content: from collections import OrderedDict
  from typing import Tuple, Union
  
  import numpy as np
  import torch
  import torch.nn.functional as F
  from torch import nn
  from einops import rearrange
  
  from . import evl_utils
  from .evl_utils import TransformerDecoder_uniformer_diff_conv_balance
  
  class LayerNorm(nn.LayerNorm):
      """Subclass torch's LayerNorm to handle fp16."""
  
      def forward(self, x: torch.Tensor):
          orig_type = x.dtype
          ret = super().forward(x.type(torch.float32))
          return ret.type(orig_type)
  
  
  class QuickGELU(nn.Module):
      def forward(self, x: torch.Tensor):
          return x * torch.sigmoid(1.702 * x)
  
  
  class ResidualAttentionBlock(nn.Module):
      def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
          super().__init__()
  
          self.attn = nn.MultiheadAttention(d_model, n_head)
          self.ln_1 = LayerNorm(d_model)
          self.mlp = nn.Sequential(OrderedDict([
              ("c_fc", nn.Linear(d_model, d_model * 4)),
              ("gelu", QuickGELU()),
              ("c_proj", nn.Linear(d_model * 4, d_model))
          ]))
          self.ln_2 = LayerNorm(d_model)
          self.attn_mask = attn_mask
  
      def attention(self, x: torch.Tensor):
          self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
          return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
  
      def forward(self, x: torch.Tensor):
          x = x + self.attention(self.ln_1(x))
          x = x + self.mlp(self.ln_2(x))
          return x
  
  
  class Transformer(nn.Module):
      def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
          super().__init__()
          self.width = width
          self.layers = layers
          self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
  
      def forward(self, x: torch.Tensor):
          return self.resblocks(x)
  
  
  class CLIP(nn.Module):
      def __init__(self,
                   embed_dim: int,
                   # vision
                   image_resolution: int,
                   vision_layers: Union[Tuple[int, int, int, int], int],
                   vision_width: int,
                   vision_patch_size: int,
                   # text
                   context_length: int,
                   vocab_size: int,
                   transformer_width: int,
                   transformer_heads: int,
                   transformer_layers: int,
                   # evl
                   n_layers=4, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
                   mlp_dropout=[0.5, 0.5, 0.5, 0.5], cls_dropout=0.5, t_size=8, spatial_size=14,
                   use_t_conv=True, use_image_attnmap=True, use_t_pos_embed=True,
                   backbone='vit_2plus1d_dw_bias_b16',
                   uni_layer=0,
                   uni_type='2d',
                   add_ffn=False,
                   t_conv_type='3d',
                   pre_prompt=False,
                   balance=0.,
                   after_me=True, 
                   before_me=False, 
                   me_type='stm', 
                   me_reduction=4,
                  ):
          super().__init__()
          
          # All assertions is for adhoc clip_kc and should be removed
          # assert vision_layers == 12, vision_layers
          assert image_resolution == 224, image_resolution
          # assert vision_patch_size == 32, vision_patch_size
          assert vision_width == n_dim, (vision_width, n_dim)
  
          self.vision_width = n_dim
  
          self.context_length = context_length
  
          vision_heads = vision_width // 64
          self.visual = evl_utils.__dict__[backbone](pretrained=False)
          self.evl = TransformerDecoder_uniformer_diff_conv_balance(
              n_layers=n_layers, n_dim=n_dim, n_head=n_head, 
              mlp_factor=mlp_factor, drop_path_rate=drop_path_rate,
              mlp_dropout=mlp_dropout, cls_dropout=cls_dropout, t_size=t_size, 
              use_t_conv=use_t_conv, use_t_pos_embed=use_t_pos_embed,
              uni_layer=uni_layer, uni_type=uni_type, add_ffn=add_ffn, t_conv_type=t_conv_type, 
              pre_prompt=pre_prompt, balance=balance,
              after_me=after_me, before_me=before_me, 
              me_type=me_type, me_reduction=me_reduction,
          )
          self.visual_ln_post = nn.LayerNorm(n_dim)
          scale =  n_dim ** -0.5
          self.visual_proj = nn.Parameter(scale * torch.randn(n_dim, embed_dim))
          self.return_qk = use_image_attnmap
          self.return_num = n_layers
  
          self.transformer = Transformer(
              width=transformer_width,
              layers=transformer_layers,
              heads=transformer_heads,
              attn_mask=self.build_attention_mask()
          )
  
          self.vocab_size = vocab_size
          self.token_embedding = nn.Embedding(vocab_size, transformer_width)
          self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
          self.ln_final = LayerNorm(transformer_width)
  
          self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
          self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
  
          self.embed_dim = embed_dim
  
          # We seperate the mask embedding to load pretrained model
          self.mask_embedding = nn.Parameter(torch.empty(1, 1, transformer_width))
  
          self.initialize_parameters()
  
      def initialize_parameters(self):
          nn.init.normal_(self.token_embedding.weight, std=0.02)
          nn.init.normal_(self.positional_embedding, std=0.01)
          nn.init.normal_(self.mask_embedding, std=0.02)
  
          proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
          attn_std = self.transformer.width ** -0.5
          fc_std = (2 * self.transformer.width) ** -0.5
          for block in self.transformer.resblocks:
              nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
              nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
              nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
              nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
  
          if self.text_projection is not None:
              nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
          
          nn.init.constant_(self.visual_ln_post.weight, 1.0)
          nn.init.constant_(self.visual_ln_post.bias, 0.0)
  
      def build_attention_mask(self):
          # lazily create causal attention mask, with full attention between the vision tokens
          # pytorch uses additive attention mask; fill with -inf
          mask = torch.empty(self.context_length, self.context_length)
          mask.fill_(float("-inf"))
          mask.triu_(1)  # zero out the lower diagonal
          return mask
  
      @property
      def dtype(self):
          return self.visual.conv1.weight.dtype
  
      def encode_video(self, video, return_all_feats=False):
          if len(video.size()) == 4: #[bs * T, C, H, W]
              #set_trace()
              frames = 8
              video = rearrange(video, '(b t) c h w -> b t c h w', b=int(video.size(0)/frames), t=frames)
              video = rearrange(video, 'b t c h w -> b c t h w')
              
          # video: [N, C, T, H, W]
          features = self.visual(video, return_num=self.return_num)
          x = self.visual_ln_post(self.evl(features))
          x = x @ self.visual_proj
  
          if return_all_feats:
              return x, features[-1]  # [N, T, C], [L, N, T, C]
  
          return x
  
      def encode_text(self, text, masked_indices=None, return_all_feats=False):
          x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
  
          if masked_indices is not None:
              x[masked_indices] = self.mask_embedding
  
          x = x + self.positional_embedding.type(self.dtype)
          x = x.permute(1, 0, 2)  # NLD -> LND
          x = self.transformer(x)
          x = x.permute(1, 0, 2)  # LND -> NLD
          x = self.ln_final(x).type(self.dtype)
  
          # x.shape = [batch_size, n_ctx, transformer.width]
          # take features from the eot embedding (eot_token is the highest number in each sequence)
          feats = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
  
          if return_all_feats:
              return feats, x
  
          return feats
  
      def forward(self, video, text):
          video_features = self.encode_video(video)
          text_features = self.encode_text(text)
  
          # normalized features
          video_features = video_features / video_features.norm(dim=1, keepdim=True)
          text_features = text_features / text_features.norm(dim=1, keepdim=True)
  
          # cosine similarity as logits
          logit_scale = self.logit_scale.exp()
          logits_per_video= logit_scale * video_features @ text_features.t()
          logits_per_text = logits_per_video.t()
  
          # shape = [global_batch_size, global_batch_size]
          return logits_per_video, logits_per_text
  
  
  def convert_weights(model: nn.Module):
      """Convert applicable model parameters to fp16"""
  
      def _convert_weights_to_fp16(l):
          if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
              l.weight.data = l.weight.data.half()
              if l.bias is not None:
                  l.bias.data = l.bias.data.half()
  
          if isinstance(l, nn.MultiheadAttention):
              for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
                  tensor = getattr(l, attr)
                  if tensor is not None:
                      tensor.data = tensor.data.half()
  
          for name in ["text_projection", "proj"]:
              if hasattr(l, name) and not isinstance(l, TransformerDecoder_uniformer_diff_conv_balance):
                  attr = getattr(l, name)
                  if attr is not None:
                      attr.data = attr.data.half()
  
      model.apply(_convert_weights_to_fp16)
  
  
  def build_model(
          state_dict: dict,
          # evl
          n_layers=4, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
          mlp_dropout=[0.5, 0.5, 0.5, 0.5], cls_dropout=0.5, t_size=8, spatial_size=14,
          use_t_conv=True, use_image_attnmap=True, use_t_pos_embed=True, no_pretrain=False,
      ):
      vit = "visual.proj" in state_dict or "visual.positional_embedding" in state_dict
  
      if "visual.proj" in state_dict:
          state_dict["visual_proj"] = state_dict["visual.proj"]
          state_dict["visual_ln_post.weight"] = state_dict["visual.ln_post.weight"]
          state_dict["visual_ln_post.bias"] = state_dict["visual.ln_post.bias"]
          del state_dict["visual.proj"], state_dict["visual.ln_post.weight"], state_dict["visual.ln_post.bias"]
      # new_state_dict = OrderedDict()
      # for k, v in state_dict.items():
      #     if k.startswith("backbone."):
      #         k = k.replace("backbone.", "visual.")
          
      #     new_state_dict[k] = v
      
      # state_dict = new_state_dict
      if vit:
          vision_width = state_dict["visual.conv1.weight"].shape[0]
          vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
          vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
          grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
          image_resolution = vision_patch_size * grid_size
      else:
          counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
          vision_layers = tuple(counts)
          vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
          output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
          vision_patch_size = None
          assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
          image_resolution = output_width * 32
  
      # embed_dim = 512
      # context_length = 77
      # vocab_size = 49408
      # transformer_width = 512
      # transformer_layers = 12
      embed_dim = state_dict["text_projection"].shape[1]
      context_length = state_dict["positional_embedding"].shape[0]
      vocab_size = state_dict["token_embedding.weight"].shape[0]
      transformer_width = state_dict["ln_final.weight"].shape[0]
      transformer_heads = transformer_width // 64
      transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
  
      vision_width = state_dict["visual_proj"].shape[0]
      n_dim = vision_width
      if vision_width == 768:
          backbone = "vit_2plus1d_dw_bias_b16"
          n_head = 12
      elif vision_width == 1024:
          backbone = "vit_2plus1d_dw_bias_l14"
          n_head = 16
      else:
          raise NotImplementedError
  
      model = CLIP(
          embed_dim,
          image_resolution, vision_layers, vision_width, vision_patch_size,
          context_length, vocab_size, transformer_width, transformer_heads, transformer_layers,
          n_layers=n_layers, n_dim=n_dim, n_head=n_head, mlp_factor=mlp_factor, drop_path_rate=drop_path_rate,
          mlp_dropout=mlp_dropout, cls_dropout=cls_dropout, t_size=t_size, spatial_size=spatial_size,
          use_t_conv=use_t_conv, use_image_attnmap=use_image_attnmap, use_t_pos_embed=use_t_pos_embed, backbone=backbone
      )
  
      for key in ["input_resolution", "context_length", "vocab_size"]:
          if key in state_dict:
              del state_dict[key]
  
      # convert_weights(model)
  
      # strict=False, for parameters of decoder
      # assert False, (len(model.state_dict()), len(state_dict))
      if not no_pretrain:
          model.load_state_dict(state_dict, strict=False)
      return model.eval()
  