





'''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__				(		lowerCAmelCase  ):





      """simple docstring"""


      return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()  )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							{}

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

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

            _lowerCAmelCase												=							value.float()

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

      return upgrade
@torch.no_grad()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None  ):





      """simple docstring"""


      if config_path is not None:
            _lowerCAmelCase												=							FlavaConfig.from_pretrained(lowerCAmelCase  )
      else:
            _lowerCAmelCase												=							FlavaConfig()

      _lowerCAmelCase												=							FlavaForPreTraining(lowerCAmelCase  ).eval()

      _lowerCAmelCase												=							convert_dalle_checkpoint(lowerCAmelCase							,     lowerCAmelCase							,     save_checkpoint=lowerCAmelCase  )

      if os.path.exists(lowerCAmelCase  ):
            _lowerCAmelCase												=							torch.load(lowerCAmelCase							,     map_location="""cpu"""  )
      else:
            _lowerCAmelCase												=							torch.hub.load_state_dict_from_url(lowerCAmelCase							,     map_location="""cpu"""  )

      _lowerCAmelCase												=							upgrade_state_dict(lowerCAmelCase							,     lowerCAmelCase  )
      hf_model.load_state_dict(lowerCAmelCase  )
      _lowerCAmelCase												=							hf_model.state_dict()
      _lowerCAmelCase												=							count_parameters(lowerCAmelCase  )
      _lowerCAmelCase												=							count_parameters(lowerCAmelCase  ) + count_parameters(lowerCAmelCase  )

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

      hf_model.save_pretrained(lowerCAmelCase  )


if __name__ == "__main__":
      A__    :       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''')
      A__    :       Tuple              =parser.parse_args()

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













'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      if not (isinstance(lowerCAmelCase							,     lowerCAmelCase  ) and isinstance(lowerCAmelCase							,     lowerCAmelCase  )):
            raise ValueError("""longest_common_substring() takes two strings for inputs"""  )

      _lowerCAmelCase												=							len(lowerCAmelCase  )
      _lowerCAmelCase												=							len(lowerCAmelCase  )

      _lowerCAmelCase												=							[[0] * (texta_length + 1) for _ in range(texta_length + 1  )]
      _lowerCAmelCase												=							0
      _lowerCAmelCase												=							0

      for i in range(1							,     texta_length + 1  ):
            for j in range(1							,     texta_length + 1  ):
                  if texta[i - 1] == texta[j - 1]:
                        _lowerCAmelCase												=							1 + dp[i - 1][j - 1]
                        if dp[i][j] > ans_length:
                              _lowerCAmelCase												=							i
                              _lowerCAmelCase												=							dp[i][j]

      return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
      import doctest

      doctest.testmod()






