




"""simple docstring"""


def 	lowercase_				(			__UpperCAmelCase = 6008_5147_5143				)						->    Optional[Any]:







      try:
            lowerCAmelCase__      :			Optional[int]   =	int(_lowercase				)
      except (TypeError, ValueError):
            raise TypeError("""Parameter n must be int or castable to int."""				)
      if n <= 0:
            raise ValueError("""Parameter n must be greater than or equal to one."""				)
      lowerCAmelCase__      :			List[Any]   =	2
      lowerCAmelCase__      :			Optional[int]   =	0
      if n == 2:
            return 2
      while n > 2:
            while n % i != 0:
                  i += 1
            lowerCAmelCase__      :			Optional[int]   =	i
            while n % i == 0:
                  lowerCAmelCase__      :			int   =	n // i
            i += 1
      return int(_lowercase				)


if __name__ == "__main__":
     print(f"""{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_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      # Initialise PyTorch model
      lowerCAmelCase__      :			int   =	TaConfig.from_json_file(__UpperCAmelCase				)
      print(f"""Building PyTorch model from configuration: {config}"""				)
      lowerCAmelCase__      :			Optional[int]   =	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													=      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)





