



"""simple docstring"""



def  _UpperCamelCase	(						_A					,      _A      )    ->  Tuple:





    """simple docstring"""

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

        return "".join(snake_case__      )

    # matching characters
    _UpperCAmelCase          =   get_matched_characters(snake_case__					,      snake_case__      )
    _UpperCAmelCase          =   get_matched_characters(snake_case__					,      snake_case__      )
    _UpperCAmelCase          =   len(snake_case__      )

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

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

    # common prefix up to 4 characters
    _UpperCAmelCase          =   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 LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   LxmertConfig.from_json_file(_A      )
    print(F"""Building PyTorch model from configuration: {config}"""      )
    _UpperCAmelCase          =   LxmertForPreTraining(_A      )

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_A					,      _A					,      _A      )

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


if __name__ == "__main__":
 a					:	Dict							=		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(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a					:	Union[str, Any]							=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)