text: """
  Copyright VIP Group
  Licensed under the Apache License, Version 2.0.
  Modify from https://github.com/rwightman/pytorch-image-models
  Original copyright of Ross Wightman below, modifications by VIP Group
  
  Hacked together by / copyright Ross Wightman
  """
  from typing import Optional
  
  import jittor as jt
  import jittor.nn as nn
  
  from .drop import DropPath
  from .helpers import to_2tuple
  from .weight_init import trunc_normal_
  
  
  def window_partition(x, win_size: int):
      """
      Args:
          x: (B, H, W, C)
          win_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 // win_size, win_size, W // win_size, win_size, C)
      windows = x.permute(0, 1, 3, 2, 4, 5).view(-1, win_size, win_size, C)
      return windows
  
  
  def window_reverse(windows, win_size: int, H: int, W: int):
      """
      Args:
          windows: (num_windows*B, window_size, window_size, C)
          win_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 / win_size / win_size))
      x = windows.view(B, H // win_size, W // win_size, win_size, win_size, -1)
      x = x.permute(0, 1, 3, 2, 4, 5).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.
          win_size (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
      """
  
      def __init__(
              self, dim, dim_out=None, feat_size=None, stride=1, win_size=8, shift_size=None, num_heads=8,
              qkv_bias=True, attn_drop=0.):
  
          super().__init__()
          self.dim_out = dim_out or dim
          self.feat_size = to_2tuple(feat_size)
          self.win_size = win_size
          self.shift_size = shift_size or win_size // 2
          if min(self.feat_size) <= win_size:
              # if window size is larger than input resolution, we don't partition windows
              self.shift_size = 0
              self.win_size = min(self.feat_size)
          assert 0 <= self.shift_size < self.win_size, "shift_size must in 0-window_size"
          self.num_heads = num_heads
          head_dim = self.dim_out // num_heads
          self.scale = head_dim ** -0.5
  
          if self.shift_size > 0:
              # calculate attention mask for SW-MSA
              H, W = self.feat_size
              img_mask = jt.zeros((1, H, W, 1))  # 1 H W 1
              h_slices = (
                  slice(0, -self.win_size),
                  slice(-self.win_size, -self.shift_size),
                  slice(-self.shift_size, None))
              w_slices = (
                  slice(0, -self.win_size),
                  slice(-self.win_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.win_size)  # num_win, window_size, window_size, 1
              mask_windows = mask_windows.view(-1, self.win_size * self.win_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)
          self._attn_mask = attn_mask
  
          # define a parameter table of relative position bias
          self.relative_position_bias_table = jt.zeros((2 * self.win_size - 1) * (2 * self.win_size - 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 = jt.arange(self.win_size)
          coords_w = jt.arange(self.win_size)
          coords = jt.stack(jt.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
          coords_flatten = jt.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.win_size - 1  # shift to start from 0
          relative_coords[:, :, 1] += self.win_size - 1
          relative_coords[:, :, 0] *= 2 * self.win_size - 1
          relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
          # self.register_buffer("relative_position_index", relative_position_index)
          self._relative_position_index = relative_position_index
  
          self.qkv = nn.Linear(dim, self.dim_out * 3, bias=qkv_bias)
          self.attn_drop = nn.Dropout(attn_drop)
          self.softmax = nn.Softmax(dim=-1)
          self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
  
      def reset_parameters(self):
          trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5)
          trunc_normal_(self.relative_position_bias_table, std=.02)
  
      def execute(self, x):
          B, C, H, W = x.shape
          x = x.permute(0, 2, 3, 1)
  
          # cyclic shift
          if self.shift_size > 0:
              shifted_x = jt.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
          else:
              shifted_x = x
  
          # partition windows
          win_size_sq = self.win_size * self.win_size
          x_windows = window_partition(shifted_x, self.win_size)  # num_win * B, window_size, window_size, C
          x_windows = x_windows.view(-1, win_size_sq, C)  # num_win * B, window_size*window_size, C
          BW, N, _ = x_windows.shape
  
          qkv = self.qkv(x_windows)
          qkv = qkv.reshape(BW, N, 3, self.num_heads, self.dim_out // self.num_heads).permute(2, 0, 3, 1, 4)
          q, k, v = qkv[0], qkv[1], qkv[2]
          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(win_size_sq, win_size_sq, -1)
          relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh * Ww, Wh * Ww
          attn = attn + relative_position_bias.unsqueeze(0)
          if self._attn_mask is not None:
              num_win = self._attn_mask.shape[0]
              attn = attn.view(B, num_win, self.num_heads, N, N) + self._attn_mask.unsqueeze(1).unsqueeze(0)
              attn = attn.view(-1, self.num_heads, N, N)
          attn = self.softmax(attn)
          attn = self.attn_drop(attn)
  
          x = (attn @ v).transpose(0, 2, 1, 3).reshape(BW, N, self.dim_out)
  
          # merge windows
          x = x.view(-1, self.win_size, self.win_size, self.dim_out)
          shifted_x = window_reverse(x, self.win_size, H, W)  # B H' W' C
  
          # reverse cyclic shift
          if self.shift_size > 0:
              x = jt.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
          else:
              x = shifted_x
          x = x.view(B, H, W, self.dim_out).permute(0, 3, 1, 2)
          x = self.pool(x)
          return x
  