




'''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		)    ->   Any:
      # 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				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      snake_case__       :   Tuple	      =    {}

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

            snake_case__       :   List[Any]	      =    key.replace('heads.cmd.mim_head.cls.predictions'				,      'mmm_image_head'		)
            snake_case__       :   int	      =    key.replace('heads.cmd.mlm_head.cls.predictions'				,      'mmm_text_head'		)
            snake_case__       :   Dict	      =    key.replace('heads.cmd.itm_head.cls'				,      'itm_head'		)
            snake_case__       :   List[Any]	      =    key.replace('heads.cmd.itm_head.pooler'				,      'itm_head.pooler'		)
            snake_case__       :   int	      =    key.replace('heads.cmd.clip_head.logit_scale'				,      'flava.logit_scale'		)
            snake_case__       :   List[Any]	      =    key.replace('heads.fairseq_mlm.cls.predictions'				,      'mlm_head'		)
            snake_case__       :   Optional[int]	      =    key.replace('heads.imagenet.mim_head.cls.predictions'				,      'mim_head'		)
            snake_case__       :   Optional[int]	      =    key.replace('mm_text_projection'				,      'flava.text_to_mm_projection'		)
            snake_case__       :   Tuple	      =    key.replace('mm_image_projection'				,      'flava.image_to_mm_projection'		)
            snake_case__       :   Optional[int]	      =    key.replace('image_encoder.module'				,      'flava.image_model'		)
            snake_case__       :   Tuple	      =    key.replace('text_encoder.module'				,      'flava.text_model'		)
            snake_case__       :   str	      =    key.replace('mm_encoder.module.encoder.cls_token'				,      'flava.multimodal_model.cls_token'		)
            snake_case__       :   Tuple	      =    key.replace('mm_encoder.module'				,      'flava.multimodal_model'		)
            snake_case__       :   Optional[int]	      =    key.replace('text_projection'				,      'flava.text_projection'		)
            snake_case__       :   Optional[int]	      =    key.replace('image_projection'				,      'flava.image_projection'		)

            snake_case__       :   str	      =    value.float()

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

      return upgrade






@torch.no_grad()
def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=None		)    ->   str:
      if config_path is not None:
            snake_case__       :   Optional[int]	      =    FlavaConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
      else:
            snake_case__       :   Tuple	      =    FlavaConfig()

      snake_case__       :   Union[str, Any]	      =    FlavaForPreTraining(__SCREAMING_SNAKE_CASE		).eval()

      snake_case__       :   Optional[Any]	      =    convert_dalle_checkpoint(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      save_checkpoint=__SCREAMING_SNAKE_CASE		)

      if os.path.exists(__SCREAMING_SNAKE_CASE		):
            snake_case__       :   Tuple	      =    torch.load(__SCREAMING_SNAKE_CASE				,      map_location='cpu'		)
      else:
            snake_case__       :   List[str]	      =    torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE				,      map_location='cpu'		)

      snake_case__       :   Dict	      =    upgrade_state_dict(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)
      hf_model.load_state_dict(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Optional[int]	      =    hf_model.state_dict()
      snake_case__       :   int	      =    count_parameters(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Union[str, Any]	      =    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__":
      A_			      =		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")
      A_			      =		parser.parse_args()

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






'''simple docstring'''






import functools






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   Any	      =    len(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   List[str]	      =    len(__SCREAMING_SNAKE_CASE		)

      @functools.cache
      def min_distance(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		) -> int:
            # if first word index is overflow - delete all from the second word
            if indexa >= len_worda:
                  return len_worda - indexa
            # if second word index is overflow - delete all from the first word
            if indexa >= len_worda:
                  return len_worda - indexa
            snake_case__       :   Union[str, Any]	      =    int(worda[indexa] != worda[indexa]		)  # current letters not identical
            return min(
                1 + min_distance(indexa + 1				,      __SCREAMING_SNAKE_CASE		)				,      1 + min_distance(__SCREAMING_SNAKE_CASE				,      indexa + 1		)				,      diff + min_distance(indexa + 1				,      indexa + 1		)				,      )

      return min_distance(0				,      0		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
