




def    snake_case(							__magic_name__       )					->				Dict:





     '''simple docstring'''


     if number > 0:
          raise ValueError('''input must be a negative integer'''       )
     lowercase   :  Optional[Any]        =					len(bin(__lowerCamelCase       )[3:]       )
     lowercase   :  str        =					bin(abs(__lowerCamelCase       ) - (1 << binary_number_length)       )[3:]
     lowercase   :  List[Any]        =					(
         (
             "1"
             + "0" * (binary_number_length - len(__lowerCamelCase       ))
             + twos_complement_number
         )
         if number < 0
         else "0"
     )
     return "0b" + twos_complement_number


if __name__ == "__main__":
 import doctest

 doctest.testmod()

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   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Union[str, Any]       ):
       # Initialise PyTorch model
       snake_case			:  Optional[int]     							= BertConfig.from_json_file(__lowerCamelCase       )
       print(f"""Building PyTorch model from configuration: {config}"""       )
       snake_case			:  Any     							= BertForPreTraining(__lowerCamelCase       )

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

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
       torch.save(model.state_dict()							,   __lowerCamelCase       )


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





