effnetb7-unet-infer.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":true,"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2022-08-17T13:10:25.035433Z","iopub.status.busy":"2022-08-17T13:10:25.034417Z","iopub.status.idle":"2022-08-17T13:10:28.310438Z","shell.execute_reply":"2022-08-17T13:10:28.309104Z","shell.execute_reply.started":"2022-08-17T13:10:25.035285Z"},"papermill":{"duration":3.606112,"end_time":"2022-08-17T06:03:00.439011","exception":false,"start_time":"2022-08-17T06:02:56.832899","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/lakshita/somusan/hubmap_kaggle/.venv/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n","  from .autonotebook import tqdm as notebook_tqdm\n"]}],"source":["import numpy as np\n","import pandas as pd\n","import os\n","import cv2\n","import matplotlib.pyplot as plt\n","import time\n","import random\n","\n","import torch\n","from torch import nn\n","import torch.cuda.amp as amp\n","from torch.utils.data import Dataset\n","from torch.utils.data import DataLoader\n","from torch.utils.data import RandomSampler \n","from torch.utils.data import SequentialSampler\n","import torch.nn.functional as F\n","# from torchmetrics.functional import dice_score\n","from torch.optim.lr_scheduler import StepLR\n","import tifffile\n","from fastai.vision.all import *\n","\n","from collections import defaultdict\n","\n","import torch\n","from torch.optim.optimizer import Optimizer\n","import itertools as it\n","\n","is_amp = True\n","import logging\n","\n","from sklearn.model_selection import KFold\n","\n","\n","from itertools import repeat\n","import collections.abc\n","import math\n","import torch\n","from torch import Tensor\n","from torch.optim.optimizer import Optimizer, required\n","from collections import defaultdict\n","\n","import warnings\n","warnings.filterwarnings('ignore')\n","\n","import sys\n","sys.path.append('/home/lakshita/somusan/hubmap_kaggle/nbs/timm-pytorch-image-models/pytorch-image-models-master')\n","import timm\n","\n","sys.path.append('/home/lakshita/somusan/hubmap_kaggle/nbs/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master')\n","\n","from efficientnet_pytorch import EfficientNet\n","from efficientnet_pytorch.utils import url_map, url_map_advprop, get_model_params"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.319339Z","iopub.status.busy":"2022-08-17T13:10:28.316338Z","iopub.status.idle":"2022-08-17T13:10:28.334845Z","shell.execute_reply":"2022-08-17T13:10:28.333523Z","shell.execute_reply.started":"2022-08-17T13:10:28.319294Z"},"papermill":{"duration":0.035888,"end_time":"2022-08-17T06:03:00.529569","exception":false,"start_time":"2022-08-17T06:03:00.493681","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["root_dir = '/kaggle/working/'\n","pretrain_dir = '/kaggle/input/swin-tiny-small-22k-pretrained/'\n","\n","TRAIN = '/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-2022-256x256/train'\n","MASKS = '/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-2022-256x256/masks'\n","LABELS = '/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-organ-segmentation/test.csv'\n","\n","df = pd.read_csv(\"/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-organ-segmentation/train.csv\")"]},{"cell_type":"code","execution_count":3,"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.execute_input":"2022-08-17T13:10:28.342501Z","iopub.status.busy":"2022-08-17T13:10:28.339903Z","iopub.status.idle":"2022-08-17T13:10:28.378628Z","shell.execute_reply":"2022-08-17T13:10:28.377366Z","shell.execute_reply.started":"2022-08-17T13:10:28.342460Z"},"papermill":{"duration":0.05662,"end_time":"2022-08-17T06:03:00.635621","exception":false,"start_time":"2022-08-17T06:03:00.579001","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["def rle_encode(img):\n","    '''\n","    img: numpy array, 1 - mask, 0 - background\n","    Returns run length as string formated\n","    '''\n","    pixels = img.flatten()\n","    pixels = np.concatenate([[0], pixels, [0]])\n","    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1\n","    runs[1::2] -= runs[::2]\n","    return ' '.join(str(x) for x in runs)\n","\n","def image_to_tensor(image, mode='bgr'): #image mode\n","    if mode=='bgr':\n","        image = image[:,:,::-1]\n","    x = image\n","    x = x.transpose(2,0,1)\n","    x = np.ascontiguousarray(x)\n","    x = torch.tensor(x, dtype=torch.float)\n","    return x\n","\n","\n","def mask_to_tensor(mask):\n","    x = mask\n","    x = torch.tensor(x, dtype=torch.float)\n","    return x\n","\n","    \n","    \n","class RGB(nn.Module):\n","    IMAGE_RGB_MEAN = [0.485, 0.456, 0.406] #[0.5, 0.5, 0.5]\n","    IMAGE_RGB_STD  = [0.229, 0.224, 0.225] #[0.5, 0.5, 0.5]\n","\n","    def __init__(self,):\n","        super(RGB, self).__init__()\n","        self.register_buffer('mean', torch.zeros(1,3,1,1))\n","        self.register_buffer('std', torch.ones(1,3,1,1))\n","        self.mean.data = torch.FloatTensor(self.IMAGE_RGB_MEAN).view(self.mean.shape)\n","        self.std.data = torch.FloatTensor(self.IMAGE_RGB_STD).view(self.std.shape)\n","\n","    def forward(self, x):\n","        x = (x-self.mean)/self.std\n","        return x\n","    \n","    \n","def message(mode='print'):\n","    asterisk = ' '\n","    if mode==('print'):\n","        loss = batch_loss\n","    if mode==('log'):\n","        loss = train_loss\n","        if (iteration % iter_save == 0): asterisk = '*'\n","\n","    text = \\\n","        ('%0.2e   %08d%s %6.2f | '%(rate, iteration, asterisk, epoch,)).replace('e-0','e-').replace('e+0','e+') + \\\n","        '%4.3f  %4.3f  %4.4f  %4.3f   | '%(*valid_loss,) + \\\n","        '%4.3f  %4.3f   | '%(*loss,) + \\\n","        '%s' % ((time.time() - start_timer))\n","\n","    return text"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.388062Z","iopub.status.busy":"2022-08-17T13:10:28.385612Z","iopub.status.idle":"2022-08-17T13:10:28.400436Z","shell.execute_reply":"2022-08-17T13:10:28.399351Z","shell.execute_reply.started":"2022-08-17T13:10:28.388024Z"},"papermill":{"duration":0.02762,"end_time":"2022-08-17T06:03:00.704123","exception":false,"start_time":"2022-08-17T06:03:00.676503","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["def valid_augment5(image, mask, organ):\n","    #image, mask  = do_crop(image, mask, image_size, xy=(None,None))\n","    return image, mask\n","\n","def train_augment5b(image, mask, organ):\n","    image, mask = do_random_flip(image, mask)\n","    image, mask = do_random_rot90(image, mask)\n","\n","    for fn in np.random.choice([\n","        lambda image, mask: (image, mask),\n","        lambda image, mask: do_random_noise(image, mask, mag=0.1),\n","        lambda image, mask: do_random_contast(image, mask, mag=0.40),\n","        lambda image, mask: do_random_hsv(image, mask, mag=[0.40, 0.40, 0])\n","    ], 2): image, mask = fn(image, mask)\n","\n","    for fn in np.random.choice([\n","        lambda image, mask: (image, mask),\n","        lambda image, mask: do_random_rotate_scale(image, mask, angle=45, scale=[0.50, 2.0]),\n","    ], 1): image, mask = fn(image, mask)\n","\n","    return image, mask"]},{"cell_type":"markdown","metadata":{"papermill":{"duration":0.013668,"end_time":"2022-08-17T06:03:00.732634","exception":false,"start_time":"2022-08-17T06:03:00.718966","status":"completed"},"tags":[]},"source":["<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"color:#c3448b; background:#efe9e9; border:1px dashed #efe50b;\" role=\"tab\" aria-controls=\"augmentations\"><center>Augmentations</center></h3>"]},{"cell_type":"code","execution_count":5,"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.execute_input":"2022-08-17T13:10:28.405075Z","iopub.status.busy":"2022-08-17T13:10:28.404702Z","iopub.status.idle":"2022-08-17T13:10:28.429154Z","shell.execute_reply":"2022-08-17T13:10:28.427994Z","shell.execute_reply.started":"2022-08-17T13:10:28.405040Z"},"papermill":{"duration":0.047993,"end_time":"2022-08-17T06:03:00.794900","exception":false,"start_time":"2022-08-17T06:03:00.746907","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["def do_random_flip(image, mask):\n","    if np.random.rand()>0.5:\n","        image = cv2.flip(image,0)\n","        mask = cv2.flip(mask,0)\n","    if np.random.rand()>0.5:\n","        image = cv2.flip(image,1)\n","        mask = cv2.flip(mask,1)\n","    if np.random.rand()>0.5:\n","        image = image.transpose(1,0,2)\n","        mask = mask.transpose(1,0)\n","    \n","    image = np.ascontiguousarray(image)\n","    mask = np.ascontiguousarray(mask)\n","    return image, mask\n","\n","def do_random_rot90(image, mask):\n","    r = np.random.choice([\n","        0,\n","        cv2.ROTATE_90_CLOCKWISE,\n","        cv2.ROTATE_90_COUNTERCLOCKWISE,\n","        cv2.ROTATE_180,\n","    ])\n","    if r==0:\n","        return image, mask\n","    else:\n","        image = cv2.rotate(image, r)\n","        mask = cv2.rotate(mask, r)\n","        return image, mask\n","    \n","def do_random_contast(image, mask, mag=0.3):\n","    alpha = 1 + random.uniform(-1,1)*mag\n","    image = image * alpha\n","    image = np.clip(image,0,1)\n","    return image, mask\n","\n","def do_random_hsv(image, mask, mag=[0.15,0.25,0.25]):\n","    image = (image*255).astype(np.uint8)\n","    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n","\n","    h = hsv[:, :, 0].astype(np.float32)  # hue\n","    s = hsv[:, :, 1].astype(np.float32)  # saturation\n","    v = hsv[:, :, 2].astype(np.float32)  # value\n","    h = (h*(1 + random.uniform(-1,1)*mag[0]))%180\n","    s =  s*(1 + random.uniform(-1,1)*mag[1])\n","    v =  v*(1 + random.uniform(-1,1)*mag[2])\n","\n","    hsv[:, :, 0] = np.clip(h,0,180).astype(np.uint8)\n","    hsv[:, :, 1] = np.clip(s,0,255).astype(np.uint8)\n","    hsv[:, :, 2] = np.clip(v,0,255).astype(np.uint8)\n","    image = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n","    image = image.astype(np.float32)/255\n","    return image, mask\n","\n","def do_random_noise(image, mask, mag=0.1):\n","    height, width = image.shape[:2]\n","    noise = np.random.uniform(-1,1, (height, width,1))*mag\n","    image = image + noise\n","    image = np.clip(image,0,1)\n","    return image, mask\n","\n","def do_random_rotate_scale(image, mask, angle=30, scale=[0.8,1.2] ):\n","    angle = np.random.uniform(-angle, angle)\n","    scale = np.random.uniform(*scale) if scale is not None else 1\n","    \n","    height, width = image.shape[:2]\n","    center = (height // 2, width // 2)\n","    \n","    transform = cv2.getRotationMatrix2D(center, angle, scale)\n","    image = cv2.warpAffine( image, transform, (width, height), flags=cv2.INTER_LINEAR,\n","                            borderMode=cv2.BORDER_CONSTANT, borderValue=(0,0,0))\n","    mask  = cv2.warpAffine( mask, transform, (width, height), flags=cv2.INTER_LINEAR,\n","                            borderMode=cv2.BORDER_CONSTANT, borderValue=0)\n","    return image, mask"]},{"cell_type":"markdown","metadata":{"papermill":{"duration":0.017685,"end_time":"2022-08-17T06:03:00.830113","exception":false,"start_time":"2022-08-17T06:03:00.812428","status":"completed"},"tags":[]},"source":["<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"color:#c3448b; background:#efe9e9; border:1px dashed #efe50b;\" role=\"tab\" aria-controls=\"dataset\"><center>Dataset</center></h3>"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.434601Z","iopub.status.busy":"2022-08-17T13:10:28.430627Z","iopub.status.idle":"2022-08-17T13:10:28.453541Z","shell.execute_reply":"2022-08-17T13:10:28.452574Z","shell.execute_reply.started":"2022-08-17T13:10:28.434563Z"},"papermill":{"duration":0.041838,"end_time":"2022-08-17T06:03:00.889591","exception":false,"start_time":"2022-08-17T06:03:00.847753","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["image_size = 256\n","\n","class HubmapDataset(Dataset):\n","    def __init__(self, df, augment=None):\n","\n","        self.df = df\n","        self.augment = augment\n","        self.length = len(self.df)\n","        self.organ_to_label = {'kidney' : 0,\n","                               'prostate' : 1,\n","                               'largeintestine' : 2,\n","                               'spleen' : 3,\n","                               'lung' : 4}\n","\n","    def __str__(self):\n","        string = ''\n","        string += '\\tlen = %d\\n' % len(self)\n","\n","        d = self.df.organ.value_counts().to_dict()\n","        for k in ['kidney', 'prostate', 'largeintestine', 'spleen', 'lung']:\n","            string +=  '%24s %3d (%0.3f) \\n'%(k, d.get(k,0), d.get(k,0)/len(self.df))\n","        return string\n","\n","    def __len__(self):\n","        return self.length\n","\n","    def __getitem__(self, index):\n","        d = self.df.iloc[index]\n","        img_height = self.df.loc[index, 'img_height']\n","        img_width = self.df.loc[index, 'img_width']\n","        organ = self.organ_to_label[d.organ]\n","\n","        image = cv2.cvtColor(tifffile.imread(os.path.join(TRAIN, f'{d.id}.tiff')), cv2.COLOR_BGR2RGB)\n","        \n","        rle_mask = self.df.loc[index, 'rle']\n","        mask = rle_decode(rle_mask, (img_height, img_width))\n","        #mask = cv2.cvtColor(mask, cv2.IMREAD_GRAYSCALE)\n","        #mask = cv2.imread(os.path.join(MASKS,fname),cv2.IMREAD_GRAYSCALE)\n","        mask = np.expand_dims(mask, axis = 2)\n","        #print(mask.shape)\n","        \n","        image = image.astype(np.float32)/255\n","        #mask  = mask.astype(np.float32)/255\n","        mask = mask.astype(np.float32)\n","\n","        s = d.pixel_size/0.4 * (image_size/3000)\n","        image = cv2.resize(image,dsize=(image_size,image_size),interpolation=cv2.INTER_LINEAR)\n","        mask  = cv2.resize(mask, dsize=(image_size,image_size),interpolation=cv2.INTER_LINEAR)\n","\n","        if self.augment is not None:\n","            image, mask = self.augment(image, mask, organ)\n","\n","\n","        r ={}\n","        r['index']= index\n","        r['organ'] = torch.tensor([organ], dtype=torch.long)\n","        r['image'] = image_to_tensor(image)\n","        r['mask' ] = mask_to_tensor(mask)\n","        return r"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.461111Z","iopub.status.busy":"2022-08-17T13:10:28.458003Z","iopub.status.idle":"2022-08-17T13:10:28.524180Z","shell.execute_reply":"2022-08-17T13:10:28.523264Z","shell.execute_reply.started":"2022-08-17T13:10:28.461073Z"},"trusted":true},"outputs":[],"source":["class FPN(nn.Module):\n","    def __init__(self, input_channels:list, output_channels:list):\n","        super().__init__()\n","        self.convs = nn.ModuleList(\n","            [nn.Sequential(nn.Conv2d(in_ch, out_ch*2, kernel_size=3, padding=1),\n","             nn.ReLU(inplace=True), nn.BatchNorm2d(out_ch*2),\n","             nn.Conv2d(out_ch*2, out_ch, kernel_size=3, padding=1))\n","            for in_ch, out_ch in zip(input_channels, output_channels)])\n","        \n","    def forward(self, xs:list, last_layer):\n","        hcs = [F.interpolate(c(x),scale_factor=2**(len(self.convs)-i),mode='bilinear') \n","               for i,(c,x) in enumerate(zip(self.convs, xs))]\n","        hcs.append(last_layer)\n","        return torch.cat(hcs, dim=1)\n","\n","class UnetBlock(nn.Module):\n","    def __init__(self, up_in_c:int, x_in_c:int, nf:int=None, blur:bool=False,\n","                 self_attention:bool=False, **kwargs):\n","        super().__init__()\n","        self.shuf = PixelShuffle_ICNR(up_in_c, up_in_c//2, blur=blur, **kwargs)\n","        self.bn = nn.BatchNorm2d(x_in_c)\n","        ni = up_in_c//2 + x_in_c\n","        nf = nf if nf is not None else max(up_in_c//2,32)\n","        self.conv1 = ConvLayer(ni, nf, norm_type=None, **kwargs)\n","        self.conv2 = ConvLayer(nf, nf, norm_type=None,\n","            xtra=SelfAttention(nf) if self_attention else None, **kwargs)\n","        self.relu = nn.ReLU(inplace=True)\n","\n","    def forward(self, up_in:Tensor, left_in:Tensor) -> Tensor:\n","        s = left_in\n","        up_out = self.shuf(up_in)\n","        cat_x = self.relu(torch.cat([up_out, self.bn(s)], dim=1))\n","        return self.conv2(self.conv1(cat_x))\n","        \n","class _ASPPModule(nn.Module):\n","    def __init__(self, inplanes, planes, kernel_size, padding, dilation, groups=1):\n","        super().__init__()\n","        self.atrous_conv = nn.Conv2d(inplanes, planes, kernel_size=kernel_size,\n","                stride=1, padding=padding, dilation=dilation, bias=False, groups=groups)\n","        self.bn = nn.BatchNorm2d(planes)\n","        self.relu = nn.ReLU()\n","\n","        self._init_weight()\n","\n","    def forward(self, x):\n","        x = self.atrous_conv(x)\n","        x = self.bn(x)\n","\n","        return self.relu(x)\n","\n","    def _init_weight(self):\n","        for m in self.modules():\n","            if isinstance(m, nn.Conv2d):\n","                torch.nn.init.kaiming_normal_(m.weight)\n","            elif isinstance(m, nn.BatchNorm2d):\n","                m.weight.data.fill_(1)\n","                m.bias.data.zero_()\n","\n","class ASPP(nn.Module):\n","    def __init__(self, inplanes=512, mid_c=256, dilations=[6, 12, 18, 24], out_c=None):\n","        super().__init__()\n","        self.aspps = [_ASPPModule(inplanes, mid_c, 1, padding=0, dilation=1)] + \\\n","            [_ASPPModule(inplanes, mid_c, 3, padding=d, dilation=d,groups=4) for d in dilations]\n","        self.aspps = nn.ModuleList(self.aspps)\n","        self.global_pool = nn.Sequential(nn.AdaptiveMaxPool2d((1, 1)),\n","                        nn.Conv2d(inplanes, mid_c, 1, stride=1, bias=False),\n","                        nn.BatchNorm2d(mid_c), nn.ReLU())\n","        out_c = out_c if out_c is not None else mid_c\n","        self.out_conv = nn.Sequential(nn.Conv2d(mid_c*(2+len(dilations)), out_c, 1, bias=False),\n","                                    nn.BatchNorm2d(out_c), nn.ReLU(inplace=True))\n","        self.conv1 = nn.Conv2d(mid_c*(2+len(dilations)), out_c, 1, bias=False)\n","        self._init_weight()\n","\n","    def forward(self, x):\n","        x0 = self.global_pool(x)\n","        xs = [aspp(x) for aspp in self.aspps]\n","        x0 = F.interpolate(x0, size=xs[0].size()[2:], mode='bilinear', align_corners=True)\n","        x = torch.cat([x0] + xs, dim=1)\n","        return self.out_conv(x)\n","    \n","    def _init_weight(self):\n","        for m in self.modules():\n","            if isinstance(m, nn.Conv2d):\n","                torch.nn.init.kaiming_normal_(m.weight)\n","            elif isinstance(m, nn.BatchNorm2d):\n","                m.weight.data.fill_(1)\n","                m.bias.data.zero_()\n","\n","\n","class config:\n","    pretrained_root = '/home/lakshita/somusan/hubmap_kaggle/nbs/efficientnet-pytorch/'\n","    efficient_net_encoders = {\n","        \"efficientnet-b0\": {\n","            \"out_channels\": (3, 32, 24, 40, 112, 320),\n","            \"stage_idxs\": (3, 5, 9, 16),\n","            \"weight_path\": pretrained_root + \"efficientnet-b0-08094119.pth\"\n","        },\n","        \"efficientnet-b1\": {\n","            \"out_channels\": (3, 32, 24, 40, 112, 320),\n","            \"stage_idxs\": (5, 8, 16, 23),\n","            \"weight_path\": pretrained_root + \"efficientnet-b1-dbc7070a.pth\"\n","        },\n","        \"efficientnet-b2\": {\n","            \"out_channels\": (3, 32, 24, 48, 120, 352),\n","            \"stage_idxs\": (5, 8, 16, 23),\n","            \"weight_path\": pretrained_root + \"efficientnet-b2-27687264.pth\"\n","        },\n","        \"efficientnet-b3\": {\n","            \"out_channels\": (3, 40, 32, 48, 136, 384),\n","            \"stage_idxs\": (5, 8, 18, 26),\n","            \"weight_path\": pretrained_root + \"efficientnet-b3-c8376fa2.pth\"\n","        },\n","        \"efficientnet-b4\": {\n","            \"out_channels\": (3, 48, 32, 56, 160, 448),\n","            \"stage_idxs\": (6, 10, 22, 32),\n","            \"weight_path\": pretrained_root + \"efficientnet-b4-e116e8b3.pth\"\n","        },\n","        \"efficientnet-b5\": {\n","            \"out_channels\": (3, 48, 40, 64, 176, 512),\n","            \"stage_idxs\": (8, 13, 27, 39),\n","            \"weight_path\": pretrained_root + \"efficientnet-b5-586e6cc6.pth\"\n","        },\n","        \"efficientnet-b6\": {\n","            \"out_channels\": (3, 56, 40, 72, 200, 576),\n","            \"stage_idxs\": (9, 15, 31, 45),\n","            \"weight_path\": pretrained_root + \"efficientnet-b6-c76e70fd.pth\"\n","        },\n","        \"efficientnet-b7\": {\n","            \"out_channels\": (3, 64, 48, 80, 224, 640),\n","            \"stage_idxs\": (11, 18, 38, 55),\n","            \"weight_path\": pretrained_root + \"efficientnet-b7-dcc49843.pth\"\n","        }\n","    }\n","    model = 'efficientnet-b7'\n","    \n","class EfficientNetEncoder(EfficientNet):\n","    def __init__(self, stage_idxs, out_channels, model_name, depth=5):\n","\n","        blocks_args, global_params = get_model_params(model_name, override_params=None)\n","        super().__init__(blocks_args, global_params)\n","        \n","        cfg = config.efficient_net_encoders[model_name]\n","\n","        self._stage_idxs = stage_idxs\n","        self._out_channels = out_channels\n","        self._depth = depth\n","        self._in_channels = 3\n","\n","        del self._fc\n","        self.load_state_dict(torch.load(cfg['weight_path']))\n","\n","    def get_stages(self):\n","        return [\n","            nn.Identity(),\n","            nn.Sequential(self._conv_stem, self._bn0, self._swish),\n","            self._blocks[:self._stage_idxs[0]],\n","            self._blocks[self._stage_idxs[0]:self._stage_idxs[1]],\n","            self._blocks[self._stage_idxs[1]:self._stage_idxs[2]],\n","            self._blocks[self._stage_idxs[2]:],\n","        ]\n","\n","    def forward(self, x):\n","        stages = self.get_stages()\n","\n","        block_number = 0.\n","        drop_connect_rate = self._global_params.drop_connect_rate\n","\n","        features = []\n","        for i in range(self._depth + 1):\n","\n","            # Identity and Sequential stages\n","            if i < 2:\n","                x = stages[i](x)\n","\n","            # Block stages need drop_connect rate\n","            else:\n","                for module in stages[i]:\n","                    drop_connect = drop_connect_rate * block_number / len(self._blocks)\n","                    block_number += 1.\n","                    x = module(x, drop_connect)\n","\n","            features.append(x)\n","\n","        return features\n","\n","    def load_state_dict(self, state_dict, **kwargs):\n","        state_dict.pop(\"_fc.bias\")\n","        state_dict.pop(\"_fc.weight\")\n","        super().load_state_dict(state_dict, **kwargs)  \n","        \n","\n","class EffUnet(nn.Module):\n","    def __init__(self, model_name, stride=1):\n","        super().__init__()\n","        \n","        cfg = config.efficient_net_encoders[model_name]\n","        stage_idxs = cfg['stage_idxs']\n","        out_channels = cfg['out_channels']\n","        \n","        self.encoder = EfficientNetEncoder(stage_idxs, out_channels, model_name)\n","\n","        #aspp with customized dilatations\n","        self.aspp = ASPP(out_channels[-1], 256, out_c=384, \n","                         dilations=[stride*1, stride*2, stride*3, stride*4])\n","        self.drop_aspp = nn.Dropout2d(0.5)\n","        #decoder\n","        self.dec4 = UnetBlock(384, out_channels[-2], 256)\n","        self.dec3 = UnetBlock(256, out_channels[-3], 128)\n","        self.dec2 = UnetBlock(128, out_channels[-4], 64)\n","        self.dec1 = UnetBlock(64, out_channels[-5], 32)\n","        self.fpn = FPN([384, 256, 128, 64], [16]*4)\n","        self.drop = nn.Dropout2d(0.1)\n","        self.final_conv = ConvLayer(32+16*4, 1, ks=1, norm_type=None, act_cls=None)\n","        \n","        self.rgb = RGB()\n","        \n","    def forward(self, batch):\n","        x = batch['image']\n","        B, C, H, W = x.shape\n","        x = self.rgb(x)\n","        enc0, enc1, enc2, enc3, enc4 = self.encoder(x)[-5:]\n","        enc5 = self.aspp(enc4)\n","        dec3 = self.dec4(self.drop_aspp(enc5), enc3)\n","        dec2 = self.dec3(dec3,enc2)\n","        dec1 = self.dec2(dec2,enc1)\n","        dec0 = self.dec1(dec1,enc0)\n","        x = self.fpn([enc5, dec3, dec2, dec1], dec0)\n","        x = self.final_conv(self.drop(x))\n","        x = F.interpolate(x, size = 512, mode = 'bilinear')\n","        return x"]},{"cell_type":"markdown","metadata":{"papermill":{"duration":0.009489,"end_time":"2022-08-17T06:03:01.501741","exception":false,"start_time":"2022-08-17T06:03:01.492252","status":"completed"},"tags":[]},"source":["<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style=\"color:#c3448b; background:#efe9e9; border:1px dashed #efe50b;\" role=\"tab\" aria-controls=\"folds\"><center>Folds</center></h3>"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.525890Z","iopub.status.busy":"2022-08-17T13:10:28.525349Z","iopub.status.idle":"2022-08-17T13:10:28.537370Z","shell.execute_reply":"2022-08-17T13:10:28.536410Z","shell.execute_reply.started":"2022-08-17T13:10:28.525856Z"},"papermill":{"duration":0.023331,"end_time":"2022-08-17T06:03:01.534954","exception":false,"start_time":"2022-08-17T06:03:01.511623","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["def make_fold(fold=3):\n","    df = pd.read_csv('/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-organ-segmentation/train.csv')\n","\n","    num_fold = 5\n","    skf = KFold(n_splits=num_fold, shuffle=True,random_state=42)\n","\n","    df.loc[:,'fold']=-1\n","    for f,(t_idx, v_idx) in enumerate(skf.split(X=df['id'], y=df['organ'])):\n","        df.iloc[v_idx,-1]=f\n","\n","    #check\n","    if 0:\n","        for f in range(num_fold):\n","            train_df=df[df.fold!=f].reset_index(drop=True)\n","            valid_df=df[df.fold==f].reset_index(drop=True)\n","\n","            print('fold %d'%f)\n","            t = train_df.organ.value_counts().to_dict()\n","            v = valid_df.organ.value_counts().to_dict()\n","            for k in ['kidney', 'prostate', 'largeintestine', 'spleen', 'lung']:\n","                print('%32s %3d (%0.3f)  %3d (%0.3f)'%(k,t.get(k,0),t.get(k,0)/len(train_df),v.get(k,0),v.get(k,0)/len(valid_df)))\n","\n","            print('')\n","            zz=0\n","\n","    train_df=df[df.fold!=fold].reset_index(drop=True)\n","    valid_df=df[df.fold==fold].reset_index(drop=True)\n","    return train_df,valid_df"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.539398Z","iopub.status.busy":"2022-08-17T13:10:28.538831Z","iopub.status.idle":"2022-08-17T13:10:28.549728Z","shell.execute_reply":"2022-08-17T13:10:28.548794Z","shell.execute_reply.started":"2022-08-17T13:10:28.539357Z"},"papermill":{"duration":0.021001,"end_time":"2022-08-17T06:03:01.585250","exception":false,"start_time":"2022-08-17T06:03:01.564249","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["def compute_dice_score(probability, mask, smooth = 1):\n","    N = len(probability)\n","    p = probability.reshape(N,-1)\n","    t = mask.reshape(N,-1)\n","\n","    p = p>0.5\n","    t = t>0.5\n","    uion = p.sum(-1) + t.sum(-1)\n","    overlap = (p*t).sum(-1)\n","    dice = 2*overlap/(uion+0.0001)\n","    return dice"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.555068Z","iopub.status.busy":"2022-08-17T13:10:28.554133Z","iopub.status.idle":"2022-08-17T13:10:28.559952Z","shell.execute_reply":"2022-08-17T13:10:28.559006Z","shell.execute_reply.started":"2022-08-17T13:10:28.555031Z"},"papermill":{"duration":0.01817,"end_time":"2022-08-17T06:03:01.632670","exception":false,"start_time":"2022-08-17T06:03:01.614500","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["def get_learning_rate(optimizer):\n","    return optimizer.param_groups[0]['lr']"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.563542Z","iopub.status.busy":"2022-08-17T13:10:28.561367Z","iopub.status.idle":"2022-08-17T13:10:28.572179Z","shell.execute_reply":"2022-08-17T13:10:28.571054Z","shell.execute_reply.started":"2022-08-17T13:10:28.563499Z"},"papermill":{"duration":0.018826,"end_time":"2022-08-17T06:03:01.660971","exception":false,"start_time":"2022-08-17T06:03:01.642145","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["# organ_threshold = {\n","#     'kidney': 0.50,\n","#     'prostate': 0.50,\n","#     'largeintestine': 0.50,\n","#     'spleen': 0.50,\n","#     'lung': 0.20,\n","# }   \n","\n","organ_threshold = {\n","    'Hubmap': {\n","        'kidney'        : 0.40,\n","        'prostate'      : 0.40,\n","        'largeintestine': 0.40,\n","        'spleen'        : 0.40,\n","        'lung'          : 0.10,\n","    },\n","    'HPA': {\n","        'kidney'        : 0.50,\n","        'prostate'      : 0.50,\n","        'largeintestine': 0.50,\n","        'spleen'        : 0.50,\n","        'lung'          : 0.10,\n","    },\n","}"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.577078Z","iopub.status.busy":"2022-08-17T13:10:28.576620Z","iopub.status.idle":"2022-08-17T13:10:28.590110Z","shell.execute_reply":"2022-08-17T13:10:28.588888Z","shell.execute_reply.started":"2022-08-17T13:10:28.577042Z"},"papermill":{"duration":0.02046,"end_time":"2022-08-17T06:03:01.691215","exception":false,"start_time":"2022-08-17T06:03:01.670755","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["# Stochastic Weight Averaging\n","# https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/\n","def do_swa(checkpoint):\n","    skip = ['relative_position_index', 'num_batches_tracked']\n","    \n","    K = len(checkpoint)\n","    swa = None\n","    \n","    for k in range(K):\n","        state_dict = torch.load(checkpoint[k], map_location=lambda storage, loc: storage)['state_dict']\n","        if swa is None:\n","            swa = state_dict\n","        else:\n","            for k, v in state_dict.items():\n","                #print(k)\n","                if any(s in k for s in skip): continue\n","                swa[k] += v\n","    \n","    for k, v in swa.items():\n","        if any(s in k for s in skip): continue\n","        swa[k] /= K\n","    \n","    return swa"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.592484Z","iopub.status.busy":"2022-08-17T13:10:28.591756Z","iopub.status.idle":"2022-08-17T13:10:28.603746Z","shell.execute_reply":"2022-08-17T13:10:28.602440Z","shell.execute_reply.started":"2022-08-17T13:10:28.592449Z"},"papermill":{"duration":0.017319,"end_time":"2022-08-17T06:03:01.718102","exception":false,"start_time":"2022-08-17T06:03:01.700783","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["#root_dir = '../input/swin-10k'"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.605857Z","iopub.status.busy":"2022-08-17T13:10:28.605378Z","iopub.status.idle":"2022-08-17T13:10:28.621106Z","shell.execute_reply":"2022-08-17T13:10:28.619997Z","shell.execute_reply.started":"2022-08-17T13:10:28.605818Z"},"papermill":{"duration":0.020596,"end_time":"2022-08-17T06:03:01.748321","exception":false,"start_time":"2022-08-17T06:03:01.727725","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["# #out_dir = root_dir + '/result/upernet-swin-v1-tiny-aux5-768'\n","# all_df = []\n","# valid = {\n","#     0: [\n","#         '../input/fold-0/result/upernet-swin-v1-tiny-aux5-768/fold-0/checkpoint/00004992.model.pth',\n","#         '../input/fold-0/result/upernet-swin-v1-tiny-aux5-768/fold-0/checkpoint/00007776.model.pth',\n","#         '../input/fold-0/result/upernet-swin-v1-tiny-aux5-768/fold-0/checkpoint/00009216.model.pth',\n","#         ],\n","#     1: [\n","#         '../input/fold-1/result/upernet-swin-v1-tiny-aux5-768/fold-1/checkpoint/00007296.model.pth',\n","#         '../input/fold-1/result/upernet-swin-v1-tiny-aux5-768/fold-1/checkpoint/00008736.model.pth',\n","#         '../input/fold-1/result/upernet-swin-v1-tiny-aux5-768/fold-1/checkpoint/00009120.model.pth',\n","#         ],\n","#     2: [\n","#         '../input/fold-2/result/upernet-swin-v1-tiny-aux5-768/fold-2/checkpoint/00005376.model.pth',\n","#         '../input/fold-2/result/upernet-swin-v1-tiny-aux5-768/fold-2/checkpoint/00008832.model.pth',\n","#         '../input/fold-2/result/upernet-swin-v1-tiny-aux5-768/fold-2/checkpoint/00009888.model.pth',\n","#         ],\n","#     3: [\n","#         '../input/fold-3/result/upernet-swin-v1-tiny-aux5-768/fold-3/checkpoint/00008019.model.pth',\n","#         '../input/fold-3/result/upernet-swin-v1-tiny-aux5-768/fold-3/checkpoint/00008910.model.pth',\n","#         '../input/fold-3/result/upernet-swin-v1-tiny-aux5-768/fold-3/checkpoint/00009900.model.pth',\n","#         ],\n","# }\n","\n","# all_score_df = []\n","# for f, checkpoint in valid.items():\n","#     if len(checkpoint)==0: \n","#         continue\n","\n","#     #project_name = out_dir.split('/')[-1]\n","#     #fold_dir = out_dir + '/fold-%d' % f\n","\n","#     #checkpoint = [fold_dir + '/checkpoint' + c for c in checkpoint]\n","#     checkpoint = [c for c in checkpoint]\n","#     swa = do_swa(checkpoint)\n","#     torch.save({\n","#         'state_dict': swa,\n","#         'swa': [c.split('/')[-1] for c in checkpoint],\n","#     }, \n","#         f'./fold-{f}-swa.pth')\n","\n","#     net = Net().cuda()\n","#     state_dict = swa\n","#     net.load_state_dict(state_dict, strict=False)  # True\n","\n","#     net = net.eval()\n","#     net.output_type = ['inference']\n","\n","#     result = {\n","#         'id': [],\n","#         'probability': [],\n","#         'rle': [],\n","#     }\n","\n","#     start_timer = time.time()\n","#     for t, d in df.iterrows():\n","        \n","#         fname = d['id']\n","#         image = tifffile.imread(f'../input/hubmap-organ-segmentation/test_images/{fname}.tiff')\n","#         image = image.astype(np.float32)/255\n","        \n","#         H, W, _ = image.shape\n","\n","#         s = d.pixel_size / 0.4 * (image_size / 3000)\n","#         image = cv2.resize(image, dsize=(image_size,image_size), interpolation=cv2.INTER_LINEAR)\n","\n","#         image = image_to_tensor(image)\n","#         image = image.cuda()\n","#         batch = {\n","#             'image':\n","#                 torch.stack([\n","#                     image,\n","#                     torch.flip(image, [1]),\n","#                     torch.flip(image, [2]),\n","#                 ]),  # simple TTA\n","#         }\n","\n","#         probability = 0\n","#         with torch.no_grad():\n","#             with amp.autocast(enabled=is_amp):\n","#                 output = net(batch) \n","#                 probability += F.interpolate(\n","#                     output['probability'], size=(H, W), mode='bilinear', align_corners=False, antialias=True)\n","\n","#         # undo TTA\n","#         probability[1] = torch.flip(probability[1], [1])\n","#         probability[2] = torch.flip(probability[2], [2])\n","#         probability = probability.float().data.cpu().numpy().mean(0)[0]\n","#         #p = probability > organ_threshold[d.organ] \n","#         p = probability > organ_threshold[d.data_source][d.organ] \n","#         p = p.T\n","#         rle = rle_encode(p)\n","#         #rle = rle\n","\n","#         result['rle'].append(rle)\n","#         result['probability'].append(p)\n","#         result['id'].append(fname)\n","#         print('\\r', t, end='', flush=True)\n","#     print('')\n","#     submit_df = pd.DataFrame({'id': result['id'], 'rle': result['rle'], 'prob' : result['probability']})\n","#     print(submit_df)\n","#     all_df.append(submit_df)\n","#     print('submit_df ok!')\n","#     print('')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def rle_decode(mask_rle, shape, color=1):\n","    '''\n","    mask_rle: run-length as string formated (start length)\n","    shape: (height,width) of array to return \n","    Returns numpy array, 1 - mask, 0 - background\n","    '''\n","    s = mask_rle.split()\n","    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]\n","    starts -= 1\n","    ends = starts + lengths\n","    img = np.zeros(shape[0] * shape[1], dtype=np.float32)\n","    for lo, hi in zip(starts, ends):\n","        img[lo : hi] = color\n","    return img.reshape(shape)"]},{"cell_type":"code","execution_count":37,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:28.623551Z","iopub.status.busy":"2022-08-17T13:10:28.623018Z","iopub.status.idle":"2022-08-17T13:10:35.528099Z","shell.execute_reply":"2022-08-17T13:10:35.519611Z","shell.execute_reply.started":"2022-08-17T13:10:28.623381Z"},"papermill":{"duration":27.031845,"end_time":"2022-08-17T06:03:28.789540","exception":false,"start_time":"2022-08-17T06:03:01.757695","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["10488\n"," 3\n","      id  \\\n","0  10488   \n","\n","                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       rle  \n","0  4713994 7 4716992 11 4719991 14 4722990 16 4725989 19 4728987 22 4731986 25 4734985 28 4737984 30 4740983 33 4743982 36 4746982 37 4749981 39 4752980 42 4753182 8 4755979 44 4756179 15 4758979 46 4759177 21 4761978 48 4762175 28 4764977 50 4765172 35 4767977 52 4768170 40 4770976 54 4771167 45 4773976 55 4774164 50 4776975 58 4777162 54 4779975 59 4780159 59 4782975 61 4783157 63 4785974 63 4786155 67 4788974 65 4789152 71 4791973 68 4792149 76 4794973 70 4795146 80 4797973 72 4798143 84 4800972 75 4801140 88 4803972 77 4804137 93 4806971 80 4807135 96 4809972 81 4810133 99 4812972 83 4813...  \n","submit_df ok!\n","\n"]}],"source":["all_df = []\n","# valid = {\n","# #     0: [\n","# #         '../input/fold-0/result/upernet-swin-v1-tiny-aux5-768/fold-0/checkpoint/00004992.model.pth',\n","# #         '../input/fold-0/result/upernet-swin-v1-tiny-aux5-768/fold-0/checkpoint/00007776.model.pth',\n","# #         '../input/fold-0/result/upernet-swin-v1-tiny-aux5-768/fold-0/checkpoint/00009216.model.pth',\n","# #         ],\n","# #     1: [\n","# #         '../input/fold-1/result/upernet-swin-v1-tiny-aux5-768/fold-1/checkpoint/00007296.model.pth',\n","# #         '../input/fold-1/result/upernet-swin-v1-tiny-aux5-768/fold-1/checkpoint/00008736.model.pth',\n","# #         '../input/fold-1/result/upernet-swin-v1-tiny-aux5-768/fold-1/checkpoint/00009120.model.pth',\n","# #         ],\n","# #     2: [\n","# #         '../input/fold-2/result/upernet-swin-v1-tiny-aux5-768/fold-2/checkpoint/00005376.model.pth',\n","# #         '../input/fold-2/result/upernet-swin-v1-tiny-aux5-768/fold-2/checkpoint/00008832.model.pth',\n","# #         '../input/fold-2/result/upernet-swin-v1-tiny-aux5-768/fold-2/checkpoint/00009888.model.pth',\n","# #         ],\n","#     3: [\n","#         '../input/fold-3/result/upernet-swin-v1-tiny-aux5-768/fold-3/checkpoint/00008019.model.pth',\n","#         '../input/fold-3/result/upernet-swin-v1-tiny-aux5-768/fold-3/checkpoint/00008910.model.pth',\n","#         '../input/fold-3/result/upernet-swin-v1-tiny-aux5-768/fold-3/checkpoint/00009900.model.pth',\n","#         ],\n","# }\n","\n","\n","result = {\n","        'id': [],\n","        'probability': [],\n","        'rle': [],\n","    }\n","\n","start_timer = time.time()\n","\n","df = pd.read_csv(\"/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-organ-segmentation/train.csv\")\n","df = df[df['organ'] == 'lung']\n","\n","for t, d in df.iterrows():\n","\n","    fname = d['id']\n","    print(fname)\n","    image = tifffile.imread(f'/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-organ-segmentation/train_images/{fname}.tiff')\n","    image = image.astype(np.float32)/255\n","    \n","    H, W, _ = image.shape\n","\n","    s = d.pixel_size / 0.4 * (image_size / 3000)\n","    image = cv2.resize(image, dsize=(image_size,image_size), interpolation=cv2.INTER_LINEAR)\n","\n","    image = image_to_tensor(image)\n","    image = image.cuda()\n","    batch = {\n","        'image':\n","            torch.stack([\n","                image,\n","                torch.flip(image, [1]),\n","                torch.flip(image, [2]),\n","            ]),  # simple TTA\n","    }\n","    \n","    use = 0\n","    probability = 0\n","    with torch.no_grad():\n","        with amp.autocast(enabled = True):\n","            \n","            net = EffUnet(config.model).cuda()\n","            state_dict = torch.load('/home/lakshita/somusan/hubmap_kaggle/nbs/result/effnetb7/fold-3/checkpoint/00008500.model.pth',\n","                        map_location=torch.device('cpu'))\n","            net.load_state_dict(state_dict['state_dict'])\n","#             state_dict = swa\n","#             net.load_state_dict(state_dict, strict=False)  # True\n","\n","            net = net.eval()\n","#             net.output_type = ['inference']\n","            use += 1\n","\n","            output = net(batch)\n","            #print(output['probability'].shape)\n","            probability += F.interpolate(\n","                output, size=(H, W), mode='bilinear', align_corners=False, antialias=True)\n","\n","        #probability = probability / 4\n","        probability[0] = probability[0]   \n","        probability[1] = torch.flip(probability[1], [1])\n","        probability[2] = torch.flip(probability[2], [2])\n","        probability = probability.mean(0, keepdims=True)\n","        probability = probability[0,0].float()\n","        #probability = probability.float().data.cpu().numpy().mean(0)[0]\n","        \n","    probability = probability.data.cpu().numpy()\n","    #p = probability > organ_threshold[d.organ]\n","    p = probability > organ_threshold[d.data_source][d.organ]\n","    p = p.T\n","\n","    rle = rle_encode(p.T)\n","    \n","    result['rle'].append(rle)\n","    result['probability'].append(p.T)\n","    result['id'].append(fname)\n","    print('\\r', t, end='', flush=True)\n","    print('')\n","    break\n","submit_df = pd.DataFrame({'id': result['id'], 'rle': result['rle']})\n","print(submit_df)\n","print('submit_df ok!')\n","print('')"]},{"cell_type":"code","execution_count":38,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["[10488]\n"]},{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f77a591c320>"]},"execution_count":38,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["print(result['id'])\n","plt.imshow(result['probability'][0])"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f77a3946a90>"]},"execution_count":34,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.imshow(cv2.imread(\"/home/lakshita/somusan/hubmap_kaggle/hubmap_data/mask_png/train_binary_masks/10488.png\",0))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["(3000, 3000)"]},"execution_count":91,"metadata":{},"output_type":"execute_result"}],"source":["result['probability'][0].shape"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.imshow(rle_decode(result['rle'][0], result['probability'][0].shape))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["CONFIG = {\n","    \"in_channels\" :3,\n","    \"num_classes\": 1,\n","    \"BATCH_SIZE\" : 8,\n","    \"NUM_EPOCHS\" : 40,\n","    \"n_accumulate\": 1,\n","    \"competition\": \"HuBMAP-Kaggle\", # HuBMAP-Kaggle\n","    \"model_name\": \"Vanilla_Unet\",\n","    \"LEARNING_RATE\": 1e-4,\n","    \"DEVICE\": \"cuda\" if torch.cuda.is_available() else \"cpu\", \n","    \"AUG\": \"No\",\n","    \"SEED\": 42,\n","    \"opt\": 'Adam',\n","    \"Normalization\": \"L2\",\n","    \"img_size\": 256\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/html":["Finishing last run (ID:2zubmuiu) before initializing another..."],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Synced <strong style=\"color:#cdcd00\">Vanilla_Unet_1</strong>: <a href=\"https://wandb.ai/somusan/HuBMAP-Kaggle/runs/2zubmuiu\" target=\"_blank\">https://wandb.ai/somusan/HuBMAP-Kaggle/runs/2zubmuiu</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Find logs at: <code>./wandb/run-20220819_161945-2zubmuiu/logs</code>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Successfully finished last run (ID:2zubmuiu). Initializing new run:<br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["wandb version 0.13.1 is available!  To upgrade, please run:\n"," $ pip install wandb --upgrade"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Tracking run with wandb version 0.13.0"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Run data is saved locally in <code>/home/lakshita/somusan/hubmap_kaggle/nbs/wandb/run-20220819_162202-19bbi4xt</code>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Syncing run <strong><a href=\"https://wandb.ai/somusan/HuBMAP-Kaggle/runs/19bbi4xt\" target=\"_blank\">model_load_1</a></strong> to <a href=\"https://wandb.ai/somusan/HuBMAP-Kaggle\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"}],"source":["import wandb\n","\n","# try:\n","#     from kaggle_secrets import UserSecretsClient\n","#     user_secrets = UserSecretsClient()\n","#     api_key = user_secrets.get_secret(\"WANDB\")\n","#     wandb.login(key=api_key)\n","#     anonymous = None\n","# except:\n","#     anonymous = \"must\"\n","#     print('To use your W&B account,\\nGo to Add-ons -> Secrets and provide your W&B access token. Use the Label name as WANDB. \\nGet your W&B access token from here: https://wandb.ai/authorize')\n","# wandb.init(project=\"PogChamp2 Baseline\")\n","run = wandb.init(project=CONFIG['competition'], \n","                 config=CONFIG,\n","                 job_type='Train',\n","                 tags=['semantic segmentation', CONFIG['model_name']],\n","                 anonymous='must',\n","                 name = \"model_load_1\",\n","                 notes = \"\")"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Saving files without folders. If you want to preserve sub directories pass base_path to wandb.save, i.e. wandb.save(\"/mnt/folder/file.h5\", base_path=\"/mnt\")\n"]},{"data":{"text/plain":["['/home/lakshita/somusan/hubmap_kaggle/nbs/wandb/run-20220819_162202-19bbi4xt/files/00008415.model.pth']"]},"execution_count":99,"metadata":{},"output_type":"execute_result"}],"source":["wandb.save('/home/lakshita/somusan/hubmap_kaggle/nbs/result/effnetb7/fold-3/checkpoint/00008415.model.pth')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["best_model = wandb.restore('00008415.model.pth')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["/home/lakshita/somusan/hubmap_kaggle/nbs\n"]}],"source":["!pwd"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["total 714344\n","drwxrwxr-x 8 lakshita lakshita      4096 Aug 19 16:19  .\n","drwxrwxr-x 7 lakshita lakshita      4096 Aug 18 22:54  ..\n","-rw-rw-r-- 1 lakshita lakshita 721737883 Aug 19 00:00 'archive (1).zip'\n","-rw-rw-r-- 1 lakshita lakshita   1322432 Aug 18 23:59  archive.zip\n","drwxrwxr-x 2 lakshita lakshita      4096 Aug 19 00:16  checkpoint\n","drwxrwxr-x 3 lakshita lakshita      4096 Aug 19 00:00  efficientnet-pytorch\n","-rw-rw-r-- 1 lakshita lakshita    530766 Aug 15 20:18  effnetb0_unet_org_data.ipynb\n","-rw-rw-r-- 1 lakshita lakshita     60477 Aug 19 12:34  effnetb7-fold3.ipynb\n","-rw-rw-r-- 1 lakshita lakshita      2316 Aug 19 00:12  effnetb7-train.ipynb\n","-rw-rw-r-- 1 lakshita lakshita    417346 Aug 19 16:26  effnetb7-unet-infer.ipynb\n","-rw-rw-r-- 1 lakshita lakshita     48542 Aug 19 15:06  hubmap-effnet-b7-unet-train.ipynb\n","-rw-rw-r-- 1 lakshita lakshita    766565 Aug 15 19:49  hubmap-effnetunet-tiled-data-no-schd-train.ipynb\n","-rw-rw-r-- 1 lakshita lakshita    432226 Jul 31 11:16  hubmap-effnetunet-tiled-data-train.ipynb\n","-rw-rw-r-- 1 lakshita lakshita    426269 Aug  5 15:04  hubmap-effnetunet-tiled-data-warmup-train.ipynb\n","-rw-rw-r-- 1 lakshita lakshita   4330560 Aug 14 14:59  hubmap-multiorgan-segmentation-1-3-data-prep.ipynb\n","-rw-rw-r-- 1 lakshita lakshita    921813 Aug 11 11:49  hubmap-tiled-data-generation.ipynb\n","-rw-rw-r-- 1 lakshita lakshita     29468 Jul 31 13:54  hubmap-unet-semantic-approach-infer.ipynb\n","-rw-rw-r-- 1 lakshita lakshita    357182 Aug 19 00:09  hubmap-vanilla-unet-w-b-pytorch-2-3-train.ipynb\n","drwxrwxr-x 2 lakshita lakshita      4096 Aug 19 12:31  .ipynb_checkpoints\n","drwxrwxr-x 3 lakshita lakshita      4096 Aug 19 00:19  result\n","drwxrwxr-x 3 lakshita lakshita      4096 Aug 19 00:00  timm-pytorch-image-models\n","-rw-rw-r-- 1 lakshita lakshita     34064 Aug 14 20:42  training_summary.csv\n","drwxrwxr-x 4 lakshita lakshita      4096 Aug 19 16:22  wandb\n"]}],"source":["!ls -la"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["[10044, 10274, 10392, 10488, 10610, 10611, 10651, 10666, 10703, 10892]"]},"execution_count":83,"metadata":{},"output_type":"execute_result"}],"source":["result['id']"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["(2023, 2023)\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAARIAAAD8CAYAAAC7DitlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUeklEQVR4nO3df5BV5X3H8fdnF1gUVJag1CgqmNWIJkFlhLbG2pgo0kzQdGqxTqTGBK2Y0U5+FJNMNTHpJEZjQ0zMYEOiqYXaJhqaahSJbZIZUfwVFBT5IUYRQSCAEQR2+faP89xyB9ns7n3u7r13/bxmdu65zz333OeZ3f3MOc+553wVEZiZ5WiqdQfMrPE5SMwsm4PEzLI5SMwsm4PEzLI5SMwsW58HiaRJkpZLWilpZl9/vplVn/ryeySSmoHngQ8BLwOLgQsjYlmfdcLMqq6v90hOA1ZGxOqI2AXMA6b0cR/MrMoG9PHnHQG8VPb8ZWDCvitJmg5MB2im+dQDObhvemf2NvQmb7ArdipnG30dJN0SEbOB2QAHa3hM0Fk17pFZ//VILMzeRl8f2qwFRpU9PzK1mVkD6+sgWQy0SRotaRAwFZjfx30wsyrr00ObiGiXdCVwP9AMzImIpX3ZBzOrvj6fI4mIe4F7+/pzzaz3+JutZpbNQWJm2RwkZpbNQWJm2RwkZpbNQWJm2RwkZpbNQWJm2RwkZpbNQWJm2RwkZpbNQWJm2RwkZpbNQWJm2RwkZpbNQWJm2RwkZpbNQWJm2SoOEkmjJD0kaZmkpZKuSu3XSVor6an0M7nsPdekUp3LJZ1TjQGYWe3l3LO1Hfh0RDwh6SDgcUkL0ms3R8SN5StLGktx1/gTgXcCD0o6LiI6MvpgZnWg4j2SiFgXEU+k5deBZykq6XVmCjAvInZGxAvASooSnmbW4KoyRyLpGOBk4JHUdKWkJZLmSGpNbfsr17nf4JE0XdJjkh7bzc5qdNHMelF2kEgaCvwYuDoitgG3AscC44B1wE093WZEzI6I8RExfiAtuV00s16WFSSSBlKEyJ0R8ROAiFgfER0RsQe4jb2HLy7XadZP5Zy1EfB94NmI+GZZ++Flq50PPJOW5wNTJbVIGg20AY9W+vlmVj9yztr8KfAx4GlJT6W2zwMXShoHBLAGuAwgIpZKugtYRnHGZ4bP2Jj1D4qIWvfhDzpYw2OCzqp1N8z6rUdiIdtis3K24W+2mlk2B4mZZXOQmFk2B4mZZXOQmFk2B4mZZXOQmFk2B4mZZXOQmFk2B4mZZXOQmFk2B4mZZXOQmFk2B4mZZXOQmFk2B4mZZXOQmFk2B4mZZatGOYo1kp5O5TkfS23DJS2QtCI9tqZ2SZqVynYukXRK7uebWe1Va4/kzyNiXESMT89nAgsjog1YmJ4DnEtx9/g2YDpFDRwza3C9dWgzBbg9Ld8OnFfWfkcUFgHD9ilfYWYNqBpBEsADkh6XND21jYyIdWn5VWBkWu5W2U6X7DRrLDl1bUpOj4i1kg4DFkh6rvzFiAhJPap5ERGzgdlQlKOoQh/NrBdl75FExNr0uAG4m6JE5/rSIUt63JBWd9lOs34ot/bvEEkHlZaBsylKdM4HpqXVpgE/TcvzgYvT2ZuJwNayQyAza1C5hzYjgbuLMsAMAP4tIn4uaTFwl6RLgReBC9L69wKTgZXAduCSzM83szqQFSQRsRp4337aNwFvqbMZRX3QGTmfaWb1x99sNbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy1ZxkEg6PpXpLP1sk3S1pOskrS1rn1z2nmtSuc7lks6pzhDMrNYqvmdrRCwHxgFIaqYoK3E3xQ2db46IG8vXlzQWmAqcCLwTeFDScRHRUWkfzKw+VOvQ5ixgVUS8+AfWmQLMi4idEfECxZ3kT6vS55tZDVUrSKYCc8ueXylpiaQ5klpTW7fKdYJLdpo1muwgkTQI+AjwH6npVuBYisOedcBNPd1mRMyOiPERMX4gLbldNLNeVo09knOBJyJiPUBErI+IjojYA9zG3sMXl+s066eqESQXUnZYU6r5m5xPUcITinKdUyW1SBoNtAGPVuHzzazGsirtpXq/HwIuK2u+QdI4IIA1pdciYqmku4BlQDsww2dszPoHFVU069fBGh4T9Jbqn2ZWJY/EQrbFZuVsw99sNbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy9atIEn1aTZIeqasbbikBZJWpMfW1C5Js1JpziWSTil7z7S0/gpJ06o/HDOrhe7ukfwQmLRP20xgYUS0AQvTcyjKU7Sln+kUdW6QNBy4FphAUaLi2rLiWWbWwLoVJBHxS2DzPs1TgNvT8u3AeWXtd0RhETAslag4B1gQEZsj4nfAAt4aTmbWgHLKUYyMiHVp+VVgZFrurDRnj0p2UuzNMJgDM7poZn2hKpOtUdS0qFpdC5fsNGssOUGyvlRVLz1uSO2dleZ0yU6zfionSOYDpTMv04CflrVfnM7eTAS2pkOg+4GzJbWmSdazU5uZNbhuzZFImgucCYyQ9DLF2ZevAXdJuhR4EbggrX4vMBlYCWwHLgGIiM2SrgcWp/W+HBH7TuCaWQNyyU6ztzmX7DSzuuAgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsDhIzy+YgMbNsXQZJJ+U6vyHpuVSS825Jw1L7MZJ2SHoq/Xyv7D2nSno6lfKcJSnr1m5mVj+6s0fyQ95aEW8BcFJEvBd4Hrim7LVVETEu/Vxe1n4r8En2lvN0lT2zfqLLINlfuc6IeCAi2tPTRRQ1ajqV6t4cHBGLUjGtO9hb4tPMGlw15kg+DtxX9ny0pCcl/a+k96e2IyhKdJZ0Wq4TipKdkh6T9Nhudlahi2bWm3Jq/yLpC0A7cGdqWgccFRGbJJ0K3CPpxJ5uNyJmA7OhKEeR00cz630VB4mkvwU+DJyVDleIiJ1Q7EJExOOSVgHHUZTmLD/8cblOs36kokMbSZOAzwEfiYjtZe2HSmpOy2MoJlVXp5Kd2yRNTGdrLmZviU8za3Bd7pF0Uq7zGqAFWJDO4i5KZ2jOAL4saTewB7i8rCznFRRngA6gmFMpn1cxswbmkp1mb3Mu2WlmdcFBYmbZHCRmls1BYmbZHCRmls1BYmbZHCRmls1BYmbZHCRmls1BYmbZHCRmls1BYmbZHCRmls1BYmbZHCRmls1BYmbZHCRmlq3SSnvXSVpbVlFvctlr16RqesslnVPWPim1rZQ0s/pDMbNaqbTSHsDNZRX17gWQNBaYCpyY3vNdSc3phtDfAc4FxgIXpnXNrB/o8ubPEfFLScd0c3tTgHmpLMULklYCp6XXVkbEagBJ89K6y3reZTOrNzlzJFemIuJzJLWmtiOAl8rWKVXU66zdzPqBSoPkVuBYYBxFdb2bqtUhcMlOs0ZTUZBExPqI6IiIPcBt7D18WQuMKlu1VFGvs/bOtj87IsZHxPiBtFTSRTPrQ5VW2ju87On5QOmMznxgqqQWSaMpKu09CiwG2iSNljSIYkJ2fuXdNrN6UmmlvTMljQMCWANcBhARSyXdRTGJ2g7MiIiOtJ0rgfuBZmBORCyt9mDMrDZcac/sbc6V9sysLjhIzCybg8TMsjlIzCybg8SsHjU1owEDQJ3MgTY1921/utDl6V8z610DRh/Ns3//Rwx8vYlBW8T29+1gwug1DBu0g1+8cAKDf30Qu4dC85sw4pldbDhlEO86dxVLF43huFteov2ll2s9BAeJWS1t/+gEpn7lXv572Ev7X+GIRXD63qe/3/MmB2gQzWqCNjip7SKOvOBVor29bzrcCR/amNVI88jD+Mvr72dGZyGyH0ObBhchkvzjST+jeeRhvdG9HnGQmNXI9lOP5hOHPFfx+5/dtZ2bvvI3tK99pYq9qowPbcz6kAYMoPmoI4nBg1h/yZsMbRpc0XYe3bmb6Td/hpE/erjKPayMg8Ssj+w6Zzw7rtrCrBPmMqxpF6MHDKa49KxnFu5o5quXXcrIhQ9DnVzi4iAx621NzWy56DS+9aVbmDi4GRiYfnpud3Rwxdy/45gH62NPpMRBYtaLmoYMYdUX38uDF32DowYMzdpWR+zh5Ecu5tgbl9FRpf5Vi4PErBeopYXXp5zM4Z9aydNjZtGi/BA5fclfcfQVG2nfsrVKvaweB4lZlTWPPIxnvzaKJz74TVqbD6TSw5hyn3jpz2j92BbaN27K72AvcJCYVYFaWtj60ZN59QMdXPHHv+C/Wn9Osw7M3m5H7OHbW8bw8mffRdPGJ6vQ097hIDHL0DR4MHvGHccrM3fzq/E3c0jTAaVXKt7m7ujgt+07+J/t7+Lr95xP2/depunF+g0RcJCY9VjzCW288sFD2XpCO1MmPMGnRnyXYwcOBQ7o8r1deX73G5w3+7Mcc88mWL+R0RsfprZffu+e7tyzdQ7wYWBDRJyU2v4dOD6tMgzYEhHjUiGtZ4Hl6bVFEXF5es+pFFX7DgDuBa6Ker/Po9k+4k/ex0U/+BkXHVQ+V5E3kVqydNcOPvb1zzDqew/T0WD/Gt3ZI/khcAtwR6khIv66tCzpJqB8GnlVRIzbz3ZuBT4JPEIRJJOA+3rcY7MaaRoyhPbrN+8TIpXZGbt5aMdQ/mXd+3lhy3A2bzyIMf8Kh/5iUd18yawnskp2ShJwAfCBP7SNVL7i4IhYlJ7fAZyHg8QaRNPgwTz/lfew7N3fJucszOM7d/HpFRfwxtzDOeyBF+lYv4ER7RsZUb2u1kTuHMn7gfURsaKsbbSkJ4FtwBcj4lcU5TnLb5rgkp3WEJqGDOG1C99L64Uv85vj/5kWVXZtzLzXW7n2P6dy7J2bGPz8alra1zTE3Ed35QbJhcDcsufrgKMiYlOaE7lH0ok93aik6cB0gMHkn0Izq9TyG07imSnf4sCmQUDPQ2Tprh18+OdXMfaGDRyz+uG6+0ZqtVQcJJIGAB8FTi21RcROKIr1RsTjklYBx1GU5zyy7O1dluwEZkNR16bSPpplaxct6v6/ye86trOifSAPvP4e5jx0Jm13vsFxixfT3oDzHj2Rs0fyQeC5iPj/QxZJhwKbI6JD0hiKkp2rI2KzpG2SJlJMtl4MfDun42Z94d3/tJobP3A8//CO4uj9t+2/50uvTOLRdUcxatgWVm0YQfu6A4kBwcCtTRzx0C4OWPkae17bRNsbi2rc+75TUcnOiPg+Rf3eufusfgbwZUm7gT3A5RGxOb12BXtP/96HJ1qtAXS8toknt42iY/hyJj03hbj+UAY8vJR37lxGh8Qx8dabCvWnuY/ucslOsy5svWgir42Hd9+whvZ1r9a6O1VXjZKd/marWRcOuXMRh9z59tzT6C7fs9XMsjlIzCybg8TMsjlIzCybg8TMsjlIzCybg8TMsjlIzCybg8TMsjlIzCybg8TMsjlIzCybg8TMsjlIzCybg8TMsjlIzCybg8TMsnUZJJJGSXpI0jJJSyVdldqHS1ogaUV6bE3tkjRL0kpJSySdUrataWn9FZKm9d6wzKwvdWePpB34dESMBSYCMySNBWYCCyOiDViYngOcS3H3+DaK2jS3QhE8wLXABOA04NpS+JhZY+sySCJiXUQ8kZZfpygSfgQwBbg9rXY7RQlOUvsdUVgEDEslO88BFkTE5oj4HbCAov6vmTW4Ht38OdUAPpmiNs3IiFiXXnoVGJmWjwBeKntbqTxnZ+37+xxX2jNrIN2ebJU0FPgxcHVEbCt/LYqaFlWraxERsyNifESMH0hLtTZrZr2kW0EiaSBFiNwZET9JzevTIQvpcUNqXwuMKnt7qTxnZ+1m1uC6LJAlSRRzIJsj4uqy9m8AmyLia5JmAsMj4nOS/gK4EphMMbE6KyJOS5OtjwOlszhPAKeWVeLr7PNfB5ZXNLr6NQLYWOtO9AKPq7GUxnV0RByas6HuBMnpwK+ApynKcAJ8nmKe5C7gKOBF4IJU41fALRQTqduBSyLisbStj6f3Anw1In7QZQelxyJifE8HVs/645jA42o01RxXl5OtEfFroLNyfm+ppZnmS2Z0sq05wJyedNDM6p+/2Wpm2RohSGbXugO9oD+OCTyuRlO1cXU5R2Jm1pVG2CMxszrnIDGzbHUbJJImSVqeriKe2fU76oukNZKelvSUpNLp7x5fMV1rkuZI2iDpmbK2hr7yu5MxXSdpbfp9PSVpctlr16QxLZd0Tll7Xf2N1vRK/Yioux+gGVgFjAEGAb8Bxta6Xz0cwxpgxD5tNwAz0/JM4OtpeTJwH8Vp9onAI7Xuf1mfz6D4EuEzlY4DGA6sTo+tabm1zsZ0HfCZ/aw7Nv39tQCj099lcz3+jQKHA6ek5YOA51P/e/33Va97JKcBKyNidUTsAuZRXFXc6Hp6xXTNRcQvgX2/fdzQV353MqbOTAHmRcTOiHgBWEnx91l3f6NRwyv16zVIun2lcB0L4AFJj6ermaHnV0zXq1678rvGrky7+HPK7pXTkGPqqyv1S+o1SPqD0yPiFIobPc2QdEb5i1HsQzb8uff+Mg6KG3AdC4wD1gE31bQ3GfrySv2Seg2Shr9SOCLWpscNwN0Uu8I9vWK6XvW7K78jYn1EdETEHuA2it8XNNiYanWlfr0GyWKgTdJoSYOAqcD8Gvep2yQNkXRQaRk4G3iGYgylGfBpwE/T8nzg4jSLPhHYWrYrWo96Oo77gbMltaZDhrNTW93YZ07qfIrfFxRjmiqpRdJoiluIPkod/o1KEvB94NmI+GbZS73/+6rlLHMXM9CTKWadVwFfqHV/etj3MRSz+L8Blpb6D7yD4v62K4AHKW69AMWs+XfSWJ8Gxtd6DGVjmUuxq7+b4lj50krGAXycYqJyJcUV4fU2ph+lPi9J/2CHl63/hTSm5cC59fo3CpxOcdiyBHgq/Uzui9+XvyJvZtnq9dDGzBqIg8TMsjlIzCybg8TMsjlIzCybg8TMsjlIzCzb/wHc3KivQvpAeQAAAABJRU5ErkJggg==","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# plt.subplot(1,2,1)\n","plt.imshow(result['probability'][0])\n","\n","# plt.subplot(1,2,2)\n","# plt.imshow(result['probability'][0].T)\n","\n","print(result['probability'][0].shape)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fdb51b66ef0>"]},"execution_count":53,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.imshow(plt.imread(\"/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-organ-segmentation/test_images/10078.tiff\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:35.529820Z","iopub.status.busy":"2022-08-17T13:10:35.529425Z","iopub.status.idle":"2022-08-17T13:10:35.545460Z","shell.execute_reply":"2022-08-17T13:10:35.543153Z","shell.execute_reply.started":"2022-08-17T13:10:35.529779Z"},"papermill":{"duration":0.016838,"end_time":"2022-08-17T06:03:28.817009","exception":false,"start_time":"2022-08-17T06:03:28.800171","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["# import matplotlib.pyplot as plt\n","# plt.imshow(p)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["before IMAGE SIZE (3000, 3000, 3)\n","AFTER IMAGE SIZE (256, 256, 3)\n"," 0\n","      id  \\\n","0  10044   \n","\n","                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       rle  \n","0  1555737 12 1558724 30 1561719 38 1564715 46 1567711 53 1570708 60 1573704 66 1576702 70 1579699 75 1582696 80 1585693 85 1588691 88 1591688 93 1594686 96 1597684 99 1600681 104 1603679 107 1606677 110 1609675 113 1612673 116 1615671 119 1618669 122 1621667 125 1624665 128 1627663 131 1630661 134 1633659 137 1636657 140 1639655 143 1642653 146 1645651 149 1648649 151 1651647 154 1654644 158 1657642 160 1660640 163 1663637 166 1666635 169 1669633 171 1672630 175 1675628 177 1678626 179 1681624 182 1684622 184 1687620 187 1690618 189 1693616 191 1696614 194 1699612 196 1702610 199 1705608 201...  \n","submit_df ok!\n","\n"]}],"source":["all_df = []\n","\n","\n","result = {\n","        'id': [],\n","        'probability': [],\n","        'rle': [],\n","    }\n","\n","start_timer = time.time()\n","for t, d in df.iterrows():\n","    fname = d['id']\n","    image = tifffile.imread(f'/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-organ-segmentation/train_images/{fname}.tiff')\n","    image = image.astype(np.float32)/255\n","\n","\n","    H, W, _ = image.shape\n","    print(\"before IMAGE SIZE\", image.shape)\n","\n","    s = d.pixel_size / 0.4 * (image_size / 3000)\n","    image = cv2.resize(image, dsize=(image_size,image_size), interpolation=cv2.INTER_LINEAR)\n","    print(\"AFTER IMAGE SIZE\", image.shape)\n","\n","    image = image_to_tensor(image)\n","    image = image.cuda()\n","    batch = {\n","        'image':\n","            torch.stack([\n","                image,\n","                torch.flip(image, [1]),\n","                torch.flip(image, [2]),\n","            ]),  # simple TTA\n","    }\n","    \n","    use = 0\n","    probability = 0\n","    with torch.no_grad():\n","        with amp.autocast(enabled = True):\n","            \n","            net = EffUnet(config.model).cuda()\n","            state_dict = torch.load('/home/lakshita/somusan/hubmap_kaggle/nbs/result/effnetb7/fold-3/checkpoint/00008910.model.pth',\n","                        map_location=torch.device('cpu'))\n","            net.load_state_dict(state_dict['state_dict'])\n","\n","            net = net.eval()\n","\n","            use += 1\n","\n","            output = net(batch)\n","\n","            probability += F.interpolate(\n","                output, size=(H, W), mode='bilinear', align_corners=False, antialias=True)\n","\n","\n","        probability[0] = probability[0]   \n","        probability[1] = torch.flip(probability[1], [1])\n","        probability[2] = torch.flip(probability[2], [2])\n","        probability = probability.mean(0, keepdims=True)\n","        probability = probability[0,0].float()\n","\n","        \n","    probability = probability.data.cpu().numpy()\n","\n","    p = probability > organ_threshold[d.data_source][d.organ]\n","    p = p.T\n","\n","    rle = rle_encode(p)\n","    \n","    result['rle'].append(rle)\n","    result['probability'].append(p)\n","    result['id'].append(fname)\n","    print('\\r', t, end='', flush=True)\n","    print('')\n","\n","    break\n","submit_df = pd.DataFrame({'id': result['id'], 'rle': result['rle']})\n","print(submit_df)\n","print('submit_df ok!')\n","print('')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["[10044]"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["result['id']"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fdb51ba9a58>"]},"execution_count":65,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQgAAAD8CAYAAACLgjpEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAtnklEQVR4nO3dd3hUVfrA8e87JZUAoYUAAUIvoggI2FFWQCzYxbVg+cm6gmtlF92irq7rqoiiiIuKi2tBFxsqgqDLqvQiIkVCIJRgqAEkfcr5/TEXDJBJJsmdQng/zzNPZs7cufedmeTNueeeIsYYlFKqIo5oB6CUil2aIJRSQWmCUEoFpQlCKRWUJgilVFCaIJRSQUU8QYjIEBFZLyLZIjI20sdXSoVOItkPQkScQBZwAZALLAWuM8asjVgQSqmQRboG0RfINsZsMsaUAdOAYRGOQSkVIleEj9cS2FbucS7Qr/wGIjISGAngxNk7ifqRi06pE9BB9u0xxjSt6LlIJ4gqGWMmA5MB6ksj008GRjkipeq2uWb6lmDPRfoUYzuQUe5xK6tMKRWDIp0glgIdRSRTROKA4cCMCMeglApRRE8xjDFeERkNzAacwBRjzJpIxqCUCl3E2yCMMTOBmZE+rlKq+rQnpVIqKE0QSqmgNEEopYLSBKGUCkoThFIqKE0QSqmgNEEopYLSBKGUCkoThFIqKE0QSqmgNEEopYLSBKGUCkoThFIqKE0QSqmgNEEopYLSBKGUCirmJq1VxxdxuUAC/2eMpyzK0Si7aYJQNeKsX5+dw7uTeeMG2iTl48PBp+t70mxGAg1nrcO3/0C0Q1Q20AShqs1/Vk9aPLORDzMmEC/uw+XPpS9j39lF/OfhDjy35nySZ6bQdEYWvj17oxitqo2ILr1XXbouRuzxDuzN3S+/w6XJRVVuW+Qv47TFt5Bx7XqM1xsoFMGZkoLv55/DHKkK1Vwzfbkxpk9Fz2kNQoXMmZrKyU+vCCk5ACQ54pje+xWu+v0DOEvBVWRourwA19O7Wb+1Pe0n+XEsWfNL8lAxRxOECln+0M48nvYC4K5y20O6xiWxatSLOK2GzC+LnXR2H6BVx3osP6uMhzZdgfN3yfjWrA9T1Ko29DKnCtneHnJEm0OoDiUHgIGJPlq56gHQOz6O2V0/ZXe/RoefF5crcGVExQRNECp0Yf5t2f6HM2j5bQItv01g49s92Xfz6ThSUsJ7UFUpTdUqJji7deLFkS8zINEfKGj9LUXnlHHDrRdx4JFOuOatBL8vqjGeiLQGoaLKZ/w4vPDjHam/JAdLkiOODzrM4dXXnyfnb31xJCREKcoTlyYIFVUbvcU0WnWAgf1WB90m012PlTc+z9a32uPKaBXajh3OQFuGw2lTpCemWiUIEdksIj+IyEoRWWaVNRKROSKywfqZapWLiEwQkWwRWSUivex4A+r4VmKc4PfTO2VzpdslOeJYc/pbnDQjF995QX51HE685/dmw9ReNP02hTYL3DT+pj4bpvYi66W+HBzeH2f9+va/iTrMjjaI84wxe8o9Hgt8aYx5UkTGWo//AFwIdLRu/YBJ1k91AvumqCOOfQU4JLQOe/9IW8nyKUu49v3f0fmF7Xi3bAMCXb9z7j2JT299ivbueke+KPO/ABRdWsZd95zP96+dTrPpP+Lbt8/W91IXheMUYxgw1bo/FbisXPkbJmAR0FBE0sNwfHUc+V9+J0xBYbVec1Kc8OYVE/FOMWwfewbZ4/tz2jd7WTny+WOTQzlJjjhea/0tCx95kaQZTuTU7rUNv86rbQ3CAF+IiAH+aYyZDKQZY/Ks53cAadb9lsC2cq/NtcryUCesMp8LTEm1XhMvbnrH+/i0y8e4u5ZvYwitj4ZbnExvP5fLnhtM2RWN8O3Nr9bxTyS1rUGcZYzpReD0YZSInFP+SRMY6FGtwR4iMlJElonIMg+ltQxPxbpVq9qCv/rjgdzixC21a4B8v8PnxH3gxtmpfa32U5fVKkEYY7ZbP3cBHwJ9gZ2HTh2sn7uszbcDGeVe3soqO3qfk40xfYwxfdzE1yY8dRyIy3cSrQGDTnHwUcfZrHuwofbeDKLGCUJEkkUk5dB9YBCwGpgBjLA2GwF8bN2fAdxkXc3oDxwodyqiTlDir3qbcFv8qwnsufm0aIcRk2pTg0gDvhWR74ElwGfGmFnAk8AFIrIB+JX1GGAmsAnIBl4B7qzFsVUd4DN+UrYEag8eE73+Cs2cyTzz4D8pvqxv1GKIVTVOEMaYTcaYU6xbd2PM36zyvcaYgcaYjsaYXxlj8q1yY4wZZYxpb4zpYYxZZtebUJFhxN79FZhSGuSU4C8oYNyci+zdeTUNSPTz8LjX8A7sHdU4Yo32pFQhkfh4OvXZYus+/caAz4AxdJ5ygEUl0R1rMTDRh+8Pe7X3ZTmaIFRISn51Mm90+I+t+yw0fpzFHgDM2o1M2xf9fnOTO7+FOb1HtMOIGZogVEj2dnfTxJls6z7nFrXDsTHX1n3WVid3MhtujIt2GDFDE4SqmgiuM+3vTJRT2hRTEnt9XW46fT6OpKRohxETNEGoKknPbrx68hu273faj73xl1SvF2UkXNlgOZLRItphxARNEKpKOVfWp3e8/dVuT0lsdk7q7o5jx3lNox1GTNAEoSol7jjOuWBVeHZu93VTmzjFQWHLaEcRGzRBqEo5mzfj5qbfhGXf8Ztityt9er88xK2NlZogVHAiZI/M4NQ4+9et8Bk/DTbGQD/rICZ0moajbYizV9VhmiBUUM6GDfnj1f8hyWH/f9ICU0rCvtidhLaDy0FBtybRDiPqNEGooPIv6swV9cLTT+G70mSSvz9mMG/MSHLEUdREe1RqglAVcqU3Z/iDs6jnCM9M0s/mDsK3a0/VG6qo0gShKnTgjDaMbJAVln3PK3ZQdl9jjKcsLPtX9tEEoSpU2NwZlraHecUOHnjyN5jla2zft7JfbPZUUXXOjMIk7pl1I52mFND4u4UVblPsq/66nyq8tAahKiQ1mCfyaKXGwxs/N6H9V7cw6aphdLxrMea7imsOxlPGtx+eisfYe2XjiT2dmVUUu/0tYp0mCFWh9Jm5tH/vDm7YPIA9vupNS1/gL+GyDYPp/8TdTDvrFDrcuBL/9+uqfF2bVzfwxB57h1q/+Z+BPH/Vldyfp+s01YSeYpzoRHA2qI8kJOBtm0ZRi8RAsd/Q+bEs9hQUc+UF95I70IEjreqBVX6/kPq/BJpOW02zgwuoTn3At3s3s548h1FPLbVvaLkB/8q1rP5tDx5+pZRHm2rbR3VogjiBiMuFo10bTIKbsibJbB0UR1rPndzQejHt4nbRLW4fac5AgvDj5+R//Y62f1rE1qHwz0GvhXycg30TmXrDGfywqRMJW+Jp8r2PlAU5mKJiTHExxhu8Z2aD6Ss47bx7WDl0Ag0cibV+z4ct+YGlV3biqtfTmd5+rn37reM0QZxgSlo3pOi+A8zoMYFmx/yXLr8qlRNfZjGOevW485wvGZTkqcZRPFzZcVZgkUWgyF/GewWteHThJSRlxdP6+ZX4i4oqfKXxlNF51Pf0f/B+Pr7taTq5a16T+KwogeZLfrmU6svOofiGDG54ZwBvtp1X5etDXA2wTtM2iBOExMfjaNeG7QPieKrL9AqSw7H8ZU4krQmD69WuWp7kiOPm+rvIGfwan97xFDtu7gkSfCSn8ZTR+rGFjLzjHobnnM8Bf3G1jucxPq7ZNJAXrr0S9xdHzo3s3bKNXWPaMnjdxVyRfQG3bD2bXG/BMfs44C8mZav209AaRF0ngueC3uy5s5B3e75GO7ebeKn6cmKBv4S0L9zkDkunq9u+y4+Z7nq8+ftxXNPwfjKeWhL8dMMY4j9fyoH/JTH04nvJO9tgnFX/S3cVOGm0Ghp/8iNmX8WJTeavhF8JhUCR08nV1zzAFQ/NYUyjjYe3We9xkbhxD/YPUzu+SLRWNQpFfWlk+snAaIdxfBLB0aMzWWMS+fDsSZwcF3qX6RxPAYPeHUOn8TmcMvMnnkizfz6IPb5Czn5lDK0fWwgx8DvoapNB7mUZ+KyPKWGPocnUpZW2l9QVc8305caYPhU9pwmiDhJ3HHl39mHqPePpGV+9PgAbPQVc/eQYmr68iF2/PZ1Ff3w+pBpHTSwvLePPg6/Dl7Wx8g1FcDZpAqn1MYlxSGEJ7MnHt/9AWOI60VSWIPQUo45xNU9j7eMZLB38dLUvFeZ5C7jsxd/T4uWFOBs2ZNDtC8KWHADaubx4m6YgQYZ8iDsOc2pn8s5KocXFW9j8TRquIkhfVIw7Pg5n2rHTwklRCb4du3Sch000QdQR4o5j+z19GPt/73JNvZm4pXrJwWf8nPvmGDLHLQZj2H5TVz5s9jwQvgQxu6gl7ty9x5znO+vXZ/+F3Si9IZ+PT5lEujMJpzigS+D5raMKKAkyXd0OXzIv5Z3PskWd6DRpJ77snLDFfyLQBFEXiJDzSG+WjBhn9R2o/jwGD+3qRccJm/D6fbhatuCG22eHtfbw2oHmTB17KYlblgDgSEmh+OwubB3i4OIzVvBk8wnWYLF6x7y2tevYskM6ueGczK8g8yse/VU3llzcDu+22Fp743iiCeI458poRdZdGcy69mkaOIL/4VRlxodn0HrHAgA2j2jLfamfEo6r4D7jZ9iGi/CPTCIxawmujFbsHJzBeXcu4qGmz5PqPLQeRe1Hko5t8j3nnXEOKe9qgqipKn8DRGSKiOwSkdXlyhqJyBwR2WD9TLXKRUQmiEi2iKwSkV7lXjPC2n6DiIwIz9s5gYhw4Pr+XDBrNRtumER7d82TwwF/MS3mBxawcWW04o7rPwtU6W3mM36u3TQI/9VefFkbMWecwsDP17Lw0Rd5uvl35ZKDPeLFzf6O2tWnNkL59P4FDDmqbCzwpTGmI/Cl9RjgQgL95zoCI4FJEEgowMNAP6Av8PChpKKqz9khk5wn+vPGE+O4J3VzrffnNwZncaAlYPvlrbmzof3n7VmeQjq9eyfFVzvx7d6N/9xTueq1OdzXaBNu0andYlWVpxjGmK9FpO1RxcOAAdb9qcA84A9W+RsmcO10kYg0FJF0a9s5xph8ABGZQyDpvFP7t3BicTZsAK+UktV5EmDvWpnictHw4p9srz08nd+ejx79FR2mL8ZrDM5O7bl80heMbPCTrcdR9qtpG0SaMSbPur8DSLPutwS2ldsu1yoLVn4MERlJoPZBAro+YnnO+vXJfrkNazq9Tk0aIqvcf/M0xrb73NZ9Pr6nCwsv7US9zYuBQJfvLX9P4I6GkZmw1lH3+zmFVa0bKY0xRsS+YS3GmMnAZAh0lLJrv8c7x8ld2P2EnzWnvh62Kvme81ozMLEIO5KPx/i4ZP2lMKYhZvMvXZ5/Gt2bxf2eBcIzGW55+3xFNF8ce2t/Hk9qmiB2iki6MSbPOoXYZZVvBzLKbdfKKtvOL6ckh8rn1fDYJxRX8zRyh7fnsVH/4tJke/54j7bbb3DtL2Zf16RaJ58D/mJuy7mEDe93ouW/f8S3N1BTcCQlsenBU3j7hufCNlP20e7OHULcdxurNSeFOlJNTzZnAIeuRIwAPi5XfpN1NaM/cMA6FZkNDBKRVKtxcpBVpoJwJCSw+7en0/GzvawY86KVHMLj04M9MNvyMLVoevAYH0/nt+eCP91H4QUFNH9uAb69+SCCnNqdfdNb8N0tz4dlEeCKHPAXkzWxm3bHrqUqaxAi8g6B//5NRCSXwNWIJ4H3ROQ2YAtwjbX5TGAokA0UAbcAGGPyReQxYKm13V8PNViqo4jg6N6ZjX+MZ8XZ463/tuG7VOczfl59ZwgZBxfUah9nf38tjW8rIDVvIYcW1BN3HNvv7sO/Rx0aExK5tS5PnTuaTu+uQM9RayeUqxjXBXnqmFFU1tWLUUH2MwWYUq3oTjCu9Oas/Utr3hz8MmcmOIjEefoPZR7afJKPH5AaLJWZ5y1g2A+30OTWA3h37jpc7mrVknV/S+O7gePsnRkqBBesu4SuY3Px6niMWtOelDHAkZTEzptO4bzbF/NZ+iwiNY+Pz/i5fPZddFq9HID2b++jXYvb6NR6B+c1zSLVVflktRPXn0vT5xNpvGwDvoMHA4UOJwVX9qH/2KXMaP4JTolschi6fihxN/nw7tgZ0ePWVZogoqzo8n5kPJDFf9qMj1jj3SHvF6bS7e95eP2BZjz/6h/peDMYEeYlNgVn80pfn168AeP1Ur7isX1MP74a/bQ1Y1VkezE+m98ORqfg3b4+osetyzRBRNGB6/sz8fEJVsNdZJLDtIOpPPjtlbh3ucmYU4Zr64pjNzIm6JyRlSkb3Ic/3fpOSNPZ2e2tg42ZdfvZyJrvI37sukwTRJSUXngazz02MWKt+oc8Mu06Oj1c8wbJoETY8X+lDE/ZZ/++q/DWwca8fscwnAsrSHaqVnQkSxQ4Gzei5+Pf0T8h8mMQXDW4WuqsX7/SSWYBxOnkvLYbahhVze3zFfHSn6/G+V9NDuGgNYgoKD0lk8fTJhKp04qquFq2IPeatvgrmP4h7oDBXWhI2VyC49uVle7HF2QSl3Dq/dVoOn38nV7ODBNNEFFQmw5JdpP4eLZNbMCqvi9V+LzP+PlPQWMeXnkJmYtcQSdxNV4v//u8L+tGfEnTEGafBkh1JNZqYNiz+e3o+pfdeEtLa7wPVTlNEFGQ8NNBlpUmMSCxBh0PbJZ3Z28W9Ak+NsIpDoan7OP8MyZx0Y0P0Oj1ilfmBmjz6GLuf+8WfMkhtKs4hG0D6/HcLa9Uc1GeQN+LM+f9ji7/KMS3Ra9YhJMmiCjwrc3itk9GsvGal6MahyMpiSE3LQjp8mozZzJD7/maJe82CH6Fw+/Dtyb0P9iMRfDch5dzx6hUpg19kb7xVU9xt9FTwGUv/J6Ozy7G59dRFuEWQ5XdE0uXCTt4r6BBxI9b2O6X/9aSmcE1DZeE/NrhDZZiurfHmdYMObU7zsaNah2Pb20WHUcv4eHLR9DhnTt4Yk/nirczfh7d3Y1bfncfLcYtBE0OEaE1iCjxbtrMn9/9NVfeOjEs07sF07bdrsAVCWPIuapxtS6zdo1LImtEEr8+K487Gn3Ahwe788KqAXj2hb72hqPUQeeX9+Bbn/1LoTH4V66l/UqY36oDp7zYi6vbfXf46R2lDfjys960e2UzidtDT2iq9jRBRFG79w+w9aYiMmsxn2RtlLav/lwJmV3yGNVoIemuetyVuoW7zp1a7X307nANaTc2qHCkpTd3O+lX7WS+q+HhMuPz09qz4IRfBi8a9BQjmjZu460DFS5oFH4OJ+d2qn6/hfvafoG7ij4RVVnU6x3Wv9AOcVdcezFeL/6SksM3XQQnejRBRJH/4EHeeed8ivyR+wOIdwb+D4tDcNSg94DThh4HbnGybMBE1v/zZBxJOq1gLNMEEWWtx6/g/FW/jtjx7sz4L85mTTF+w+aC6jcylhknHhvWc011JrFm0Ets+uMpiEvPdGOVJogo85eU4H61MaWmen0Baqpn/C5Ku2cELkmOS2Ojp6Bar/9ifw/+sH2oLbEkOeL46PpxSPeOtuxP2U8TRAxw/+zFF6FV1lu76pH22Cacac2In72CCz69H48J/ZLhfk8iG8d3I8tT+VwRoerkTmBvz4a27EvZTxNEDPC7Q/safMbPrKJ4bt92JsNzzmdNWXGNjvfvtl+y7vE2iEPo8scf6fzBnSHXYLzGQcoHy7hofoUTh1WbUxwYXTcnZmmCiBJxuSgbchrZz/bnwqfmWQvVBucxPjrM/A0vnHcB284s5ecLvbxXwysgTnGwdMhzrJ8QWBmx89jV9Pj6/0KqSSzOysR4vXR8tCAqHb1UZGmCiCBxx+Hs2I68+86AL9J4e/J4Ng5/mT80rvpy47CsS+g6JgvvttzAgKnmTbmmwbIax9LEmUzWsEm4Pk7E0bQxHX67me7f3FJlkojfGugU5VufzTNP/JolpbVrOynylxFXoGMxY5UmiEgQ4eDw/jT7OoEnvniHpfc/z6wun5FeyTL25b12oDlyW9wRHYt2DGxGB3ftWv/d4mRGx1k0m7YPz0mZdLhjCz0X3YTPVDyI7IsiN5kf7j/8OPVfC7l9wt3keavX0FlettdPgxU6f2Ss0gQRZq6MVuT8rT///sczvNHma3rGxxMvVQ9KKu/xry/Bu2nz4ceOhAS63biu2vsJ5vXW33DX6+9x8LwutLljF32W/fqImkSp8fCnXT3469hb8a9ce8Rrmz+/mDNm38s+X83W7Xh1z9mYnzRBxCq9AB1GzoYN2P9KHFknTwJq1p16j6+QNh8dWVYw9BTezhgPNq5demlyEc3GvcQDY+4kfcSP9Lj3LkqbeRGf0PxboeGcLJL3Lj72hX4fXe5ewyUX3svQh+dxT6MfqmxPOWRJqYdvXj+NZkVhmAJP2UJMhC6v1UR9aWT6yTHLbxwfHE6yJp9K1oX/rNVydktKPTw64Aq8WwJrH7tatuCUT3N5Im2VXZEe4bOiBJ4ZdQNxs6vfvuFs0pj9v+rIz21DqJgaaPFtETJ/ZfWDVLaaa6YvN8ZU2OKtNYgwKb60N98Meha32DAQy/9LEt97XmsebfYJ4VijE+CipBLmP7mE71e1wJu3o1qv9e3ZS8q0vaSEJTIVDdoGEQaOlBRO+/MyWoXYCFmZfF89KDfN2+5ehG1170Mea7aStY9kBB1MpU4cmiDCIPe3PfhL2nxb9vXQ2ssOrxLlbNKY6863Z7+VcYqDuUPG4+/bLezHUrGtygQhIlNEZJeIrC5X9oiIbBeRldZtaLnnHhSRbBFZLyKDy5UPscqyRWSs/W8lNrgy2zByxGe2rEdZajw4P2wEVjvRz+d24M9NIzO9e3t3Pbbf5wGHdnM8kYVSg/gXMKSC8vHGmJ7WbSaAiHQDhgPdrde8JCJOEXECE4ELgW7Adda2dYvDyfrHG3FX6hZbdre6zNB00Z7Dj7cP8tt2aTMUd3WdhyNZh2OfyKpMEMaYr4H8EPc3DJhmjCk1xuQA2UBf65ZtjNlkjCkDplnb1im+c05hzlkv2La/q2aPxvdjYGo2iY/nur4VXGYMI4fE7hUuFRm1aYMYLSKrrFOQVKusJbCt3Da5Vlmw8mOIyEgRWSYiyzwcP+sdiMvFzrtLbJs+LsdTQJtPzOHTC+nanmsaLrVl30qFqqYJYhLQHugJ5AHj7ArIGDPZGNPHGNPHTeiToUZb6cCefN57si378hk/F736e+Jn/tIXYeO1DegZf/x8HqpuqFE/CGPM4b6xIvIK8Kn1cDuQUW7TVlYZlZQf9xwpKSQ/uN2Wy5oAy8t8tPk4H/+h2oM7jg797WnXqI5VBRngicxENio21agGISLp5R5eDhy6wjEDGC4i8SKSCXQElgBLgY4ikikicQQaMmfUPOzYsmNED97r8LFt+7v2q9/iX/Xj4cf+vt2Y3P492/Yfin2+IpaPPxV/SfVnvlZ1R5U1CBF5BxgANBGRXOBhYICI9AQMsBn4DYAxZo2IvAesBbzAKGMCo35EZDQwm0AXwCnGmDV2v5mIczjJH9GXh+9+I+TxB1U54C+m9YdH5u2cSxJtq52E6roNV9Pwg5VEf3FAFU1VJghjzHUVFL9WyfZ/A/5WQflMYGa1ootxO0f349MHnrL1j/fzwhYkL9rIobGUroxW3H9pZCtbi0p8+P/UBCmpM2eBqoa0J2UNudpkMGbUu7b/Z//Likvx5e8//HjL9a25vcG24C+wWanxcOuUu5AF30fsmCp2aYKoAXG5WPuXNIbX223rfnf5CmnxZtzhdSednTvw+G1vRHRpvls2D6LNeE0OKkATRHWJkPPwaawYNMH2P9x7tl5MwlxrGLfDybp7G3FZcs1na6quUuNh/Rtd8BfaM2O1Ov5pgqgm6dmNqde/SKrT3i7IPuNn7btdMaWBzmGe83sy98JnbT1GVe796WzSpq+P6DFVbNMEUQ3Orh3p/uo6+ifYP4Bpq7eItEUHA8dJTaXVYxtoH8FFfbd6C1j7lx749obaq16dCDRBhMhZvz5xL//MuPTwjKbc4q2Pa1ugTWPb7V15rfV/w3KcYC5ZcTsJc76L6DFV7NMEEaJtd5zEux0+rXrDGvIRWDHb2bQp512zNOyTwpS31VtAkwlJgen0lSpHE0SIEs/dHZGh1hvGdGB8euRGbXqMj/OmP4Br3sqIHVMdPzRBhCgSQ59N/Xr87qKZEb2seebK4XT+67rDl1aVKk8TRAwxbhft4iOzRsQ+XxGdvr6JpiMLj1iQR6nydFbrE4jP+PmkqD5PZF2I883GtJu+Aq+nLNphqRimCSJGOAnPKYzP+PFjyPGWcPFbD9Dxpa2kbs8GsyFMR1R1iSaIEO3ZF97VHlq4DuJPcjPnwElclFTzRXkhkBS+KXFx6/ybafJVPHEFfpJ+KiFz4SK8MbxQkoo9miBC1PCrBEoHeMJ2JSPTlUBh6yS+ersvnvsW1+gyZ46ngH/t78e0T8+h/es76Ljxu8NT1ilVE5ogQpT22Saeu6cbf2i8ISz7dyAUNnfSaup6rh02hA86zKnyNR7jI8dbwl3Z1/LT561pNXc/ZG2mbdFC9JqEsoNexQiRd8dOvrqtP5mf3M6SUvunYXOKg/0ne/Dt2UvpTYkM+fEifObY6Vp8xs9z+9rS7v3fcO79o7j70v9DBu+gxTML8K9ci7+oZqtsK1URrUFUx5If6LQE/tz/NrY94Gd+v8n2DtryB3pTejdvxXldMzo8eTvfDHyeTd56fF3Qhbk7urBvZgta/WczHbcHOlPpjE8qnHR17xpypKTg6d2RbQPjSe+XR4Ir9FqFMUJWTnMSc+I41P/K4YVWs/KPmItS4uPhpA449xXi/2kH/jJP5R2aRBDnkW0Xxm+q7gTlcCIO0a7WJ6jKVvfWBGEHkeq/xubP3ZmayoaJbRjccd0R5av3pbP3ixa0nPczjo3b8RcUYqy+D+JysfM3fWlzzUbquUv57pNuZMz5GbN8jTZunkAqSxB6imGHGPhjyr+oM2vPnXjs1Y+WwEmwbnQRK0ozmLe/C1/N70WXpzfja9mEt8c8Q9e4wGmSb/RXZP2mhKsnPUDLpxbGxPtS0aUJ4jjkG9CLjde4sAaAEr/LhXGYSi+Ndo1LomvcXq5PmY/v2m/414UtSHB4DicHCDSUdo1L4sYb5jDvhaba4Kk0QRxvXJlt6D1+GXPTVh1RvtFTAIQ2wYxTHNzWYEfQ5x2iTZ8qQC9zHkfEHUfW31J54qjkANDWlcRnRQlRiErVZZogjiP+07oy44yXKnzOKQ4+2tubUqNL5Sn7aII4jmy8OvGINoOjXdZ4OZt0LU1lI22DOI744ytvG+gVtwd3TS65HiWnuCnGp521ldYg6pR0Vz2aOJNrvZ/PV3c/PP2+OrFVmSBEJENE/isia0VkjYjcbZU3EpE5IrLB+plqlYuITBCRbBFZJSK9yu1rhLX9BhEZEb63pWrFX/taiKobQqlBeIH7jTHdgP7AKBHpBowFvjTGdAS+tB4DXAh0tG4jgUkQSCgEVgbvB/QFHj6UVJRSsanKBGGMyTPGrLDuHwTWEeifNwyYam02FbjMuj8MeMMELAIaikg6MBiYY4zJN8bsA+YAQ+x8M0ope1WrkVJE2gKnAouBNGNMnvXUDiDNut8SKL8cda5VFqz86GOMJFDzIAF7l7c7XjiSkig9oyt+t5W/HbD7FDfXnf5tdANTJ5yQE4SI1APeB+4xxvws5VrLjTFGxJ554Y0xk4HJEBisZcc+jyeO5GR+fKErKwe9QIL88vVEYk0OpY4W0lUMEXETSA5vGWM+sIp3WqcOWD93WeXbgYxyL29llQUrV+Xk/vYUfhw8iQaOROLFffimVDSEchVDgNeAdcaY8stNzwAOXYkYAXxcrvwm62pGf+CAdSoyGxgkIqlW4+Qgq0xZnI0bMei6RZoQVMwI5RTjTOBG4AcRWWmVPQQ8CbwnIrcBW4BrrOdmAkOBbKAIuAXAGJMvIo8BS63t/mqM0aWkyynq257H0iYCcdEORSkghARhjPmWwwOLj3HMbC4mMAPNqCD7mgJMqU6AJ5KDrV3Ei3ZuVbFDe1LGkPz+ZRFdl1OpquhvY4wQdxwXnrQm2mEodQRNEDHC0SCF/inZ0Q5DqSNogogRvnYtuCBpc7TDAMBxUNtBVIAmiBiROzCFdFdoU8aFU6nxkLYo2lGoWKEJIgaIO44eF/9Y9YYR8GFBM1Ln5UQ7DBUjNEHEAEdyIkMbHzvPZDQ8tOhyvDt2RjsMFSM0QcQIpz1DWWrFZ/ykzo+PdhgqhmiCUIft9RfTaG1xtMNQMUQThDpsZmEm7tXa/qB+odez1GEvbzqHBj8HEoQzNZXsMV3wpvgRr5C2GFLn5+Lfm68rbp1ANEHEAH9xCa9uPYvru39c9cZhsqTUQ/3Hkg+vBL5/cGd+GDHh8MjS0ms8fFuSwCs7zmXv2M44F/6gq4GfAPQUIwaY0lKSfiP0/e5qfCY6y96NWnM9suiXKynFjR1HDDuPFzcDE31My/yKiW++SPYbJ+HKbIO4deRpXaYJIkZ4N22myU176Tp1FLdvO5M9vsKIHXuXr5CkfzYMeTXvTu5k1p87hTFzP8HMaobjpC7hDVBFjSaIGOLbm0/mQwvJPdfHlXfey+nfX8n8kvDXKM5ZeAeJX3xfrdc4xcGARD+zu37K6I8+YtM/TsfVskWYIlTRogkiBvlLSkj4ZAn1h27ib5dcR7eX7uTp/PZhOVaet4DmUxNqtVDORUkl/HjDRAbMXs/+G09HXNq0VVdogohlxuBbs56Mxxcw79IePLy7u+2HGLDwThJmf1fr/TjFwZhGG7ng/m9xttKaRF2hCeI44d20mW/u789HhfYM6PIZP2etuoIO9+yy9WrEmCZL2H3uMasZqOOUJojjiHvucsb9/no2egpqtZ8cTwEdPrmD+iMK8ObtsCm6gAaORPqOXoGzfn1b96uiQxPEcSbpoyUMm/R75pf4KTWekF/nMT6mHUwlc8ZIRt5wF53uXI5v566g2xtnzWN8Kv0bfhpxUs13oGKGmBAvbUVDfWlk+skx8+IqhxNX65bkn96CHQO9OBJ89G67lfMbVTxkfMGB9qx5vTvNP8kJqcbgap5G5if7ebHl4hqHOO1gKv8efDbezVtrvA8VGXPN9OXGmD4VPacJoo4QdxySUPFITFNWVq2rFFsfOYPVt79Y6wl0T106nPSR+ZXWVFT0VZYg9BSjjjCeMvwHD1Z4q05ycLVswc1XzrFldu1lfd4m95+NkXgdQn680gShjrDp9rY80Gi9LftyioMFfV4n58+9tG/EcUoThDqseFhfJt34sq1rc9RzJDB/xDPkje5r2z5V5GiCUAAUXtmPh599jQGJ9nftbuJM5h+jX8PZIdP2favw0gShEHccTX63mYGJvrAdY2BiEft7p4Vt/yo8QlndO0NE/isia0VkjYjcbZU/IiLbRWSldRta7jUPiki2iKwXkcHlyodYZdkiMjY8b0lVl6N+PW5uMT+8x0Bq1bdCRUcoLUde4H5jzAoRSQGWi8gc67nxxphnym8sIt2A4UB3oAUwV0Q6WU9PBC4AcoGlIjLDGLPWjjeilLJflTUIY0yeMWaFdf8gsA6orLP9MGCaMabUGJMDZAN9rVu2MWaTMaYMmGZtq6LN42V9SXpYD+HH4NAJqI471WqDEJG2wKnAoS52o0VklYhMEZFUq6wlsK3cy3KtsmDlKsp8P//MjL+ez1Zv7cZ4VObZ/C40XJgbtv2r8Ag5QYhIPeB94B5jzM/AJKA90BPIA8bZEZCIjBSRZSKyzEPN5yhQ1VPv/WVc/PzvyanlQLCjlRoPl20YzH9v7Y93myaI401IvVdExE0gObxljPkAwBizs9zzrwCfWg+3AxnlXt7KKqOS8sOMMZOByRDoah3Su1C15/eRPn4xv857gKl/H0cnd3KVL8nxFDB4wSi8ZcFbH+utSqDl5B8wB3+wM1oVIVUmCBER4DVgnTHm2XLl6caYPOvh5cBq6/4M4G0ReZZAI2VHYAkgQEcRySSQGIYDv7brjSgb+H3Un7aUG10P8Mc/T+XS5ODT2+/yFTLonTG0e2jJ4Zmwg+7W7jhVxIRSgzgTuBH4QURWWmUPAdeJSE/AAJuB3wAYY9aIyHvAWgJXQEYZY3wAIjIamA04gSnGmDW2vRNlD7+Phm8s5IUt1/Le33N4ufUs6jkSjtgky1PI9Y8+QPs3lmKqSA7q+KajOVVQjuRkDlzSgwOZRzZVNcz2UW/6kpBnwVaxrbLRnDqCRgXlLywkZdoiUqIdiIoa7WqtlApKE4RSKihNEEqpoDRBKKWC0gShlApKE4RSKihNEEqpoDRBKKWC0gShlApKE4RSKihNEEqpoDRBKKWC0gShlApKE4RSKihNEEqpoDRBKKWC0gShlApKE4RSKihNEEqpoDRBKKWC0gShlApKE4RSKihNEEqpoDRBKKWC0gShlApKE4RSKihNEEqpoDRBKKWC0gShlApKE4RSKigxxkQ7hqBE5CCwPtpxHKUJsCfaQZSj8VQu1uKB2IupjTGmaUVPuCIdSTWtN8b0iXYQ5YnIsliKSeOpXKzFA7EZUzB6iqGUCkoThFIqqFhPEJOjHUAFYi0mjadysRYPxGZMFYrpRkqlVHTFeg1CKRVFmiCUUkHFbIIQkSEisl5EskVkbASPu1lEfhCRlSKyzCprJCJzRGSD9TPVKhcRmWDFuEpEetkUwxQR2SUiq8uVVTsGERlhbb9BREbYHM8jIrLd+pxWisjQcs89aMWzXkQGlyu35TsVkQwR+a+IrBWRNSJyt1Uelc+oknii9hnZxhgTczfACWwE2gFxwPdAtwgdezPQ5Kiyp4Cx1v2xwD+s+0OBzwEB+gOLbYrhHKAXsLqmMQCNgE3Wz1TrfqqN8TwCPFDBtt2s7yseyLS+R6ed3ymQDvSy7qcAWdZxo/IZVRJP1D4ju26xWoPoC2QbYzYZY8qAacCwKMYzDJhq3Z8KXFau/A0TsAhoKCLptT2YMeZrIL+WMQwG5hhj8o0x+4A5wBAb4wlmGDDNGFNqjMkBsgl8n7Z9p8aYPGPMCuv+QWAd0JIofUaVxBNM2D8ju8RqgmgJbCv3OJfKP3A7GeALEVkuIiOtsjRjTJ51fweQZt2PZJzVjSESsY22quxTDlXnIx2PiLQFTgUWEwOf0VHxQAx8RrURqwkims4yxvQCLgRGicg55Z80gTpiVK8Nx0IMwCSgPdATyAPGRToAEakHvA/cY4z5ufxz0fiMKogn6p9RbcVqgtgOZJR73MoqCztjzHbr5y7gQwLVvp2HTh2sn7uiEGd1YwhrbMaYncYYnzHGD7xC4HOKWDwi4ibwx/iWMeYDqzhqn1FF8UT7M7JFNBtAgt0IDCLbRKAB51BjTfcIHDcZSCl3fwGBc9KnObLx6ynr/kUc2fi1xMZY2nJko2C1YiDQ8JZDoPEt1brfyMZ40svdv5fAOTVAd45sgNtEoPHNtu/Ueq9vAM8dVR6Vz6iSeKL2Gdn2exjNg1fxoQ8l0Bq8EfhjhI7ZzvpSvgfWHDou0Bj4EtgAzD30S2T9Yky0YvwB6GNTHO8QqJJ6CJyH3laTGIBbCTSAZQO32BzPv63jrQJmHPXH8EcrnvXAhXZ/p8BZBE4fVgErrdvQaH1GlcQTtc/Irpt2tVZKBRWrbRBKqRigCUIpFZQmCKVUUJoglFJBaYJQSgWlCUIpFZQmCKVUUP8PbiziUA8rhgEAAAAASUVORK5CYII=","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.imshow(plt.imread(\"/home/lakshita/somusan/hubmap_kaggle/hubmap_data/mask_png/train_binary_masks/10044.png\"))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["(3, 256, 256)\n"]},{"data":{"text/plain":["<matplotlib.image.AxesImage at 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LK80sOsdTLfG/sZ9yqJgPM34vf/+h5DsMZpFXH//Vzh/rsXCyhKmZZ5s8iN7EnGyrcUzEOB5Q+4X7gT2+sv/agLE6+iIl83iJdM/V2PxVJXYqfKes1eVSKIgZvP+Fjc/vE+WhGRZwLt/4vtceeMiUkoURakUnRJMU6coSoa9IeQxSZyRJgW2yPFqJcHMx3F0StVDpD46YBgOeZohhSBNYgq1RstO0J063/6172M5NpuPtul0m4AkzwqmoxmzYY8wkiSFSRmG+LOEtXmB1eoiFcjDAcgWSZyhGwZpXkAZ0+rUyApJlibYroVhWQTTiCKNWO5CLm3SXCEbjbHTmHqrQZZkmHbJ1o6PVV/gwlvL3PnkDv27EfPr61z51nVMy6DerIEQKOKsAHnHZBRncH5HgPJLRF9NgHgOvfaTnE4bUX2mvJ7G/RxaLR2qK5+8X8ryMVciJUVRksYpP/2Dn3L3o49pzc9z/s0rvPudNypp+hFIHRdwCqIwZndjn1JKDFMhypuEhY5Lyvz8HGVZMJ6F5KWOV2+iGRphkBBHCfW6iqkKuqtXWLtyHsM02d8dsrq+dPReZVGyef8hw/0hxEOyNEYoKkaZkikLNGyDVrfLvfsD6tMJnWWvUiWLDJmOeHR/h2vf+3VkmTMbTak16wgkmqbhxwqj0YQgFlhGSbOU1Jsu8+trPPjkBjUzZRJq9DZ3cbwW4/0Udqek8c8BSWNukWvvXMJ17QPN8FlS4SdRWUooygJN0x436Zlc49cLQL6WAPHaO0mAkOL1bPFOiRYOtQzixMXjSR7LGRShIIGyKAimAZsPthnsTdnbnqI7Hlfeu87FqxeO4U9VwGHu4kBAYFom7bbDaE8wnuTYnk23rTO/soYwLcLBkGF/RM01sZ1l0iynLAuSYMzGg49RVI2r779LrVFD01VqdbcyZjqg/l6fQW/KcBDgmSqodcospObVsBsWXs1DEQXtukrgz1CHPkUhKfMAr7XCqmGzv7kBWcDeSKfbrVGrOeiWxcW3vsXOvdvcv32f+Y7NbpSjyATTNnnr+99hOp6w82CD6SShKFVaC6tMB/tomopShuzth0xGIy5dv8j6heVjY+WkfOc0kAtFoAr17H58+qWvPH0tAeKV6GmT/7jx4vEV/KXzf7rcRJxYtE6vYMegQ0oCPyIJIz74/R+TRjGKblLmEzrzc6yfX0PXVQa9KXEQ0plvoeoamlZZ4kkgz3KCWYjXrFPKEs2wOH++zfzaGoalceNnd5BlQcOBNM/oD3waDQdd16g1auwrJrOg5Mf/7I944zvvcPWdq0d2CgCylIwGE1AdbGOE4XhMQpsymZGpdS4vWsytreKPxxjaiOFM0poXGLqkv9lnb6tgYbHO/EKTNHFJRMxcR6UsE3a3BlDmWJbD6qWrbD+4i65PsE1Ikk1GvQGX377OheuXuf2zT7FlQqk06M5dIc8S0iRCU/vc++gjJpOUOJZcub5yAG6ngZnHWqJDWeUpW5YnthxfM+4BPiNACCEeADOgAHIp5XeFEG3g/wmcBx4Af1lKORJVS/6fgH8DCIH/hZTyJ5+l/NdKT+vbYwvMaXB4NZbyQCh2fNt7onh5OjUAeZbx8NY9PvrZNkkc0+3YWM0ml66v8+CTm4SzMQ9uPeCNd6+RpRnbDzYQQtJdmj/KtihKRoMJtz/4EEXmNDwFlBr7I1i8oCFQUMiZTsYIa57ltWVsx0IoKkmcMPPzSoOhzUCv0d/aptFpsbgy91ilrAiW15e5+dObxJqNaeros11Uo2D+XIely+dZv3qRPE354e+OMRwbVRUM9gaMxiGqYeLW67SXVzAMlYtva4yGE/zBiDLrkec5WTShtXSeev1ttu58ynTsM4vq2LMJ7aUZiytzJKXJaDClzDZpNus4zXmKPKbQupx7Y4XuygL9YUR9d8biYp0njS4PhZfiMVPB44OkOSHD4CkLzAuynF+89vKF6XUYSv0pKeV7UsrvHvz+a8DvSimvAL978BvgzwNXDv7/NvCfv0phzzyz4LPS6WyfU4wQ4sWMsKqlhiMh2GNvq+rjGY/meUaWptz6+Q0e3rhF3SkQecTW5hRByf52j1qzQ5xo9DY26O32abRqGLZFlstjcojKKq9Wsxn3B9x/MGNrNyPNwDFL8iSkLArcWg3DbaMbFsNhgKYpCAGaqmJZBmmuopgN0gz6g4T7n9wmy4oTdW51G7z/699hfqlDFBcEfkh75RxX33uT6++/iWUbmJZOmcbYlkaaFiiajtdssLLSpDnXYXmty/xSm3a3zuWra7z9vbd489tXaTdNTEPh4Y2P6e/uEaQ2syCDdEQQloz2dhGKgmvmSFQc16a3s8lPfu/v8uDTn5AmGXkhiKdTDEXy8O4ejx70qwAwPD7m73G/HoK5PDJ7PnMMnmAuDhHlBcfH5wIOr2eOfB5bjL8A/ObB9/8C+KfAf3Rw/b+UVYv9oRCiKYRYklLuPDO303vBz5ONO53104o6riJ/Ge+9I6OnkyvTmUkP4iiURUl/Z59xf0Kj02U66FMzYzS9jsx89u7tECQ6pu2wtevTfLRFa67FhWuXqlJOLAGyMkhCYzhOcPQMx8jQdY/pcIjj2vhhjqVGRLmgNddkNgmpN13CNOPWpw/QihGYJrquUOZQFjZFUaBL7agMhODc5VUs26C/s08av8H86jIr51eO2mvr/iZxbkKYY9gFhu3hz2a05josrMyhKILAjyrbCkDVVLxmEykURoFFno+5d6+HlPDud95g2OtT5gnkCQ8+uYksJZ2WRl4oNLtdgtkMz1UJo5zuvEWe5UBKXkr2docYts7iQp00K9BUSeAHJGHE/PLC8U45GotPdPNZhnmCZxqzfb70eubJZwUICfxDIYQE/s9Syr8BLByb9LvAYQuvABvHnt08uPZsgPgy7utOV+l4ABZx+trh72NfTtk0nNrqUpYFn/7kI4pScP3b19Etj+2dEE1kJLMh9VadTqtFGidomqAc7RAUK+iawnB/wKg/xZ9MMWwbr2Yfa8JKvVermTSdEYk/IQx1dDthbnGRsghp1wVZ6uCUkjQcodhdSgmapuDpM6IkIhY2jlAo8gxVN1AOvA2rOfJ4kiyuLjC/PP9ErIjJYMStn9/Cq5lkeSVEtUww1Zg0y7Adg9kkYG9vwOWr60fP1ZseXr3GcjFiLCUra0tE/gRdTqnVLHqbO9y5o2DoA+LSwVBzOi2NtfU5HFMSBDH3790hCnze+NZV0rTAcg2uv7XOcDBDSrBMHYnkzs1N/uCf/Cv+J7/9b9PqNNjdG2HbJo2GeywkwOHHM7aaX8bx+xL0WbcYvyalfJ9q+/BXhRC/cfymfByH7IVJCPHbQog/FkL8cb/f/4zVe4106i0OXaYrEicNoODg9+F/nslqPl6TqsE2GYz5+//N3+UP/+VP+L/8p/8Pfv7Dj1EViapIDF1FsxooSkmSK1iuy72bH0GRYlkq/jRg4+ZNBrv7GKZBmuYnojgpqsosKMkLmCQeWV7iWRJNUzDdKpCL5bjk0iLOwPVM0rQgzzJ0He7evo3fv0sQpJimglAq8Dh65VN0Vli2ex99Sn9/wmB7E1AwTYOyKCiLnGg6IY4SXM8+AQ5SSqajKbqu82jTRzUcZn5OWlqMJyHT4QhHT5nu3iYJAwxdwXZt6nPzXPzWWyiaRpSqqDLm7o1bbNz4iK2HO9y9tYs/i9nbGVEUh5GhBM2mTRql/OPf+V0++fAe/e0+v/sP/+hkP4pDucTXlz4TQEgptw4+94G/A3wf2BNCLAEcfO4fJN8C1o49vnpw7XSef0NK+V0p5Xe73e5nqd7rpVOjQDnu1HNaCH60R32Mj8cPiDmOmiccNpH4E58f/u4fsLR4jmgmiIc+/t5dPEsymWYYps7+3gSVnMV5HU1kXDi/xM6tf4lna+TxlNifsnpxnXanjmloTwjfLr11mdX1Lq7nkhU6W/slg91taq0O7cUlilKlzGOaNZUkSsjykqKQdJcvYdkOjq2hahqLK/MIRX88sV5gKYiCmDiBvFRJUhCUKKqgzGLKQiBRURTlpHs9sHnvETd//BM2H27jaCFFllLkBXMdk7m5OrpdQ62tYXktNEWy0NV489tX+M6vfYckjon8AKWIUZWcc4s6USpRRYzMpvzB7/0M09ROvMe5S+u886vfxShjfvh7P+Jv/mf/d25/fIf+3oA8L4468USYgLPo85KXfUH0ygAhhHCFELXD78BvAR8BvwP8lYNkfwX4uwfffwf4n4uKfgWYPFf+8GWkw8l/xDAcN0LiGOdwSCcHz0m+okpeHgVvkext7jAb9NFMD01VqHs6UvMYTjNkMqhUrTIjzXJWL19k+fIVCuciDU9n49ZHaLpJOB1T5DmH8smyLCnlY6CaW2jhuA62WdLwJHmWMAvAH4/ZvL/J3ftjwliQJWkVa7KoJvFklrC6vkJnbo5ossunH2+wu7VHHCVnveqZFIcxaTDh/JqDYdkURaUaDeOSqZ/iT2bc/PnNo8l62L6aXnmKRn7IbBqy20vRNbDcOrph4nk6rbZHvd3Bz130WofLb16sAHVzlyTTKIsIzyqQmosUGnNzNouLdaLpPvduPuD2zR3yoqyEsprKn/qt79K58A4XL19FUQR3Pr5Lb+CfijB3sGU8HsrvRId/tfmLzyKDWAD+zsHKqAH/lZTy7wshfgT810KIfx94CPzlg/R/j0rFeYdKzfnvfYayv1B6Up15Smh6Wohwkp14bv5Flh+YP4/49IO7ZNGUXPNQKJhOxjSaJlkiqbkaaa6AorF4/gJzS/O05josfXqbiVxn0OtRlDq9cUrt7kMmwxGlhHAyQLc8vvW9d9B0jXqzTiklaZrRqDt4LZfLb17CNHVqdYf6aIihO2j2HKpROT5lKZRSwak3EaZFNg1xdIPltUVs23ruOx5SvVXjwltvsvvgPrYVVkZYEnTDQJARBBnnXOux9+VBu7cXujiteeRWnySVOK6CZZQ05pcxbINOGuKPp2iGydyqxpvvXUFVFaSU+NMZWRISZg5G7QItkYGaUpYF564uU+YxDx4NcCwL2zZYP9/FMDRUVeHt9y/S3x3z9ltvcfP+TmUUpqjP7uMvsdryZemVAUJKeQ9494zrA+DPnHFdAn/1Vcv7QugpHftcY5hTRngvQ3EUcfODD9FMjyKcEgUxQV4j9CNMy8NtSExvgZ3eLoauYVigO3WW1pcRikDTVFRVJaZFbb6BpqtMe5Ltu7doLZ3HdRR6exMcJ2DYHzG/NIdmaDTmuqytzzBMgze++x3a3SYf/+EP2dud0uuHnL/UJitULNMiyzIsx6ZeN5nEJqZpMNfN6C7WWb20xnQa0m7XnjkpDptW01TWrpwjCmb09mdE/qzidIqUwfYmS99pMd7Z4Md7O7QXFzl3teICiixBNxTqnTbTUFAqJYZl4zY81i8uIhRBlmY8urtDd7FDs+UelV3kBcNhyKUry/jTCWlhMzfnsn71EkvnVti4eQNLL7l/+x5SrcLZX7i8gABc1+TBxGfr7h/yp3/rL7K6cta290A4fSLa13FVF7xWc/0vkH6pLCllWVYGLq+rkw61Eccs8Z63eFSPVGmH+wMe3rrLbDTE8QoGuwPyUuC5GoaYIXRQrTlmfoaqm7iegWGqrL25jlerVu2yKPE8g26jZHekMgkEisy4d/MGLV+hO9ciy8EQPmlaxWtQhODN999m7fJ5dF1FN3RufvAJO5t9ojBD0Uz2hvDWOw3itLIe7e2PCWYhquWwtNqks3wd3XS4//Etli+u0e7UntlUh9uqQ5OQzuIC929tY9iCMs/RDAOVjMEoQ9UtRoMRtz99RDDz+dYP3kM3TFYurmOaJk6thpQFK5cusHZh4UgQaug6i6vzR21TSRFL7JrH0moXw9LoWB0UTaW5sMTKhXXC6ZTJNKMsS/LZDhu3FVbWWgSzCNeziMOUJMv5tX/z30Y1ver0s6OOPNnfp/v+sQBTHPz7aoEDfBUB4qwZ+ILu38/3sHxWuafKkFRKbnkkTTj4K4+qeFZVq6CxJTsb2/z49z9BU3LOXVpmd2OfaVBW7LzSoGGp9EaSov+A6e5NzPoSI/c8c4stLlw9f5Sv70cousveIKfV1FF0FSWDhbfe5d6DMY6eUyg2dddhsLXFyvoCCMH9Ww8IJyPOXb3IbDxj4+4Dev2AmmcgZUm9WSMKE4RQSNOCcDZDZCNM28RrtTh/9RJpmrO7NaDRbj1uk6e0Y+BHTEYz5hZaBBMfVVUxHRvFUEEomKbFeOaj9iYgM+IwYTSDB7cfce29t7Edk7mFDt35NlfeOZBLaMqxAiu5kFezqn46AG+hKHzr++9y/dtvUZQlUoJtm2iaQp4X3HnwCFGmuFqM2u3S377JrQ/nQCh4DRvb0ml26vSLgg9/eoM3ri/R6rSZzWY0GjU++fAm3fkO3bkuqnYqbL88A0VeZGx9ieirF3LurHb8Qhr3oMPP2noeN53mbPyqwqJV4DHsDfjkD/+IaW8HITSm/T7+cA8ZPML3M9bWmuRSQ1cLzPoCimFT5hH1moZlqSfybrQ82kvz1OoGri1wrJxENJjFOnVPQXW6RDH4kcK4P2aw26PIC0Z7PTZv3+bH//T36W1scu7SOm9c6+I1GtRrJrZjk2Qlmq4xGoxpNXSSyCcvBLNpiAQMQ+PyWxcrF+qnvTwQRwl3fn6Lzdv3ufnBp/z49/45t37yU4pwTDSdIKUkjlMst8nKvIqSz0hylbVFnfnVVYyD0PpQbfc0TT0GDsc7gjOv6bqGpmuYho5paFVsCQmhH7L9YJP9scos1im1Bs12i+nODWLfR8gC17PQRYmtlsy35/g7f/ufcefGFkUuKIqSv/V/+3/zv/1f/x+YTPyT739kE3JyUZGnx1D1UmfU/8tBXz0O4nXR8ywez7SqPG79eOoAG3ns2vFHDtKUpSRLUlRNY/v+I6KwwK3XaXVqKIrCNB4TZwaKDkmc0mzXkcIkjArmzr2DbUiyJKbWnj9pqCMEaxdW0dQ/SZ5mbN66wcUVwdZ2QZiGqIVPs24xGsyIYknj3gNGvQF+f4skKZlMRyTBlJpnMQkEpgjJ45CiKCllQWGVtBoacZRQq3vohiBLEtKkUrlKyQlPzieaUsKD25uE0ymthS5ZNGW/F1CUKmGYobtt/FnIZLDLxStXSDBoL9rMWxa6qXH922+cEbj1OcKe41GvpWTrwQ6j/gjTtskin97WNte/+y3mlhe58t47ROGPcFtrxOMtYtGilArb925jWm/RnWuQpgWGafPmex0U5So3P9nk7q19fuU3rvGX/p2/xP/v//P32d7s0Wx6CFEBh6app+p0AA5HthNfXlA4Tr+8AHHc4vH0AD+j7yqXbHFi1ovj6q0zHpUHoLG3ucOHP/op/fGEv/Q//Uukccx0lnPuYhtFKdnbnVVBWrIE28pozzVBFhj6kNlozGgqaTQtDLfB+pVz6MbJblM1ldWLK/izkP2tLaJxH8uUKEsXCXwfXY9ZWplnPJrxyQd3qHsCXSlwDImugFTrbPYgDfYxdA3dm6fVcimKyn8jygz6vSGWmjEKNa4tG1WkJwm2Y54c8KfANctyBtvb5OEEVemC6bC06DEYBpRKDcd20VTQFIV6ex63plMkAW/+4D1qDeclozof39hV/TIZ+exubHH35ibf+s51mt0WGzc/5cMf/owf/Gt1zl85h1Nz0A2L/c15BlsP2d6NUDWF6XDMZNJCN02KUsV2DfKsZH6hjh9kbDwccuWNi/yH//H/ipufbPDxz+7w9nuXD05CO9h+HlXlmIDyaUKLLyH90gDEUz0vXyDC9JH0+QnrJvFk4mPlpXHChz/8KR/88GPyvODf/J/9jw5UbyqlohPFJYKQYDIG1SIrdYpEx657tOc6TMfzhDOfhTim1mqyuL5EveHyNPI8hyvvvsWjm/dRjR7hzCf0VdLSYOvRDoZWkiQZRa5Sc1UKqWPokk5DZxrk2LU5NNMiCCurxlJqJHGKQKKrBZmoszQHF69frhzVOBbx+VBge7gVOwDT3k6fcDJCExlSQuJPKBWHJJ5QbwscR8c0dcKwiVAFqqrQPX8Rr2ZVkbAOWHLxQpGl5alPqDVcrr33Fm98+y22797l/s8/IE8j0tJgd3OPK29dZGl1gTiMEStzqEqO7U6Z+RmjwZjOJKLedCjTkAcf3eTCO+/S7HjYXoldMwmjjE7b4dLVFQb9EUUp0U6coXLS8vIEfcnBAX6JAOKVNBfHkf747yMB1HFWloPwbpUH33Q45sM//pTNe1vooiAqVWq1SgreWlqisReyfmWZzTsPEWVMliZ0WxaWq5NECWlWsnJ+CVVV0TT1BSZIJR5tdpq433mb+zfu8fEf/ZTVFRdZ5IShTp5LSllZ/s1CgWYaFFlBf5jSblkkUYJuqcioRxy0Mb02aZyS+XtkwT5maw3NNNENgzhK0TQF3dAfO6pyfO2WyEJy5+M7zPo72PU2huPgj/pMBn1u/Pj3+NN/6X95IEuQeDWHwJ/Sbtk8vHGH3uYW9U6XcDZBorJy8RxLq92TXNwLkKoq2LbBxu17fPzTOwR+iirBrpU4rkWeFYRBxO/+N/8d4WiPMouw6gvU212SWOCPfXQN+hu3iHOLRtNjNNjDrTuVBkgTBDOfH/9kA4WcLEpYv7hyIDeRT8TBfCxQ/QqgA79EAPEkPYW/O67LPkh2gn1+CgnBkfv3xt1HfPRHHyGzGZoocRp1VpZWMC2DMEhYXlvk/OVVRoMRNz58SJiaeIZPvTlHa2mZ699+k8FgimkZL+5SzmOjLcPSWb20Rm9njyIOiKMp7U6NUX+CrWekVKBhqTHduTqaaRMECaUUyDzBtOuoukkcZ8iiIA5m3H+wzRV3niip/CZs2zoArUNNzuE248DACejtDdi4t0U2iyl0SZkmlKpLb/shy5e/h6o5REFMvemhipyWV3L/YUASFVjDLR7d2SQtVIo84+6nd/iNP/frLJ1bfqn9uyxL7n74CTd+fht/muH7EZ42or2+gO1Y5HlBkmRkfo88kwhhURjzWLZGnEv80QjT1Fm4+Da1qGRns0eW5PiDPn/8xzd4640um3duMytXaNVK7t9UKqGtZtBuu8e2SMeEVZ8XOJzaLr+OEHi/PADxhKzhKQ13ljziSNZwDP2fMmf7O3vc+NEHTGcqq0sNLMtHs2v86r/+A3RdY/vhNtNJwKXrF4j8AF0pkUVMiYaiGZiOg6IpzM83T3TuC3f2QRXdmsP3/9SvEvghcRgRBzP2N/4hUWLQ6UqEYmJqBV67jV1v0//4AUUy4cbtEYbl8L21c0yHKbsPHxDt32Bu8Tw1T2BrGUmcUG94Vb2O/T0CBwF5VnD/k1s0PI2dmUezpjIdjSFLmVtaJU5gFsQsLFYq0jSaEoz2UPU6bSekt91jd+M+3aVVFL3FeJjz4M4jls4tPykPegpJKfEnU+7deEhvpwdFhCVSGo0mdr2J6ThYtnHQ5YIkyckVDycMGe8O6C4ukwOyLIj9CUWSMok0Nh5sM7fYZe/ex+zcGPKD3/zzrLSa5GlMFA+58eFtFKtOo1Xj7W8duh8dCrDF5yd7ODU+Xoe9zy8PQLxAY8myPAgKckrgJg41mc/u2TRJufmzGwz7I5bOrXHhnTeIZjMUwyGJUqajCR9+cI+9zR2iICCejoiiDM0wGfqgDabozgB/tkTjmKzh5Tr6UOUqMCwdw2oADeKojfJn/4fEYUw0G1OWGf54gtdskUZ+5fwUjVno6LjtecoSZB4QjbfQDAtXD0lij7nlOdrd1lHzHG8mKSVxlFSGS0IwGw7ojzKWltugVpyHYWisrC+x1y9ACJK0oMwLdFUyP+/RG0t279/i5r0pYe8OuuFi1TU0w30cZ+eg1OPu5Yc1KUvJdDSp7DeSnPHeNoaSsjdI0JId5pbXQPdoLc7juJVBlW7orL3xFuM//BEyn5AlFnOXzqNpOkUSg1BxGnOEQYyjprzxrQtsPOhjioy1d/8MiqpRr9ssnruG6d5jNp5w4cIqWamdAQaHC8wpTvVLSl9zgHg5qD4BDqeeF8dZRPlkijwvuP3JPWb9Hkkq+KM/+oD582ucv3iOmz/9lI1PP6I1v8Csv0fdKdCUktkkwvcjVBmh6S7BeEy5PIemqkfnQR4xPi/yKkeD7sl6WrbJysVVdh9tkcYBhmaTRBmaaWOYJq69wzBdRBYSQwREk32Guz0S6WG5NfzMpCFSrr335gnNwvEq9ff6fPQHP2La2+TtX/11dNtEV2f4QYEwDIosBanQ3x/hNNfQDYsozDBNjVQ06Q9AJUYxO0TJhMVz11Fa13HdjGbL5vJbV49ZYx7baEgoioI4jHh48yY79zeQssS0LDRdx23UuXTOJQmXMRsLrFy9WBmbHWSmCMF3f/PXOXftOmWZkecFnbkOtz74KdGgT0e7QJrm6KZBlkv88Yx0usXKxTewLUEW+tz92RaG7fKdP/EeSZKhqgp5npOmOeaBPOJoDEn5uTERr5u+NgCRZwVpmuO4JnColuTleuGslfoZ239JZeefZTlIUIWkUOoIEWMoBv/Jf/gf82/9xX8L23AQQmBYIYXUcbwGjueRS4Oaq+KaFvW5edqLy6Dp6IbKyZDsL7HSHL20PC4xpCxKPvnxx/ijIcgCKVWcmkut1aC/tcPc0hymF7O/ucNwd4/CKBgNhjhuF2HqzLk5V99/B6/mHmesjppnMpzx6U9usrs9pUwFD2/eodFu4nkWupoz31HQDI0syXC8BpbnkWUlhqnguAaqkpFlNbbubjHtDVlZXcC0HFw9olZzWLt2lc58+7F6+aADirLgD/7pz3EdgweffsTy6gL+NGDmZ3TmNXJ0LC3j/o2fcemt77J6rsOFaxdRFUGa5jy4s0U0GaGaHs2mRVnm2I7Lp3/8E/I0I4wSwjDFrXtMxiHBxGfS3wVZUHMFsyAkSGzajkpvt8flNy/iHozBvFCPVN2HFZZAkhyAxosC/0vS6wy//7UBCE1XTxinfJbj2x932PEp8GR+eZoRxwm6YaBpCtPBmHA2ozW/gGrU+Nd+TbC60CIMEkbTnPEoQCkDPK+FU68jZUkcJ/iBQLPG2PUG9e48ZSERujhZ/PMqe2iEIyVpUsWxNAwDw9KPVJAlKqqq4fsFO3sBKwsJTn3vIFhLgaFK8qDH/iChvazTbapkoqBR17n67ltcfuuNJ0BUSsnGnQds3t1ga3NKlKlIWWdrN4WyT91TmQWSrFBwTAvd9MjKBNPU0NSSvMiQpUTTLRp1SdLUqDkXyIuMEhM/LHFbHd54761q63JqX7P9qM+dTx7xW//jP8Hq+T/N7oMNBrM+pbQohUU8mzCLYyaTkCyX6JoCQiFLM/r7E1J/ws2f3cRyXYLxgNWL56k1a+zshiST/QPNlEqS5NiOzoNbQ8b9KbZTJ01LkiQgzzOEohFFCUVRIg7C42uqcqz7Hku9y1LyuSDDYUmvUQj6tQEI4LO392lBpoDT52EeJ03XcTUNIcCf+oBE0UwMXcMyJW//md9k3N8nSgWijEjCjDxNyHOJ7TosLjcw1AxkFfT14d0dzIePUMS3Wb+0iuDQseyY0c1RPY8q/dj8oJRMhlO27j1k+8EGrYUl3v7eW1i2iaIqrF1a5+HHAY4rsNQxs5nJYHefZruBXW+gxSFOa5nVlsdkNEZodZa7Ot31Va69++aRCpcDGwiA2WjGvY9uMpvGaJQYhHS6BmleMh5OWFpbRiLxgxSvliNEQSZ1VE1D0SDzMzRDB1my/eABimHSqHtIKRlNMhabCt2VJTRdPWZrwVE/FWVJKTRQVGxbZe/hXTSRkGUp4SRFlAmthsn6xcsMB1N2Hz5As2ze+dXv0u7U+KP//o+YDSOiicAPc4SzyEoR4Tk6dbPOfi9AKAqaBllaYmopCJVSVlHCNUXSbDrYXkat2TiwD5FHdiKCKt7HIbYJwLGNo64ryxJZlqj6l3Mqfjlr9VnoJYFZluVjJ67jYeMeK68fS5+PCqiolCWT0QwBZElMHvu0Oi0e3t9hacljYX2R5Ysr/OxHN8lLheksoF6rQRGg6RrdpSWyOCT2Z8xmGUGQotUVyvJJP4PB/pAojDAsi2a7jqapBH6EEALHrVyU/VnE7sYuG7fvsd8LydOMjyl479e+g6ooNFsue67JdDQmL3XqTg1Th/7ugByThlugWTatzjJBmONaObprc/077x1FeDoeGWt/c5cHN+4zHc9QBVg6pBT0hxEL83X2ApfdnQlCs1g+10RRVabTHFXTQBwEpJEQhRGOY0GZUG+6uDUXVTfRlS2Wr7zBlbcvnOpSgSxLZhOfZDbm7ettXFvFch3mVtdQ1B67vQiZjjAsk/5YwWicQ8sHDAMTt7dPElfC1EnWwaiXlEVM28koshi3MY8RJ+wN63SW6kzHPnc++jnXvvUWAgXKBFPJmRQGstAx8hFCn2dxdRFFedJbuFJVn2a+qn5VVAVeylr0i6WvH0C8JBfxVHbsCQO405aSsLvZ58aPf8ryuRUMy6SUJpNJQt0TGLZJvVnDckx+8Bvv8OEffsB0kGE5FnPr53FcG211EUVTGe7ukuebzIIUt1Fjfrlzwt8iSzNu//SnzIYT0lyyfH6d1tIiwXiKIkpM12P1QmVnoRsaUmh0uzX6owTL7hEHMaqmoapQa7WYDGeIYoBqLnL77h66rpFnIYkLUm9S5BmuWaLoFlffeQvTMiiynM37G9QaHq25Dtv3N7j3yR02Ho1J4oz5xTnS0iTXbUwRMBzlyHjELHdQTIOigMlEoph1bMOgKEos28DvP0K3amSaRrPVpN52ufbumxRlSXrtCvWmW20dj++myiq4zj//+39INOlTs1IEBeffus761SuMe0NqnsnuRknuw+LaPLrfpyg6VcQtPyGYBbS6Td58a5XR9kOQGlEkyaOHGOYqimZiBRMa7SZoNhffuEJZQo6NZUBSWDRqKhopVn2ec9evsXpuobINOa2gOIEOxwUPL2bf8oukrx9AvCydVmlWFznZeY+FfkVekiYZ+7tDPvzhxyhFTDwbMxxY9KcKlqUiDBPHKOht7zO3PIdbs7n41hV2HpjopsXF6xcOApwkDLY2uXdvhFpmNJoGyxcvYln6UVXKsmTj7gMmo4CdnRjVMGmMx1iug66pREFIb2e/OgvDMqm1mnTmamxs+Li2wLY1bv/0Q5x6g9mojxAqcSLpLrTpzLdodtuoikKWhOzvTbAtFU0TuJ6JrgvG/SGzachs0MOf+uSl4Hv/gx9w/9M7bNzfZa8XML96js7iAjsPbtBstlGMBXQ1x3auV9uREkqh47UsilJWQXBziVB0pLmI7ugYlo5ULWbjGb3dHqsXV6jVH59DWn1WW5zJcMadn30MeQRCwU8spsMhlm1iOxa17jxp3kPKnGx8E/PadcqopN2ySUudZsPCdh00TeP8tQtoaklZCvYf3MT1lkjiFK/ZouGOmextsnT1XWyvThJlGJaJYbmVKbhI8Trnufz2NTqdGgJxYtQcCXMPthuHNiNnLThfVvr6AsQL+9ifpY8+wyT2oIcDP+TRnQ3SYEbiT1FUCIOMyazAdV3S2S6F6rDfD8myG2zefcCld95kfnmeuaW5I/WcPwu58cEn3PzwHmkm6HYt3v7euzi1Og9uPUI3VMLplDQKmAynTGc5WTzDMyOmM5tVxyIOQqLZhN6gYNzrY7kus+EIRRGoSkEcpUxCg6RI6O/dQ6CQxDGq02Fu6QLRbIpllAxn4DmCVkMnTjLcmoXpnGO0s8nuVo+pL1EoqLkSUym488EHlHmOXW/RziWmmpHEMYZpYtoG7fkaQijVPr0E309RjiTrBYOte7TnupR6DbWYkIQGrmdiWA79rR28rU3G+3s0Om28ZpPOQvugNyTD3ojtexvsbu4TRzmUBUHuILX6gbBYxa27xP6MlXPLhOE8RQGF6hElOZlUsWwTw9QJwxjLttBNC38S4DTaGIaBqlUyEn8WMuiPseublAur6IZGWeSkuUTTYPH8Ja68dflAOC5OiEjgJK8AJ4fYyW3rl5e+vgDxvEjDx1m+Z4GJfGwrmCYpn/74Y8pSIZ6OSZIUt1E/CBBroSggdJN2u04yieiNVaLQZ+p/yHd//V0azRq6oRHMQj76gx+zt/EIP9LRDIv2QgevUePRjRts3nmA5nYwDBUoSTMVpMR1VBQFGk0by3EospTe7gBddZgOehRZQjgZUqQZqgLRZJ84FViOhW1qZKVCwwbHNXj06U/JlDqXr8yhiBRTVzHVnDyOUKiRZiZWcwFdyYnDbZIophdILNvEa6gUpWQ2S5Cy4OZP/zk677G5PaLZicjCHkKx0C2HvaGC5+lcvLRIkWfMpgHdpWUs1yHypwhdJw5Tth7u0O62mVta5t5Hf0xr5RqR72P2x3TmW5TAZDDl7s8/YrC9SRoETIcBRZ6zsLKEpmYUeQbo5GmCMFxUB7oNnUanxXSioOYjVFWiKLBx5xH9nW10XcNyHAwtR63VOZzowWSKZnm0ljpkoo6uGzy418M2VTQZkOUWe9t9rr515XCgPB5PB+NGHsprDoSRT7Mf+TLT1xcgzqJDIHgBucNjhrD6k+c5N37yCXlahW2fBBrNTov1cw3yosQsHMpSIRrNcE2HmayEkZ1aQpkKfu+//QPOnXN5/zd/DSlLNh/tM5koCM3k0qUml9+6QpZkPLyzhaJoRKNtElUnjCWlMGnUVESSopltRjNJMJkyHY5pdxqMhgGBn1PKkGA8ZjgBy1KRZhvDrtFZmENTIS9K4vE2g5sf4Icl196eo9ltYTsR0+GY3X7O6oLJZG8Tv+iilD5JZhCHOiKfVcfe2TqzYR9FCjwrI1FrvPnd3yTKBN/+/hqCnK1HPQwzY+oPIYlJSo/hXkEQazSbLpZtk6cZnueSRAGqohDMUnrb2yyurbJ09Xvsbg/QNQ1VTOht9wmnY/a3dsjThLiwyVWdQpHERUqYCOppwXQ4w7JNylKSRhG6Ig/CxCm05zpEM8Gw1yfOYvJsm6zQGE8zlMEAx8hxax7+rAKdRruNqYHlNXDrHmVZsHauzcb9fTJpYdo2ZZmfmOl5XkXiVhSBcjx6meDIM/W4duPzVHWeRfLAkO5ltKC/XADxtJZ5Rosdsrb3b9xj8+5DdNNhfmWePB8TTqeYzgqdVh3VrLFxZxfPcxgOAixLJwtjZvsPMGsdVKXO5saYy+MAz7MAhTILuXKtRb1uYDkWaRwhdJc82EWkE6axTipa1OyI2IfxKGR14QprazVmwx67O1Mcx2H+3BKz8RgRSya+isymqLUFLl7rYFkG/WGMzHvEokl3ocO5K2sUec7c6jrzq/OM+iNG+3s4asTDhyUgWVjRefSoJAn20bWC7sICs6jE0QqmkYFpmiTZiCSH1UaOZlooKtRbXaJYYuAjS4UoSCkUgaFlTMIxvvSZzVw68x0kAt1ycJs2iX+L/t5DVs6v4DXbNBPB1l6flu0T/PCHGJaNIgomgUYYKyRRimVIFhfmKMoSU4ONmzcY7NYY9yaMp5I8GHHl3RVUXWWwvcmgt8+Fa28RTCakeYZq2HTmmwyGEVHcR6gpXqNJiUIhJUZ9EUU1UDXtKCiuaduEsSCOC2z72MFAspqAqqagCOUp+4szjPW/YJPKlzWR+OoDxKvE8zsrviSckE0e77c4Tvjwhx/QqhuMBzs4dRsUAz8QTKcZSxe77G4NUIQgljVSfwiiwNAE89/6Ho82plhaTlxYFEWBqqq4NQt/otLbG+E4OkWe0Wg16HQspkqb6cjAaWgoSUZY1OguLuCqLaL+Q2Z1nTwX5KJJvdtFN3VUw2aycxenVkdr1dl58AARbdJavszK2hq7ew1WFhyUMiZPE6SEQkp0Q0MRsN9LME2DIptRq1nMJj4kPdbXutiuw3jsc+HiAisX18jSDH8WEgc/YTTN8COdleUFLr55lTSKCf2AvHCRZcYbv/In2HqwTZnFNOZdKDL2ezMMQ0Gf6/Lg7jbtuXmM5hrvXb7O/Rs36M4vsLg2h1BVEt+gIGUyTTEsi/FwhpAptp4TKS1Up4VME2Z+Spyp7O9t0GjWaNdBb3dASspkSqPTpbPQRbNqmI6LbqpkaUEwmXLl2gqb93P6gwGrtToPNiIuX57DtC3iqDobJIkSZtOYIivotG3yPKMxt/JYjiAqY70nNBenZd2ceekLoVcxoPrqA8QrxXk49cxxA6QzLkV+RDANqHk1/FRwvtmlVHJaiyq54tDrTdh51Kde93Bcj8mgj2Ol2I5Oc67LuetXUdVqFaq3aqiayqW3LiGyGf2x4MFDn87ygFa3xdL5VYrkJkFcpzvnEUYlShHi1HSarfPs7jjk0qU530IPUlRVICgZjyIwuvhTn2D3J9Xxc+482/slbivm3IUO4XRIfGB7YDo13JqDAIZ7PZp1hbiw0YwE02mQlCbjUEcfhjRRcC0FWRZ05tooqkKWFaRxgvFog87SIutXLuDVbArHZP78ebyai6opNBouXrNOEiX0tnbwR0MGY5twtEu90eDcpXOopkGzZROHKWuXLpOmBbqusrjcJphZpHFC3dRQVZ1ap1qls2DM5v2HtDtNpGZjapAkBZ3lC5VJd5pDmVLkObV6DYSk9+Am9bllap15/FmIH0imk4RmV7Jy4Ry1Rh3DMrj2RgfTNkmiBFWvBLeWpdHUPIaDGZalkOaCab/PdBocBfERR3+PLy9nL2BFXlDkBaZl8MXCxMvRVx8gXpaexnE8NeaCZDocUms4DMYxnqej5iV1V6NRa+G2PaIgw9RlZXIrJKYh0XWNWqOGLLJqINg2qqocmYOvXVxlsPkItIjJLGMyHFOWJYvrK9z5+D6OU1Kvm9RrJcNeRG93D9PS6XYadBaalKWg2XIoixxFU2i2HTbuzXBdg5Xv/Aa7O2M0YnpbG1DUmV9Z4nZ/n34voN5u4Oga7W6ToihJkxShGATTBFFKTLdBEcYsznsUJQSzEKWmM5vFxEmG45goimD98nlWL61jOxb6wXsJBKvnFk8ofyzbwDA0dh5WlqSyzCmSKbIsUADHUsmSjDxJQUpabZdgGpLGCbYBluWSZwVZmpFkJYONMQopbs0iS2JazQZpkqOqGaPeLtPxlPUrV1AUgWVX4fFKKWmuXMN0TNJcQTVclHBKs+miCEGWRNSbNYRmVjEigilSlkhp4bgmhqEy7E2q9ypLAr+g1bK5f2eT7tI8K8stHmvDjlvjPv4uywNNjqjCBKZZQVFIVO3zAQgpqyMLPgt9JQDidTqfPJvjOGn/IKXkzse3GO2NuPjW+ziuRRxEaIZGKaEsIQ1zRv0Jt3/8T7j49neRepeyKDC1kr0huMmMSW8Xq97FH49pLCxz9e1LmLbN2htvEX/wM9L9KbdvbHD++mXa3SZvfvdNRvsjvEYdVaksPYWj0Kop7Gz1EatLBx7DkqKQTKdjpKJimiXj/V0sc4nFOZXxxKQ1v0QwHWM6Nk6tjjIIGfamzK2sUEpJHET096aEscAUAaZnoOk6gT9G0XSELKm5GnbNo7WwgGFqSEnlGOfZVci5Y01axWM82XdZkqObGguri/jjEUsLGtFsgdEkZsF26e2N0DWFICgo8pRwNobUJwwTsnBErb1IgclsGiAlGGqBW7dJw5IwShh+coPtrX063TaKouBqUwrfJdVbZElEGqdIRcWPFVbrdWSeQ5FgmdVWz3JNgv2UtJDYdkGZZRQFaBo4loZmaAilArosLRhlCnmeUJYFs94Oi8vVVqbXm7C7sUNZpOh2jStvrD+OHyo5GRVMws0bO6yudZibe/aZIq9KT1ejvrjg4ysBEJ/raURHHMVJcAiDiJ0HjxjsDWi0F9A0jTiM0E0NzTDI8hJKgBLdMPj+b/1FWnM1BvtTDHGBIvXJo5ThsGShYzIZ+biOyXDrAVt1j/OXlrFMldk0oNZqU6tb5FmOoiisXVhjaX2ZIi/ZuPMAoerMtQVBCO1ulzQpUBRBEoTcv7eDa5U0uouASloYyDSge/4iqjHioZ8i4xRFVVDtGlE6olbTcOsOZSHxJxPiOMU2dZJMkkuNUgo27t1hcbHN0moX01JANWm0m6hqxSmYlo5AkOUFihDVmRCnyJ9GTIYTejt7zC20WVxf5s3vfZe9Rxv0twvCXCOOU8o0Ydjro1k1ZtMEpVRJgzGmqTL0UzIRYVop7baFphsYhsKwP6PZdinRSUIDQ5NIxaTRaRMEc0zCEsf2KVCIo4hGTaXdXGLUn6GpgjxNSeMJuw8HrF17h/7+lIWlBqoCfpSChP29AN0sWFjqoKgamq6g6ipC1fAcBc8zUXWDTrfFg7s7fPSjn+LPIoQsgAJFllx+80LlR3J6CAs4f3EeBYijFN3QUI9M/l907MIzT+t66rx58fn0lQCIz5WOHbJy+Duchfzj//aH1FyHxbUFDF0nDBLCIMVEw9Gr06dVXTDpj0mTgtRS6e+HKELBbdaYjRRII7JoSG8/R9dVstyl3aoD8iC+gIXmtGm3LTqL87S6LcIgYrjfp9as43guF9+8TBonDHsjglsbbO6mXKpJkiikSEZYloIfFsSP7iBUG6JdomiFPMvoLC0wnfiY5mXG44Bmu85cR0N3G8wtzyOQ7D7aoigkUtFJpEc8K5FKn61P/xmW8+dZFjm208TrzlNrPhnERtfUA2+Vk5TEGfc+vcvmg11MQ1BEE/rbO9iuy3Swz97WgPpC5asx6e9gGAq2q1BvesRxRpB5hFKnPlej3VRRdJPlC+s4rsXe5i6NEjRVZTQK0A2VtDTJggml6lVxNAodT0g0tWBja4/JfsLlby8TBgmNuomqady//YjV86s4rkm746BpKmmcoWsQBQmNhoNuOfT3R6xdmK8O+zFUlDIkKQRJELB0cY5Rr8/9Tz7l0aMZlilwjQDLsnh05x5SUbj+9oUzJ2uzbrG7OyGJU9rdQy7iJdQa4ulcQlmWJ1Wtr0jfAMQRHSg0y5L7d3ao1et05loAzGYJpVQPjKIKhvtjHFdH1XRG/SGrq01KIQlmEaZtYBgGSTKh1a7juDajvYdQxMSJR56mKLIgDGKEUFhaqrN8fp255Q4g+cf/33/G/sYm1948T3NxidVL57AdC9OysWydpjtl2BsSBT7tts5cC2IL/Jnk7kf/En/cw/MMHt5WeOPbb7F+9TJZLml3GxR5QWNxhVrDwzR0Hty4ReL7JElOrVZiqz7NuSb9UcjypXfQdZUyL8gKsF0HxzafaLHDP8dEctU9AVkSMZ0lXLo8Txr5QMw0TQmCHGl2qDcd4mAf2zapNwwKLNIkQ8enaZcMZgamYeG128ytrtBoOGw/2mc6mqDrGl6zRZ4mRHFOWmi4tSZhMMOzMkrFIlAM4ijGaSzSmauhqCrttl3ZKwh44/1vY9smaVKgComqCrJCkOclEhXL8ZiOZ0xnMVGYHtjDSChSXNuCMmW832P7YclwGFfObnaDVr1GOJswG435l7/7Qy5dWcWwDj04HwOAEAJNV7nx8TbvfVvHqzkvGL37+VTZXXx2GcRznxZC/E0hxL4Q4qNj19pCiH8khLh98Nk6uC6EEP+pEOKOEOLnQoj3jz3zVw7S3xZC/JXPVOvXQSdOOJJHA73Ii+p8CgOKLKPISkzbxrJN8qJkPJqi6YIyzyjznIWlOdJcIIVGvWlTa1johk5elKR5gW4aLF+6TmvpEho5s2nI/oPb9DcfMdzdZTQKuPHzGzy4/YiyhFanjuE0mU18Nu5uMO6P2b6/ydad2wRTn7xUmfU3mZuzmF+Z5/y33sF0LToL88wtX+TKG2/SH5YM9oZs398iy0rcmoumqgR+TJykLK3NsXl/m60H+5QSui2FPPHRTRPdMPFqHosX38Z2qgN3gvGQNElPyt547AQvgKKU+LMYfxoiS4lh6lx99xpvv7OKUkQMxykoNooQaKrC+qVzyBJmviRPfGzbBJkxnSbESclgoiAynyyuPFa9mk1ZFvS2tqCshJ07924SBiG+H1OzS5JSp1YzQfUgj4mijLxQUHWNNKncqo/qLsF1bcqiZDz0MUyDMq9sQKrgPiaBH5EmCd1uDRAoqsJsPGU0q+qjagaxP2OwP6KIJ+TSqOq4N2ISGSh6jTxJ8f3o2HirZDaHjOtct8Z3f3CR7a0eQRBT5EVlhfmCgYqfRZ8VHODFOIj/K/CfAf/lsWt/DfhdKeVfF0L8tYPf/xHw54ErB/9/APznwA+EEG3gPwG+S9UuPxZC/I6UcvSZ3+BV6UTch8fr30c/vUV/c0h7roOiKJRSUsQhml55ShqWiW1pFHmGaelkWUmeK6AoaLqGqmrohmDt/DyKqpElKUJVK3WkouAPthjPoJR7KKqOH0gUMib9PuHiHK5r8caby0RBQJ5lhJMBWQb9UYmpSGQp8Kwc04BGt8v8YgdZXufOx/exXAc/aiClSh4+Ik2XWT63gG0bKKrC3HwTr2azt7HDo08+YhoazM3bTCOF+TkDFBPfzwhnI9oNExDkhWDl8mUWlip/iKIoUA7kEFLCcDAlCWOmwxHDvR6mqaGaNq25Fs12Hdt1yKMZhoi4c3uPuY6J3Vrl4f19zGKfLIxpz7XR1Zy8UCllwSwyaHsJ02GIa+igKKiK4NHth+RJgKYZRP4Mu94m9EMcR2Pt0hr1VoM4DBlsb2EYHoGfMppKHAt00yTyQ0wTZClI4xTHsxFCQdMUDKvijnTArVkEswTTLOjM1ZhOQoo8PxBSj9DUkqkvScQM1fAwbJdZ7xHtZgs/KdA1SRBk7MU+3YU5TOsk51WNtMd4axgqtZpNmmYHmhfziSH6eVEVvObp9FyAkFL+cyHE+VOX/wLwmwff/wvgn1IBxF8A/ktZwd8fCiGaQoilg7T/SEo5BBBC/CPgzwF/6wXf4+VIPpYnvFj66qMoSnYfbtNdWEHTDYSqcOPHf0Cj3WL16rfIsoL5hSZZHKObBoZpEMURummDAoZZ+RVAgeVYlEWGECplXiJ0FbVex3Itdh7u4E8Dpolkcc4gyzUoc+Iwpru8TBwEzJs2wWRAlqTcuz/B1CR6s0uabJOINo1CwXHtI01GveEy6jVo2RLTdag3V3j7e+9hOweDjcdS9NF+r/KKnASIMiTKPdKooChTFKDuqRi6pNbu0lpcYmGli6ap1aA+MBmWQH9vyNbdh4STEYoooZCMphl7A8m59QmbFBi6wmCUUmQKRRIi7HMgQSkTskKlVneIcx2nXmfRlPjjlNEkZzwtEbpHqZgsri4RRQmbdx+QxyGm10BYHs2lJZYcG9M2qNedI0e4pfUlNu48IknHzHUkhqFQajoKBZZjE8xiLMdB1VQmo4AszXFrJqZpIKkmjeVU0a5lKdnZHrK41KJWt4kmfdxaG6EoTCIXK8+o1wrqnUWCyRBLV1E0lYWORlzYvPHuNRznmK3DkatGJYdSVQUhoNlpcvfT+9TqLqvnlyvB5gsNdXmkIXkVbZ/ynC3Nq8ogFqSUOwffd4GFg+8rwMaxdJsH1552/QkSQvw28NsAa2trZyV5Pp3WRZ+m03KgA54zmIV05rpYnoOuafR2B7iNZZqdBq5nUhSSNIpRNBVN08gKiMKYzYd7XLi8TJEXWLZGGhcoAhRNRVEUZkGIlAVCUZmMZiysr9HbHZL17yCaNWzTxrQ8wukUTYVoNmE2DkjjkDCIUIsZ0XgfP5SURUGzZZPkKrZrEQUxt35+mySMCKKC7//G26xePEeaZFi2UenelcduyPubOzy6u0OZJ3iuiUZIzTG5/zBi/dJFJr0e62s1ijLn2ntvYZj6EbDIUjIZT9nf3EM3dHbv3ydLC+JwhmkaTAKVUjiYus9oOMN2PdIMRBGRFyXtxQVMXSELRziOiml2cBydOI7wmg2auk4ejWh4glqzSxbN0O3KLdv3QxRVo7GwyMW338QwDWzLqKI1KRUwJElKf7eHECrBLCBMFSxTxazVSVIVReZV7NIkwal7lMVhFHMwDJ3NRz32Njd48703MZ3K4zNPczrdOllWATgyI/RnLMyVxEIHWeAP+4R+hmMJyrJgHLnUayqdOYfL11YfRwUDZAnTaYhtGfz+7/2EMgkxbYNr771JmmT89Ecf0eg0aDZrB1uRZ0/gw/offX/N9JmFlFJKKYT47Bumx/n9DeBvALz//vuvmO9zGur07QOOY2erT1aouIrAn4XUm96BEZBCkqSAIM1LkAJDFsRhiqFKlpYa5FmKptnEUUYSpiRxgQQ0XcOwLXRdI5z0MZUETRUsrnSpuSplvE+ZxAy37jPYEmiGTX9qImQKqo2ieeg1D01TqLVdpjMVrRyzuLxaGfhkOXniMx6MMN06qqIyGUwYDnzWLixW5z4cvGaW5Ww93Mar2/SHGtfO1QgDh71eTHepOg0qjUOmM435pQ6j3oAoKZhb7FCr2cRRwu0P77G7uYfnVJaV2/sFrldjEhXoSo6qBth1B9eIKUnY6+csLs3jGR6akjPq91CyffTaIoICy6tx5d1r1OouOxv7lCW4ro4sM+rzS1x44xKOaxKGMVfff49Wp340EYSoIksFs4jRcMa032PW20HRDOLcZDYJmL/YJo4leZZgWwpZXlKUVYRpWRY0W07VjmlOd66OIhdJwhDbs3Bdk0GYUGs4RLOQshTMnbtO79FtgtDE0PTqZLJByWimsr5Yxd9wRImiqFx64xy2ZRxbkAS9/TE3PrxNp+1RJj7+JGJjc4KUn/DW975Fq1sjmIVYlnG01Xgld4LXRK8KEHtCiCUp5c7BFmL/4PoWcHzZXz24tsXjLcnh9X/6KgW/VqOpU0Fimu0mimrQaLrEcRW6fDackqUFsqjiBjZqgqkv0C2DOEwYT2KWVjtYrkVRQprkjPoDikKhu1BHVTTSrEAoAsNtoVl1EAr+LKLe7pBnNab9fXYHO5iWRduqYTAiLSRuo8N0OKIsCygVGqpgZa2DVbvA2sXlg9VDoTm/gO16rFw+j26oPLxxB9NrMB3NMK02vh+jqgqmpfOdX/8uD249oNHxScIBpttA9kqaTY8yCTAMgWGa5GnKvU9uYjs2ilDwPAspS/IsJwgljiUZTRV0rcBWI2QZMp7ltBbX0ERCGElGozGpaFDrzBNMxgz2tlAUSKVJFuU0mya6aVOre9X4VwSKbqLbDqbrcO1bV44sNLvzTfKsYNCb0Gi6bN7fgTJj1BuBTBGI6uSyzKBuGhiqgSJCikIhTzM0XaAZBrIssWwTKQtAQ5YlhmsRRwnJbECj00DTNBSgt1tZTh7aeUgk3cV58mjMnZubWLZBjkdjbhXbm2E5BUtri/SHCWbNYX558aR5jZTkSUQy3KQ/Uxnt7hPmNfKsZH93TPPeI1YvnuPujQfEUcrFK6sItZJongiN+AXSqwLE7wB/BfjrB59/99j1/0AI8bephJSTAxD5B8D/8VDbAfwW8L95lYJfK44eZnZwkMlkHKIAeZJSpCWFUjAehbieharpFFnGeOcmjbW3URWBYWrYtkYcxRi2SZokJOGYhdUWO5sDGnN1tu7uojkNTNuAMidLIgb9kFrdQyJQdRPVaXPhzXnu3rjPdFY5BbUXFkhmfWxLRSgaEoMs8pE1h/VLq1imBgLqTZcLB4fpGobCR3/4AQ/v73PlTRXHXWPUGzPY7ZHlBRevX8K2Td545yqBH7N97wGjvV2KPKHMEzTDwHZdhFDJs5goSHn0aMIVxWLl3DyyLKnVLWxbJw7GKFLHMnL2t4eMNj9iNBpy7sq7xKXB0uoal968jlRtBvsDwv49pNnFMnVqWkGa5ZRFhu06R4vj3EKHevM7GIaObmgIIcjz4igKdJZVPhq3fnaTeDYmz3M2d3MuX+5w996EuXbOdJJi2h6mXlCvGUjVRVFzoNpv+36CH2Qsr9ehLCnLkizLmU1m5JmGmVZHGCSJIE0LLMsAoeB4Lnmek6cZbmOea+80UDSTmZ8SzUZkeYFlmURhyMJyl/NvXEZRVPZ2hhiGjluzmYx8PvngU9KkYDTTGQeNKj5FPsGyOgTjMT/744w7n9zCrbk0O026c/VnL4qfM3fxXIAQQvwtqtW/K4TYpNJG/HXgvxZC/PvAQ+AvHyT/e8C/AdwBQuDfA5BSDoUQ/3vgRwfp/neHAsuXpldsjPKY9J2qUift5KkMb8IDNV2/HzC/UGd+uYVpGwihkMYC+9J7ZFnJdDRD01Sa3SamZWBYBpuPBjTrGutX1lm9tM5kNKOUZSX9zwtqNROvVsWqLIuCJM4IgwTL0gmCBNu10LUcw6lhmZK6a4OUFCUodgNdU1i6sFaBw5FrkKTdrVMWJT///T/g3q0NMmyG/Rk7j7aIJkP8qY9m2kxHUyyrg5Sws7HPdNAnCDKEZh6EoFdZWq5TpAmBn+I6GnEEmqaT5wWGZWFYFssrdfzAQ4x2GO7uEmYencu/zppj0J1vU+9WwVuLUhD7Y/y9G9Tn12g0HMoyp5AWRp7gdhZptB6bGeuGim44IKuAwHGYUJQlg70+s9EYiUAUGWkwQxYpwTTB1jW2t6ZYelE5X+3dRmMJ27Goz18kL1WSJKLWcNF0lXrLpdHRDuQLaaXWLnTyHOI4x7AsNKP6rQiFQ/VmGicUWc5oOCNPE1qdOpbj4Lgxt/fvI9GJEhBaiTL1ufXjHyN0G13XmQxH2K5TCZM9nbs9l2jSwzIMNDlD2BauayPLkroH587PEScFgR/R7tROuJQ/aZH5ErK2V6AX0WL8u0+59WfOSCuBv/qUfP4m8DdfqnavkQ7ZM1lWwsLTDZulGVkcUaQpmZA0vCrIqywlg50xiiKwaw5BmJBEMe35FmVZeRYmsx5JWEmRx4OYf/VPPqS7UCeYxiiqiWNrmKaCqqmVu7emoZs6hmWS5oIsLejt9mk3wJ8l1DwNt+ZgN9p4dRev4VLkOV7NxTQf2/ZXL1YdJDPsjxGqyeJSg0JqdLo1VF0jzzJG44yyiLCd+8Shz8LqMgopD+/v0+7UUIspmtrBD1NqVkkhFcahwfKcwsq6zdxihziMqsjYnQbRdEicKTiew8DssH7hHO1OA4nAn8zY3+mh6jYqITLzmb9wHX8WYrgei+dWKhdsCZ1uHe0gypIEZpOQsigpS4muKwz2hhR5zs7d20wmCbVWi7nFFsE0IC+glIJJoGLoOZqhE036bG9ssbY+T6PdJsoKGo4gkgppWlLXNMLxlFl/k5UrbwGC6SigkDFezUTTFFzPpihKfD/m/OVFkijFn1VBfzVNY2G52n6Nhz5p6tNs13j7u98hDFNm4zE7m9s0OgZzzUpLpBoWJiXReEBeFMRlHdssyQyH2B/iGTGKmrCxOaZRt2iVOvVWHdkf8+jegIXFFpZtnApm9KKD/pWmygn66lpSHp0N8WKtcCTYUo5FSD76lOxs9MnTkrIsyBKBYWnIslIh6rpKkeeUJaiagmlbJHGKoiikSUYehdi1LqocVdGYo5w8sjANixKYzWZ0F5YOjGAqK7ciL6pBFheM93dBptTqDWoNh8Zcl9XzK2i6eiJMWfXa1Ug5YWIrqvMla61WFYxV14njhHjmE0wDwqgABINeSOhvMO0PqTXrrF9cpSwy/KmP4TaRk31+/sefML9+Gc+u3NN1w2TS32PvwRTLa5DmgrIocU3QpEaj3WFxeQ7T1AmCEIREEwW+P2N1xUYUDqqp0p5bZv3yGo5jHmv8xzQdTnh4bwdKWVlhpgmWbdLfnzGbxFiWimkqGLZHqVZALaREpD285jz+aIc0THn31/4sbrfJrY9/wuVv/zr1ukEUhBimRjQdcvcnv09rYZk4SsiSjMk0RLcc5l2TLK2iLUVBgOdVJ49zIH8wFA1VFQz7E7KkcuVXNY0kTpFSHnCTHQzbIQxTpkGCoeX0ehMcW6HVcNAomfYjpFTwjBluU8UPbTxHILMRg4GLVk6oNWt4jRZJlhMnBZb9uJ+/aPrqAsRn2pMdIMNBsjhO2bi3S6vVQFH1alIKlTTJKickAbqpoygKcZxi2Qama5LnJUnoc+fjTzj/5rcp8xjDsGl0F0jThCiKaLY9FhY7FHlJUZQoiiAvcjYfDtA1hb1HG4RhQqcJXvsiK+eXsWzjCWA4fLen7UXrTQ9/POXeRx+TpxlRbiNkimeXiGAHq76IP9PZufMpi+trpPESuungTzLq3WXKomRursn+/YxHn/4Yq3OVpK1imQKv4TEapyjTAWmuYmsZ/YlCq66xdr4LojoZWxECRcDMz6nXdNyax9zyRbxGjTiq2k1She9ThEBVKz8Ofxrw4Q9/gkCi6hamqaFbHnatRvhoxNzKMoZpIoWKVXNZWO4iNnfoDxJmu58g/NsYbov5+TVUzcKPJJfe+RXqdQsptIO4FyXhdIpquqxff580LYijiHC8z5vfeY8kinE8hyzLMUwTTa+OzUuTFNMySKKULPQZDgMWFhpHTmuKqpIm6cHvysW33mqgHUQcV/Uxo0GPSaCjCBVTzSjKmNmsRyJroOoUwqLZaeLEAePQpsgnZJpk/dqVA+9ZyXMD3H5OsoivJkA8a2/1Qo0kThhT5VlJreZQHpjiZkmCVFT0g713HCWomk5epqiGgVQUhFKdd6mZFlfe/1VqzQa63WBno0c+m3Lh8vLBkXLVwJEHnIOqawx3+qiawWQ8YzKZsLTUxGvWWD6/guualQnzWf39zNB4gnF/wHgwpCwVCt0izwVCUWm2G5RlTK516F74DlN/hpilOJ5JkKicu7SEzEvSTHDte7/BzmYfQxckhVEJJOMZ7bkug1FM3YN+P6PR9LA8F81ymAUJqhAM98cEkxHLa83qrI0kZDIYkSYZZVkdMhT4IVkcYzo2V9+6QJZm3Pv0Pnv7KY2GRsdVEELBa3jUGjXe/9U3ydLKihEBtZpDMBrhuBZLtoNt/yp5MMSud6i15xG6hWmaqCooCmRJWtlyaCqmvU5jboVZfxfdrtHfvE8w3kGW7xBHGbZbCTJNSyXPclRVQxEKWVKQpykf/v4/4lt/8l/Hcl1kWYG9UJTqhC1dIQljyrJkPJjg1R0s267KVlXi2QhFhuwP+oy2blSxNY2LzC00GU8jFD1Cr69zaWGRwfYWqb9Pza2ifVXj/dSgf+IUuCfHRppWToKfRev31QSIzwiUh96Hhw03Gs3oLLYJphFZnFKWlXWboio4TnVqVVlWT01HU+aWuuRpWZn5eg6mZTEbh0RBim5qXLi2iCaqZ5CQFwX7Wz0W1xdpdjw+/tlDmg0HTRQszNeo1SzcZgdFEaRZga4rL/+SAlTdQlUVai5oRkSQGDjalOb8Mv1BSuIPaZy7TndpGSEzZB4xv1AnmY0IBlvYrSWanUVAx7Aq+4nqlOou47FPq92qQtRpBmkwxHEaSCCaTvEcjTBKaDZ1DK2ozgfVdO7f2WU23GdxZZ7lixcok5g8CZlb6rK31Wfj9l3G+z2yTCUKM2RbQRY5WZrx8MYNaq0W8+urlYCzKNm8v8m0t4vvp4SxpGFn7AY2waigu+IymqSoSoZtWWRpQVHklepS10niykYlGu+imB5pEjLY2yOYTmm225QHqsQszSkLWW1DsgJd11BUje/91l8EoVT9KiqbCg6MrxS1MrUXas7cQh3TMgmCDN00UAwX09MY9Ppsbd+jiE0WF1rU7TY1T6NUF+guNah1Fuj3Z2zvJXS7y0zHAbdu3Oftb12u/CpOxLg8NT7OWFEM47NP768mQLw0nURfISqnLEVRqmCkcUY0SwiCGM8zqXs1VEMnLyrVmqrrtFsO03HE/EoHWQqSMGIyjimlgmVqzCYhcZhw5c1lFlY6DPYnlGmOP/GJ4pSl9UXSJOPTnz3Ets3qpOipT1lI6i1BGscMe1M0ozI/dpwn7fcf05N7eCEEa5fWaM21yZKY0X4PKFla/wHT0ZS83EMXKVkSk2kpluuhGnU0TSHNCroX3iFNUvY2dylRSJMEz7MQqkIWBQfnWpjVhDEMhPSQKOSxj21WsS27bQdNjZHozKIcTS9ZW2/z0djH68yRxjFp7KMIBcez2bxzH8qURquOVAtEMavOtjAtsihCkTmqItHVSgA86o0Y7/UwTR0l3UIkgiArqbUvoOkaumUyb2gImQMCwzIwTYdmt87e9pgiqwTEnfNvIyVcf/9dWvMLNLstsixHCEGZScbjEF1TMC0DVRHkWY6hGkdm5hsPdlhYmgNAVQRe3aEoSqSioGsZqqYjFAXb0knSDNsxMZsOTs1hGoCt5zhWiaBgfnkJhIKmqzy4t4dXr/Gtd9dBUZhMA7J4TByfw3bMIyezMxmClzkD5usWMOaz0ymNRZaz8aDH+vl5FCnY2x3SqNWxTJUiy8gU9ejQl0yCYWnMJjFSSsqsZDoNKbIc0xDIIkXXDWSRU29YqLrG/vYIWVQH8rp1B7dRQyiCm59uMrfQpd1xGRQ5+0HK/JyBUEykhNl4yuJa5Vz1uL9PSFOfeJfjYOHVHby6gyxLOotzIEs27z4iGA8YjCJkWhIGQ+aXWuTZgO7CAmnkIxSNcBqTSwXblCATECUbj6bU6xaBH+F6FlKA7ViksYJluUTTEZoKrW4LIQRJNKPZrOw7DENQa7dpzHX4lU4H2zK49dEd4jCj1alVXp95BkXKfr+gWdewnSaqqjDZfUReCpqdJrbX4OYHHzG3usp0MCIOA0ajECUKkWqL7uoqQnUp0XBsnSTOQSrkWYbb8CikZOvRgCRIgZyyKKg1G5iWTqPdrjw/EdiuTRJnCFlSb7gIUUWxBolp6Vi2UcWdSHK68+0qxoWstqqarqBoKpOhj+1VMTOKvCDLigMX8ZIszfBqNt/5/hX6+2Mmowmz6Qw3zHBcj6zUmFusrDfDKGcyjqDIGM4S/sk/+Am//mfepVF3Hvf+K6swX04d8ksCEBVVwh7QNJXzFxdQVIVgFiGzsjr8RtWqiELTAMs20S0TRVVRKcnTnKKQ5FmGYxvkhkocQhikeLUC3dSpN20s2yCYhgclVjp0KSWzSUS9UcN1dJIwxp/OaDY1OnMtWkvLRH7AZDQhjpq02u7xWnOGbfgZ9x9fE4qC41pMx1P6mw+ZzXJkVhCnClkWM5vlzC3UiMMZ/mhEqTUI/RlFIal5Cg1PQTMENcOn9AcoUUShtMAxkLnAslSSKMG0bEzHxGs4JGGM49WJwylRfABQKOw/2iaLQ2ZBiamXNOsqmiIIxhOyNKOUKu2WznTio+kO6e5DklzBqbkIzUaiYHs1Jr198iRhMC7IohhHNbGcJmWpomsSQYI/KVCExB8PqXUWCP0Aw9SYDSfcvbXF+YtL1BvOgQWqqI7/EwpCUcmy4iiQrBCCLJdEYYLrWSAEs0lEliSYtk694RFOJwjNrraFSY6iqti2ceCElR3Y3SggBe2Oi6ZrZElOFAS0Oh6GrrCw1KYoQSoqjVadsixJohTT1Gk0BME0w7LaaIbJoB+gayqqpmKaWhVJ6sjz6/EQOfTOfLoTljimAXw+fcUB4uVg9EjVCQhVEIcxNz+5RV7k6IZanToN6GnGcBTRbArcmsFsFFEUBZphIFQdRYBtaZWPRpwRhVkVbcgyiIL4yNd/0BvR7jbZfrhPlArm5uv404D+3ogym7KwMs/8+QvUGg5Z0yMMY2zHOF3rM9755HUpZSV9P7bnlAfyj1qrzXS6i0qMq+e4pk5nzsLzNIYPPyFTmvjTMUvLDTxPx6l5lYPZeExncRFZphRZTphqDPcGNDoKWVmdLtVo1wnjgnpLQVFVdMsmkSqmUhD5IcI1GfdHBEEVo3E4LriwbtLfH1TxLjOfMJLMZgGGodMbCVaWFrCojLLceoNg1CcYjRCqShYFqPmERqfG3p6CpigsuDa6rpEmGWkSk6cpqmGTpQGf/PAfc/37v8F47xF7Gw9Zv7BaBehVq3banYRYtkEpQVVVFLtSW2dJBrLEdi0UTSfPSqBEN6vzSxCgmRXQKIo4aGtZCRIlOJ7FoDej0ajM8bNUOeAmSkzHRFE1bNdhNvHxTI08k0R+TJFnCEVBt0yGe/sYpoph2Zi2Tm9vSrNpY5hgmtpjweOpKRBHMY5rczYdeXVVXmNHoRafTl9xgHhRcDgbSOIwpNX0kElInpUIURAnRRVnsGNQlpIsSqqTmg5WCV3TkJQoioYAPE9H0xQCPyX0QxqtRhVkVgg63SZpkmDZFoYeIkRlV5GFfUy3TmthDss20TQVy9KrQ2NqzsGrHWpaznI9ffyt4nJlpWZUqtgGUJ0hevfjm5RpSomO7SqoJIRhAYoK5MxCgTAVOl2PpbUFGp0mrmshFEiTBfp7w0pYF8xQg4jNhzG2m1Brt8gyhTQTGJpg80GPrUd7tFouXr2OoamUpcJsVhCnBmVZUmYpDUeSxRGyhGg2o153iCchwWgXd+0CYa5SlAJTV0CWBKMeo8GU8QxqdZUsKqm1u/T2I/JSZX2liWHpCEWDJGd/a5dGu4NmGkjFoDm3SDibsbGdcun6W7S7NZIoJsvA9jQaB3Ey0iQ/aD+BqlVBeTVdQzeqeB5FDnGUoeg6EsiygrKs4jgkSVZpCpTKIzRNMmbjkDhK0FSl0nbkJaaloBkmQhXUag6D/Rn1hkuWZZDnCEDRDBRVrcILhgH15gIShTTOSZIpjcZ51FMnnZ8e1o7zNHA4TYfg8Owtxxfv/fELIFmeitBzoEe0PQcpVKIorVgyWRLOIkI/RjlA6EIqlRXlgU9/6IcIUYKiYNp6pX4rC0xLIwoLkiRDSkme55RFjj8N+dm/+oegVPvc0J+g6QLPU9EMHcc1jia1YeknV4aTb/Hke1GtXIfOVNqxwLG2a1Nrz1VaB90mjgss1yPOVMaTGKG5tBfXsbWAxaUmTs1jMg6pDoUSmKbB0to8K+cWWL24jm6ZNOoGcZygafDo/iajXp9wuEUy2aLMY/p7Pbbv32Zv4z737vXY39phtLdBNBsg0TEslyR3UPQac8urOM0u8WSf9sIqEg1DDiiFie8nBGHOXi9hFkhE0sfv7zCLbKa+Qbvb4uLVZeaWOji1OkjJdOxj17sUwmKvF2FaFguXvs32TsD5S+usnF/BsPRKM+UZuDWTWtM5iMJdCVkdrwrnX2s4uJ5JHCbEUQqKUtm+GCqyKCmysto+iOqwnFJClskj9XRZShSliltquxaGWYFNEmWYus544B9Eq8qQZaUpEkrlHSvLnDjK8FwPRTVQFEhCn09/+Pv0e6PnM80vtGYeH0tfaw7ixSiJ00ptdzj5DhZn0zQpSh27Vqc53yCLE+pZQVlU6jFN1ZBCoBkaeZqBlOi6TlEqiFyiqgqKqoHIcBwDoQimkxjH1RFINh71sdWU1XMXsGybNPRxPQtRemxtB2TJh2zcbdGaa3L+yhq1mn1MZ30aEM4UXR+lOx5zACoVrWkZbO3tkPkFFZx4dDuV/CSYDMnSmO7KebxWi1rdwbSMEwY5ysFevSwlcRig6QqaVuJPZqyvd9AJcZttwskU3YYiDiiKnOFEomsJtmvgjybkRYlb+/+z92exlmVpfh/2W2vPw5nvfGOOyLnGrO5qkhLZlAUOImDLejHkB4uwDdAPFGADerDsFxkmBPjBA2DYEEBDhC3AkCDAAswHGhIlkaJoq9ndYg/VVVk5xRw37nTms+e91vLD2vfGjcjIzKjsKpJVzQVkxr377HvO3vvs9e1vfd9/SDlfaxbzDVvbYzZrhygK8JItzk7neKHBzX5CEjv0tw54+ugYpTSeo5GOTzzc53CU4gcW4RlEAa7nsV5sWM4WICR71yYEScxk21Kmf+u/+j2CKOX6rV0c1+nalRLPc2hry4VxXKerFcByurGtSiGo6wbP92jqmnyVIaR9UMSJdUdL+ilN3aJabQvarQIjCUIXY3zC2McPPdpGYbRGtVaGb3q6pKlrEJK26b4716UqchCSKl+zmj5h98Y7ls5uIFueo70xs9mG3b2tnwMoyoL1XPfrp/+vbIC4ZMAZEE4HFrnMIl5MA89zcTsVY6tFGNLUzWU9wugWYwQIi+eXUhG7Aq1bjPDQqsZoBVKiak0Y+ghpe+6TrTGPPv0pb3/7A6R0UapBuDEff/wJcdBy1ji4QUkxP2Z2cspv/Eu/geOI13z/Vx4bL8UBc2XfV9qeQG+Y4gQR+cmcJJY8fFLgiZbBvoVQ53plZefbFtdz8HzntXGprmuE0RaGXLb0xjH54jnTTcWekEhhSGOopMf5zMH3KpQMCP2am2+/jcLHcw0OLXFQ02IY9lo2WcPhjT0GkwknzxcEo7dZHT1g/fwTilJSOAccXtti++AAz3dRraZtDUHoopuWdVExPT7DC0J2r++ytT/GD2wBdTlbU1Vw5+1djLFGNVXV4Pk+nu+BsNJyy3nG82dLJmPr/SmllRF0XZcqL9BdfUogLZeiKFFKM+/0K1VrAE1RNGztDABbk2hbRdq3bmGOIyiKmqaqO1yJxg88qiLHD0PausEPbeakm4bBeEy/H6CUoilziqJkMExB1d13Il5Xinp1BnzVi9bA6Q0Cza9sgLiaLQSB9+KFy3W9HXESMj1ZcX6+wpEOoS9xJEip2awKWi1IEr/TLrSBR7UNAslqmTEY90Aomrq+RNeptkUpQZbXvPPtD4j6CeUmRzo+gob3v3WDx4+mNM0CT65YZz3qdsr0bM7O3sQi877gM2Fe+ufFeb5m45VzSwYDglTjODWiMURJwnAYEiYRVd5jNT2zaXAcMZ6kXNWnvrjFkjQG4eCRUze2IKrahtaEnB+fsXOwhR/3uPn+DYJP7tv6wvYOu9d2LdCsmxSnz04JIlv4wxicsGE5WzHc2WUwTDk7WXNyHLM4OaI/nnD3/XtMtlJ832V+vkAKQW+QUtcNZatYL9fEvQFuEDLeGdAfRuRZhVKGqlb4Uc8KDKsCYzzms5LBIKRpNNoYHNehzBvGo8hiYtwAP/Qo8ppsVVBscsI4sF4YwpLDhHDQWtluRdVQFC2jccwoDtBaU5YtdV3jOC4nT2cs1yV7u1YN3fNtDSPp2+JllKZMjx7hBz6D7QPGE48880iHAt0Fh8ef/oTdm3fJV1Yn9SWawFeWD15MfLvkeU0geIMk5Fc2QLzpCEPJejHj8PqIyf4uTx6d4wo6fUML0hHGyqT7gYN0bEvMday2A3JAf9QnW29A28njuhLXcRj0L2TqavzARUpBEUgkPfb3FadHBbguWWmt87LVhnZryPRsRZJGpL2QqmpfMDh/xiGlxPUjhoMNbSPpu4bNukBVCwqd4/ghbhBTbjZd0LvQNQSbZZnLm9DxPISXUnbr5DCURP0Q0w5pW8Pm5JRr927z7vc/QKkuk7oopxg4fX7K+dEZUm/wghDHj5ktGgJRYlTFeLuH6/sEUcTBrWv0ByFb2ylSQL4prHjPumS1zhgMYqvW3e/heg6PHx3h+JL+MKFtNUdPZmSbksPrA7b3R2ilmE0zfF/SKo3KSqqqxRHY7wVDVbeEQUBTt7YT0i1FXLcjzAmBo7Wd6J5LEHpIIfC6h8/0bI0jDUka4noenu/T1A290HQZqeWd5JsKP3CZ7AyoK8Vwew8/CCzQCnBcl826QApNvl4R9beY7AyIEqtL8YVZ/QaNvNe3PP85UOqVYb4QcbUxbJYb6mLNf/af/Ff8hf/+f4+bd65z/vwclKY/GaJaRZ1viHv9jq5tb7Cnj6fsHUxsIBEQxjFaKbSxMu1aa1RbY5SmKit7o7l2HRsmMSPpUZQti0e/zWDvbXzZsJpOeaw1TUcBD0LvFflzQ1nUzM/X7OyPX+tmdXW4rkPa7/Hk82esM0U/KFifnzFLA4TO2bv3XSQa3RSspgv6g5S6bun1I66WNDzfJR0MqMuCYbOmrmr6cUAyHqKVZr3KCfs9NuucvV780mUu8pIHH33K7PiUR88abh4G9FwFdcYgNczOJZMkIVvN8eJ9pN/itorxJKWpSqbnOVHk4kcBQ9cliu0kDlPP0uC14QeH22htmE/XrBdrVNsyGvcJAgetNEoJ4sjK2Pu+rWE4jpXr0xrwHLSC9aqkyEqk0Iy2+pZDYiQagSMtXkI4ygaRRtE0LUiJ79kCY9VAFBsGo7jrekmmi4q9xB5nFHjoCFbLNa7n0tYaP4xomgbVGqQjcFwP15McP3yEMprhIMBoxff/1AddoHrlS/6aeX41ezDGMJvNmUzGX/1HV++hN97zl3W8ZK0HV6NEU9UsZwuu3TwgjiM++73fpyoFi/mMMPAZ7e5bdyIvolUGIzTruZ0gt9++RhBaOHZZNOi2wI9SgjiizAuaWoGwGhC+7+D5rjXLARzPx/M1ewe7rJ73Ob3/h4xHP6Apcnq3b7NeFxijaRpF1GkBlGXFg59+zuL0FBA8f5Tw7R9+74X34+tOHUNdFtRFRhCkrAuJcXyOz2r6o13rL9rWnG5cvGjKo49r4uGIKPLxvE7FugusdVWhW0VdK6JAEfUGlOsl0gvJNjW6npH0fPYOtmiblqNnZwSBz5NPHvDg/ox+kOMYeHYsmBQ5UZIQRDGeWbKennF28pTv/9nbRJHH9GxFXdVURW27Mb3AGvAqRatAeA6jrT5JL6CpFdm6QCDI1wUgmGwPyNYFfuCBVjQNGBT16gi1bpnc+oBSKZS2gUK1iulszc7ukPWiYbHI8UOfdNCjrhqWp09JRrtdB0sy2h6xmm9e1Kscq42RZxVN2/mlzldI6XD73gHSlVRFQ1UpFouCXi9BKUOYBDiOY8WD2hrPca2juBQ0WjCeDMlXc9brHM91X/Y9fUNA5NXsQQhBr/ez+YD+6rc5hbiUBn/1qpZlTZ7XbO1v4zpQibEFuIwmjPf2bCFHCuraUrUdx64NwyigP0y7AqeDMoZPPn2G40m0hv6oTxBFVLUVSDXGUOaFVVHutCCqosb1HK7d+wAhBM9PGhwvoDdIqIuSsqhtFqIUeV7w8e/9iKNPP+H0aMbDh3PWswWf/OhTlNJfdfLUTUO/byXfpXS489Ytbu4pEm+B0C3p5JBGSdbrlrooefSTj7j/8WOKor5c5pZlTVUUTOcVR8cZVaVoqoKm1WTrnMV8xSZrCROL4XBdF6Fbfu8f/mM+/3xGna+oaut6PhzYNfzZtGaxUmSFQEqXO+9/j7YuwLQMBwG6bckKTRT5TE/nPHvwBI1ktD1ge2+IlBD4Lk2t7CTTGse1XQqAIArQBpaLtdWkdH22rt8hmRxaGTulrEWfgc26YG8nwg8cxlsDtrZSgtBSvqMkoLd1iOO6+IEPRrGarYiSgCB08Vz78Al8B9cReK5gs8oJowjPt8C5YlNitMZxJDt7Q6IkJggDHCmQEmsKZJEQuK6kKCoOrk1IY0MYhbz3vXe7Nnz3tV4sK95glWAML2Whvu99xd5fHL/6GQRdz6JbV3PlYq3mC67fucZ40uc49Rn1VxSbJQspqCtF2k8IYp/xJEa6DlVe4TgOySBhsyrwfGsrn/ZDvvW9d3CkQ75ZUhUOwnHY3RtaqXQ0fhhCWXU3pUE6DmXRUlQa448wbUFT5mSrjMB3Wc6m1FsJritZzjbkywWbwmGTVSgjmC8NjnfG6dGE/es7rz9vIXjnu++jmwaOT8icmMr0qMsCJ7CKRsLrs39tl/X5c56v5yhnAI8esJnPuH73Ftv7YzarDdnslPHAo1xrkD6PHpyRDsfkeYnA4PuG/rB/+dlVXiLaDVGQIoOE3b2UdNjHcTzqbMlm03B2vkSqjMXZMVoIEB7KCKLIJx0kLOcZx09PydYrJrs7aG1YzdZWgk8KiqxGSAt0ssa6rkUIIhBC0yqDFyTW5axRuI5EEXL6bEUv9XF9TVM19PqxFeeREsdVxEmIIwFjrMp2L6JtlC1UKkVdtwSthdfb7pairgpc3yMrFFvbKYAVGxYCKYztpGF5QFVR4br2PoiSCOlZBbCiaGgbQ5vPOTt7xmR3j8nBLsPhBXiOl/99k3v/4r7/huOXJkD8LKq+F5yLy3aQ4EVg6NqdFyIcqm1p6pr+aMx83tCsp7hG0laGpg2JXaej8xqKvCEIfYLOICeMPYQWtHVLuSkoqMjWGXWl2b8+sYArV+K6DlobhOOhtMJoa66aZQXlZkXoNpT5mlYfki1XnDz8jMXpc4rNig//3G+QL2cU6yUYB+P2iMWaYvaQIti71LD4suEHPve+8y28MGV+vmJ6NiNwNVvbPrVyyNY540mPIBny8Ec/IhnfxO1HLLMlQXhEb5AgjCFIUhzHxfXWCDRNsaKK7JP29r09gjhkMOxRVTWe5+H4Idv7uxRFQ5RuMdoeYVSLE4TU+Zq2VYShh/C2KRpB0usRxgECQ1XWCC0J44i+73Ln3Rv4oY9SlqugmxZtBH7kE4Y+XuASJwGz8xVNabU7wLqA+YGPg11K5EUNxhD4EqNbXC+maRTFaonjh5yebNjdTYgTj7ZuUG1D3Qja1mpYhlFIXdV4nka1FY7vUteWnj/esdqkShl7nGV9+TDAcRDYutQFaG+1yhkMexhE1xVSNLWibRWmtcK76XDEtz9874XrN3yxNfmNSVtvNn5pAsQXgsNX9HCFEF8UqX1lX6U0s7MZt9+5y8nROcvZgmqzom49isrgkRO2EW0TIl2HIPSIIpe2aSk2BUJAlVf4UUBblcxPjjk+l+zuRBRFTr4piJMQIcB1IC9r6tKSeIq8tkrSxYxkOKSsbqLzE5r1KW2zh9GtFX0pV1aOvSiI0hTlOEwXK/y0x2C7T9XYivvXjaQXcfv9u3gPnuE6DUF6wM7hAc8fPKKuW5bnx6TDbW5/589SrmdEAYhSMzs5RwqDFAbX88mzgka59HwfL93F6BZPSuoiQ7cVDz9p2Kxzbty9zu7BhJPHTzg6qRhWa3qDBFWXuKpBeiFtm+MGMXXlsr/t4/m+zZSyCj8MGU9S+mOPJA3xAo+maUn6EXEasllZbowUgvVyjeP6zKcZdVXRVjnjnW1Uq9lkDZGy2AnpOiSxbzM0TyKlYXl2ynB7l9ZzaKqS23e3cKRASFt4ztYFQnq4jqSpWsLIo2lBGmOXU8aiLm3xWbKcZzie3T8MvA5ZKQGLi/B9l7BjhrphQBBaGX4DaG2h8hhNq5QtMA96X+xAvHrP/yzB4RsArH5pAsQXxtec6JcqWF/ACYwh7vdI+wl+4HF+smA46THZcTGqJO71WK1WSCDu9/EGkQW5NFDlBa7nUFeK85MZad9qBbzzzoC6KkjiiP4ooCha6qzl44fHuK5EFTOk67FcK5JQEcUBo75PkUWs2zF1K1lPTzmb1tR5RZwUPPn4E4Tj09SKPFMkYct4ZwdX1pRljhe+Su56/Ygin7fev03z1nUcxyomnTx8gDQN02XJevWY0fY+g8kuy+kp+WaF8PbINjVp4vL48Zph0rK7P6ERPkm/z2Z+yryW9AYG3TZsZlOk43H85Ij9G9dwHcHtW2OSxEHVBefnGUZrkthBq4aydgnchtkyIhwIXN9jFEWEoYvjOLieFe4RGFxHspplYOwT2nElxeqM3/nP/z/88C/9dxltbVEVAXkWkG1K0jRkZ7dn6yXr6pJQ5YceTVlz/PCnLFcb3v5ujOeC5/kspkuENPT6PeqyIogiyqKmaTSeb4lcYeDStrbmEQSd4pOUtI0mCANrS6itcbMfeASBZQwnaUjbtDTaBoAo9DpFKod8k7NaFXhBQBA4NK0gyxtWswXHR5ae77nOy/P7m2QO3wB9+UsXIF7vEfAVV+vq5ivW647jcOftWwgBjueye7jDs/s5pikZ7WyxODvj2ZMl125HfPLRH/LDP/s9G+21oi4rMD7rTUZ/kpAO+8zP1rTlmsc//REHd96mzQVV4bCcLXH1AqMTvChEa0hTqNqAUSwpi5LANbiTHkHgYLQichuMZ8FF5WaDMRrVanq9kOUmpa4VjS7wfMnJ4yO2d7ctCvINhufZr1xrjdYCowrCwEWrmtnZGb1hn+HOPtCymZ9RlwlVL0SiCQZ7BHFK3WiKTNCYGFcY1osNWekwShuS4ZimKHj8yWfEScB6XTObNfiBT9sossJw9uQhmdnh2n7L4c0bOEGC63sYbay2o4B8U1FX4LqW03Ch2tTUNQbJZlOSbVpufe9fpqx9/LrtvleJ61i4dLaucFyH0Simqltc38ULHHJjuP7udxmvC8osI94aMjtfUGQlk50R5yfnjHe2yLMa172wHhQEkVXamk/XuK6L1rDJLAPXC32M7ohzZUNVthaNqxV+4FiFcaXxPAddWxi7MYa60QjHIe0lGEC1jRUdzudUecaDn37K+cmI/evb7OxNXtzHX6rJ+uVT4ZuMX7oA8Xp9va+7IheoSluTaFtFUzeEUQiA60iu3dpla28MprOoe3rGzt6Y6dmKb/3gfVplTVzQGsd1aBpNOugTRz6z5zOK1Zrpw88Y7+8w3N5mdjzj6HmG67RsMrh2aHBpqYoMN7ZOznc++JDz56eU6znDQYqQkqqVLOZrApnjB0Ok41AVLU+PMga9mv5whzAQZLMlq3WI568vvTN/lhFGATffe5uj+4/QJ8eE8YAyL6A+I19rDm/f5fHnn7Gcr9gUEMc+VdWwez2lqlo8VxLF15nPNjQ6Z9iHqqyYPTgmSTwm20POj885W/mkMWRZjWzWUBlKM+HafkQYp5SNR5tnDEYJQWC9NnVrJ5LrOZewZa01bVPx+Y9/n7vf/pBeP2YwSi0hKvaoK0XbaHzfwfWCS73Ium5RtTUqqitF3Sjrqr7OLUZAF5w/WeIkI3av7bBZbtgUkolwcVzNdLpme6ePMRKtNAhpRXC7Fk+SRggpkICRBtUqq77thWzWBb5vZQA2y4y21ZDY7gVAU9d4foCS4PtWoKjMFUmSEIa+VU43K842S5bnp0R/5kPCKERrbXklUtAqbbttXzUV/hjcjV+6APEzj5eWFza8Oo6DcnQnumFrG0LYNLxpWvJVxq07Bzz6/BmBBwjLA6jLmrZpuyIjOFLz7NEpSRrhpmMOd39IEMdkWUtVKfLNmjDtsbUzxDRz3HTI+NotBttbKCMZDFIMLrptqYqC3mhMVVYY4bCuIlg1IFccHRdEScpiteLaTUW1mVK1Hv245a1vvfMaWDZ8GRb36gNma2fAYPQB+eY2+XrDcjrj5GiOrhY8+OiUG+98wL1vBShtSNKIpiNj1VVD0gupa4Uf9tFNjGpq/DAhCAsWy4z1RvPsBBzWxIPQKoK7Ozi+w/W9kDT1bSamGjzXwfM8Hn5+xN7+iDBJaOoarSzLMssqVquSw2tDPvjhvwBCWG+Muibp91jNCrSxRsnG2IeA51s1aN+FZz/9Aw7f+S5Pn2VMtns8+vyI3f0Raepx9qzEaMX1rQFnZ2uiJOHGJMBgiOKAHc/aH1jehUY6IIy5LDb6vmMLixqqqkVIgdtpmC7nK9J+gkF0RsGGuqwJAtcKxSiD50pbaBYCrRrCOOTZY8VsJUl7CikkN+5dZ73KuP/JEXffvYHryUtsj3uxlP7nqtbfcFyQtK4s3oQA1Vrr9eVyxWqxRmIwWqGNYHp0ZNWXqgYvHgEG1SjyrKauG9IkwA9dHGlFbYPAxQ9cqkqxXNZ4lExPzzg8iAlCl81qTasLTmfnRIMxca9HEge0SuO4HtJP8A3EgxFxX7F/cMJqOiNrIxYrxWh7l0Y5lGXN7HxK3OszHgluvvcWw62RfYq8TibfXoArP5svPGA8z2Ew6jEY9RhvT9jM/hsa1xbetGooC0ndwnDs8tk//gOc5IDRzg5WHMXQ78fUVQ2tsEuiqiXtD/CDgHfeT9CdeJHruQx9lzAKENhg2+tHBFGAdARVpdg73CVOA9oOqbicnhAlEY6f0O9H1HVruwGqRQpDEPogLD/Cwp9dyvWGppuMxXLKs49+hxvf/g2yXLO7N6A/ijHtNvl6xaoNSEY7rNcVVWMYbQ3RqpM0VgqlNW2jMcZ2T2ZnU7b3di0kusNQ5JucpBfRtIq2biyyNvCo65qt7SFuB2QrqgrP91gtC3LXoi+LvERIyXSes7Pdx/c9tIGtvQmL2ZytnR553rCYrXFdSRiHBKEtgl5mjZekrdcHB6PNF1++Mh9eRup+cfzyBoifJWJe7vfiqeq6Dn/42z8mn51icJmvDeOkQomws3k3NAoGg5IgdMkXGWEgicIQOll2q1moMMajLCqUts5YARuCJCVIYpIITk80oePQknD/kyN623ukaYgnrBWcH17DkZI8r61wzFtvYfRHLJ5lhNGEOEmoqppkMIRmhSNhMJmwf+MaZ6crbtzcvnJ+dOdoXvn91de/OOqqJkx6+NpwdqKYPn+MKxW3vv3rPLp/Tm/7Jn6YsFoWHXDIpchKwgCWx/cZ3vg2ealpqxrXlyS9HllWIQWXywWjFa7vEMYhq1WJcD1EY+0Ffd96kdguVM16U5IMBrSt6VJqpxPdEbi+AzhIodEohJEd9do+wYUUDHf30M6fI+ilOB3+YDXHYlKkQ57VJGlEHLqcPfqE3Vtvd0JABqTtTKi6uASNDYYDS6JqWqR0AN0tWW0mIaTA6zKOuqpxXWsObIzucA+CwHfxA4f1qiCKAoSUbG9Z3ETTWFnD8VaPvYMdss2GomxZLQuiQHHt3i1LCOs6IgIugWFfNmbnS7zApz+IXzMfvmzJ/mL80gQIY7SV/r4YbxocvqRok29yHnz8lCiUVJXCYDhZzSkanyQJ2D0YkvZ86sahWGdkm9K2r2KftrW4NwzdmtBOSN1UGNWyrhTvvH+dMs/Yu3EdJ+jx+R/9hKOpZGdXsloV3TlAHAeXCtZJTyGloCl65Ks5QkRoPDZ5Qxw6OPUZYW9AXbcYad26R+P0NZdCvJI4vI7J9zJoDCBKI7wwIt+UCMclTntEcUS+qaBaUDSG3nDMcNuqOAsERinqxtA/fI+2afEcwTIrcF2fVtkOgZTSZiTa0LaaIAooG2uXB9Y7JPRdyqJBtbbFJ9yIu+/do64aZKNsfQQroNI2VsugrhuLpRBWcGU5W+N2dokGUE1LnFid0KpTi7K4EUEQuoBEOA6bVYOX7HJ+umY0TgkTC6FWbU1d5niuh0FihG2P2qzIYJTqAoJCacl6XbG15aG0JV2dnszZ3hkBdiILCVEaoJXtYL34Ggx1VeJ6fqcnYQV/ik1NXRUEgcPW/g6DUY/p6ZyybLh5ex/xWnOlF/d7ti74+KMnfOcHb712arzJ+FrkkRDibwkhToUQf3Rl2/9GCPFMCPH73X9/5cpr/yshxGdCiI+FEH/pyva/3G37TAjxb/9MR/kmzkJfegJAp7xkzUfsOH12RCQWZOuMpm0p85zaJPR6IX7g2OKTbjBGc/LsnDCKCOPQ6hMa65mgtWa1LJmeLZFSkmUlRVZQ5hmb5Rwn7DHcHnHng3vc/tYHXLs25p3v3uH23d3XHmpR1JRlQzrsEw+2SYIW3WSEARTLU6TrEyR9Ku0x3t/DcST9fsQXcLevSxxeQuF98Vpq3Slgz885OZqh6oqi0Kw2hvnplKSXcHBjh3y9YrPM0I1Ctw1N3drvRzio1loE7O6NbQGybjBYkJhWlluCkBR5Q1m2xElgtRm6Y/V9tytO2hS8qWtcRyDQeC5WtEcbgjAADHVZsVqs0VqwWmR4nttxGwxN2VhCVd2QZzVFYY/VGCv043oe/UFEbxCSDnr4gU+2sdlCXbU2kIUh0vFYLDKCwLXqX37AcJRgtMEPAoTj4DgOjhTEobRLCqOJooDD6zt4vmUAS8ezRc7uVlRVhhSW+CUdlygO8Txbj1J1w3DgMdzaweiW5arhfFbx+NNH3P/JZzz9/D6dls+X3O8vhh941qH8G443ySD+78D/BfgPXtn+fzLG/O9fOjYh3gf+deAD4AD4z4UQb3cv/1+BvwA8BX5HCPG3jTE/eaOjfM2V+DJL9NdvF1wIil6M8/M1dWPwHFgVoJXh7t0BUS/lxts3mT0/IlvkZFlO2wgwyhYUtX3KV0WN1ookDQlDqzxUtx79tEIQki2WCH+AUvaGvvPeHcK0z+HN3Zf1Ka6MNA0tDFv4RMNdHjw4J4hy5mcSSYMb9UlSn5vv3ub6zavwavMyavTKab9ydbjMLq5kD0IInjx4xv2ffEYYOJa16HmMxgFGeuTrgsFkhMGKpKzOzxlsbSNlp9otJEZgJ4s2GCFBSEyjQRr7M9aWL04C8qLB912M0jSN9basSwnCtjlVoy85Jq4rMYHXeaS6VEWJ0zQ0rSFMIrQyVFWDFOC50hYMsX6qAo10vI6hCXVbY7AKUnES0qqaqoDZ2QLPdTk4GGKEi+u5OK5tS85mGY6EqmpIfA8QNJ3knHQEfmCVxLTSVJWVi9PaMjYHwxjXsQze6XTDeJIghKSpG6psjtJ9/EjS1pK6aQkC35LHGoUQPn4Au9dvszx9Qi+RTI9PqMuaVd5Y4JV8Dbvzygginxu3915v4/iG403cvf+BEOLWG77fvwr8R8aYCngghPgM+GH32mfGmPsAQoj/qNv3zQLEa8aXrZ2EEF8RJLicGG+9f49yfka2aZAmZ2srZLA14Oa796yYapaTrzZsMo0UtgXlmI46a3308LzwEgnXKs1o0kdVirwEoTdsZmdsVgeMt3pEccjttyxRqGkU/mtwC5Y6LqjKitX5Ef1YU5UziqZCRmOu9Vy2rx1y887ey+f1JTWFL7zyEljs4lW7bTQZkQyH6CpDOi5VUZNtGsLIpd+3WADVwtnJEumlOOvCyuw5grgX0FSNFcsBHM8+NYfbMVVh+RL5uiAIfZpGI7SyvpfKYIzG8z2KzGIWRODQNjWe55BnFUb7l6VVP/QQaKqqwvUDgijAqIbldEXSS7qulLFkMQFtC6ZtWC4y4iTAkXQCMoamsgHeTnTHSgc6XqckZrOMIms4vLFjYf4dOhfhAALPtRaBda2IYh8tIBmknZShx87emAutTMd1LCLUczFG44cBve1rSGnlA7RqcaRAtQ1SegShT1lU1GVJfzRAaXjy+DlF0eD7toORZaVlq37FcF2Hnd3hV+7zdeOPU4P4N4UQ/wbwu8C/ZYyZA4fAb13Z52m3DeDJK9t/43VvKoT4a8BfA7h+/fobHsrLj87XBo+X1tuC4bjP3p07LKcLxvsC1/N46zvvEcUBZV5w/PABjoReVOG4cYfgUziuREjXGvE6Erui1R0QqCGfnxL0JwRSUhYrHn7ygOH42/ZpK4VdX39F/WR6csb9H39GkTdk6xLj9QjjAdtbTsdHubqUEN3ZCHsFxMsB4eUzvrgM5uUA2v3TG6S89+EHtFXF0eefk2eO7bJIawwspeHZ/UekaYjsZNuCKMAIgdFQFzWyk8tzXOtpWXVpPqbFdQRKaYq8pMgr0l6XUptOgMdzsUhji0C8oMeD7Rg4cUDbKgt7lyFn5xnSgSSxnRCEZT5aTkNN0xi0Fjw/mnL9xnZ3uQyqtYCqtm5BuhgEw/HQwp1VSxQ61mrR2NqI1hopLR/H9VzaTrN0PlshHI/t3YENesLK8Letsp2uXtTVrQRx561R5A1GN0RxiJRRZ+fYoJXuNFONzXZcWwD1AktzH036hKG9L9fnTzg+z8iygtH4Z6Nuf5PxTQPEvwf8Dew9+DeA/wPwP/l5HJAx5m8CfxPgww8/fEPW+xvUJ65MSqM1ZVnz7nffvWzDqQ7lZoyhURrH95lsT6jaBZ5n3baXi4w4diy6z43AWGKR0YZsk1EXKxp8dNEQ96FVgvVixf2fPuD2O7esyO1FBvKaYz49OuXH/+j3ODmzuhHrxZS737nBuvDwPJd0e4/DGztcPv27tzG8Jlt4JYu6fE2IF/WcK/AQIQT9QUJVuihtKLKMXPXYv7vF+fEpZydL8trlrbcndnlgrO4BGOr24kAsxkQIBzfwrFhrXdtg6nrdSshSolVriVTCGFzH1i8A2sYC0fKNxSfUVY2UEi/w8DzZaWQE3LkTWZ5KpzMZhJYnY5A0LR3zsmVnZ4gQhqbRYCzF+9IfxXHI84ZRHLBZFSSxZVxetAajyKPVmrPTDUYZHEcRRR7CkYy2xwiMLcz6Ho5jm8ibRYnnOZdSAHUlUMqgje6UwVwrb4fGaI3reSjRWJHhwKMsrDiyH4S2VqG0ldrrh939cxNtHvH4wXOuXdv58tv/54So/EaLE2PMiTFGGWM08H/jxTLiGXD1sX+t2/Zl2//pDCGwS1yB60gcR9q2UZfi93oJt997h/nZlE8+OcJogzYGhEO+WYOQxGnAfJ6BEBgMTx6fka/XFLWL7yqaxipdRz48/vwZ0+nm6gF8YVXQ1A33f/wRZe2ytxMRypLx/i28IGY0cGlaTRhHlyi87jQu3u012ULX537t+fOSYtRFEVNrzcnRGauNpJU9dve30K1muL3Fjbdu8d4H1+gPrUuUNiBd97IILF2XILbWg44jKbOS+dkSZQR+ZJ+gjmfT5zgJOq6MsZO+blCt7hSmbRZS15qmtZJwTVVYD9Gypq4aVNuyWqxpakVd2hpGqwRNC8tVQ5wmpP2Y4bjHeLvHYNInSgL8MMBgj8MNQ4QUDIYxAmtr4Pg+rTJs1laSrmkUTaWZjFOC0CEvGuuXEbhWWs51GI5Twsi3RCsk40nPOrRrhRCmM7HppAulsKpk2uB6LmFoHzza2HNrakWSxhgjLKGve2gJKdFGECUhw60+/Z1rPPzsMUrrrwgCX/1s/Tr8w8X4RgFCCLF/5dd/DbjocPxt4F8XQgRCiNvAW8BvA78DvCWEuC2E8LGFzL/9TT77Gw1jXi7KAcvZkqqqX+30XY6mrjGqpt+LUcZy+gfDmF4HdNLaIi893yfPa1azM4rNCkfnlGWLalvKfE22XDDe2WLQ9aHrqn5RArgynn7+iNX5nDCQlJVmuVY4fkS23qBxqWtDEIUv/lBcDQNf/PdqZnH1OrxYalx5q+6/0+M5i+MzhsOAOHIQxlDmJXVnBxcloV0zG2sF4Dhcyuu5ruzeR1FsVmAsCEw1DafPnuM6tqApHAclPEuEKhuqoraQd2kZk8ZYSHtVVYSxT13XtAbyvMAIGE36PD+a8/x5zv37c1oFq3XJdLrGCEnoQ1NVtoPRappWUxQ1nu+S9HyCyEW6kjIvEF2tQanm0hypLitWq5zVfMNiuqQsKgyGXj9i/2BoZeEkZJ1O5mqRk20K8ry0UGopQToodbGUs0VapWzGp5RGNa2F7QuD61j9C9dzMXApUqO0fS+jTScsIzHAelMT93oId8RikfOl42tgAF+Hf7gYX7vEEEL8h8CfB7aEEE+Bfwf480KI72HvwYfA/wzAGPNjIcR/jC0+tsBfN8ao7n3+TeA/BRzgbxljfvxGR/jzGC/DyABYz2Y8+uhjtq9f591v33tpl806Z/r8lCJv7M3lWmt3hMBIKxdeZDaVbJqWk6dPEcVD0v071EayWBkC37fWaekE6fuXtnh+8MWWkzGG6dmCRRUTqQqlFHUrCXEw0od6jgDC0BbQXv3ybZD74vaXy5B02c6VF196E1vQWy9WtHWFdsf0Ry4XxQ3VtrRNDcra6PmBjzFQ5Vb2v+hcrJVq0UYitGa+KNiaxIy3t2xnRIDjSnphTNsqYjelrVua1uD5fhc4BaiCaze2WMyXqFZcASgpFvOMIIpIerazsV4sSXqJTfMluIFni45S2qzPmMvOwXp1TrZek4z20driItpW2SVm016Ssvr9CD+wWg9BFNoMUivapkFIa7TkOALXcXEci3sQnauZxYdYQRutDI5rP7uuWusW7liG6oVnq0Dg+fLySzBaoxUkSYDnOWRZhdFWJl8KQdTpYty5s8tsmjGZpC/f2m8KD/qSLuCr4026GP/D12z+979i/38X+Hdfs/3vAH/na4/oFzm6x2bbWgDNtClYnU+pqhsv9YqfP3rK4vSM6aymra0AiVJWVDRMYoqssik2groqOfvs/0t/5zYyGBD7PcTyjLZQxJM9+uMxt9658SUB3U5hrQz9Qco4eoxxetS1pUTnhSaJK+q6Zf/eHbZ2Ry+dx9W04aKuoLS+NLx5/ae9/POFWa0QEIQBeV6j24bDe0P8yGcx3ZCmPkK4+L6HahTGaIqspshK2rolHSSdWIpitShYT58RJSm7+9cQjkRgadYgUK3FQ0hX4klBU1cIXNqmYbOxhc5e32M5zzDKFoRbZdN3umwljHyaxiBp0MYwO5+xOX/A7fe+TZQOUG1Do7QFsBnTFZYlXjxglIwwGuLAw/Ed0FAWNX5kVaiFsVleUzeWz9FWGOykdhxBWVYs52sGg4SmtcVUKaw4rJCCttRdRiVwpEB32cMFvkMCrYK6slqWris720dB3SiMEChVs1mVJGnUaYM6uJ4tjLuOLaj7hpey4i+Vtv+S8XPLIH4lxkVk7cr8ruswX1acTRuC9Smff/SA97/3DmAlxcqsQGuYny8Z7R6CsFwBzzNWjBaQ0lrBV5s5Go/BzgFJLyZOXMTWNepGEI8npOPhFVPd17cklba6inESooVrOQduQiByUC2D/WvcfveexeAL8dKy6OLHC+S54EVwMK/s89IlMYaTp6c8+fRz4v6QMPKo8pzxOEYxIcsVwmkYTRLyrGZTFOzv90n6Y1aLDUePzxiNU+IOu6GURmsY7wyZ7I4s07BWtkIfW2CTai22QQhBU1QUTYOQLtIVyNDHrCt8B0yrcT2Xom06eHaNVoogCmgakNLDlSWtFuzsb5OdPyXxDgiThCDyUa3i7GSJ79oaBsIWVV3Po2mspFxTtxTLkjDscA+OQAQBqmnwIysme1G8btsaIZzu9nHwwxAhHeqysnB74YK0SFGtLV7DD1yqsgFhCALPivCWTScZYHA9ad3FscEiToOudWo1Pf3Ap24UsWdbtlpr21VyJMZo8lwx2X3hw/lScPg5FSjhT0qA6AppF4V/1WrOj094+uiU7f2JtUPr3LGV0hTrJUpLgt4Wu3sjgjiibY2F3tYXaDj7hGrqkhvvfJvdvT798Yjx3h6O69EbxEhHXiFRvdKe7Ax8sk3B5z/6MbOTKUXpoYvnOEGfMISiCFhXDn/qe+8zHNpU0lwpHpjL//Gi5vAVT5GrgaJtFY8/+Zzp8xO22oI2ts7jjhsQ9VPbOkTQ1C1xL2AYeHihR7bOWS8zkn5qi49a4fkujufZQqQjydYlShnasrD2dSWMtvssppmVrI98HM+hqRt8T3Ch2bl3bUxTtfZclCZOpLUSUBo3CRGOA1IhMSij8RyBaSv8ZMRwL8Ya3das5mvKbEM46Yx8DczOVwzHKVESsV5tMG1DEFlvTiEEy0VuBWoA13FBCp4/PWM4THF9D20EjjR4rqDfCy3bRUqUNkhH4ggbTIyxT/OyaKxruFS4rkRrF9U2HdRbY3kkkOcVSWzxIU3dXrbL015IWbbkWdGJ5zgEvm9bv0LiOIJeL3zpnrgsPP+cggP80geIn+Vq2KtX11byrddPee9bN5nNG06ePUWIDwFYzFZIJ2CzOmP/4JDeMEUbgZSaZNS3bluNhRebJgfpsXdth81yxfs//AFpz0KfBVh0oQFzQZ4SthV45XAAQ5VlSGGXFZmK2JSSqijYPxyxf+c2o0n/RVfzapv0yjry6pUwr16Xq33QLljmG2twa7whq43Bqwt0uaF3aH0sbYNK4vkeEokUgmJdUBYNge/iuXB2umY4CIhSh7aucX2PumwZTayo73yj0bri6Ok5dVmDcJCua4ucrtOpfNugXBc25cZYstfZ40+o8hW93bvgpAzHLn6HjnQ8gagd/DCgaRTrVYMX2SWX9a+wYrCgmU/XeK6klwY4aJqyIPBdnDjg+MlzJjsTgjhiMEyAbnlRVuRZzmAQdWI9LZ7nIoymrCqEkDiebeP6nkvTNEjXkrAAyqJFCEFRWm6KwdLWgzAg3xQYZKeK5VgGJ5arslmVVGXDw0fn7O70GG8NaBsbcIyxOBLR0QWUMpdgu6vLBdW5u73pEuLrxi95gPhZL4IgCFwC3+H2O3d4Iu4T9gzStci6tm55+slPqUtD2NsmTT2QDrTWvVsbqyillS1sFXnG1k6PMO3z9g++3yHb7Ew0V5/y3dLm6hy9WCa0jcaLUupao5uK2byiLmaspk+5fffPc3hzF0cKW7EWouuGdB2Mq0XHbpir1+VyadX9aiwnQrUtTz97gFYNRjosjz9GGkV/7y6OZ6nsbuhhtKAua7RqO8EWW31vqpr+MGH/wD6dm1pRFi35PGN/d4hqWtI0JIw8Pv7xEUEU0dQVxXrG1o27eL4t7grHpSoKm9aHIUbaY1d1jhYB4+vvId3IQtvL1hrgdlme0pKyqIjikINrI7sGFxZz0B8NLwvJAijLhiSxSw+jLKjJC3yiUJItznD9Q+I0pC4tMMuPAuJedHm9QKBUS9tY3IIxdsLTFT+tXotkMStxHAFCEoYunmt9UWyB17YsRztDlrNNR2CzfilN1YKB3iCiyCoG/YimURitcByXpm3tV9ghUKHz3lgWbO+8DJaSP6fAcDF+yQPEzzBeoocLXM9h+/oBris5e3LE6dOjzuo9Ii/WDMZDNllJ3O/ZNNR1rXFsGuGUgtOjc4SUhHHMZpNztxe/UPa52oK8gDyIF6mguYgWWD2G3nDAZnZKVvl4UtEIw7Vb14l6fYq8oD+I6Q/iF5nIK0HoS8dlJLL7nx/PWcyWCNNQlxXZcsEwDaj0gNXGIRzs4bgOjuexOp/jRynL2ZqkFxInHqDJs4peL7ACMHmJQeD61s16OEkwytrTKWUr74c3J6wWJW7gsjW4iVGaMq9s0U/qriYkUU2D8WwHwvECtq/dwPMD2qa2mgydoIrWgrZucIRd4rVNg2gMjuthpLC+ElmLalpc117rwSCmyCu0sujEuiqIexFBMuyMc1qaqqEpK1RroddVU9u2rLCgJqtHqjuats/ydMVo3CNfL3FoiEd7GCyd3fMsQrPILQckCD28wCpeZ6uSqtIkqYfjOGxWOa7r0LYGo8EPffYPJ6jGihVLNyDpJXzyj/9/9Mf7pONdPN+hzAtWy/wLAULYQtQbdym+bvzJCBAXOGS4DBSO49LWFQ9/ekyvH7I4X7BYKvr9hMFoRBDHBHFsUXVSEsUBjx/O8F1YTBcEgU0jz58fMzq43rWtXlDSr2YJl+Oi9nBle5SEtmXmOlRFgZtsMUoNXhTT5Gv+8L/5b/nBv/RnGI/SK39nXjqXq4jKKyf80v6z8wVPPvkE1VSXiFHXlTR1gfIO2bsZkwzsk34+PccLe6hNTq8f4vo+dSeG0u8HaA3rxZqmbgmiAMezILPNxqp21x0vQwBB7JP2QzzfQWtbBNZtywXPyPFckD5t07JcVQyHDkY4uI6FPDsXk6dtMcJQFSWeZ9f+TV1b+nercT1DlIRgNPmmsIHGEQyHsa1h+B5lUeN6knQ4tMCpwKUuW1bLjJPnC7Z3eni+y9Gzc7K8xaiWW7d3EUKgtAEjGU0GaK2sQEyVE6c9iqK1waWqwJXUlSLuWUSpdATaCFbzFXEvxfMcirLGCyz92/M9q0wuLEo09izvRToe9WaDowXSEdx699s4XoDrWTBXlERUVdvdBl+Ezv/zJcbPMl7T81dac/L4mLrI2TQLijwnHu4jHQ/XE3ieNWSxaz2XfFOilUZLOD+dce/eCNf3Obz7Pmk/uUztDOYV4ujVWsAXQdFKac6eH9PUDbo8ZbD/rjVvpUE6AcPx8EU383UEtFd+uyxcXiwtjN2q6hphGjzfo25dm5HIHOk2pPEYL/BBQLaY4Xk9fFcgHb/Df0ii1MeVktV8Q54VuNIwm+VMtn3UIidMfDzfpe6Kt1FiOxfSkfihxY7otrUOX50QrRASgwRpCL3AYiG0QkGnA2Gr/r7vs5wXpL2QLKsRwoqylGVjjWdMJ1UvBXWek6Y+fhzZliwQdBDqOI1IB1Gn3wHnx3OePz6h3/dZLyvGWz2kMdy8t8ez4wV/+NsfMR4PCEIbXNLUCtMKIQlDj8ef/gS0ZnJ4B99PrMGxELjC4hlct8NGaIXr2WtQN4p3P9jnyaMpbaMoywbfdbrOhdXH0I2xhL4gBjo6veMjHdtur2sLKOv1oy+5L1653f8Y2cQvfYDQ2ur2fR1y7MWw+43HKcNJj14/pi4WuEFI2B9a+HAHvHR9q1ysmhYlBMNRwvLshINtqKuS3tY24+3BRUmye/eXI/nL1UPxynEY8k1GvlpSloazp5/jRNvWpi0I8PyB7bsvNoxGVwAxVz/AvPyjvSaa6dkK1dQMx0OeP36KH3gYrWgbxWZTcHq6YXt3TNwf4QcuWjhkizk7124AEoOmKlvcwCPpRRRZgfAc0lGKkQ5Ct9x5p4/BoakVRimaUiGMsL4TAqTjkq8yeqMU13dRtUJ3mAKlBGVh28lB4OC4Tte9sH4XRhpAWuKXtKK5qmkYjhK77g9chGO7J62ygU8KwdPPP0ZKyfW3v90tXy74ISAciVaGMis4OZqymC3oDfoMRjGjnYBVVqFXGUbAvbev4WnwXfsZjoTVqiDwrYmv53tsX7uNEJaYZoxGug5RFFA3NjhUdYXQVrJfC8tExQiOHk1BaY5O5hxe36JtWwxW51Rr0wUTj7rUYKxAsuM4VMpmGlJKyqruRG/e4I7/Y2QTv/QB4mrf/6vHFXCRsUi27cN9pk8fc3y2pFIpuxEYR7NY2Ap21I9pa01dNfiBh1KK87M1e9sByWhEFAcvAsKraYp59eer2OaLTgR4rkvS6wMFva3b9AYDlusKISWPn64ZjxzGr2XtvdzftCxNK48/PTnn6WefoeqKs8EYYRQbA8Yb0rQFZX6O9IYI6eP6Lq4fUFUN450xfhQghezIU4Iw8shW+SUbta4awlBSFRKEg1EGKSy+wYKuJJ7vEMU+TdWwc21M29i6Q1naZZmUBtexk34537CpjGU4OhI/tGrNgePjBVaLQkrrsuX51hvC8yw3xXMNy9mKttV4rsC4klvvfbcrLrZUZdNpP8jLqzVbLBFCIt2A8fY2aWqFgLIsR7Qt/WGfqlI8+OSUIq8pZEUQ+PQHEcNhgjGaTd5S5y1S+uhWI6kJgphWGabnGzzfZdkpfwvdQqdk5Th+V/SUSAn9nk9b13iBj9YGxxG2W9EqXN8WcB1HUtXqsosRJwFlUTGepC9QlD/n1ubV8csfIN7Qju/qY9Z0PUMhLSU56Q1I3Z4VKZEOw0naWctDW69oatVJgWl6vYCyXOO3hnSQvkjfLmLPKyDHl7AKl8Nu0NqmkoPJFnV1hN+/RqtdPK9mOPTxSkPc6xMn4VeeWV23nJ+tWJ08t1TnckNdVLjSkC+XKC0oGwfHqekPEqLbb9E0hv4w6Qp9NVIItJZWDEVYJSbdtizyAtd1KeuGpqypqxohHYs5KKrOr6IrynZwaiGsn2jSi8jXhe06tLY86/sOcexbC7sgIAg7OTdjlyN1WeH6Hq7j4DjSitwKYYlTtcILXIuxaDWBJxmMUmtN1118rWyBNElD4jRkvcgo8wKDFc61oCMrMmMFdxvaVjOdrQk8h8V8RW/UR2GoF2sCz4UumxLCBsA09kHCarFCCIsXydYlXugThzbI+c6LLpPneyCF7aBgM5kLoN2jByccXhsjPR9HGIIgZrPO8VxbsxHCmvtWVUtdW1Ea6TncvDN5Ycn3dcHhjxFAfukDxM88LrAJAlRTk+Ua6Q8wuJRly3DLxe1AQVQtXuChtaCuVWe7N2N7b0x/mNIfJK+nVPNygdLWEQVoW42/gERgDA9/+gmrsxM0AVK2uKJmuBWilGE4TNi/e8uayXzF+RR5wenDB2xmM1rtEASS5calKnOEtt6bnmhJe2NcP8ULPCIAo5FCEwQ+0vNRrfWk0Nh/z07XDIcJF/eh1gaEZRcKY3sq0hG4nsELAqI4YnG+osgKwFAVVk+hqRqElB202KGqGpqqJs8VVd3SS32E4yC1pixqyryyzEvTFSAdexEv6OUg8DvzW4y2XhpNg5YWt2GMZjHboKoNx4/vc+f9b6OF1YL0fInW0jIppWSTFTRKkPZTpAvvfnBIFPt88tPnPP3knEo6mEZyujFcv7WNdnwePjhjPO7jSjg9nVG1VlFqMgpIexF+5PP86AxjBNs7A6QjUI3AtCVFZUjTECXtddza6nF2POPGnT00lrBlDFR1i5EOxtg6ih84nJ2t6UnJoB8zHMQ/GwToG44/eQECQAjLVCwVvm91B7XW9AY9HN+zasaNlZbzfJem1uhWka/WDAcho509xjvbaGOQV7OFV4PClSbDxS8C+8T3fdeKm5Q5m1VGVW/QjeLJsiBMIhyp+PXfvEMY+S+voC7LHaL7DMP0dEFTbAiThOUi43RqCOWG2dMHrNdroh/+BSY7A6TrUrfghRKJxgt8pGtVjVDmEuhTZCUg6A9S/MgCgqIkom6sz0Tb1FRlybNnCw72I/7gv/6v+f5v/gWqvKGpKjabgqrIOLxzG9W00AnsXvANPM/BcV280CNOg0tIs1IGP/Av247SkQS+7UY0rcYVNW1tMQwCwenJCteBKLYoTk+6FLnt0qwXK4rFE8psRavBdJXjumoJ4og4CSgKhef7uAbKLKMqc35nfk5V16S+4umDzzk8nNB4CbvbMU8ePEXlU/JNzv2fRGxvRTheyHCYomVNkqTEScJ6ZWs/Zb7h2dGM4ahv5fLmD0knBwhCVNvgeQ5VaRGa9z9+QjpIiZKYwahH3WiSJEArbTMPKdjZHdA26iXK/y96/MkMEAACxtsjju4/xxMufhBYia9J36a8WpFtKgLfxQhBo2yRanHymOH2Fs/uZ2wf7lLXmvH2gDgOLtNX0xG7LoVJgKbVl07cp0dTHGkQKLwgQDgBxlRgGrSS9MOGcLDF4uSE2dmCb//ae1Y3QGvyTUnajzukozVoUXWJF8Y8f55jRMiwV7Feeezc+oBfv3sDpE9VtwghiUOBUQ3Gkd3fNuhW2aewspNYYCX0BIpn95+xdbCP50ny5QI/tOg/5Qpu3d4hHiT88C/9a2yWJaot2NqfoIzBFQ3nz55hVEteuxwcjkgGfcDyBoyQbJYrTKsI0x5RYkFXdaU7YRQbB60xrkAojXFCjj/9Q7Rq2bv7XeazNaKe0SjD7k5CunW9W2kYBsOAyc53MQiauibuWdHYVaVZHS9xPYflfEnkG1zHurPXmS1Wx70eUTrgB3/m10hTnyrPiNOAs2fPkEmfuFnw+Md/j8+14Ns/+LME7gGYhoc/fsZb3/sN0JK2bhmNBmzvuFStoS4b+rt3O/3KBsd1LWJVOEznJW2Z0ypQCtJBQlWUJEmA9K0QjjbQNlbv0g88dMcE/Tnjor4w/mQGiO5J70hJmCa4jkMQh1SFVVzWRiCFRqDJ1jkIh/WqJEoSGE+o8pwqX5FNjxHhkKyoefudQ5bzDWdHJ6ym56STHW7eu07QkXaePTpBtQ2DUcr6/JQy21Dla/buvIUAZvOG6emCftrDeBrpBPiBz/jgwAYHpXly/xlVVXPz3g2qsqLKC7KsoFrPqYqarYlHtrYZxGi8xfbeGDcMaMsCB43X6S8qQ6f+bO3mVWuJUHXVXsrISVfSNC1aK4pCAyXj7ZTFdIPr+gjhWFShhrps8FyBdCLOjmZkixPqusGPetStA7pkefqU05MB12/t4Ac+VVmQpAFPH57gFZr9YNxhFxxa1SClZY+aDh4PEs+TTPYPUW3Dar4kDRVO2qdpBavZMX5v32Zm2uB4YcfNKBBeSJ5Z6n6VFwg0q9ma0LNPY+kFHN7axQ1vkw569IcpbaOYns6ZbI/YrHPOnz5BuyN8R8LoLu98GBNHDr3hFsmghzQ18XDM00dHTLa3qVtN3+3z+PP7BK4mGu6zyBWuI/A8QRJbw5/+IMIcTvjkx3N6A2Mp675Ea5hON/QGiVWayi1wygiX8ST5SsTkzwskBb/sAeJnMc+5OjqIo2rrrsfsEPVCgjhgObdioEJItvZGlFnF+emasmrY3emRJjd5eP8xe/spvcmEtq4wVcFmnfP084c8+vwUtX7KeH/DeGeCTgKefP4UXWdUeUFbrGmbBtdz6B3sU6yX+L5HL26YeyGqLRns7iL1CqM8yiynKmJOnx1z+vgpfhjw4KOSpiztxE5SND6z02dMtreI+9vc203xPcl6VVDklVVNcgytBqkFfuhRFaXt2buWH+F4HnHPY3q27BSSDHles3f9Oj/9nX/Arbfu4Dv7LFctUjY4nktTN7TKLr/axlCWBdl6heMGXLu9g6SmzHKCZBtBy8cfHTM9gcNb1ywwqGrwAxdxAbFuFZvlmjLPrdXg3Hp20hGitFIk/RHnJ2csFyWSlmI9wwsTfG9EVbVkmw1xrw9KcfLsEXUr2bt+iFE1ps05efocqQt2D3c4fOtthpM+89mKw+tWvs115WVNr9/B2qUjOH8e4piC87OGw4MeH3zrGnmh6Q9T4l7KcGsLL/B5/viU06dPCKKU+ZliOJ5w9uQBi80p6WBIpRV7ewPKWhNJTV7WbG33+BTL8ajLkkefPOWtb93m7HiNFMJK6RlN0xqivkOafnnR2uqWvr5wbztdrzA/v2b8cgeIC3DSVTbb140L0VYpOTtdIF2XfFMz3BKMt/sspxuMdqgaQ5FVBKFnvRP6IU2tMdpnsrdHVcw4fjZnNPSQ1Zo/+t2PacsM3xO4wz6DUcL8+ITMl6jK2tbPpiuScIGT7OI5UGQFrudTmxA3ckj8BaXw6A1TitmU1WyFUpAtlxw/nSKFZlMIDuKITQHr2RrdnhHFPr3xHuFwx3750iIUB5M+ddlQlIYgkFYkVtvCZBhbdSpbaLQ1iaoo6Q0Sq8u5OWMwSBiOE9o64/TZEX5vj53dlKay3ILj4wW37u5RacF8PqcoCvqpYLKV0pZrtNI4XoBuS6p8w3AYgMooiorD2/s8f3SC67oUraCpG9JBxOykBOGxXlf0hjFh5JNtKtqqBWMzlaqR+JHDoJdyhs06XD+kyabkmwVpv4fwXYY7e0ihefz5A1vM1JqtoWDn5rcQXsRg0qfXj0l60VdOmiSN2Lm+h3BcwuMz9q7v2ZrF8bGloScDesMebuDT25SMJ+/x7MFjPv/0EVHc54/+8W+ztX0DmeyzvzvkTMBoa2gLkkLSNIY7777f4UBaXD/gyYMpo3FK0xlG6w4Y9p0f3rXt0E5/4gtT4iu6euKiQv8zjF/uANGNr0ynXm3xdLqTRluhlrZWeJ4gW2RUZYPumHei1WxWBVWpUK0Bo4jjkLKoGe9uc3R/w3o6o24G7GzTAVpA1zmNE1BVBumsOH64pMXHlYajR0ecPvh94v6IrYO7bF27ifQkul7guxo/6uP2R/ihz1mdIIRBrwu0cEAXrGZnJMMdzk4gCl3Gk6HVvuwPCKOUPCvRjmAw7FHmDbrVrJY5Td2ys5OisbZxTaFZrlsOrk+Y7PSZnixZLdZs1iW9Ycpg3MNxYD6bIVFEWx/gJQmuH1r/iSBkvcw4vLlN0yqOn68QusETBU3tMpuVODq3JK28xPEjpBsiHMVyXdOvazbLjCiJEO2GgeOgqwXZouX3/95/wnf+7F9hONrhyaMTjBoz2R4yP19R5RkqjNjdn1yKpzieNVwOIx+hPcLIB61olCSMYvL1gvFA4AcJVVUx2Brx1rfeskGxa0+/mq5fNWk6P1swngzYPxgznvR5/3v3MMaw2ZQMJiPm0xWe7+JHAacnS7b2xjR5gRCwNRKczTPufPCnOXn0EaIsOJc3CBOfpioJg5g47Cz7XIHwBJ5n0ZyNErRNQ5a1uI4V4J3sDRkOOvSkFF+8t792orx8bm8yfiUCxFeOL7keVVmzON8Qx3HXhmqtEYrnUlcVW7tDnj9bcPZkys27OxTrnM1yjSMF2aZga/8abjigXJ9zflawuz+gWNsevCbk+aPHBOkEqStKQpLYYe/WO7z9ne/iuRLpBQgMZdUSDFK00tTLktC3rM6D63usVzm6LdD1inI1BW/McLIFSBw/xAs8pHRsIa5VaOEw6Mdkq5LFbMV43GPQ86kqQVsuqbXAyBAwnXNVy3K2purUoU9P5lbSfbrk4No2g4lNV9967zYGOu8LiREapRSRo6lrw/7hgNOnx7hug9+boOoc1WryvOX5aY3rGYSyywVHWLr9pCM/bWYn5Ktz9t750xSbnMM77yHdgHy9QpqC2ckZruvy9OmMyVbPqmNj/S+aWqFVi+8J/NCnLiVVtaQRhiCAPMs5e/QR0nHQaQ8n7PPu99/vuChXboaXJtrLN8xw1LNFVcOlmjdYWbog2MUPI4aTXidNaPUldduQ9vuURU7+8AHp5Ca7t79LvXqK4xquX99hMT1j/vw5k8N7GCOQrsug71E1Fimpy4b5bEPSTynyAtXU3L45eRHMfoHgqKvjVz9AfMmQbqdbqC34xRg4ejKzCMCmZTBK6PUjNquMuqwI08havykLK8a0TLYHsDOkLCu0FISpy2hriEDTNhO7Ng58LgxdjDYoZR2qjbAJfhB4+EHAZl0wGMSWvCQkjiuI4gDpRGg1oDfqzHKMoq5K6sqyFZtWIR0XgSCNXdq6IfAdRqOEuqsJGAFVvSHfbDh78oBrb3+f3mSXYpNT5VZSfjzp40jJ9HyJUobjZzbFNdgUO88KMBbpV9U1J48fELxzj/nJlOXpUxZrhzB0GWw+JRkO6W3vsTg7JwxtC1lhgUBF47Fc1oQnSybbfZ4+esZb3/0+XuBjjODmt36dpqxo2xbfcxnvbuP5LtdubNHWDWXRUOT1pcy91orFsmFoPFzPob+1z+xsDsaQz49pW0PThoy2Y6JBfOlqdtmGvky7X5ltXTvZ7SDgl0DcSxyLsAY5ByNcx6JHf/xHnzPe6pNnFa2qbSvZ0QwSzfbBAY5zndPzJet1RpSkrJdWkVsbjUaijCTPSsLYuqsniU8QuKjWY1VWTLZ63b1kM5+rJbivK0x+03Ldn7wAcfWLlg51VaPalqSXkqYBnu8Q9yJ6w5jmfMPtezuX3AHVtMTDBDf0KYuaqmrZ3klp6gDddrJrppMYqwuydUaU+FRFQ9s2zGcZk60ejmuxDVKAcC6eTg5+6ONHPm2tqGsrQ+d4LlpIGtMghcRxHIIktYrHrUK0dpJc4Ph12+B6kipfc3qeIxyH64cpjjfiwadHyKhPWUypT0CVc5QI2NkdUTeGKOmxtW3NgaUriXoRnu9TlzW9YUpdthgh6Y1CHHGb6WnGH/3epwxHKY4LrfHZFC1+mPPsk486VqZH4EtM4EJbEoqWNBmR9n3apub6Wx/gRwNU03Zra0ndtSa9Lli2TYMf+LidX0ZvmOK6FtMwO3pKv7dl26Ge4Pz5knVWEWto6px00OP4rObBwxk//M0bL+llCHlxP3zJzLmy+RId/womRXZLVtd1+PZ3bl+a+9x+7x1++jsZH3z3Pe4/nKOePWOwvc/u3thK/beGsL9HXih8zy536kIxm27YD6PL98w3JcK1LvBh4L60RBAvHd9Xz/5v2tT4kxcgugulWsukG/QCslXNZrnC9X3iQWqdmZ8vrRNW7BPFPkXeUOWwOM+I+wHj7R5N1eC41pzWcyFOrcyctXjzbKwXEtf3cXyPnrJLA6MvvCMFxlgCVZTGCEegtUUMSmGxCK5vEMLa/7m+FTBdbyqSxKfMcjDKaji4Vuh1er5k1Gtw/IDR0MdxDOiGuijYGglu3Pw1MIrHj+b4UYR2UtrshONzwdNnP+XGjW2kl/KtD2/TGyZk6xrXFaimoVUG4QjaRnP+7D6zk1N2JiG7h0OQPo8+fUDWBswXDf3ItjG39sf0+gOeffYRq9KQDvfI89aStfKaxkQ8fjQn9A3TJz/l5nvfwQ98jp5VrE8fMF5Oufn2HXzPp2ghiAOiJKDMS549eARIkq2wU9huqGpNGAacnk452Bvx5Mkc14G7793g9lvXXsCT4c3TdHOFZ3flbyQCnK6OgWA8GWC0IUlCTo8XxIMxVbZistVjPVvgufuUtUIphTKSMHBYrmriyPqFGgy6eI6UY0uCq6z5ses4ljLvfQWi9hc0/uQFiI4cEQQeGivlpYGmMuzeGLF9MEZ2uorZurz0S8iziiAKaZuMpigxHcKtzErSQYLru/iRVcbeLAvifsBqkZEvS5JeRNsolBGUZUuShlYFWYDrBWihUUaAAmEUZZbh+j5F0ZCtrbS551uzmiIvAIFuG9q6pFw8ZjEvGO8dsNwYsqzGkxq39Rgla6J0ZGXgPR+BRLcVfhSRxJKy7eOIhrWaUJVHGK2p8zm9ZMPseUKc9ugPI2anC6TjkJclulrT1iWmXDDev06xXtC2LfONJE5jKAxhGPLgYcXBLqTxCR//9n9Jf7LN/s13yQqH6emSMJQ4nkO2KRlvD4kCw/MHNb4vaZQhiaGNJFVdMz1boptjXA/COOQsS/CCkDLP2b52B+tnWpOtMkxb8/z5Eft7MUdHC5LYYffgOu9//90rgj4v3xKvxogvkPJfRbK+bnR/IITVsKirurMZbDk7L9DKYb1ckAwnrEtNEDj4QYSQLWEU0LYa6UoMPnVREKQDpLTCtm3T0NRfcpC/4PGrHyCutDW7DV0nA27f2eHp58cMhn20MYRxSF239AcRbeiymFq4b1PVaAVaqM6dCYLQpcgKpCPJC0XiOBRZgVaGs+MlnueQ9CJc34qGuDikifVFkA4obEvLDwJcz7otXXRXgjimyjN6PYu3ryor9ea4PmXZkqY+TTZjMV0yGAy4ub3Lw6cVkZOTuks8b0wvMQjXoyoyPD9gMc9x/Jjd63u05Yo09SjPM3IVsDUU5KEhZIbv7fLWt97j5Pncvv/IktaiNCDYZPzeP/o9+mkATsyW15L7Y+oWQrliqQKiWHP0dE7gGuZLxWa1ZuQH5JlksWg5PSswTkyrJAe3toijFWFin47f+lN/Ds/zaNY5o8mI63dvUeYFDz76KVWxYWtnyNl5QRSsSCfX2L1xF8fzWc/PaVvBapUTewWDUULYG/LdexOG4x6DUZ8w9m1r8IrADi/uCC78Tb84876iLfgVE3QwTDmWDi4VkbNCeUOyxTl1NuXavfdAeggpCQIrfmOAomi4du8tjIamueDtGBazDOnGb/zZP8/xqx8gxCsCnld+Pry5zWpZUOcFYWIn4+nzOb1eSBj6DEYpddViHAdfGh7fP2EwSMBxSXsBJok7NyhNkVUYZZUUhkOruuRIaHTHPAx9njz4KVp4eMkeceIT+Yb1Yk3USy9l41Xb4rouk90JSinaViOkQ28UsF7mpImPbmvOpwVpam3shHR4696YT388RxVLRnuHuE6LFyQ0dc1mnRFGPlG/x/behDyLKDY5kbvAEQ3GJLz9nfe5/e5b6LZkuapZLZbMp/+YdDikKQre+cEPOH/0MT13w3RuGIxjZmuHOLKEKa9juwrd4Jg5/fEOvcgwWw+JhiN6owlgKOua/tBlMPSpywo/CpFSsFps2L22xex0xXxeWJPeWLM4m1I2DklvQF5opBeRNT59L+qg7Yrp8RHpYEwxe0C8t8/hjV0++PBd60NxZUnxBdzAZf3BXAkOXP5+sd5/PSP36nt88fVeP8bxQ1TbInRNryd5frQG3bJ1UOF4L1jA6+WGOE1wpKCpNXEa0rQGbazTexj5JGn42s/5RY9f7gDxx0yzpCO5dW+PT378DI1kObeqzbNpxmiSoLWm2FSUZUMQuhzc2KZtG/wgoKpbtDKd6GhlxVC0xnGllYM3xiIdQ6trUBYV6WiENoZkmBAEkqbWqLJhPT3j2ePnHN6+RRAnGAN50eBIjTIXqXiBI7EiK/WcwSgkjV2S0Yi6qnn82fPu6dgS+NbLcby/x3BrxGa5wfV9hHTxAo++1+NIW7fy4dYu29cP2L++R1NWfP6HPyIKAla+pm494v4OpVvx+U8ekM2Omdx4l/OPHhMEEtfz8FhTKZ8wcNBIZFORJgGh15JOrjHc86iqllXWEPiSw8NR15JsAEFTFgjHRzhYYhIQRRYSXhY1z56vETIgjlqW65ZBkkFknc4Xq4IwdEjGNzl79pDzpeLgls9ga2j9ML7qvnn9L6/s9vrXLufpV0xWx3W48+4NfvvZY7z+NZLUJUljPN/H8VzaVlOuSobDkLaucTyHtmytx0irrbiwMtS6ZZQm7B+Muq7LP9nxjbw5/5kZf5yr1X25URIQRD5+4KKahqMHD3n40SM+++hZxwpc0tZWCr2tNXGS2PV4VlNXlgIupUQp25MPY98a3HYsyaZpydcFVV6BSNk+uM5gFFPXGiNclBFMj55x/fY1EJ51nipLVNOgtCSKfRts6paqyNnMj5HhgP1rWwx39zi4eQ3XkaS9AM+BT3/8Yx5/9HssM5f56RQhJNt7WzTKMBonBIGL1obR7h5elHLz3Xtcu7WPIwWnx6eESczz44xah2wd3GC9qgnCCNfxGF+7SzrZJZQ5i+kSSYv0YibbQ8pSUTcuy7pHnIYIXbBcbNDGQUqX6azibFqzWhVIWppas1luQLfEaUCcxJw+nxGEPmEUohpr0FuWiiJb4/keh9eGSMchSEYslzlJEuAHgQW65SX37mzjxz1u3N7nC7PXXNkkrvz36g3xhYKD+MKPL2EoxBd3vXir/jAmSUN6YctmlREFsFjmlixnNEVeouqap/fv09StfbiEIVXVMju6T7lZ8Na7+zjuCw+M157PLzCr+OUOEG8yXjHuvRwXlWghuPfuHpvpMY5j1Y6aqqXOK+qiQhpDsV4jjELVFflqg6pbPNd6NOSrDbpzS1oucvJ1hWo1QeQzGPcZbg1wwwCkQ1lVVJW1ngujgGxdALBz+12EG+P5niVkFXknhW44e3rK/GSKBmbzAi8IGAwDFuczK4XmSoTrc3pesak8rt1+1/pNqIKz4zlnz88RUrC11e+YmoLNck1v2GP/7l1a1Vr3Kix4bLPakPo1/Z4DQjAaJ9Y70nFYLdb0BgkHd+5imoLlfEVda9brHN+DWns27TceRnq4JuPsdMWzozVGGwY9SVlWrFc5q8WGs6dPOD064/jxU+oix6iW1WJDtlrRVhsWsxWq1VRlxfFM4IUpNWOKWnaalDVVWbGZHeOYmrTfQ7rupRrWKzfCm94wr/xsurkvvoBCfKl1+Jq3dz2Xu99+n1ZZXkdWe0yGPmW2xnUtBd51Xa7dPKQsG8pKARor/6ExCNsixqJGzdUU4mpA+gWmFV8bIIQQ14UQf08I8RMhxI+FEP/zbvtYCPF3hRCfdv+Ouu1CCPF/FkJ8JoT4Q3HhSGNf+6vd/p8KIf7qL+603nB0SxTHEVRFSVM1XL9zh6RvFaWWiwwcBy/wybOCIi9oG83yfI5pa6SwSsp+4NF2eHmlDatFRqvA8RwcV7J7MGD3cMxgPMKVNU22pKlqPN/h9Nlzjp6cWAHWEO68cx0lAtJhgue7fPrRA4TjWin2YkNR1VRFiTGa+dmMtlGk/YStkUQIh+07P0BHN8mWS9oq4+TxEzbLzK7FDcxmK37nt36MkIL/8j/7+4RxBMLKyUVxgueH5CUIf0y2LqlrhR/47Bxs896v/ZC6Vnz4m3+aYLDD5vwp82f3OT+egvBI5JxR2qBkhBI9VrlDlpcEoc9obI1phsMIZVwwhs9+9Dssz084efKUKi9oqhZVFeTz56znU+o659Z1n73DfTaLOX/0+085O7f4kyD0idMI1bRs1hlBf0KeZeSbDavl61yv33QWiZd+ejkGmC++1dUn+WvGcDLADXyqsmQ4GuJ7ksXZU6bHTy2GQgri3hBHWGctIQRFVtHbvkXUGzA9WzGe9O13dCku8s3GlznZf9V4kwyiBf4tY8z7wJ8C/roQ4n3g3wb+C2PMW8B/0f0O8K8Ab3X//TXg3wMbULDO4L8B/BD4dy6Cyi9iGKMxF5pdXwOEcRyXd77/AWVRoRQEkY90nI6pF+OFEatNSxRHRElIlCYYHITj4gY+dd3iSEGcBMyna4th6ERhMIaT5yumZ+uOau1RVpBvcoLAZTzps3uwTZoIHn7ykLpsqLINq/maIi+5ee8mfhDy9NEpTZFTbdaU6wVlrlicPGc5X5H0EhzXYRBXBFEETkhtfFrZs/b2ndsSAgaDlD//F3/Ib/3Dn/Dut75Pvx8jpdU7XM2XnJ0XBFHI4uwc14+IIp/FbMNmMeX40VMrKS8F733nLq12+IPf+gccf/rbzM+nHJ3UlHnBeBQzSA2SijSNGA4jXM8hyxsGg5j9gxGClmR4gOPHHOymbDbWKGZ9foQfeGRNSC8sqWrF9Vv7fOv777B34xqtUvT6MUHo0dQN9z97RBJqbt9IcfyApJ8wHL8Q+P1CHeFrJvRr76VX3udqLiFeu1y5vAmJ4pDB1g5N3bBZnOCFMZvCZbx7eKk3qZQmDDzLyxBWeetCtCftJ9y8Obxq6fK6j3mj8QtBUhpjngPPu5/XQoiPgEPgXwX+fLfb/wP4+8D/stv+HxgLWv8tIcRQCLHf7ft3jTEze7Di7wJ/GfgPf/bD/vohhHzjh4bB0B+mbIqKKIlxvM7kJG9QytYpeq0VjlXKWFj0yHYegsBFeC4yjdBtSxi6ly5U87MNQezhCMN8XeF5rqUTtw1aS/JNzdmsYhtNudyg2xLPd+y62g84OZox3hqQbXLaas1oHDOdezy9/wQZjtnaGTA9PmE0eZubb9/hMZInT04IzIa0FxKlgv1bN+n1Y7RSVm/BlaS9mP/OX/51S//FXqYiK1mcHrOYFriODXBCCKbna8rNnIIlQZzgtQ0PPn6C47jcePt95huHol3y/MkxYehwcrZF6B5TVA5ZKXl7O6c3nJAvljhRi3QDqqqlrAT97dsMxg5ub4xpJUW24Xxes7UzZGdbUC6XxIlECEVZtZRVzfVbe9S1IgytY1Xs5CzXDkEwJeiNGW+PXgIUvbQseKP7wXzJbxfa5eZKj+M1ZcyXsgvbTr/1zi2efPoZedFSPJtTb45p6nsI6aE0CGHgilye51vhnraxqlNOZwz8ZRoQr27+prDq142fqQYhhLgFfB/4R8BuFzwAjoHd7udD4MmVP3vabfuy7a9+xl8TQvyuEOJ3z8/Pf5bD+2bjImuUMByGrJYbNsuMfFNQV8riGIQhTgK8MEC3DWHko1vF2fGczSqnLGprxBJHpIPUqkRJB20MZd7gBT47e327ZBGGOA2ZTjOENCSJT618kvE+t959D6PtU6dtWgaDlCrP2SymRHHIatOwNfGJ04SyUjw/qVgvck6ez5jsbSGdliSGoob5UjOdZjz7/HNm50tUp2Z1MYLAIwi8y5uuaRV+ELC1lXBwbYuwP+L50ZI08ZG07BzucXDrBr1RQrauGIxTDm4f8P637pCOD8hrH10t8dSMzbokmz7ErY/wggBjFMcPP2b2+McUeU2WtZRFyyrT7F6/wWypODtdgKkY9CSe03ByWtO0gk1Wo5sKzw8Z9CM8F5RSLKdTFtMF+WZNoxzceMxgZ5fDG3u8NhK82aYv3hovrSougNpfwom8WjS8UvMcjHrs3bjG/k7K3pbL/U9/BNgWbV011LWyOB3p0DYNxhhapSmK9lJy8IJ5+ibj56ky9cZtTiFECvy/gP+FMWb1klirMUaIn5Fo/iXDGPM3gb8J8OGHH/7867OvwdNb6S7Be9+5w/1Pj6nLligOaFpolens6G27Lo5d6rpAtYZe6pOtMtJhn6pUnRiH9VzUdUtdK+LOwakqavYPR7ieJeGMRjVh6BGGY4RpaduGqlJkWYUwmjD0KPOMdnOE5w/wvZatke2gnG58drc9NlnOYtqyWBb0B9/n/V/7kE/+4COrPbECUTXkqxWf/Ogzfu03f/DSZbArrxcXoj9I2Do44PzxfVRbMxzG9Hr2XMLIw/N9qjzn5CTHEZrZacDe9R2k4yC8hDSGJBGsZgtWucfZgw272xECzeb8CKUlw+0dothlOS9wfZfvfXiL1WJDWxWU6ykrL6UxIfXKKmvrtqLWIauV9cGQwiClJAgEnpuyPPuUUgccDgPCJOHuu7c6tOTr+99GG9arjKZuAENd2U7UcNJnMOpdYOgwhs5c9yILFVw1Onxd+7Pbzb7yStKiWkV/ss1HP3rE7sRBuAHaGHttQxfHsa5antt9irDGv45/RRzmn3R/sxtvFCCEEB42OPw/jTH/Sbf5RAixb4x53i0hTrvtz4DrV/78WrftGS+WJBfb//43P/Q3H6+zJnt5B/tPEAVMtnucPl92qR+o1qoQ12VNuckZDMbotqWsGvojy4DUTcMyL1Ftg+P7KOUyO11QVJq7b+9Z6HZWIQVURYsrWmgW6NZH4yFRaC2YnllNimHfo9wsmR4fEwQunl8yGPSRrsfZs6cYGVIUDeNxD2M0Z9OMJ/ef8d533+L6W7fYLJekA58iy0FakVmBlTAbDGNc54uJowBaLQiSHgY4OcmYbG+R5Q2tcbj2zts8+fQBm3VB352iyojlIuXG3QMczyPwJOVmhePGiEVF/zu/jsieMD89xaiG7Rtv4/kBEkOS+ri+T77e8PjhM+LQQeoMrX3eemuHz3/6iDiJCd0Ux/cR0uBQU+drvJ61wlst1mzOPufmrXeAlqSfEHbCuPb7fuUr1po/+K3f59Gnj3GERgjB6bnVffRDjz/3r/w5/NAnTSOapuXxw1PuvnXQgR7Mi8l/ZWlxtat5mW28UsA0AjzfZf/GNofXx8SB5P0PfxNoEcbFaMjLGhyJ5wWWvq8VQeAx2usTBO4LBOg/hSDxJl0MAfz7wEfGmP/jlZf+NnDRifirwP/7yvZ/o+tm/Clg2S1F/lPgLwohRl1x8i92237h4+Km+apxsaqM4pAyyzBaIQR4vmSzLMjXBUdPzpmfrXA9j3TQs87X2liYdhIx2h1fgmF2Dybcvrdj1ZOzEj/0CKKAwcjy+9frEuG6OA5oZUA4bG8n9Hoe6/kpm9WSJIJGu/ix5R4YrXB9j/E4QLoeJ+ctjuvTiw3rZUFeNAzGI/o7Bzw/qRjv7fL2r32fD374HTzPZTxJcS8l9F/KACmKivV0SlXkrOYrHGGdpVEl12/vI6UkW+e4smK28ZidL2mqirpqSfspWngoGbHcNDiOIXZL1qXPbKVZlR7DrR28ILYOU8KwOH3Ow0/v0ywfc3J0wvPnC4b9iE1uIBjRNIqnz5ZUpSLpxyT9AOH3KPLa6jMohdO/xeTwGve+/13e+uDuVxbyzk9mfPz7HzOf5Xh+wHpV4kUJq3XLYrbhyf2n5JucPC+RjqTKS85O5tbo5nVlDPOaeueXFBCFFAzHPa7fu4VG0usPrM2eaalbRV60gOVdmO5+zbKSw4NB19r8pxMc4M0yiH8B+B8BPxJC/H637X8N/O+A/1gI8T8FHgH/g+61vwP8FeAzIAf+xwDGmJkQ4m8Av9Pt97+9KFj+kxhvSodN0pA8z2mVQ9JPiVwP6bkMeiGuJwGB67mdNZ9HmZd4UWQDQOiRtSVl0RDFPkppFrMcx3UQRiAdSVk2TI9P2D44oKmtRVvdgOdrzp6fs1xkbI8Fwo2oK49ACRYLS0kfDlP8aECerShKWK01caDoj8YMdrbZbCri2Ofe+7cRbsDdtw8JQ4/FsvjK81fKsFllFKsZmBaBpFGWc1IVG+LeNSvRX1UUlcTzfLL1muXZnIefT6nrhv39gXVM30r5g9/9hP2DIeHkGodjTSAbhISkFzI9m1PMnlLkGePUY954rJcle/t7rDPNbq9GGI3vu/SHI/LaZ8eBqjRsMoXvSzCa+aICXTEYD7j7zo2Xzu8LhUkDZ8/POZuW9GPBYq0xqiJfl5SNFd+dnpwz3h6BEMRJiOs5fP7R54Thu/SGr+mKXIU9f9mlfWVeO65HUVijnvPzktHAp6kqjLZyeOt1gee7RKEVLdJa22zvn1JwgDfrYvxDvvwQ/+XX7G+Av/4l7/W3gL/1sxzgP5FxJVcUUhL2YkbDAUobqsoCibJNjhdYmnFTtRgMfuARhAGOFCynS3qDBCklrmPt7P3Ah069yHEFZdmA1gRxjB/4NK1AKZCui1YVedEyGfsEkbWay9drgtBnMSs4OcpZr1p8mZMkPpOxSxg0TOc1flTx7rURcUc2cxyX979ziwthkX4/+hJBEft6qzTZpmJTCAJHYFRJVXpoZZgta3zfQWlbhzGqJq8dlA5JNgV1ocjzBvfaECEsE/P9D99lMOmzfzhGKc1P/uBz8tmM4TDl+MkTivWcNBYo7RGFkl5Y4oiWsnZ5+jRH0OIGkk2tEYEkTIfkZ2u0Fh29XkN1wvXbh+zf2H19Ue5iAneT2HFddsewWmY0s4bReEhZZYzinFXdI45CpqcrBmPIeyXLxYbBeIjuyH5SiNfqQXz5DfVKN6T726qsMNLlWx/e4rf+3u8j1JrdG+8QhgFtk5Gtc+J4xPd+eJetrfTNAtEvcPzScTG+saS3sdVvx3G+vA4hLK//ez94l6cPTlhNl3hhhOv7SFwMgnRkhWCN0kjHoWltCuh7Lk1ZWSs7pfF9h7qw3hVKWdNaRxocV9AfbeM4WJmyMMIPBPVmweFBiBSC7WvX6A0T8rxCNQ1ePKNpHnF6Mmdr7BMkNtXO84zhJOXud94nSUL7fr5r0ZWXuGC7/i7LijiJeWmh3K2vfc9h/9oWp096rOdLVOshpaCtM/a37NJiFLgszqcUTYRua7b7Gbr1kUBVGzabmvF2SNKTDCYTkl5IGFrZ+ne+fYef/LcZD370u1SlJMtrFCnRZELgzvCWObN5Sa5W3Ly1Q7/voY2LKJaMt3pUZYV0JGmv032oFDLoc/u9t+j149d/mebqOUIQRQy3tliuT/GiAbsHI3qJ4PRcYpyYvFA0asmnHz/l7be3SXo97r13k9l0RdwoPN99UY+4mhp8CR/o1XltMCzOp7RKc/3OhP2DIUa4CBkThAF1XRNGPkHscn624L3vHLwQ0v1nOYP4Z218U71/g0Fp/eU2dpdfvt0brEJU7NnX6qJFNTVBFBAmAWWpqWqNENK2qAQs52vSXoTn2sva1C0CzXKxwRgYTEZoIwlCAVrjeFbjsCpbVsuaOPEJAkG2yQk7n4r9a1ts703YOdzj0eePkcJFOi5hT3F47w4Iwf7BBCEsQchznZcKslXZ8MmPH3Ht5jZxcnGiL04zz0qW8xX9Yc/yJByf9dowmvQ4n5bEnmK0PebhRx/jyYZ+ZJjPS55+8kfs3fshmyrg5s0ho0kf0a75/Hf/kG//i/8i0fbty4/xPAcvcHn44DFh/4DBMCaKQ6I4JPFGnJ2sadfnOP0+/dSlzBuEC7s7Q6Rnnc6qSuEHHlVRUSxPuXZ9zP71HV73tL5iZ4YN0C3buyOe9UfcuO3SH/UZbo0RUnJn+wZ5UTHeSjl6ek5TFjx/csrtD4ZIKVgva8KgZuC73SWzbY6L7OzL+p2vFi2NNjTAt37tPW6/c4uqbG03xYRsNjlJEtFUFdvXtrj77h5p6n/NHX3l036OuIdXx68+F6MbQgh83/vqaNyhLr3/f3tvGmNJlh3mfTf2ePuWa1XWXl1VvU1Pd0/PyuEMaXLIIWDKMmwtXmTLgAxYAiTD/kFZfwTol2VLNgwIAiRYgCTIJmxTAijZEs0RaWlIzsLunu6e3qqrurrW3Le3xh7XP268l++9fJmVWZW1TedpZFe8iBv3nrjLuWe751gmcaQ03FIK5ZFJOjCPeZ0eGgkiCUmiQCWgSSRhBHEMUZQg0VVI81iSKxSw3DxOTsU9MAydOFWTuNX0EJpga7OD1+0QeV3Wb13ng7fe5cYnN0kT5QVZa5R45Y0Xeen1y5x+7iTTp+c5c2GeM+fVTiOEYGujzcpyU03P7Ds/vXqLG1dvYtrjE04tqnazw91by5iWyexskVI+ZbqmUcgbaCKlVFPEJ00iDNOgXNRwzJBeexs/gNm5ErlijkLFJVcp4tZmyRXHspFLycfvL4JVA8ALJGGqsbnlc2sxojI9h25a5MyQu3ebXL+2ROjHdLs+hUqRKFbh9HRNZ2v1Hndu3CJfymEauxWuYwNKHCXq+2yTy6+/hF2qsb3to5s65ak5SvUaX/vFN3CLBXw/obW+RBhFNKYrANQaBZrNLmki6XZ8NUWG8i2OtDyuB99h4tB0jVe/9CJIjSROaTV9ttbW0V2dK184hWVrRInG1maXudnKvolxJrX3IG7UB4FnjoN4HCDTlHYrQUiBKVM0U8O0DdJIJW+xcw5RGKtITUhkmuD3AjRNhTAXQmDaJk7eIQ5jwiBE02NaW22cnIOWcSeW6+CmKvGr4xisrrSpTVVJgoQobCFEk3d+8C7Pv/oibs4c+C6Uii4Id8D09DeqxlSJz26sqhOEJQcBXH7pHOeeO6WCtWabbberiB/AzHyDfCnP5tp2doJUgIzRNUnOlRiGQavlZRYUGz2JaUyVyVtfojBVoXFimoVz0xSLDn6QcOa5M+hDZlQB+D2frZUldSQ8TTh3fp7t7R6mIdH1AIRJvebSbrWp5BosnGogSHEKRXJ5h431LoahEUURQWuNMAEz507MC6EGkMHibLe7zJ+awzR06o0yL3/pMp+8+yHLt5dU6gMpcHIO5VoJIWDuZJ2v/9JXqTVKaEJQr+cpV1yEpsyV4/zKjhghVP7PMRguH4Yx77/1LhtLd3jxq19Gz7m89uVzlAomP/x0iZn5GfIFpS/iIMlt+kTqEYogP1McxP1MmZNf2n1L1zWVur4XksQSmSgFXb5aJooi4jDqxxlBIImCgHLZwbIEhmmSK+VxczZpHBPHcXZqT1DKtOEqJwMqEauuEXhddN1AxG1WV5p47W06vkUSJ7Q3t1lZXMsyW+8/EyzL4MLFOfJZQlxQBMV1LaIwGpjsHMdieOrqmuDOJ9dI4pitVorpVvE6HVrbbWX/TyJF/IRGEBsEiYWZr1CuV+l2/MHpyULBUs5FfciQiLpbnJh1cUszSD2HpklyeZc4SdnqGKzdvcbayia2KanXbErlHJquM32iQRInJJGKj9BrrbN87x6nLpzi8gvn9h7A/m2JsgT0cRJQrZcoVmu8/841rn2yRGtzna3lFW58dJswSvnmr/0i9ekKXk+ZsPvzIY5iFm+vcP3j23jd7JuHlKDjzlPDNhWZSuIowbIM5k+f5Ob1u6yvNfl3/9S3uPL8Sba3PQp5G8PQaEwVVdzMR8QRHBZ+pjiIB9FPSCRCjtmjhGD2RI2NlRYSCIME3VBur7abQzNg5d42ui6o1YtIKRCa8uAzTB1NQ6VLQ2A7Jqal4/VU0FeZJmiaWqBJItF1wfryMpV6gzs3IuZmQzQ9j9f26KYOmAbrS6uUamVqtSGtdp9eiJ1bAnUyFZQnYLfjsXh3gzPn54jjWEWJlgzt8IKNtS3KlSKFSoXFW8v0mlsUS2UMiyx6Vcza3SWEhDSO8AKdUi6lHVbpdrr4XkAcTSsT8PCkHto6S/UG+bxBkGgIs8bGdoQgRZhK7Nle+oxYq+GePoVMAnSjiO4Y5HI26ytdoigBKYnjhKn5k1x4/owSF4dGcRfxzH7WG5XRxwKee+k8y/eWuf7+dZZXYnK5FTq+pFKyMEwDEORyaoxazR4Syftvvs/6vWVWVjoIDRbOnaJcsnjupUvUpioTlZYCRaD+7ffexrQMLr10loULZ+j4KfmCQ2NK6Tlm5ytc+8gmCBNq9dwTVUqOw88UgXgQGBCVscldruaxXIOg59HqBNQpKffrMEZoFk7ORjN04iRVmaiFIFfMkyu6hEGk0tnHCRg6vp9iWKbye9DEIMu3alpQLrn4vRAjV2dzc4NK2cVAkkhBKe/Q2u6p3T+DJEkHnnUii6/ZX4+9js/mZodczub/+kf/nG/9yteRSPKFIW3/0LfWGmXufLZMc2MTQ0vByKMbFs17V6nMnmZxqUfekeSLLttNn822hZ2rU66XWVvZxNRRdY8Th6EfummgmS5hqmOZKXGvRSQdcoaklBfopy8TJSam5bC6kYADlVqOzlZbmYF18L0Q084zfXKOM+d3HeHZDXuaBpUfy7e++3MUizY3P1kkV60zU3HJFcsUS+pYukwld24t8+kHn+A4JmvL23zywU1002FmusDV929SKui02yFfeOMFStUCtm0OiT1qRFaX1vnjH1/lT/6pnyPvWpRLOerTNbq9YIBavuBw/sos6+s98jlzHGFA6WAOk1PzQP1zgOo+9wQCGLdHgVQ+/2cvzfNH3/sJYSAxdAPbNnFdk267h+OYVBolOp0A01AWCzfvqIUslaJSRQnSiQKVZ0LTVfZm1OE9pQ0XGoXqLMl2F8c2ibUyqZWjURWZeKJTrkxRq+846/RZ5q3tLlGgzo2Yps7S3XW6HY+ZE1NUagV+/c98h1q9POAchmeFsp74rC5tcuOn77O6BVNTJfKFEOIukVbCKjSoOzHe9io3b6zw0Q/+OVfe+FXKtYsYWsJ6uEkrtJVX4D7OQqZlc/blLyI+us7GRkiiVzF1sM0ImURYpQW0KKbnJWy0VNJjy8wykcc+lqmxce82tp2nWC9mu/vw94iR9nb5QY/jJpWY98pXX2PqxBxJmJAv5ijVygCkScKt67f54MfvoSVd2lFApDfUcX8zZPHeOpgVVjyfYqXJ+29/TKFS4eXXLuC61qDBJFGWqv/wP/o2Qgg+fPcjTp07hZPLKzFiCNeLz81z4eLe+oQDKS0PAwes7tkjEAekfA9TXyolyJTGVImv/cIL/MHvvMnHH15HyrM0GgU0Q8sIgUph5+Zt4jAhzRyrhFAZmXRdRT7K5U28XuY7YWrK8UbTiONokA/BEl0MEVBuFMg7KM/MfINUMzhxWrk697Mq9VEu5B0SJ8W0DFaWttja6GLaBu3tDoWCzexcffCJ4zv8xuo2N97/gLDXRUYBJTPEEg65qRqeF3LquUsITeDmLTQxS8c3Of/y16hMneAnf/gHaMJgcXGL51+5QhInSNmfSn2n9dF+1Qwd3SpQLDQRWh5Tj9G1FNNSbt6umRIJF12XtFoBZx0bP0jRDQO/F9BudogKDrXZ0XbuO57jBGMIL8PUMbWUj9/8I7ZbMY3ZKYrVKlNzU3z05tukoc+9lR6Fgsu5KyrTe32mirG0Shx42G4e1zFI4xhdJFmYQUkmbbK4tEm9VqJcLXH39hpJouH5EcWKQdE2duG/Lw2Y9KkP6hN0CHj2lJRH3R8T6tOEUKcUhaBUKVMsV7l8oUrsbyF0A8sycDIZNYpUYBqJUkTZtq52EQFJdk4DIRCahtCyqEDZoBqGOudv6IJP3v4+MzMOCJOtZoDX7ZErlbnyhYtUawXIiMMwyqap4ziW8vJLU3Rdo1zJ09zaZmVxY2DhGOEeMuVoa7NJ5HVJEuiELm3fpeXZeH6Km89hWTqBF7D62Ud88sf/mlIeZi9+hU5zmySxMC2DxvwCcSR5/+0brCxuk6Yp6mTs7n49cXIa01IZvzQZZOZgaLcCNjZD/NgkjmF6usjUdEFF50ogiVPiOGH+3PO0tpvKE1HuMxHG1EkyHdP2Df1cvrPEv/nnv8enn21x626X2zcW+fidj7jx3nukYY/b97q0mm38MGH53jLdIEFqJnahQblWpdg4idRdDMtmeq6BYWh4XqhwThKmpyoITbCyvE0aR9y9u47jOtjWbuJwUOjr4dW0evTKimePg8jgoZ1D9uJEBMoLTzJwp37uC2d574efEEVSPcqUhFoWJzBJJEbmoKQJQRyrCNeaJjBNY3CEWJklFeKaJlRglhSEaXPx9V/ANtq0222KBZPK7AmKlTKWOezBN/lDkjRlc70NQiARXH7pPLZt7Oyegw9TvzdWN/nk7bcplvJohoNtp1imTrVRJAwCcnkHhEbitxC6RbfTJfS6zJ2d4dTC80TyKndvr2HoHoEnuXdrjfZWk8uvXKRcL1DI2wzMPFnTnXbA+lqPdifEdk0ivwVpSNxeo5yvUquYrG2GVKcrFPMWYRATBDEIjVwhTxj4xInEsvdyG98ZVzn0KEnSnYQ5Q92gvCsdDAM0zcC0TNKgie2arC36eAEYhsGVL36BmfkpLDfHlXyB1sYqp07V+fi9a3ib90g1jde//otUG2WCIKbd9pXOYq2JIQS2a5NEITeu3+Hr33qVRqbQ7Fs9hPpxYHqxY506WPmHhWePg8hgz/lx4Phbe7wzYh1Q/y2cmeGVb1xheW2drfXNAbeg6xr5gk0YRCrgrRBIobgK01Qihu9H+F4MaaTyXCTpoGlNqF0/iVMsJ09rs0Ox5ODmXWrTdRrTpZEZsdc3d9o+lm3i93p4vS6tZncoUdDO9wCEYcS9z+5Rnaqh6TpdH4JAUp6aw87lKNUr5IouuYKDZrmg5bj40hcxtIiP37/Gxx/eoVAq8cZXLnH6VI183qTXWqfZ9NjebA24p3E7XbnisjBvIoWJoUviGHptj15coNqY5vb1T7DMlFzOUlagOMuULkHKVOVQDXo0N5sEfjQ0WKPfN77QDFMfI5Q7UJuq8bXv/jIzMzlydow0Cmx3ddbXmniRTqVksr3ZwvM8LNdm/mSFk2dmuf7JEqWiQRQlVOoNVhdXePsH79FpexQKNlJKdCT/8rd+j5uf3CZOJK9/9WVq9dKAoxkJgnuYxf6YLRzPLIHYCw7FdvXn10hiHUauZWZTPHGyzn/2X/0aF19YwC24+F5IFCaAyufQaXnEsUqgAyo1fRQlKnRYrBSWCD07C5Il8xEqUIhh6KAZuJUTbLUgkg5ba+vcubXM0uImG+vtHXGBUWZCAmsrTTrNHqBlOTyskW/oF4yimE67h20JtjZ7bLchCQNMt0SxnFfiUd7F64YkiSTwEzZWV7HyFQqVCjNTNtPTBcIwpLndZGqmwtRUiUIxh9/ZJuhs0esGE/tZ0zXmT5/gwpkCadRDkxFRnFCdmsELJF63x9qWQKBl4yGwcy5RGBP0PHpbK6RouHlXxfocH6z77Qvj00IAQnDy7Ele+tqXKeSg41t0Qxs/dWm2E1JhMzeb5+0fXmV9eRXHsShWShRrZYqNWerzc5w8PcOH71zNuMqUVqvLxmYbP4gpN0okMqVYtClX8sr/JTND74ylfDD/nccEz6yIcSQwrOkmk1n6HMRgc9qZWa5rc/L0rPKCtC2VeVkI4igmSaBad0AI0kQ5DiVxSpKkrK1uk887FMsqY3N/UgiEMt9ZBkkqkJpJqVSktb5O2oPu1jamY7Pd7PDN73wdoYkd7XcGSZJmsSUltqNz9vw85XJuZ71kF52Ox51P7xJ0mrTXl7Fth3ZPw81XEKbN8q2bFPKCytw5JUv3Qvxum9nZItMnG1iuhaYbeJ0eaeyzui5YWethax7FgoukyN3PVthuxnz12y/juOq4uKZpdDo+SRJjugX81KFUtFlaapFz8qQih2FanLj0Bs2OQCcmjjXQpVJSyBSpa+haQpqoCNsIMhFOG/nG+8IkVl4ILr54Cc10+Mkfvs3mvZt4vYBi0eClN15i7e5darU83//eW2xsbPMn/vQv8/Vvv0yaQhhEFAoOG5ttmptNlu+tqERF7QiERm32NCdPT1GfqgzM0f3JtcMYDilNDig3H1Q5OVLdAyr3nw0CcVCFw4NaOMZV7nKUWAzSEWS/dV1j9mSV6x/cRdcN8sU8S/c2VfxA3UATAstSrK2FrnJSaCpasdBAJmpiGJaOlClJnJLLm4hUx/MCwkjiB5v0opi6AfWGOjatoyHFTvDSNE1ZureJm3c4caqOZRqKAI1/m5RoSFY/+wQvANuyaHdCTENSrc1iuw6djU3qs3OkQCqzLFdxkAXtdZk/PQsSej2frdVVaqWAWOYJeil+BP76R5AmFIsv8v3f+SGv/dxr3Lq5zukzUyA0ymWX2LY4e+Uim6vrXDDrRKHAshwsW2BgUg1jhOkMFlOcSmxHhWfreNBsRyr2pqbd/+zBRGKw1/gLzl86w8LZeVYXV+h0PEzL4uyFBX7Y3CYOlmk0yqxnAWQcx0JKVNpDCV/91qt8+O5VUgTtpsfKSpOLl05y7sKU0vNoYuDcJsbmmsyUJiLjaA4CB+WSR4o9oGjybBCIg4oNRyGfjROLDAbzLZuYhqGRL+cIvJDmdgevGxCUXHJ5Ux35jtNMfktx8zYySel0AizTHrRhmgaNusvqmodpgEaMZZk4uQLrbY/2xiKlynlMGdPreDS3mgRhyvnnTmIYujrAZOhUqwVc1xzguXtHVWHtU+GwvhVQdbfQNRvNmae1ehvTsqmfPIvUdLqtrsoc1umCTAgDwerSCnOnZtCERj7v8vo332BteQPf81m/d4/VVQ9f1Fi+/odQOI+Vy7N8Z416o8jS4hZbd2/RmGswc2qBQqlImmoYZh6NlI2NHpatc/HyWW58ci+LSC3odEIV5Zk0O6S1SDXXo1jK7YqnOaJjmOAaMQlGOJAMbNtien6WKSlJ4pg4SnjpS6+wtbqKrlu0OhGLd1eYX5hFAnrmgeu4NldevojXC/npWx/T7gQsL7u8+PLZXRnF07Tv5CYyKecxKxUOCc8GgXjCMD6EfX+EhTMNut2Q3/onv0+1lGdOr6sQdCmEaYpADgKfRlGiLAsZeyizHcNyLHJumCn9NeIoolTNI3QtC3YbUCw6fPb+B6yttvB7LZIk5eyFE+RyFvNZzsZdXMMOsoCk1+7i5BwIbuMlIXOXv4Gm63Q22uQLdXwvRDdcDFMdJ1/87BqpsDh9dprq9PRIpZqmMXNiCoDGbAPv3/wA+8wsbvHX0AyL+ZM1DNPizo1VTp+bpq3H3Ln6AUFkKcImJY5rYuhKz2BYBp1ugGGYyiqUgjoxr865GKZJe+MOen4W09DVCdfhY/t7enHuDZoYEk+GCMr66jYf/vgnkEY02zGnzp2gUJ1WyYOv3eDdtz6h22wRBCHFconpuWkMyyCKUgzLYnphji+fnR1YtfSMCA1C5Wt9JNXvw/oy7MVMP6oj358vAnHYXpw0EOzoJYQQ5PM23/iFV/i9//uHPPf8WTRNHbt2cxYaKWliUijZJFGSTX6pPCGlHLDJlqM03zKO0FKdJI6wLQPLLaIZMVvr22hERN0NajML2JapgrH0XRvEmKOkVMFxfC8gCkJ0XWNtcYmer2GZGmZxQQVoLTo49kmiWKJpKXGUkiSwuXybj37yB1x48StUpuucPju3pyLXcWycUpW71zYgalMt5li5u4gwXeI45caHN9n87Ke8+M1fo+elmKaJ0HQsS6Pb9iiW88RxQhhFinBmWn7DMBF6Fj1KMygWXaQm+b1/+v9Qm53hhS+9TH26tu/i2sN4MfoNQ4aEMIy58+k9Pvt0lZyjgabOy+TLVSxLJ9YtZosGv/+v3qLX87lwYQZdF5y6eIYzz50ln3c4f2F+SIk67KQrJv4+Cg7iUcaD+HwRiIP24j7eeOMneqWUnDjZ4LWvvIgmJDLtR5MKVLq1epVeJwRNGyx8BjqNHQ22rusYloaZxKwurlOZmmJ6tsjaShsTkyAIKc1dwNZ9gl4nO6WoD5LfDPwOpFJcfvjWh3z6yR0KeYfp+QYAliW4+MUv0e1JTEvFzPS8mFLZJQzCwW4WRyFf//a3KM6cYuHcwp4RwVUoPY0LL1xgY3mFjbRKOzSIQo/Ya1Mu1zCNHJ3mJfz2NilKk58kKYEXsvbpm7gvfQ1hGFiGQ7vpYeh6dopSEMcS07bY3u5RnL7Ares3SPUSPf8ecZzwpW9/lVI5v+cYykMumjs3V/ns2m2CTpPtbZ0v//xrXHn1CqWyyvRenaqytbpKpexi0eP2zRXQDLwwJV+tYLvWjlv7eF8dVPa5D0yawp/r495HZQGSu/jw+70w9nuSnV2qAzSOY3D+8jw9L8T3fKrVPIVSjkq9ktnhlaZz5BxPpr1WjlYJcZyqKNmJZHZhGse1yOVsSkWL8tQcufI0URjTbIPXbnHtvY9ZXVzn6k+v89knd+i2PTotjzCI2VxrEvR6aJpJoVwmX3DZ2AxpbnXwfZierWAZkjiI0LUEzTAAjU7bY/XubWJ/m0JjjnOXzoJMx7558OmDiVmuFrj0xRc4fdKlXoqp1Et4XsLGWhupmZw8e5okiUmTBL/rk8YRUQzF2Uvcub1NHKVEfpRlkOoTHhX+PY7UcW23NItlCCza9DzJxsoGn350iyROiONk97iJUQvUTkDJyatJAmcuzPGdP/ltKjMz/NwvvsLLb1xBonxH3v6Dt/hX/+z/o7nVxrYkfmJg5/LkKlVe+8Zr1Bv9My9ylMvMah/G5enWOozCU89BHCl1nCi7DYUP26fczgtDz6UYTMhSyaVUzdNrefhBCKjoyEEQsbnWxrJ1pmb6kZZ2bE+aJnAdg+3tAENTfhN2zlU6C8fCNHWEgHK9hmGabG9ssLq0Tam9zd2by2hJB8u22ViqY9smURQyfWKOyGuSRgGCkMAL6HUCHBNsW8fvegjNwLBM2q0YEJi2xcbqKr1QUC43uPTKC5iWnsnqQ6zTrr5RGvjT5xeYPTnD3Rv32FhcZnktppDrImWF7VbIyYV57t1eI4xSimUX23UIPJNcThJH0Y6GX2iIJCUKU3o9n2KpQODHCCTrG1tUSiZhYNJsm3z83nVOX1ygVi8OozMyWAMxY9izcw/QhMB2LL707Tc4c25OxffUBFEUs7Gywt3P7jI3X81ysYIwJN/65deYmqlSKLg7hF/ucAw752eGycYkXJ9OeOoJxFHAREVQ/4zE8P39zKTjG9DYXBOa4MyFGe7cWGX53jaWYyE0jfXVJpVqgULRQsCODiKTtyUppqWyV6Wxcr9WvhEmm6vbyERFrvLabZCCan2KOCrS21qi0+phCEnXT/DXNykXUkwDfC8gSItoWkDOMYgjSa1WIVdwFbKagWaYWLbB1Pwcq/eWETLE624hNIPZUxcza4HY6Ssylj1byOPmOiHAsky21tbRNCjklPgkk5Qz52dpri7SvPcRjTMv4bguMg3p9XyiMKJcKRP4AUmSoukG21sdDBHS2u5RLuVV0Juci2FX6LXbWDkdW9eQicHN64tUqs/tHIUe4iBG9DKwr6wusu9zHJOLlxfQh45Wm6bB6z//FdBdSuUCzfVtKrNTvPqVL3D2/PxOBUMKjZ1pMqnBB7XHP3546kWMo4D+RB8RVyYKc3tUMNgRdpdNZZrZstUOVJsuoZuGytLtxxRLeXRDnfhstzy6XZXvQPYPdKAImJGd8uy0e5imQRRGRGFCnGr0uh5RFGI5JpouWb1znUJjgblTp6k06sQx5K2A5rbHVkvQbCZsra1hOznaPYNUczFsmzgRCN1EM0w2N1qsLW8QeB6eH+F5EeVKgRMLDeYXprI+GzLeC5XIN8qiR01iuTRNMHt6Ab8XoOuCONXwegGBH2E5BfxeC9tM0XSN21c/pru9jpsvEIURgRewudkDJIYhuP3RD7GNAKEpnYVpmViWztKWTiJsQgp0ujHv/OBNVpfWdoZqXCbtE4dUEgbxULnRAv2fuq6pxDxjU8CwVEqBD967xvSpeX7+l77M+edOKp8NbTc5UD23m2OR/Tbvz9A8FfC54CD6MNk8lA1Z/0TVpIK75FkGE29gLsugWHS5cHmGH//BNRVNyhTINEEFwzUQQpCmEpGmWfxKTZlDNUGcgFsoZvJ3zNWfvElp+hSFSoGp6QqdZo98McfpKy9hOznFhcgGpSmPwAuoRjG6qdhiZIOelyJMGzfvYibqiHmaphiaJO/qCEOF7K43aqwuLzHbqKCZjorv0P/wIcjl7czvao8cG6jDbYkw2NwKqZU0us0N8gWXfD7HlS//PEjlC+B5IdVaCZmmyg0dga7raJpGqVzk9ItfR2imCggcJ+iaTqGQp9XsUi7oYCT0eiEaOttbXWZP9IdOTCT0QhPYjjGCbX8wh/N0j1oZdkyRt28sYlg63/n3f5m5+boKgz8BBsrIvjl7iPUUO1U+NHF4lJaLYfhcEYhJMGq+O2CPjyssxwa7XMnzwhdPs3R7A11Tpzk3V9vopsoUjlSnQgUCKdMsbqTKuqTpmVOT1Hj+S6+haRaannLh8jxbGx2iUO327VYb0zSz8HECw7RwXIckTZES0tSgMW0jhQq8YooUhKZiPALFahEhBL4fEwUejg0r91bQDJXEeBKYhs6N68ucPT87YsobhkqtTLlWQpfrpGmK73WIwx6hZWBYeZQresyJU/NIYZCgDUybuZxFkkVOskwNyy2QpAmmHuH1ImwjplEvUijlEZry1/ATC90Yi8I0JGbcdwwZ3un7ZEEO7qVZ1qtcweGbv/x1iqWcOsI/VplgjGMQO092iMYYfgeFCRLJjvT3aGNCfO4JxEFhQLEzY8jwAMEohyGEYP5klULR4ebVReIwQQgNwzaJophS0cKwzMGhpL6gly9YhIEKVddptjEtgdB1Vpa3ufSSxolT9QEuXi+g2wtJ4wTfD/n02hKuY2HqOoHfDyQbE0cppmWpQ0IyVmkB8wVAw7L1LKS/ZHoqT7Opk6QJa2tNZudqg+/rdnwKRRWyP190M3fuUc18fxZblsGVVy7z6cc3uXH1LtOzdUoVhyCU6Jlfg0QDs0y+4JDECV4vQggNy1aHmULPJ4oiDDvB9xMMDQwt5fS5k9y4fhep5ajWc7Ddo16qqFOvEweNURM1ozuv3LWV7xYKdE0dHrtw6fRAzzDJJWT4+PYEdeQk3e7BCcU+6/9Re2IeE4gD6osG4zA20QaJgccGXCAoFB10QyPwIoRICToxpm3glvO0NtrkCw5pKrEdkzhK2dzoKpFFqqjZdz55n3MvvUGlmlMmtCHI5W1yWeh6gFNnp5VHpZT4fkgcJziORbfdI06gWHSwbZP11W3e/dFHCD3HqbPTmKbOVqeHPVWi1nCQukMQxgPnqySRfPrpKi9/QeW/PHGytmvyDzom6yfbtbj88kV00yTseqwvLpGrzCPIUt3HKYlUC8l2baIohTRRXoaaxta2R+j5WDmBa+toiUEURRTr84QfL+J1miQli9mFGc5cOke1ViRJEtrNHk7OxrJU1mzd2AmkK6Xk5o0lPr16h1I5z4XLp6lm1g+BIJVpFrxH39Fj9LmAIW5g6HZfQ9MXIMZ6YjcMpto4B/oUw0Gyey8IIX5fCPGhEOIDIcRfzu7/dSHEPSHEO9nfd4fe+atCiOtCiKtCiO8M3f+V7N51IcRvPCzy6tTkw8EkRdJBYFQyGVJ0DenvdE3j8sunOXNlnplTdXRDJwki7txYZ2mpzdamR+B5tDabSCGo1NQ5gzQVCMNm4fJrdNs9Llw+MZprYo+JpQmlKMznbEqlHLZlUK2XqNXyOI4KqDo9W+W1b7xMzwu4/skKXi/EMA06zR6B1yGJI9KETBmpQuI/d2luYCUQI8tj+F+1CJWruYrWfe65UxiWgePmgAShCeXnkKYUig6appOkKjM6upkF44ViuYDj2li2gZQJdq5AvlzDsIsUSlU21rbY2ApozE6j6zqfXb3F9/7Fj/nt//P7vPWDD1m9t0KaJsRRnEUXl2ytt/iD7/0xizdu8u4P3+d7/+KHI0pLgVCxOZKYNE1GZ4VU8Sl21Mo7JGGUh5o8l8QQ8RzrsqceDsJBxMB/I6V8WwhRBN4SQvxu9ux/klL+j8OFhRDPA38aeAGYB74nhHgue/x3gF8C7gJ/LIT4bSnlhw+KvLZXGr3DwhFZnfoKvH4wWlCxKaemywjAtk3e/dE1alNV6vUiSRzS8mIs16Gz3EITkjBMif0OtmNh5EtsrDd50T07gmp/gg30qmIEg5Gy926t8s6bN7jy/AlOX5xHpmAZGvmcyidq2zo5t0qn42HokjQOieOYOE4wDbWAm81uFoxV0uv6BH5Ita7Y+jhOuXntDrZr05ip4ToWvh8RC+h2PPz2FkksyRcaKvp3Pzh3mmJYOiQ9pFHAcS38boSQEpH2qE5Pk8YBlmXR7njomsAwNE5fOMUHby2zubLMj7+vMTOdY321iVFoMNuwWfr0Bks3brJwdp6lxU1Mx+Ub3/4CvW6AhY9mxIRJjlzeHiK6O3L8v/7dPyLwUn7tT/z8DjEUKrqU2NXFo/09Kav4hGKPnXN4GD3FQbJ7LwFL2XVbCPERsF/c8V8HflNKGQCfCSGuA29kz65LKW8ACCF+Myv7wATiSOEgiq393hlwpYIdL8nsMns2NVtm4fw8vbZH6KmUe6ZlKgsGEikFrdXb3HjvB5x94TWqJy4iNZHt3kOqtN0WxgFCfVTSVHL700VWl1qcPX+CdivivR+8z2YzolorMz03g9/tqbR9mo5AI0klEFMtO9hZ3gld15idrRKFET998yM2V9aQCF756ssUSgVWlzZ5581rzM7VKZaLSAlba9us3r6Fbhh4XkKuWEEglfhk7OQyTVMwrRxoBlEYE8UQRQGFUpl2J8AyDVIJjm3R66lQboVSmee/+BqfffwRS7fvEYezJEnEcwspd28u0/VNSGO2VpYJPUka9njr+z8iVyyji5St9U2cskVjurSLE5NIvvj6yzSbHlKmSKHtEh927/67J4xEjfmAdo+/M6YbedSwF3E4SKCaQ+kghBBngC8CPwK+DvwlIcR/CryJ4jK2UMTjh0Ov3WWHoNwZu//lw7R/JJD5NDyMcmfExDQ20IPYEeyeHJrQOH9plg9+coPAT7FMG8PS2Vi6Q65URzNdUr3A5a99Vy2S7W2ef/kMjmPu1DdoblgeHqVUaZxw59Yqn11fZ/6k8mnQNI0b199lZv4kTs4hiWLCSCIEGLogX8xjOy5+a4tPPrzF6SAmX8xhmjqbG23aW03W7i6RL9jYuQJXf/IhuYKDEDqnZk2cnGT13gq9Vpt8MY+uqVQACXkMK49bcJCpJIlTpEwwDEPl2tjoEPjbOLlcFjbPI44Scq6N0HVkHCNMjXKlQOCHmLZFpTHFlddK3Pn0U5rrtygXXRZvhSRRQjlv40U2mkjJlUoY+OhINldXKZZLuIUSpVqNSq3C3bubLCzUEZpGu+2xsd2jXivQaJSHOlo83Dqe8GKfgDxIpWEQq43FfHju+SBr4MAEQghRAH4L+CtSypYQ4u8CfwP1vX8D+FvAn38wVEfa+QvAXwBYWFjYefAAHSpTtZLHd9xxi8Og/gPjuFPXyL9yeMFORtayDF587RzbWz1WlzYxDZNUmnS3V6nPn+PEwgxC14mCiFzR5eSp+mgDu2reaSsMIm59ukyvGxB4IbNzdcV9CEEShCxcfBHLcUlSidB1KpU8Uajcu4XQuHVjg9bmOvMnKmxvtOj1Ihxbo7m+yepKk0TLk6CxtrxJteYS9nosrgSU8xIpHO6trFKtueidNr4fI7GoNSqYtkW37WNZBnGc0Gt75IoO3Y5A0wXbm22qmomupRSKLkmiFqWUicqNkajgMZVGmSgMiWMT3084f+USmytFNm9/wJ31NqkUpPE9GvNniNIqQSIol2D51odIZwE3X8LUI/JFm5mT0/zhv/mY27fWqNfz5ApFiiWXQs4aES36fhL7QSplxjSO6hpEprQYUU5mGu44kTspAQ8BypfmcO88jM/EgQiEEMJEEYd/IqX8p6pRuTL0/O8D/yL7eQ8YWtmczO6xz/0BSCn/HvD3AF599dVhAS8rwMEJhThExww4gMwb7jA9uqPKHrWB7iGHmqbB1FSJxlSRNJHkSw6dpsf2Rhc3Z5MkKe1Wh+n5EsaInmWUXRk2NbZbPa59dJd206dcyuG6FmkSg6kRej4gcPIFNE0j8UPlTCV0HEeHTLlYrlXotDv0eiFpeI/cwhniIGV75S55J0+5nOfeYoeKm2AYJhsbMZHfY6UdsbqpznmYmmQ71cnli5TKjoqslTlQRb6HaTmEYUR70acxXUamCeVqGU0TJLEEmeCHEjdnq8THUagCu8iUbjdkeXGVWjmPaZv0uh6lqZOYTpngsyVib4XPPvyIzbVl5s9cpNqos9RJWd9MCaK7nDx3jplz05w4d4rV1RbVWpGP3rtBq9viP/7zv0qtmpvA+Ss/BpVPd/Kc2OXmPSauDjjJId2F9oA+zA+y0Ic95g/7/n0JhFBb7P8KfCSl/NtD9+cy/QTAvwe8n13/NvC/CSH+NkpJeRH4MaqfLgohzqIIw58G/uzh0OVAxKGvlHkQMeLQQW/HVdnjr+9Zncw058p0GEzF3JArrC83cXMOwjA5eWZ2p/QuRZOabd2Oz8riFjeuLVOrl6lUS8RRROS3ufvxH3Pi+W/g5BxyeQcpIQp84jCitdXEtG1M2yaXs9F0jVzOZma2gfRXkVLSXl8hX6kxc/oczY0tWk2PYtHBFBFLyz3K9TKN2Sm8Xohu6Oi6Qa5YoNX2qZZtkiTB8wJsx8bvBpimhm7qVGpFfD8emCFzBZMohiAKieIUQxckcYIgIeca+J0tNKtMGEQUi0ViCSXXIgkjJAKnUGThootlnOLkwgxbzYCbV9/jxk//kC/+3K9gOWVu3LyGnbN541uvcu2jVT69vsy5yydwKgW++gsv0hjKXDbex522z4cf3KNctrn8/MLe3OYwdRkhCKPzQHDIeXZE8CBNHoSD+DrwnwA/FUK8k93774A/I4R4BdUNN4H/EkBK+YEQ4v9AKR9j4C9KKROFoPhLwO8AOvAPpJQfHB7l+8MDdf6DkNdJxffjcEY4CjEygWzH5Lkr8/hRSOSHvPTaGfIFZ+fVXXINdFse7/34E0CjWimgiZQwTOh0Qmoli5MXX6BYKRFHMZ3tNmkiVeBbXaNQKqhj1bok8Hw0XVOZyfMOorAAMmRttY1bloR+QLOdEvke5YqBpMKF5xvops7GrY/RnSkKJeU2LeMI10zodX0Vuq4boglBmqQIy8Drheqsg5QEPR/DNBCOUoZGiQRNJ5d3iIKQKFYh9XRTnZTM5S1kzibo+bRbPdy8TRJLOh0f29LodFLqC5epL8DM6Yus3rlBFGwzc/4ytfkap8+doFSrIO92mD8zxcKZKb72c5cy1/Sx8Rnqazdn0Wx6tNoetfzwsgAAFgBJREFUl68MM8FjYz5p7HfVeZ858pSBeJpDbr/66qvy+9///uNp7GEHbfz9SVaR4TKTTGAjYs79w5Et313n7mfrmJbOR2//mMtfeAXNsskXCkSBp9LOJypEXGurjWkINF1TOT1MA9t1MtY+Uuc0pLIsJIkkDkOi0MOkS5Ra9HxB6PWYPjGPm7OVi7gmQKZIqSl+SIOw56m4F0JDN1TynjAISVNwXZMoSskX86RJNDjopGkis2SoMyvKVBwT9EK6vZhcziaVKlGR1wsJ/QjfDylV8upErJTIJCaVOltND8fSshgbCaZrk8iEb/7CFcpFB6FpWVRxJRqkqRyEhRsdzlHdQ5IqR6qBcnAfs+WwonrXnBpfbk8BoSgUCm9JKV+f9OxzcZrzQCDI3I5HIR26ty8xnWAym7h7DJefoOTs/1Qp5uQeE0gV3N7q4vkhEo3Tl66og1aWhdf16bZDklTLrAYpTs7J0glqWI6F49rZAlFnQXrdgDhLI7iyuEUSpzhunlztJJpVRjcspmansG2TwPPptj3u3VohiiW2a2E7Oq5r4+Zz6JaJ7Sov0SRWgXg315vIVDkmxXGEZelZ0qAE34vRhEpykyYJoR8gpYZhGtiWSlmYxhFISc41KZVdpqfy9DpdZJqgaQIvSLh9Z4UgCNBESi+KWXhuluqUxb/znZcoF11lXs282TRNJa4ZDlw7PFRhNOQsJRSHM1AqThIth17ed82Lnb+H3poPWUGfMB4GjgnEEIgJO8nwBNov3dv4YE0sO0wwJKPvDREJmeGyd7p3db/aKGNZKop2oVzHME0K5RxxHKObpvI90JQCMpEyO3odcuP6KlEMvW6A70WkqUrZ124HJCmcON1ANw0s10HTLYpll3qjBEIjjpNBCP9czkVDHaPuNLt4vYA4TtFNBzQNy3WwHFs5UU2VSaRGu+VnofLg3t1N2s0eURiSxDJLAgyGaWLZOqZt4LimYms0Hd8LlY+HlKyudZiZzRNFPo5rUC67XL5yirn5OpbrMl3Pc2KuyO/89g/4/u//NAtjx0Bx3T9ElSbJyIqWSFKZYlvGzu3+q+OxQ4bHbfj+sM5Sjj2THFmUtMNyHzu5OQ4Ox2cxMngYU9C+fhV7KS0nFc8IiBBCHbkeTCSZ4SdGiEy54hKGIU7OVSKDpasMW6lEM9RMjcIEw4L6VJH1OMJ2TKZmVF2GIYijlG67R6VRxrQiHNcijBLypQKBFxFrEQJJq+kTBD65gsCxlLOT5dgDMaFULdJp+aRJjGEbWZIdD00XrK5uUygYOK6TnUAVtNZXEXGHJKnhb6uoWBIVOFYRZRUzo+tlIefyJoEnsn7QyOUdmlsBpmERRBFRECNEgps3WTg3ixCCnGvwH/zZX6LaKI3m5xzqcE3XdqKK9e8N6YeGjVKDG0NjtRcXwfh7Q2Mu9nr+mOEgc/7zQSDkhIEeg4cZrIfSSI9PsgnXsn89RBykVKz969+4wvpqi821NmmstP9a5kIcRTFpKlm5t00YxMydnsJ2bGK5QSoTlXcijUkMMGwLI5F4nQ6abhClgEjRSOj2Ynw/wBARMo5ptzfQ7BKbaxs4uTxTczOkaUoYJRiaRhQk6AYq83nPRyYxurDQNchVc3RbPmura+iGiSYlpuOQpAnNZoCUCY6l4/UgSmB2vkyn6RGHMTJR6QulpvQba6sdleh3aw2Jjpe6/PwXn2d6qjDovBe/kFPXWd/JXVu/GPgo9M2R/bkyUQc0Jg7KzNd91zmL4bEa+rkrJsQTJBIHmbafDxFDHI4AHIQFHE/4K6WcqMN4aJBqCg/mX6aXiKKYKIxxXJOTp+ssnG3g5m1C38freqyttFSW8UQRDMMwWF9pY9sGpWoOKSWRH5IkMU7OolrLY5oC23UQ2cEpGQf0WluYeoxrxAjNpNnuYZiC7XsfUClbNGaqxH6Txas/ASS6bYEQpDJBN3Q8P6YxU1UHsAoOjZkKwtCYP/8c82efw83nKVTyxFGC6wjqjSJhDL6fIOOY9cU1kAmd7W3SNKXX8/C7Pe7dWcF24cylaeoLJwilhm4FlMvuSPcN+k7uuqN+DRTDg8e7Tm+Oj0e/3L46pqEyYvhfhuaOYBALYz84cjPCISr8fHAQh4SDEJPxgznisFRo6N0R2JOjEBluiv01TIM4SgYiSa1epFovMHuiwuKdDVhtZ+y/Tq1RIg5DvK7PB+0uUugkSYpt6fheRN4wuXt7HRmqJDVhBMuLm+SsGLSUtLvF4s3rTM2eJdVsrHwVzVwi6K1jOynXPrpBrVJma6vHrOsQJxJSMEwN07ZVOz0foUMURkiZUm2U6LR8FU1KCgxTp1TIsXR7nSBSkbJlqhLkrG90KLjKC7M6VaHb7jA9X0PTU85emOPcBcE7tsGF5+ZVWgHuYywYiHKMls36fiSw2Nh4qAA/Q3qpcT3RpCkwxjEMv7Pr/Qlw5EzGISr8fBCIR8HKTWIp90PhMCfqJuE71E7f21NFvFZRl3VDRxOCXMHh7MU5ytUCn32ySM8LMCOdJE5oNbvUZmrYjknkqSzc5XqBQtHB92O6nZheJ6BUL1MuGbS3PPw4oFKMSNIelXoOodvops3LX/8mS59+TLsnOXn2OSqNWdBMhWiaYLsWhaKNTNSxaykld26u0+n4KgGvBE1IBAm6YVGu5em0lAt2vLFOrTZFp91lfa3N0vIWX/nGJVbvbTN7skYUFbAdi3otTz925quvnwd2zk3sS3flBBo8TO/Hx3a47Lgie4iojNQzLNaKkaKjrz+E7mu0nr3n1yN3tX7m4RHKeQc9+DWxzF6ONXtWwkDkUGYyNYN1Qx+Z/IahMzVbplIvKP8Bkfk2xAk516Lnhdy4ughS0txss7W6iWHbGLpBmqrDQLZl0jMMinpCFFrMnnwRrxvTDSKm53KsrwVo9hQ5w1SEyjAIg5iyayNNgWnpBL1AZe9yLba2OtiOid+LiEOI/EiJSVFKpeEQx6nKBVJwEUJw9/YWvh/Ra7epVV2SOGFqvkJjKo8QO8lydke26vfLTlTJoa4b7eP7bRx7lBshBn1xZMI4irF3d5U5MuKwvx5sxPhyyKPfnw8C8QhhNGL2IU+J7seO9q+zcgPWdliO7bOsQzvYcKBUazgKlQkSdSq0UHB4+dWzhGHM1mZ70NbqcovFW00qsgSaSX2mQavZxRSaCvziWFRNHcs2SKIUDBvTsdjabDGVr5AmsLXVw7J0ZMdj4dwMcSppbnRpbXnYtkUUqZSAYSiwLAvL1lhbapIkCa1WgG6AY2nEUYBp5jh/5QSNmQqFokuh6A5N9p3O2614HO/anU4V+xCHPXfasXcmjvBexGY/ZeT9psoBOd9DTblDUqRjAnFEIMb51IPAQXawgTw85KTDDhfRvx6VpzOiNSZUSykzFlk9sWyD2bnaYIHNzFVZOFMnimJWF7dpNz1KtSKRHxKHMUmssmOlScL6WovZOXVUulIrE0US3/OxHRMhNNZX25TrJTbWe7S3u9QaZQw9JY5ikjAiRJAkSpfS6fggwbF1ckUHQw9YXlznyuVXOXlqilyWuXxn2x53Lhi9tzv8m+qrflyOKFQBcdycfSCF3WDX7Vd7iMU+Fr3ucPAIOd+DwjGBGIeH1VdMso3vVd/Y/b5X44ib9R6Tcg9GY2ip9MO5M9hihs9zCNEvNbRCBFRq6tBSpVoklSocf3O7y+1PV4lDH8tx2Fxv4zgmUZJCrILe+l6IYys9iOmYFOtF3v7Rh5SKBepzUzSbPUwtIQgiTNvNIlSlFIo2EFObraDpOidPVHEcA/jChI5kiL8f78Q9OnmYvc7+7XYDhADHtXcUkTvdtLuK/Rzk9mm6X+dTfJrhvnBMIPYD2d91H0JsOCSxuW/W5/ENdGhnHC1yP0F7rKIximNa+uBxvVGi3igNzHNra03yeWVO3N7q0mp2MSzY2uywurzN61+7xKUXLtDc3uT8cyc4d2GejY0u21tdcnmbajWPZSu9iWHqQ9q8cRhGSuGrdA6KmxqXp/dk/fsPs76qVPJDvg67xY5dCse9YLgL5VBTY+/tcrR6CjiDg8Izd1jrYeLrHUib+yQHcL+295KZJxTrQ39JTS6116oYhqEVMMTJDzP1UkrleSjA9yOWlrZYWGhg6Fq2CIdW4RFnexlWRwwHFh9RUzzEeE7M28oedT5jC38YfqYOa40Th8MQuH3n5gHNlYdxMjk03E8fMYxDf62NsNCSsVvq/oRELzsV7df2+KzPZHkBCJVbdOD/IVVey7Nnp7Pco/3+HhYNJrTb/5hDduwIukNVD3b+cU7rAWCvLF0PpHB8RPCoN/hnjkCMw5EF3phQTRagevTerovxl+7f1MDj8pBjO4LLBFFmF/FEiSya6Asuk1PJ7qpo+G9gLRlfcTILeJvd76/M4bUuhxCdqDcYb3MSyFGMMqIzQlb2YqPGq32ItTRxIe5R377ekUe8nu8XDu9h4ZknEI8SxGAXHL2nLpg82AeYkANnm8PqJ4bbPkDZ3eqK8aS0Oytod5aH8SITWKwRoiF27g18i7P3RoLjjFPd3YRn+Mlwe3Lwv50nYvSVURhnTg7a38Oqmf2sEHvUN66z2vNzj4JYPLD4dLBynx8CcZ8OOSyrtneshiF4XGzn2BobnPsfIij3+zo54Wqn8mHiMb7a9tOCqOejfSV2aMdQmYFUkO4mYSPX+zAkA9ox/tJhxcZxGsgBFtQeUtJAsrrfZjLhvX2be2jR4mDvP5ME4n6dM/H5fSbJYUWV+5q+HhYOUs/wDjmyKEbP/Y92h/qx/9dO0hEMKmd/Ab//bGeb7OcgFSNlRtvqL25tzJV5Em+hWtidx2pfbmI/OAhHdj9L1gRCJCUkSToYHzlc9n7tPex8nSQfH+b9DJ5JAnHkAT+PUi58WNQOww6Pb+LZRBz87LPHE7bbvTZNwehpx8lb8PiyHV8Zk5DbKTn8RPT1HBOgTzrkOFETGZ4TuIUBWZKTSMhQ3Y9CuTdWpRAMFLYZeo8PJsnHDwDPJIG4HzzKiMH3IcwPDw/KDk+Qcycp4Q/C+OymUZPEinEuYKgGsetiwq/+m1LFrUjSiWX7jmO7KtjNhGTlh94bcYMfR/Hh58guIrNXlY/W0PBI4WeSQBwW9hy/SZLKhEX3WGGYIAz9DWJg9p+NLaBRWVoeZK2NlBhTu429vZ+gv8/qyDgByzYHQW6G8dl13Hov5d6kD5hANPb86AeEAyddeqIT5uHgmECwz/jt9WBP+f4Rwl66wwEeuwOSDhSVYuy1jMoNmwz3Wzu7NQ6HmfFi5/9irJ6hxbznWhtufK8y4/TqQeFBx/IZJgD3g2MCAQ81wI9QmhlraJ/7mXJvnLPf92Ti0I37E4eD+E9MamTMNrKX4WeIu+kTkv0anBjebS8UDgMHfWcPa8WB332GRI5nhkCkafrIvcaeeZikjxh/vovLSAc7+940aJIu4SBjMaHGPXSeyoWeyd9wAE7uscIen3UQmEQfpHwEoQqPCJ4ZAhEnCUmSPPqGHoAGPXVka2/DwATdhDbY2Uf0/nIPMyKTDaX32fTZm31QSWk67d6+eD71bPwB8ZtsXDi6jzvqTfSZOc1pmebjaegBxmq/V/oD9rhzMR6ouWGFq5zMKUzST8gJz4bVHZM2/4GQMq4XkSopTaGYZ094QsThYQ4GHqwBMl3Q0bUxOXKZPOCE2A3PDAcxCR6VxJFm6dyOAh40ifB+cJBIyLB3/8hJfO6YMaKPsxgvww4RSNNkxNfgUOL1mLl4zy56guzZg+V4PUARCXEUH77uB4WHmH/PNIGAw6cSOwho2tEv6j4oa8PDIX3Q+BR7B0Bhoh6g/xcnyeR5Lkf1gpqmM8qGHBCynVNOEGMmFT1wnU8IRkTfA/SFECqS1lMvNvGME4gHSSV2JPAQk/GhOAp5hOtgL+4CMHR9x5oA7LWMB3RGTtJByJ1Cw8+GCilCvD+aB+6qScrNR0Q0xom8rutDz/Yq/xhN4oeB+yD1TBOI+4GUo8l3Rx8+RMVjk3bPNo4aHmCz3q+uSb/HnYrGicOwMnMylyH7L9KnaEeyLg5ayX422yOEvYj85Nuq1x77ZnYgQ9P+SP1MEwghdh/+2Xl4BA1kA7BnG88qDO3GO0FhGPz1ObfdxAQGrqZ9F+k+UXtIHcMT23wnNPwgHODjVlKrRkd/Poho+1SHnBNCtIGrTxqPMWgA608aiSE4xmd/eNrwgacPp9NSyqlJD552M+fVvWLlPSkQQrz5NOF0jM/+8LThA08nTnvBzxhvfAzHcAxHCccE4hiO4Rj2hKedQPy9J43ABHjacDrGZ3942vCBpxOnifBUKymP4RiO4cnC085BHMMxHMMThGMCcQzHcAx7wlNLIIQQvyKEuCqEuC6E+I3H2O5NIcRPhRDvCCHezO7VhBC/K4S4lv1bze4LIcT/kuH4nhDi1SPC4R8IIVaFEO8P3Ts0DkKIP5eVvyaE+HNHjM9fF0Lcy/rpHSHEd4ee/dUMn6tCiO8M3T+SMRVCLAghfl8I8aEQ4gMhxF/O7j+RPtoHnyfWR0cGfb/yp+kP0IFPgXOABbwLPP+Y2r4JNMbu/U3gN7Lr3wD+++z6u8C/RPmsfQX40RHh8E3gVeD9B8UBqAE3sn+r2XX1CPH568B/O6Hs89l42cDZbBz1oxxTYA54NbsuAp9k7T6RPtoHnyfWR0f197RyEG8A16WUN6SUIfCbwK8/QXx+HfiH2fU/BP7E0P1/JBX8EKgIIeYetjEp5b8FNh8Sh+8Avyul3JRSbgG/C/zKEeKzF/w68JtSykBK+RlwHTWeRzamUsolKeXb2XUb+Ag4wRPqo33w2QseeR8dFTytBOIEcGfo91327/CjBAn8v0KIt4QQfyG7NyOlXMqul4GZ7Ppx4nlYHB4Hbn8pY9n/QZ+df9z4CCHOAF8EfsRT0Edj+MBT0EcPA08rgXiS8A0p5avArwJ/UQjxzeGHUvGIT9Q2/DTgAPxd4DzwCrAE/K3HjYAQogD8FvBXpJSt4WdPoo8m4PPE++hh4WklEPeAhaHfJ7N7jxyklPeyf1eBf4Zi+1b6okP27+oTwPOwODxS3KSUK1LKRKqIq38f1U+PDR8hhIlajP9ESvlPs9tPrI8m4fOk++hI4EkqQPb6Qx0iu4FS4PSVNS88hnbzQHHo+o9QMun/wKjy629m17/GqPLrx0eIyxlGlYKHwgGlePsMpXyrZte1I8Rnbuj6v0bJ1AAvMKqAu4FSvh3ZmGbf+o+A/3ns/hPpo33weWJ9dGTz8Ek2fp9O/y5KG/wp8NceU5vnskF5F/ig3y5QB/41cA34Xn8SZRPj72Q4/hR4/Yjw+N9RLGmEkkP/iwfBAfjzKAXYdeA/P2J8/nHW3nvAb48thr+W4XMV+NWjHlPgGyjx4T3gnezvu0+qj/bB54n10VH9HbtaH8MxHMOe8LTqII7hGI7hKYBjAnEMx3AMe8IxgTiGYziGPeGYQBzDMRzDnnBMII7hGI5hTzgmEMdwDMewJxwTiGM4hmPYE/5/dH9hD8d2AcYAAAAASUVORK5CYII=","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["print(image.cpu().detach().numpy().shape)\n","plt.imshow(plt.imread(\"/home/lakshita/somusan/hubmap_kaggle/hubmap_data/hubmap-organ-segmentation/train_images/10044.tiff\"))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fdb51884898>"]},"execution_count":85,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.imshow(result['probability'][0].T)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:35.546968Z","iopub.status.busy":"2022-08-17T13:10:35.546621Z","iopub.status.idle":"2022-08-17T13:10:35.930211Z","shell.execute_reply":"2022-08-17T13:10:35.929259Z","shell.execute_reply.started":"2022-08-17T13:10:35.546934Z"},"papermill":{"duration":0.210441,"end_time":"2022-08-17T06:03:29.036978","exception":false,"start_time":"2022-08-17T06:03:28.826537","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["import gc\n","gc.collect()\n","torch.cuda.empty_cache()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-08-17T13:10:35.932177Z","iopub.status.busy":"2022-08-17T13:10:35.931590Z","iopub.status.idle":"2022-08-17T13:10:35.947185Z","shell.execute_reply":"2022-08-17T13:10:35.946308Z","shell.execute_reply.started":"2022-08-17T13:10:35.932143Z"},"papermill":{"duration":0.019919,"end_time":"2022-08-17T06:03:29.066378","exception":false,"start_time":"2022-08-17T06:03:29.046459","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["submit_df.to_csv('submission.csv',index=False)"]}],"metadata":{"kernelspec":{"display_name":"Python 3.7.0 ('.venv': venv)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.0"},"vscode":{"interpreter":{"hash":"21a2a557654fc1676068684031cf9bb9dfda94e124d3623f4e9c9ed764d794ac"}}},"nbformat":4,"nbformat_minor":4}