content: import math
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from functools import partial
  
  from modeling_finetune import Block, _cfg, PatchEmbed, get_sinusoid_encoding_table
  from timm.models.registry import register_model
  from timm.models.layers import trunc_normal_ as __call_trunc_normal_
  
  
  def trunc_normal_(tensor, mean=0., std=1.):
      __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std)
  
  
  __all__ = [
      'pretrain_videomae_base_patch16_224',
      'pretrain_videomae_large_patch16_224',
  ]
  
  
  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, num_classes=0, 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, tubelet_size=2,
                   use_learnable_pos_emb=False):
          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, tubelet_size=tubelet_size)
          num_patches = self.patch_embed.num_patches
  
          # TODO: Add the cls token
          if use_learnable_pos_emb:
              self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
          else:
              # sine-cosine positional embeddings
              self.pos_embed = get_sinusoid_encoding_table(num_patches, 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(depth)])
          self.norm = norm_layer(embed_dim)
          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)
  
          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)
  
      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, mask):
          _, _, T, _, _ = x.shape
          x = self.patch_embed(x)
  
          x = x + self.pos_embed.type_as(x).to(x.device).clone().detach()
  
          B, _, C = x.shape
          x_vis = x[~mask].reshape(B, -1, C)  # ~mask means visible
  
          for blk in self.blocks:
              x_vis = blk(x_vis)
  
          x_vis = self.norm(x_vis)
          return x_vis
  
      def forward(self, x, mask):
          x = self.forward_features(x, mask)
          x = self.head(x)
          return x
  
  
  class PretrainVisionTransformerDecoder(nn.Module):
      """ Vision Transformer with support for patch or hybrid CNN input stage
      """
  
      def __init__(self, patch_size=16, num_classes=768, 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_patches=196, tubelet_size=2
                   ):
          super().__init__()
          self.num_classes = num_classes
          assert num_classes == 3 * tubelet_size * patch_size ** 2
          self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
          self.patch_size = patch_size
  
          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 = norm_layer(embed_dim)
          self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
  
          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)
  
      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(self, x, return_token_num):
          for blk in self.blocks:
              x = blk(x)
  
          if return_token_num > 0:
              x = self.head(self.norm(x[:, -return_token_num:]))  # only return the mask tokens predict pixels
          else:
              x = self.head(self.norm(x))
  
          return x
  
  
  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_num_classes=0,
                   encoder_embed_dim=768,
                   encoder_depth=12,
                   encoder_num_heads=12,
                   decoder_num_classes=1536,  # decoder_num_classes=768,
                   decoder_embed_dim=512,
                   decoder_depth=8,
                   decoder_num_heads=8,
                   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,
                   tubelet_size=2,
                   num_classes=0,  # avoid the error from create_fn in timm
                   in_chans=0,  # avoid the error from create_fn in timm
                   ):
          super().__init__()
          self.encoder = PretrainVisionTransformerEncoder(
              img_size=img_size,
              patch_size=patch_size,
              in_chans=encoder_in_chans,
              num_classes=encoder_num_classes,
              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,
              tubelet_size=tubelet_size,
              use_learnable_pos_emb=use_learnable_pos_emb)
  
          self.decoder = PretrainVisionTransformerDecoder(
              patch_size=patch_size,
              num_patches=self.encoder.patch_embed.num_patches,
              num_classes=decoder_num_classes,
              embed_dim=decoder_embed_dim,
              depth=decoder_depth,
              num_heads=decoder_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,
              tubelet_size=tubelet_size)
  
          self.encoder_to_decoder = nn.Linear(encoder_embed_dim, decoder_embed_dim, bias=False)
  
          self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
  
          self.pos_embed = get_sinusoid_encoding_table(self.encoder.patch_embed.num_patches, decoder_embed_dim)
  
          trunc_normal_(self.mask_token, std=.02)
  
      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)
  
      def get_num_layers(self):
          return len(self.blocks)
  
      @torch.jit.ignore
      def no_weight_decay(self):
          return {'pos_embed', 'cls_token', 'mask_token'}
  
      def forward(self, x, mask):
          _, _, T, _, _ = x.shape
          x_vis = self.encoder(x, mask)  # [B, N_vis, C_e]
          x_vis = self.encoder_to_decoder(x_vis)  # [B, N_vis, C_d]
          B, N, C = x_vis.shape
          # we don't unshuffle the correct visible token order,
          # but shuffle the pos embedding accorddingly.
          expand_pos_embed = self.pos_embed.expand(B, -1, -1).type_as(x).to(x.device).clone().detach()
          pos_emd_vis = expand_pos_embed[~mask].reshape(B, -1, C)
          pos_emd_mask = expand_pos_embed[mask].reshape(B, -1, C)
          x_full = torch.cat([x_vis + pos_emd_vis, self.mask_token + pos_emd_mask], dim=1)  # [B, N, C_d]
          x = self.decoder(x_full, pos_emd_mask.shape[1])  # [B, N_mask, 3 * 16 * 16]
  
          return x
  
  
  @register_model
  def pretrain_mae_small_patch16_224(pretrained=False, **kwargs):
      model = PretrainVisionTransformer(
          img_size=224,
          patch_size=16,
          encoder_embed_dim=384,
          encoder_depth=12,
          encoder_num_heads=6,
          encoder_num_classes=0,
          decoder_num_classes=1536,
          decoder_embed_dim=192,
          decoder_num_heads=3,
          mlp_ratio=4,
          qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6),
          **kwargs)
      model.default_cfg = _cfg()
      if pretrained:
          checkpoint = torch.load(
              kwargs["init_ckpt"], map_location="cpu"
          )
          model.load_state_dict(checkpoint["model"])
      return model
  
  
  @register_model
  def pretrain_videomae_base_patch16_224(pretrained=False, **kwargs):
      model = PretrainVisionTransformer(
          img_size=224,
          patch_size=16,
          encoder_embed_dim=768,
          encoder_depth=12,
          encoder_num_heads=12,
          encoder_num_classes=0,
          decoder_num_classes=1536,
          decoder_embed_dim=384,
          decoder_num_heads=6,
          mlp_ratio=4,
          qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6),
          **kwargs)
      model.default_cfg = _cfg()
      if pretrained:
          checkpoint = torch.load(
              kwargs["init_ckpt"], map_location="cpu"
          )
          model.load_state_dict(checkpoint["model"])
      return model
  
  
  @register_model
  def pretrain_videomae_large_patch16_224(pretrained=False, **kwargs):
      model = PretrainVisionTransformer(
          img_size=224,
          patch_size=16,
          encoder_embed_dim=1024,
          encoder_depth=24,
          encoder_num_heads=16,
          encoder_num_classes=0,
          decoder_num_classes=1536,
          decoder_embed_dim=512,
          decoder_num_heads=8,
          mlp_ratio=4,
          qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6),
          **kwargs)
      model.default_cfg = _cfg()
      if pretrained:
          checkpoint = torch.load(
              kwargs["init_ckpt"], map_location="cpu"
          )
          model.load_state_dict(checkpoint["model"])
      return model