






"""simple docstring"""





import itertools






def 							A(    snake_case_						):
       """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 							A(    ):
       """simple docstring"""





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


if __name__ == "__main__":
  print(solution())










'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(__A ,     __A ,     __A       )

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
      torch.save(model.state_dict() ,     __A ,     _use_new_zipfile_serialization=__A       )


if __name__ == "__main__":
  a__    :   str								=			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(
      '--bert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  a__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





