content: from functools import partial
  import numpy as np
  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': 400, '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., 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
  
          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):
          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))
  
          
          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,
                   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, 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 > 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):
          if self.gamma_1 is None:
              x = x + self.drop_path(self.attn(self.norm1(x)))
              x = x + self.drop_path(self.mlp(self.norm2(x)))
          else:
              x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
              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, num_frames=16, tubelet_size=2):
          super().__init__()
          img_size = to_2tuple(img_size)
          patch_size = to_2tuple(patch_size)
          self.tubelet_size = int(tubelet_size)
          num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) * (num_frames // self.tubelet_size)
          self.img_size = img_size
          self.patch_size = patch_size
          self.num_patches = num_patches
          self.proj = nn.Conv3d(in_channels=in_chans, out_channels=embed_dim, 
                              kernel_size = (self.tubelet_size,  patch_size[0],patch_size[1]), 
                              stride=(self.tubelet_size,  patch_size[0],  patch_size[1]))
  
      def forward(self, x, **kwargs):
          B, C, T, 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
      
  # sin-cos position encoding
  # https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31
  def get_sinusoid_encoding_table(n_position, d_hid): 
      ''' Sinusoid position encoding table ''' 
      # TODO: make it with torch instead of numpy 
      def get_position_angle_vec(position): 
          return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] 
  
      sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) 
      sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i 
      sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 
  
      return torch.FloatTensor(sinusoid_table).unsqueeze(0) 
  
  
  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=0.,
                   use_learnable_pos_emb=False, 
                   init_scale=0.,
                   all_frames=16,
                   tubelet_size=2,
                   use_mean_pooling=True):
          super().__init__()
          self.num_classes = num_classes
          self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
          self.tubelet_size = tubelet_size
          self.patch_embed = PatchEmbed(
              img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, num_frames=all_frames, tubelet_size=self.tubelet_size)
          num_patches = self.patch_embed.num_patches
  
          if use_learnable_pos_emb:
              self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
          else:
              # sine-cosine positional embeddings is on the way
              self.pos_embed = get_sinusoid_encoding_table(num_patches, embed_dim)
  
          self.pos_drop = nn.Dropout(p=drop_rate)
  
  
          dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
          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)
              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 use_learnable_pos_emb:
              trunc_normal_(self.pos_embed, std=.02)
  
          trunc_normal_(self.head.weight, std=.02)
          self.apply(self._init_weights)
  
          self.head.weight.data.mul_(init_scale)
          self.head.bias.data.mul_(init_scale)
  
      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)
          B, _, _ = x.size()
  
          if self.pos_embed is not None:
              x = x + self.pos_embed.expand(B, -1, -1).type_as(x).to(x.device).clone().detach()
          x = self.pos_drop(x)
  
          for blk in self.blocks:
              x = blk(x)
  
          x = self.norm(x)
          if self.fc_norm is not None:
              return self.fc_norm(x.mean(1))
          else:
              return x[:, 0]
  
      def get_spatial_features(self, x):
          x = self.patch_embed(x)
          B, _, _ = x.size()
  
          if self.pos_embed is not None:
              x = x + self.pos_embed.expand(B, -1, -1).type_as(x).to(x.device).clone().detach()
          x = self.pos_drop(x)
  
          for blk in self.blocks:
              x = blk(x)
  
          x = self.norm(x)
          x = x.transpose(1,2).reshape(-1,768,8,14,14).mean(2)
  
          return x
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.head(x)
          return x
  
  @register_model
  def vit_small_patch16_224(pretrained=False, **kwargs):
      model = VisionTransformer(
          patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model.default_cfg = _cfg()
      return model
  
  @register_model
  def vit_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 vit_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 vit_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 vit_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 vit_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
  