






'''simple docstring'''



import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[int]    )  ->			Optional[Any]:

   """simple docstring"""
   # encoder.embeddings are double copied in original FLAVA
   return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()    )



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	Optional[int]    )  ->			Optional[int]:

   """simple docstring"""
   __a				     =    {}

   for key, value in state_dict.items():
      if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
         continue

      __a				     =    key.replace('heads.cmd.mim_head.cls.predictions',					'mmm_image_head'    )
      __a				     =    key.replace('heads.cmd.mlm_head.cls.predictions',					'mmm_text_head'    )
      __a				     =    key.replace('heads.cmd.itm_head.cls',					'itm_head'    )
      __a				     =    key.replace('heads.cmd.itm_head.pooler',					'itm_head.pooler'    )
      __a				     =    key.replace('heads.cmd.clip_head.logit_scale',					'flava.logit_scale'    )
      __a				     =    key.replace('heads.fairseq_mlm.cls.predictions',					'mlm_head'    )
      __a				     =    key.replace('heads.imagenet.mim_head.cls.predictions',					'mim_head'    )
      __a				     =    key.replace('mm_text_projection',					'flava.text_to_mm_projection'    )
      __a				     =    key.replace('mm_image_projection',					'flava.image_to_mm_projection'    )
      __a				     =    key.replace('image_encoder.module',					'flava.image_model'    )
      __a				     =    key.replace('text_encoder.module',					'flava.text_model'    )
      __a				     =    key.replace('mm_encoder.module.encoder.cls_token',					'flava.multimodal_model.cls_token'    )
      __a				     =    key.replace('mm_encoder.module',					'flava.multimodal_model'    )
      __a				     =    key.replace('text_projection',					'flava.text_projection'    )
      __a				     =    key.replace('image_projection',					'flava.image_projection'    )

      __a				     =    value.float()

   for key, value in codebook_state_dict.items():
      __a				     =    value

   return upgrade



@torch.no_grad()
def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	str,					SCREAMING_SNAKE_CASE__:	List[Any]=None    )  ->			Optional[Any]:

   """simple docstring"""
   if config_path is not None:
      __a				     =    FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE__    )
   else:
      __a				     =    FlavaConfig()

   __a				     =    FlavaForPreTraining(SCREAMING_SNAKE_CASE__    ).eval()

   __a				     =    convert_dalle_checkpoint(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					save_checkpoint=SCREAMING_SNAKE_CASE__    )

   if os.path.exists(SCREAMING_SNAKE_CASE__    ):
      __a				     =    torch.load(SCREAMING_SNAKE_CASE__,					map_location='cpu'    )
   else:
      __a				     =    torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__,					map_location='cpu'    )

   __a				     =    upgrade_state_dict(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
   hf_model.load_state_dict(SCREAMING_SNAKE_CASE__    )
   __a				     =    hf_model.state_dict()
   __a				     =    count_parameters(SCREAMING_SNAKE_CASE__    )
   __a				     =    count_parameters(SCREAMING_SNAKE_CASE__    ) + count_parameters(SCREAMING_SNAKE_CASE__    )

   assert torch.allclose(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__,					atol=1e-3    )

   hf_model.save_pretrained(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 __UpperCamelCase      :       int      			=      argparse.ArgumentParser()
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
 parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 __UpperCamelCase      :       int      			=      parser.parse_args()

 convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Any      			=      {
    """google/vivit-b-16x2-kinetics400""": (
        """https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vivit"




      def __init__(  self ,   lowerCamelCase=224 ,   lowerCamelCase=32 ,   lowerCamelCase=[2, 16, 16] ,   lowerCamelCase=3 ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu_fast" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps

         __a				     =    image_size
         __a				     =    num_frames
         __a				     =    tubelet_size
         __a				     =    num_channels
         __a				     =    qkv_bias

         super().__init__(**lowerCamelCase      )