# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_activation_layer, build_norm_layer
from mmcv.cnn.bricks import DropPath
from mmcv.cnn.bricks.transformer import PatchEmbed
from mmengine.model import BaseModule, ModuleList
from mmengine.model.weight_init import trunc_normal_
from mmengine.utils import to_2tuple

from ..builder import BACKBONES
from ..utils import resize_pos_embed
from .base_backbone import BaseBackbone


def resize_decomposed_rel_pos(rel_pos, q_size, k_size):
    """Get relative positional embeddings according to the relative positions
    of query and key sizes.

    Args:
        q_size (int): size of query q.
        k_size (int): size of key k.
        rel_pos (Tensor): relative position embeddings (L, C).

    Returns:
        Extracted positional embeddings according to relative positions.
    """
    max_rel_dist = int(2 * max(q_size, k_size) - 1)
    # Interpolate rel pos if needed.
    if rel_pos.shape[0] != max_rel_dist:
        # Interpolate rel pos.
        resized = F.interpolate(
            # (L, C) -> (1, C, L)
            rel_pos.transpose(0, 1).unsqueeze(0),
            size=max_rel_dist,
            mode='linear',
        )
        # (1, C, L) -> (L, C)
        resized = resized.squeeze(0).transpose(0, 1)
    else:
        resized = rel_pos

    # Scale the coords with short length if shapes for q and k are different.
    q_h_ratio = max(k_size / q_size, 1.0)
    k_h_ratio = max(q_size / k_size, 1.0)
    q_coords = torch.arange(q_size)[:, None] * q_h_ratio
    k_coords = torch.arange(k_size)[None, :] * k_h_ratio
    relative_coords = (q_coords - k_coords) + (k_size - 1) * k_h_ratio

    return resized[relative_coords.long()]


def add_decomposed_rel_pos(attn,
                           q,
                           q_shape,
                           k_shape,
                           rel_pos_h,
                           rel_pos_w,
                           has_cls_token=False):
    """Spatial Relative Positional Embeddings."""
    sp_idx = 1 if has_cls_token else 0
    B, num_heads, _, C = q.shape
    q_h, q_w = q_shape
    k_h, k_w = k_shape

    Rh = resize_decomposed_rel_pos(rel_pos_h, q_h, k_h)
    Rw = resize_decomposed_rel_pos(rel_pos_w, q_w, k_w)

    r_q = q[:, :, sp_idx:].reshape(B, num_heads, q_h, q_w, C)
    rel_h = torch.einsum('byhwc,hkc->byhwk', r_q, Rh)
    rel_w = torch.einsum('byhwc,wkc->byhwk', r_q, Rw)
    rel_pos_embed = rel_h[:, :, :, :, :, None] + rel_w[:, :, :, :, None, :]

    attn_map = attn[:, :, sp_idx:, sp_idx:].view(B, -1, q_h, q_w, k_h, k_w)
    attn_map += rel_pos_embed
    attn[:, :, sp_idx:, sp_idx:] = attn_map.view(B, -1, q_h * q_w, k_h * k_w)

    return attn


class MLP(BaseModule):
    """Two-layer multilayer perceptron.

    Comparing with :class:`mmcv.cnn.bricks.transformer.FFN`, this class allows
    different input and output channel numbers.

    Args:
        in_channels (int): The number of input channels.
        hidden_channels (int, optional): The number of hidden layer channels.
            If None, same as the ``in_channels``. Defaults to None.
        out_channels (int, optional): The number of output channels. If None,
            same as the ``in_channels``. Defaults to None.
        act_cfg (dict): The config of activation function.
            Defaults to ``dict(type='GELU')``.
        init_cfg (dict, optional): The config of weight initialization.
            Defaults to None.
    """

    def __init__(self,
                 in_channels,
                 hidden_channels=None,
                 out_channels=None,
                 act_cfg=dict(type='GELU'),
                 init_cfg=None):
        super().__init__(init_cfg=init_cfg)
        out_channels = out_channels or in_channels
        hidden_channels = hidden_channels or in_channels
        self.fc1 = nn.Linear(in_channels, hidden_channels)
        self.act = build_activation_layer(act_cfg)
        self.fc2 = nn.Linear(hidden_channels, out_channels)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.fc2(x)
        return x


def attention_pool(x: torch.Tensor,
                   pool: nn.Module,
                   in_size: tuple,
                   norm: Optional[nn.Module] = None):
    """Pooling the feature tokens.

    Args:
        x (torch.Tensor): The input tensor, should be with shape
            ``(B, num_heads, L, C)`` or ``(B, L, C)``.
        pool (nn.Module): The pooling module.
        in_size (Tuple[int]): The shape of the input feature map.
        norm (nn.Module, optional): The normalization module.
            Defaults to None.
    """
    ndim = x.ndim
    if ndim == 4:
        B, num_heads, L, C = x.shape
    elif ndim == 3:
        num_heads = 1
        B, L, C = x.shape
    else:
        raise RuntimeError(f'Unsupported input dimension {x.shape}')

    H, W = in_size
    assert L == H * W

    # (B, num_heads, H*W, C) -> (B*num_heads, C, H, W)
    x = x.reshape(B * num_heads, H, W, C).permute(0, 3, 1, 2).contiguous()
    x = pool(x)
    out_size = x.shape[-2:]

    # (B*num_heads, C, H', W') -> (B, num_heads, H'*W', C)
    x = x.reshape(B, num_heads, C, -1).transpose(2, 3)

    if norm is not None:
        x = norm(x)

    if ndim == 3:
        x = x.squeeze(1)

    return x, out_size


class MultiScaleAttention(BaseModule):
    """Multiscale Multi-head Attention block.

    Args:
        in_dims (int): Number of input channels.
        out_dims (int): Number of output channels.
        num_heads (int): Number of attention heads.
        qkv_bias (bool): If True, add a learnable bias to query, key and
            value. Defaults to True.
        norm_cfg (dict): The config of normalization layers.
            Defaults to ``dict(type='LN')``.
        pool_kernel (tuple): kernel size for qkv pooling layers.
            Defaults to (3, 3).
        stride_q (int): stride size for q pooling layer. Defaults to 1.
        stride_kv (int): stride size for kv pooling layer. Defaults to 1.
        rel_pos_spatial (bool): Whether to enable the spatial relative
            position embedding. Defaults to True.
        residual_pooling (bool): Whether to enable the residual connection
            after attention pooling. Defaults to True.
        input_size (Tuple[int], optional): The input resolution, necessary
            if enable the ``rel_pos_spatial``. Defaults to None.
        rel_pos_zero_init (bool): If True, zero initialize relative
            positional parameters. Defaults to False.
        init_cfg (dict, optional): The config of weight initialization.
            Defaults to None.
    """

    def __init__(self,
                 in_dims,
                 out_dims,
                 num_heads,
                 qkv_bias=True,
                 norm_cfg=dict(type='LN'),
                 pool_kernel=(3, 3),
                 stride_q=1,
                 stride_kv=1,
                 rel_pos_spatial=False,
                 residual_pooling=True,
                 input_size=None,
                 rel_pos_zero_init=False,
                 init_cfg=None):
        super().__init__(init_cfg=init_cfg)
        self.num_heads = num_heads
        self.in_dims = in_dims
        self.out_dims = out_dims

        head_dim = out_dims // num_heads
        self.scale = head_dim**-0.5

        self.qkv = nn.Linear(in_dims, out_dims * 3, bias=qkv_bias)
        self.proj = nn.Linear(out_dims, out_dims)

        # qkv pooling
        pool_padding = [k // 2 for k in pool_kernel]
        pool_dims = out_dims // num_heads

        def build_pooling(stride):
            pool = nn.Conv2d(
                pool_dims,
                pool_dims,
                pool_kernel,
                stride=stride,
                padding=pool_padding,
                groups=pool_dims,
                bias=False,
            )
            norm = build_norm_layer(norm_cfg, pool_dims)[1]
            return pool, norm

        self.pool_q, self.norm_q = build_pooling(stride_q)
        self.pool_k, self.norm_k = build_pooling(stride_kv)
        self.pool_v, self.norm_v = build_pooling(stride_kv)

        self.residual_pooling = residual_pooling

        self.rel_pos_spatial = rel_pos_spatial
        self.rel_pos_zero_init = rel_pos_zero_init
        if self.rel_pos_spatial:
            # initialize relative positional embeddings
            assert input_size[0] == input_size[1]

            size = input_size[0]
            rel_dim = 2 * max(size // stride_q, size // stride_kv) - 1
            self.rel_pos_h = nn.Parameter(torch.zeros(rel_dim, head_dim))
            self.rel_pos_w = nn.Parameter(torch.zeros(rel_dim, head_dim))

    def init_weights(self):
        """Weight initialization."""
        super().init_weights()

        if (isinstance(self.init_cfg, dict)
                and self.init_cfg['type'] == 'Pretrained'):
            # Suppress rel_pos_zero_init if use pretrained model.
            return

        if not self.rel_pos_zero_init:
            trunc_normal_(self.rel_pos_h, std=0.02)
            trunc_normal_(self.rel_pos_w, std=0.02)

    def forward(self, x, in_size):
        """Forward the MultiScaleAttention."""
        B, N, _ = x.shape  # (B, H*W, C)

        # qkv: (B, H*W, 3, num_heads, C)
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, -1)
        # q, k, v: (B, num_heads, H*W, C)
        q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(0)

        q, q_shape = attention_pool(q, self.pool_q, in_size, norm=self.norm_q)
        k, k_shape = attention_pool(k, self.pool_k, in_size, norm=self.norm_k)
        v, v_shape = attention_pool(v, self.pool_v, in_size, norm=self.norm_v)

        attn = (q * self.scale) @ k.transpose(-2, -1)
        if self.rel_pos_spatial:
            attn = add_decomposed_rel_pos(attn, q, q_shape, k_shape,
                                          self.rel_pos_h, self.rel_pos_w)

        attn = attn.softmax(dim=-1)
        x = attn @ v

        if self.residual_pooling:
            x = x + q

        # (B, num_heads, H'*W', C'//num_heads) -> (B, H'*W', C')
        x = x.transpose(1, 2).reshape(B, -1, self.out_dims)
        x = self.proj(x)

        return x, q_shape


class MultiScaleBlock(BaseModule):
    """Multiscale Transformer blocks.

    Args:
        in_dims (int): Number of input channels.
        out_dims (int): Number of output channels.
        num_heads (int): Number of attention heads.
        mlp_ratio (float): Ratio of hidden dimensions in MLP layers.
            Defaults to 4.0.
        qkv_bias (bool): If True, add a learnable bias to query, key and
            value. Defaults to True.
        drop_path (float): Stochastic depth rate. Defaults to 0.
        norm_cfg (dict): The config of normalization layers.
            Defaults to ``dict(type='LN')``.
        act_cfg (dict): The config of activation function.
            Defaults to ``dict(type='GELU')``.
        qkv_pool_kernel (tuple): kernel size for qkv pooling layers.
            Defaults to (3, 3).
        stride_q (int): stride size for q pooling layer. Defaults to 1.
        stride_kv (int): stride size for kv pooling layer. Defaults to 1.
        rel_pos_spatial (bool): Whether to enable the spatial relative
            position embedding. Defaults to True.
        residual_pooling (bool): Whether to enable the residual connection
            after attention pooling. Defaults to True.
        dim_mul_in_attention (bool): Whether to multiply the ``embed_dims`` in
            attention layers. If False, multiply it in MLP layers.
            Defaults to True.
        input_size (Tuple[int], optional): The input resolution, necessary
            if enable the ``rel_pos_spatial``. Defaults to None.
        rel_pos_zero_init (bool): If True, zero initialize relative
            positional parameters. Defaults to False.
        init_cfg (dict, optional): The config of weight initialization.
            Defaults to None.
    """

    def __init__(
        self,
        in_dims,
        out_dims,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=True,
        drop_path=0.0,
        norm_cfg=dict(type='LN'),
        act_cfg=dict(type='GELU'),
        qkv_pool_kernel=(3, 3),
        stride_q=1,
        stride_kv=1,
        rel_pos_spatial=True,
        residual_pooling=True,
        dim_mul_in_attention=True,
        input_size=None,
        rel_pos_zero_init=False,
        init_cfg=None,
    ):
        super().__init__(init_cfg=init_cfg)
        self.in_dims = in_dims
        self.out_dims = out_dims
        self.norm1 = build_norm_layer(norm_cfg, in_dims)[1]
        self.dim_mul_in_attention = dim_mul_in_attention

        attn_dims = out_dims if dim_mul_in_attention else in_dims
        self.attn = MultiScaleAttention(
            in_dims,
            attn_dims,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            norm_cfg=norm_cfg,
            pool_kernel=qkv_pool_kernel,
            stride_q=stride_q,
            stride_kv=stride_kv,
            rel_pos_spatial=rel_pos_spatial,
            residual_pooling=residual_pooling,
            input_size=input_size,
            rel_pos_zero_init=rel_pos_zero_init)
        self.drop_path = DropPath(
            drop_path) if drop_path > 0.0 else nn.Identity()

        self.norm2 = build_norm_layer(norm_cfg, attn_dims)[1]

        self.mlp = MLP(
            in_channels=attn_dims,
            hidden_channels=int(attn_dims * mlp_ratio),
            out_channels=out_dims,
            act_cfg=act_cfg)

        if in_dims != out_dims:
            self.proj = nn.Linear(in_dims, out_dims)
        else:
            self.proj = None

        if stride_q > 1:
            kernel_skip = stride_q + 1
            padding_skip = int(kernel_skip // 2)
            self.pool_skip = nn.MaxPool2d(
                kernel_skip, stride_q, padding_skip, ceil_mode=False)

            if input_size is not None:
                input_size = to_2tuple(input_size)
                out_size = [size // stride_q for size in input_size]
                self.init_out_size = out_size
            else:
                self.init_out_size = None
        else:
            self.pool_skip = None
            self.init_out_size = input_size

    def forward(self, x, in_size):
        x_norm = self.norm1(x)
        x_attn, out_size = self.attn(x_norm, in_size)

        if self.dim_mul_in_attention and self.proj is not None:
            skip = self.proj(x_norm)
        else:
            skip = x

        if self.pool_skip is not None:
            skip, _ = attention_pool(skip, self.pool_skip, in_size)

        x = skip + self.drop_path(x_attn)
        x_norm = self.norm2(x)
        x_mlp = self.mlp(x_norm)

        if not self.dim_mul_in_attention and self.proj is not None:
            skip = self.proj(x_norm)
        else:
            skip = x

        x = skip + self.drop_path(x_mlp)

        return x, out_size


@BACKBONES.register_module()
class MViT(BaseBackbone):
    """Multi-scale ViT v2.

    A PyTorch implement of : `MViTv2: Improved Multiscale Vision Transformers
    for Classification and Detection <https://arxiv.org/abs/2112.01526>`_

    Inspiration from `the official implementation
    <https://github.com/facebookresearch/mvit>`_ and `the detectron2
    implementation <https://github.com/facebookresearch/detectron2>`_

    Args:
        arch (str | dict): MViT architecture. If use string, choose
            from 'tiny', 'small', 'base' and 'large'. If use dict, it should
            have below keys:

            - **embed_dims** (int): The dimensions of embedding.
            - **num_layers** (int): The number of layers.
            - **num_heads** (int): The number of heads in attention
              modules of the initial layer.
            - **downscale_indices** (List[int]): The layer indices to downscale
              the feature map.

            Defaults to 'base'.
        img_size (int): The expected input image shape. Defaults to 224.
        in_channels (int): The num of input channels. Defaults to 3.
        out_scales (int | Sequence[int]): The output scale indices.
            They should not exceed the length of ``downscale_indices``.
            Defaults to -1, which means the last scale.
        drop_path_rate (float): Stochastic depth rate. Defaults to 0.1.
        use_abs_pos_embed (bool): If True, add absolute position embedding to
            the patch embedding. Defaults to False.
        interpolate_mode (str): Select the interpolate mode for absolute
            position embedding vector resize. Defaults to "bicubic".
        pool_kernel (tuple): kernel size for qkv pooling layers.
            Defaults to (3, 3).
        dim_mul (int): The magnification for ``embed_dims`` in the downscale
            layers. Defaults to 2.
        head_mul (int): The magnification for ``num_heads`` in the downscale
            layers. Defaults to 2.
        adaptive_kv_stride (int): The stride size for kv pooling in the initial
            layer. Defaults to 4.
        rel_pos_spatial (bool): Whether to enable the spatial relative position
            embedding. Defaults to True.
        residual_pooling (bool): Whether to enable the residual connection
            after attention pooling. Defaults to True.
        dim_mul_in_attention (bool): Whether to multiply the ``embed_dims`` in
            attention layers. If False, multiply it in MLP layers.
            Defaults to True.
        rel_pos_zero_init (bool): If True, zero initialize relative
            positional parameters. Defaults to False.
        mlp_ratio (float): Ratio of hidden dimensions in MLP layers.
            Defaults to 4.0.
        qkv_bias (bool): enable bias for qkv if True. Defaults to True.
        norm_cfg (dict): Config dict for normalization layer for all output
            features. Defaults to ``dict(type='LN', eps=1e-6)``.
        patch_cfg (dict): Config dict for the patch embedding layer.
            Defaults to ``dict(kernel_size=7, stride=4, padding=3)``.
        init_cfg (dict, optional): The Config for initialization.
            Defaults to None.

    Examples:
        >>> import torch
        >>> from mmpretrain.models import build_backbone
        >>>
        >>> cfg = dict(type='MViT', arch='tiny', out_scales=[0, 1, 2, 3])
        >>> model = build_backbone(cfg)
        >>> inputs = torch.rand(1, 3, 224, 224)
        >>> outputs = model(inputs)
        >>> for i, output in enumerate(outputs):
        >>>     print(f'scale{i}: {output.shape}')
        scale0: torch.Size([1, 96, 56, 56])
        scale1: torch.Size([1, 192, 28, 28])
        scale2: torch.Size([1, 384, 14, 14])
        scale3: torch.Size([1, 768, 7, 7])
    """
    arch_zoo = {
        'tiny': {
            'embed_dims': 96,
            'num_layers': 10,
            'num_heads': 1,
            'downscale_indices': [1, 3, 8]
        },
        'small': {
            'embed_dims': 96,
            'num_layers': 16,
            'num_heads': 1,
            'downscale_indices': [1, 3, 14]
        },
        'base': {
            'embed_dims': 96,
            'num_layers': 24,
            'num_heads': 1,
            'downscale_indices': [2, 5, 21]
        },
        'large': {
            'embed_dims': 144,
            'num_layers': 48,
            'num_heads': 2,
            'downscale_indices': [2, 8, 44]
        },
    }
    num_extra_tokens = 0

    def __init__(self,
                 arch='base',
                 img_size=224,
                 in_channels=3,
                 out_scales=-1,
                 drop_path_rate=0.,
                 use_abs_pos_embed=False,
                 interpolate_mode='bicubic',
                 pool_kernel=(3, 3),
                 dim_mul=2,
                 head_mul=2,
                 adaptive_kv_stride=4,
                 rel_pos_spatial=True,
                 residual_pooling=True,
                 dim_mul_in_attention=True,
                 rel_pos_zero_init=False,
                 mlp_ratio=4.,
                 qkv_bias=True,
                 norm_cfg=dict(type='LN', eps=1e-6),
                 patch_cfg=dict(kernel_size=7, stride=4, padding=3),
                 init_cfg=None):
        super().__init__(init_cfg)

        if isinstance(arch, str):
            arch = arch.lower()
            assert arch in set(self.arch_zoo), \
                f'Arch {arch} is not in default archs {set(self.arch_zoo)}'
            self.arch_settings = self.arch_zoo[arch]
        else:
            essential_keys = {
                'embed_dims', 'num_layers', 'num_heads', 'downscale_indices'
            }
            assert isinstance(arch, dict) and essential_keys <= set(arch), \
                f'Custom arch needs a dict with keys {essential_keys}'
            self.arch_settings = arch

        self.embed_dims = self.arch_settings['embed_dims']
        self.num_layers = self.arch_settings['num_layers']
        self.num_heads = self.arch_settings['num_heads']
        self.downscale_indices = self.arch_settings['downscale_indices']
        self.num_scales = len(self.downscale_indices) + 1
        self.stage_indices = {
            index - 1: i
            for i, index in enumerate(self.downscale_indices)
        }
        self.stage_indices[self.num_layers - 1] = self.num_scales - 1
        self.use_abs_pos_embed = use_abs_pos_embed
        self.interpolate_mode = interpolate_mode

        if isinstance(out_scales, int):
            out_scales = [out_scales]
        assert isinstance(out_scales, Sequence), \
            f'"out_scales" must by a sequence or int, ' \
            f'get {type(out_scales)} instead.'
        for i, index in enumerate(out_scales):
            if index < 0:
                out_scales[i] = self.num_scales + index
            assert 0 <= out_scales[i] <= self.num_scales, \
                f'Invalid out_scales {index}'
        self.out_scales = sorted(list(out_scales))

        # Set patch embedding
        _patch_cfg = dict(
            in_channels=in_channels,
            input_size=img_size,
            embed_dims=self.embed_dims,
            conv_type='Conv2d',
        )
        _patch_cfg.update(patch_cfg)
        self.patch_embed = PatchEmbed(**_patch_cfg)
        self.patch_resolution = self.patch_embed.init_out_size

        # Set absolute position embedding
        if self.use_abs_pos_embed:
            num_patches = self.patch_resolution[0] * self.patch_resolution[1]
            self.pos_embed = nn.Parameter(
                torch.zeros(1, num_patches, self.embed_dims))

        # stochastic depth decay rule
        dpr = np.linspace(0, drop_path_rate, self.num_layers)

        self.blocks = ModuleList()
        out_dims_list = [self.embed_dims]
        num_heads = self.num_heads
        stride_kv = adaptive_kv_stride
        input_size = self.patch_resolution
        for i in range(self.num_layers):
            if i in self.downscale_indices:
                num_heads *= head_mul
                stride_q = 2
                stride_kv = max(stride_kv // 2, 1)
            else:
                stride_q = 1

            # Set output embed_dims
            if dim_mul_in_attention and i in self.downscale_indices:
                # multiply embed_dims in downscale layers.
                out_dims = out_dims_list[-1] * dim_mul
            elif not dim_mul_in_attention and i + 1 in self.downscale_indices:
                # multiply embed_dims before downscale layers.
                out_dims = out_dims_list[-1] * dim_mul
            else:
                out_dims = out_dims_list[-1]

            attention_block = MultiScaleBlock(
                in_dims=out_dims_list[-1],
                out_dims=out_dims,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                drop_path=dpr[i],
                norm_cfg=norm_cfg,
                qkv_pool_kernel=pool_kernel,
                stride_q=stride_q,
                stride_kv=stride_kv,
                rel_pos_spatial=rel_pos_spatial,
                residual_pooling=residual_pooling,
                dim_mul_in_attention=dim_mul_in_attention,
                input_size=input_size,
                rel_pos_zero_init=rel_pos_zero_init)
            self.blocks.append(attention_block)

            input_size = attention_block.init_out_size
            out_dims_list.append(out_dims)

            if i in self.stage_indices:
                stage_index = self.stage_indices[i]
                if stage_index in self.out_scales:
                    norm_layer = build_norm_layer(norm_cfg, out_dims)[1]
                    self.add_module(f'norm{stage_index}', norm_layer)

    def init_weights(self):
        super().init_weights()

        if (isinstance(self.init_cfg, dict)
                and self.init_cfg['type'] == 'Pretrained'):
            # Suppress default init if use pretrained model.
            return

        if self.use_abs_pos_embed:
            trunc_normal_(self.pos_embed, std=0.02)

    def forward(self, x):
        """Forward the MViT."""
        B = x.shape[0]
        x, patch_resolution = self.patch_embed(x)

        if self.use_abs_pos_embed:
            x = x + resize_pos_embed(
                self.pos_embed,
                self.patch_resolution,
                patch_resolution,
                mode=self.interpolate_mode,
                num_extra_tokens=self.num_extra_tokens)

        outs = []
        for i, block in enumerate(self.blocks):
            x, patch_resolution = block(x, patch_resolution)

            if i in self.stage_indices:
                stage_index = self.stage_indices[i]
                if stage_index in self.out_scales:
                    B, _, C = x.shape
                    x = getattr(self, f'norm{stage_index}')(x)
                    out = x.transpose(1, 2).reshape(B, C, *patch_resolution)
                    outs.append(out.contiguous())

        return tuple(outs)
