""" Swin Transformer V2
A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
    - https://arxiv.org/abs/2111.09883

Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below

Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
"""
# --------------------------------------------------------
# Swin Transformer V2
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import math
from typing import Tuple, Optional

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import PatchEmbed, Mlp, DropPath, to_2tuple, trunc_normal_, _assert
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._registry import register_model

__all__ = ['SwinTransformerV2']  # model_registry will add each entrypoint fn to this


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'patch_embed.proj', 'classifier': 'head',
        **kwargs
    }


default_cfgs = {
    'swinv2_tiny_window8_256': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth',
        input_size=(3, 256, 256)
    ),
    'swinv2_tiny_window16_256': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth',
        input_size=(3, 256, 256)
    ),
    'swinv2_small_window8_256': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth',
        input_size=(3, 256, 256)
    ),
    'swinv2_small_window16_256': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth',
        input_size=(3, 256, 256)
    ),
    'swinv2_base_window8_256': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth',
        input_size=(3, 256, 256)
    ),
    'swinv2_base_window16_256': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth',
        input_size=(3, 256, 256)
    ),

    'swinv2_base_window12_192_22k': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth',
        num_classes=21841, input_size=(3, 192, 192)
    ),
    'swinv2_base_window12to16_192to256_22kft1k': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth',
        input_size=(3, 256, 256)
    ),
    'swinv2_base_window12to24_192to384_22kft1k': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth',
        input_size=(3, 384, 384), crop_pct=1.0,
    ),
    'swinv2_large_window12_192_22k': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth',
        num_classes=21841, input_size=(3, 192, 192)
    ),
    'swinv2_large_window12to16_192to256_22kft1k': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth',
        input_size=(3, 256, 256)
    ),
    'swinv2_large_window12to24_192to384_22kft1k': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth',
        input_size=(3, 384, 384), crop_pct=1.0,
    ),
}


def window_partition(x, window_size: Tuple[int, int]):
    """
    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[0], window_size[0], W // window_size[1], window_size[1], C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size[0], window_size[1], C)
    return windows


@register_notrace_function  # reason: int argument is a Proxy
def window_reverse(windows, window_size: Tuple[int, int], img_size: Tuple[int, int]):
    """
    Args:
        windows: (num_windows * B, window_size[0], window_size[1], C)
        window_size (Tuple[int, int]): Window size
        img_size (Tuple[int, int]): Image size

    Returns:
        x: (B, H, W, C)
    """
    H, W = img_size
    B = int(windows.shape[0] / (H * W / window_size[0] / window_size[1]))
    x = windows.view(B, H // window_size[0], W // window_size[1], window_size[0], window_size[1], -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
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
    """

    def __init__(
            self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
            pretrained_window_size=[0, 0]):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.pretrained_window_size = pretrained_window_size
        self.num_heads = num_heads

        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))))

        # mlp to generate continuous relative position bias
        self.cpb_mlp = nn.Sequential(
            nn.Linear(2, 512, bias=True),
            nn.ReLU(inplace=True),
            nn.Linear(512, 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 pretrained_window_size[0] > 0:
            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
        else:
            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) / math.log2(8)

        self.register_buffer("relative_coords_table", relative_coords_table, persistent=False)

        # 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, persistent=False)

        self.qkv = nn.Linear(dim, dim * 3, bias=False)
        if qkv_bias:
            self.q_bias = nn.Parameter(torch.zeros(dim))
            self.register_buffer('k_bias', torch.zeros(dim), persistent=False)
            self.v_bias = nn.Parameter(torch.zeros(dim))
        else:
            self.q_bias = None
            self.k_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: Optional[torch.Tensor] = 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, self.k_bias, 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)
        q, k, v = qkv.unbind(0)

        # cosine attention
        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
        logit_scale = torch.clamp(self.logit_scale, max=math.log(1. / 0.01)).exp()
        attn = attn * logit_scale

        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
        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
        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
        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):
    r""" Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        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
        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
        pretrained_window_size (int): Window size in pretraining.
    """

    def __init__(
            self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
            mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
            act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
        super().__init__()
        self.dim = dim
        self.input_resolution = to_2tuple(input_resolution)
        self.num_heads = num_heads
        ws, ss = self._calc_window_shift(window_size, shift_size)
        self.window_size: Tuple[int, int] = ws
        self.shift_size: Tuple[int, int] = ss
        self.window_area = self.window_size[0] * self.window_size[1]
        self.mlp_ratio = mlp_ratio

        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
            pretrained_window_size=to_2tuple(pretrained_window_size))
        self.norm1 = norm_layer(dim)
        self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
        self.norm2 = norm_layer(dim)
        self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        if any(self.shift_size):
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
            cnt = 0
            for h in (
                    slice(0, -self.window_size[0]),
                    slice(-self.window_size[0], -self.shift_size[0]),
                    slice(-self.shift_size[0], None)):
                for w in (
                        slice(0, -self.window_size[1]),
                        slice(-self.window_size[1], -self.shift_size[1]),
                        slice(-self.shift_size[1], None)):
                    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_area)
            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))
        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)

    def _calc_window_shift(self, target_window_size, target_shift_size) -> Tuple[Tuple[int, int], Tuple[int, int]]:
        target_window_size = to_2tuple(target_window_size)
        target_shift_size = to_2tuple(target_shift_size)
        window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
        shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)]
        return tuple(window_size), tuple(shift_size)

    def _attn(self, x):
        H, W = self.input_resolution
        B, L, C = x.shape
        _assert(L == H * W, "input feature has wrong size")
        x = x.view(B, H, W, C)

        # cyclic shift
        has_shift = any(self.shift_size)
        if has_shift:
            shifted_x = torch.roll(x, shifts=(-self.shift_size[0], -self.shift_size[1]), dims=(1, 2))
        else:
            shifted_x = x

        # 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_area, C)  # nW*B, window_size*window_size, C

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

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size[0], self.window_size[1], C)
        shifted_x = window_reverse(attn_windows, self.window_size, self.input_resolution)  # B H' W' C

        # reverse cyclic shift
        if has_shift:
            x = torch.roll(shifted_x, shifts=self.shift_size, dims=(1, 2))
        else:
            x = shifted_x
        x = x.view(B, H * W, C)
        return x

    def forward(self, x):
        x = x + self.drop_path1(self.norm1(self._attn(x)))
        x = x + self.drop_path2(self.norm2(self.mlp(x)))
        return x


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

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(2 * dim)

    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        _assert(L == H * W, "input feature has wrong size")
        _assert(H % 2 == 0, f"x size ({H}*{W}) are not even.")
        _assert(W % 2 == 0, f"x size ({H}*{W}) are not even.")

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

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.reduction(x)
        x = self.norm(x)

        return x


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

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        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
        pretrained_window_size (int): Local window size in pre-training.
    """

    def __init__(
            self, dim, input_resolution, depth, num_heads, window_size,
            mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
            norm_layer=nn.LayerNorm, downsample=None, pretrained_window_size=0):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.grad_checkpointing = False

        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(
                dim=dim, input_resolution=input_resolution,
                num_heads=num_heads, window_size=window_size,
                shift_size=0 if (i % 2 == 0) else window_size // 2,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                drop=drop, attn_drop=attn_drop,
                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                norm_layer=norm_layer,
                pretrained_window_size=pretrained_window_size)
            for i in range(depth)])

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = nn.Identity()

    def forward(self, x):
        for blk in self.blocks:
            if self.grad_checkpointing and not torch.jit.is_scripting():
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        x = self.downsample(x)
        return x

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


class SwinTransformerV2(nn.Module):
    r""" Swin Transformer V2
        A PyTorch impl of : `Swin Transformer V2: Scaling Up Capacity and Resolution`
            - https://arxiv.org/abs/2111.09883
    Args:
        img_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
        pretrained_window_sizes (tuple(int)): Pretrained window sizes of each layer.
    """

    def __init__(
            self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, global_pool='avg',
            embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24),
            window_size=7, mlp_ratio=4., qkv_bias=True,
            drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
            norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
            pretrained_window_sizes=(0, 0, 0, 0), **kwargs):
        super().__init__()

        self.num_classes = num_classes
        assert global_pool in ('', 'avg')
        self.global_pool = global_pool
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))

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

        # absolute position embedding
        if ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)
        else:
            self.absolute_pos_embed = None

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

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(
                dim=int(embed_dim * 2 ** i_layer),
                input_resolution=(
                    self.patch_embed.grid_size[0] // (2 ** i_layer),
                    self.patch_embed.grid_size[1] // (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,
                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,
                pretrained_window_size=pretrained_window_sizes[i_layer]
            )
            self.layers.append(layer)

        self.norm = norm_layer(self.num_features)
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

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

    def _init_weights(self, 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)

    @torch.jit.ignore
    def no_weight_decay(self):
        nod = {'absolute_pos_embed'}
        for n, m in self.named_modules():
            if any([kw in n for kw in ("cpb_mlp", "logit_scale", 'relative_position_bias_table')]):
                nod.add(n)
        return nod

    @torch.jit.ignore
    def group_matcher(self, coarse=False):
        return dict(
            stem=r'^absolute_pos_embed|patch_embed',  # stem and embed
            blocks=r'^layers\.(\d+)' if coarse else [
                (r'^layers\.(\d+).downsample', (0,)),
                (r'^layers\.(\d+)\.\w+\.(\d+)', None),
                (r'^norm', (99999,)),
            ]
        )

    @torch.jit.ignore
    def set_grad_checkpointing(self, enable=True):
        for l in self.layers:
            l.grad_checkpointing = enable

    @torch.jit.ignore
    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=None):
        self.num_classes = num_classes
        if global_pool is not None:
            assert global_pool in ('', 'avg')
            self.global_pool = global_pool
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        x = self.patch_embed(x)
        if self.absolute_pos_embed is not None:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)

        for layer in self.layers:
            x = layer(x)

        x = self.norm(x)  # B L C
        return x

    def forward_head(self, x, pre_logits: bool = False):
        if self.global_pool == 'avg':
            x = x.mean(dim=1)
        return x if pre_logits else self.head(x)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.forward_head(x)
        return x


def checkpoint_filter_fn(state_dict, model):
    out_dict = {}
    if 'model' in state_dict:
        # For deit models
        state_dict = state_dict['model']
    for k, v in state_dict.items():
        if any([n in k for n in ('relative_position_index', 'relative_coords_table')]):
            continue  # skip buffers that should not be persistent
        out_dict[k] = v
    return out_dict


def _create_swin_transformer_v2(variant, pretrained=False, **kwargs):
    model = build_model_with_cfg(
        SwinTransformerV2, variant, pretrained,
        pretrained_filter_fn=checkpoint_filter_fn,
        **kwargs)
    return model


@register_model
def swinv2_tiny_window16_256(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=16, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), **kwargs)
    return _create_swin_transformer_v2('swinv2_tiny_window16_256', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_tiny_window8_256(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=8, embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), **kwargs)
    return _create_swin_transformer_v2('swinv2_tiny_window8_256', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_small_window16_256(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=16, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), **kwargs)
    return _create_swin_transformer_v2('swinv2_small_window16_256', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_small_window8_256(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=8, embed_dim=96, depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), **kwargs)
    return _create_swin_transformer_v2('swinv2_small_window8_256', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_base_window16_256(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
    return _create_swin_transformer_v2('swinv2_base_window16_256', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_base_window8_256(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=8, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
    return _create_swin_transformer_v2('swinv2_base_window8_256', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_base_window12_192_22k(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=12, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), **kwargs)
    return _create_swin_transformer_v2('swinv2_base_window12_192_22k', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_base_window12to16_192to256_22kft1k(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=16, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32),
        pretrained_window_sizes=(12, 12, 12, 6), **kwargs)
    return _create_swin_transformer_v2(
        'swinv2_base_window12to16_192to256_22kft1k', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_base_window12to24_192to384_22kft1k(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=24, embed_dim=128, depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32),
        pretrained_window_sizes=(12, 12, 12, 6), **kwargs)
    return _create_swin_transformer_v2(
        'swinv2_base_window12to24_192to384_22kft1k', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_large_window12_192_22k(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=12, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), **kwargs)
    return _create_swin_transformer_v2('swinv2_large_window12_192_22k', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_large_window12to16_192to256_22kft1k(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=16, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48),
        pretrained_window_sizes=(12, 12, 12, 6), **kwargs)
    return _create_swin_transformer_v2(
        'swinv2_large_window12to16_192to256_22kft1k', pretrained=pretrained, **model_kwargs)


@register_model
def swinv2_large_window12to24_192to384_22kft1k(pretrained=False, **kwargs):
    """
    """
    model_kwargs = dict(
        window_size=24, embed_dim=192, depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48),
        pretrained_window_sizes=(12, 12, 12, 6), **kwargs)
    return _create_swin_transformer_v2(
        'swinv2_large_window12to24_192to384_22kft1k', pretrained=pretrained, **model_kwargs)
