text: '''
  Modified from https://raw.githubusercontent.com/pytorch/vision/v0.9.1/torchvision/models/shufflenetv2.py
  
  BSD 3-Clause License
  
  Copyright (c) Soumith Chintala 2016,
  All rights reserved.
  
  Redistribution and use in source and binary forms, with or without
  modification, are permitted provided that the following conditions are met:
  
  * Redistributions of source code must retain the above copyright notice, this
    list of conditions and the following disclaimer.
  
  * Redistributions in binary form must reproduce the above copyright notice,
    this list of conditions and the following disclaimer in the documentation
    and/or other materials provided with the distribution.
  
  * Neither the name of the copyright holder nor the names of its
    contributors may be used to endorse or promote products derived from
    this software without specific prior written permission.
  
  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  '''
  import sys
  import torch
  import torch.nn as nn
  from torch import Tensor
  
  try:
      from torch.hub import load_state_dict_from_url
  except ImportError:
      from torch.utils.model_zoo import load_url as load_state_dict_from_url
  
  from functools import partial
  from typing import Dict, Type, Any, Callable, Union, List, Optional
  
  
  cifar10_pretrained_weight_urls = {
      'shufflenetv2_x0_5': 'https://github.com/chenyaofo/pytorch-cifar-models/releases/download/shufflenetv2/cifar10_shufflenetv2_x0_5-1308b4e9.pt',
      'shufflenetv2_x1_0': 'https://github.com/chenyaofo/pytorch-cifar-models/releases/download/shufflenetv2/cifar10_shufflenetv2_x1_0-98807be3.pt',
      'shufflenetv2_x1_5': 'https://github.com/chenyaofo/pytorch-cifar-models/releases/download/shufflenetv2/cifar10_shufflenetv2_x1_5-296694dd.pt',
      'shufflenetv2_x2_0': 'https://github.com/chenyaofo/pytorch-cifar-models/releases/download/shufflenetv2/cifar10_shufflenetv2_x2_0-ec31611c.pt',
  }
  
  cifar100_pretrained_weight_urls = {
      'shufflenetv2_x0_5': 'https://github.com/chenyaofo/pytorch-cifar-models/releases/download/shufflenetv2/cifar100_shufflenetv2_x0_5-1977720f.pt',
      'shufflenetv2_x1_0': 'https://github.com/chenyaofo/pytorch-cifar-models/releases/download/shufflenetv2/cifar100_shufflenetv2_x1_0-9ae22beb.pt',
      'shufflenetv2_x1_5': 'https://github.com/chenyaofo/pytorch-cifar-models/releases/download/shufflenetv2/cifar100_shufflenetv2_x1_5-e2c85ad8.pt',
      'shufflenetv2_x2_0': 'https://github.com/chenyaofo/pytorch-cifar-models/releases/download/shufflenetv2/cifar100_shufflenetv2_x2_0-e7e584cd.pt',
  }
  
  
  def channel_shuffle(x: Tensor, groups: int) -> Tensor:
      batchsize, num_channels, height, width = x.size()
      channels_per_group = num_channels // groups
  
      # reshape
      x = x.view(batchsize, groups,
                 channels_per_group, height, width)
  
      x = torch.transpose(x, 1, 2).contiguous()
  
      # flatten
      x = x.view(batchsize, -1, height, width)
  
      return x
  
  
  class InvertedResidual(nn.Module):
      def __init__(
          self,
          inp: int,
          oup: int,
          stride: int
      ) -> None:
          super(InvertedResidual, self).__init__()
  
          if not (1 <= stride <= 3):
              raise ValueError('illegal stride value')
          self.stride = stride
  
          branch_features = oup // 2
          assert (self.stride != 1) or (inp == branch_features << 1)
  
          if self.stride > 1:
              self.branch1 = nn.Sequential(
                  self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
                  nn.BatchNorm2d(inp),
                  nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
                  nn.BatchNorm2d(branch_features),
                  nn.ReLU(inplace=True),
              )
          else:
              self.branch1 = nn.Sequential()
  
          self.branch2 = nn.Sequential(
              nn.Conv2d(inp if (self.stride > 1) else branch_features,
                        branch_features, kernel_size=1, stride=1, padding=0, bias=False),
              nn.BatchNorm2d(branch_features),
              nn.ReLU(inplace=True),
              self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
              nn.BatchNorm2d(branch_features),
              nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
              nn.BatchNorm2d(branch_features),
              nn.ReLU(inplace=True),
          )
  
      @staticmethod
      def depthwise_conv(
          i: int,
          o: int,
          kernel_size: int,
          stride: int = 1,
          padding: int = 0,
          bias: bool = False
      ) -> nn.Conv2d:
          return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
  
      def forward(self, x: Tensor) -> Tensor:
          if self.stride == 1:
              x1, x2 = x.chunk(2, dim=1)
              out = torch.cat((x1, self.branch2(x2)), dim=1)
          else:
              out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
  
          out = channel_shuffle(out, 2)
  
          return out
  
  
  class ShuffleNetV2(nn.Module):
      def __init__(
          self,
          stages_repeats: List[int],
          stages_out_channels: List[int],
          num_classes: int = 1000,
          inverted_residual: Callable[..., nn.Module] = InvertedResidual
      ) -> None:
          super(ShuffleNetV2, self).__init__()
  
          if len(stages_repeats) != 3:
              raise ValueError('expected stages_repeats as list of 3 positive ints')
          if len(stages_out_channels) != 5:
              raise ValueError('expected stages_out_channels as list of 5 positive ints')
          self._stage_out_channels = stages_out_channels
  
          input_channels = 3
          output_channels = self._stage_out_channels[0]
          self.conv1 = nn.Sequential(
              nn.Conv2d(input_channels, output_channels, 3, 1, 1, bias=False),  # NOTE: change stride 2 -> 1 for CIFAR10/100
              nn.BatchNorm2d(output_channels),
              nn.ReLU(inplace=True),
          )
          input_channels = output_channels
  
          # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) NOTE: remove this maxpool layer for CIFAR10/100
  
          # Static annotations for mypy
          self.stage2: nn.Sequential
          self.stage3: nn.Sequential
          self.stage4: nn.Sequential
          stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
          for name, repeats, output_channels in zip(
                  stage_names, stages_repeats, self._stage_out_channels[1:]):
              seq = [inverted_residual(input_channels, output_channels, 2)]
              for i in range(repeats - 1):
                  seq.append(inverted_residual(output_channels, output_channels, 1))
              setattr(self, name, nn.Sequential(*seq))
              input_channels = output_channels
  
          output_channels = self._stage_out_channels[-1]
          self.conv5 = nn.Sequential(
              nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
              nn.BatchNorm2d(output_channels),
              nn.ReLU(inplace=True),
          )
  
          self.fc = nn.Linear(output_channels, num_classes)
  
      def _forward_impl(self, x: Tensor) -> Tensor:
          # See note [TorchScript super()]
          x = self.conv1(x)
          # x = self.maxpool(x) NOTE: remove this maxpool layer for CIFAR10/100
          x = self.stage2(x)
          x = self.stage3(x)
          x = self.stage4(x)
          x = self.conv5(x)
          x = x.mean([2, 3])  # globalpool
          x = self.fc(x)
          return x
  
      def forward(self, x: Tensor) -> Tensor:
          return self._forward_impl(x)
  
  
  def _shufflenet_v2(
      arch: str,
      stages_repeats: List[int],
      stages_out_channels: List[int],
      model_urls: Dict[str, str],
      progress: bool = True,
      pretrained: bool = False,
      **kwargs: Any
  ) -> ShuffleNetV2:
      model = ShuffleNetV2(stages_repeats=stages_repeats, stages_out_channels=stages_out_channels, ** kwargs)
      if pretrained:
          state_dict = load_state_dict_from_url(model_urls[arch],
                                                progress=progress)
          model.load_state_dict(state_dict)
      return model
  
  
  def cifar10_shufflenetv2_x0_5(*args, **kwargs) -> ShuffleNetV2: pass
  def cifar10_shufflenetv2_x1_0(*args, **kwargs) -> ShuffleNetV2: pass
  def cifar10_shufflenetv2_x1_5(*args, **kwargs) -> ShuffleNetV2: pass
  def cifar10_shufflenetv2_x2_0(*args, **kwargs) -> ShuffleNetV2: pass
  
  
  def cifar100_shufflenetv2_x0_5(*args, **kwargs) -> ShuffleNetV2: pass
  def cifar100_shufflenetv2_x1_0(*args, **kwargs) -> ShuffleNetV2: pass
  def cifar100_shufflenetv2_x1_5(*args, **kwargs) -> ShuffleNetV2: pass
  def cifar100_shufflenetv2_x2_0(*args, **kwargs) -> ShuffleNetV2: pass
  
  
  thismodule = sys.modules[__name__]
  for dataset in ["cifar10", "cifar100"]:
      for stages_repeats, stages_out_channels, model_name in \
          zip([[4, 8, 4]]*4,
              [[24, 48, 96, 192, 1024], [24, 116, 232, 464, 1024], [24, 176, 352, 704, 1024], [24, 244, 488, 976, 2048]],
              ["shufflenetv2_x0_5", "shufflenetv2_x1_0", "shufflenetv2_x1_5", "shufflenetv2_x2_0"]):
          method_name = f"{dataset}_{model_name}"
          model_urls = cifar10_pretrained_weight_urls if dataset == "cifar10" else cifar100_pretrained_weight_urls
          num_classes = 10 if dataset == "cifar10" else 100
          setattr(
              thismodule,
              method_name,
              partial(_shufflenet_v2,
                      arch=model_name,
                      stages_repeats=stages_repeats,
                      stages_out_channels=stages_out_channels,
                      model_urls=model_urls,
                      num_classes=num_classes)
          )
  