# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, visit
# https://github.com/NVlabs/FAN/blob/main/LICENSE

""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
    - https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
"""
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
#
# Modified by: Daquan Zhou
# --------------------------------------------------------
import math
from copy import deepcopy
from typing import Optional

import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models.fx_features import register_notrace_function
from timm.models.helpers import build_model_with_cfg
from timm.models.layers import PatchEmbed, Mlp
from timm.models.layers import _assert
from timm.models.vision_transformer import checkpoint_filter_fn, init_weights_vit_timm

from timm.models.layers import DropPath, trunc_normal_, to_2tuple


def overlay_external_default_cfg(default_cfg, kwargs):
    """ Overlay 'external_default_cfg' in kwargs on top of default_cfg arg."""
    external_default_cfg = kwargs.pop('external_default_cfg', None)
    if external_default_cfg:
        default_cfg.pop('url', None)  # url should come from external cfg
        default_cfg.pop('hf_hub', None)  # hf hub id should come from external cfg
        default_cfg.update(external_default_cfg)


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 = {
    # patch models (my experiments)
    'swin_base_patch4_window12_384': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22kto1k.pth',
        input_size=(3, 384, 384), crop_pct=1.0),

    'swin_base_patch4_window7_224': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth',
    ),

    'swin_large_patch4_window12_384': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth',
        input_size=(3, 384, 384), crop_pct=1.0),

    'swin_large_patch4_window7_224': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22kto1k.pth',
    ),

    'swin_small_patch4_window7_224': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth',
    ),

    'swin_tiny_patch4_window7_224': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth',
    ),

    'swin_base_patch4_window12_384_in22k': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth',
        input_size=(3, 384, 384), crop_pct=1.0, num_classes=21841),

    'swin_base_patch4_window7_224_in22k': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth',
        num_classes=21841),

    'swin_large_patch4_window12_384_in22k': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth',
        input_size=(3, 384, 384), crop_pct=1.0, num_classes=21841),

    'swin_large_patch4_window7_224_in22k': _cfg(
        url='https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth',
        num_classes=21841),

}


def window_partition(x, window_size: 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, 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


@register_notrace_function  # reason: int argument is a Proxy
def window_reverse(windows, window_size: int, H: int, W: int):
    """
    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
        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, attn_drop=0., proj_drop=0.):
        """Initialize WindowAttention class"""
        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5

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

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

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

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

    def forward(self, x, mask: 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 = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)

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

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

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

        attn = self.attn_drop(attn)

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


class PositionalEncodingFourier(nn.Module):
    """
    Positional encoding relying on a fourier kernel matching the one used in the "Attention is all of Need" paper.
    Based on the official XCiT code
        - https://github.com/facebookresearch/xcit/blob/master/xcit.py
    """

    def __init__(self, hidden_dim=32, dim=768, temperature=10000):
        """Initialize PositionalEncodingFourier class"""
        super().__init__()
        self.token_projection = nn.Conv2d(hidden_dim * 2, dim, kernel_size=1)
        self.scale = 2 * math.pi
        self.temperature = temperature
        self.hidden_dim = hidden_dim
        self.dim = dim
        self.eps = 1e-6

    def forward(self, B: int, H: int, W: int):
        """Forward function"""
        device = self.token_projection.weight.device
        y_embed = torch.arange(1, H + 1, dtype=torch.float32, device=device).unsqueeze(1).repeat(1, 1, W)
        x_embed = torch.arange(1, W + 1, dtype=torch.float32, device=device).repeat(1, H, 1)
        y_embed = y_embed / (y_embed[:, -1:, :] + self.eps) * self.scale
        x_embed = x_embed / (x_embed[:, :, -1:] + self.eps) * self.scale
        dim_t = torch.arange(self.hidden_dim, dtype=torch.float32, device=device)
        dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode='floor') / self.hidden_dim)
        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack([pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()], dim=4).flatten(3)
        pos_y = torch.stack([pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()], dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        pos = self.token_projection(pos)
        return pos.repeat(B, 1, 1, 1)  # (B, C, H, W)


class FANMlp(nn.Module):
    """FANMlp"""

    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., linear=False):
        """Initialize FANMlp"""
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.dwconv = DWConv(hidden_features)
        self.gamma = nn.Parameter(torch.ones(hidden_features), requires_grad=True)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)
        self.linear = linear
        if self.linear:
            self.relu = nn.ReLU(inplace=True)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        """Initialize weights"""
        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):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W):
        """Forward function"""
        x = self.fc1(x)
        if self.linear:
            x = self.relu(x)
        x = self.drop(self.gamma * self.dwconv(x, H, W))  # + x
        x = self.fc2(x)
        x = self.drop(x)
        return x


class DWConv(nn.Module):
    """Depth-wise convolution"""

    def __init__(self, in_features, out_features=None, act_layer=nn.GELU, kernel_size=3):
        """Initialize DWConv class"""
        super().__init__()
        out_features = out_features or in_features

        padding = kernel_size // 2

        self.conv1 = torch.nn.Conv2d(
            in_features, in_features, kernel_size=kernel_size, padding=padding, groups=in_features)
        self.act = act_layer()
        self.bn = nn.BatchNorm2d(in_features)
        self.conv2 = torch.nn.Conv2d(
            in_features, out_features, kernel_size=kernel_size, padding=padding, groups=out_features)

    def forward(self, x, H: int, W: int):
        """Forward function"""
        B, N, C = x.shape
        x = x.permute(0, 2, 1).reshape(B, C, H, W)
        x = self.conv1(x)
        x = self.act(x)
        x = self.bn(x)
        x = self.conv2(x)
        x = x.reshape(B, C, N).permute(0, 2, 1)
        return x


class ChannelProcessing(nn.Module):
    """Channel Processing"""

    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,
                 sr_ratio=1, linear=False, drop_path=0.,
                 mlp_hidden_dim=None, act_layer=nn.GELU, drop=0., norm_layer=nn.LayerNorm, cha_sr_ratio=1, c_head_num=None):
        """Initialize ChannelProcessing class"""
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        num_heads = c_head_num or num_heads
        self.num_heads = num_heads
        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))

        self.cha_sr_ratio = cha_sr_ratio if num_heads > 1 else 1

        # config of mlp for v processing
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.mlp_v = FANMlp(in_features=dim // self.cha_sr_ratio, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, linear=linear)
        self.norm_v = norm_layer(dim // self.cha_sr_ratio)

        self.q = nn.Linear(dim, dim, bias=qkv_bias)

        self.attn_drop = nn.Dropout(attn_drop)

        self.linear = linear
        self.sr_ratio = sr_ratio
        self.apply(self._init_weights)

    def _init_weights(self, m):
        """Initialize weights"""
        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):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def _gen_attn(self, q, k):
        """Returns attention"""
        q = q.softmax(-2).transpose(-1, -2)
        _, _, N, _ = k.shape
        k = torch.nn.functional.adaptive_avg_pool2d(k.softmax(-2), (N, 1))

        attn = torch.nn.functional.sigmoid(q @ k)
        return attn * self.temperature

    def forward(self, x, H, W, atten=None):
        """Forward functions """
        B, N, C = x.shape
        v = x.reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
        k = x.reshape(B, N, self.num_heads,  C // self.num_heads).permute(0, 2, 1, 3)

        attn = self._gen_attn(q, k)
        attn = self.attn_drop(attn)

        Bv, Hd, Nv, Cv = v.shape
        v = self.norm_v(self.mlp_v(v.transpose(1, 2).reshape(Bv, Nv, Hd * Cv), H, W)).reshape(Bv, Nv, Hd, Cv).transpose(1, 2)

        repeat_time = N // attn.shape[-1]
        attn = attn.repeat_interleave(repeat_time, dim=-1) if attn.shape[-1] > 1 else attn
        x = (attn * v.transpose(-1, -2)).permute(0, 3, 1, 2).reshape(B, N, C)
        return x

    @torch.jit.ignore
    def no_weight_decay(self):
        """layers to ignore for weight decay"""
        return {'temperature'}


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

    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., mlp_type=None,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        """Initialize SwinTransformerBlock class

        Args:
            dim (int): Number of input channels.
            input_resolution (tuple[int]): Input resulotion.
            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
        """
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        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,
            attn_drop=attn_drop, proj_drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp_type = mlp_type
        if mlp_type == 'Mlp':
            self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
        else:
            self.mlp = ChannelProcessing(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
                                         drop_path=drop_path, mlp_hidden_dim=mlp_hidden_dim)

        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 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))
        else:
            attn_mask = None

        self.register_buffer("attn_mask", attn_mask)

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

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

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), 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_size * self.window_size, 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, self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # 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
        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)
        if self.mlp_type == 'Mlp':
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.mlp(self.norm2(x), H, W))

        return x


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

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        """Initialize PatchMerging class

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

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

        Args:
            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 and 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.norm(x)
        x = self.reduction(x)

        return x

    def extra_repr(self) -> str:
        """exptra representation"""
        return f"input_resolution={self.input_resolution}, dim={self.dim}"

    def flops(self):
        """returns FLOPs"""
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops


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

    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., mlp_type=None,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
        """Initialize BasicLayer

        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
            use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
        """
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # 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, mlp_type=mlp_type,
                drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer)
            for i in range(depth)])

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

    def forward(self, x):
        """Forward function"""
        for blk in self.blocks:
            if not torch.jit.is_scripting() and self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x)
            else:
                x = blk(x)
        if self.downsample is not None:
            x = self.downsample(x)
        return x

    def extra_repr(self) -> str:
        """exptra representation"""
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"


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

    def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
                 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,
                 use_checkpoint=False, weight_init='', mlp_type='Mlp',  **kwargs):
        """Initialize SwinTransformer class

        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
        """
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio

        # 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)

        self.patch_grid = self.patch_embed.grid_size

        # absolute position embedding
        if self.ape:
            # self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            # trunc_normal_(self.absolute_pos_embed, std=.02)
            self.absolute_pos_embed = PositionalEncodingFourier(dim=embed_dim)
        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
        layers = []
        for i_layer in range(self.num_layers):
            layers += [BasicLayer(
                dim=int(embed_dim * 2 ** i_layer),
                input_resolution=(self.patch_grid[0] // (2 ** i_layer), self.patch_grid[1] // (2 ** i_layer)),
                depth=depths[i_layer],
                mlp_type=mlp_type[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                mlp_ratio=self.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,
                use_checkpoint=use_checkpoint)
            ]
        self.layers = nn.Sequential(*layers)

        self.norm = norm_layer(self.num_features)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

        assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '')
        head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0.
        if weight_init.startswith('jax'):
            for n, m in self.named_modules():
                init_weights_vit_timm(m, n, head_bias=head_bias, jax_impl=True)  # pylint: disable=E1123
        else:
            self.apply(init_weights_vit_timm)

    @torch.jit.ignore
    def no_weight_decay(self):
        """layers to ignore for weight decay"""
        return {'absolute_pos_embed'}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        """keywords to ignore for weight decay"""
        return {'relative_position_bias_table'}

    def get_classifier(self):
        """Returns classifier"""
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        """Redefine classifier of FAN"""
        self.num_classes = num_classes
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        """Extract features"""
        x = self.patch_embed(x)
        B, N, _ = x.shape
        H = W = math.sqrt(N)
        if self.absolute_pos_embed is not None:
            # import pdb; pdb.set_trace()
            x = x + self.absolute_pos_embed(B, H, W).reshape(B, -1, x.shape[1]).permute(0, 2, 1)
        x = self.pos_drop(x)
        x = self.layers(x)
        x = self.norm(x)  # B L C
        x = self.avgpool(x.transpose(1, 2))  # B C 1
        x = torch.flatten(x, 1)
        return x

    def forward(self, x):
        """Forward functions"""
        x = self.forward_features(x)
        x = self.head(x)
        return x


def _create_fan_swin_transformer(variant, pretrained=False, default_cfg=None, **kwargs):
    """Create FAN Swin Transformer backbone"""
    if default_cfg is None:
        default_cfg = deepcopy(default_cfgs[variant])
    overlay_external_default_cfg(default_cfg, kwargs)
    default_num_classes = default_cfg['num_classes']
    default_img_size = default_cfg['input_size'][-2:]

    num_classes = kwargs.pop('num_classes', default_num_classes)
    img_size = kwargs.pop('img_size', default_img_size)
    if kwargs.get('features_only', None):
        raise RuntimeError('features_only not implemented for Vision Transformer models.')

    model = build_model_with_cfg(
        SwinTransformer, variant, pretrained,
        default_cfg=default_cfg,
        img_size=img_size,
        num_classes=num_classes,
        pretrained_filter_fn=checkpoint_filter_fn,
        **kwargs)

    return model
