"""simple docstring"""





from functools import lru_cache


def    lowercase_   (   _lowerCamelCase:	int      ) ->      set:



       '''simple docstring'''

       __lowerCamelCase					:		Optional[Any]		   =     2
       __lowerCamelCase					:		Tuple		   =     set()
       while i * i <= n:
              if n % i:
                     i += 1
              else:
                     n //= i
                     factors.add(_lowerCamelCase      )
       if n > 1:
              factors.add(_lowerCamelCase      )
       return factors


@lru_cache
def    lowercase_   (   _lowerCamelCase:	int      ) ->      int:



       '''simple docstring'''

       return len(unique_prime_factors(_lowerCamelCase      )      )


def    lowercase_   (   _lowerCamelCase:	list      ) ->      bool:



       '''simple docstring'''

       return len(set(_lowerCamelCase      )      ) in (0, 1)


def    lowercase_   (   _lowerCamelCase:	int      ) ->      list:



       '''simple docstring'''

       __lowerCamelCase					:		str		   =     2

       while True:
              # Increment each value of a generated range
              __lowerCamelCase					:		int		   =     [base + i for i in range(_lowerCamelCase      )]

              # Run elements through out unique_prime_factors function
              # Append our target number to the end.
              __lowerCamelCase					:		Dict		   =     [upf_len(_lowerCamelCase      ) for x in group]
              checker.append(_lowerCamelCase      )

              # If all numbers in the list are equal, return the group variable.
              if equality(_lowerCamelCase      ):
                     return group

              # Increment our base variable by 1
              base += 1


def    lowercase_   (   _lowerCamelCase:	int = 4      ) ->      int:



       '''simple docstring'''

       __lowerCamelCase					:		Any		   =     run(_lowerCamelCase      )
       return results[0] if len(_lowerCamelCase      ) else None


if __name__ == "__main__":
    print(solution())
"""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_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     TaForConditionalGeneration(_lowerCamelCase      )

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

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


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