



def 	UpperCAmelCase__      (      UpperCAmelCase__ :Union[str, Any]					):






 '''simple docstring'''

 a						=		[0] * len(SCREAMING_SNAKE_CASE_					)

 for i in range(1 ,					len(SCREAMING_SNAKE_CASE_					)					):
  # use last results for better performance - dynamic programming
  a						=		prefix_result[i - 1]
  while j > 0 and input_string[i] != input_string[j]:
   a						=		prefix_result[j - 1]

  if input_string[i] == input_string[j]:
   j += 1
  a						=		j

 return prefix_result


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Union[str, Any]					):






 '''simple docstring'''

 return max(prefix_function(SCREAMING_SNAKE_CASE_					)					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






import argparse

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


logging.set_verbosity_info()


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






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

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

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     Union[str, Any]        =     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.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

