






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()






def    UpperCAmelCase__	(						lowerCamelCase  : Tuple ,lowerCamelCase  : Optional[int] ,lowerCamelCase  : int						):
   # Initialise PyTorch model
   lowerCamelCase											=RoFormerConfig.from_json_file(lowerCamelCase						)
   print(f'''Building PyTorch model from configuration: {config}'''						)
   lowerCamelCase											=RoFormerForMaskedLM(lowerCamelCase						)

   # Load weights from tf checkpoint
   load_tf_weights_in_roformer(lowerCamelCase ,lowerCamelCase ,lowerCamelCase						)

   # Save pytorch-model
   print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
   torch.save(model.state_dict() ,lowerCamelCase ,_use_new_zipfile_serialization=lowerCamelCase						)


if __name__ == "__main__":
   _UpperCAmelCase				:	int    						=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--bert_config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained BERT model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _UpperCAmelCase				:	Dict    						=    parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







from typing import TYPE_CHECKING

from ...utils import _LazyModule


_UpperCAmelCase				:	Dict    						=    {'tokenization_bert_japanese': ['BertJapaneseTokenizer', 'CharacterTokenizer', 'MecabTokenizer']}


if TYPE_CHECKING:
   from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer

else:
   import sys

   _UpperCAmelCase				:	Tuple    						=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)