XfTImIiIjEoJspERERkRh0MyUiIiISQ0FnpoqLi0M9w+dnsLbMVBT8DJcHjOTBbE3vCZuZMm2DTOQQMpGR4nlyjRSuoWLKDPCz9g8++MDX5nol77zzjq/NAw4DwbpRtn3EbdOxZMvG8Hui5DY4M+CSOwg7T84/cS4QCGbMOO9kO9dMdaY4R2WrJ5YEl0GyM1HbKx+Ytmc+5JuSYDsXTX/nc9w2qLiJLdPDy+3Ro0fatgsegJkzUi61mGzr5pKx4mmSyIRmIiPVtu9hrsH6ZkpEREQkBt1MiYiIiMSgmykRERGRGAr6YX9JSUkqr+BSI4VxboPzOpyFcHm2zFkRrnHEWRwgfu2WXNbWCMO0npw7KCsr87V79uyZdnogmJnijMArr7zia3NmwGX72TJSLuNh2eqYcb4piQxfJurlcL0YU00tPld4G9vWzdRv3ve2sTWZKaPB29yU1WrLlI8KO6Zn2H5H0V6yTS5M+9VWby2JWn62zw9TPtZ2rbHV2TO93zZO6Pvvv+9r79q1K20fTMJeR0zbM+z1zDR9pjNSgH+bh8mc6pspERERkRh0MyUiIiISg26mRERERGIo6MxU2DpTtlpB/IyWn1+bntfyM1V+Xr1z586005uWY8PrESWzkglRsh+8zbt16+Zrc30izkMBwL59+3ztf/3rX7726tWrfe0oGSmbJMY1y8QYbVHqTNmyXcy0rnxMmsbva4vzUKbpk6hTY2PLrZn2YZT8nGQWZ99s557pGmpjy8ZF0blzZ1+bj3lTDquxsdHX5rFJuZ0JUa5/fG3na78Jf5665JhtbDlJV/pmSkRERCQG3UyJiIiIxKCbKREREZEYdDMlIiIiEkNBB9A7dOiQCoi6hFE5AMfBbw7BugTTbKFYDrRG6act0GrqJw8+y+0oRU5ZJgKXHErkwCG3gWBYmQOXUcKlYblsTz5W+PhLIlBtC5y7FN+zhbBdisbyjwJ4XfnvLkUUTQVvk8bbh7eFqV/tdaDjfGUrbGliu765XCNsP2DiY5oLOAPB48t2DeV5mgYV5+udaZqk8bXK5ZoQ9gc4pgLNpm2arh8ufUjqhyz6ZkpEREQkBt1MiYiIiMSgmykRERGRGAr6YX/Hjh1Tz1WTKErJ2STOIrlkI/iZbVVVla+9YcOGwHtsz5JtWS+XvBMXN+OMQCYGW2Uuz6tt+3Hr1q2B12wFIW1Mz8zDZshcimOG3c/chyjP9l0yUrZ15X3Cx1L37t0D77FtD1vGzHQcZGLQZltGykU2BjIOOzCvaZmFOvgxZ2c4VxmFLYtkKg7Mx30SOVP+zLEVWzYV7eR1yVe8LrYBl03HMK9/2M/9TJ4D+mZKREREJAbdTImIiIjEoJspERERkRgKOjNVUlKSyg+5DDppe75qm4dL9qFPnz6+ds+ePa3vsbHVI3LJctlyHJl4lmzLegHBzAoP2sl/d8lHhV0Xl/0aNgPEtZlcRKn9lUQeJ+z24uld6umEZRrA1JZptNWgcWHLUJmOA76ucD95AFeXwZNtbO8ppHwU15qrqKhIOz3vA9N1ndefryvbt2/3tbNRmwmwn+O2XFYSA/tGkUQtJl4Xzu02NDTEXkYu6ZspERERkRh0MyUiIiISg26mRERERGIo6MxUUVFR6lmuSw4hbDaBnxO75EC45kcmxoWzjdVnwnklfvaeRIbKtn1N/baNd5WJjFTS7wfcjq1s1PKyyUSWxlTnJmy9Jt42pswZ1xsqLy/3tTk7k0TtOZdznpfD687nHtdJytWxk4tclSlD6jIGalsu2cJNmzb52pzHydVYpGFzkZmorSaZkfuru4iIiEgB082UiIiISAy6mRIRERGJoaAzU8XFxYd9jm16Fs3ZBVumgnMbphwIP9Petm2br71ly5a0y3DB2S3Ojphw3/k9nHOxZZeiiFKni/dJvtbLsdV/MdXUcqkHVohMuTYed6tHjx6+dllZWdp5mrYV15HiekR8nuzcuTPt+4HsjPfHx7TLMsOef1HOtSh1zWzXCc6Dca4tCtuYbps3bw68h8dwS+I6ko1rUb5kpMLWlcpEpi8T40u6XJfbXpvC5C71zZSIiIhIDLqZEhEREYlBN1MiIiIiMehmSkRERCSG9pmIhVt4zTaopEtgmANqXBxux44daZfhIkookQPoXBjPVgwziaJ2UUKwSRRazAWXwX95e4QtVpivTEU7eSDZmpoaX7uystLX5oKupuPPFuTm7ck/uuA+AcH9lEQA2Fbs1yX4HbYAbpRzj7kMoM6vJfGjCl7/pqYmX5t/1MMBdFNh5LhB5XwJgueLbBQc5vM1yg8imO0HEoD9xzCu9M2UiIiISAy6mRIRERGJQTdTIiIiIjG028yUCWekOM/ERTk5Q+Dy3Jif5+/ZsydMF42SyBFxtsFWlC2JZ+Qu2y9K4cBCYMpc8H4Mm5kybT/TgMC2frBMDBTNOZfGxkZfmwe8dcnecAFIW8bRZdBsU7YtLt7mfF3h48C2D4FgRiqJrFISAzDb8D7i6yMA1NfX+9pccNM2zySuGUdyRsr0WZCNjJQt55eJPnBWM0n6ZkpEREQkBt1MiYiIiMSgmykRERGRGI6ozBTXIzENXNwWZwpM03MOq6WlJdQykpCJASGT4JLr4L635+yCbZ+YaqC05TL4qG2Q3Wzh5dbV1fna/fr187W7du3qa5uOad4+tkF3XTJqnFeKMiixDV8jeBkuA5cngY8fl6xWWLb9zlm6KKJkWY9kYQctzhZbZjSJc69Xr16x5+FKR6GIiIhIDLqZEhEREYlBN1MiIiIiMRxRmanOnTv72pzBMI3x1BZnH4BgRorr1nCuw+U5sC0TkERGwJZVSmJsPlvdnyMN50ls2zjK2H22eWYjw+eyXD5vqqqqfG2XY4W3D7+Hz0VTJo3nwf1MIrdhqztl2mf8Gr/Hlkd0Ge8vyjnOx/DOnTt9bR6bNEqdPc5y2er/ZWpdC0UmMlG2PGK+4nps2VQYW0hEREQkT+lmSkRERCQG3UyJiIiIxNBuM1Om58hcz4VrUPBz4aamJl/b9Px/3759adtRntWHzWmYnmfza7axvZJ4Rm7LeuVrvZNcsdUfipKZsuXW+Pg0vSeJfAmPo9ejRw9fm3Mw3K8otdP4+OI+8HiApn7w9uJx4pLYNrax+kx4+/C1iHNGpkwVb1Oep2ncPJtM1DGzXSfacy06m1xdQ5M47rNRTy2JMSuj0jdTIiIiIjHoZkpEREQkBt1MiYiIiMSgmykRERGRGNptAN1lEE8u4llRUeFrc9E/U/jPpQBfXFxM1CUsbgsicztsQUkTXmaUgVSPpJC6bVDdJH68wMewiyQC6bwfbT942L17t69tKqAb9tjg449D8Cb8Qw0OzXKRSsBemNL2Y44krhmZuO6Yzl9eF/6RBBdfdQkE875vbGz0tW0/BHJZd9sxzcdWvoTcj6TrYRIyMXi3K30zJSIiIhKDbqZEREREYtDNlIiIiEgM7TYzZWLLLnTr1s3X5nwEZ6wAe8YiiQEiOZfFGQNTv2wDy9pyBlGKJiZRlI2Xm6uBeXPBdqy47JMo2ysTmR4+3viY5UGHOevA56JpmrB5EtN5wjkq7hcX+jQVqeRMz7Zt23xt3ha8HpkYuDwKl8Kf/Brvp65du4ZeLvedC6Xajuko16ps5AJNxxvj48mWjy0ULsdjErk0WzY4m/TNlIiIiEgMupkSERERiUE3UyIiIiIxtJvMVJR6HPy8lducnzDlOHgwVX6Py0CzYfGApC4DlPKzd1utKtOzettz8ChZkCQGWM4HLoM6h91+nNcpKysLvIfrSHENHs5kcMbPhLOCfHy5DG7L2Y8dO3ak7QevuylvkokaMlwniftlG4waCOaE+JrA29O2jwB7TshWJynK+WtbRhJM17+GhgZf21TLq60o1yq+Dtvyd6bsDWdCbflYlywX58Ns05uOlXzIVUW5bke59vN+Mn0mt8XbK2y+LsxA3oX5ySUiIiKSJ0LfTL3wwgu4+OKLUVtbi6KiIjz55JOpvx04cAA33XQTjj/+eHTt2hW1tbX44he/iE2bNvnm0dDQgEmTJqGiogJVVVW45pprnL5dEREREck3oW+mdu/ejRNPPBFz584N/K2lpQWvv/46ZsyYgddffx1//OMfsXLlSowfP9433aRJk/DOO+9g0aJFePrpp/HCCy9gypQp0ddCREREJEdCZ6YuvPBCXHjhhca/VVZWYtGiRb7X7r//fpx66qlYt24dBgwYgHfffRfPPvssXn31VZx88skAgPvuuw+f/vSncffdd6O2tta98yUlh60rkUT2hudtyqzwWFTc5gwV19vJlDDPepMSpU4Sb2POqHA7H/IBgFtGKizO6/Tp08fX7tevn/U9vL1c6k5xhmfDhg2+9qpVq3ztKMcWZ7s4O+OSTeJ8UxJ4v3GWhmvhmPrF7+ExPjlDxetu+laea1XZxv9jpusiHyu28RJNbPuJjzfOztXX1wfmyWPxMVveyaUeVti2KVuYiVpetpypSx0l236z5WWzdU0NuxzT9uZ8Yth6YWH7EGb6jGemmpqaUFRUlLrJWLp0KaqqqlI3UgAwduxYFBcXY9myZZnujoiIiEiiMvprvr179+Kmm27CFVdckfq/tbq6usD/cZeUlKBHjx6oq6szzmffvn2+/5uz/QJCREREJFsy9s3UgQMH8LnPfQ6e52HevHmx5jVr1ixUVlam/vXv3z+hXoqIiIjEk5GbqUM3UmvXrsWiRYt8GYKamhps2bLFN/2HH36IhoYG1NTUGOc3ffp0NDU1pf6tX78+E90WERERCS3xx3yHbqRWr16N559/PlB4cMyYMWhsbMTy5csxatQoAMBzzz2H1tZWjB492jjP0tJSY/i0uLg4o0UeXULGHIjr1auXr83h0+3btwfmEXfAxygBwmyEEDksyY93gWAAc+vWrb42B5dzJWzg3OW45GOazxX+n4tBgwYF5mH6UUQ6LgP1snXr1vnaSRSetcnE8Ril6GcSA0fzecA/SjGFnTmUzgF0DoIz037mMHiUAa7D7pckfgjD2y/KYPKZKDgcBW+/JM6lsH3l6fm4iCKJzyCXsH3YHzll8wdLoW+mdu3ahX/+85+p9po1a/Dmm2+iR48e6Nu3L/793/8dr7/+Op5++mkcPHgwlYPq0aMHOnXqhBEjRuCCCy7AV77yFcyfPx8HDhzAddddh4kTJ4b6JZ+IiIhIPgh9M/Xaa6/hU5/6VKo9bdo0AMDkyZNx66234k9/+hMA4KSTTvK97/nnn8c555wDAHj00Udx3XXX4bzzzkNxcTEmTJiAe++9N+IqiIiIiORO6Jupc845J+1jKZdHVj169MDChQvDLlpEREQk77SbgY4zweXZPL/WvXt3X5uLePI8gdxkULKRkeL82DHHHBN4D+dFuFhhS0uLrx2lLIYt3+TyPwBJZKR4+/Cxwo+5q6urfW3enkAws2crxGjK2vB7uLCiy+DINmGLE7oMSGorgGjrg8ty+T2mc5X7xYM08z7i7WnavjwwNGcto2S5CgVfI02DXoeVLwOo8/GTiyLESeSbmMs11JaRculXlMGRw2o7zzDzz48jTERERKRA6WZKREREJAbdTImIiIjEUNCZqbZ1plyet4Z9vsr1YLhtmifXDnLJTMV9bm6q5ZKLwSx53XjdTbXCOCfEg/lyfZ033ngjMA/OVeVDPsLUBz5+uO7W0KFDfe1hw4b52qbMFOdtOLvA+91UU4aPnyQyKnElsQ/5eDvcoOhtcb6Jj+FMaGhoCLzG9dbaMz7eTNfIsOIeP6brZdh5mnJtSdR0suG+c60+W42yJLjki7mftsGnoyzXZZ8l9dmY+08dERERkQKmmykRERGRGHQzJSIiIhJDQWemSkpKUpkRlzGgwo6Bx89b2w7YfAjXDeFxznr37u1rd+vWLTCPsOPPDRw40Nc2PfPlwaS5flMm8PblPI8pc8bTVFZW+tqHquYfwpkWAHjvvfd8bdNA2m1lIj/Gx4ppXXmsveHDh/vaXIeL93MSTLWWuHbX7t27fe1sjI8YJePCx1uUvAQff6ZcX1u2emNRmGpXNTY2+trZyNpkAudeTHm8fMg48rFk2s+26wZnkXh8RdM0SeB+cc40ifERw4qSYbbVeDPNN2zdKZcsV1S5P4pFRERECphupkRERERi0M2UiIiISAwFn5k6XC0K0zPbuFkZlzpJ/Hx648aNvjaPxwYE68yEHevL9MyXMwDZyEzxs3rOB5jqx3CmrKyszNfmOj+muj9DhgzxtdetW+dr81hzXMPHVOeHc0OM14X3CeejgGBG6mMf+5ivnY2aRqbMRl1dna+9efNmXzuJvA7nH/hcykZuxrQMvn5kox98fK5YscI6TaHgTGg+5KGiMOVr+drO1zvOFobN6EbF/cpFRsqF7fOX/+4y/iSvK5/PLuPrJqUwj3QRERGRPKGbKREREZEYdDMlIiIiEoNupkRERERiKOgAetuBjjloZgrhxQ3Smgq5cbCWA+Y9e/b0taurqwPz4CKTXMTPNvBslAJpmShcyYFMDnabij9y0UTbIKe8PYHgunGQmwsg7tixI20/geAPBzi0zkFHLrDJgxgDwODBg31t3q8ugcuw+FjiYw0ANmzY4Gvz9olyrPA+4f3K289UoI/ZBnG24WPN9JptcFVTqNhWyPODDz7wtd9//31f27RPMnEsZEKUwWgLgenzg69f+VIMM18D52Hxerj8CMD2d26bimaraKeIiIhIHtDNlIiIiEgMupkSERERiaGgH3gXFRUdNq9gykdkYrBQzjbY8jtcpBIIFjC0DbbqkhXZuXNn6PfExcvgrBLnjoBkBv7kQYXLy8vT9ounNxXY7NWrl6/997//PW0feIBm0zxt+9U04G1YnFvj/JMpn8P7iQuWhh1MFAief9zmjFQSAx3buBTtjDKQsa0fvD1bWlp87UwMfpst2SpMmWm2AXTzhalfhboPeF34mhAln8jv4XPLlLmy5XRd6ZspERERkRh0MyUiIiISQ0E+5jv0VR4/yrLhr9eTwI/5bF/pmx7lhP1pq8tjAe5XLr625j6YHrPyY6koX7nyT5ZtY2bx9jNtG95Ptn3E62baz9wPXlc+Vnbt2pV2mSa8DJ4nbxvAvq5RfqbPj9R4nrwPePu5jDdpWyZP71J+IcojTRvevi6PHgqlNALjdcnV2Hxhl8v73WWf2NqZePxmOi4K5RGljcvYfFEew7dl2q/pjpVD07vsyyKvAB+4btiwAf379891N0RERKSdW79+Pfr165d2moK8mWptbcWmTZvgeR4GDBiA9evXo6KiItfdKnjNzc3o37+/tmeCtE2Tpe2ZLG3PZGl7Ji+X29TzPOzcuRO1tbXWbzsL8jFfcXEx+vXrh+bmZgBARUWFDtwEaXsmT9s0WdqeydL2TJa2Z/JytU35l9qHowC6iIiISAy6mRIRERGJoaBvpkpLS3HLLbdYiyGKG23P5GmbJkvbM1nansnS9kxeoWzTggygi4iIiOSLgv5mSkRERCTXdDMlIiIiEoNupkRERERi0M2UiIiISAwFfTM1d+5cDBo0CJ07d8bo0aPxyiuv5LpLBWHWrFk45ZRTUF5ejj59+uDSSy/FypUrfdPs3bsXU6dORc+ePdGtWzdMmDAB9fX1OepxYbnzzjtRVFSEG264IfWatmc4GzduxJVXXomePXuiS5cuOP744/Haa6+l/u55Hm6++Wb07dsXXbp0wdixY7F69eoc9jh/HTx4EDNmzMDgwYPRpUsXDBkyBD/84Q99441pe6b3wgsv4OKLL0ZtbS2Kiorw5JNP+v7usv0aGhowadIkVFRUoKqqCtdcc02k8Tfbg3Tb88CBA7jppptw/PHHo2vXrqitrcUXv/hFbNq0yTePfNueBXsz9dvf/hbTpk3DLbfcgtdffx0nnngixo0bhy1btuS6a3lvyZIlmDp1Kl5++WUsWrQIBw4cwPnnn+8bpPnGG2/En//8Zzz++ONYsmQJNm3ahMsvvzyHvS4Mr776Kn7+85/jhBNO8L2u7elux44dOOOMM9CxY0c888wzWLFiBe655x507949Nc1dd92Fe++9F/Pnz8eyZcvQtWtXjBs3LjDQswCzZ8/GvHnzcP/99+Pdd9/F7Nmzcdddd+G+++5LTaPtmd7u3btx4oknYu7cuca/u2y/SZMm4Z133sGiRYvw9NNP44UXXsCUKVOytQp5Jd32bGlpweuvv44ZM2bg9ddfxx//+EesXLkS48eP902Xd9vTK1CnnnqqN3Xq1FT74MGDXm1trTdr1qwc9qowbdmyxQPgLVmyxPM8z2tsbPQ6duzoPf7446lp3n33XQ+At3Tp0lx1M+/t3LnTGzZsmLdo0SLvk5/8pPfNb37T8zxtz7Buuukm78wzzzzs31tbW72amhrvxz/+ceq1xsZGr7S01PvNb36TjS4WlIsuusi7+uqrfa9dfvnl3qRJkzzP0/YMC4D3xBNPpNou22/FihUeAO/VV19NTfPMM894RUVF3saNG7PW93zE29PklVde8QB4a9eu9TwvP7dnQX4ztX//fixfvhxjx45NvVZcXIyxY8di6dKlOexZYWpqagIA9OjRAwCwfPlyHDhwwLd9hw8fjgEDBmj7pjF16lRcdNFFvu0GaHuG9ac//Qknn3wyPvvZz6JPnz4YOXIkfvGLX6T+vmbNGtTV1fm2Z2VlJUaPHq3taXD66adj8eLFWLVqFQDg73//O1566SVceOGFALQ943LZfkuXLkVVVRVOPvnk1DRjx45FcXExli1blvU+F5qmpiYUFRWhqqoKQH5uz4Ic6Hjbtm04ePAgqqurfa9XV1fjvffey1GvClNraytuuOEGnHHGGTjuuOMAAHV1dejUqVPqwD2kuroadXV1Oehl/nvsscfw+uuv49VXXw38TdsznPfffx/z5s3DtGnT8L/+1//Cq6++im984xvo1KkTJk+enNpmpvNf2zPoe9/7HpqbmzF8+HB06NABBw8exI9+9CNMmjQJALQ9Y3LZfnV1dejTp4/v7yUlJejRo4e2scXevXtx00034YorrkgNdJyP27Mgb6YkOVOnTsXbb7+Nl156KdddKVjr16/HN7/5TSxatAidO3fOdXcKXmtrK04++WTccccdAICRI0fi7bffxvz58zF58uQc967w/O53v8Ojjz6KhQsX4thjj8Wbb76JG264AbW1tdqektcOHDiAz33uc/A8D/Pmzct1d9IqyMd8vXr1QocOHQK/hqqvr0dNTU2OelV4rrvuOjz99NN4/vnn0a9fv9TrNTU12L9/PxobG33Ta/uaLV++HFu2bMEnPvEJlJSUoKSkBEuWLMG9996LkpISVFdXa3uG0LdvXxxzzDG+10aMGIF169YBQGqb6fx3853vfAff+973MHHiRBx//PH4whe+gBtvvBGzZs0CoO0Zl8v2q6mpCfw46sMPP0RDQ4O28WEcupFau3YtFi1alPpWCsjP7VmQN1OdOnXCqFGjsHjx4tRrra2tWLx4McaMGZPDnhUGz/Nw3XXX4YknnsBzzz2HwYMH+/4+atQodOzY0bd9V65ciXXr1mn7Gpx33nl466238Oabb6b+nXzyyZg0aVLqv7U93Z1xxhmBUh2rVq3CwIEDAQCDBw9GTU2Nb3s2Nzdj2bJl2p4GLS0tKC72X+o7dOiA1tZWANqecblsvzFjxqCxsRHLly9PTfPcc8+htbUVo0ePznqf892hG6nVq1fjr3/9K3r27On7e15uz5zE3hPw2GOPeaWlpd7DDz/srVixwpsyZYpXVVXl1dXV5bpree/rX/+6V1lZ6f3tb3/zNm/enPrX0tKSmuZrX/uaN2DAAO+5557zXnvtNW/MmDHemDFjctjrwtL213yep+0ZxiuvvOKVlJR4P/rRj7zVq1d7jz76qFdWVuY98sgjqWnuvPNOr6qqynvqqae8f/zjH94ll1ziDR482NuzZ08Oe56fJk+e7B111FHe008/7a1Zs8b74x//6PXq1cv77ne/m5pG2zO9nTt3em+88Yb3xhtveAC8n/zkJ94bb7yR+nWZy/a74IILvJEjR3rLli3zXnrpJW/YsGHeFVdckatVyql023P//v3e+PHjvX79+nlvvvmm7zNq3759qXnk2/Ys2Jspz/O8++67zxswYIDXqVMn79RTT/VefvnlXHepIAAw/luwYEFqmj179njXXnut1717d6+srMy77LLLvM2bN+eu0wWGb6a0PcP585//7B133HFeaWmpN3z4cO/BBx/0/b21tdWbMWOGV11d7ZWWlnrnnXeet3Llyhz1Nr81Nzd73/zmN70BAwZ4nTt39o4++mjv+9//vu+DSdszveeff954zZw8ebLneW7bb/v27d4VV1zhdevWzauoqPCuuuoqb+fOnTlYm9xLtz3XrFlz2M+o559/PjWPfNueRZ7XpgyuiIiIiIRSkJkpERERkXyhmykRERGRGHQzJSIiIhKDbqZEREREYtDNlIiIiEgMupkSERERiUE3UyIiIiIx6GZKREREJAbdTImIiIjEoJspERERkRh0MyUiIiISg26mRERERGL4f2q3udTI27jAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    index = np.random.randint(0, len(train_dataset))\n",
    "    image = train_dataset[index]\n",
    "    print(np.unique(image['mask']))\n",
    "    visualize_mri_sample(image, \"Train_Image \" + str(i))\n",
    "    \n",
    "    index = np.random.randint(0, len(val_dataset))\n",
    "    image = val_dataset[index]\n",
    "    print(np.unique(image['mask']))\n",
    "    visualize_mri_sample(image, \"Val_Image \" + str(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3a1e3fd-290a-4de5-ab58-4a44e610cd16",
   "metadata": {},
   "source": [
    "**Create a dataloader for the corresponding data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "076fac0b-ab36-47cb-9054-243330ee4630",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=False)\n",
    "val_dataloader = DataLoader(val_dataset, batch_size=16, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c87256-6710-4a59-ba79-30b9df4cfad0",
   "metadata": {},
   "source": [
    "**Visualization of preprocessing impact on 'broken' masks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91ee608c-6f50-4225-ac5c-ed7d6b47462d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# longMasks = []\n",
    "# for batch in tqdm(train_dataloader):\n",
    "#     masks = batch['mask']\n",
    "#     for mask in masks:\n",
    "#         if len(np.unique(mask)) > 4:\n",
    "#             mask=mask[0]\n",
    "#             print(np.unique(mask))\n",
    "#             mask[np.logical_and(mask>170,mask<255)]=1\n",
    "#             mask[np.logical_and(mask>3,mask<85)]=2\n",
    "#             mask[np.logical_and(mask>85,mask<170)]=3\n",
    "#             print(np.unique(mask))\n",
    "#             longMasks.append(mask)\n",
    "# print(longMasks[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "958bf831-5c0a-44e2-a0f1-acecba10c20d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# print(type(longMasks[0]))\n",
    "# for mask in longMasks:\n",
    "#     plt.imshow(mask)\n",
    "#     plt.show()\n",
    "#     print(f'       ^^ {np.unique(mask)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145e609e-d2ec-4061-9b87-fb9bf2622798",
   "metadata": {},
   "source": [
    "**The selection of the correct and availble GPUs on the virtual machine.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5c4e731f-4b12-4cf4-9f92-fb687ec4a55d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean(lst):\n",
    "    return sum(lst) / len(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d0f99157-9073-421f-942b-10b081778f0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The used device is cuda:4\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:4\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f'The used device is {device}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b7a58f-e2c0-4666-bba8-320616d7f235",
   "metadata": {},
   "source": [
    "**Definition of the basic U-net model based on the tutorial.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bab17942-5359-411f-aea8-e4612f257611",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Things to tweak: Kernel size, channels, amt of layers (6 layers only made it worse),\n",
    "# learning rate, strides (5*2 with 6layers was worse), num res units, batch size, transforms\n",
    "\n",
    "model = monai.networks.nets.UNet(\n",
    "    spatial_dims=2,\n",
    "    in_channels=1,\n",
    "    out_channels=4,\n",
    "    channels=(8, 16, 32, 64, 128),\n",
    "    strides=(2, 2, 2, 2),\n",
    "    num_res_units=2,\n",
    ").to(device)\n",
    "\n",
    "loss_function =  monai.losses.DiceLoss(softmax=True, batch=True, to_onehot_y=True)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c75e4dac-f50f-4305-852d-1850eb5ef845",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/150 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val loss: 0.41637861728668213\n",
      "Best val loss: 0.37444570660591125\n",
      "Best val loss: 0.362487256526947\n",
      "Best val loss: 0.3558954417705536\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|▎         | 5/150 [01:29<42:07, 17.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val loss: 0.15414686501026154\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 15/150 [04:12<37:16, 16.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val loss: 0.12118552625179291\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█▎        | 20/150 [05:37<36:54, 17.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val loss: 0.1092509776353836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 25/150 [07:00<34:52, 16.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val loss: 0.10567030310630798\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 30/150 [08:29<34:34, 17.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val loss: 0.09852516651153564\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 45/150 [12:34<28:25, 16.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val loss: 0.09104892611503601\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 50/150 [14:04<30:12, 18.13s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val loss: 0.08776289224624634\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 37%|███▋      | 55/150 [15:33<27:46, 17.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val loss: 0.07816566526889801\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 115/150 [32:24<09:30, 16.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val loss: 0.07540352642536163\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 150/150 [43:36<00:00, 17.44s/it]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import wandb\n",
    "import datetime\n",
    "# now = datetime.datetime.now() # current date and time\n",
    "# nu = now.strftime('%c')\n",
    "\n",
    "# run = wandb.init(\n",
    "#     project='ACDC_Segmentation',\n",
    "#     name='BasicTrans_SimpleModel_AllData_',\n",
    "#     config={\n",
    "#         'loss function': str(loss_function),\n",
    "#         'lr': optimizer.param_groups[0][\"lr\"],\n",
    "#         'transform': from_compose_to_list(composed_transform),\n",
    "#         'batch_size': train_dataloader.batch_size,\n",
    "#         'model': model,\n",
    "#     }\n",
    "# )\n",
    "# # Do not hesitate to enrich this list of settings to be able to correctly keep track of your experiments!\n",
    "# # For example you should add information on your model...\n",
    "\n",
    "# run_id = run.id # We remember here the run ID to be able to write the evaluation metrics\n",
    "\n",
    "def wandb_masks(mask_output, mask_gt):\n",
    "    \"\"\" Function that generates a mask dictionary in format that W&B requires \"\"\"\n",
    "\n",
    "    # Apply sigmoid to model ouput and round to nearest integer (0 or 1)\n",
    "    # sigmoid = torch.nn.Sigmoid()\n",
    "    # mask_output = sigmoid(mask_output)\n",
    "    # mask_output = torch.round(mask_output)\n",
    "\n",
    "    # Transform masks to numpy arrays on CPU\n",
    "    # Note: .squeeze() removes all dimensions with a size of 1 (here, it makes the tensors 2-dimensional)\n",
    "    # Note: .detach() removes a tensor from the computational graph to prevent gradient computation for it\n",
    "    mask_output = mask_output.squeeze()\n",
    "    mask_gt = mask_gt.squeeze().detach().cpu().numpy()\n",
    "\n",
    "    # Create mask dictionary with class label and insert masks\n",
    "    class_labels = {1: 'left-ventricle', 2: 'right-ventricle', 3: 'myocardium'}\n",
    "    masks = {\n",
    "        'predictions': {'mask_data': mask_output, 'class_labels': class_labels},\n",
    "        'ground truth': {'mask_data': mask_gt, 'class_labels': class_labels}\n",
    "    }\n",
    "    return masks\n",
    "\n",
    "def log_to_wandb(epoch, train_loss, val_loss, batch_data, outputs):\n",
    "    \"\"\" Function that logs ongoing training variables to W&B \"\"\"\n",
    "\n",
    "    # Create list of images that have segmentation masks for model output and ground truth\n",
    "    log_imgs = [wandb.Image(img, masks=wandb_masks(mask_output, mask_gt)) for img, mask_output,\n",
    "                mask_gt in zip(batch_data['img'], outputs, batch_data['mask'])]\n",
    "\n",
    "    # Send epoch, losses and images to W&B\n",
    "    wandb.log({'epoch': epoch, 'train_loss': train_loss, 'val_loss': val_loss, 'results': log_imgs})\n",
    "\n",
    "# ⌨️ WRITE YOUR TRAINING LOOP HERE\n",
    "from tqdm import tqdm\n",
    "\n",
    "def train_medmnist(model, train_dataloader, val_dataloader, optimizer, epochs, device, val_freq=1):\n",
    "    train_loss = []\n",
    "    val_loss = []\n",
    "    best_dice = -1\n",
    "    best_loss = 10\n",
    "    \n",
    "    for epoch in tqdm(range(epochs)):\n",
    "        model.train()\n",
    "        steps = 0\n",
    "        epoch_loss = 0\n",
    "\n",
    "        for batch in train_dataloader:\n",
    "            optimizer.zero_grad()\n",
    "            images = batch['img'].float().to(device)\n",
    "            labels = batch['mask'].float().to(device)\n",
    "            output = model(images)\n",
    "            loss = loss_function(output, labels)\n",
    "            epoch_loss += loss.item()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            steps += 1\n",
    "            train_ep_loss = epoch_loss/steps\n",
    "        train_loss.append(epoch_loss/steps)\n",
    "\n",
    "        # validation loop\n",
    "        if epoch % val_freq == 0:\n",
    "            steps = 0\n",
    "            val_epoch_loss = 0\n",
    "            model.eval()\n",
    "            for batch in val_dataloader:\n",
    "                optimizer.zero_grad()\n",
    "                images = batch['img'].float().to(device)\n",
    "                labels = batch['mask'].float().to(device)\n",
    "                output = model(images)\n",
    "                loss = loss_function(output, labels)\n",
    "                val_epoch_loss += loss.item()\n",
    "                steps += 1\n",
    "                val_ep_loss = val_epoch_loss/steps\n",
    "\n",
    "                # ---------- OLD DICE SCORE -----------\n",
    "                # Run images through inferer and transform for Dice score calculation\n",
    "                # batch_dices = []\n",
    "                # inferer = monai.inferers.SimpleInferer()\n",
    "                # discrete_transform = monai.transforms.AsDiscrete(argmax = True)\n",
    "                # Softmax = torch.nn.Softmax()\n",
    "                # diceImages = batch['img'].float().to(device)\n",
    "\n",
    "#                 for image in diceImages:\n",
    "#                     with torch.no_grad():\n",
    "#                         output = discrete_transform(Softmax(model(image).cpu())).squeeze()\n",
    "#                     dice_mask = np.argmax(output, axis=0)\n",
    "\n",
    "#                     print(f'dicemask: {np.unique(dice_mask)}, gtmask: {np.unique(labels[i])}')\n",
    "\n",
    "#                     #Calculate mean Dice score for validation batch\n",
    "#                     dice = metrics(labels[i], dice_mask)\n",
    "#                     print(dice)\n",
    "#                     batch_dices.append(dice)\n",
    "#                 mean_dice = mean(batch_dices)\n",
    "\n",
    "                # Save the network if its Dice score is better than the current best\n",
    "                if val_ep_loss < best_loss:\n",
    "                    best_loss = val_ep_loss\n",
    "                    print(f'Best val loss: {best_loss}')\n",
    "                    torch.save(model.state_dict(), r'ACDC_Best_DenoisedROI.pt')\n",
    "\n",
    "            val_loss.append(val_ep_loss)\n",
    "        # log_to_wandb(epoch, train_ep_loss, val_ep_loss, batch, output)\n",
    "\n",
    "    return train_loss, val_loss, model\n",
    "\n",
    "val_freq = 5\n",
    "n_epochs = 150\n",
    "\n",
    "train_loss, val_loss, model = train_medmnist(model, train_dataloader, val_dataloader, optimizer, n_epochs, device, val_freq)\n",
    "\n",
    "torch.save(model.state_dict(), r'ACDC_Last_DenoisedROI.pt')\n",
    "# run.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65dd6127-459b-481c-8784-38e8802e6c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot training and validation loss\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_loss, label='Training loss')\n",
    "plt.plot(np.linspace(0, n_epochs, int(n_epochs/val_freq)), val_loss, label='Validation loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe09f90d-d611-4158-bfad-d4123d2c8acf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = \"./preprocessed/\"\n",
    "if not os.path.exists(data_path):\n",
    "    print(\"Please update your data path to an existing folder.\")\n",
    "elif not set([\"training\", \"testing\"]).issubset(set(os.listdir(data_path))):\n",
    "    print(\"Please update your data path to the correct folder (should contain training and testing folders).\")\n",
    "else:\n",
    "    print(\"Congrats! You selected the correct folder :)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ba278ee-1bb6-4d18-81e9-20a060bda03f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_dict = build_dict_ACDC(data_path, mode='training')\n",
    "print(len(test_dict))\n",
    "test_transform = monai.transforms.Compose([\n",
    "                                           LoadMriData(),\n",
    "                                           monai.transforms.AddChanneld(keys=['img', 'mask']),\n",
    "                                           monai.transforms.NormalizeIntensityd(keys=['img']),\n",
    "                                           # monai.transforms.Zoomd(keys=['img', 'mask'], zoom=0.25, keep_size=False, mode=['bilinear', 'nearest']),\n",
    "                                           # monai.transforms.Resized(keys = ['img','mask'],spatial_size=(1024,1024), mode=['bilinear','nearest'])\n",
    "                                            ])\n",
    "test_set = monai.data.CacheDataset(test_dict, transform=test_transform)\n",
    "test_loader = monai.data.DataLoader(test_set, batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7bd2035-d66e-4b4e-b15e-4460cc6d3541",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dice scores of models with this architecture:\n",
    "# - Best_Basic.pt: 0.6613432727090607\n",
    "# - Last_Basic.pt: 0.7322..\n",
    "# - Best_NewTrans: 0.7589075271760529\n",
    "# - Last_NewTrans: 0.7562161966539251\n",
    "# - Best_6Layers: 0.6607905967465393\n",
    "# - Last_6Layers: 0.6596011656943401\n",
    "# - Best_HIghLR: 0.5132312381186614\n",
    "# - Last_HighLR: 0.5132307676388257\n",
    "# - 32 Batch size: 0.59287373909797\n",
    "# - Best_Kernel5: 0.7030439132476438\n",
    "# - Last_Kernel5: 0.7308205497238879\n",
    "\n",
    "model = monai.networks.nets.UNet(\n",
    "    spatial_dims=2,\n",
    "    in_channels=1,\n",
    "    out_channels=4,\n",
    "    channels=(8, 16, 32, 64, 128),\n",
    "    strides=(2, 2, 2, 2),\n",
    "    num_res_units=2,\n",
    ").to(device)\n",
    "\n",
    "model.load_state_dict(torch.load(\"ACDC_Best_Denoised.pt\"))\n",
    "model.eval()\n",
    "print(\"Model loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67ff63ea-e1d8-4a5e-a4c6-d72a21a2a1ae",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dices = []\n",
    "lvs = []\n",
    "rvs = []\n",
    "myos = []\n",
    "hds = []\n",
    "volpreds = []\n",
    "volerrors = []\n",
    "best_dice = 0\n",
    "worst_dice = 10\n",
    "current_patient = ''\n",
    "initialize = False\n",
    "\n",
    "for i, sample in enumerate(tqdm(test_loader)):\n",
    "    # Initialize empty masks once\n",
    "    mask = sample['mask'].squeeze().numpy()\n",
    "    \n",
    "    # Get voxel_size and patient from file names\n",
    "    voxel_size = test_dict[i]['mask'].split('_')[-2]\n",
    "    patient = test_dict[i]['img'].split('_')[0][-10:]\n",
    "    \n",
    "    # If first patients, initialize (3D) arrays\n",
    "    if patient in ('patient001' or 'patient101') and initialize == False:\n",
    "        best_seg = np.zeros(mask.shape)\n",
    "        worst_seg = np.zeros(mask.shape)\n",
    "        threedmask = mask.reshape(mask.shape[0], mask.shape[1], 1)\n",
    "        seg = evaluation(sample, model)\n",
    "        threedseg = seg.reshape(seg.shape[0], seg.shape[1], 1)\n",
    "        current_patient = patient\n",
    "        initialize = True\n",
    "    # If patient is new, but not the first patient\n",
    "    elif patient not in current_patient:\n",
    "        # ----- Calculate all metrics on 3D-mask -----\n",
    "        dice, lv, rv, myo, haus, volpred, volerror = metrics(threedmask, threedseg, voxel_size)\n",
    "        \n",
    "        lvs.append(lv)\n",
    "        rvs.append(rv)\n",
    "        myos.append(myo)\n",
    "        volpreds.append(volpred)\n",
    "        volerrors.append(volerror)\n",
    "\n",
    "        if dice > best_dice:\n",
    "            best_seg = seg\n",
    "            best_dice = dice\n",
    "        elif dice < worst_dice:\n",
    "            worst_seg = seg\n",
    "            worst_dice = dice\n",
    "        dices.append(dice)\n",
    "        print(f'Patient: {current_patient}')\n",
    "        print(f'--Dice per class--\\nLV:{lv}\\nRV:{rv}\\nMyo:{myo}')\n",
    "        print(f'Dice: {dice}')\n",
    "        # hds.append(haus)\n",
    "        # print(f'Hausdorff: {haus}')\n",
    "        \n",
    "        # Reset 3D arrays\n",
    "        threedmask = mask.reshape(mask.shape[0], mask.shape[1], 1)\n",
    "        seg = evaluation(sample, model)\n",
    "        threedseg = seg.reshape(seg.shape[0], seg.shape[1], 1)\n",
    "        current_patient = patient\n",
    "    # If same patient, add current mask to the 3D mask\n",
    "    elif patient in current_patient:\n",
    "        # Make mask&seg fit 3D shape\n",
    "        mask = mask.reshape(mask.shape[0], mask.shape[1], 1)\n",
    "        seg = evaluation(sample, model)\n",
    "        seg = seg.reshape(seg.shape[0], seg.shape[1], 1)\n",
    "        \n",
    "        # Concatenate in 3rd dimension\n",
    "        threedmask = np.concatenate((threedmask, mask), 2)\n",
    "        threedseg = np.concatenate((threedseg, seg), 2)\n",
    "\n",
    "meanlv = mean(lvs)\n",
    "meanrv = mean(rvs)\n",
    "meanmyo = mean(myos)\n",
    "meandice = mean(dices)\n",
    "# meanhd = mean(hds)\n",
    "meanvolpred = mean(volpreds)\n",
    "meanvolerror = mean(volerrors)\n",
    "\n",
    "print(f'Total mean Dice: {meandice}, mean Dice LV: {meanlv}, mean Dice RV: {meanrv}, mean Dice Myo: {meanmyo}, mean Hausdorff: {0}, mean Volume error: {meanvolerror}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e52735dd-3970-426d-b902-12696300d7a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "roiModel = monai.networks.nets.UNet(\n",
    "    kernel_size=21,\n",
    "    up_kernel_size=21,\n",
    "    spatial_dims=2,\n",
    "    in_channels=1,\n",
    "    out_channels=1,\n",
    "    channels=(8, 16, 32, 64, 128),\n",
    "    strides=(2, 2, 2, 2),\n",
    "    num_res_units=2,\n",
    ").to(device)\n",
    "\n",
    "roiModel.load_state_dict(torch.load(\"trainedUNetROI_Final.pt\"))\n",
    "roiModel.eval()\n",
    "print(\"ROI model loaded\")\n",
    "\n",
    "model = monai.networks.nets.UNet(\n",
    "    spatial_dims=2,\n",
    "    in_channels=1,\n",
    "    out_channels=4,\n",
    "    channels=(8, 16, 32, 64, 128),\n",
    "    strides=(2, 2, 2, 2),\n",
    "    num_res_units=2,\n",
    ").to(device)\n",
    "\n",
    "model.load_state_dict(torch.load(\"ACDC_Best_ROI.pt\"))\n",
    "model.eval()\n",
    "print(\"Segmentation model loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51451a1a-b277-40fd-abea-3898b05ef5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.measure import regionprops, label\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "## function for finding the largest connected component and it's centre\n",
    "## make ROI around centre of size:\n",
    "\n",
    "# According to Gray’s Anatomy, the heart length, width, and thickness are 12 cm, 8.5 cm, and 6 cm, respectively, doi: 10.15171/jcvtr.2016.25\n",
    "target_resolution = (1.36719, 1.36719)\n",
    "\n",
    "def get_ROI(image):\n",
    "    # Give as an input an image (in the form of a dictionary with labels 'img'and 'mask) \n",
    "    # and outputs an ROI centred around the centre of mass of the heart. \n",
    "    # Second input is the resolutoin (mm/pixel) of the input image.\n",
    "    \n",
    "    # split input into image and mask\n",
    "    im = image['img']                          \n",
    "    mask = image['mask']\n",
    "    \n",
    "    # find target size of ROI, based on pixel size (mm/pixel) and maximum expected size of heart (doi: 10.15171/jcvtr.2016.25)\n",
    "    nr = int(128/2) # number of rows/2\n",
    "    nc = int(128/2) # number of columns/2\n",
    "\n",
    "    if len(np.unique(mask))==1:                                            # if there is no heart in the image, just use the central part of the image as an ROI\n",
    "        labels = label(mask,background=1)                                  # find all connected components\n",
    "        largestCC = labels == np.argmax(np.bincount(labels.flat)[1:])+1    # largest connected components\n",
    "        mask = largestCC*1                                                 # True --> 1, False --> 0     \n",
    "        properties = regionprops(mask.astype(int))                         # Geometrical properties of image\n",
    "        com = np.round(properties[0].centroid).astype(int)                 # Centre of mass = centre of image in this specific case\n",
    "        com_r = com[0]                                                     # row index\n",
    "        com_c = com[1]                                                     # column index\n",
    "        ROI_mask = mask[(com_r-nr):(com_r+nr),(com_c-nc):(com_c+nc)]     # ROI mask\n",
    "        ROI_mask = 0*ROI_mask\n",
    "        im = im.squeeze()\n",
    "        ROI = im[(com_r-nr):(com_r+nr),(com_c-nc):(com_c+nc)]            # ROI of grey scale image\n",
    "        \n",
    "    else:                                                                  # if heart is present\n",
    "        # Find Largest connected component:\n",
    "        labels = label(mask)                                               # find all connected components\n",
    "        largestCC = labels == np.argmax(np.bincount(labels.flat)[1:])+1    # largest connected components\n",
    "        largestCC = largestCC*1                                            # True --> 1, False --> 0\n",
    "        properties = regionprops(largestCC)                                # Geometrical properties of largest connected component\n",
    "        com = np.round(properties[0].centroid).astype(int)                 # Centre of mass\n",
    "        com_r = com[0]                                                     # row index\n",
    "        com_c = com[1]                                                     # column index\n",
    "        ROI_mask = largestCC[(com_r-nr):(com_r+nr),(com_c-nc):(com_c+nc)]\n",
    "        im = im.squeeze()\n",
    "        ROI = im[(com_r-nr):(com_r+nr),(com_c-nc):(com_c+nc)]    \n",
    "    # plt.figure()\n",
    "    # plt.imshow(np.squeeze(ROI),'gray')\n",
    "    # overlay_mask = np.ma.masked_where(ROI_mask==0, ROI_mask)\n",
    "    # plt.imshow(overlay_mask, 'Reds', alpha = 0.5, clim=[0,1], interpolation='nearest')\n",
    "    # plt.scatter(x=nc, y=nr, c='g', s=40)\n",
    "    # plt.figure()\n",
    "    \n",
    "    return ROI, com_r, com_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a59bd4a9-affa-46dc-871f-eccd6deeadf9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from medpy.metric.binary import hd, dc\n",
    "def metrics(img_gt, img_pred, voxel_size):\n",
    "    \"\"\"\n",
    "    Function to compute the metrics between two segmentation maps given as input.\n",
    "    ----------\n",
    "    \n",
    "    Abbreviations:\n",
    "    ----------\n",
    "    LV: left ventricle\n",
    "    RV: right ventricle\n",
    "    MYO: myocardium\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    img_gt: np.array\n",
    "    Array of the ground truth segmentation map.  (LV --> class 3, RV --> class 1, myocardium --> class 2)\n",
    "\n",
    "    img_pred: np.array\n",
    "    Array of the predicted segmentation map.\n",
    "\n",
    "    voxel_size: list, tuple or np.array\n",
    "    The size of a voxel of the images used to compute the volumes (in mm).\n",
    "    \n",
    "    For example: for voxels of size 2mm by  1mm by 8mm, use voxel_size = np.array([2 1 8])\n",
    "\n",
    "    Return\n",
    "    ------\n",
    "    A list of metrics in this order, [Dice LV, Volume LV, Err LV(ml),\n",
    "    Dice RV, Volume RV, Err RV(ml), Dice MYO, Volume MYO, Err MYO(ml)]\n",
    "    \"\"\"\n",
    "\n",
    "    if img_gt.ndim != img_pred.ndim:\n",
    "        raise ValueError(\"The arrays 'img_gt' and 'img_pred' should have the \"\n",
    "                         \"same dimension, {} against {}\".format(img_gt.ndim,\n",
    "                                                                img_pred.ndim))\n",
    "\n",
    "    lv = 0\n",
    "    rv = 0\n",
    "    myo = 0\n",
    "    dices = []\n",
    "    hds = []\n",
    "    voxel_size = float(voxel_size)\n",
    "    \n",
    "    # Loop on each classes of the input images\n",
    "    for c in [3, 1, 2]:\n",
    "        # Copy the gt image to not alterate the input\n",
    "        gt_c_i = np.copy(img_gt)\n",
    "        gt_c_i[gt_c_i != c] = 0\n",
    "\n",
    "        # Copy the pred image to not alterate the input\n",
    "        pred_c_i = np.copy(img_pred)\n",
    "        pred_c_i[pred_c_i != c] = 0\n",
    "\n",
    "        # Clip the value to compute the volumes\n",
    "        gt_c_i = np.clip(gt_c_i, 0, 1)\n",
    "        pred_c_i = np.clip(pred_c_i, 0, 1)\n",
    "        \n",
    "        # Compute the Dice\n",
    "        dice = dc(pred_c_i, gt_c_i)\n",
    "        \n",
    "        # Save the dice score per class\n",
    "        if c==3:\n",
    "            lv = dice\n",
    "        elif c==1:\n",
    "            rv = dice\n",
    "        elif c==2:\n",
    "            myo = dice\n",
    "\n",
    "        # Append to this list to calculate mean dice\n",
    "        dices.append(dice)\n",
    "\n",
    "        # Compute Hausdorff Distance\n",
    "        # hausdorf = hd(pred_c_i, gt_c_i)\n",
    "        # hds.append(hausdorf)\n",
    "\n",
    "        # Compute volume\n",
    "        volpred = pred_c_i.sum() * np.prod(voxel_size) / 1000.\n",
    "        volgt = gt_c_i.sum() * np.prod(voxel_size) / 1000.\n",
    "\n",
    "    return mean(dices), lv, rv, myo, 0, volpred, volpred-volgt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c0b65bd-706a-4703-a4d8-2b8fd28fda8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluation(img, mask, seg, model, visual=False):\n",
    "    \"\"\"\n",
    "    Given a model, calculate the dice score of a sample image and ground truth pair.\n",
    "    Model will be used to create a predicted mask, dice score is calculated between this and ground truth.\n",
    "    \n",
    "    If visual=True:\n",
    "    Allow the visual inspection of one sample by plotting the X-ray image, the ground truth (green)\n",
    "    and the segmentation map produced by the network (red).\n",
    "    \n",
    "    Args:\n",
    "        sample (Dict[str, torch.Tensor]): sample composed of an X-ray ('img') and a mask ('mask').\n",
    "        model (torch.nn.Module): trained model to evaluate.\n",
    "        visual (Boolean): True if you want to see the predicted masks visualized -> will take much longer\n",
    "    \"\"\"\n",
    "    \n",
    "    # infer output of network\n",
    "#     model.eval()\n",
    "#     inferer = monai.inferers.SimpleInferer()\n",
    "#     Softmax = torch.nn.Softmax()\n",
    "    \n",
    "#     with torch.no_grad():\n",
    "#         output = Softmax(inferer(sample['img'].float().to(device), network=model).cpu()).squeeze().detach().numpy()\n",
    "#     classes = np.argmax(output, axis=0)  # find class with highest probabillity per pixel\n",
    "    \n",
    "    \n",
    "    \n",
    "    if visual:\n",
    "        # print(f'For image below, dice = {dice}')\n",
    "        fig, ax = plt.subplots(1,3, figsize = [12, 10])\n",
    "\n",
    "        # mask = ground truth segmentation\n",
    "        mask = np.squeeze(mask)\n",
    "        print(f'mask: {mask[np.where(mask!=0)]}')\n",
    "        # create mask for background\n",
    "        overlay_mask1 = np.ma.masked_where(mask!=1, mask)\n",
    "        overlay_mask2 = np.ma.masked_where(mask!=2 ,mask)\n",
    "        overlay_mask3 = np.ma.masked_where(mask!=3, mask)\n",
    "\n",
    "        # Plot MRI image\n",
    "        ax[0].imshow(img.squeeze(), 'gray')\n",
    "        ax[0].set_title('Grey scale MRI')\n",
    "\n",
    "        # Plot ground truth\n",
    "        ax[1].imshow(img.squeeze(), 'gray')\n",
    "        ax[1].imshow(overlay_mask1, 'Reds', alpha = 0.7, clim=[0,1], interpolation='nearest')\n",
    "        ax[1].imshow(overlay_mask2, 'Blues', alpha = 0.7, clim=[0,1], interpolation='nearest')\n",
    "        ax[1].imshow(overlay_mask3, 'Greens', alpha = 0.7, clim=[0,1], interpolation='nearest')\n",
    "        ax[1].set_title('Ground truth')\n",
    "\n",
    "        mask = np.squeeze(seg) # rename (because code for ground truth was copied, and there I named them 'mask' instead of 'classes')\n",
    "        # select the different components (= value 1, 2 or 3 for myo, LV and RV)\n",
    "        print(f'seg: {mask[np.where(mask!=0)]}')\n",
    "        # create mask for background\n",
    "        overlay_mask1 = np.ma.masked_where(mask!=1, mask)\n",
    "        overlay_mask2 = np.ma.masked_where(mask!=2 ,mask)\n",
    "        overlay_mask3 = np.ma.masked_where(mask!=3, mask)  \n",
    "\n",
    "        ax[2].imshow(img.squeeze(), 'gray')\n",
    "        ax[2].imshow(overlay_mask1, 'Reds', alpha = 0.7, clim=[0,1], interpolation='nearest')\n",
    "        ax[2].imshow(overlay_mask2, 'Blues', alpha = 0.7, clim=[0,1], interpolation='nearest')\n",
    "        ax[2].imshow(overlay_mask3, 'Greens', alpha = 0.7, clim=[0,1], interpolation='nearest')\n",
    "        ax[2].set_title('Prediction')\n",
    "        plt.show()\n",
    "    \n",
    "    # return classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c36bf38-7aed-4a29-8c67-94f76fe4e774",
   "metadata": {},
   "outputs": [],
   "source": [
    "dices = []\n",
    "lvs = []\n",
    "rvs = []\n",
    "myos = []\n",
    "hds = []\n",
    "volpreds = []\n",
    "volerrors = []\n",
    "best_dice = 0\n",
    "worst_dice = 10\n",
    "current_patient = ''\n",
    "initialize = False\n",
    "\n",
    "for i, sample in enumerate(tqdm(test_loader)):\n",
    "    # Get ROI\n",
    "    inferer = monai.inferers.SimpleInferer()\n",
    "    discrete_transform = monai.transforms.AsDiscrete(threshold=0.5)\n",
    "    addchannel = monai.transforms.AddChannel()\n",
    "    norm = monai.transforms.NormalizeIntensity()\n",
    "    Sigmoid = torch.nn.Sigmoid()\n",
    "    inputImage = denoise_tv_chambolle(sample['img'].cpu().detach().numpy(), eps=1e-6, weight=0.1, max_num_iter=1000)\n",
    "    inputImage = torch.from_numpy(inputImage)\n",
    "    with torch.no_grad():\n",
    "        output = discrete_transform(Sigmoid(inferer(inputImage.float().to(device), network=roiModel).cpu())).squeeze().detach().numpy()\n",
    "    \n",
    "    roiDict = {'img': sample['img'], 'mask': output}\n",
    "    nr = nc = int(128/2)\n",
    "    ROI, com_r, com_c = get_ROI(roiDict)\n",
    "    \n",
    "    # Get segmentation on ROI\n",
    "    Softmax = torch.nn.Softmax()\n",
    "    with torch.no_grad():\n",
    "        output = Softmax(inferer(torch.from_numpy(np.resize(addchannel(ROI), (1,1,128,128))).float().to(device), network=model).cpu()).squeeze().detach().numpy()\n",
    "    seg = np.argmax(output, axis=0)\n",
    "    img = sample['img'].squeeze()[(com_r-nr):(com_r+nr),(com_c-nc):(com_c+nc)]\n",
    "    mask = sample['mask'].squeeze()[(com_r-nr):(com_r+nr),(com_c-nc):(com_c+nc)]\n",
    "    print(img.shape, mask.shape)\n",
    "    evaluation(img, mask, seg, model, visual=True)\n",
    "    \n",
    "    # Place segmented ROI back in full image size\n",
    "    finalseg = np.zeros((256,256))\n",
    "    finalseg[(com_r-nr):(com_r+nr),(com_c-nc):(com_c+nc)] = seg\n",
    "    seg = finalseg\n",
    "    \n",
    "    # ---- ROI done ----\n",
    "    \n",
    "    # Initialize empty masks once\n",
    "    mask = sample['mask'].squeeze().numpy()\n",
    "    \n",
    "    # Get voxel_size and patient from file names\n",
    "    voxel_size = test_dict[i]['mask'].split('_')[-2]\n",
    "    patient = test_dict[i]['img'].split('_')[0][-10:]\n",
    "    \n",
    "    # If first patients, initialize (3D) arrays\n",
    "    if patient in ('patient001' or 'patient101') and initialize == False:\n",
    "        best_seg = np.zeros(mask.shape)\n",
    "        worst_seg = np.zeros(mask.shape)\n",
    "        threedmask = mask.reshape(mask.shape[0], mask.shape[1], 1)\n",
    "        threedseg = seg.reshape(seg.shape[0], seg.shape[1], 1)\n",
    "        current_patient = patient\n",
    "        initialize = True\n",
    "    # If patient is new, but not the first patient\n",
    "    elif patient not in current_patient:\n",
    "        # ----- Calculate all metrics on 3D-mask -----\n",
    "        dice, lv, rv, myo, haus, volpred, volerror = metrics(threedmask, threedseg, voxel_size)\n",
    "        \n",
    "        lvs.append(lv)\n",
    "        rvs.append(rv)\n",
    "        myos.append(myo)\n",
    "        volpreds.append(volpred)\n",
    "        volerrors.append(volerror)\n",
    "        \n",
    "        if dice > best_dice:\n",
    "            best_seg = seg\n",
    "            best_dice = dice\n",
    "        elif dice < worst_dice:\n",
    "            worst_seg = seg\n",
    "            worst_dice = dice\n",
    "        dices.append(dice)\n",
    "        print(f'Patient: {current_patient}')\n",
    "        print(f'--Dice per class--\\nLV:{lv}\\nRV:{rv}\\nMyo:{myo}')\n",
    "        print(f'Dice: {dice}')\n",
    "        # hds.append(haus)\n",
    "        # print(f'Hausdorff: {haus}')\n",
    "        \n",
    "        # Reset 3D arrays\n",
    "        threedmask = mask.reshape(mask.shape[0], mask.shape[1], 1)\n",
    "        threedseg = seg.reshape(seg.shape[0], seg.shape[1], 1)\n",
    "        current_patient = patient\n",
    "    # If same patient, add current mask to the 3D mask\n",
    "    elif patient in current_patient:\n",
    "        # Make mask&seg fit 3D shape\n",
    "        mask = mask.reshape(mask.shape[0], mask.shape[1], 1)\n",
    "        seg = seg.reshape(seg.shape[0], seg.shape[1], 1)\n",
    "        \n",
    "        # Concatenate in 3rd dimension\n",
    "        threedmask = np.concatenate((threedmask, mask), 2)\n",
    "        threedseg = np.concatenate((threedseg, seg), 2)\n",
    "\n",
    "meanlv = mean(lvs)\n",
    "meanrv = mean(rvs)\n",
    "meanmyo = mean(myos)\n",
    "meandice = mean(dices)\n",
    "# meanhd = mean(hds)\n",
    "meanvolpred = mean(volpreds)\n",
    "meanvolerror = mean(volerrors)\n",
    "\n",
    "print(f'Total mean Dice: {meandice}, mean Dice LV: {meanlv}, mean Dice RV: {meanrv}, mean Dice Myo: {meanmyo}, mean Hausdorff: {0}, mean Volume error: {meanvolerror}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b4b3f9e-3aca-4960-a086-be9267f3488d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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