content: import logging
  import numpy as np
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.checkpoint as checkpoint
  from functools import partial
  
  from timm.models.layers import drop_path, to_2tuple, trunc_normal_
  
  logger = logging.getLogger(__name__)
  
  
  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)
          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])
          )
          logger.info(f'Num of patches: {num_patches}')
  
      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, ckpt_num_frame=-1, cur_frame=12): 
      ''' 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)] 
      
      if ckpt_num_frame != -1 and ckpt_num_frame != cur_frame:
          logger.info(f"Interpolate position embedding")
          logger.info(f"Testing frame: {cur_frame}")
          logger.info(f"Checkpoint frame: {ckpt_num_frame}")
  
          T = ckpt_num_frame # checkpoint frame
          new_T = cur_frame # testing frame
          n_position = n_position // new_T * T # generate checkpoint position embedding
          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 
          sinusoid_table = torch.tensor(sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0)
          # interpolate
          P = int((n_position // T) ** 0.5)
          C = d_hid
          sinusoid_table = sinusoid_table.reshape(-1, T, P, P, C)
          sinusoid_table = sinusoid_table.permute(0, 2, 3, 4, 1).reshape(-1, C, T)  # BHW, C, T
          sinusoid_table = torch.nn.functional.interpolate(sinusoid_table, size=new_T, mode='linear')
          sinusoid_table = sinusoid_table.reshape(1, P, P, C, new_T).permute(0, 4, 1, 2, 3) # B, T, H, W, C
          sinusoid_table = sinusoid_table.flatten(1, 3)
          return sinusoid_table
      else:
          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.tensor(sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0) 
  
  
  class PretrainVisionTransformerEncoder(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, 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, num_frames=8, tubelet_size=1,
                   use_learnable_pos_emb=False,
                   use_checkpoint=False, checkpoint_num=0, 
                   ckpt_num_frame=-1, with_ln=True, return_index=-1
                   ):
          super().__init__()
          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_frames=num_frames, tubelet_size=tubelet_size
          )
          num_patches = self.patch_embed.num_patches
          self.depth = depth + return_index + 1
          self.use_checkpoint = use_checkpoint
          self.checkpoint_num = checkpoint_num
          logger.info(f"Use checkpoint: {use_checkpoint}")
          logger.info(f"Checkpoint number: {checkpoint_num}")
          logger.info(f"Real runing depth: {self.depth}")
  
          # TODO: Add the cls token
          if use_learnable_pos_emb:
              self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
              self.img_pos_embed = nn.Parameter(torch.zeros(1, num_patches//(num_frames//tubelet_size) + 1, embed_dim))
          else:
              # sine-cosine positional embeddings 
              self.pos_embed = get_sinusoid_encoding_table(num_patches, embed_dim, ckpt_num_frame=ckpt_num_frame, cur_frame=num_frames//tubelet_size)
              self.img_pos_embed = get_sinusoid_encoding_table(num_patches//(num_frames//tubelet_size), embed_dim)
  
          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(self.depth)])
          
          if with_ln:
              self.norm = norm_layer(embed_dim)
          else:
              self.norm = nn.Identity()
  
          if use_learnable_pos_emb:
              trunc_normal_(self.pos_embed, std=.02)
  
      @torch.jit.ignore
      def no_weight_decay(self):
          return {'pos_embed', 'cls_token'}
  
      def forward_features(self, x, use_image=False):
          x = self.patch_embed(x)
          
          if use_image:
              x = x + self.img_pos_embed.type_as(x).to(x.device).clone().detach()
          else:
              x = x + self.pos_embed.type_as(x).to(x.device).clone().detach()
  
          B, _, C = x.shape
          x_vis = x
  
          for idx, blk in enumerate(self.blocks):
              if self.use_checkpoint and idx < self.checkpoint_num:
                  x_vis = checkpoint.checkpoint(blk, x_vis)
              else:
                  x_vis = blk(x_vis)
  
          # with ln ot not
          x_vis = self.norm(x_vis)
          return x_vis
  
      def forward(self, x, use_image=False):
          x_vis = self.forward_features(x, use_image)
          return x_vis
  
  
  class PretrainVisionTransformer(nn.Module):
      """ Vision Transformer with support for patch or hybrid CNN input stage
      """
      def __init__(self,
                   img_size=224, 
                   patch_size=16, 
                   encoder_in_chans=3, 
                   encoder_embed_dim=768, 
                   encoder_depth=12,
                   encoder_num_heads=12, 
                   mlp_ratio=4., 
                   qkv_bias=True, 
                   qk_scale=None, 
                   drop_rate=0., 
                   attn_drop_rate=0.,
                   drop_path_rate=0., 
                   norm_layer=partial(nn.LayerNorm, eps=1e-6), 
                   init_values=0.,
                   use_learnable_pos_emb=False,
                   num_frames=8,
                   tubelet_size=1,
                   use_checkpoint=False,
                   checkpoint_num=0,
                   ckpt_num_frame=-1,
                   return_index=-1,
                   with_ln=False
                  ):
          super().__init__()
  
          self.encoder = PretrainVisionTransformerEncoder(
              img_size=img_size, 
              patch_size=patch_size, 
              in_chans=encoder_in_chans, 
              embed_dim=encoder_embed_dim, 
              depth=encoder_depth,
              num_heads=encoder_num_heads, 
              mlp_ratio=mlp_ratio, 
              qkv_bias=qkv_bias, 
              qk_scale=qk_scale, 
              drop_rate=drop_rate, 
              attn_drop_rate=attn_drop_rate,
              drop_path_rate=drop_path_rate, 
              norm_layer=norm_layer, 
              init_values=init_values,
              num_frames=num_frames,
              tubelet_size=tubelet_size,
              use_learnable_pos_emb=use_learnable_pos_emb,
              use_checkpoint=use_checkpoint,
              checkpoint_num=checkpoint_num,
              ckpt_num_frame=ckpt_num_frame,
              with_ln=with_ln,
              return_index=return_index
          )
          logger.info(f'With LN: {with_ln}')
          logger.info(f'Total {encoder_depth} layer')
          logger.info(f'Return {encoder_depth+return_index+1}-th layer')
  
          self.apply(self._init_weights)
  
      def _init_weights(self, m):
          if isinstance(m, nn.Linear):
              nn.init.xavier_uniform_(m.weight)
              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)
  
      @torch.jit.ignore
      def no_weight_decay(self):
          return {'pos_embed', 'cls_token', 'clip_pos_embed'}
  
      def forward(self, x, use_image=False):
          T = x.shape[2]
          x_vis = self.encoder(x, use_image) # [B, N_vis, C_e]
          B, TL, C = x_vis.shape
          x_vis = x_vis.view(B, T, TL // T, C)
  
          return x_vis
  
  
  def build_vit_clean(config):
      model = PretrainVisionTransformer(
          img_size=config.vision_encoder.img_size, 
          patch_size=config.vision_encoder.patch_size, 
          encoder_embed_dim=config.vision_encoder.encoder_embed_dim, 
          encoder_depth=config.vision_encoder.encoder_depth,
          encoder_num_heads=config.vision_encoder.encoder_num_heads, 
          drop_path_rate=config.vision_encoder.drop_path_rate, 
          num_frames=config.vision_encoder.num_frames,
          tubelet_size=config.vision_encoder.tubelet_size,
          use_checkpoint=config.vision_encoder.use_checkpoint,
          checkpoint_num=config.vision_encoder.checkpoint_num,
          ckpt_num_frame=config.vision_encoder.get('ckpt_num_frame', -1),
          return_index=config.vision_encoder.get('return_index', -1),
          with_ln=config.vision_encoder.get('with_ln', False),
      )
      model.default_cfg = _cfg()
      if config.vision_encoder.pretrained is not None:
          logger.info(f"Loading pretrained weights from {config.vision_encoder.pretrained}")
          state_dict = torch.load(config.vision_encoder.pretrained, map_location='cpu')
          model.load_state_dict(state_dict, strict=False)
      else:
          logger.info("No pretrained weights!!!")
      return model
   
  
  if __name__ == '__main__':
      import time
      from fvcore.nn import FlopCountAnalysis
      from fvcore.nn import flop_count_table
      import numpy as np
  
      seed = 4217
      np.random.seed(seed)
      torch.manual_seed(seed)
      torch.cuda.manual_seed(seed)
      torch.cuda.manual_seed_all(seed)
      num_frames = 4
  
      config = {
          'vision_encoder':
              {
              'img_size': 224,
              'patch_size': 16,
              'encoder_embed_dim': 768, 
              'encoder_depth': 12,
              'encoder_num_heads': 12, 
              'drop_path_rate': 0.1, 
              'num_frames': num_frames,
              'tubelet_size': 1,
              'use_checkpoint': False,
              'checkpoint_num': 0,
              'pretrained': '',
              'ckpt_num_frame': 8,
              'return_index': -1,
              'with_ln': False,
          }
      }
      from easydict import EasyDict
      model = build_vit_clean(EasyDict(config))
  
      # flops = FlopCountAnalysis(model, torch.rand(1, 3, num_frames, 224, 224))
      # s = time.time()
      # print(flop_count_table(flops, max_depth=1))
      # print(time.time()-s)
      print(model(torch.rand(1, 3, num_frames, 224, 224), False).shape)