


'''simple docstring'''







import functools






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[Any]       =							len(_A       )
       _lowerCAmelCase     :				Optional[Any]       =							len(_A       )

       @functools.cache
       def min_distance(_A ,    _A       ) -> int:
              # if first word index is overflow - delete all from the second word
              if indexa >= len_worda:
                     return len_worda - indexa
              # if second word index is overflow - delete all from the first word
              if indexa >= len_worda:
                     return len_worda - indexa
              _lowerCAmelCase     :				Dict       =							int(worda[indexa] != worda[indexa]       )  # current letters not identical
              return min(
                  1 + min_distance(indexa + 1 ,    _A       ) ,    1 + min_distance(_A ,    indexa + 1       ) ,    diff + min_distance(indexa + 1 ,    indexa + 1       ) ,    )

       return min_distance(0 ,    0       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()





'''simple docstring'''







import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							TaConfig.from_json_file(_A       )
       print(f'Building PyTorch model from configuration: {config}'       )
       _lowerCAmelCase     :				Union[str, Any]       =							TaForConditionalGeneration(_A       )

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

       # Save pytorch-model
       print(f'Save PyTorch model to {pytorch_dump_path}'       )
       model.save_pretrained(_A       )


if __name__ == "__main__":
       lowerCAmelCase     :  str											=    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 T5 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."""
       )
       lowerCAmelCase     :  int											=    parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

