





def   __lowercase  ( _SCREAMING_SNAKE_CASE = 20      )					->  int:


      '''simple docstring'''


      try:
            SCREAMING_SNAKE_CASE						=		int(_SCREAMING_SNAKE_CASE      )
      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."""      )
      SCREAMING_SNAKE_CASE						=		0
      while 1:
            i += n * (n - 1)
            SCREAMING_SNAKE_CASE						=		0
            for j in range(2					,		_SCREAMING_SNAKE_CASE      ):
                  if i % j != 0:
                        SCREAMING_SNAKE_CASE						=		1
                        break
            if nfound == 0:
                  if i == 0:
                        SCREAMING_SNAKE_CASE						=		1
                  return i
      return None


if __name__ == "__main__":
      print(F'''{solution() = }''')









import argparse

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


logging.set_verbosity_info()



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


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

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

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


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


