

import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig





def      A__					(			__A  :						Any     ,				__A  :						Union[str, Any]     ,				__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Any:
      __A    =s.rsplit(__A     ,				__A			)
      return new.join(__A			)





def      A__					(			__A  :						List[str]			)					->Dict:
      # 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      A__					(			__A  :						List[Any]			)					->Dict:
      __A    ={}

      __A    =['''group_1''', '''group_2''', '''group_3''', '''group_4''']
      for key, value in state_dict.items():
            for group_key in group_keys:
                  if group_key in key:
                        __A    =key.replace(F'''{group_key}.'''     ,				F'''{group_key}.group.'''			)

            if "res_path" in key:
                  __A    =key.replace('''res_path.'''     ,				'''res_path.path.'''			)

            if key.endswith('''.w'''			):
                  __A    =rreplace(__A     ,				'''.w'''     ,				'''.weight'''     ,				1			)
            if key.endswith('''.b'''			):
                  __A    =rreplace(__A     ,				'''.b'''     ,				'''.bias'''     ,				1			)

            __A    =value.float()

      return upgrade





@torch.no_grad()
def      A__					(			__A  :						Union[str, Any]     ,				__A  :						Tuple     ,				__A  :						Dict=None     ,				__A  :						Optional[int]=True			)					->Any:
      from dall_e import Encoder

      __A    =Encoder()
      if os.path.exists(__A			):
            __A    =torch.load(__A			)
      else:
            __A    =torch.hub.load_state_dict_from_url(__A			)

      if isinstance(__A     ,				__A			):
            __A    =ckpt.state_dict()
      encoder.load_state_dict(__A			)

      if config_path is not None:
            __A    =FlavaImageCodebookConfig.from_pretrained(__A			)
      else:
            __A    =FlavaImageCodebookConfig()

      __A    =FlavaImageCodebook(__A			).eval()
      __A    =encoder.state_dict()

      __A    =upgrade_state_dict(__A			)
      hf_model.load_state_dict(__A			)
      __A    =hf_model.state_dict()
      __A    =count_parameters(__A			)
      __A    =count_parameters(__A			)

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

      if save_checkpoint:
            hf_model.save_pretrained(__A			)
      else:
            return hf_state_dict


if __name__ == "__main__":
   _lowerCamelCase      :					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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   _lowerCamelCase      :					Union[str, Any]					    =      parser.parse_args()

   convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





from typing import TYPE_CHECKING

from ...utils import _LazyModule


_lowerCamelCase      :					Tuple					    =      {'''tokenization_byt5''': ['''ByT5Tokenizer''']}


if TYPE_CHECKING:
   from .tokenization_byta import ByTaTokenizer
else:
   import sys

   _lowerCamelCase      :					Optional[int]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


