#  --------------------------------------------------------
# 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 numpy as np
from timm.models.layers import DropPath, to_3tuple, trunc_normal_

from typing import Sequence, Tuple, Optional, Type, Union, List
from torch import Tensor

from ..builder import BACKBONE

class Mlp(nn.Module):
    """ Multilayer perceptron."""
    def __init__(
            self,
            in_features: int,
            hidden_features: Optional[int] = None,
            out_features: Optional[int] = None,
            act_layer: Type[nn.Module] = nn.GELU,
            drop: float = 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: Tensor, window_size: int) -> Tensor:
    """
    Args:
        x: (B, D, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, window_size, C)
    """
    B, D, H, W, C = x.shape
    x = x.view(B, 
               D // window_size, window_size,
               H // window_size, window_size,
               W // window_size, window_size,
               C)
    windows = x.permute(0, 1, 3, 5, 2, 4, 6, 7).contiguous().view(-1, window_size, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, D, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, window_size, C)
        window_size (int): Window size
        D (int): Depth of image
        H (int): Height of image
        W (int): Width of image

    Returns:
        x: (B, D, H, W, C)
    """
    B = int(windows.shape[0] / (D * H * W / window_size / window_size / window_size))
    x = windows.view(
            B,
            D // window_size, H // window_size, W // window_size,
            window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).contiguous().view(B, D, 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: int,
            window_size: Tuple[int, int, int],
            num_heads: int,
            qkv_bias: bool = True,
            qk_scale: Optional[float] = None,
            attn_drop: float = 0.,
            proj_drop: float = 0.
        ):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wd, 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) * 
                (2 * window_size[2] - 1) 
                , num_heads))  # 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_d = torch.arange(self.window_size[0])
        coords_h = torch.arange(self.window_size[1])
        coords_w = torch.arange(self.window_size[2])
        coords = torch.stack(torch.meshgrid([coords_d, coords_h, coords_w], indexing='ij'))     # 3, Wd, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)                                               # 3, Wd*Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]               # 3, Wd*Wh*Ww, Wd*Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()                         # Wd*Wh*Ww, Wd*Wh*Ww, 3
        relative_coords[:, :, 0] += self.window_size[0] - 1
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 2] += self.window_size[2] - 1
        relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1)
        relative_coords[:, :, 1] *= 2 * self.window_size[2] - 1
        relative_position_index = relative_coords.sum(-1)                           # Wd*Wh*Ww, Wd*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, Wd*Wh*Ww, Wd*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))

        num_windows = self.window_size[0] * self.window_size[1] * self.window_size[2]   # Wd*Wh*Ww
        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            num_windows, num_windows, -1)   # Wd*Wh*Ww, Wd*Wh*Ww, nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()                   # nH, Wd*Wh*Ww, Wd*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: int,
            num_heads: int,
            window_size: int = 7,
            shift_size: int = 0,
            mlp_ratio: float = 4.,
            qkv_bias: bool = True,
            qk_scale: Optional[float] = None,
            drop: float = 0.,
            attn_drop: float = 0.,
            drop_path: float = 0.,
            act_layer: Type[nn.Module] = nn.GELU,
            norm_layer: Type[nn.Module] = 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_3tuple(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)

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

        Args:
            x: Input feature, tensor size (B, D, H, W, C).
            D, H, W: Spatial resolution of the input feature.
            mask_matrix: Attention mask for cyclic shift.

        Return:
            x: Output feature, tensor size (B, D, H, W, C).
        """
        B, D, H, W, C = x.shape
        shortcut = x
        x = self.norm1(x)

        # pad feature maps to multiples of window size
        pad_w0 = pad_h0 = pad_d0 = 0
        pad_w1 = (self.window_size - H % self.window_size) % self.window_size
        pad_h1 = (self.window_size - W % self.window_size) % self.window_size
        pad_d1 = (self.window_size - D % self.window_size) % self.window_size
        x = F.pad(x, (0, 0, pad_w0, pad_w1, pad_h0, pad_h1, pad_d0, pad_d1))
        _, Dp, Hp, Wp, _ = x.shape

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size, -self.shift_size), dims=(1, 2, 3))
            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, window_size, C (nW: num_windows)
        x_windows = x_windows.view(-1, self.window_size * self.window_size * self.window_size, C)
                                                                                # nW*B, window_size*window_size*window_size, C

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

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

        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size, self.shift_size), dims=(1, 2, 3))
        else:
            x = shifted_x

        if pad_d1 > 0 or pad_h1 > 0 or pad_w1 > 0:
            x = x[:, :D, :H, :W, :].contiguous()

        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x    # B, D, H, W, C


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(8 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(8 * dim)

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

        Args:
            x: Input feature, tensor size (B, D, H, W, C).

        Return:
            x: Input feature, tensor size (B, D/2, H/2, W/2, 2*C).
        """
        B, D, H, W, C = x.shape

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

        x0 = x[:, 0::2, 0::2, 0::2, :]  # B, D/2, H/2, W/2, C
        x1 = x[:, 1::2, 0::2, 0::2, :]  # B, D/2, H/2, W/2, C
        x2 = x[:, 0::2, 1::2, 0::2, :]  # B, D/2, H/2, W/2, C
        x3 = x[:, 1::2, 1::2, 0::2, :]  # B, D/2, H/2, W/2, C
        x4 = x[:, 0::2, 0::2, 1::2, :]  # B, D/2, H/2, W/2, C
        x5 = x[:, 1::2, 0::2, 1::2, :]  # B, D/2, H/2, W/2, C
        x6 = x[:, 0::2, 1::2, 1::2, :]  # B, D/2, H/2, W/2, C
        x7 = x[:, 1::2, 1::2, 1::2, :]  # B, D/2, H/2, W/2, C
        x = torch.cat([x0, x1, x2, x3, x4, x5, x6, x7], -1)  # B, D/2, H/2, W/2, 8*C

        x = self.norm(x)
        x = self.reduction(x)           # B, D/2, H/2, W/2, 2*C

        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
    """

    def __init__(
            self,
            dim: int,
            depth: int,
            num_heads: int,
            window_size: int = 7,
            mlp_ratio: float = 4.,
            qkv_bias: bool = True,
            qk_scale: Optional[float] = None,
            drop: float = 0.,
            attn_drop: float = 0.,
            drop_path: Union[float, Sequence[float]] = 0.,
            norm_layer: Type[nn.Module] = nn.LayerNorm,
            downsample: Optional[Type[nn.Module]] = None,
        ):
        super().__init__()
        self.window_size = window_size
        self.shift_size = window_size // 2
        self.depth = depth

        # 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 if isinstance(drop_path, float) else drop_path[i],
                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: Tensor) -> Tuple[Tensor, int, int, int, Tensor, int, int, int]:
        """ Forward function.

        Args:
            x: Input feature, tensor size (B, D, H, W, C).
        """
        B, D, H, W, C = x.shape

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

        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, window_size, 1
        mask_windows = mask_windows.view(-1, self.window_size * 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:
            x = blk(x, attn_mask)
        if self.downsample is not None:
            x_down = self.downsample(x)
            Wd, Wh, Ww = (D + 1) // 2, (H + 1) // 2, (W + 1) // 2
            return x, D, H, W, x_down, Wd, Wh, Ww
        else:
            return x, D, H, W, x, D, 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,
            in_chans: int = 3,
            patch_size: Union[int, Tuple[int, int, int]] = 4,
            embed_dim: int = 96,
            norm_layer: Optional[Type[nn.Module]] = None):
        super().__init__()
        self.patch_size: Tuple[int, int, int] = to_3tuple(patch_size)

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size,
                              bias = (norm_layer is None))
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

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

        Args:
            x: Input feature, tensor size (B, C, D, H, W).
        Return:
            tensor size (B, D, H, W, C').
        """
        # padding
        B, C, D, H, W = x.size()
        if W % self.patch_size[2] != 0:
            x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2]))
        if H % self.patch_size[1] != 0:
            x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1]))
        if D % self.patch_size[0] != 0:
            x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - D % self.patch_size[0]))

        x = self.proj(x)                        # B, C, Wd, Wh, Ww
        if self.norm is not None:
            _, _, Wd, Wh, Ww = x.shape
            x = x.flatten(2).transpose(1, 2)    # B, Wd*Wh*Ww, C'
            x = self.norm(x)
            x = x.transpose(1, 2).view(B, self.embed_dim, Wd, Wh, Ww)  # B, C', Wd, Wh, Ww

        return x


@BACKBONE.register()
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:
        in_channels (int): Number of input image channels.
        image_size (int): Input image size,
            used in absolute postion embedding. Default 48.
        patch_size (int | tuple(int)): Patch size. Default: 2.
        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.
    """

    def __init__(
            self,
            in_channels: int,
            image_size: int = 48,
            patch_size: Union[int, Tuple[int, int, int]] = 2,
            embed_dim: int = 64,
            depths: Sequence[int] = [2, 2, 6, 2],
            num_heads: Sequence[int] = [3, 6, 12, 12],
            window_size: int = 6,
            mlp_ratio: float = 4.,
            qkv_bias: bool = True,
            qk_scale: Optional[float] = None,
            drop_rate: float = 0.,
            attn_drop_rate: float = 0.,
            drop_path_rate: float = 0.2,
            norm_layer: Type[nn.Module] = nn.LayerNorm,
            ape: bool = False,
            patch_norm: bool = True,
            out_indices: Union[int, Sequence[int]] = [0, 1, 2, 3],
        ):
        super().__init__()

        self.image_size = image_size
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        out_indices = out_indices if not isinstance(out_indices, int) else [out_indices]
        self.out_indices = [(index if index >= 0 else self.num_layers + index) for index in out_indices]

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

        # absolute position embedding
        if self.ape:
            patches_resolution = [imgsz // patchsz for imgsz, patchsz in zip(to_3tuple(image_size), to_3tuple(patch_size))]
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, *patches_resolution))  # (1, C, D, H, W)
            trunc_normal_(self.absolute_pos_embed, std=.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic dept
        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)
            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)

    def initialize_weights(self):
        """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, nn.Conv3d)):
                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.BatchNorm3d, nn.GroupNorm, nn.LayerNorm)):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)
        self.apply(_init_weights)

    def forward(self, x: Tensor) -> Sequence[Tensor]:
        """Forward function."""
        x = self.patch_embed(x)             # B, C, D, H, W -> B, C', Wd, Wh, Ww

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

        outs = []
        for i in range(self.num_layers):
            layer = self.layers[i]
            x_out, _, _, _, x, _, _, _ = layer(x)             # B, Wd, Wh, Ww, C' | B, Wd', Wh', Ww', C''
            if i in self.out_indices:
                norm_layer = getattr(self, f'norm{i}')
                x_out = norm_layer(x_out)
                x_out = x_out.permute(0, 4, 1, 2, 3).contiguous()     # B, C', Wd, Wh, Ww
                outs.append(x_out)

        return outs
