


"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str       ):






					"""simple docstring"""

					def get_matched_characters(_lowerCAmelCase     :					str,     _lowerCAmelCase     :					str       ) -> str:
										_a    	=				[]
										_a    	=				min(len(_stra       ),     len(_stra       )       ) // 2
										for i, l in enumerate(_stra       ):
															_a    	=				int(max(0,     i - limit       )       )
															_a    	=				int(min(i + limit + 1,     len(_stra       )       )       )
															if l in _stra[left:right]:
																				matched.append(_lowerCAmelCase       )
																				_a    	=				f'{_stra[0:_stra.index(_lowerCAmelCase       )]} {_stra[_stra.index(_lowerCAmelCase       ) + 1:]}'

										return "".join(_lowerCAmelCase       )

					# matching characters
					_a    	=				get_matched_characters(_lowerCAmelCase,     _lowerCAmelCase       )
					_a    	=				get_matched_characters(_lowerCAmelCase,     _lowerCAmelCase       )
					_a    	=				len(_lowerCAmelCase       )

					# transposition
					_a    	=				(
					    len([(ca, ca) for ca, ca in zip(_lowerCAmelCase,     _lowerCAmelCase       ) if ca != ca]       ) // 2
					)

					if not match_count:
										_a    	=				0.0
					else:
										_a    	=				(
										    1
										    / 3
										    * (
										        match_count / len(_lowerCAmelCase       )
										        + match_count / len(_lowerCAmelCase       )
										        + (match_count - transpositions) / match_count
										    )
										)

					# common prefix up to 4 characters
					_a    	=				0
					for ca, ca in zip(stra[:4],     stra[:4]       ):
										if ca == ca:
															prefix_len += 1
										else:
															break

					return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(jaro_winkler('''hello''', '''world'''))



"""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       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Union[str, Any]       ):






     """simple docstring"""

     _a    	=				BertConfig.from_json_file(_lowerCAmelCase       )
     print(f'Building PyTorch model from configuration: {config}'       )
     _a    	=				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__":
   __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.'''
   )
   __snake_case		=       parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)