111593 lines
3.5 MiB
111593 lines
3.5 MiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Using device cuda:0'"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import torch\n",
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"from pathlib import Path\n",
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"\n",
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"DATA = Path('/mnt/wsl/PHYSICALDRIVE1/data/unsplash').glob('*.jpg')\n",
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"CACHE_PATH = Path('/mnt/wsl/PHYSICALDRIVE1/data/cache2')\n",
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"CACHE_PATH.mkdir(exist_ok=True, parents=True)\n",
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"BINS = 32\n",
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"NUM_EPOCHS = 20\n",
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"BATCH_SIZE = 64\n",
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"LEARNING_RATE = 0.005\n",
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"SCHEDULER_GAMMA = 0.7\n",
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"EDIT_COUNT = 25\n",
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"LOSS_DAMPING = 2\n",
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"MODELS_PATH = Path('models')\n",
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"MODELS_PATH.mkdir(exist_ok=True, parents=True)\n",
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"\n",
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"device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
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"f'Using device {device}'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Loaded 561982 training images and 62443 test images'"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from torch.utils.data import DataLoader, random_split\n",
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"from editor.training import HistogramDataset\n",
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"\n",
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"dataset = HistogramDataset(DATA, edit_count=EDIT_COUNT, bin_count=BINS, delete_corrupt_images=False, cache_path=CACHE_PATH)\n",
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"total_size = len(dataset)\n",
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"train_size = int(0.9 * total_size)\n",
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"test_size = total_size - train_size\n",
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"train_dataset, test_dataset = random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(42))\n",
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"\n",
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"train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=32)\n",
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"test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=32)\n",
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"\n",
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"f'Loaded {len(train_dataset)} training images and {len(test_dataset)} test images'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"\n",
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"class HistogramRestorationNet(nn.Module):\n",
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" def __init__(self):\n",
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" super(HistogramRestorationNet, self).__init__()\n",
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"\n",
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" self.conv1 = nn.Conv3d(in_channels=1, out_channels=16, kernel_size=3, padding=1)\n",
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" self.bn1 = nn.BatchNorm3d(16)\n",
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" self.conv2 = nn.Conv3d(16, 32, 3, padding=1)\n",
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" self.bn2 = nn.BatchNorm3d(32)\n",
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" self.conv3 = nn.Conv3d(32, 64, 3, padding=1)\n",
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" self.bn3 = nn.BatchNorm3d(64)\n",
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" \n",
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" # Adjusted residual connections with proper downsampling and channel matching\n",
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" self.res1 = nn.Sequential(\n",
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" nn.Conv3d(16, 32, 1, stride=1, padding=0), # Match channels\n",
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" nn.BatchNorm3d(32),\n",
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" nn.MaxPool3d(2) # Downsample to match size\n",
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" )\n",
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" self.res2 = nn.Sequential(\n",
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" nn.Conv3d(32, 64, 1, stride=1, padding=0), # Match channels\n",
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" nn.BatchNorm3d(64),\n",
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" nn.MaxPool3d(2) # Downsample to match size\n",
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" )\n",
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"\n",
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" self.fc1 = nn.Linear(64 * 4 * 4 * 4, 512)\n",
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" self.fc_bn1 = nn.BatchNorm1d(512)\n",
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" self.fc2 = nn.Linear(512, 32 * 32 * 32)\n",
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" self.apply(HistogramRestorationNet._init_weights_he)\n",
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"\n",
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" @staticmethod\n",
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" def _init_weights_he(m):\n",
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" if isinstance(m, nn.Linear) or isinstance(m, nn.Conv3d):\n",
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" torch.nn.init.kaiming_uniform_(m.weight, nonlinearity='relu')\n",
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" if m.bias is not None:\n",
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" torch.nn.init.zeros_(m.bias)\n",
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"\n",
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" def forward(self, x):\n",
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" # Input dimensions: (batch_size, channels(1), 32, 32, 32)\n",
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"\n",
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" x = F.relu(self.bn1(self.conv1(x)))\n",
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" x = F.max_pool3d(x, 2)\n",
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"\n",
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" # Apply first adjusted residual connection\n",
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" res = self.res1(x)\n",
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" x = F.relu(self.bn2(self.conv2(x)))\n",
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" x = F.max_pool3d(x, 2)\n",
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" x += res # Add adjusted residual\n",
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"\n",
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" # Apply second adjusted residual connection\n",
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" res = self.res2(x)\n",
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" x = F.relu(self.bn3(self.conv3(x)))\n",
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" x = F.max_pool3d(x, 2)\n",
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" x += res # Add adjusted residual\n",
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"\n",
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" # Flatten for fully connected layers\n",
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" x = x.view(x.size(0), -1)\n",
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"\n",
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" x = F.relu(self.fc_bn1(self.fc1(x)))\n",
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" x = self.fc2(x)\n",
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"\n",
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" # Reshape back to the histogram shape\n",
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" x = x.view(-1, 32, 32, 32)\n",
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" x /= torch.sum(x, (1, 2, 3)).view(x.size()[0], 1, 1, 1)\n",
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"\n",
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" return x\n",
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" \n",
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"edited, og = next(iter(train_dataloader))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"def plot_histograms(original_histogram, edited_histogram, predicted_histogram):\n",
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" fig = plt.figure(figsize=(15, 5))\n",
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" tensors = [original_histogram.numpy().squeeze(), edited_histogram.numpy().squeeze(), predicted_histogram.numpy().squeeze()]\n",
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"\n",
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" for i, tensor in enumerate(tensors, 1):\n",
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" ax = fig.add_subplot(1, 3, i, projection='3d')\n",
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"\n",
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" x, y, z = np.indices(tensor.shape)\n",
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" x = x.flatten()\n",
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" y = y.flatten()\n",
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" z = z.flatten()\n",
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" values = tensor.flatten()\n",
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"\n",
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" sizes = values * 5000 \n",
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"\n",
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" colors = np.vstack((x, y, z)).T / 31\n",
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"\n",
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" sc = ax.scatter(x, y, z, c=colors, s=sizes, marker='o', alpha=0.5)\n",
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"\n",
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" ax.set_xlim([0, 31])\n",
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" ax.set_ylim([0, 31])\n",
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" ax.set_zlim([0, 31])\n",
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"\n",
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" ax.set_title(f'Tensor {i}')\n",
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" return fig"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/andras/miniconda3/envs/bipolaroid/lib/python3.12/site-packages/matplotlib/collections.py:996: RuntimeWarning:\n",
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"\n",
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"invalid value encountered in sqrt\n",
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"\n"
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"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from torch.utils.tensorboard import SummaryWriter\n",
|
|
"from torch.optim import Adam\n",
|
|
"from tqdm.notebook import tqdm\n",
|
|
"from torch.nn.utils import clip_grad_norm_\n",
|
|
"from editor.training import ProgressivePoolingLoss\n",
|
|
"# from geomloss import SamplesLoss \n",
|
|
"# import numpy as np\n",
|
|
"\n",
|
|
"writer = SummaryWriter()\n",
|
|
"model = HistogramRestorationNet().train().to(device)\n",
|
|
"writer.add_graph(model, next(iter(train_dataloader))[0].to(device))\n",
|
|
"writer.add_scalar(\"Bins\", BINS)\n",
|
|
"writer.add_scalar(\"Batch size\", BATCH_SIZE)\n",
|
|
"writer.add_scalar(\"Learning rate\", LEARNING_RATE)\n",
|
|
"writer.add_scalar(\"Scheduler gamma\", SCHEDULER_GAMMA)\n",
|
|
"writer.add_scalar(\"Loss damping\", LOSS_DAMPING)\n",
|
|
"\n",
|
|
"optimizer = Adam(model.parameters(), lr=LEARNING_RATE)\n",
|
|
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=SCHEDULER_GAMMA)\n",
|
|
"loss_function = ProgressivePoolingLoss(target_sizes=[16, 32], damping=LOSS_DAMPING).to(device)\n",
|
|
"# loss_function = torch.nn.KLDivLoss(reduction='batchmean')\n",
|
|
"# loss_function = SamplesLoss(backend='online')\n",
|
|
"\n",
|
|
"last_model_path = None\n",
|
|
"try:\n",
|
|
" for epoch in range(NUM_EPOCHS): \n",
|
|
" epoch_loss = 0\n",
|
|
" writer.add_scalar(\"Actual learning rate\", scheduler.get_last_lr()[0], epoch)\n",
|
|
" for batch_id, (edited_histogram, original_histogram) in enumerate(\n",
|
|
" tqdm(train_dataloader, desc=f'Epoch {epoch}', unit='batch')\n",
|
|
" ):\n",
|
|
" edited_histogram = edited_histogram.to(device)\n",
|
|
" original_histogram = original_histogram.to(device)\n",
|
|
" \n",
|
|
" optimizer.zero_grad()\n",
|
|
" predicted_original = model(edited_histogram)\n",
|
|
" # predicted_original = torch.clamp(predicted_original, 0.0000000000000000000000001, 1)\n",
|
|
" # histogram_points = torch.from_numpy(np.array(np.meshgrid(np.arange(BINS), np.arange(BINS), np.arange(BINS))).T.reshape(-1,3)).to(device)\n",
|
|
" # original_weights = original_histogram[:, histogram_points[:, 0], histogram_points[:, 1], histogram_points[:, 2]].float()\n",
|
|
" # predicted_weights = predicted_original[:, histogram_points[:, 0], histogram_points[:, 1], histogram_points[:, 2]].float()\n",
|
|
" # histogram_points = histogram_points.unsqueeze(0).repeat(BATCH_SIZE, 1, 1)\n",
|
|
" # loss = loss_function(original_weights, histogram_points.float(), predicted_weights, histogram_points.float())\n",
|
|
" # loss = loss_function(torch.log(predicted_original.unsqueeze(1)), original_histogram)\n",
|
|
" loss = loss_function(predicted_original.unsqueeze(1), original_histogram)\n",
|
|
" epoch_loss += loss.item()\n",
|
|
" writer.add_scalar(\"Loss/train/batch\", loss, epoch * len(train_dataloader) + batch_id)\n",
|
|
" writer.flush()\n",
|
|
"\n",
|
|
" # loss = loss.sum()\n",
|
|
" loss.backward()\n",
|
|
" # clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
|
|
" optimizer.step()\n",
|
|
" \n",
|
|
" writer.add_scalar(\"Loss/train/epoch\", epoch_loss, epoch)\n",
|
|
" with torch.no_grad():\n",
|
|
" model.eval()\n",
|
|
" loader = iter(test_dataloader)\n",
|
|
" edited_histogram, original_histogram = next(loader)\n",
|
|
" edited_histogram = edited_histogram.to(device)\n",
|
|
" original_histogram = original_histogram.to(device)\n",
|
|
" predicted_original = model(edited_histogram)\n",
|
|
" writer.add_figure(\"Histograms/train/original\", plot_histograms(\n",
|
|
" original_histogram.cpu()[0], edited_histogram.cpu()[0], predicted_original.cpu()[0]\n",
|
|
" ), epoch)\n",
|
|
" model.train()\n",
|
|
" last_model_path = MODELS_PATH / f'model-{epoch}.pth'\n",
|
|
" torch.save(model.state_dict(), last_model_path)\n",
|
|
" scheduler.step()\n",
|
|
"except KeyboardInterrupt:\n",
|
|
" print('Interrupted, saving last model')\n",
|
|
" last_model_path = MODELS_PATH / f'model-final.pth'\n",
|
|
" torch.save(model.state_dict(), last_model_path)\n",
|
|
"finally:\n",
|
|
" writer.close()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = HistogramRestorationNet().to(device)\n",
|
|
"model.load_state_dict(torch.load(last_model_path))\n",
|
|
"model.eval()\n",
|
|
"loader = iter(test_dataloader)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.plotly.v1+json": {
|
|
"config": {
|
|
"plotlyServerURL": "https://plot.ly"
|
|
},
|
|
"data": [
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{
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"marker": {
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"color": [
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"rgb(40.0,32.0,32.0)",
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"rgb(40.0,40.0,32.0)",
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"rgb(72.0,96.0,80.0)",
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"rgb(72.0,96.0,88.0)",
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"rgb(72.0,96.0,96.0)",
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"rgb(72.0,104.0,96.0)",
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"rgb(72.0,104.0,104.0)",
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"rgb(80.0,64.0,64.0)",
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"rgb(80.0,72.0,64.0)",
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"rgb(80.0,72.0,72.0)",
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"rgb(80.0,72.0,80.0)",
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"rgb(80.0,80.0,64.0)",
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"rgb(80.0,80.0,72.0)",
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"rgb(80.0,80.0,80.0)",
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"rgb(80.0,80.0,88.0)",
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"rgb(80.0,88.0,80.0)",
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"rgb(80.0,88.0,88.0)",
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"rgb(80.0,88.0,96.0)",
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"rgb(80.0,96.0,88.0)",
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"rgb(80.0,96.0,96.0)",
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"rgb(80.0,96.0,104.0)",
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"rgb(80.0,104.0,96.0)",
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"rgb(80.0,104.0,104.0)",
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"rgb(80.0,112.0,104.0)",
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"rgb(80.0,112.0,112.0)",
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"rgb(88.0,72.0,72.0)",
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"rgb(88.0,80.0,72.0)",
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"rgb(88.0,80.0,80.0)",
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"rgb(88.0,80.0,88.0)",
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"rgb(88.0,88.0,72.0)",
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"rgb(88.0,88.0,80.0)",
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"rgb(88.0,88.0,88.0)",
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"rgb(88.0,88.0,96.0)",
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"rgb(88.0,96.0,88.0)",
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"rgb(88.0,96.0,96.0)",
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"rgb(88.0,96.0,104.0)",
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"rgb(88.0,104.0,96.0)",
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"rgb(88.0,104.0,104.0)",
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"rgb(88.0,104.0,112.0)",
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"rgb(88.0,112.0,104.0)",
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"rgb(88.0,112.0,112.0)",
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"rgb(88.0,112.0,120.0)",
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"rgb(88.0,120.0,112.0)",
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"rgb(88.0,120.0,120.0)",
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"rgb(88.0,128.0,128.0)",
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"rgb(136.0,136.0,152.0)",
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"rgb(136.0,144.0,144.0)",
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|
"rgb(136.0,144.0,152.0)",
|
|
"rgb(136.0,144.0,160.0)",
|
|
"rgb(136.0,152.0,144.0)",
|
|
"rgb(136.0,152.0,152.0)",
|
|
"rgb(136.0,152.0,160.0)",
|
|
"rgb(136.0,160.0,152.0)",
|
|
"rgb(136.0,160.0,160.0)",
|
|
"rgb(136.0,160.0,168.0)",
|
|
"rgb(136.0,168.0,160.0)",
|
|
"rgb(136.0,168.0,168.0)",
|
|
"rgb(136.0,168.0,176.0)",
|
|
"rgb(136.0,176.0,168.0)",
|
|
"rgb(144.0,144.0,144.0)",
|
|
"rgb(144.0,144.0,152.0)",
|
|
"rgb(144.0,152.0,152.0)",
|
|
"rgb(144.0,152.0,160.0)",
|
|
"rgb(144.0,160.0,152.0)",
|
|
"rgb(144.0,160.0,160.0)",
|
|
"rgb(144.0,160.0,168.0)",
|
|
"rgb(144.0,168.0,160.0)",
|
|
"rgb(144.0,168.0,168.0)",
|
|
"rgb(144.0,168.0,176.0)",
|
|
"rgb(144.0,176.0,168.0)",
|
|
"rgb(144.0,176.0,176.0)",
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|
"rgb(152.0,152.0,152.0)",
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|
"rgb(152.0,152.0,168.0)",
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|
"rgb(152.0,160.0,160.0)",
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"rgb(152.0,160.0,176.0)",
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"rgb(160.0,160.0,168.0)",
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"rgb(160.0,168.0,168.0)",
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"rgb(160.0,168.0,176.0)",
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"rgb(160.0,168.0,184.0)",
|
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"rgb(160.0,176.0,168.0)",
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"rgb(160.0,176.0,176.0)",
|
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"rgb(160.0,176.0,184.0)",
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"rgb(160.0,184.0,176.0)",
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"rgb(168.0,176.0,184.0)",
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13,
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13,
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1,
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1,
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1,
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3,
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4,
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4,
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4,
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3,
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7,
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23,
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10,
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10,
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14,
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24,
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25,
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14,
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15,
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15,
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16,
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25,
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25,
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25,
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25,
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15,
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16,
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17,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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25,
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25,
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25,
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25,
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25,
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26,
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26,
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17,
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17,
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18,
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18,
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18,
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18,
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18,
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19,
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19,
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19,
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19,
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19,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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23,
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23,
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23,
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23,
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23,
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23,
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23,
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23,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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25,
|
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25,
|
|
25,
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25,
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25,
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25,
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25,
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26,
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26,
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26,
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26,
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26,
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|
18,
|
|
18,
|
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19,
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19,
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19,
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19,
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19,
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19,
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20,
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20,
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20,
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20,
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20,
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20,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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|
23,
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23,
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23,
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23,
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23,
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23,
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23,
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23,
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|
24,
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24,
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24,
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24,
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24,
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24,
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24,
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25,
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25,
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25,
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25,
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25,
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25,
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26,
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26,
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26,
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26,
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26,
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26,
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27,
|
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27,
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27,
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27,
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27,
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19,
|
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20,
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20,
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20,
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20,
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20,
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21,
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21,
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21,
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21,
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21,
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21,
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22,
|
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22,
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22,
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22,
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22,
|
|
22,
|
|
22,
|
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22,
|
|
23,
|
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23,
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23,
|
|
23,
|
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23,
|
|
23,
|
|
23,
|
|
23,
|
|
24,
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|
|
11,
|
|
12,
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13,
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14,
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15,
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|
16,
|
|
4,
|
|
5,
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6,
|
|
7,
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8,
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9,
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10,
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11,
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12,
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13,
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14,
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15,
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16,
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17,
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6,
|
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7,
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8,
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9,
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10,
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11,
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12,
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13,
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14,
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15,
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16,
|
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17,
|
|
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|
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6,
|
|
7,
|
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8,
|
|
9,
|
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10,
|
|
11,
|
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12,
|
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13,
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14,
|
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15,
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16,
|
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|
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|
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8,
|
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9,
|
|
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|
|
11,
|
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|
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13,
|
|
14,
|
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15,
|
|
16,
|
|
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|
|
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|
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|
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|
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|
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13,
|
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14,
|
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15,
|
|
16,
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
7,
|
|
8,
|
|
9,
|
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10,
|
|
11,
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12,
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13,
|
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14,
|
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15,
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16,
|
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5,
|
|
6,
|
|
7,
|
|
9,
|
|
10,
|
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11,
|
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12,
|
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13,
|
|
14,
|
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15,
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16,
|
|
17,
|
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7,
|
|
8,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
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15,
|
|
16,
|
|
17,
|
|
18,
|
|
8,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
13,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
21,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
21,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
21,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
10,
|
|
11,
|
|
12,
|
|
14,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
7,
|
|
8,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
9,
|
|
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|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
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|
|
16,
|
|
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|
|
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|
|
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|
|
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|
|
10,
|
|
11,
|
|
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|
|
13,
|
|
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|
|
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|
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|
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|
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|
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|
|
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|
|
11,
|
|
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|
|
13,
|
|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
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|
|
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|
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|
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|
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|
|
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|
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|
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21,
|
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5,
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10,
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30,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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12,
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12,
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14,
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14,
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14,
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14,
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14,
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15,
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15,
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15,
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18,
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18,
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18,
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21,
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21,
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21,
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21,
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21,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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23,
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23,
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23,
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23,
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23,
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23,
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23,
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26,
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26,
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26,
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27,
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27,
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27,
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27,
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27,
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27,
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27,
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27,
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27,
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27,
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27,
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28,
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28,
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|
28,
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|
28,
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|
28,
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28,
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28,
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|
28,
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|
28,
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|
28,
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|
28,
|
|
28,
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|
28,
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|
28,
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|
29,
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|
29,
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29,
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|
29,
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|
29,
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|
29,
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|
29,
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29,
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29,
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30,
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30,
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30,
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30,
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30,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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|
2,
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|
2,
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|
2,
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|
2,
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2,
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2,
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2,
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2,
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2,
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2,
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3,
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3,
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3,
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3,
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3,
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3,
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3,
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3,
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3,
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|
3,
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|
3,
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3,
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3,
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|
3,
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|
3,
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|
4,
|
|
4,
|
|
4,
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|
4,
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4,
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|
4,
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4,
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4,
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|
4,
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4,
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|
4,
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4,
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|
4,
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|
4,
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|
4,
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|
5,
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|
5,
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|
5,
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|
5,
|
|
5,
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31,
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31,
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0,
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0,
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2,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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9,
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9,
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9,
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9,
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9,
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9,
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9,
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9,
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9,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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12,
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13,
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23,
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23,
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23,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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25,
|
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25,
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25,
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25,
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25,
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25,
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25,
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25,
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25,
|
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25,
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|
26,
|
|
26,
|
|
26,
|
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26,
|
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26,
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26,
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26,
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26,
|
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26,
|
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26,
|
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26,
|
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26,
|
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26,
|
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26,
|
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26,
|
|
27,
|
|
27,
|
|
27,
|
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27,
|
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27,
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27,
|
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27,
|
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27,
|
|
27,
|
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27,
|
|
27,
|
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27,
|
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27,
|
|
27,
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|
28,
|
|
28,
|
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28,
|
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28,
|
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28,
|
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28,
|
|
28,
|
|
28,
|
|
29,
|
|
29,
|
|
29,
|
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29,
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19,
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11,
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11,
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27,
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27,
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27,
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28,
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28,
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28,
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28,
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28,
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28,
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29,
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29,
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29,
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29,
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29,
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29,
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29,
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29,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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1,
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1,
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1,
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1,
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1,
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1,
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2,
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2,
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2,
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2,
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2,
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2,
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2,
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3,
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3,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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8,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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13,
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13,
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13,
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13,
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13,
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13,
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13,
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13,
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13,
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13,
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13,
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13,
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13,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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15,
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15,
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15,
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15,
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15,
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15,
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16,
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16,
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16,
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16,
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16,
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16,
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16,
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16,
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16,
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16,
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16,
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16,
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16,
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16,
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17,
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17,
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17,
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17,
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17,
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17,
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17,
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17,
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18,
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18,
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18,
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18,
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18,
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18,
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18,
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18,
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18,
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18,
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18,
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18,
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18,
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18,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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|
19,
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|
19,
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|
19,
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19,
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19,
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|
19,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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23,
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23,
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23,
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23,
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23,
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23,
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23,
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23,
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24,
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24,
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25,
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25,
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25,
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25,
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25,
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25,
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|
25,
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|
26,
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26,
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26,
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|
26,
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|
26,
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|
26,
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|
26,
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|
26,
|
|
27,
|
|
27,
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|
27,
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|
27,
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|
27,
|
|
27,
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|
27,
|
|
27,
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|
27,
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|
27,
|
|
27,
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|
27,
|
|
27,
|
|
28,
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|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
29,
|
|
29,
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|
29,
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|
29,
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|
29,
|
|
29,
|
|
29,
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|
29,
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|
29,
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|
29,
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|
29,
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|
29,
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|
29,
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|
29,
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|
30,
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|
30,
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|
30,
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|
30,
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30,
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|
30,
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30,
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|
30,
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|
30,
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|
30,
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30,
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|
30,
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30,
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|
30,
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31,
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31,
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|
31,
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|
31,
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31,
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|
31,
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|
31,
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|
31,
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|
31,
|
|
0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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1,
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1,
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1,
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1,
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1,
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1,
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|
1,
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|
1,
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|
1,
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|
1,
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|
1,
|
|
2,
|
|
2,
|
|
2,
|
|
2,
|
|
2,
|
|
2,
|
|
2,
|
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2,
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20,
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20,
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20,
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20,
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14,
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14,
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|
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27,
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27,
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27,
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27,
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28,
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28,
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28,
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29,
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29,
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29,
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29,
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29,
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29,
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29,
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29,
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29,
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29,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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30,
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31,
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1,
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1,
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1,
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2,
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2,
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2,
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2,
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2,
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3,
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9,
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9,
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9,
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9,
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9,
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9,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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11,
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12,
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|
12,
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|
12,
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|
12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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12,
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13,
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13,
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13,
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13,
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13,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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15,
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16,
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16,
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16,
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16,
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16,
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16,
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16,
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16,
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17,
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|
17,
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17,
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17,
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17,
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17,
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17,
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|
17,
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|
17,
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|
17,
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|
17,
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|
17,
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|
17,
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|
17,
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|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
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|
18,
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|
18,
|
|
18,
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|
18,
|
|
18,
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|
18,
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18,
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18,
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18,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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23,
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23,
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23,
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|
23,
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23,
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23,
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23,
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23,
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23,
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23,
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|
23,
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23,
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23,
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23,
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24,
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|
24,
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|
24,
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|
24,
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|
24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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24,
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25,
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25,
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25,
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25,
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25,
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25,
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25,
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25,
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25,
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26,
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26,
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26,
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26,
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26,
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26,
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26,
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26,
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26,
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26,
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26,
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26,
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|
26,
|
|
26,
|
|
26,
|
|
26,
|
|
26,
|
|
26,
|
|
27,
|
|
27,
|
|
27,
|
|
27,
|
|
27,
|
|
27,
|
|
27,
|
|
27,
|
|
27,
|
|
27,
|
|
27,
|
|
27,
|
|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
28,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
29,
|
|
30,
|
|
30,
|
|
30,
|
|
30,
|
|
30,
|
|
30,
|
|
30,
|
|
30,
|
|
30,
|
|
31,
|
|
31,
|
|
31,
|
|
31,
|
|
31,
|
|
31,
|
|
31,
|
|
0,
|
|
0,
|
|
0,
|
|
0,
|
|
0,
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|
0,
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|
0,
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0,
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0,
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0,
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0,
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0,
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|
0,
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1,
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|
1,
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|
1,
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|
1,
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|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
1,
|
|
2,
|
|
2,
|
|
2,
|
|
2,
|
|
2,
|
|
2,
|
|
2,
|
|
2,
|
|
2,
|
|
3,
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21,
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22,
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22,
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22,
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22,
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22,
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|
22,
|
|
22,
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18,
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18,
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18,
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18,
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18,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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23,
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24,
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24,
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24,
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24,
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26,
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26,
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26,
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26,
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26,
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26,
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27,
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27,
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27,
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27,
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28,
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28,
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28,
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28,
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29,
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29,
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30,
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30,
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30,
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30,
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30,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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31,
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0,
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0,
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0,
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0,
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0,
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0,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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|
1,
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|
1,
|
|
2,
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2,
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2,
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|
2,
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2,
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|
2,
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|
2,
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|
2,
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|
2,
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|
3,
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|
3,
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|
3,
|
|
3,
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|
3,
|
|
3,
|
|
3,
|
|
3,
|
|
3,
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|
3,
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|
4,
|
|
4,
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|
4,
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|
4,
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|
4,
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|
4,
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|
4,
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4,
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4,
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|
4,
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|
4,
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5,
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|
5,
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|
5,
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5,
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5,
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5,
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|
5,
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|
6,
|
|
6,
|
|
6,
|
|
6,
|
|
6,
|
|
6,
|
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6,
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27,
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27,
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27,
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28,
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28,
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28,
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28,
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28,
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28,
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28,
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28,
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28,
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28,
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28,
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29,
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29,
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29,
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29,
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29,
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29,
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|
29,
|
|
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26,
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26,
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21,
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22,
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22,
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22,
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22,
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23,
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23,
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23,
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23,
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23,
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23,
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25,
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25,
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25,
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25,
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25,
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30,
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30,
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30,
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30,
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30,
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|
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|
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|
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|
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|
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|
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23,
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0,
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|
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|
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|
|
0,
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|
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|
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|
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" if (!display || display === 'none') {{\n",
|
|
" console.log([gd, 'removed!']);\n",
|
|
" Plotly.purge(gd);\n",
|
|
" observer.disconnect();\n",
|
|
" }}\n",
|
|
"}});\n",
|
|
"\n",
|
|
"// Listen for the removal of the full notebook cells\n",
|
|
"var notebookContainer = gd.closest('#notebook-container');\n",
|
|
"if (notebookContainer) {{\n",
|
|
" x.observe(notebookContainer, {childList: true});\n",
|
|
"}}\n",
|
|
"\n",
|
|
"// Listen for the clearing of the current output cell\n",
|
|
"var outputEl = gd.closest('.output');\n",
|
|
"if (outputEl) {{\n",
|
|
" x.observe(outputEl, {childList: true});\n",
|
|
"}}\n",
|
|
"\n",
|
|
" }) }; }); </script> </div>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from editor.ploting import plot_histograms\n",
|
|
"\n",
|
|
"\n",
|
|
"edited_histogram, original_histogram = next(loader)\n",
|
|
"edited_histogram = edited_histogram.to(device)\n",
|
|
"original_histogram = original_histogram.to(device)\n",
|
|
"predicted_original = model(edited_histogram)\n",
|
|
"plot_histograms([\n",
|
|
" original_histogram.cpu().numpy().squeeze(),\n",
|
|
" edited_histogram.cpu().numpy().squeeze(),\n",
|
|
" predicted_original.cpu().detach().numpy().squeeze()\n",
|
|
"])\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"torch.Size([1, 1, 32, 32, 32])"
|
|
]
|
|
},
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import torch.nn.functional as F\n",
|
|
"\n",
|
|
"original_histogram.size()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.plotly.v1+json": {
|
|
"config": {
|
|
"plotlyServerURL": "https://plot.ly"
|
|
},
|
|
"data": [
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{
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"marker": {
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20,
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21,
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22,
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23,
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23,
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0,
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0,
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0,
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1,
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2,
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|
3,
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3,
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3,
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5,
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5,
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6,
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6,
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7,
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7,
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8,
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9,
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9,
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9,
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12,
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13,
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13,
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|
13,
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|
13,
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|
13,
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|
13,
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13,
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13,
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13,
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14,
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14,
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14,
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14,
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14,
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16,
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16,
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16,
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16,
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17,
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|
17,
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17,
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17,
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17,
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17,
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17,
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18,
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18,
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18,
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18,
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18,
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18,
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19,
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19,
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19,
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19,
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20,
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20,
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20,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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22,
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22,
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22,
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22,
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22,
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22,
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22,
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23,
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23,
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23,
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23,
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23,
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24,
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24,
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24,
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0,
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0,
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0,
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1,
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2,
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2,
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3,
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3,
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4,
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5,
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5,
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6,
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7,
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7,
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8,
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8,
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8,
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9,
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10,
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10,
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11,
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11,
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11,
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11,
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12,
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12,
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12,
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12,
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12,
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13,
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13,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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21,
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21,
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21,
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21,
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21,
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21,
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21,
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22,
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22,
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22,
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22,
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22,
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22,
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23,
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23,
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23,
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23,
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23,
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23,
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23,
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24,
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24,
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24,
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24,
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0,
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0,
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0,
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1,
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1,
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2,
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2,
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2,
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3,
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3,
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3,
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4,
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4,
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4,
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4,
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5,
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5,
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5,
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6,
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6,
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6,
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6,
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7,
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7,
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9,
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9,
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10,
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10,
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10,
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11,
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11,
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11,
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12,
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12,
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13,
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13,
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14,
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21,
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22,
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22,
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22,
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22,
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22,
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23,
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23,
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23,
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23,
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23,
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23,
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23,
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24,
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24,
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24,
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24,
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|
24,
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|
24,
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|
24,
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|
25,
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|
25,
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|
25,
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|
25,
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|
25,
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|
0,
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|
1,
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1,
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2,
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2,
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2,
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2,
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3,
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3,
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3,
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4,
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4,
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4,
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5,
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5,
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5,
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6,
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7,
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8,
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10,
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10,
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10,
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11,
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11,
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11,
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11,
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12,
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12,
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12,
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13,
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13,
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13,
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13,
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13,
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13,
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13,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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14,
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15,
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16,
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16,
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16,
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17,
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17,
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17,
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17,
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17,
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17,
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17,
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17,
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17,
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18,
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18,
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18,
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18,
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18,
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|
18,
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|
18,
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|
18,
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18,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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19,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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20,
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21,
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21,
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21,
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|
21,
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|
21,
|
|
21,
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|
21,
|
|
21,
|
|
22,
|
|
22,
|
|
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|
22,
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|
22,
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|
22,
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|
23,
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|
23,
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|
23,
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23,
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|
23,
|
|
24,
|
|
24,
|
|
24,
|
|
24,
|
|
24,
|
|
25,
|
|
25,
|
|
25,
|
|
25,
|
|
25,
|
|
25,
|
|
25,
|
|
26,
|
|
26,
|
|
26,
|
|
26,
|
|
0,
|
|
1,
|
|
1,
|
|
2,
|
|
2,
|
|
2,
|
|
3,
|
|
3,
|
|
4,
|
|
4,
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|
5,
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|
5,
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|
5,
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6,
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6,
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7,
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7,
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7,
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8,
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8,
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9,
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9,
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9,
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10,
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10,
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11,
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11,
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11,
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11,
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11,
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|
12,
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12,
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12,
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12,
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12,
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|
13,
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13,
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13,
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13,
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13,
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13,
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13,
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13,
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14,
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14,
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14,
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|
14,
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|
14,
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|
14,
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|
14,
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|
14,
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14,
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|
14,
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|
14,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
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|
|
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|
|
16,
|
|
16,
|
|
16,
|
|
16,
|
|
16,
|
|
16,
|
|
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|
|
16,
|
|
16,
|
|
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|
|
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|
|
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|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
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|
|
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|
|
19,
|
|
19,
|
|
19,
|
|
19,
|
|
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|
|
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|
|
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
22,
|
|
22,
|
|
22,
|
|
23,
|
|
23,
|
|
23,
|
|
23,
|
|
23,
|
|
23,
|
|
24,
|
|
24,
|
|
24,
|
|
24,
|
|
24,
|
|
25,
|
|
25,
|
|
25,
|
|
25,
|
|
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|
|
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|
|
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|
|
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|
|
26,
|
|
26,
|
|
27,
|
|
1,
|
|
2,
|
|
2,
|
|
3,
|
|
3,
|
|
3,
|
|
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|
|
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|
|
4,
|
|
5,
|
|
5,
|
|
6,
|
|
6,
|
|
6,
|
|
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|
|
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|
|
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|
|
8,
|
|
10,
|
|
10,
|
|
10,
|
|
11,
|
|
11,
|
|
12,
|
|
12,
|
|
12,
|
|
13,
|
|
13,
|
|
13,
|
|
14,
|
|
14,
|
|
14,
|
|
14,
|
|
14,
|
|
14,
|
|
14,
|
|
14,
|
|
14,
|
|
14,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
15,
|
|
16,
|
|
16,
|
|
16,
|
|
16,
|
|
16,
|
|
16,
|
|
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|
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|
|
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|
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16,
|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
17,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
18,
|
|
19,
|
|
19,
|
|
19,
|
|
19,
|
|
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|
|
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|
|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
|
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
27,
|
|
27,
|
|
27,
|
|
27,
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
|
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|
10,
|
|
11,
|
|
11,
|
|
11,
|
|
11,
|
|
11,
|
|
12,
|
|
12,
|
|
12,
|
|
13,
|
|
13,
|
|
13,
|
|
14,
|
|
14,
|
|
14,
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|
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3,
|
|
1,
|
|
2,
|
|
3,
|
|
4,
|
|
0,
|
|
1,
|
|
2,
|
|
3,
|
|
4,
|
|
5,
|
|
6,
|
|
1,
|
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6,
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|
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|
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|
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|
4,
|
|
5,
|
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6,
|
|
7,
|
|
8,
|
|
9,
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|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
4,
|
|
6,
|
|
7,
|
|
8,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
5,
|
|
6,
|
|
7,
|
|
8,
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|
9,
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|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
8,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
9,
|
|
10,
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|
11,
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12,
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13,
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14,
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15,
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16,
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15,
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16,
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17,
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|
16,
|
|
18,
|
|
19,
|
|
20,
|
|
19,
|
|
20,
|
|
20,
|
|
21,
|
|
0,
|
|
2,
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|
3,
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|
4,
|
|
0,
|
|
3,
|
|
1,
|
|
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|
|
1,
|
|
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|
1,
|
|
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|
|
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|
|
0,
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|
1,
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|
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|
19,
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|
20,
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|
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|
|
21,
|
|
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|
|
0,
|
|
2,
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|
3,
|
|
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|
|
0,
|
|
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|
|
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|
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|
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|
|
20,
|
|
21,
|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
|
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|
|
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|
0,
|
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|
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|
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|
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|
|
14,
|
|
15,
|
|
7,
|
|
8,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
16,
|
|
17,
|
|
9,
|
|
10,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
17,
|
|
18,
|
|
11,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
12,
|
|
13,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
21,
|
|
14,
|
|
15,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
21,
|
|
16,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
21,
|
|
22,
|
|
17,
|
|
18,
|
|
19,
|
|
20,
|
|
21,
|
|
22,
|
|
23,
|
|
20,
|
|
21,
|
|
22,
|
|
23,
|
|
24,
|
|
25,
|
|
22,
|
|
23,
|
|
24,
|
|
25,
|
|
26,
|
|
24,
|
|
25,
|
|
26,
|
|
27,
|
|
28,
|
|
22,
|
|
23,
|
|
26,
|
|
27,
|
|
28,
|
|
23,
|
|
1,
|
|
0,
|
|
1,
|
|
0,
|
|
1,
|
|
2,
|
|
3,
|
|
4,
|
|
1,
|
|
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|
|
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|
|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
|
8,
|
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9,
|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
12,
|
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" \n",
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"var gd = document.getElementById('1e164548-65ce-4b80-8b51-f9ba78d24397');\n",
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" var display = window.getComputedStyle(gd).display;\n",
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" console.log([gd, 'removed!']);\n",
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" Plotly.purge(gd);\n",
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" observer.disconnect();\n",
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|
|
" x.observe(notebookContainer, {childList: true});\n",
|
|
"}}\n",
|
|
"\n",
|
|
"// Listen for the clearing of the current output cell\n",
|
|
"var outputEl = gd.closest('.output');\n",
|
|
"if (outputEl) {{\n",
|
|
" x.observe(outputEl, {childList: true});\n",
|
|
"}}\n",
|
|
"\n",
|
|
" }) }; }); </script> </div>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"# 32 / 16 -> 2\n",
|
|
"# 32 / 8 -> 4\n",
|
|
"# 4 -> 8\n",
|
|
"\n",
|
|
"a = F.avg_pool3d(original_histogram, 4)\n",
|
|
"a /= torch.sum(a)\n",
|
|
"plot_histograms([\n",
|
|
" original_histogram.cpu().numpy().squeeze(),\n",
|
|
" a.cpu().numpy().squeeze(),\n",
|
|
"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"todo: try different colour spaces, see the results applied to images"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "bipolaroid",
|
|
"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": "3.12.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|