{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":71549,"databundleVersionId":8561470,"sourceType":"competition"},{"sourceId":9538089,"sourceType":"datasetVersion","datasetId":5726807},{"sourceId":9539917,"sourceType":"datasetVersion","datasetId":5726703},{"sourceId":194279540,"sourceType":"kernelVersion"},{"sourceId":204685366,"sourceType":"kernelVersion"}],"dockerImageVersionId":30787,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport cv2\nimport pydicom\nimport numpy as np\nimport os\nimport glob\nfrom tqdm import tqdm\nimport gc\nimport pickle\n\n!pip install segmentation_models_pytorch\n\nimport torchvision\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset\nfrom fastai.vision.all import *\nimport segmentation_models_pytorch as smp\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2024-11-02T22:55:55.187439Z","iopub.execute_input":"2024-11-02T22:55:55.187836Z","iopub.status.idle":"2024-11-02T22:56:13.116607Z","shell.execute_reply.started":"2024-11-02T22:55:55.187792Z","shell.execute_reply":"2024-11-02T22:56:13.115500Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"FOLDS = [1,2,3,4,5]\nPATCH_SIZE = 512\npatch_size = 64\nLmax = 5\nBS = 16\nEPOCHS = 16\nSEED = 777\nlevel_swap = .1\nSWAPS = [[1,2],[2,3]]","metadata":{"execution":{"iopub.status.busy":"2024-11-02T22:56:13.119042Z","iopub.execute_input":"2024-11-02T22:56:13.119405Z","iopub.status.idle":"2024-11-02T22:56:13.124810Z","shell.execute_reply.started":"2024-11-02T22:56:13.119370Z","shell.execute_reply":"2024-11-02T22:56:13.123793Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![image.png](attachment:image.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"code","source":"def seed_everything(seed):\n    random.seed(seed)\n    np.random.seed(seed)\n    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n    \n# CosineAnnealingAlpha\ndef nt(nmin,nmax,tcur,tmax):\n    return (nmax - .5*(nmax-nmin)*(1+np.cos(tcur*np.pi/tmax))).astype(np.float32)\n\nplt.plot(nt(.25,1,np.arange(EPOCHS),EPOCHS))\nplt.show()\n\n# callback to update alpha during training\ndef cb(self):\n    alpha = torch.as_tensor(nt(.25,1,learn.train_iter,EPOCHS*n_iter))\n    learn.dls.train_ds.alpha = alpha\nalpha_cb = Callback(before_batch=cb)\n\ndef augment_image(image,alpha):\n#   Randomly rotate the image.\n    angle = torch.as_tensor(random.uniform(-180, 180)*alpha)\n    image = torchvision.transforms.functional.rotate(image,angle.item())#,interpolation=torchvision.transforms.InterpolationMode.BILINEAR)\n\n    return image","metadata":{"execution":{"iopub.status.busy":"2024-11-02T22:56:13.126966Z","iopub.execute_input":"2024-11-02T22:56:13.127695Z","iopub.status.idle":"2024-11-02T22:56:13.444288Z","shell.execute_reply.started":"2024-11-02T22:56:13.127661Z","shell.execute_reply":"2024-11-02T22:56:13.443347Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"lsubarticular = [\n    'left_subarticular_stenosis_l1_l2',\n    'left_subarticular_stenosis_l2_l3',\n    'left_subarticular_stenosis_l3_l4',\n    'left_subarticular_stenosis_l4_l5',\n    'left_subarticular_stenosis_l5_s1'\n]\nrsubarticular = [\n    'right_subarticular_stenosis_l1_l2',\n    'right_subarticular_stenosis_l2_l3',\n    'right_subarticular_stenosis_l3_l4',\n    'right_subarticular_stenosis_l4_l5',\n    'right_subarticular_stenosis_l5_s1'\n]\n\nlabels = {\n    'Normal/Mild':0,\n    'Moderate':1,\n    'Severe':2,\n    'UNK':-100\n}\n\navailable_flips = [[True],[False]]\nfor _ in range(4): available_flips = [v+[True] for v in available_flips]+[v+[False] for v in available_flips]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T22:56:13.446809Z","iopub.execute_input":"2024-11-02T22:56:13.447564Z","iopub.status.idle":"2024-11-02T22:56:13.454061Z","shell.execute_reply.started":"2024-11-02T22:56:13.447515Z","shell.execute_reply":"2024-11-02T22:56:13.453147Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/sagittal-t1/train_split.csv')\ntrain = train[['study_id','fold']+lsubarticular+rsubarticular]\ntrain.tail()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T22:56:13.455492Z","iopub.execute_input":"2024-11-02T22:56:13.455770Z","iopub.status.idle":"2024-11-02T22:56:13.496312Z","shell.execute_reply.started":"2024-11-02T22:56:13.455740Z","shell.execute_reply":"2024-11-02T22:56:13.495409Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train = train.fillna('UNK')\ntrain[(train[lsubarticular+rsubarticular] == 'UNK').sum(1)>0].reset_index(drop=True).tail()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T22:56:13.497900Z","iopub.execute_input":"2024-11-02T22:56:13.498907Z","iopub.status.idle":"2024-11-02T22:56:13.529638Z","shell.execute_reply.started":"2024-11-02T22:56:13.498869Z","shell.execute_reply":"2024-11-02T22:56:13.528633Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"with open('/kaggle/input/axial-t2-centers-generation/axial_centers.pkl', 'rb') as f:\n    coord = pickle.load(f)\n\nwith open('/kaggle/input/getting-true-axial-levels/level_assignments.pkl', 'rb') as f:\n    assignments = pickle.load(f)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T22:56:13.530843Z","iopub.execute_input":"2024-11-02T22:56:13.531302Z","iopub.status.idle":"2024-11-02T22:56:16.228243Z","shell.execute_reply.started":"2024-11-02T22:56:13.531243Z","shell.execute_reply":"2024-11-02T22:56:16.227240Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df = pd.DataFrame({'key':assignments.keys()})\ndf['study_id'] = df['key'].apply(lambda v:int(v.split('_')[0]))\ndf['series_id'] = df['key'].apply(lambda v:int(v.split('_')[-1]))\ndf.tail()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T22:56:16.229549Z","iopub.execute_input":"2024-11-02T22:56:16.229930Z","iopub.status.idle":"2024-11-02T22:56:16.248543Z","shell.execute_reply.started":"2024-11-02T22:56:16.229883Z","shell.execute_reply":"2024-11-02T22:56:16.247444Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df = df.merge(train,left_on='study_id',right_on='study_id')\ndf.tail()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T22:56:16.249858Z","iopub.execute_input":"2024-11-02T22:56:16.250158Z","iopub.status.idle":"2024-11-02T22:56:16.272041Z","shell.execute_reply.started":"2024-11-02T22:56:16.250125Z","shell.execute_reply":"2024-11-02T22:56:16.271002Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df['flip'] = False\nfdf = df.copy()\nfdf['flip'] = True\ndf = pd.concat([df,fdf]).reset_index(drop=True)\ndf.tail()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T22:56:16.274992Z","iopub.execute_input":"2024-11-02T22:56:16.275333Z","iopub.status.idle":"2024-11-02T22:56:16.301653Z","shell.execute_reply.started":"2024-11-02T22:56:16.275295Z","shell.execute_reply":"2024-11-02T22:56:16.300605Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class subarticular_Dataset(Dataset):\n    def __init__(self, df, f, VALID=False, P=patch_size, alpha=0):\n        self.data = df\n        self.VALID = VALID\n        self.f = f\n        self.P = P\n        self.alpha = alpha\n        self.resize = torchvision.transforms.Resize((PATCH_SIZE,PATCH_SIZE),antialias=True)\n        self.indices = torch.arange(Lmax).float()\n\n    def __len__(self):\n        return len(self.data)\n\n    def __getitem__(self, index):\n#       Is not the best solution but will work\n        try:\n            return self.__original__getitem__(index)\n        except:\n            images = torch.zeros(5,2,Lmax,self.P,self.P).to(device)\n            slices_mask = torch.ones(5,2,Lmax).bool().to(device)\n            label = torch.zeros(2,5).long().to(device)\n            label[:] = -100        \n            return [images,slices_mask],label\n\n    def __original__getitem__(self, index):\n\n        row = self.data.iloc[index]\n        \n        sample = '/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/train_images/'\n        sample = sample+str(int(row['study_id']))+'/'+str(int(row['series_id']))\n\n        images = [x.replace('\\\\','/') for x in glob.glob(sample+'/*.dcm')]\n        images.sort(key=lambda k:int(k.split('/')[-1].replace('.dcm','')))\n        instance_numbers = [int(k.split('/')[-1].replace('.dcm','')) for k in images]\n        images = [torch.as_tensor(pydicom.dcmread(img).pixel_array.astype('float32')) for img in images]\n        shapes = [img.shape for img in images]\n        H,W = np.array(shapes).max(0)\n\n        images = torch.concat([torch.nn.functional.pad(\n            images[k].unsqueeze(0),(\n                (W - shapes[k][-1])//2,\n                (W - shapes[k][-1]) - (W - shapes[k][-1])//2,\n                (H - shapes[k][-2])//2,\n                (H - shapes[k][-2]) - (H - shapes[k][-2])//2\n            ),\n        mode='reflect') for k in range(len(images))]).float()\n\n        if H > W:\n            d = W\n            h = (H - d)//2\n            images = images[:,h:h+d]\n            H = W\n        elif H < W:\n            d = H\n            w = (W - d)//2\n            images = images[:,:,w:w+d]\n            W = H\n\n        images = self.resize(images/images.max()).float().to(device)\n\n        c = coord[self.f][row['study_id']][row['series_id']].clone()\n        c[c < 64] = torch.nan\n        c[c > 512 - 64] = torch.nan\n        instance_to_k = {instance_numbers[k]:k for k in range(len(c))}\n        \n        img = torch.zeros(5,2,Lmax,128,128)\n        slices_mask = torch.ones(5,2,Lmax).bool()\n        for k in [1,2,3,4,5]:\n            instance_numbers = assignments[row['key']][k]['instance_numbers']\n            if len(instance_numbers) == 0: continue\n            distances = assignments[row['key']][k]['dis']\n            dis_sign = np.sign(distances)\n            if dis_sign[0] != dis_sign[-1]:\n                c_k = torch.stack([c[instance_to_k[i]] for i in instance_numbers])\n                c_mean = torch.nanmean(c_k, dim=0)\n                for i in instance_numbers:\n                    mask = torch.isnan(c[instance_to_k[i]])\n                    c[instance_to_k[i],mask] = c_mean[mask]\n        \n                images_k = torch.stack([\n                    torch.stack([\n                        images[\n                            instance_to_k[i],\n                            c[instance_to_k[i],0,1].long()-self.P:c[instance_to_k[i],0,1].long()+self.P,\n                            c[instance_to_k[i],0,0].long()-self.P:c[instance_to_k[i],0,0].long()+self.P\n                        ] for i in instance_numbers\n                    ]),\n                    torch.stack([\n                        images[\n                            instance_to_k[i],\n                            c[instance_to_k[i],1,1].long()-self.P:c[instance_to_k[i],1,1].long()+self.P,\n                            c[instance_to_k[i],1,0].long()-self.P:c[instance_to_k[i],1,0].long()+self.P\n                        ] for i in instance_numbers\n                    ]).flip(-1)\n                ])\n\n                if len(distances) > Lmax:\n#                   abs_dist = [abs(v) for v in distances]\n#                   abs_dist.sort()\n#                   dis_th = abs_dist[Lmax]\n#                   img[k-1] = images_k[:,abs(distances) < dis_th]\n#                   slices_mask[k-1] = False\n                    indices = [i for i in range(len(distances))]\n                    indices.sort(key=lambda i:abs(distances[i]))\n                    indices = indices[:Lmax]\n                    indices.sort()\n                    img[k-1] = images_k[:,indices]\n                    slices_mask[k-1] = False\n\n                else:\n                    d = (Lmax - len(distances))//2\n                    img[k-1,:,d:d+len(distances)] = images_k\n                    slices_mask[k-1,:,d:d+len(distances)] = False\n\n        images = img.view(-1,2*self.P,2*self.P)\n        if not self.VALID:\n            images = augment_image(images,self.alpha)[...,self.P//2:self.P//2+self.P,self.P//2:self.P//2+self.P]\n        else:\n            images = images[...,self.P//2:self.P//2+self.P,self.P//2:self.P//2+self.P]\n\n        images = images.view(5,2,Lmax,self.P,self.P)\n        \n        label = torch.as_tensor([labels[x] for x in row[lsubarticular+rsubarticular]]).view(2,5)\n        label[slices_mask.sum(-1).T == Lmax] = -100\n        \n        if row['flip']:\n#               Flip L to R\n                images = images.flip(1)\n                label = label.flip(0)\n\n        return [images.to(device),slices_mask.to(device)],label.to(device)","metadata":{"execution":{"iopub.status.busy":"2024-11-02T23:11:38.767391Z","iopub.execute_input":"2024-11-02T23:11:38.767795Z","iopub.status.idle":"2024-11-02T23:11:38.800528Z","shell.execute_reply.started":"2024-11-02T23:11:38.767759Z","shell.execute_reply":"2024-11-02T23:11:38.799418Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"ds = subarticular_Dataset(df,1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T23:11:43.609672Z","iopub.execute_input":"2024-11-02T23:11:43.610805Z","iopub.status.idle":"2024-11-02T23:11:43.615856Z","shell.execute_reply.started":"2024-11-02T23:11:43.610751Z","shell.execute_reply":"2024-11-02T23:11:43.614778Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sample = ds.__getitem__(np.random.randint(len(ds)))\nprint(sample[1])\nfig, axesL = plt.subplots(1, 5, figsize=(10,10))\nfig, axesR = plt.subplots(1, 5, figsize=(10,10))\nfor k in range(5):\n    axesL[k].imshow(sample[0][0][k,0].sum(0).cpu())\n    axesR[k].imshow(sample[0][0][k,1].sum(0).cpu())\nplt.show()\nplt.imshow(sample[0][1].cpu().view(-1,Lmax))\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T23:11:45.842465Z","iopub.execute_input":"2024-11-02T23:11:45.842869Z","iopub.status.idle":"2024-11-02T23:11:47.549087Z","shell.execute_reply.started":"2024-11-02T23:11:45.842833Z","shell.execute_reply":"2024-11-02T23:11:47.547962Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class myUNet(nn.Module):\n    def __init__(self):\n        super(myUNet, self).__init__()\n\n        self.UNet = smp.Unet(\n            encoder_name=\"resnet18\",\n            classes=2,\n            in_channels=1\n        ).to(device)\n\n    def forward(self,X):\n        x = self.UNet(X)\n#       MinMaxScaling along the class plane to generate a heatmap\n        min_values = x.view(-1,2,PATCH_SIZE*PATCH_SIZE).min(-1)[0].view(-1,2,1,1) # Bug, I've been MinMaxScaling with the wrong values\n        max_values = x.view(-1,2,PATCH_SIZE*PATCH_SIZE).max(-1)[0].view(-1,2,1,1)\n        x = (x - min_values)/(max_values - min_values)\n        \n        return x","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T22:56:19.360786Z","iopub.status.idle":"2024-11-02T22:56:19.361511Z","shell.execute_reply.started":"2024-11-02T22:56:19.361220Z","shell.execute_reply":"2024-11-02T22:56:19.361258Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class SinusoidalPosEmb(nn.Module):\n    def __init__(self, dim=16, M=10000):\n        super().__init__()\n        self.dim = dim\n        self.M = M\n\n    def forward(self, x):\n        device = x.device\n        half_dim = self.dim // 2\n        emb = math.log(self.M) / half_dim\n        emb = torch.exp(torch.arange(half_dim, device=device) * (-emb))\n        emb = x[...,None] * emb[None,...]\n        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)\n        return emb\n\nclass myViT(nn.Module):\n    def __init__(self, ENCODER, dim=512, depth=24, head_size=64, **kwargs):\n        super().__init__()\n        self.ENCODER = ENCODER\n        self.AvgPool = nn.AdaptiveAvgPool2d(output_size=1).to(device)\n        self.slices_pos_enc = nn.Parameter(SinusoidalPosEmb(dim)(torch.arange(Lmax, device=device).unsqueeze(0)))\n        self.side_pos_enc = nn.Parameter(SinusoidalPosEmb(dim)(torch.arange(2, device=device).unsqueeze(0)))\n        self.level_pos_enc = nn.Parameter(SinusoidalPosEmb(dim)(torch.arange(5, device=device).unsqueeze(0)))\n        self.slices_transformer = nn.TransformerEncoder(\n                nn.TransformerEncoderLayer(d_model=dim, nhead=dim//head_size, dim_feedforward=4*dim,\n                dropout=0.1, activation=nn.GELU(), batch_first=True, norm_first=True, device=device), 24)\n        self.side_transformer = nn.TransformerEncoder(\n                nn.TransformerEncoderLayer(d_model=dim, nhead=dim//head_size, dim_feedforward=4*dim,\n                dropout=0.1, activation=nn.GELU(), batch_first=True, norm_first=True, device=device), 12)\n        self.level_transformer = nn.TransformerEncoder(\n                nn.TransformerEncoderLayer(d_model=dim, nhead=dim//head_size, dim_feedforward=4*dim,\n                dropout=0.1, activation=nn.GELU(), batch_first=True, norm_first=True, device=device), 12)\n        self.proj_out = nn.Linear(dim,3).to(device)\n    \n    def forward(self, x):\n        x,slices_mask = x\n        \n        '''for kk in range(BS):\n            fig, axesL = plt.subplots(1, 5, figsize=(10,10))\n            fig, axesR = plt.subplots(1, 5, figsize=(10,10))\n            for k in range(5):\n                axesL[k].imshow(x[kk,k,0].sum(0).cpu())\n                axesR[k].imshow(x[kk,k,1].sum(0).cpu())\n            plt.show()'''\n        \n        x = self.ENCODER(x.view(-1,1,patch_size,patch_size))[-1]\n        x = self.AvgPool(x)\n        slices_mask = slices_mask.view(-1,Lmax)\n        mask = slices_mask.sum(-1) < Lmax\n        x = x.view(-1,Lmax,512) + self.slices_pos_enc\n        x[mask] = self.slices_transformer(x[mask],src_key_padding_mask=slices_mask[mask])\n        x[slices_mask] = 0\n        d = (~slices_mask).sum(1).unsqueeze(-1).tile(1,512)\n        x = x.sum(1)\n        x[d > 0] = x[d > 0]/d[d > 0]\n\n        side_mask = slices_mask.view(-1,2,Lmax).sum(-1) == Lmax\n        mask = side_mask.sum(-1) < 2\n        x = x.view(-1,2,512) + self.side_pos_enc\n        x[mask] = self.side_transformer(x[mask],src_key_padding_mask=side_mask[mask])\n\n        level_mask = side_mask.view(-1,5,2).permute(0,2,1).reshape(-1,5)\n        mask = level_mask.sum(-1) < 5\n        x = x.view(-1,5,2,512).permute(0,2,1,3).reshape(-1,5,512)\n        x = x + self.level_pos_enc\n        x[mask] = self.level_transformer(x[mask],src_key_padding_mask=level_mask[mask])\n\n        x = self.proj_out(x.view(-1,512)).view(-1,2,5,3)\n\n        return x","metadata":{"execution":{"iopub.status.busy":"2024-11-02T22:56:19.362713Z","iopub.status.idle":"2024-11-02T22:56:19.363156Z","shell.execute_reply.started":"2024-11-02T22:56:19.362933Z","shell.execute_reply":"2024-11-02T22:56:19.362957Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def myLoss(preds,target):\n    target = target.view(-1)\n    preds = preds.view(-1,3)\n    Loss = nn.CrossEntropyLoss(weight=torch.as_tensor([1.,2.,4.]).to(device))(preds,target.long())\n    return Loss","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-11-02T22:56:19.364375Z","iopub.status.idle":"2024-11-02T22:56:19.365080Z","shell.execute_reply.started":"2024-11-02T22:56:19.364804Z","shell.execute_reply":"2024-11-02T22:56:19.364833Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![image.png](attachment:image.png)\n\n![image-2.png](attachment:image-2.png)","metadata":{},"attachments":{"image-2.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAOgAAAGJCAYAAACAdn8zAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAFTTSURBVHhe7Z17sBVVdribSaqUDEQkhkEkIVAijyEwGIcgSBXBspBoKjFWIU+r4qMANcgQQaYGBS8YEIwiCYIFOKU8vVUToZRHBiUkPCRUEgJDeDpUKgpOjFESdGD+GX73W/esZt++vfuc09333j4/11fVdc7Z3fu19lp7r92w1213pYHAMIxC8o3Sp2EYBcQM1DAKjBmoYRSYxD3oZ599VvpmGEZLccMNN5S+NcdWUMMoMGaghlFgzEANo8CYgRpGgTEDNYwCYwZqGAWm8AZ66dKl4C/+4i+CBx54IPj8889LqW3Hhx9+GIwcOVLaRNuqoWh9qYZ/+qd/Cn7zN38z+NGPfpQog3J9rBUZaH//6q/+qpTSNnwtV1AUAwXJIvxu3boF7du3L/36evL/kwx00mECcvmd3/md0re24WtpoGfOnAl2795d+lUdN998c7Bnzx5ZBb6u/P8ogyNHjgT//u//XvoVBL//+78f/Pd//3dw//33l1LahkwGqrMOroC6P+rCkD527Ngm90DvazrPUY6iroXeO3v2rKT/13/9VzB16tTwHs+lgXz33nuvfF+8eLGspP/yL/8StpdP2nfs2DH5rvWpS6Z9ZvX19bXStiXJQld5vUd9cWlpYCzIr2Pi9knvJdXhPu/+5vnf/u3fDt58801Jr5RondquuP5GZcZ30rJAfegW8EmZ//iP/xi2Rfun+kE6n88991zYDldOrg5zpdVVSG2gNPqRRx4J/vzP/1xmmjlz5gR//dd/HRoUsxEd/c///M/gwQcflHvkefXVV8UgPvjgA7n3e7/3e1IO99R4KIsymaV79eol5cFf/uVfBqdOnQpGjRoV1NfXpxoYZsZ3331XvlPPW2+9FVx33XXyu127dsHf/u3firAHDBgg9dMOnmfF/fu//3t5Lg7aRp++/e1vV9w2nyyQ3fPPPy+TEvdow2OPPdYsDfmm4Q/+4A9EhgcOHJB26upxxx13yIpB2VzI55133pGx8UH+lStXBt/61rdkbFQGlcKYYxSrVq2SOvnk9+bNm2P7i8yYABgT0hirrG42faZe4JMyr7nmGvnt8lu/9VvBjh075Bnk1aFDh1C/VU6qw7RP77300ksy2aQhtYHqoOqqxmrE7//5n/+R+yhA7969RXjDhg2Te//xH/8RHDp0SBTxpptuanKPfPv27ZPB/eM//mMpwwUF6Ny5c+lXEHz88ceZZ84oQ4YMaVIHAmem1BWX9sehbePieyVtu3DhglcWX375pezv+H777bfLLM79aFpaaCd9pZ20A0NFkQYOHCj3KTtuTOM4d+6cTDIqO/pCnypFx3zQoEHym09+U25cf3VPyJi4q1ZrEN1z0xYdF4X+AO1Tb4JJptUNVNGZTK/vfve7pTu1DYOPYuAhVLsq5AGrha706naxikbT0k5SrJZ4Bbt27RIDY3KgLFxKvB36rKtKWxEngz/8wz8Mx0O3KGmVv6Wgbbrqc+GJsW9PQ2oD7d69u3y67hzL/+XLl+U7g8/LGITHM6yot956q8y0KAQzJPmYvXW1ZUZixty6dauU0VL8xm/8RqLB0a7z589Lu3AHWUFoV5506tQpURaAO64uva7KcWlpoA7KQOmBlYuxYrb/oz/6I1Eon8fgol4D3gD5dUWtFCYKZItHBq67DXH9pW3oGqt+ltXJRfU5K1Edpr20NS2pDVT3cizhLOW4RO+9917pbiMs83369BEhsp9gMFkFcIFYncjHYOo93QswM1Ie7qXuafOEAUYJdQb+3//939KdRnBbeCHAJEP7WU1bYgX1yYL6MRxkoPJ79tln5Z6bpnJLA/mYIEDdbFcu1HPy5Em5nwTlzJw5M5QVe+guXbqU7pYHPWLMdavEJ79xt6MyoL8//OEPJU3dxxdeeCH16uRCfRi8rtS/+MUvSneqI6rDtPP48eOlu9WT+3lQZoy5c+fKbMfLg7QKZBhfF+w8aBugL1rcK8uLnVrEZJAdW0ENo41JWkEt5IlhtDHm4hpGjWIGahgFxiLLG0aBsRXUMAqMGahhFBgzUMMoMGaghlFgzEANo8CYgRpGgTEDNYwCUzMGypnA73znOxKWhCNBac9BlmPDhg3e8jkXevfdd0sbaAttciEPeSlD2b9/vzzPtWDBglJqIN/j0t3nqUujGbjP69VSckiSgUJ73HYryGT06NGhbFQm2mafbNy+RvPwXJRK2lgOX/3uOPvq9+UF916cjKqhJgyUzs+fP1/iB/H/Kji7SOygPNEJgAPDv/7rv15KvQqK8IMf/CCYPHmytGHFihXByy+/3ERB/vVf/zV47bXXSr8a2/03f/M3cq7y5z//uRxmZvBIHzp0qJSj/09EFfeb3/ymPEs6dRGDB5555pnwea66ujq574bgyEo5GYAqXxQ1KsapZ8+epdRA+sJ5UdrM/+3mDLEaL7+1r/QP+VLOiy++KGNMOrJDhq4B8H3dunWlX+nxyZo2rFmzxls/+PL6xjwtNWGgdLZ///5ycBc4TI0SucaRFcr+t3/7t+Dhhx8upTQFYQOzJRAdAiUmMBTQFg4tEyBKQQEJNMWhXQxpxIgREqWAiA533XVX6amgSWA0DESN7rbbbgsuXrzYrJ8oOOm0IU/KyQCGDx8uSkmEAxfazEHl119/vYlx09dbbrkl/M446iEMYk9pX/kP4+Sjr/SNMQbaRB50QNm5c2fwp3/6p95JpFJ8sia6gkZYYOwYw+jBEV9e35inpSYMlA66Ssxg/t///V9oNK0Bgo9GXqANOnCs6LTRjSZAOz/66CMxYgaPSYXBjELUiLgAyf/8z/8soUhUERTSXeWuFVhdWFH69u1bSrkKcuzYsaMYPzKLokrO5IQcNRpEXvhkrRMwBkfds2fPbjZhunkrHfNKsZdEFcIAEZ5TZ3IG4ODBg/JdlYZZ3YUVA5ePsCq/9mu/JkalXoCC+4PS6mrIoOr+CyZOnCifCkq+d+/eWCUvKuoW0xfCliAXF3ULWTV1lUXp9Z66iMhm7dq1ssLnMTklyZotB+lsY4h/G63Pl7eSMa8GM9AKYYC+973vBY8//ng4cMSf+dVf/VX5/vTTTzcbRBSLe0R4Y2XABXZfkvACgTRcQ83LJ795HqIvQpggcJuiSl5k1C2m7yiyuyfjO2kYqCoysZrYY6pR89IJD4O9aVaFd0mSNfWSzpj/yZ/8iUzC1LtkyRLJ58tbbsyrpSYMlMFxg4fhDrH/YIZqTRgg9mgInpWAlY/fvBjCtUGhJk2aJJcan/six907c599HC9HfOg+S90sIHpeFpepLWFSob/IBVBmvrOndCccvpOGnN9+++3gk08+kT0e4VKI9oec8SCWLl0a3HfffbLKZiVO1sCYE0xMV/Q43LxJY56GmjBQBoPIaMxiUF9fH7tfaAlQorhX5by1YyWbMmWKKJJe69evlwtFZE/KSqCDwyCzz2IgiUrOyuLCcxi8wlth9rm414AiIgMmg9bEJ4NKOH36dGhA9A95MDHxHWVmtUyCvT0y4+WcTo5ceBKzZs0SA07jTSTJmnQdM9rOm2cmRd2DEubTl9c35ml1tSYMlAF44oknxFCjPn9rgpJSPxcDUa4N3OefC1jpycPe8amnnhIPgH960LK4cJGAPyGgadTHSq2D21aeQxYwpjvvvFP6Q7uZ1JiYeMGH4avnwcWbUYxA939ceE5JXkZakKlP1l999VU4ZrQP3XPd6qS8vjFPix3YNowCYy+JDKPAmIEaRoExAzWMAmMGahgFxgzUMApM4lvc1vy/robxdSXpn81sBTWMAmMGahgFxgzUMAqMGahhFBgzUMMoMGaghlFgaspAOfrDgV2NZJA3ixcvlmBQXHF1aP3cJ+iXHqXSNPfavHlzKVcj/J4+fXpw+fLlUkoj1EN+ykkqy22bXnHl5YGvrS60hytKnAyRE/KKa7Pvnm8s3HQdg7RQrpalYwC0gbbovehYKr42QtK9aqgZA6XDDz30UNCjR49SSr7oIHDU6PDhw/Ln+3XAgEEj9MX48ePlmWXLlkkUAAZg69atksbFWU9CcrhBwShn06ZNpV9XocyNGzeWfjUeq/OVNWfOnDCdiyNYEyZMCK699tpS7uyosRw7dkyOtcWhSh0HY8Them0jZdFH5IS8SOvWrVuwZcsWeZ76OKa1bds2ubd8+XLpj28sKOvLL7+UNO7RFg1Ilgb+/ZFyKYtxXb16taQj90ceeUTSuX/gwIFmRpakL3FySEvNGCgKivIywHnDwKOUGlOIQe/Xr19w5swZ+Q0IGtTwOLsYdwJ/165dwbBhw5ocIiaNGTqq9Bz6pS7fpBNXFqDYKCptyBPagiISQcAHyoYsRo4cWUppRNtEeBAXlVvv3r3lk3waAIzD2NOmTWvSv3JjwUH3qDzSMnDgwHCCIyYU7ad+6uQecD+qc0lt9MkhLbYHbQAl4kS8O/DMgG64RGbHuKh+nK5XGDjyuKsnA8ZgRsOUUB6rp4bxjBJXlsIJ/nvvvTfX1TMryKFr164SLIsVVl1GXW3VwPbs2SNGSv+AiAM8w8WqlDQW3CPWD5EL9Pm8QKYDBgxoJlPaicF17txZxnLu3LnSV18bfXJIixlohWhUP1U0Vk7iA7loGAwdOAb3zTfflBUpOvC4U7io119/fSmlKdGyFAYbl0tXpKKAchLqhThBGJK6jPR73rx5waJFi0RhUWRdhTE2lJ/vyHPHjh3BT3/601KJzUEWugXgebYNWfZ3jI/uNWHcuHHy6YI7zqrNKlkJPjmkxQy0QlA0Ql/MmDFDBpR9FW4MM6vCaqBuD7DvYqWLDi4zvyqqj2hZChNEnNtbBJCH9lVdxp/85CcS3oWA1igssEeDTp06hX2kP7iJFy5ckN/l4HmUH4NIC2PKvlfb5b6kUuMFtldA3xYuXBhcc8018ttHnBy03GoxA20Ag2N/6LoiDDxG5ILQmbEZUFaF8+fPh4ZCXgZCA3zhDjH7Eo+H8gcPHizhGFFIVlVe/pDO88R6veeeeyQPRMtywXDzjiifB1FZKXgCY8aMCeWEy47cUFgisEfBaCoZi7zBqPSdAm3DEJmQ41bVJH3Ju51moA2gFOw/9O+9YCgnTpwQNxKD1BnfBbfFXcnU9aUscI2Zizd9xFilju3bt4fpKATGyptMnXWjZSkoBG3TOlsLnwxckBUy00mGfiJT0nFFVZm1b/QhTubf/va3vWPhQnm4xGknK4zw6NGjpV+NE4lG5uM7bdDxUGgLe1DwtdEnh+hYVooZaAlmUGZ2Zkf2jPwhn6ghoKTc52KmdGfXPGd5X1koJTM39RcNZIXMkJ22D/ngxuOKoviksxfF+0BhVX6kuzL3jQVKT3mkUx5vgKNGVCnUz0RJWdF2IX/1cPSKTlC+NvrkkBY7D2oYbYydBzWMGsUM1DAKjBmoYRQYM1DDKDBmoIZRYMxADaPA2B9PMowCYyuoYRQYM1DDKDBmoIZRYMxADaPAmIEaRoExAzWMAlMzBrpgwYKgXbt2cvG9JXDr2L9/fym1kVOnTkmQLr3vPscxMGILadrUqVODS5cuST7ua7rbbrc893k++a153Hb4ysqTJBkocW138+nFvR/+8IfN0pEVMnP7o2ngk0FUzr72VYqvfoV+jh49Wj59aFs3bNggv31y0PGtlpowUATHOUD+yVb/2VYFkhdaHuWfPHlSQpq4A9anTx85yKtt4JlZs2bJgeHPPvtMosBxPI97q1atCtq3by/5KYdnuXfu3DlRCgaL6AorVqyQ52+66abw8O+LL74ov6Pt8JWVJ+VkACjr/Pnzg/fff79JXwkDym+96urqgsmTJwd/9md/1iSdeD2kc24SuanMyM85SmTjkwH31qxZ0yw9LZzX1Ppp06uvvirpanT0s2fPnpLmg2Bjr732WulX4JUDMkpDTRgog+lGt+vVq1fpWz4wIEeOHAnGjh0rvzHG/v37ixL4qK+vl4htKvgbb7yx2SCggIT14HAx90aMGCGHgVEK6Nu3r3yOGjUqOHv2rLTj4sWLse3wlZUXlcqAfhMKhDHxgRHTj2i0A4xp7969slqBK78bbrhBDqMnyaB79+5yAXJAHsglLXgBWj9RF6mX+klj4iGOEm3ywbO7d+8OXnrppVJKU3xyqIaa3IOizHnGfsFgCHeBkihMAj4D0FlbBY+SEBgLd4ZBV5eI8j766CMJa6IGgCLoAV1VfgYZI6UdPB+FdvjKyotKZEC9QDRDdd/iPBniJrnGp9BfJpY440aGHTt2lFXHJwMX5AAYah7Q5kGDBjVrswvjOnv27FAOeD3IqEuXLvI7ik8O1VBzBopbh3uXZVbKys6dO2VgVPDDhw8PXRrc1ieffFKMGEXETSJGKkbJYLEikI+I6rpfoSzK4HlWCwYWKEPdWF9ZrQlGTHsIaEZfMap33303nJBAV0n1DhSUmpCZcZMKeXBXWTWTZABMCMiMLQKR/rMoP23ClaU8mDhxonxWAn1mkoyLvAg+OVRLTRkoCs1qo/uetoBBZQVXNy0KEwf7UZQXxUKRiP+KQtN2FIzBxU3kO+lA3+Cxxx4L1q1bJ0qDwvCSAm/BV1ZrQxxfdT/VmFw307dKsuKxDYhOKvSLfmKges8nA+A3/ScAG3GB3MmhWtAhdEnHoNzLHNq3ZMkSmagYi6efftqrh0neQjXUjIGiwLiBbMLzhhWJvYaraD43GkXrUOGfH8CI3BcEKDazLoGniZerZWDseAXMuqSxQqM0b7/9dvDJJ5+I2+krK0mhqqESGfBMXKhMF9zfuFWSFXHIkCGlX41gnPSL/rry9MnABWMhMJeutFlhJaT/6jonQdt4MUSbmEQmTZokl06y4JNDtdSEgTJLYhS4gS0BSs/+o76+Xn5T3/Hjx8U9QYlcwaMQvj0H8FZPwzfiurISqBGRl30WkehIxyBB96IYgAt7HJ5HGX1l+WbwaqlEBknPAP0hLWpMtJnJxE0nDeNktUzClQFv0bX/1IV7ndYIKIfyFHfcfNA39qAYM5OHXryZ5tLFwyeHNNSEgTKr60sYvbL821IcCJ1VjLLZ57Hfi1sl41ZW3RdxocjsL1Fm3DH+uQDD4x57kqeeekomGlZDnYHdPG5Z1KWD7isrTyqRAe2AuGcYJ1ah6EQT9wJK97MqAy59weaTAXGEtf/kY5ugbnG1IOt33nknrMcdg6z45JAGOw9qGAWm5t7iGsbXCTNQwygwZqCGUWDMQA2jwJiBGkaBsT+eZBhtTNI/x9gKahgFxgzUMAqMGahhFBgzUMMoMGaghlFgzEANo8CYgRpGgakJA718+XIwffp0icLGtXnz5tKdfFm8eHFYx8GDB0upTTl9+rREGOQZ2kTbou2L5o2WW64/nCckpAn3+NRzo+TVPJTZEiTJwO27e7nP8Qxt5tNF++Q+68qBct08vnbkKQO3LFfOiq8vkJQ3zzbWhIFyDpCB5JMT7zt27IgVWhbUSKjj8OHDwcqVK2MHjDOD27Ztk+eWL18eXHvttcGyZcuCbt26xeZlgDg/yj0uFJFPX3/Ix9nXpUuXyn3i+HDeknTKpXy+nz9/voni5kE5GXDQnDq5r88QeoRznGpsyKdHjx6lHI0gg4ceeqhZOnIbNmyYlMV3wp5Qjq8dyIjvyCwPGfAfBCiHesaPHx+sXr1a0pP6ovjykpbnONWEgaKgN998c/i9X79+weeffy6/84ABOXbsWBgACkWkjjNnzshvhdP906ZNa3KImbwE0YrLi0Jxj9g5Lkn92bVrVzBmzBgpx4XB1rCbTAoodp5hNyuVgQvyIHQL7eFiwiIUCIeVXebMmSMTDZOYosqr4VQxcvJRn68dRD1ANsiM+iZMmBDs2bNHnksDsaMoB4glxVghh6S+KL68eY9Tze1BdWB79+5dSskOsyAn/l3DY9VzBYvwgVAj6r4w05PXFyYSo+vatatEHohzhSDaH07j818soy4SbdOwm2pMeUY2rEQGLtoPDCsN5Ef56SOgzIS1If6Qrx1cbjSLzp07h4aRFYx/wIABodHFwYQ7d+7cZvW5efMep5oxUPXrcZXmzZvXZABbAxSYiHooBN/VNUXRmOEZJOA3zwEKRawadVddVyiuPwwoSkCsHMrhUheJ+0ReJ1AW31m5oqtsa8JKj7EkKXTRQd66B4Zx48bJZyX48uY9TjVjoLp3I9o3Sp3Fr09Lp06dQtcL4atr+uijjwabNm0KDQ63TWd63FsdINcVSuoPrhuKz6UuEvfZo+HSkY/P6Mul1oL20yY32n8tgnxxZZEnYHD0zQfjuHDhwnBs4vLmPU415+JiGN///vcz7T2iYFi4W6xYStSd4hlfyEnaxB6LAXnrrbeCn/3sZ+J+ufl9uP1h0N19mgv31XCBiQL3KUmhqqESGSh4D5WGHvVBXlxZVXD6weRF7FxfO7j4rjA50g6VSRaYSKmXvlWLmzfvcaoJA/3www/DAaOjGzduDEaOHCm/8wBhsofgpQfgZp44cUL2hcyI7AOTnnHZsmWLKA2zLfd4hmeBvJTx8ccfe/vDJ79J5xncaFZelFPTAZc6L+WESmSgUHfWkJIYKJMRrjLg1mOw1OdrB3LQbUVWPSD/0aNHS7+u1p8UdpO2sAe9cOGCN2/e41QTBvrLX/4yuOeee2SWZ2Bx+3AR84RZkP0edbB/YB8RXSF0nxF9BheGNC5meN5aAvd0P8I9oIyk/vDJb9IZcN4aY+zkQ6FJJx/Br2fMmCF58qISGYBvZa0Wd2uwaNEi2YujyL52IAfe4iKXqNyqhXq2b98udUTrL0dS3rzHyQ5sG0YbYwe2DaNGMQM1jAJjBmoYBcYM1DAKjBmoYRQY++NJhlFgbAU1jAJjBmoYBcYM1DAKjBmoYRQYM1DDKDBmoIZRYGrOQPfv3x/cfffdTc4L5sWCBQuCdu3ayUU9cZw6dUrCfPDM1KlTg0uXLpXuBPKdtA0bNpRSrkKa+zzla13RcuLaoWVrelwdeVBOBm47kAPyAMaDcUnKS1rc2GneaJ44efKM1kFbs+CW5barUln7ZJVnG2vKQBHcunXrSr/yRQeBfxY+efKknIqPKhLKOH/+/OD999+X51atWhW0b9++dLfx7B+BpqJQjttuyqmrq5N6KOemm24Kzz/62sFhYJSGdE4Z7d27t5lCZ6USGdBO2sszK1asCF5++WUZF641a9Z48yaN3c6dO4O/+7u/K/26SlSelEe5lI8Mzp07l0kGHAejHNo8efLk4NVXX5X0SmTtk1XebawpA2XA+vTpk8tZRBeU58iRI8HYsWPlN3X0799fhOxSX18fPPHEE7FnJClj9+7dwUsvvVRKuQoKyMl6Tt0DgcHuuOMOqQeol/qJEOBrB981QBeTAkaSJ5XK4OzZs8GoUaPke9++feUTRezevbtcwHlNok/QT8U3dig0Zc6aNauU0kicPClPI+YhgxEjRjSJsFAtyFMn2Ntuuy24ePGi1FtO1kmyyruNNWOgDCQzMAed8wYF40S8GyWgV69eTQTLoMChQ4dC98V1fVhZyNOlS5dSSiOslgzmkCFDSimNB56jEAmOPpZrB9AWlClrVAOXSmQApGE4gEKivNEJS8OGoKSQNHasWhh8x44dSymNxMmTtmnEPDUSDCsPiNY4aNCg0GAVV9aM5ezZs2Ui9ckq7zbWjIEykLghcatXa4AC46oQNwe3hpny3XfflUFTI9SAYgoDtHbt2uDhhx9uMvAM2PHjx0VxAeX44osv5HsloLwoNDN3azNx4kT5ZILC6J566in5DUxYpOP2Pvfcc2GffWOnrt/w4cPlU/HJk/xsMQhjyiFnwphmkQHjo3tN0L65VCvrvNtYEwbKwDNDRQeytbn++utDt4aBwK356U9/Kgr59NNPN5t9X3zxxdgB4jfhGJltUY5PP/1U+pZ0sh5UoeCZZ56Rz9ZE62fFY5IiqjwRCXWiQcE1nbAlGJpv7LhHoDXXwIGyfPLEoLlHWFPqYSV3vZhqoXzeI1AW0Df6CHGyZtyWLFmSGBYl7zZSSKFpWKmujB49Ggk2uRrckSsNLlbpqWw0rI5XGvZATcqrq6u7sm/fvtIv/zPPPvtss7ZxTZ8+XdoYTacv9MmFeigrqR1x9/KkEhlwj2d4Vok+o6xfv/5Kg/J7xw75RNO5pk2bFptOPZW0Jy1u/8vJOklWebex8CsoKxUvWRraKheu5ZQpU+RNahbXwYWZlP1HfX29/GZ2xwXlJQgzIq/Kfc8QD1XbxkWgaq5XXnlFor1pesNAyYuQt99+u4mrx4rBWz9W5qR28JKFe3n1OUolMmDFJ5odbQHazjOk01ddfUjH/Sfinm/skI+mczUodtCg2OIOu+kqT1YxVmLcaq2HrQHuJ22vFsqgzQp90sh8PlkjE/ag4JNVnm2EmnqL25Kw3+GVOC4n+wf2Ea4hgbv/8j1TCSiw/pvhnXfeKeWoMvjawQuISZMmSbpeWf+NLUo5GfAdw3r88cflGQyTt9q0nfi2uOjR9DxB/ryU0nr454+oi1wpGMw777wj5XAhS/5YEumVyNonqzzbCHYe1DAKjK2ghlFgzEANo8CYgRpGgTEDNYwCYwZqGAXGDNQwCoz98STDaGOS/ounraCGUWDMQA2jwJiBGkaBMQM1jAJjBmoYBcYM1DAKTM0Y6ObNmyUKm14HDx4s3cmPxYsXly3/9OnTwdChQ+UZzoJevnxZntV8RFDgOFkU2q/Pg5vHlx5XFvWTzmdLkFYG4OblvrbR9zyf/I4+D3HtcNP0csurFp+s+eS33vPJAaLj6pJ0r1JqxkA5o8dBX84dcjGgeYIwgbIPHz4crFy5MtY4ODO4bds2eW758uUS/oJ/x+JZ0saPHx+sXr26lKMR7m3atKn0q7GcRYsWST3k6datW7Blyxa55ytLlZn6e/ToIWl5k0UGtI94TdonlPqWW26R/Dz/5ptvNuvrsmXLgmHDhkk63zm4TjnkPX/+vOR12zFnzhx5Vi8OcU+YMCExBEkSSbLm4DjpSXJAB48dOxZGa3TheXfM01IzBtqhQ4egc+fOpV/5woAgaA1ShWL169cvOHPmjPxWCCA1bdq0Zoe0Bw4cGCrJrbfeKopKmcquXbtkRtaBJCrc7bffLvUA9VI/eXxlkYYxECc2TiGyklUGwBhF08lPOdG+YoBcd911l6QT5pJ+EQ2PyRjDpc8awjPOQJCNhsdMg0/WHLjWUJu++ukPE8mDDz5YSmlKdMzTUhMGitAYkMGDB4vLgauTJ8yUhLtwlYv4rSiKQhuAEBbq+uiK40K4jAEDBoQDT7tRSDf0oluuQqhG2uESLaslySoD8hMoC4V208nvxsKlfOohGgHKy7NAHzFwJi+eP3DggNQXDeGpIBsCr+UlG5+s3foZy7lz54Zy8BE35mmpCQNFaKweKAEzGTNvnHG0JKqAzLJ8Z+B27Nghg8GA4X6qso0bN04+Sce1Y5Z1B57Z+sSJE+GsjHJcuHBBvvvKKgJJMsDwiNKn6bh3SXu3JHAdWUEpkzhORAl05YfcMODevXuXUtKRJGv0i3Tcboyy0onAN+ZpqRkXV6HT7DviVqGWplOnTqELiPLgujHj0yadQIBBZ6DYVzHLq3un8HvMmDHhakMwLVxevvvKKgo+GbiQzp4u7Riph4QMXn/99WDmzJkyCSi4zWrAWUiSNcZKOrGViDlE/YzbwoULEw3PN+ZpqTkDbQkwDNwtd58Rdc14hr1IOYgHS1kEi+JlCEHByIt7TrxUBpt6VAG41BWKDryWpW5WS5KnDFyibjLlUw97PFxdNQ4Mg5X5G9/4RpO9KUbIZIaXoeBi44XkiU/WGBqTjVu/D4w4aczTUHMGykBu3LhRQjrmBYbB/oMXIICgcUFxoXDTmNF9z6CwhKJUCOWI4vE3OcirRsjbQFy1t956q8nMz8DxlpBVib7FlRXdf7UEWWQQdTXpE64vRsQ9nuFZIC9l8DaXi5cpoH3VlUfTkQnurE4UlE1ZWVfPJFmTzn1w+0K9SXtQ2l7JmFdDTRgoQtJ/l6KjuDd5/zMLMygzN3Wwf/jBD37QTKi6R3GfYSXYvn27pHHxzyfz5s1rthq6uP3h75VQDoNLnmrLypO0MuAZlJcxIR0l500vfeIe3/UFH2gZjz76qOxV3b7iQvOp6dHxRnasdFpWWpJkzTFL6iXd7UtbYOdBDaONsfOghlGjmIEaRoExAzWMAmMGahgFxgzUMAqM/fEkwygwtoIaRoExAzWMAmMGahgFxgzUMAqMGahhFBgzUMMoMGaghlFgaspAN2zYELRr104uvufNggULwvL3799fSm3KqVOnJFAVz0ydOjW4dOmSpMfl5WjU3XffHVum+zzfFbd8Pvntwu/Ro0c3S8+LtDJw8+ml9yBu7Chf05AT8lJc2ek9yqLMaDlp8dWfNG4uPlm55bpjmwr+o0ItsH79+it1dXWlX/njln/y5Mkr48aNu/LZZ5/Jb8WX7sv70UcfyRVN5/rxj38s6UBeygDStXw3z89//vMrU6ZMkd98ci9vssggCuXs27dPvvvGbsuWLdIv4Fn6xW/Kpo5oH/l9+PBh+a7y0DrSQFlav9tG37i5+GTltj2PNtbECsqMRgiRxx57rJSSL8zMR44cCcaOHSu/+/TpE/Tv3z9oELL8Vurr6yVGjXuIOSlv9+7d5QIO/hJ9oWEAJb+G9IBevXqVvgWSruXfcMMNwXXXXSff27dvH6xatUpi9HBgOW+yyCAKK+zFixclCkHS2HFonX4BfdV+7dy5U+L60AYXfmuYTfJpaMy0UJbWT9gZ2owcfOOmJMmK53iefJRNZI0s8bNqwkDpNIPB6X3chjjXLwsNM52Eu0BJFIzGFSyDAocOHQrdF1ysSvKCxrph4KKcPXu2SewfhX6jtEkHevMiiwyiEDNIja/SseO5jh07Sp5PP/1UQoZoHXFuIm3BoNz2ZoE2Dxo0KDRYxR032j179mwJkuaTFWmEUCWfGnKW8Js1Y6DPPvtsMHnyZFzyYMWKFRKMSRWmNUCB2VsQ2Io20KZ33303+PDDD0tPxKN7L9r73HPPNVMAyiRGbDQIFn0jj7vKtDU+GbgGpytm37595XclY0cewluyIpFOeQTpoj4u5OPu8YDYRhh0dJWtBurSPS1MnDhRPqHcuPnAs5g/f74ECmNiZfyytLFmXhI1+PKhEuvgM3ityfXXXx+6NQwEbg3KlQSDjmISPIqYP64yszLs3r1bXFdXAXiGZ8kzfPjwUmoxiJMBRqjg5uHWuS5w0thheMgIA3UVGYNGJlyum6hGBfzphyxQNrJnfMB9qRU3brRvyZIliTGi6A9GTfxg8jO+WV5m1YSB4ja0xL5LYaajfFfRom4nz8SFnGSwyuUFBpcgW7hSgHGOGjWqmZKhCGvXrpXwjVlm3mrJIgMX3F/XpUsaO5QZBWbPqQaN0fj2lhgPgb0wGne1ywOiKtLOaNjN6LhBkqzoj04uwGSGm6uGXy01YaD4//j8GpuUAWUQ3Vk6CwiT/QcvQAAjOX78uMz2KBHG5HuGNF9eQjnqwLDS4g6ivDzToUOH2NWRSHMPP/xwOMCtRRYZ6KpIH0lz92a+sUPJUea4l0dMXOvWrRPZuXKjDOrPY+KibMZHoWzaSXuTxo09KPjkwF5U2w4Ytu6tU9GwDNcEDbPVldGjR+OLhK/j84TyKJfyG4QfvuLnFbn7TwR8jz6TlJc0vfR1ezSdi/wNyhCW417ua3rqmjVrVlhHnmSRAfCdtkXHJm7s3DS93PL4ZwxN1/67aXq57aoW7QcXbaFNEB0frd/tn09W4Jar/U2LHdg2jAJTMy+JDOPriBmoYRQYM1DDKDBmoIZRYMxADaPA2B9PMow2hn8T9mErqGEUGDNQwygwZqCGUWDMQA2jwJiBGkaBMQM1jAJTEwa6efPm4Jvf/GaTi5PqHAXKk8WLF4flHzx4sJR6ldOnTwdDhw4Nn+F5JS4v7aOd0XRwy5o+fXpw+fLl0p1G6HM0nfxallt3nmSRgTtOce2jPHfc6Bt95HnKpOyozPSibPd5TcuCK09fu8rVo8/GPUNadAyrpSYMdNy4cRKjRi8ONI8fPz6386CgAqb8w4cPBytXrmw2ARCLhtP12o45c+ZIOgN9/vx5ed7Ny8C8+uqrzcoknQgCy5Ytk3vdunWTA9qgBnDs2LEmB51JJz8HiimD+uIMKAtZZbBp0yZv++jzxo0bS78aoc/0nXKQBTLBILZu3RqWT3mcjyWYGt8feeQRSaeOAwcOZJIB//5IOZSHPq1evVrSq6mHs6PoYxTyIY+s1JyLqwJzo+JlBeXBIDhVD7fcckvQr1+/4MyZM/LbJS64F+E4hg0bJtEVOPBL1AHaycFkjQ7gpjPw0Lt3b/kcOXJkGNKDulEGTvG7cKB4zJgxMilRz4QJE4I9e/aU7mYnDxnopBnXPhSZMnv06FFKacxD30FlobJRdu3aJbKlXPIPHDhQ0qkD484CZVEOEJKFWEvIodJ6eJY+vvDCC6WUq9BuVmVfNIlKqTkDRWF0wPICpeA0vVsmSqhGo/Cb2TzqEvEskwYDxuwLGKSLm05+UOVnkFVRfVC3axidO3cOFSoPssog+hywitI+nmH1JBi0C+WrESMLDMGtn7yUGzcZc4/+I4c8YAIcMGBAaLCKWw9ezNy5c0OZ4wHQBzeCBPAck12WaH5KTRkogiH8RDQCXmvhutqsFkR7o024pDppzJo1S2Lm6EDjNqLMuG8MLulcxNZZtGiR3GOQKaMW8MmACUYnKXBXT1xHVlQCjrlQFiADDHjGjBnyW9GQJHGTMcZB2BhWu7TQVt1rgrbHxVePGiHuvgtlvvnmm+IBRY09DTVloKxCXbt2zTQoeaGzOoqqL0T4TmDpmTNnygCCKjTBngnFSDoXz/As96ClXvq0JK4MmGB0BUThu3TpIuOkq0x0AlLjwLBVPlOmTAlXZCCej7rciuYD3f+mBQNavnx5OAbuC524eujPwoUL5XkmXHciVthLE3Q7Lx2tKQPFDcnDbYiCQrFXcJUj6lL6+OKLL8SVU2VFQdkr0lYXBowVh3R3Pwnk1ZdMPmiL60bysoaZPY9ZGrLIQEGRUV4uXsAQKIsXJeoS497zQuWee+6R2LnUp5HikY+756UduJbuVgGjwUAw5rjVLgushLSHRaBcPewv6YduV+gfF94TE9Kdd94p6YMHD5YQnEzMSWObRM0YKELDpchrz+GCkrP/IBgysMKdOHFCXlzwwiZudWOQWDH0TwTwG2gnrh6KffToUfkNDNCOHTvEPeceiquDpkrJoPogH/nJQ5m4hOX2rdWQRQZRF5S8tBWlj3sju23bNunPT37yk9CNpV/Up+OrMnEnIJ6ljXmsTsiQ8VEo243qF1cP/WKb4r7F5sJYuZYuXSqy0nTehLPKvvXWW7FueiXUjIHS4ehLjDxB6KxiGAn7B/5UQbQulJT7XBgheyYUiP0kBkc6ediP4tJxXI/fpDPw06ZNk0HnHqupzsDsRSkjaTUkH6suedw68iStDADlpT2ks5K89NJLzfK6cA+FJj95XPlA3OpNmq7GeqXdGiBrQpxqOe4Y5FlPVuw8qGG0MXYe1DBqFDNQwygwZqCGUWDMQA2jwJiBGkaBMQM1jAJjfzzJMAqMraCGUWDMQA2jwJiBGkaBMQM1jAJjBmoYBcYM1DAKTM0Y6KlTp+Rwb7t27YKpU6cGly5dKt3JjwULFkj5XPv37y+lXsVtAxfPK768bjp5KQOS+sPZSOL3cI9PPTcK5Bs9enRYTt5kkQHPx6Un9RV4Vp93++5eGzZskPtQro2V4rY3KmeoRNb0hT5p+6Ltz9I+qAkDRQicTF+xYkXAP9sSKU8PFueFCpjyT548KSEtogP22WefBffff788w/XMM89IOoNw7tw5OZ7n5qXdFy9elDSe5yBwnz595N78+fPlIG+0P9zjJP8rr7wi93bu3ClnJ1URyNezZ095Nm+yyIDneJ58yAF5IJeksVMDcaGv9FnLpz5CoaD0gHH26tUrvD98+HBJTwPnPGkr5UyePFlCpEI1siY6xmuvvVb61Zh3zZo1UqZPhtVQEwaq51L79u0rn6NGjQrOnj0r3/MAoR45ciQYO3as/MaI+vfvLwKOgnJE4YDviBEjJMQHB48Jr4liQceOHZtFfaNcyqceoF7qpx0oJzFt9J5C2atWrZI4RllDOcaRVQb0l37Tf9qKPJBL0thhXCgyaT6QB2VhuKxkTHjROEVpYVWnrUAoHcpGDpXKmmd3794th9MVImxolI2oLqShJgxUD7SqsiCUpEGtFpSIaA2uIaGEKJgLvydNmtTMJeLkPzF2GDA3vCblskpQLnl0haIcV8m5T/08/+mnn0r0CHWRXFexJckqA/J99NFH0n81dpQ+y9hRDsasqyeKfuONNwb33Xdfs/qzQoCyQYMGhQYbBxPE7NmzpV2AJ4CMCJAWh6sLaakJA0Vozz//fLj3QChZXJu0TJw4MXStcIkICcJg0RZmeZTxySefDJ5++mlps+uuoVzr1q1L3JMQJwclwG3CYLjUVSwKPhnQV1xCAmQhB2LmsgpnGTsNvEbZwOTwxhtvhO6/65amgXbjytIuoG+VwjgxCcWt5kzElIlrT1jSJKMvR00YKMJgX0bHGRhorZXFh87qGJG2hbbRRtpKm11QMhQquiLFwXMMKpe6ikXElQGTCAr5wQcfiBxYKZFFlrE7dOhQ6HIr7H/V/Xfd0jQgX1xZbRfGmlQW9S5ZskT6S191Io6ikxgBw4jzFNWFaqgJA8X9YF+mMymKwcqSl3vDjM9ew90r4FpVEnKS8Je0RZWVNtJW2uyDct09NPVSPxHt9E9FtDZZZAAYpE4soPtqAoulGTvuR8NuVtqWNLAS0n91S5PAK+LFkG5dcPm5ohMPBk3wtSRdKEdNGCgDg3uog6r7Gd3fZAWlYv9RX18vv5nxjh8/Li82WBniZnwGCWPSFwL8BmZg9qNRZaLtRMVn1qdcyteZlXp1/8P+jL5SjpunpckiA4wP11XbDSglL8iI0pdm7PQ5d4VKklu10E4N+Qm40xp20wf1sgfFmFkh9Vq/fr1cvNGmTJVBLuPXUEFN0CAA/BC5Ro8efaVhpi/dyYcGt+XKlClTpPyGQb/SoCCSvm/fvit1dXXynU9tA8+SB2gLbdJ7tBUog7I0nbIUvmu6lq+4fXXzAHXOmjUrbF+eZJEB+O6VGzu3fIU8KkcXV6bRPNXitjeuXVFZ88lvt8/gttUdV67o+FWLnQc1jAJTEy6uYXxdMQM1jAJjBmoYBcYM1DAKjBmoYRQY++NJhtHGJP2bsK2ghlFgzEANo8CYgRpGgTEDNYwCYwZqGAXGDNQwCowZqGEUmJox0IMHD0oUNq7p06dLeJC8Wbx4cVgH9UU5ffp0MHTo0PAZnlc4+0eYD9L51POPmzdvbva8m6aXmwd4xu2nWz5XXPvyIIsM4vrqQnnRfrrlaX/dsY4+796Lq6MakuoB2kY6n3EkyaqcHCulJgwUAS1atCg4fPiwBNTq1q1bsGXLltLdfEC5gPKpZ+XKlc0GjOgJhLDgGa45c+ZIOs/NnDkzWLp0qaRv3bpVDjEzMJs2bZJT+jxz/vx5SRs3blxYBtfatWuD8ePHSx5V2GPHjjWJKIfiEn+H533ty0oWGfj6qtD+jRs3ln41Ql+JV7Rt2zYpa/ny5cG1114r/3BPGaQhl9WrV4fP0yZfHdXiq4e2MlnQth49ekhalCRZYZwc2OceF+OZlpowUJTi9ttvl9P5wIl2FFhXl6xQDuVpACjq6devX3DmzBn57RIXdmPXrl3BmDFjwvYpxBJSw0PxJkyYEOzZs6d0txEGlbAgd911l/ymDJSOUBkuRC7QcCgazlEVIg+yyqBcX4k0QJmuwhMVb9q0aZLHZeDAgVIG3HrrrRL6hPYR9QA5J8mzGnz1kMZkQVgTd5JUkmTFJEI5TGJ5UBMGGhc0ixCPzE55QDmEu3AVBSWM1svvhx9+uJlLRBwf/lukujTqesW1m1mfAVYY1GHDhjVT0iTyCOcYJasMkvrKM6yeGrcJVAaERlG56arkglEOGDBAjIY63MmBGE5qVFlx6/GB8c2dO1cWDJ+suNe1a1eJbhiVURpqwkCZ3U6cOBF2FGFeuHBBvrcmrmvKakFIRdrBwLFC0D4udb1Gjhwpq6MqUHS2J52YNfSvEnSPR7RyFCVJmVqKOBnQj6S+4jqy2l1//fWllMYJgQiAGBjfmXR27NghsqQMXEz6CtTZErREPRjp+vXrw+2O6zqnoSYMFBcC14YVA2GyYuHyqmDbAnVJGQRAATEYLlZEBoq9B/tlZlraSoBj+qKGhVIy25JWCWochLFkhkaZ2xJXBr6+8q6A1SVuH9apU6fQTSQfbiIrEPLBxVTZui/L8qTaeujPwoULg2uuuaaUEg/urY6p6zqnoSYMFNyZW6OkqaJnBYVir+G6IlF3ygdtQDF98BJF281LCTdyO55AmohvDD4zM/nzIosMlGhfibbHiyN1iZlgeSF2zz33iJfBProcKLuGw1Q3UsGYO3TokIseuPWUI0lW1cirEmrGQBWEwhsznXnzgAFm/6F/1IeVCZe6d+/e4qrGvc7nxZCuGLh37LGYJWkfrlrUbaVM0nXV4VleNLCPqoSjR4+Gs7CvjixklYGL9hWl5422Gi3Kj7Hy1vZ3f/d3Y+vDaOmrwtZBw2HSX8ql/8gCmSP7NJDfV48P2sjWAnyy4uI7acAz5fa2SdSEgTIgbLaZuZh9+XMDlbqFlYIyMatTB29QqSOqeO6/bbHfmjFjhqTjvumLHgaYN5O0j0HiHs/PmjVL/siOlonCRl80JMFLKJ6lLLeOPMkig6S++tA9n1sfb6q3b98e1sE/r82bN08UnP7qVoeykXmc61wJlOerpxJ8suLiO2ncgyx7WzuwbRhtjB3YNowaxQzUMAqMGahhFBgzUMMoMGaghlFg7I8nGUaBsRXUMAqMGahhFBgzUMMoMGaghlFgzEANo8CYgRpGgSmkgZ46dSoYPXq0fCp8/853vhO0a9cumDp1anDp0qXSnfxYsGCBlM+1f//+UupV3DZw8Txw2oZwHpqu7ePiu6Zv2LBBnge+azpXtD7N6+aBcm3MSloZKHH5+dQ0+kTfouXoxbM+eUbTs/bfbRflUr4LbYzqYRTGR9unuDKgj0n5y1EoA1WlnD9/ftCzZ89SaqMBkPbWW28F/LMtR5L0LF5eqCFQ/smTJyWsSHTAiORw//33yzNczzzzTJhOACpO/5C+atUqOazM+Uf6Qxr39u7dGyrV2bNng3379oVlDR8+XNIVDmMTtMqFge/Vq5c3T1ayyADi2ody1tXVSXmk6dj16dNHzmDqs9znmBpnPn3yRD/WrFkTPh/XvmrgOJjWMXnyZImaCD49dNEJ5siRI3J4WyHvxYsXw/7SR/qalkIZKIPAYLz++utNOk1n+/fvH3Z07NixIhh31soC5VAe5QL1UB/1RkEB47jxxhul/S6UwyAC9zQqH3To0KFJdAUX2rN79245U6mgEAx8ngfVXbLKwNc+jO2OO+4oO3b19fVy5ldlGCfP7t27ywWcCeVwN+WnhbHROohsQftpF2lxeuhCfzA+DqBH6dixo3dsq6Um9qCEk3CVgs5z2Dmv86qUQ3muUKnPDa8B/J40aVIzlwglIS5ukkvDwKMA1MF3nunbt6/kibqJrDDUT1wfhTpQ2vvuu69Z/XmQhwzi2hfND0RkdMdOy9AIEZXIU0OTJEVAqAaiCw4aNKjZpOBCO2bPnt1scnGhX3hJyJH2R7co1WIviapg4sSJ4raoS8TJeQYLV07TV6xYETz55JPNjAejY2Zl5tUZmucZ0HPnzoUDiRKwwkRXIhT9jTfeCF555ZWwfnXJWhOfDHztY2U6fvx4KA8M4YsvvpDvys6dO2UyUONIkidyQvFffvlliSiYZFDloN24spQH9C0rRFSgP7SdiWbdunWZ9spmoClhhQB3JQBWAfZPDA6oEoC7X1NQMJSZPSlKiOI9/fTTsYrH3k9dRdclayuiMohrHyvcvffeG64on376qRigRhGg/fRdy4oSladOEN/73vck7Ejc6lop7kQJjFOSPOnbkiVLKp4UMFbGNs6LqJSaMFAipTGICoPF3iApVEQ1UA7lqRIA9WWN0MZgE+cGZapkdmbm5cWQKjOuJBcucN7R4qJklUHSc+6qO2TIEElTJcdVZT+OMlcDxkLcH1bkPMBjof+VRPVrTWrCQNmr4SbpbFlfX192v1ANlEN5lAvUQ33Ui3sS3SMCxsRLn6hi8fZVo8PxnXJ1VYkDI8YNGjVqVBNF5iIAMhcrb9FlUEn78BB486ovogADc/faUVx58lJGVzjKIuh3mrClQDmUp7j1+KBv5fagLlnbKDQoQuFocJmuzJo168rJkydLKVeu7Nu3Dz9Errq6ulJqflDnlClTpPwGxQrrpl6tj09tA8+SBxqMKEwfPXr0lYZVqFm6XpTBfZ7TNJ6Lg3T3Hm2ibVpO3mSRAcS1z+2rW6bCc5Tv4pOnqwNc0XzV4vbFrUeJ6iGf/I722U1zZZBHG+08qGEUGHtJZBgFxgzUMAqMGahhFBgzUMMoMGaghlFgzEANo8DYH08yjDbG/niSYdQoZqCGUWDMQA2jwJiBGkaBMQM1jAJjBmoYBaaQBnr69GkJIMWnC+frSD948GApJV8WL14skd644uqgPUOHDg2f4XmXy5cvB9OnTw82b95cSmlaJnm1T266W46Wofei7SjXxqyUKz9JBjwfl448NJ3LLdctj37Tf4V80TS3fa480+C2F71Cv1woO04PXeLaCJXkrYRCGagq5/PPPx/06NGjlNoIA/PQQw81S88LNaqvvvoqOHz4cLBy5cpmA/b5559LmA2e4ZozZ07pTiMcAF67dm3pV2N/vvzySymP51GIW265Rcr97ne/G5YDWv+yZcuCbt26SXq0HciAyAWaDwXNkywy4DmeJx/fz58/HxoiIT/ef//9Zu1GeRnrbdu2Sfry5cuDa6+9VtJ55tixY02i6vnkmRb+/ZG2Utb48eOD1atXS3qSHipJbSyXtxoKZaAMDoNE2A+304AibN26VZQ3bxAqgtZAXQx6v379gjNnzshvF19oD8rYs2dP8MILL5RSGokL58HvO++8s/TrapmqgHHtQCG4h3G0BFllgKITBpOIBIzjsGHDwlg8yKBz587y3YVAatOmTWsmH+rG+AhpEiVNeBQfxDqirUDsI+SLHJL0UPG1sZK81WB70AaYQQl34Q48ShgN9sRv4qDGuURbtmyRPG7YSsr94IMPRGnJoytUFMolL88TkjIK91m5unbtGjzwwANelywLWWVAPtpOTB81dpSe70wugwcPljzq+pIOhDwhPUk+SqXyTAMhTwYMGBAabBz0Y+7cuWHbWwMz0CoYN26cKAkXLhFhH1UBUcjo6obSsurzPIq7adOm0O1T+I07SPxXnmfVQlkA5UchAcNYv359sHTp0rB+dclaE58MaDshOFlR+E4kP1YZXVF4nv7QVwyL3/SNVYvvyGfHjh0iSx+VyLMaaDfuKMYO9K1omIGm5K677pLPjz/+WAJhEbkvafZFuVBod0ViNcEt1r0XPProo6J4KA17bupRl5IJAKUH1yVrK1QGGAyGghzoD7/5jK5w9HHChAmhDDp16hS61Do54SlUQpw8q8WdPABjTZInsl+4cGHiOOeNGWgDGAP7BddlZODVMJL4h3/4B3kxpG4X7h+XunI+uD9y5MhmL5rcVYK/RfOzn/1M9m+VtCULWWQAGCTGp8qL4eFV+BSe+tizFgUmP/pvYTcLCErF/kP/IBNu1okTJ4LevXvLyhBnbLt27ZIXVhgjxqQXxsoVNTwUHxeOlY/yedlR7i0s+1qeY+amLbRJXUDaWm7PVA1ZZMCkgiFv3LgxNEjcdNruto97PMPElFRfJbjyTANtOXr0aOlX4xv4cmE3aaPtQdsIZlD2R8zs7KPYT6F4Ligp97kOHDgQzJgxo3QnHgYUI+R5Bp43lhgbbhzR47UsLnWvcAs1jRVMDZ226B6Pe5D3nimLDGiLGqt7D0PiZRJp3OPtrk5M2v6k+lx88kwDE8T27dulLK5FixZJkPG8Jry8sPOghtHG2HlQw6hRzEANo8CYgRpGgTEDNYwCYwZqGAXG/niSYRQYW0ENo8CYgRpGgTEDNYwCYwZqGAXGDNQwCowZqGEUGDNQwygwhTTQU6dOBaNHj5ZPZcGCBUG7du3k4ntL4Naxf//+UupVaA+hSXzt2LBhQzB16tTg0qVLpZSmZZKXMrjPc5pOPsWtQ5+PEldPXqSVgZtPL22jm8dttyuHuL7qfVc+tEnLj8q/Wtyy7r777iaH1YH2RPXQxSerPNtYKAPVAZk/f37Qs2fPUmrj4VzOAfJ/KvT/VbiDlgdaHuWfPHlSwndEB+yzzz4L7r///rAdzzzzjKSrAh45cqRJJDf6c/HiRSmP5zkU3KdPHznGN3PmTEmjzHfffTdUAk70E6KSe0RUQBZuO/i+bt260q98ySIDPjWNq66uLpg8ebL0lT7QF9Jvuumm8JA2n/wmfcWKFcHLL78cGi9w6JvoeAoyok20gXLPnTsXO4lUCudAKYf6aeurr74q6T49dPHJiovvpOXRxkIZaPv27YNVq1YFr7/+ehNF5xCvxr+BXr16lb7lAwOCcY0dO1Z+Y0T9+/cXIUeJq5vnMT6iK0Tp2LFjk0h/QH/0oDHfqQulA/qph5bJd91118l3ZefOnRJOxJVPHmSVgQuGxMREtAPyUw7lAeVTD/WdPXs2GDVqlKT37dtXPlFq4P7u3buDl156SX4DEQAJRoZ80BWMimfSwqRKOXDbbbdJm6nXp4dKkqwYRw0/SjkjRozIFDepJvegDGyeMXpQCsJduIaEEkYFy+9JkyZ5XaIolMvsSbnkiVv1KYNZVhXUhcFGQfRAL4qPYgwZMkR+50meMsCQiKKAgvK8a9CUTz3UR7oaGMrNaqqTE6sr97t06SK/ITrulKVGlRXaPGjQoNBg40D+s2fPlogYPlmRpuFH1ZAx/rTUnIGi8Ch02lg0WZg4caK4NOoSEaIjSTlQNlY8nsfYcE3V3eETJadMopCrYiqUi8unis5vYh2xSicpUUtTTgYY7N69e2MnnCiUBcgB2Tz11FPyWycijfjXUtBu3QODticLjCOuMfGLmVgZP/Ue0lBTBsqGmxkX96MtlRRYPUBdsnIwcCi0rkjDhw8XJWdVRTHcfQoKSnwgQnnyHLz44ouZBztv4mTASohbF51wohB/CePAxUUO9JUwo0SyZ2IiZlNLjzHlo0vUD7QnacJF9kuWLEmMW8Q40n5i/lIu+hrnOVVKzRgoxslg6kuJPGGmw5XUfSDk7Ub7QJHpk7p6GCcrJRH91BhJ+9GPfhTccccdMtuzOhHA+r777ivrZldKXjI4dOhQE5eO/JSjqNuun+oJ6T6O4Ny8GMJVpK+401yMf9Tlpgz2+HkYsu7rKwm7mSQrxpGJWNvk7rnTUBMGioISwlFXk7xBmOw/6uvr5Tf1HT9+XAyBGTHuVTmuq7tnKgeGxNtalJfodGpYDBzunb4sIdJc1I1FeXkJxYzMxSo1a9as4O233664/nLkIQP6RD53b0Z+yiEdKJ96unfvLmEv3Sj6PEdsXe0nFwbLxSSG7JAhz0blVi3kR6YK7SgXdpM+sAcFn6yYRGiXGiR72yyTSE0YKDMV/yzBjKpXOXekWphB2dtSNvsH9hFR5UdJtX72Wbpn8sHA6b//obRPPPGEGBuKxx9PIp3ZGJeQyYf+kIeB1nq4XPe3JckqA10V6ZNCfvqtfQJcetJfeeWV4PHHH5d0Vz4+uMdbXJ515ZYGDOadd94J+0K/eBdQqSH5ZEXfmLRoH/cq0ZMk7MC2YRSYmnuLaxhfJ8xADaPAmIEaRoExAzWMAmMGahgFxv54kmG0Me4/S0WxFdQwCowZqGEUGDNQwygwZqCGUWDMQA2jwJiBGkaBKaSBchyLw8l8Aod7p0+fLkGeuDZv3izpebN48eKwjoMHD5ZSr0J7CF6mz/B8NJ1Pbbdbnl70g/5QfjRN8bXDTXfryZO0MlDi8jNemuamKxwfY7yj6Tru7njnKQPq07KoX48Aanv0XpwcoJysaHd0bKulUAaqA8Kxnx49epRSg+Crr76SdD45ULtjx47clVOVgDoOHz4crFy5MhwwhVg0RDrgGa45c+ZIOjFotm3bJmlvvvmmtJ+83NdnuTjTyHlH+rBo0SKph/Ru3brJAW3wtQPZfPnll2EeFEIDj+VFFhkACsuhZb2HAQGHrIlUGE1Xdu3aFbz33nulX1fhvCaH15W8ZcC/P9I/yho/fnywevVqSaceIvyR7pNDOVnxfdOmTaVf6SmUgRJKYvny5XKinnOFCufsbr755vB7v379RFHyggE5duxYGAOHQacOwm9EiYswwNlO2gV8RiPxARMKysX5UNp+++23h8pFvdR/4cKFxHZwaF3ryZusMtD+YbxRaHfnzp1Lv5qCImPAhDxxoT179uwJXnjhhVJKI3nKYODAgWH4EiI70H7q5TwnF3CAmyh9rvFVIismHVZhV4/TUHN7UAR1/vz5oHfv3qWU7DALcpreHXiU0A2vAfwm2kHUJXIhjUHhGRdO7HPYGIWIlguswhx49rWDNhLnBoWhbJ3B8yKrDJh0unbtKoeX3XsoM8Y7ePBgSY+6xKxaI0eOFMNzwaOgfjc6Q0vKgPEZMGBAs3hDGgKFOunH3Llzpa9JsuI5DDhLND+lZgxU9wsElpo3b15us2g1jBs3TpSEC5foueeeEwVU+E7QYjVEBUU9cOBAOKkwW584cSJUbpSD1TMJ+rt161apG6XBffLtjVoSnwxQTkKTECtJ72F86hWRRn+ZXNWwtP1Rl1cVPLoa5y0D2s3WCb0C+qbQRtIZT4wyarg+KJNtzoMPPlhxniRqxkAZRAaGYMIYaVsop4sG0qZNgFKxehC2I6pwuD7Dhg0LJxVcojFjxoQrASsnLm/S/8l0oRwMILq6tTZRGWBQ6ra7LqOCwrIHp93Ii/hCM2bMKN1tBCPGKHB5kxQ8Dxm4kwe4L3R0ImI8GVfaS98WLlwYXHPNNfJMHMuWLZMJWuWQlZpzcRmY73//+7I/yQuMBLdUVzRg4HFbKoHBY9bkzxvEDQyBo6JxfN2VSF0h9mlZ2pGFrDKoto0EpiY8JeNJ3cQEYi/PJy+GdPLCneaKusZ5w+RC/9WlVRhPJgINbgY+Wf3Kr/yKuOb0g2dw6+kjBu4+Ww01YaAffvhh2EFmuI0bN8q+JS+YSdl/6N8MweBwQXFJWanjlIOXALx9RcF+/OMfe10a2k15PBcH93kDyAuHpHa4kIc32VGjz0JWGfAcz5MPKCe6p3PHLu4NN296WYHcdIyVy31bDFllQFuIKqjwxlij+pGuK6lbD33D3YU4WRFhEFlp23m7iyfAxO0b/3LUhIH+8pe/DO655x6Zlego7mLUjcwKMyj7I+rA2IiYHhUqSsp9LvaUuGcMJAOkL0H0UhecAWa2JU0hTf+djX5Rl668vnZQB30mHSWaNm1abm6UklYGwHM8Tz7uAV6C29esY5enDJg4CHGqfeGfvXi3QTrHLGlrUj2VyCoP7DyoYbQxdh7UMGoUM1DDKDBmoIZRYMxADaPAmIEaRoGxv81iGAXGVlDDKDBmoIZRYMxADaPAmIEaRoExAzWMAmMGahgFxgzUMApMIQ301KlTwejRo+Uzyv79+4O7775bjjHlDYeF27VrJxf1RKE9BP3SZ3he8eXdsGFD7PNuWVOnTg0uXbok6eTV591+uuXr5ebLiywygGh+t/96ab/ce25f+OS33tN2kIe80fS0+GRdaT1Jsionx0oplIHqwMyfPz/o2bNnKfUq3F+3bl3pV76gLMD/2zh58qSE3dABUwhNcv/998szXBwyBl9eBob2ko+je+fOnZM0+sFJ+xUrVkgeIsjp4V/OF/Is6ZMnT5bwj0BdWi9XXV2d3G/fvr3cz4MsMgCUslevXuG94cOHBxMnTgx/cxG3iHZzdpIoDJruyuDFF1+U36S77UBua9asaZaeFp+sK6knSVZxckhLoQwUZVu1apXEHeKQcxTCTvTp0yf3ECAMyJEjR4KxY8fKb+ro37+/CD4KgndJyksYDFVG+sb33bt3h+ds+/btK5+cxD979qx8Z3VSoyMUysWLF6UOF1Yx0vOMqJBFBqBt0lCUcaDAe/fuldUJXMVVGdAOyolrR/fu3eUCDlITDpMJIy0+WZerJ0lWlcihGmpmD8rgshoRgSBvMBjCXbghHlFCDMyF35MmTRK3RV2ipLxqdC6souQHVX6MFgWNQiyjQYMGNVslSSdKQTQ9C1lkACjwjTfeGNx3333N7in0d8SIEbGRB1QGtIMQpFGi7XDDYeaBT9ZuPRjf7Nmzw7CbcbKqRA7VUDMGivuhq1Fb4bprtIUwFxq7Jg4UjhVDV0CUEFACos/rPoXB1dWEZ3X/BdTpwmBTpq6+rU2cDGgzyvnGG28Er7zySnhPXUbgGUJmurFi6QsKTF+RFTJgfFmNMBh9xt3D6b6VLQIhP7NMUkmyTltPOTlUS00YKMJylbgIoFig7moctJe9FCEtGOxvfetb4g4xKxPOkX4xiICxAoqAm6/pKJAaOCStQq1NVAbsTekfRN1z+szKoveBPuzcuVP6yuSlMnjsscfEW0JmGA0vDHVboxMEwbiIC8SqlpYkWcfVQ9uXLFnSJBBaHElyqJbCGygzKIOlbhVuBX8agtCGWQbHBQNiz+vuM3BPK9nrlsvrvtzhpUSXLl1kdSB2qhoZio7rG3WF2MdQthsK8tChQ01WobzIIgMo9xx9HjJkSOlXczBKlYFruG+//XbwySefNHEnAQMgWJeutFmJkzXE1ZMkq0rlVSmFN1B3sLgQypQpUyREozsbZ4GZlP1HfX29/Mbwjx8/Lm4k7pXO7C60idWRy5fXhXQCNWOMDCKTjhqk7kWZgAj/qPBSTENBAs9TTlRZ8yCLDBgjnuN58gHl6J6O1YOXKm676QtR+hS3LBfe7Hbs2FHGGtnoSkR+5Jl2sqIcn6x99dA39qDgk1WSHFLRoPSFo8FlujJr1qwrDYpbSrlKg4FeaTBQ+cwT6qRcRNIg0LDuffv2Xamrq5PvfHKfi2fJA768fPKb9AY3rUmb169fH5bl3nPriOahPOSi9eZNFhmA2199HuLGzK0rWpYrG7cc2qHpXPzOgk/Wvnpc+ftkBT45pMEObBtGgamZt7iG8XXEDNQwCowZqGEUGDNQwygwZqCGUWDsjycZRhvDf3zwYSuoYRQYM1DDKDBmoIZRYMxADaPAmIEaRoExAzWMAlNIA+UYEiE93ONImzdvlvOUeh08eLB0Jz8WL16cWD7tGTp0aPgMzyu+vG46ed0+cZSJfkbriiuLyA3Tp08P05FHS5BFBhCXv9zYxckhrhw3TS9kkhTVIgnK1XKon3ZAJbL25QX3XlQ+1VIoA1XBEA6kR48epdRGCCXBGdCvvvpKLpQkT3QQKPvw4cPBypUrmwgdiEXD6Xptw5w5cyTdl5f+fPnll5LGPQbulltukWcZuIceeqhZP3nm/Pnzkt8ti4PEjzzyiJTD7wMHDsizeZJFBkCfOOuq93SMyo3drl27gvfee6/0yy8D6tIyuDgMP2HChLIRDnzw74+US1njx48PVq9eLemVyNqXlwmM9lIG9+lHlnEqlIEi6OXLl0vEBE6su3To0CHo3Llz6Ve+YEjHjh0LI7FhRP369QvOnDkjv12iJ+bL5aXd0UPIgLIRo6dbt26llEZQ5mHDhoksNKIcA025AwcOlGe4F82XlSwyABSTyQjjjZI0dvSNPhNaRPHJwEXrIzJfWpCnGjcREikPOVQia19eDn6PGTNGxpz7TCB79uyR59JQE3tQOs6ADB48OBe3IQqzIKfpXUNCCVEUF34//PDDTdyapLzc++CDD0TJyBPnKkUhLzM2fdbwGxpRQeEeCpHnhJVFBsDK2rVr1+CBBx5ocq/c2LHyjBw5UoxYqUQGGAJhY9RIskJ5AwYMaFaeK2v6MXfuXElzcfMiH9qvkE+NNw01YaB0nJUVJWLQcRsqUfa8GTdunLSBC7eGaG+/+MUvSnebg7KzSvI8irZp06ay7g7uH6sHeWfNmiUrS1RptmzZIgrNTN/axMkA5UMxCUq9dOnS8B7GlzR2Kouoy1tOBpSDAffu3buUkg7arXtNoG9RfLKuJG8e1ISBujBQuA3Rmb21ueuuu+Sz0v+vjLKhtOXarSsMCk0A75kzZ8rMDaoU4O792gqVAW0F3FtVZNftU9yxo0/E+pkxY0bp7lWSZAC43WrAWXAnD0C22t44WdO3hQsXSr6kvHlScwbaEjALsudVdw2irooPgkGlzRtFVxhVfBSQ/QwuFIOPchCusyVm6ywygGr7SzAwYs7SR+omKBmRGteuXeuVgUKEvTyj6gOTC/3H06lW1m5e5OBOwrj+rMAYdBpqzkAR3saNG2XfkhcIjz2E/m0QZusTJ06IC4UbFrfn5c0jLw+IROfL64Li79ixI1Gx1F2ibKCvuHIMOpHmqKel3NosMsCIeI7ndaWjHN2XKe7Yxb2R5U2vBo+OkwEgR+rIunpS7tGjR0u/ApGvG9UvTtbUyx70woUL3ryML+NMO/PQ1ZowUDrLSwcUmIHBvYnuW7LCLMjMTR3EQSVielQJUFLuc6E06p758jKgtJN0Bm/atGmJBoYyz5s3T/aq5HH7yqysL2f0ijOaLGSRAc/xPPm4B6w+1Y5dkgyA8littI60UM/27dulHK5FixZJvaSXk3VSXsaXFZ/xrqS/5bDzoIbRxth5UMOoUcxADaPAmIEaRoExAzWMAmMGahgFxgzUMAqM/fEkwygwtoIaRoExAzWMAmMGahiFJQj+HwZAGAq/pEsQAAAAAElFTkSuQmCC"},"image.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"![image.png](attachment:image.png)\n\n![image-2.png](attachment:image-2.png)","metadata":{},"attachments":{"image-2.png":{"image/png":"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"},"image.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"![image.png](attachment:image.png)\n\n![image-2.png](attachment:image-2.png)","metadata":{},"attachments":{"image-2.png":{"image/png":"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"},"image.png":{"image/png":"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"}}},{"cell_type":"code","source":"for f in [4]:#FOLDS:\n    seed_everything(SEED)\n    seg_model = torch.load('/kaggle/input/axial-t2/Axial_T2_axial_side_segmentation_'+str(f))\n    model = myViT(seg_model.UNet.encoder)\n    \n    tdf = df[df.fold != f]\n    vdf = df[df.fold == f]\n    tds = subarticular_Dataset(tdf,f)\n    vds = subarticular_Dataset(vdf,f,VALID=True)\n    tdl = torch.utils.data.DataLoader(tds, batch_size=BS, shuffle=True, drop_last=True)\n    vdl = torch.utils.data.DataLoader(vds, batch_size=BS, shuffle=False)\n    dls = DataLoaders(tdl,vdl)\n\n    n_iter = len(tds)//BS\n\n    learn = Learner(\n        dls,\n        model,\n        loss_func=myLoss,\n        cbs=[\n            ShowGraphCallback(),\n            GradientClip(3.0),\n            alpha_cb\n        ]\n    )\n    learn.fit_one_cycle(\n        1,#EPOCHS,\n        lr_max=1e-3,\n        wd=0.05,\n        pct_start=0.02\n    )\n    torch.save(model,'subarticular_DICOM_V2_ViT_'+str(f))\n    del model,seg_model,tdf,vdf,tds,vds,tdl,vdl,dls,learn\n    gc.collect()","metadata":{"execution":{"iopub.status.busy":"2024-11-02T22:56:19.366991Z","iopub.status.idle":"2024-11-02T22:56:19.367510Z","shell.execute_reply.started":"2024-11-02T22:56:19.367236Z","shell.execute_reply":"2024-11-02T22:56:19.367263Z"},"trusted":true},"outputs":[],"execution_count":null}]}