


'''simple docstring'''

import itertools



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Union[str, Any] )		->       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 							SCREAMING_SNAKE_CASE__(		)		->       Optional[int]:





   '''simple docstring'''







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


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



'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase:		Tuple    						=  {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		List[str]    						=  ["ImageGPTFeatureExtractor"]
       __lowercase:		Tuple    						=  ["ImageGPTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		str    						=  [
           "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ImageGPTForCausalImageModeling",
           "ImageGPTForImageClassification",
           "ImageGPTModel",
           "ImageGPTPreTrainedModel",
           "load_tf_weights_in_imagegpt",
       ]


if TYPE_CHECKING:
       from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
              from .image_processing_imagegpt import ImageGPTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_imagegpt import (
                  IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ImageGPTForCausalImageModeling,
                  ImageGPTForImageClassification,
                  ImageGPTModel,
                  ImageGPTPreTrainedModel,
                  load_tf_weights_in_imagegpt,
              )

else:
       import sys

       __lowercase:		List[str]    						=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)