text: import logging
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.nn.utils import spectral_norm
  
  from src.models.dropouts import WeightDropLinear, WeightDropConv2d
  
  logger = logging.getLogger(__name__)
  
  
  # Credits to https://github.com/kekmodel/FixMatch-pytorch/blob/master/models/wideresnet.py
  
  
  # class PSBatchNorm2d(nn.BatchNorm2d):
  #     """How Does BN Increase Collapsed Neural Network Filters? (https://arxiv.org/abs/2001.11216)"""
  #
  #     def __init__(self, num_features, alpha=0.1, eps=1e-05, momentum=0.001, affine=True, track_running_stats=True):
  #         super().__init__(num_features, eps, momentum, affine, track_running_stats)
  #         self.alpha = alpha
  #
  #     def forward(self, x):
  #         return super().forward(x) + self.alpha
  
  
  class BasicBlock(nn.Module):
      def __init__(self, in_planes, out_planes, stride, drop_rate=0.0, weight_dropout=0.0, dropout_method=F.dropout,
                   spectral_normalise=False, activate_before_residual=False):
          super(BasicBlock, self).__init__()
          if weight_dropout == 0.0:
              self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
              self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
          else:
              self.conv1 = WeightDropConv2d(weight_dropout, in_planes, out_planes, kernel_size=3, stride=stride, padding=1,
                                            bias=False)
              self.conv2 = WeightDropConv2d(weight_dropout, out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
  
          if spectral_normalise:
              self.conv1 = spectral_norm(self.conv1)
              self.conv2 = spectral_norm(self.conv2)
  
          self.bn1 = nn.BatchNorm2d(in_planes, momentum=0.001)
          self.relu1 = nn.LeakyReLU(negative_slope=0.1, inplace=True)
          self.bn2 = nn.BatchNorm2d(out_planes, momentum=0.001)
          self.relu2 = nn.LeakyReLU(negative_slope=0.1, inplace=True)
          self.drop_rate = drop_rate
          self.equalInOut = (in_planes == out_planes)
          self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
                                                                  padding=0, bias=False) or None
          if spectral_normalise and isinstance(self.convShortcut, nn.Conv2d):
              self.convShortcut = spectral_norm(self.convShortcut)
  
          self.dropout_method = dropout_method
          self.activate_before_residual = activate_before_residual
  
      def forward(self, x):
          if not self.equalInOut and self.activate_before_residual:
              x = self.relu1(self.bn1(x))
          else:
              out = self.relu1(self.bn1(x))
          out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
          if self.drop_rate > 0:
              out = self.dropout_method(out, p=self.drop_rate, training=self.training)
          out = self.conv2(out)
          return torch.add(x if self.equalInOut else self.convShortcut(x), out)
  
  
  class NetworkBlock(nn.Module):
      def __init__(self, nb_layers, in_planes, out_planes, block, stride, drop_rate=0.0, weight_dropout=0.0,
                   dropout_method=F.dropout, spectral_normalise=False, activate_before_residual=False):
          super(NetworkBlock, self).__init__()
          self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, drop_rate, weight_dropout, dropout_method,
                                        spectral_normalise, activate_before_residual)
  
      def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, drop_rate, weight_dropout, dropout_method, spectral_normalise,
                      activate_before_residual):
          layers = []
          for i in range(int(nb_layers)):
              layers.append(block(i == 0 and in_planes or out_planes, out_planes,
                                  i == 0 and stride or 1, drop_rate, weight_dropout, dropout_method, spectral_normalise,
                                  activate_before_residual))
          return nn.Sequential(*layers)
  
      def forward(self, x):
          return self.layer(x)
  
  
  class WideResNet(nn.Module):
      def __init__(self, num_classes, depth=28, widen_factor=2, drop_rate=0.0, weight_dropout=0.0, dropout_method=F.dropout,
                   after_bn_drop_rate=0.0, spectral_normalise=False, scale=4):
          super(WideResNet, self).__init__()
          channels = [16, 16 * widen_factor, 16 * scale * widen_factor, 16 * scale**2 * widen_factor]
          # assert ((depth - 4) % 6 == 0)
          n = (depth - 4) / 6
          block = BasicBlock
          # 1st conv before any network block
          self.conv1 = nn.Conv2d(3, channels[0], kernel_size=3, stride=1, padding=1, bias=False)
          if spectral_normalise:
              self.conv1 = spectral_norm(self.conv1)
          # 1st block
          self.block1 = NetworkBlock(n, channels[0], channels[1], block, 1, drop_rate, weight_dropout, dropout_method,
                                     spectral_normalise, activate_before_residual=True)
          # 2nd block
          self.block2 = NetworkBlock(n, channels[1], channels[2], block, 2, drop_rate, weight_dropout, dropout_method,
                                     spectral_normalise)
          # 3rd block
          self.block3 = NetworkBlock(n, channels[2], channels[3], block, 2, drop_rate, weight_dropout, dropout_method,
                                     spectral_normalise)
          # global average pooling and classifier
          self.bn1 = nn.BatchNorm2d(channels[3], momentum=0.001)
          self.relu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
          if weight_dropout == 0.0:
              self.fc = nn.Linear(channels[3], num_classes)
          else:
              self.fc = WeightDropLinear(weight_dropout, channels[3], num_classes)
          self.channels = channels[3]
          self.after_bn_drop_rate = after_bn_drop_rate
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d) or isinstance(m, WeightDropConv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1.0)
                  nn.init.constant_(m.bias, 0.0)
              elif isinstance(m, nn.Linear) or isinstance(m, WeightDropLinear):
                  nn.init.xavier_normal_(m.weight)
                  nn.init.constant_(m.bias, 0.0)
  
      def forward(self, x):
          out = self.conv1(x)
          out = self.block1(out)
          out = self.block2(out)
          out = self.block3(out)
          out = self.relu(self.bn1(out))
          out = F.adaptive_avg_pool2d(out, 1)
          out = out.view(-1, self.channels)
          if self.after_bn_drop_rate > 0.0:
              out = F.dropout(out, p=self.after_bn_drop_rate, training=self.training)
          return self.fc(out), out
  
      def disable_batch_norm_update(self):
          for m in self.modules():
              if isinstance(m, nn.BatchNorm2d):
                  m.eval()
  
      def enable_batch_norm_update(self):
          for m in self.modules():
              if isinstance(m, nn.BatchNorm2d):
                  m.train()
  