





"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						List[str]							,						SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      lowerCAmelCase  						=       BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
      print(F'Building PyTorch model from configuration: {config}'			)
      lowerCAmelCase  						=       BertForPreTraining(_SCREAMING_SNAKE_CASE			)

      # Load weights from tf checkpoint
      load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE			)

      # Save pytorch-model
      print(F'Save PyTorch model to {pytorch_dump_path}'			)
      torch.save(model.state_dict()							,						_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   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."
       )
       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)













"""simple docstring"""





def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->bool:





  """simple docstring"""


  return numa ^ numa < 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()
