text: # --------------------------------------------------------
  # BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
  # Github source: https://github.com/microsoft/unilm/tree/master/beit
  # Copyright (c) 2021 Microsoft
  # Licensed under The MIT License [see LICENSE for details]
  # By Hangbo Bao
  # Based on timm and DeiT code bases
  # https://github.com/rwightman/pytorch-image-models/tree/master/timm
  # https://github.com/facebookresearch/deit/
  # https://github.com/facebookresearch/dino
  # --------------------------------------------------------'
  import math
  from functools import partial
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from timm.models.layers import drop_path, to_2tuple, trunc_normal_
  from timm.models.registry import register_model
  
  
  def _cfg(url='', **kwargs):
      return {
          'url': url,
          'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
          'crop_pct': .9, 'interpolation': 'bicubic',
          'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
          **kwargs
      }
  
  
  class DropPath(nn.Module):
      """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
      """
      def __init__(self, drop_prob=None):
          super(DropPath, self).__init__()
          self.drop_prob = drop_prob
  
      def forward(self, x):
          return drop_path(x, self.drop_prob, self.training)
      
      def extra_repr(self) -> str:
          return 'p={}'.format(self.drop_prob)
  
  
  class Mlp(nn.Module):
      def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
          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.act = act_layer()
          self.fc2 = nn.Linear(hidden_features, out_features)
          self.drop = nn.Dropout(drop)
  
      def forward(self, x):
          x = self.fc1(x)
          x = self.act(x)
          # x = self.drop(x)
          # commit this for the orignal BERT implement 
          x = self.fc2(x)
          x = self.drop(x)
          return x
  
  
  class Attention(nn.Module):
      def __init__(
              self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
              proj_drop=0., window_size=None, attn_head_dim=None):
          super().__init__()
          self.num_heads = num_heads
          head_dim = dim // num_heads
          if attn_head_dim is not None:
              head_dim = attn_head_dim
          all_head_dim = head_dim * self.num_heads
          self.scale = qk_scale or head_dim ** -0.5
  
          self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
          if qkv_bias:
              self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
              self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
          else:
              self.q_bias = None
              self.v_bias = None
  
          if window_size:
              self.window_size = window_size
              self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
              self.relative_position_bias_table = nn.Parameter(
                  torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
              # cls to token & token 2 cls & cls to cls
  
              # get pair-wise relative position index for each token inside the window
              coords_h = torch.arange(window_size[0])
              coords_w = torch.arange(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] += window_size[0] - 1  # shift to start from 0
              relative_coords[:, :, 1] += window_size[1] - 1
              relative_coords[:, :, 0] *= 2 * window_size[1] - 1
              relative_position_index = \
                  torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype)
              relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
              relative_position_index[0, 0:] = self.num_relative_distance - 3
              relative_position_index[0:, 0] = self.num_relative_distance - 2
              relative_position_index[0, 0] = self.num_relative_distance - 1
  
              self.register_buffer("relative_position_index", relative_position_index)
          else:
              self.window_size = None
              self.relative_position_bias_table = None
              self.relative_position_index = None
  
          self.attn_drop = nn.Dropout(attn_drop)
          self.proj = nn.Linear(all_head_dim, dim)
          self.proj_drop = nn.Dropout(proj_drop)
  
      def forward(self, x, rel_pos_bias=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 = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
          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[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
  
          q = q * self.scale
          attn = (q @ k.transpose(-2, -1))
  
          if self.relative_position_bias_table is not None:
              relative_position_bias = \
                  self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
                      self.window_size[0] * self.window_size[1] + 1,
                      self.window_size[0] * self.window_size[1] + 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 rel_pos_bias is not None:
              attn = attn + rel_pos_bias
          
          attn = attn.softmax(dim=-1)
          attn = self.attn_drop(attn)
  
          x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
          x = self.proj(x)
          x = self.proj_drop(x)
          return x
  
  
  class Block(nn.Module):
  
      def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                   drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
                   window_size=None, attn_head_dim=None):
          super().__init__()
          self.norm1 = norm_layer(dim)
          self.attn = Attention(
              dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
              attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim)
          # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
          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 = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
  
          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 = None, None
  
      def forward(self, x, rel_pos_bias=None):
          if self.gamma_1 is None:
              x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
              x = x + self.drop_path(self.mlp(self.norm2(x)))
          else:
              x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
              x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
          return x
  
  
  class PatchEmbed(nn.Module):
      """ Image to Patch Embedding
      """
      def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
          super().__init__()
          img_size = to_2tuple(img_size)
          patch_size = to_2tuple(patch_size)
          num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
          self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
          self.img_size = img_size
          self.patch_size = patch_size
          self.num_patches = num_patches
  
          self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
  
      def forward(self, x, **kwargs):
          B, C, H, W = x.shape
          # FIXME look at relaxing size constraints
          assert H == self.img_size[0] and W == self.img_size[1], \
              f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
          x = self.proj(x).flatten(2).transpose(1, 2)
          return x
  
  
  class RelativePositionBias(nn.Module):
  
      def __init__(self, window_size, num_heads):
          super().__init__()
          self.window_size = window_size
          self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
          self.relative_position_bias_table = nn.Parameter(
              torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
          # cls to token & token 2 cls & cls to cls
  
          # get pair-wise relative position index for each token inside the window
          coords_h = torch.arange(window_size[0])
          coords_w = torch.arange(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] += window_size[0] - 1  # shift to start from 0
          relative_coords[:, :, 1] += window_size[1] - 1
          relative_coords[:, :, 0] *= 2 * window_size[1] - 1
          relative_position_index = \
              torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
          relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
          relative_position_index[0, 0:] = self.num_relative_distance - 3
          relative_position_index[0:, 0] = self.num_relative_distance - 2
          relative_position_index[0, 0] = self.num_relative_distance - 1
  
          self.register_buffer("relative_position_index", relative_position_index)
  
          # trunc_normal_(self.relative_position_bias_table, std=.02)
  
      def forward(self):
          relative_position_bias = \
              self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
                  self.window_size[0] * self.window_size[1] + 1,
                  self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
          return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
  
  
  class VisionTransformer(nn.Module):
      """ Vision Transformer with support for patch or hybrid CNN input stage
      """
      def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                   num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
                   drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None,
                   use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False,
                   use_mean_pooling=True, init_scale=0.001):
          super().__init__()
          self.num_classes = num_classes
          self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
  
          self.patch_embed = PatchEmbed(
              img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
          num_patches = self.patch_embed.num_patches
  
          self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
          # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
          if use_abs_pos_emb:
              self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
          else:
              self.pos_embed = None
          self.pos_drop = nn.Dropout(p=drop_rate)
  
          if use_shared_rel_pos_bias:
              self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
          else:
              self.rel_pos_bias = None
  
          dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
          self.use_rel_pos_bias = use_rel_pos_bias
          self.blocks = nn.ModuleList([
              Block(
                  dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
                  drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                  init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None)
              for i in range(depth)])
          self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
          self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
          self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
  
          if self.pos_embed is not None:
              trunc_normal_(self.pos_embed, std=.02)
          trunc_normal_(self.cls_token, std=.02)
          # trunc_normal_(self.mask_token, std=.02)
          if isinstance(self.head, nn.Linear):
              trunc_normal_(self.head.weight, std=.02)
          self.apply(self._init_weights)
          self.fix_init_weight()
  
          if isinstance(self.head, nn.Linear):
              self.head.weight.data.mul_(init_scale)
              self.head.bias.data.mul_(init_scale)
  
      def fix_init_weight(self):
          def rescale(param, layer_id):
              param.div_(math.sqrt(2.0 * layer_id))
  
          for layer_id, layer in enumerate(self.blocks):
              rescale(layer.attn.proj.weight.data, layer_id + 1)
              rescale(layer.mlp.fc2.weight.data, layer_id + 1)
  
      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)
          elif isinstance(m, nn.LayerNorm):
              nn.init.constant_(m.bias, 0)
              nn.init.constant_(m.weight, 1.0)
  
      def get_num_layers(self):
          return len(self.blocks)
  
      @torch.jit.ignore
      def no_weight_decay(self):
          return {'pos_embed', 'cls_token'}
  
      def get_classifier(self):
          return self.head
  
      def reset_classifier(self, num_classes, global_pool=''):
          self.num_classes = num_classes
          self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
  
      def forward_features(self, x):
          x = self.patch_embed(x)
          batch_size, seq_len, _ = x.size()
  
          cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
          x = torch.cat((cls_tokens, x), dim=1)
          if self.pos_embed is not None:
              x = x + self.pos_embed
          x = self.pos_drop(x)
  
          rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
          for blk in self.blocks:
              x = blk(x, rel_pos_bias=rel_pos_bias)
  
          x = self.norm(x)
          if self.fc_norm is not None:
              t = x[:, 1:, :]
              return self.fc_norm(t.mean(1))
          else:
              return x[:, 0]
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.head(x)
          return x
  
      def get_intermediate_layers(self, x):
          x = self.patch_embed(x)
          batch_size, seq_len, _ = x.size()
  
          cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
          x = torch.cat((cls_tokens, x), dim=1)
          if self.pos_embed is not None:
              x = x + self.pos_embed
          x = self.pos_drop(x)
  
          features = []
          rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
          for blk in self.blocks:
              x = blk(x, rel_pos_bias)
              features.append(x)
  
          return features
  
  
  @register_model
  def beit_base_patch16_224(pretrained=False, **kwargs):
      model = VisionTransformer(
          patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model.default_cfg = _cfg()
      return model
  
  
  @register_model
  def beit_base_patch16_384(pretrained=False, **kwargs):
      model = VisionTransformer(
          img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model.default_cfg = _cfg()
      return model
  
  
  @register_model
  def beit_large_patch16_224(pretrained=False, **kwargs):
      model = VisionTransformer(
          patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model.default_cfg = _cfg()
      return model
  
  
  @register_model
  def beit_large_patch16_384(pretrained=False, **kwargs):
      model = VisionTransformer(
          img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model.default_cfg = _cfg()
      return model
  
  
  @register_model
  def beit_large_patch16_512(pretrained=False, **kwargs):
      model = VisionTransformer(
          img_size=512, patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model.default_cfg = _cfg()
      return model
  