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# Source code for mmseg.models.backbones.swin_transformer

    # --------------------------------------------------------
    # Swin Transformer
    # Copyright (c) 2021 Microsoft
    # Licensed under The MIT License [see LICENSE for details]
    # Written by Ze Liu, Yutong Lin, Yixuan Wei
    # --------------------------------------------------------

    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.utils.checkpoint as checkpoint
    import numpy as np
    from timm.models.layers import DropPath, to_2tuple, trunc_normal_

    from mmcv_custom import load_checkpoint
    from mmseg.utils import get_root_logger
    from ..builder import BACKBONES

    import pdb

    class Mlp(nn.Module):
        """ Multilayer perceptron."""

        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 WindowAttention(nn.Module):
        """ Window based multi-head self attention (W-MSA) module with relative position bias.
        It supports both of shifted and non-shifted window.

        Args:
            dim (int): Number of input channels.
            window_size (tuple[int]): The height and width of the window.
            num_heads (int): Number of attention heads.
            qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
            qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
            attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
            proj_drop (float, optional): Dropout ratio of output. Default: 0.0
        """

        def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):

            super().__init__()
            self.dim = dim
            self.window_size = window_size  # Wh, Ww
            self.num_heads = num_heads
            head_dim = dim // num_heads
            self.scale = qk_scale or head_dim ** -0.5

            # define a parameter table of relative position bias
            self.relative_position_bias_table = nn.Parameter(
                torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH

            # get pair-wise relative position index for each token inside the window
            coords_h = torch.arange(self.window_size[0])
            coords_w = torch.arange(self.window_size[1])
            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
            coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
            relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
            relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
            relative_coords[:, :, 1] += self.window_size[1] - 1
            relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
            relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
            self.register_buffer("relative_position_index", relative_position_index)

            self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
            self.attn_drop = nn.Dropout(attn_drop)
            self.proj = nn.Linear(dim, dim)
            self.proj_drop = nn.Dropout(proj_drop)

            trunc_normal_(self.relative_position_bias_table, std=.02)
            self.softmax = nn.Softmax(dim=-1)

        def forward(self, x, mask=None):
            """ Forward function.

            Args:
                x: input features with shape of (num_windows*B, N, C)
                mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
            """
            B_, N, C = x.shape
            qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
            q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

            q = q * self.scale
            attn = (q @ k.transpose(-2, -1))

            relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
                self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
            relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
            attn = attn + relative_position_bias.unsqueeze(0)

            if mask is not None:
                nW = mask.shape[0]
                attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
                attn = attn.view(-1, self.num_heads, N, N)
                attn = self.softmax(attn)
            else:
                attn = self.softmax(attn)

            attn = self.attn_drop(attn)

            x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
            x = self.proj(x)
            x = self.proj_drop(x)
            return x

    class SwinTransformerBlock(nn.Module):
        """ Swin Transformer Block.

        Args:
            dim (int): Number of input channels.
            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.
            qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
            qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
            drop (float, optional): Dropout rate. Default: 0.0
            attn_drop (float, optional): Attention 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, num_heads, window_size=7, shift_size=0,
                     mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                     act_layer=nn.GELU, norm_layer=nn.LayerNorm):
            super().__init__()
            self.dim = dim
            self.num_heads = num_heads
            self.window_size = window_size
            self.shift_size = shift_size
            self.mlp_ratio = mlp_ratio
            assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

            self.norm1 = norm_layer(dim)
            self.attn = WindowAttention(
                dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
                qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)

            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)

            self.H = None
            self.W = None

        def forward(self, x, mask_matrix):
            """ Forward function.

            Args:
                x: Input feature, tensor size (B, H*W, C).
                H, W: Spatial resolution of the input feature.
                mask_matrix: Attention mask for cyclic shift.
            """
            B, L, C = x.shape
            H, W = self.H, self.W
            assert L == H * W, "input feature has wrong size"

            shortcut = x
            x = self.norm1(x)
            x = x.view(B, H, W, C)

            # pad feature maps to multiples of window size
            pad_l = pad_t = 0
            pad_r = (self.window_size - W % self.window_size) % self.window_size
            pad_b = (self.window_size - H % self.window_size) % self.window_size
            x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
            _, Hp, Wp, _ = x.shape

            # cyclic shift
            if self.shift_size > 0:
                shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
                attn_mask = mask_matrix
            else:
                shifted_x = x
                attn_mask = None

            # 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

            # W-MSA/SW-MSA
            attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C

            # merge windows
            attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
            shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C

            # reverse cyclic shift
            if self.shift_size > 0:
                x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
            else:
                x = shifted_x

            if pad_r > 0 or pad_b > 0:
                x = x[:, :H, :W, :].contiguous()

            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

    class PatchMerging(nn.Module):
        """ Patch Merging Layer

        Args:
            dim (int): Number of input channels.
            norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
        """
        def __init__(self, dim, norm_layer=nn.LayerNorm):
            super().__init__()
            self.dim = dim
            self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
            self.norm = norm_layer(4 * dim)

        def forward(self, x, H, W):
            """ Forward function.

            Args:
                x: Input feature, tensor size (B, H*W, C).
                H, W: Spatial resolution of the input feature.
            """
            B, L, C = x.shape
            assert L == H * W, "input feature has wrong size"

            x = x.view(B, H, W, C)

            # padding
            pad_input = (H % 2 == 1) or (W % 2 == 1)
            if pad_input:
                x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))

            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

    class BasicLayer(nn.Module):
        """ A basic Swin Transformer layer for one stage.

        Args:
            dim (int): Number of feature channels
            depth (int): Depths of this stage.
            num_heads (int): Number of attention head.
            window_size (int): Local window size. Default: 7.
            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
            qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
            qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
            drop (float, optional): Dropout rate. Default: 0.0
            attn_drop (float, optional): Attention 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.
            This is a test
        """

        def __init__(self,
                     dim,
                     depth,
                     num_heads,
                     window_size=7,
                     mlp_ratio=4.,
                     qkv_bias=True,
                     qk_scale=None,
                     drop=0.,
                     attn_drop=0.,
                     drop_path=0.,
                     norm_layer=nn.LayerNorm,
                     downsample=None,
                     use_checkpoint=False):
            super().__init__()
            self.window_size = window_size
            self.shift_size = window_size // 2
            self.depth = depth
            self.use_checkpoint = use_checkpoint

            # build blocks
            self.blocks = nn.ModuleList([
                SwinTransformerBlock(
                    dim=dim,
                    num_heads=num_heads,
                    window_size=window_size,
                    shift_size=0 if (i % 2 == 0) else window_size // 2,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop,
                    attn_drop=attn_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(dim=dim, norm_layer=norm_layer)
            else:
                self.downsample = None

        def forward(self, x, H, W):
            """ Forward function.

            Args:
                x: Input feature, tensor size (B, H*W, C).
                H, W: Spatial resolution of the input feature.
            """

            # calculate attention mask for SW-MSA
            Hp = int(np.ceil(H / self.window_size)) * self.window_size
            Wp = int(np.ceil(W / self.window_size)) * self.window_size
            img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
            h_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            w_slices = (slice(0, -self.window_size),
                        slice(-self.window_size, -self.shift_size),
                        slice(-self.shift_size, None))
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1

            mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
            attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))

            for blk in self.blocks:
                blk.H, blk.W = H, W
                if self.use_checkpoint:
                    x = checkpoint.checkpoint(blk, x, attn_mask)
                else:
                    x = blk(x, attn_mask)
            if self.downsample is not None:
                x_down = self.downsample(x, H, W)
                Wh, Ww = (H + 1) // 2, (W + 1) // 2
                return x, H, W, x_down, Wh, Ww
            else:
                return x, H, W, x, H, W

    class PatchEmbed(nn.Module):
        """ Image to Patch Embedding

        Args:
            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, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
            super().__init__()
            patch_size = to_2tuple(patch_size)
            self.patch_size = patch_size

            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):
            """Forward function."""
            # padding
            _, _, H, W = x.size()
            if W % self.patch_size[1] != 0:
                x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
            if H % self.patch_size[0] != 0:
                x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))

            x = self.proj(x)  # B C Wh Ww
            if self.norm is not None:
                Wh, Ww = x.size(2), x.size(3)
                x = x.flatten(2).transpose(1, 2)
                x = self.norm(x)
                x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)

            return x

    @BACKBONES.register_module()
    class SwinTransformer(nn.Module):
        """ Swin Transformer backbone.
            A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
              https://arxiv.org/pdf/2103.14030

        Args:
            pretrain_img_size (int): Input image size for training the pretrained model,
                used in absolute postion embedding. Default 224.
            patch_size (int | tuple(int)): Patch size. Default: 4.
            in_chans (int): Number of input image channels. Default: 3.
            embed_dim (int): Number of linear projection output channels. Default: 96.
            depths (tuple[int]): Depths of each Swin Transformer stage.
            num_heads (tuple[int]): Number of attention head of each stage.
            window_size (int): Window size. Default: 7.
            mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
            qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
            qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
            drop_rate (float): Dropout rate.
            attn_drop_rate (float): Attention dropout rate. Default: 0.
            drop_path_rate (float): Stochastic depth rate. Default: 0.2.
            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.
            out_indices (Sequence[int]): Output from which stages.
            frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
                -1 means not freezing any parameters.
            use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
        """

        def __init__(self,
                     pretrain_img_size=224,
                     patch_size=4,
                     in_chans=3,
                     embed_dim=96,
                     depths=[2, 2, 6, 2],
                     num_heads=[3, 6, 12, 24],
                     window_size=7,
                     mlp_ratio=4.,
                     qkv_bias=True,
                     qk_scale=None,
                     drop_rate=0.,
                     attn_drop_rate=0.,
                     drop_path_rate=0.2,
                     norm_layer=nn.LayerNorm,
                     ape=False,
                     patch_norm=True,
                     out_indices=(0, 1, 2, 3),
                     frozen_stages=-1,
                     use_checkpoint=False):
            super().__init__()

            self.pretrain_img_size = pretrain_img_size
            self.num_layers = len(depths)
            self.embed_dim = embed_dim
            self.ape = ape
            self.patch_norm = patch_norm
            self.out_indices = out_indices
            self.frozen_stages = frozen_stages

            # split image into non-overlapping patches
            self.patch_embed = PatchEmbed(
                patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim,
                norm_layer=norm_layer if self.patch_norm else None)

            # absolute position embedding
            if self.ape:
                pretrain_img_size = to_2tuple(pretrain_img_size)
                patch_size = to_2tuple(patch_size)
                patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]

                self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
                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),
                    depth=depths[i_layer],
                    num_heads=num_heads[i_layer],
                    window_size=window_size,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    attn_drop=attn_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)

            num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
            self.num_features = num_features

            # add a norm layer for each output
            for i_layer in out_indices:
                layer = norm_layer(num_features[i_layer])
                layer_name = f'norm{i_layer}'
                self.add_module(layer_name, layer)

            self._freeze_stages()

        def _freeze_stages(self):
            if self.frozen_stages >= 0:
                self.patch_embed.eval()
                for param in self.patch_embed.parameters():
                    param.requires_grad = False

            if self.frozen_stages >= 1 and self.ape:
                self.absolute_pos_embed.requires_grad = False

            if self.frozen_stages >= 2:
                self.pos_drop.eval()
                for i in range(0, self.frozen_stages - 1):
                    m = self.layers[i]
                    m.eval()
                    for param in m.parameters():
                        param.requires_grad = False

        def init_weights(self, pretrained=None):
            """Initialize the weights in backbone.

            Args:
                pretrained (str, optional): Path to pre-trained weights.
                    Defaults to None.
            """

            def _init_weights(m):
                if isinstance(m, nn.Linear):
                    trunc_normal_(m.weight, std=.02)
                    if isinstance(m, nn.Linear) and 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)

            if isinstance(pretrained, str):
                self.apply(_init_weights)
                logger = get_root_logger()
                load_checkpoint(self, pretrained, strict=False, logger=logger)
            elif pretrained is None:
                self.apply(_init_weights)
            else:
                raise TypeError('pretrained must be a str or None')

        def forward(self, x):
            """Forward function."""
            x = self.patch_embed(x)

            Wh, Ww = x.size(2), x.size(3)
            if self.ape:
                # interpolate the position embedding to the corresponding size
                absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
                x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
            else:
                x = x.flatten(2).transpose(1, 2)
            x = self.pos_drop(x)

            outs = []
            for i in range(self.num_layers):
                layer = self.layers[i]
                x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)

                if i in self.out_indices:
                    norm_layer = getattr(self, f'norm{i}')
                    x_out = norm_layer(x_out)

                    out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
                    outs.append(out)

            return tuple(outs)

        def train(self, mode=True):
            """Convert the model into training mode while keep layers freezed."""
            super(SwinTransformer, self).train(mode)
            self._freeze_stages()