





import itertools



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  List[str]:


      '''simple docstring'''


      return (
          int("""""".join(combination[0:2]      )      ) * int("""""".join(combination[2:5]      )      )
          == int("""""".join(combination[5:9]      )      )
      ) or (
          int("""""".join(combination[0]      )      ) * int("""""".join(combination[1:5]      )      )
          == int("""""".join(combination[5:9]      )      )
      )



def   __lowercase  ( )					->  int:


      '''simple docstring'''


      return sum(
          {
              int("""""".join(pandigital[5:9]      )      )
              for pandigital in itertools.permutations("""123456789"""      )
              if is_combination_valid(_SCREAMING_SNAKE_CASE      )
          }      )


if __name__ == "__main__":
      print(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)


