
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
import torch.utils.checkpoint as checkpoint
from torch.nn.utils import weight_norm
from torch import Tensor, Size
from typing import Union, List
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
import numpy as np
import logging

# from mmcv.utils import get_logger

from common.utils.ckpt_load import load_checkpoint_swin


def get_root_logger(log_file=None, log_level=logging.INFO):
    """Use `get_logger` method in mmcv to get the root logger.

    The logger will be initialized if it has not been initialized. By default a
    StreamHandler will be added. If `log_file` is specified, a FileHandler will
    also be added. The name of the root logger is the top-level package name,
    e.g., "mmpose".

    Args:
        log_file (str | None): The log filename. If specified, a FileHandler
            will be added to the root logger.
        log_level (int): The root logger level. Note that only the process of
            rank 0 is affected, while other processes will set the level to
            "Error" and be silent most of the time.

    Returns:
        logging.Logger: The root logger.
    """
    return get_logger(__name__.split('.')[0], log_file, log_level)


_shape_t = Union[int, List[int], Size]


class LayerNorm2D(nn.Module):

    def __init__(self, normalized_shape, norm_layer=None):
        super().__init__()
        self.ln = norm_layer(
            normalized_shape) if norm_layer is not None else nn.Identity()

    def forward(self, x):
        """
        x: N C H W
        """
        x = x.permute(0, 2, 3, 1)
        x = self.ln(x)
        x = x.permute(0, 3, 1, 2)
        return x


class LayerNormFP32(nn.LayerNorm):

    def __init__(self,
                 normalized_shape: _shape_t,
                 eps: float = 1e-5,
                 elementwise_affine: bool = True) -> None:
        super(LayerNormFP32, self).__init__(normalized_shape, eps,
                                            elementwise_affine)

    def forward(self, input: Tensor) -> Tensor:
        return F.layer_norm(input.float(), self.normalized_shape,
                            self.weight.float(), self.bias.float(),
                            self.eps).type_as(input)


class LinearFP32(nn.Linear):

    def __init__(self, in_features, out_features, bias=True):
        super(LinearFP32, self).__init__(in_features, out_features, bias)

    def forward(self, input: Tensor) -> Tensor:
        return F.linear(input.float(), self.weight.float(),
                        self.bias.float() if self.bias is not None else None)


class Mlp(nn.Module):

    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.,
                 norm_layer=None,
                 mlpfp32=False):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.mlpfp32 = mlpfp32

        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)
        if norm_layer is not None:
            self.norm = norm_layer(hidden_features)
        else:
            self.norm = None

    def forward(self, x, H, W):
        x = self.fc1(x)
        if self.norm:
            x = self.norm(x)
        x = self.act(x)
        x = self.drop(x)
        if self.mlpfp32:
            x = self.fc2.float()(x.type(torch.float32))
            x = self.drop.float()(x)
            # print(f"======>[MLP FP32]")
        else:
            x = self.fc2(x)
            x = self.drop(x)
        return x


class ConvMlp(nn.Module):

    def __init__(self,
                 in_features,
                 hidden_features=None,
                 out_features=None,
                 act_layer=nn.GELU,
                 drop=0.,
                 norm_layer=None,
                 mlpfp32=False,
                 proj_ln=False):
        super().__init__()
        self.mlp = Mlp(in_features=in_features,
                       hidden_features=hidden_features,
                       out_features=out_features,
                       act_layer=act_layer,
                       drop=drop,
                       norm_layer=norm_layer,
                       mlpfp32=mlpfp32)
        self.conv_proj = nn.Conv2d(in_features,
                                   in_features,
                                   kernel_size=3,
                                   padding=1,
                                   stride=1,
                                   bias=False,
                                   groups=in_features)
        self.proj_ln = LayerNorm2D(in_features,
                                   LayerNormFP32) if proj_ln else None

    def forward(self, x, H, W):
        B, L, C = x.shape
        assert L == H * W
        x = x.view(B, H, W, C).permute(0, 3, 1, 2)  # B C H W
        x = self.conv_proj(x)
        if self.proj_ln:
            x = self.proj_ln(x)
        x = x.permute(0, 2, 3, 1)  # B H W C
        x = x.reshape(B, L, C)
        x = self.mlp(x, H, W)
        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):
    r""" 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.,
                 relative_coords_table_type='norm8_log',
                 rpe_hidden_dim=512,
                 rpe_output_type='normal',
                 attn_type='normal',
                 mlpfp32=False,
                 pretrain_window_size=-1):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        self.mlpfp32 = mlpfp32
        self.attn_type = attn_type
        self.rpe_output_type = rpe_output_type
        self.relative_coords_table_type = relative_coords_table_type

        if self.attn_type == 'cosine_mh':
            self.logit_scale = nn.Parameter(torch.log(10 * torch.ones(
                (num_heads, 1, 1))),
                                            requires_grad=True)
        elif self.attn_type == 'normal':
            head_dim = dim // num_heads
            self.scale = qk_scale or head_dim**-0.5
        else:
            raise NotImplementedError()
        if self.relative_coords_table_type != "none":
            # mlp to generate table of relative position bias
            self.rpe_mlp = nn.Sequential(
                nn.Linear(2, rpe_hidden_dim, bias=True), nn.ReLU(inplace=True),
                LinearFP32(rpe_hidden_dim, num_heads, bias=False))

            # get relative_coords_table
            relative_coords_h = torch.arange(-(self.window_size[0] - 1),
                                             self.window_size[0],
                                             dtype=torch.float32)
            relative_coords_w = torch.arange(-(self.window_size[1] - 1),
                                             self.window_size[1],
                                             dtype=torch.float32)
            relative_coords_table = torch.stack(
                torch.meshgrid([relative_coords_h, relative_coords_w
                                ])).permute(1, 2, 0).contiguous().unsqueeze(
                                    0)  # 1, 2*Wh-1, 2*Ww-1, 2
            if relative_coords_table_type == 'linear':
                relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
                relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
            elif relative_coords_table_type == 'linear_bylayer':
                print(
                    f"norm8_log_bylayer: [{self.window_size}] ==> [{pretrain_window_size}]"
                )
                relative_coords_table[:, :, :, 0] /= (pretrain_window_size - 1)
                relative_coords_table[:, :, :, 1] /= (pretrain_window_size - 1)
            elif relative_coords_table_type == 'norm8_log':
                relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
                relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
                relative_coords_table *= 8  # normalize to -8, 8
                relative_coords_table = torch.sign(
                    relative_coords_table) * torch.log2(
                        torch.abs(relative_coords_table) + 1.0) / np.log2(
                            8)  # log8
            elif relative_coords_table_type == 'norm8_log_192to640':
                if self.window_size[0] == 40:
                    relative_coords_table[:, :, :, 0] /= (11)
                    relative_coords_table[:, :, :, 1] /= (11)
                elif self.window_size[0] == 20:
                    relative_coords_table[:, :, :, 0] /= (5)
                    relative_coords_table[:, :, :, 1] /= (5)
                else:
                    raise NotImplementedError
                relative_coords_table *= 8  # normalize to -8, 8
                relative_coords_table = torch.sign(
                    relative_coords_table) * torch.log2(
                        torch.abs(relative_coords_table) + 1.0) / np.log2(
                            8)  # log8
            # check
            elif relative_coords_table_type == 'norm8_log_256to640':
                if self.window_size[0] == 40:
                    relative_coords_table[:, :, :, 0] /= (15)
                    relative_coords_table[:, :, :, 1] /= (15)
                elif self.window_size[0] == 20:
                    relative_coords_table[:, :, :, 0] /= (7)
                    relative_coords_table[:, :, :, 1] /= (7)
                else:
                    raise NotImplementedError
                relative_coords_table *= 8  # normalize to -8, 8
                relative_coords_table = torch.sign(
                    relative_coords_table) * torch.log2(
                        torch.abs(relative_coords_table) + 1.0) / np.log2(
                            8)  # log8
            elif relative_coords_table_type == 'norm8_log_bylayer':
                print(
                    f"norm8_log_bylayer: [{self.window_size}] ==> [{pretrain_window_size}]"
                )
                relative_coords_table[:, :, :, 0] /= (pretrain_window_size - 1)
                relative_coords_table[:, :, :, 1] /= (pretrain_window_size - 1)
                relative_coords_table *= 8  # normalize to -8, 8
                relative_coords_table = torch.sign(
                    relative_coords_table) * torch.log2(
                        torch.abs(relative_coords_table) + 1.0) / np.log2(
                            8)  # log8
            else:
                raise NotImplementedError
            self.register_buffer("relative_coords_table",
                                 relative_coords_table)
        else:
            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
            trunc_normal_(self.relative_position_bias_table, std=.02)

        # 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=False)
        if qkv_bias:
            self.q_bias = nn.Parameter(torch.zeros(dim))
            self.v_bias = nn.Parameter(torch.zeros(dim))
        else:
            self.q_bias = None
            self.v_bias = None

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x, mask=None):
        """
        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_bias = None
        if self.q_bias is not None:
            qkv_bias = torch.cat(
                (self.q_bias, torch.zeros_like(self.v_bias,
                                               requires_grad=False),
                 self.v_bias))
        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        # 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)

        if self.attn_type == 'cosine_mh':
            q = F.normalize(q.float(), dim=-1)
            k = F.normalize(k.float(), dim=-1)
            logit_scale = torch.clamp(
                self.logit_scale,
                max=torch.log(
                    torch.tensor(1. / 0.01,
                                 device=self.logit_scale.device))).exp()
            attn = (q @ k.transpose(-2, -1)) * logit_scale.float()
        elif self.attn_type == 'normal':
            q = q * self.scale
            attn = (q.float() @ k.float().transpose(-2, -1))
        else:
            raise NotImplementedError()

        if self.relative_coords_table_type != "none":
            # relative_position_bias_table: 2*Wh-1 * 2*Ww-1, nH
            relative_position_bias_table = self.rpe_mlp(
                self.relative_coords_table).view(-1, self.num_heads)
        else:
            relative_position_bias_table = self.relative_position_bias_table
        relative_position_bias = 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
        if self.rpe_output_type == 'normal':
            pass
        elif self.rpe_output_type == 'sigmoid':
            relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
        else:
            raise NotImplementedError

        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)
        attn = attn.type_as(x)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        if self.mlpfp32:
            x = self.proj.float()(x.type(torch.float32))
            x = self.proj_drop.float()(x)
            # print(f"======>[ATTN FP32]")
        else:
            x = self.proj(x)
            x = self.proj_drop(x)
        return x

    def extra_repr(self) -> str:
        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'

    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops


class SwinTransformerBlockPost(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.,
                 use_mlp_norm=False,
                 endnorm=False,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 relative_coords_table_type='norm8_log',
                 rpe_hidden_dim=512,
                 rpe_output_type='normal',
                 attn_type='normal',
                 mlp_type='normal',
                 mlpfp32=False,
                 pretrain_window_size=-1):
        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
        self.use_mlp_norm = use_mlp_norm
        self.endnorm = endnorm
        self.mlpfp32 = mlpfp32
        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,
            relative_coords_table_type=relative_coords_table_type,
            rpe_output_type=rpe_output_type,
            rpe_hidden_dim=rpe_hidden_dim,
            attn_type=attn_type,
            mlpfp32=mlpfp32,
            pretrain_window_size=pretrain_window_size)

        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)

        if mlp_type == 'normal':
            self.mlp = Mlp(
                in_features=dim,
                hidden_features=mlp_hidden_dim,
                act_layer=act_layer,
                drop=drop,
                norm_layer=norm_layer if self.use_mlp_norm else None,
                mlpfp32=mlpfp32)
        elif mlp_type == 'conv':
            self.mlp = ConvMlp(
                in_features=dim,
                hidden_features=mlp_hidden_dim,
                act_layer=act_layer,
                drop=drop,
                norm_layer=norm_layer if self.use_mlp_norm else None,
                mlpfp32=mlpfp32)
        elif mlp_type == 'conv_ln':
            self.mlp = ConvMlp(
                in_features=dim,
                hidden_features=mlp_hidden_dim,
                act_layer=act_layer,
                drop=drop,
                norm_layer=norm_layer if self.use_mlp_norm else None,
                mlpfp32=mlpfp32,
                proj_ln=True)

        if self.endnorm:
            self.enorm = norm_layer(dim)
        else:
            self.enorm = None

        self.H = None
        self.W = None

    def forward(self, x, mask_matrix):
        H, W = self.H, self.W
        B, L, C = x.shape
        assert L == H * W, f"input feature has wrong size, with L = {L}, H = {H}, W = {W}"

        shortcut = 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
        if pad_r > 0 or pad_b > 0:
            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
        orig_type = x.dtype  # attn may force to fp32
        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
        if self.mlpfp32:
            x = self.norm1.float()(x)
            x = x.type(orig_type)
        else:
            x = self.norm1(x)
        x = shortcut + self.drop_path(x)
        shortcut = x

        orig_type = x.dtype
        x = self.mlp(x, H, W)
        if self.mlpfp32:
            x = self.norm2.float()(x)
            x = x.type(orig_type)
        else:
            x = self.norm2(x)
        x = shortcut + self.drop_path(x)

        if self.endnorm:
            x = self.enorm(x)

        return x


class SwinTransformerBlockPre(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.,
                 use_mlp_norm=False,
                 endnorm=False,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm,
                 init_values=None,
                 relative_coords_table_type='norm8_log',
                 rpe_hidden_dim=512,
                 rpe_output_type='normal',
                 attn_type='normal',
                 mlp_type='normal',
                 mlpfp32=False,
                 pretrain_window_size=-1):
        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
        self.use_mlp_norm = use_mlp_norm
        self.endnorm = endnorm
        self.mlpfp32 = mlpfp32
        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,
            relative_coords_table_type=relative_coords_table_type,
            rpe_output_type=rpe_output_type,
            rpe_hidden_dim=rpe_hidden_dim,
            attn_type=attn_type,
            mlpfp32=mlpfp32,
            pretrain_window_size=pretrain_window_size)

        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)

        if mlp_type == 'normal':
            self.mlp = Mlp(
                in_features=dim,
                hidden_features=mlp_hidden_dim,
                act_layer=act_layer,
                drop=drop,
                norm_layer=norm_layer if self.use_mlp_norm else None,
                mlpfp32=mlpfp32)
        elif mlp_type == 'conv':
            self.mlp = ConvMlp(
                in_features=dim,
                hidden_features=mlp_hidden_dim,
                act_layer=act_layer,
                drop=drop,
                norm_layer=norm_layer if self.use_mlp_norm else None,
                mlpfp32=mlpfp32)
        elif mlp_type == 'conv_ln':
            self.mlp = ConvMlp(
                in_features=dim,
                hidden_features=mlp_hidden_dim,
                act_layer=act_layer,
                drop=drop,
                norm_layer=norm_layer if self.use_mlp_norm else None,
                mlpfp32=mlpfp32,
                proj_ln=True)

        if init_values is not None and init_values >= 0:
            self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),
                                        requires_grad=True)
            self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),
                                        requires_grad=True)
        else:
            self.gamma_1, self.gamma_2 = 1.0, 1.0

        if self.endnorm:
            self.enorm = norm_layer(dim)
        else:
            self.enorm = None

        self.H = None
        self.W = None

    def forward(self, x, mask_matrix):
        H, W = self.H, self.W
        B, L, C = x.shape
        assert L == H * W, f"input feature has wrong size, with L = {L}, H = {H}, W = {W}"

        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
        if pad_r > 0 or pad_b > 0:
            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
        orig_type = x.dtype  # attn may force to fp32
        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
        if self.mlpfp32:
            x = self.gamma_1 * x
            x = x.type(orig_type)
        else:
            x = self.gamma_1 * x
        x = shortcut + self.drop_path(x)
        shortcut = x

        orig_type = x.dtype
        x = self.norm2(x)
        if self.mlpfp32:
            x = self.gamma_2 * self.mlp(x, H, W)
            x = x.type(orig_type)
        else:
            x = self.gamma_2 * self.mlp(x, H, W)
        x = shortcut + self.drop_path(x)

        if self.endnorm:
            x = self.enorm(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, postnorm=True):
        super().__init__()
        self.dim = dim
        self.postnorm = postnorm

        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(2 * dim) if postnorm else 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

        if self.postnorm:
            x = self.reduction(x)
            x = self.norm(x)
        else:
            x = self.norm(x)
            x = self.reduction(x)

        return x


class PatchReduction1C(nn.Module):
    r""" Patch Reduction 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, dim, norm_layer=nn.LayerNorm, postnorm=True):
        super().__init__()
        self.dim = dim
        self.postnorm = postnorm

        self.reduction = nn.Linear(dim, dim, bias=False)
        self.norm = norm_layer(dim)

    def forward(self, x, H, W):
        """
        x: B, H*W, C
        """
        if self.postnorm:
            x = self.reduction(x)
            x = self.norm(x)
        else:
            x = self.norm(x)
            x = self.reduction(x)

        return x


class ConvPatchMerging(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, dim, norm_layer=nn.LayerNorm, postnorm=True):
        super().__init__()
        self.dim = dim
        self.postnorm = postnorm

        self.reduction = nn.Conv2d(dim,
                                   2 * dim,
                                   kernel_size=3,
                                   stride=2,
                                   padding=1)
        self.norm = norm_layer(2 * dim) if postnorm else norm_layer(dim)

    def forward(self, x, H, W):
        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))

        if self.postnorm:
            x = x.permute(0, 3, 1, 2)  # B C H W
            x = self.reduction(x).flatten(2).transpose(1, 2)  # B H//2*W//2 2*C
            x = self.norm(x)
        else:
            x = self.norm(x)
            x = x.permute(0, 3, 1, 2)  # B C H W
            x = self.reduction(x).flatten(2).transpose(1, 2)  # B 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
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
        use_shift (bool): Whether to use shifted window. Default: True.
    """

    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,
                 checkpoint_blocks=255,
                 init_values=None,
                 endnorm_interval=-1,
                 use_mlp_norm=False,
                 use_shift=True,
                 relative_coords_table_type='norm8_log',
                 rpe_hidden_dim=512,
                 rpe_output_type='normal',
                 attn_type='normal',
                 mlp_type='normal',
                 mlpfp32_blocks=[-1],
                 postnorm=True,
                 pretrain_window_size=-1):
        super().__init__()
        self.window_size = window_size
        self.shift_size = window_size // 2
        self.depth = depth
        self.use_checkpoint = use_checkpoint
        self.checkpoint_blocks = checkpoint_blocks
        self.init_values = init_values if init_values is not None else 0.0
        self.endnorm_interval = endnorm_interval
        self.mlpfp32_blocks = mlpfp32_blocks
        self.postnorm = postnorm

        # build blocks
        if self.postnorm:
            self.blocks = nn.ModuleList([
                SwinTransformerBlockPost(
                    dim=dim,
                    num_heads=num_heads,
                    window_size=window_size,
                    shift_size=0 if
                    (i % 2 == 0) or (not use_shift) 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,
                    use_mlp_norm=use_mlp_norm,
                    endnorm=True if ((i + 1) % endnorm_interval == 0) and
                    (endnorm_interval > 0) else False,
                    relative_coords_table_type=relative_coords_table_type,
                    rpe_hidden_dim=rpe_hidden_dim,
                    rpe_output_type=rpe_output_type,
                    attn_type=attn_type,
                    mlp_type=mlp_type,
                    mlpfp32=True if i in mlpfp32_blocks else False,
                    pretrain_window_size=pretrain_window_size)
                for i in range(depth)
            ])
        else:
            self.blocks = nn.ModuleList([
                SwinTransformerBlockPre(
                    dim=dim,
                    num_heads=num_heads,
                    window_size=window_size,
                    shift_size=0 if
                    (i % 2 == 0) or (not use_shift) 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,
                    init_values=init_values,
                    use_mlp_norm=use_mlp_norm,
                    endnorm=True if ((i + 1) % endnorm_interval == 0) and
                    (endnorm_interval > 0) else False,
                    relative_coords_table_type=relative_coords_table_type,
                    rpe_hidden_dim=rpe_hidden_dim,
                    rpe_output_type=rpe_output_type,
                    attn_type=attn_type,
                    mlp_type=mlp_type,
                    mlpfp32=True if i in mlpfp32_blocks else False,
                    pretrain_window_size=pretrain_window_size)
                for i in range(depth)
            ])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(dim=dim,
                                         norm_layer=norm_layer,
                                         postnorm=postnorm)
        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 idx, blk in enumerate(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)
            if isinstance(self.downsample, PatchReduction1C):
                return x, H, W, x_down, H, W
            else:
                Wh, Ww = (H + 1) // 2, (W + 1) // 2
                return x, H, W, x_down, Wh, Ww
        else:
            return x, H, W, x, H, W

    def _init_block_norm_weights(self):
        for blk in self.blocks:
            nn.init.constant_(blk.norm1.bias, 0)
            nn.init.constant_(blk.norm1.weight, self.init_values)
            nn.init.constant_(blk.norm2.bias, 0)
            nn.init.constant_(blk.norm2.weight, self.init_values)


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


class ResNetDLNPatchEmbed(nn.Module):

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

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.conv1 = nn.Sequential(
            nn.Conv2d(in_chans, 64, 3, stride=2, padding=1, bias=False),
            LayerNorm2D(64, norm_layer), nn.GELU(),
            nn.Conv2d(64, 64, 3, stride=1, padding=1, bias=False),
            LayerNorm2D(64, norm_layer), nn.GELU(),
            nn.Conv2d(64, embed_dim, 3, stride=1, padding=1, bias=False))
        self.norm = LayerNorm2D(embed_dim, norm_layer if norm_layer is not None
                                else LayerNormFP32)  # use ln always
        self.act = nn.GELU()
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    def forward(self, x):
        _, _, 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.conv1(x)
        x = self.norm(x)
        x = self.act(x)
        x = self.maxpool(x)
        # x = x.flatten(2).transpose(1, 2)
        return x


# @BACKBONES.register_module()
class SwinV2TransformerRPE2FC(nn.Module):
    """ Swin Transformer backbone.
    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.
        use_shift (bool): Whether to use shifted window. Default: True.
    """

    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=0.1,
                 norm_layer=partial(LayerNormFP32, eps=1e-6),
                 ape=False,
                 patch_norm=True,
                 use_checkpoint=False,
                 init_values=1e-5,
                 endnorm_interval=-1,
                 use_mlp_norm_layers=[],
                 relative_coords_table_type='norm8_log',
                 rpe_hidden_dim=512,
                 attn_type='cosine_mh',
                 rpe_output_type='sigmoid',
                 rpe_wd=False,
                 postnorm=True,
                 mlp_type='normal',
                 patch_embed_type='normal',
                 patch_merge_type='normal',
                 strid16=False,
                 checkpoint_blocks=[255, 255, 255, 255],
                 mlpfp32_layer_blocks=[[-1], [-1], [-1], [-1]],
                 out_indices=(0, 1, 2, 3),
                 frozen_stages=-1,
                 use_shift=True,
                 rpe_interpolation='geo',
                 pretrain_window_size=[-1, -1, -1, -1],
                 **kwargs):
        super().__init__()

        self.pretrain_img_size = pretrain_img_size
        self.depths = depths
        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
        self.rpe_interpolation = rpe_interpolation
        self.mlp_ratio = mlp_ratio
        self.endnorm_interval = endnorm_interval
        self.use_mlp_norm_layers = use_mlp_norm_layers
        self.relative_coords_table_type = relative_coords_table_type
        self.rpe_hidden_dim = rpe_hidden_dim
        self.rpe_output_type = rpe_output_type
        self.rpe_wd = rpe_wd
        self.attn_type = attn_type
        self.postnorm = postnorm
        self.mlp_type = mlp_type
        self.strid16 = strid16

        if isinstance(window_size, list):
            pass
        elif isinstance(window_size, int):
            window_size = [window_size] * self.num_layers
        else:
            raise TypeError("We only support list or int for window size")

        if isinstance(use_shift, list):
            pass
        elif isinstance(use_shift, bool):
            use_shift = [use_shift] * self.num_layers
        else:
            raise TypeError("We only support list or bool for use_shift")

        if isinstance(use_checkpoint, list):
            pass
        elif isinstance(use_checkpoint, bool):
            use_checkpoint = [use_checkpoint] * self.num_layers
        else:
            raise TypeError("We only support list or bool for use_checkpoint")

        # split image into non-overlapping patches
        if patch_embed_type == 'normal':
            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)
        elif patch_embed_type == 'resnetdln':
            assert patch_size == 4, "check"
            self.patch_embed = ResNetDLNPatchEmbed(in_chans=in_chans,
                                                   embed_dim=embed_dim,
                                                   norm_layer=norm_layer)
        elif patch_embed_type == 'resnetdnf':
            assert patch_size == 4, "check"
            self.patch_embed = ResNetDLNPatchEmbed(in_chans=in_chans,
                                                   embed_dim=embed_dim,
                                                   norm_layer=None)
        else:
            raise NotImplementedError()
        # 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

        if patch_merge_type == 'normal':
            downsample_layer = PatchMerging
        elif patch_merge_type == 'conv':
            downsample_layer = ConvPatchMerging
        else:
            raise NotImplementedError()
        # build layers
        self.layers = nn.ModuleList()
        num_features = []
        for i_layer in range(self.num_layers):
            cur_dim = int(embed_dim * 2 ** (i_layer - 1)) \
                if (i_layer == self.num_layers - 1 and strid16) else \
                int(embed_dim * 2 ** i_layer)
            num_features.append(cur_dim)
            if i_layer < self.num_layers - 2:
                cur_downsample_layer = downsample_layer
            elif i_layer == self.num_layers - 2:
                if strid16:
                    cur_downsample_layer = PatchReduction1C
                else:
                    cur_downsample_layer = downsample_layer
            else:
                cur_downsample_layer = None
            layer = BasicLayer(
                dim=cur_dim,
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size[i_layer],
                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=cur_downsample_layer,
                use_checkpoint=use_checkpoint[i_layer],
                checkpoint_blocks=checkpoint_blocks[i_layer],
                init_values=init_values,
                endnorm_interval=endnorm_interval,
                use_mlp_norm=True if i_layer in use_mlp_norm_layers else False,
                use_shift=use_shift[i_layer],
                relative_coords_table_type=self.relative_coords_table_type,
                rpe_hidden_dim=self.rpe_hidden_dim,
                rpe_output_type=self.rpe_output_type,
                attn_type=self.attn_type,
                mlp_type=self.mlp_type,
                mlpfp32_blocks=mlpfp32_layer_blocks[i_layer],
                postnorm=self.postnorm,
                pretrain_window_size=pretrain_window_size[i_layer])
            self.layers.append(layer)

        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.
        """
        self.norm3.eval()
        for param in self.norm3.parameters():
            param.requires_grad = False

        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)
            elif isinstance(m, nn.Conv2d):
                trunc_normal_(m.weight, std=.02)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

        self.apply(_init_weights)
        for bly in self.layers:
            bly._init_block_norm_weights()

        if isinstance(pretrained, str):
            logger = get_root_logger()
            load_checkpoint_swin(self,
                                 pretrained,
                                 strict=False,
                                 map_location='cpu',
                                 logger=logger,
                                 rpe_interpolation=self.rpe_interpolation)
        elif pretrained is None:
            pass
        else:
            raise TypeError('pretrained must be a str or None')

    def forward(self, x, skip_early=True):
        """Forward function."""
        if not skip_early:
            return 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.float()(x_out.float())

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

                outs.append(out)
        return outs[-1]

    def train(self, mode=True):
        """Convert the model into training mode while keep layers freezed."""
        super(SwinV2TransformerRPE2FC, self).train(mode)
        self._freeze_stages()


class SwinBackbone(SwinV2TransformerRPE2FC):

    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=0.1,
                 norm_layer=partial(LayerNormFP32, eps=0.000001),
                 ape=False,
                 patch_norm=True,
                 use_checkpoint=False,
                 init_values=0.00001,
                 endnorm_interval=-1,
                 use_mlp_norm_layers=[],
                 relative_coords_table_type='norm8_log',
                 rpe_hidden_dim=512,
                 attn_type='cosine_mh',
                 rpe_output_type='sigmoid',
                 rpe_wd=False,
                 postnorm=True,
                 mlp_type='normal',
                 patch_embed_type='normal',
                 patch_merge_type='normal',
                 strid16=False,
                 checkpoint_blocks=[255, 255, 255, 255],
                 mlpfp32_layer_blocks=[[-1], [-1], [-1], [-1]],
                 out_indices=(0, 1, 2, 3),
                 frozen_stages=-1,
                 use_shift=True,
                 rpe_interpolation='geo',
                 pretrain_window_size=[-1, -1, -1, -1],
                 **kwargs):
        super().__init__(
            pretrain_img_size, patch_size, in_chans, embed_dim, depths,
            num_heads, window_size, mlp_ratio, qkv_bias, qk_scale, drop_rate,
            attn_drop_rate, drop_path_rate, norm_layer, ape, patch_norm,
            use_checkpoint, init_values, endnorm_interval, use_mlp_norm_layers,
            relative_coords_table_type, rpe_hidden_dim, attn_type,
            rpe_output_type, rpe_wd, postnorm, mlp_type, patch_embed_type,
            patch_merge_type, strid16, checkpoint_blocks, mlpfp32_layer_blocks,
            out_indices, frozen_stages, use_shift, rpe_interpolation,
            pretrain_window_size, **kwargs)

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
    def forward(self, x, skip_early=False):
        if not skip_early:
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.relu(x)
            x = self.maxpool(x)

            return x

        return super().forward(x)
