




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowerCAmelCase       =   {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase       =   [
				    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''GPTJForCausalLM''',
				    '''GPTJForQuestionAnswering''',
				    '''GPTJForSequenceClassification''',
				    '''GPTJModel''',
				    '''GPTJPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase       =   [
				    '''TFGPTJForCausalLM''',
				    '''TFGPTJForQuestionAnswering''',
				    '''TFGPTJForSequenceClassification''',
				    '''TFGPTJModel''',
				    '''TFGPTJPreTrainedModel''',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase       =   [
				    '''FlaxGPTJForCausalLM''',
				    '''FlaxGPTJModel''',
				    '''FlaxGPTJPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_gptj import (
								    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GPTJForCausalLM,
								    GPTJForQuestionAnswering,
								    GPTJForSequenceClassification,
								    GPTJModel,
								    GPTJPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_gptj import (
								    TFGPTJForCausalLM,
								    TFGPTJForQuestionAnswering,
								    TFGPTJForSequenceClassification,
								    TFGPTJModel,
								    TFGPTJPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
				import sys

				_lowerCAmelCase       =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''simple docstring'''






import itertools

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


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



