text: import re
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.checkpoint as cp
  from collections import OrderedDict
  from .utils import load_state_dict_from_url
  from torch import Tensor
  from torch.jit.annotations import List
  
  
  __all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
  
  model_urls = {
      'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
      'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
      'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth',
      'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth',
  }
  
  
  class _DenseLayer(nn.Module):
      def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, memory_efficient=False):
          super(_DenseLayer, self).__init__()
          self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
          self.add_module('relu1', nn.ReLU(inplace=True)),
          self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
                                             growth_rate, kernel_size=1, stride=1,
                                             bias=False)),
          self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
          self.add_module('relu2', nn.ReLU(inplace=True)),
          self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                                             kernel_size=3, stride=1, padding=1,
                                             bias=False)),
          self.drop_rate = float(drop_rate)
          self.memory_efficient = memory_efficient
  
      def bn_function(self, inputs):
          # type: (List[Tensor]) -> Tensor
          concated_features = torch.cat(inputs, 1)
          bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features)))  # noqa: T484
          return bottleneck_output
  
      # todo: rewrite when torchscript supports any
      def any_requires_grad(self, input):
          # type: (List[Tensor]) -> bool
          for tensor in input:
              if tensor.requires_grad:
                  return True
          return False
  
      @torch.jit.unused  # noqa: T484
      def call_checkpoint_bottleneck(self, input):
          # type: (List[Tensor]) -> Tensor
          def closure(*inputs):
              return self.bn_function(*inputs)
  
          return cp.checkpoint(closure, input)
  
      @torch.jit._overload_method  # noqa: F811
      def forward(self, input):
          # type: (List[Tensor]) -> (Tensor)
          pass
  
      @torch.jit._overload_method  # noqa: F811
      def forward(self, input):
          # type: (Tensor) -> (Tensor)
          pass
  
      # torchscript does not yet support *args, so we overload method
      # allowing it to take either a List[Tensor] or single Tensor
      def forward(self, input):  # noqa: F811
          if isinstance(input, Tensor):
              prev_features = [input]
          else:
              prev_features = input
  
          if self.memory_efficient and self.any_requires_grad(prev_features):
              if torch.jit.is_scripting():
                  raise Exception("Memory Efficient not supported in JIT")
  
              bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
          else:
              bottleneck_output = self.bn_function(prev_features)
  
          new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
          if self.drop_rate > 0:
              new_features = F.dropout(new_features, p=self.drop_rate,
                                       training=self.training)
          return new_features
  
  
  class _DenseBlock(nn.ModuleDict):
      _version = 2
      __constants__ = ['layers']
  
      def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, memory_efficient=False):
          super(_DenseBlock, self).__init__()
          for i in range(num_layers):
              layer = _DenseLayer(
                  num_input_features + i * growth_rate,
                  growth_rate=growth_rate,
                  bn_size=bn_size,
                  drop_rate=drop_rate,
                  memory_efficient=memory_efficient,
              )
              self.add_module('denselayer%d' % (i + 1), layer)
  
      def forward(self, init_features):
          features = [init_features]
          for name, layer in self.items():
              new_features = layer(features)
              features.append(new_features)
          return torch.cat(features, 1)
  
  
  class _Transition(nn.Sequential):
      def __init__(self, num_input_features, num_output_features):
          super(_Transition, self).__init__()
          self.add_module('norm', nn.BatchNorm2d(num_input_features))
          self.add_module('relu', nn.ReLU(inplace=True))
          self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
                                            kernel_size=1, stride=1, bias=False))
          self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
  
  
  class DenseNet(nn.Module):
      r"""Densenet-BC model class, based on
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
      Args:
          growth_rate (int) - how many filters to add each layer (`k` in paper)
          block_config (list of 4 ints) - how many layers in each pooling block
          num_init_features (int) - the number of filters to learn in the first convolution layer
          bn_size (int) - multiplicative factor for number of bottle neck layers
            (i.e. bn_size * k features in the bottleneck layer)
          drop_rate (float) - dropout rate after each dense layer
          num_classes (int) - number of classification classes
          memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
            but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
      """
  
      __constants__ = ['features']
  
      def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
                   num_init_features=64, bn_size=4, drop_rate=0, 
                   header=True,
                   num_classes=1000, memory_efficient=False):
  
          super(DenseNet, self).__init__()
  
          self.header = header
  
          # First convolution
          self.features = nn.Sequential(OrderedDict([
              ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2,
                                  padding=3, bias=False)),
              ('norm0', nn.BatchNorm2d(num_init_features)),
              ('relu0', nn.ReLU(inplace=True)),
              ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
          ]))
  
          # Each denseblock
          num_features = num_init_features
          for i, num_layers in enumerate(block_config):
              block = _DenseBlock(
                  num_layers=num_layers,
                  num_input_features=num_features,
                  bn_size=bn_size,
                  growth_rate=growth_rate,
                  drop_rate=drop_rate,
                  memory_efficient=memory_efficient
              )
              self.features.add_module('denseblock%d' % (i + 1), block)
              num_features = num_features + num_layers * growth_rate
              if i != len(block_config) - 1:
                  trans = _Transition(num_input_features=num_features,
                                      num_output_features=num_features // 2)
                  self.features.add_module('transition%d' % (i + 1), trans)
                  num_features = num_features // 2
  
          # Final batch norm
          self.features.add_module('norm5', nn.BatchNorm2d(num_features))
  
          # Linear layer
          if self.header:
              self.classifier = nn.Linear(num_features, num_classes)
  
          # Official init from torch repo.
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight)
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.Linear):
                  nn.init.constant_(m.bias, 0)
  
          self.num_features = num_features
  
      def forward(self, x):
          features = self.features(x)
          out = F.relu(features, inplace=True)
  
          if self.header:
              out = F.adaptive_avg_pool2d(out, (1, 1))
              out = torch.flatten(out, 1)
              out = self.classifier(out)
          return out
  
  
  def _load_state_dict(model, model_url, progress):
      # '.'s are no longer allowed in module names, but previous _DenseLayer
      # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
      # They are also in the checkpoints in model_urls. This pattern is used
      # to find such keys.
      pattern = re.compile(
          r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
  
      pretrained_dict = load_state_dict_from_url(model_url, progress=progress)
      for key in list(pretrained_dict.keys()):
          res = pattern.match(key)
          if res:
              new_key = res.group(1) + res.group(2)
              pretrained_dict[new_key] = pretrained_dict[key]
              del pretrained_dict[key]
  
      model_dict = model.state_dict()
      pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
  
      model_dict.update(pretrained_dict)
      model.load_state_dict(model_dict)
  
  
  def _densenet(arch, growth_rate, block_config, num_init_features, pretrained, progress,
                **kwargs):
      model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
      if pretrained:
          _load_state_dict(model, model_urls[arch], progress)
      return model
  
  
  def densenet121(pretrained=False, progress=True, **kwargs):
      r"""Densenet-121 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
          memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
            but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
      """
      return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress,
                       **kwargs)
  
  
  def densenet161(pretrained=False, progress=True, **kwargs):
      r"""Densenet-161 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
          memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
            but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
      """
      return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
                       **kwargs)
  
  
  def densenet169(pretrained=False, progress=True, **kwargs):
      r"""Densenet-169 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
          memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
            but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
      """
      return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress,
                       **kwargs)
  
  
  def densenet201(pretrained=False, progress=True, **kwargs):
      r"""Densenet-201 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
      Args:
          pretrained (bool): If True, returns a model pre-trained on ImageNet
          progress (bool): If True, displays a progress bar of the download to stderr
          memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
            but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
      """
      return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress,
                       **kwargs)