content: import os
  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 flash_attn import flash_attn_func
  
  
  MODEL_PATH = 'your_model_path/videomae'
  _MODELS = {
      # see videomaev2
      "vit_g14_hybrid": os.path.join(MODEL_PATH, "vit_g_hybrid_1200e_pre.pth"),
  }
  
  
  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)
  
          x = flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=self.scale, causal=False).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, cur_frame=-1, pre_n_position=1568): 
      ''' 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)] 
      
      # generate checkpoint position embedding
      sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(pre_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)
      print(f"n_position: {n_position}")
      print(f"pre_n_position: {pre_n_position}")
      if n_position // cur_frame * 8 != pre_n_position and cur_frame != -1:
          T = 8 # checkpoint frame
          P = 14 # checkpoint size
          C = d_hid
          new_P = int((n_position // cur_frame) ** 0.5) # testing size
          print(f'Pretraining uses 14x14, but current version is {new_P}x{new_P}')
          print(f'Interpolate the position embedding')
          sinusoid_table = sinusoid_table.reshape(-1, T, P, P, C)
          sinusoid_table = sinusoid_table.reshape(-1, P, P, C).permute(0, 3, 1, 2)
          sinusoid_table = torch.nn.functional.interpolate(
              sinusoid_table, size=(new_P, new_P), mode='bicubic', align_corners=False)
          # BT, C, H, W -> BT, H, W, C ->  B, T, H, W, C
          sinusoid_table = sinusoid_table.permute(0, 2, 3, 1).reshape(-1, T, new_P, new_P, C)
          sinusoid_table = sinusoid_table.flatten(1, 3)  # B, THW, C
      if cur_frame != -1 and cur_frame != 8:
          print(f'Pretraining uses 8 frames, but current frame is {cur_frame}')
          print(f'Interpolate the position embedding')
          T = 8 # checkpoint frame
          new_T = cur_frame # testing frame
          # interpolate
          P = int((n_position // cur_frame) ** 0.5) # testing size
          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)  # B, THW, C
      if n_position == pre_n_position:
          return sinusoid_table
      else:
          print("Use learnable position embedding")
          return nn.Parameter(sinusoid_table, requires_grad=True)
  
  
  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, 
              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.,
              all_frames=16,
              tubelet_size=2,
              mae_norm_type='l2',
              mae_return_layer=1,
              mae_return_interval=1,
          ):
          super().__init__()
          self.mae_norm_type = mae_norm_type
          self.return_index = []
          for i in range(mae_return_layer):
              self.return_index.append(depth - int(i * mae_return_interval) - 1)
          print(f'Normalization Type: {mae_norm_type}')
          print(f'MAE Teacher return index: : {self.return_index}')
  
          self.tubelet_size = tubelet_size
          self.depth = depth
          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
  
          # sine-cosine positional embeddings is on the way
          if patch_size == 14:
              pre_n_position = 2048
          else:
              pre_n_position = 1568
          self.pos_embed = get_sinusoid_encoding_table(
              num_patches, embed_dim, all_frames // tubelet_size,
              pre_n_position=pre_n_position
          )
  
          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 = norm_layer(embed_dim)
  
          self.apply(self._init_weights)
  
      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 forward(self, x, mask=None):
          x = self.patch_embed(x)
          B, _, C = 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)
  
          if mask is not None:
              x = x[~mask].reshape(B, -1, C) # ~mask means visible
  
          z = []
          for idx, blk in enumerate(self.blocks):
              x = blk(x)
              if idx == self.depth - 1:
                  x = self.norm(x)
              if idx in self.return_index:
                  z.append(x)
          x = torch.stack(z)
  
          if self.mae_norm_type == 'l2':
              x = x / x.norm(dim=-1, keepdim=True)
          elif self.mae_norm_type == 'none':
              pass
          else:
              raise NotImplementedError
          
          return x
      
  
  def load_state_dict(model, state_dict):
      from collections import OrderedDict
      new_state_dict = OrderedDict()
      for k, v in state_dict.items():
          if k.startswith('encoder.'):
              new_k = k[8:]
              if new_k == "patch_embed.proj.weight" and model.tubelet_size == 1:
                  print("Kernel pooling")
                  v = v.mean(dim=2, keepdim=True)
              new_state_dict[new_k] = v
      msg = model.load_state_dict(new_state_dict)
      print(msg)
  
  
  def mae_g14_hybrid(pretrained=True, **kwargs):
      model = VisionTransformer(
          patch_size=14, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=48/11, qkv_bias=True,
          norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
      model.default_cfg = _cfg()
      if pretrained:
          print('load MAE pretrained weights')
          state_dict = torch.load(_MODELS["vit_g14_hybrid"], map_location='cpu')
          load_state_dict(model, state_dict['model'])
      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 = 16
  
      model = mae_g14_hybrid(all_frames=num_frames, tubelet_size=2).cuda().half()
      # print(model)
  
      flops = FlopCountAnalysis(model, torch.rand(1, 3, num_frames, 224, 224).cuda().half())
      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)).shape)