text: # --------------------------------------------------------
  # Swin Transformer
  # Copyright (c) 2021 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # Written by Ze Liu
  # --------------------------------------------------------
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.checkpoint as checkpoint
  from timm.models.layers import DropPath, to_2tuple, trunc_normal_
  
  
  class Mlp(nn.Module):
      def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
          super().__init__()
          out_features = out_features or in_features
          hidden_features = hidden_features or in_features
          self.fc1 = nn.Linear(in_features, hidden_features)
          self.act = act_layer()
          self.fc2 = nn.Linear(hidden_features, out_features)
          self.drop = nn.Dropout(drop)
  
      def forward(self, x):
          x = self.fc1(x)
          x = self.act(x)
          x = self.drop(x)
          x = self.fc2(x)
          x = self.drop(x)
          return x
  
  
  def window_partition(x, window_size):
      """
      Args:
          x: (B, H, W, C)
          window_size (int): window size
      Returns:
          windows: (num_windows*B, window_size, window_size, C)
      """
      B, H, W, C = x.shape
      x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
      windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
      return windows
  
  
  def window_reverse(windows, window_size, H, W):
      """
      Args:
          windows: (num_windows*B, window_size, window_size, C)
          window_size (int): Window size
          H (int): Height of image
          W (int): Width of image
      Returns:
          x: (B, H, W, C)
      """
      B = int(windows.shape[0] / (H * W / window_size / window_size))
      x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
      x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
      return x
  
  
  class SwinMLPBlock(nn.Module):
      r""" Swin MLP Block.
      Args:
          dim (int): Number of input channels.
          input_resolution (tuple[int]): Input resulotion.
          num_heads (int): Number of attention heads.
          window_size (int): Window size.
          shift_size (int): Shift size for SW-MSA.
          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
          drop (float, optional): Dropout rate. Default: 0.0
          drop_path (float, optional): Stochastic depth rate. Default: 0.0
          act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
      """
  
      def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                   mlp_ratio=4., drop=0., drop_path=0.,
                   act_layer=nn.GELU, norm_layer=nn.LayerNorm):
          super().__init__()
          self.dim = dim
          self.input_resolution = input_resolution
          self.num_heads = num_heads
          self.window_size = window_size
          self.shift_size = shift_size
          self.mlp_ratio = mlp_ratio
          if min(self.input_resolution) <= self.window_size:
              # if window size is larger than input resolution, we don't partition windows
              self.shift_size = 0
              self.window_size = min(self.input_resolution)
          assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
  
          self.padding = [self.window_size - self.shift_size, self.shift_size,
                          self.window_size - self.shift_size, self.shift_size]  # P_l,P_r,P_t,P_b
  
          self.norm1 = norm_layer(dim)
          # use group convolution to implement multi-head MLP
          self.spatial_mlp = nn.Conv1d(self.num_heads * self.window_size ** 2,
                                       self.num_heads * self.window_size ** 2,
                                       kernel_size=1,
                                       groups=self.num_heads)
  
          self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
          self.norm2 = norm_layer(dim)
          mlp_hidden_dim = int(dim * mlp_ratio)
          self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
  
      def forward(self, x):
          H, W = self.input_resolution
          B, L, C = x.shape
          assert L == H * W, "input feature has wrong size"
  
          shortcut = x
          x = self.norm1(x)
          x = x.view(B, H, W, C)
  
          # shift
          if self.shift_size > 0:
              P_l, P_r, P_t, P_b = self.padding
              shifted_x = F.pad(x, [0, 0, P_l, P_r, P_t, P_b], "constant", 0)
          else:
              shifted_x = x
          _, _H, _W, _ = shifted_x.shape
  
          # partition windows
          x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
          x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
  
          # Window/Shifted-Window Spatial MLP
          x_windows_heads = x_windows.view(-1, self.window_size * self.window_size, self.num_heads, C // self.num_heads)
          x_windows_heads = x_windows_heads.transpose(1, 2)  # nW*B, nH, window_size*window_size, C//nH
          x_windows_heads = x_windows_heads.reshape(-1, self.num_heads * self.window_size * self.window_size,
                                                    C // self.num_heads)
          spatial_mlp_windows = self.spatial_mlp(x_windows_heads)  # nW*B, nH*window_size*window_size, C//nH
          spatial_mlp_windows = spatial_mlp_windows.view(-1, self.num_heads, self.window_size * self.window_size,
                                                         C // self.num_heads).transpose(1, 2)
          spatial_mlp_windows = spatial_mlp_windows.reshape(-1, self.window_size * self.window_size, C)
  
          # merge windows
          spatial_mlp_windows = spatial_mlp_windows.reshape(-1, self.window_size, self.window_size, C)
          shifted_x = window_reverse(spatial_mlp_windows, self.window_size, _H, _W)  # B H' W' C
  
          # reverse shift
          if self.shift_size > 0:
              P_l, P_r, P_t, P_b = self.padding
              x = shifted_x[:, P_t:-P_b, P_l:-P_r, :].contiguous()
          else:
              x = shifted_x
          x = x.view(B, H * W, C)
  
          # FFN
          x = shortcut + self.drop_path(x)
          x = x + self.drop_path(self.mlp(self.norm2(x)))
  
          return x
  
      def extra_repr(self) -> str:
          return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
                 f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
  
      def flops(self):
          flops = 0
          H, W = self.input_resolution
          # norm1
          flops += self.dim * H * W
  
          # Window/Shifted-Window Spatial MLP
          if self.shift_size > 0:
              nW = (H / self.window_size + 1) * (W / self.window_size + 1)
          else:
              nW = H * W / self.window_size / self.window_size
          flops += nW * self.dim * (self.window_size * self.window_size) * (self.window_size * self.window_size)
          # mlp
          flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
          # norm2
          flops += self.dim * H * W
          return flops
  
  
  class PatchMerging(nn.Module):
      r""" Patch Merging Layer.
      Args:
          input_resolution (tuple[int]): Resolution of input feature.
          dim (int): Number of input channels.
          norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
      """
  
      def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
          super().__init__()
          self.input_resolution = input_resolution
          self.dim = dim
          self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
          self.norm = norm_layer(4 * dim)
  
      def forward(self, x):
          """
          x: B, H*W, C
          """
          H, W = self.input_resolution
          B, L, C = x.shape
          assert L == H * W, "input feature has wrong size"
          assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
  
          x = x.view(B, H, W, C)
  
          x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
          x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
          x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
          x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
          x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
          x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
  
          x = self.norm(x)
          x = self.reduction(x)
  
          return x
  
      def extra_repr(self) -> str:
          return f"input_resolution={self.input_resolution}, dim={self.dim}"
  
      def flops(self):
          H, W = self.input_resolution
          flops = H * W * self.dim
          flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
          return flops
  
  
  class BasicLayer(nn.Module):
      """ A basic Swin MLP layer for one stage.
      Args:
          dim (int): Number of input channels.
          input_resolution (tuple[int]): Input resolution.
          depth (int): Number of blocks.
          num_heads (int): Number of attention heads.
          window_size (int): Local window size.
          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
          drop (float, optional): Dropout rate. Default: 0.0
          drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
          norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
          downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
      """
  
      def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                   mlp_ratio=4., drop=0., drop_path=0.,
                   norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
  
          super().__init__()
          self.dim = dim
          self.input_resolution = input_resolution
          self.depth = depth
          self.use_checkpoint = use_checkpoint
  
          # build blocks
          self.blocks = nn.ModuleList([
              SwinMLPBlock(dim=dim, input_resolution=input_resolution,
                           num_heads=num_heads, window_size=window_size,
                           shift_size=0 if (i % 2 == 0) else window_size // 2,
                           mlp_ratio=mlp_ratio,
                           drop=drop,
                           drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                           norm_layer=norm_layer)
              for i in range(depth)])
  
          # patch merging layer
          if downsample is not None:
              self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
          else:
              self.downsample = None
  
      def forward(self, x):
          for blk in self.blocks:
              if self.use_checkpoint:
                  x = checkpoint.checkpoint(blk, x)
              else:
                  x = blk(x)
          if self.downsample is not None:
              x = self.downsample(x)
          return x
  
      def extra_repr(self) -> str:
          return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
  
      def flops(self):
          flops = 0
          for blk in self.blocks:
              flops += blk.flops()
          if self.downsample is not None:
              flops += self.downsample.flops()
          return flops
  
  
  class PatchEmbed(nn.Module):
      r""" Image to Patch Embedding
      Args:
          img_size (int): Image size.  Default: 224.
          patch_size (int): Patch token size. Default: 4.
          in_chans (int): Number of input image channels. Default: 3.
          embed_dim (int): Number of linear projection output channels. Default: 96.
          norm_layer (nn.Module, optional): Normalization layer. Default: None
      """
  
      def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
          super().__init__()
          img_size = to_2tuple(img_size)
          patch_size = to_2tuple(patch_size)
          patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
          self.img_size = img_size
          self.patch_size = patch_size
          self.patches_resolution = patches_resolution
          self.num_patches = patches_resolution[0] * patches_resolution[1]
  
          self.in_chans = in_chans
          self.embed_dim = embed_dim
  
          self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
          if norm_layer is not None:
              self.norm = norm_layer(embed_dim)
          else:
              self.norm = None
  
      def forward(self, x):
          B, C, H, W = x.shape
          # FIXME look at relaxing size constraints
          assert H == self.img_size[0] and W == self.img_size[1], \
              f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
          x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
          if self.norm is not None:
              x = self.norm(x)
          return x
  
      def flops(self):
          Ho, Wo = self.patches_resolution
          flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
          if self.norm is not None:
              flops += Ho * Wo * self.embed_dim
          return flops
  
  
  class SwinMLP(nn.Module):
      r""" Swin MLP
      Args:
          img_size (int | tuple(int)): Input image size. Default 224
          patch_size (int | tuple(int)): Patch size. Default: 4
          in_chans (int): Number of input image channels. Default: 3
          num_classes (int): Number of classes for classification head. Default: 1000
          embed_dim (int): Patch embedding dimension. Default: 96
          depths (tuple(int)): Depth of each Swin MLP layer.
          num_heads (tuple(int)): Number of attention heads in different layers.
          window_size (int): Window size. Default: 7
          mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
          drop_rate (float): Dropout rate. Default: 0
          drop_path_rate (float): Stochastic depth rate. Default: 0.1
          norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
          ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
          patch_norm (bool): If True, add normalization after patch embedding. Default: True
          use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
      """
  
      def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                   embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24],
                   window_size=7, mlp_ratio=4., drop_rate=0., drop_path_rate=0.1,
                   norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                   use_checkpoint=False, **kwargs):
          super().__init__()
  
          self.num_classes = num_classes
          self.num_layers = len(depths)
          self.embed_dim = embed_dim
          self.ape = ape
          self.patch_norm = patch_norm
          self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
          self.mlp_ratio = mlp_ratio
  
          # split image into non-overlapping patches
          self.patch_embed = PatchEmbed(
              img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
              norm_layer=norm_layer if self.patch_norm else None)
          num_patches = self.patch_embed.num_patches
          patches_resolution = self.patch_embed.patches_resolution
          self.patches_resolution = patches_resolution
  
          # absolute position embedding
          if self.ape:
              self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
              trunc_normal_(self.absolute_pos_embed, std=.02)
  
          self.pos_drop = nn.Dropout(p=drop_rate)
  
          # stochastic depth
          dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
  
          # build layers
          self.layers = nn.ModuleList()
          for i_layer in range(self.num_layers):
              layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer),
                                 input_resolution=(patches_resolution[0] // (2 ** i_layer),
                                                   patches_resolution[1] // (2 ** i_layer)),
                                 depth=depths[i_layer],
                                 num_heads=num_heads[i_layer],
                                 window_size=window_size,
                                 mlp_ratio=self.mlp_ratio,
                                 drop=drop_rate,
                                 drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
                                 norm_layer=norm_layer,
                                 downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                                 use_checkpoint=use_checkpoint)
              self.layers.append(layer)
  
          self.norm = norm_layer(self.num_features)
          self.avgpool = nn.AdaptiveAvgPool1d(1)
          self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
  
          self.apply(self._init_weights)
  
      def _init_weights(self, m):
          if isinstance(m, (nn.Linear, nn.Conv1d)):
              trunc_normal_(m.weight, std=.02)
              if m.bias is not None:
                  nn.init.constant_(m.bias, 0)
          elif isinstance(m, nn.LayerNorm):
              nn.init.constant_(m.bias, 0)
              nn.init.constant_(m.weight, 1.0)
  
      @torch.jit.ignore
      def no_weight_decay(self):
          return {'absolute_pos_embed'}
  
      @torch.jit.ignore
      def no_weight_decay_keywords(self):
          return {'relative_position_bias_table'}
  
      def forward_features(self, x):
          x = self.patch_embed(x)
          if self.ape:
              x = x + self.absolute_pos_embed
          x = self.pos_drop(x)
  
          for layer in self.layers:
              x = layer(x)
  
          x = self.norm(x)  # B L C
          x = self.avgpool(x.transpose(1, 2))  # B C 1
          x = torch.flatten(x, 1)
          return x
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.head(x)
          return x
  
      def flops(self):
          flops = 0
          flops += self.patch_embed.flops()
          for i, layer in enumerate(self.layers):
              flops += layer.flops()
          flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers)
          flops += self.num_features * self.num_classes
          return flops