




from itertools import permutations







def 			lowerCamelCase_		(       UpperCamelCase__				:      tuple      )     ->       bool:





				"""simple docstring"""


				if num[3] % 2 != 0:
								return False

				if (num[2] + num[3] + num[4]) % 3 != 0:
								return False

				if num[5] % 5 != 0:
								return False

				__lowerCamelCase		       =    [7, 11, 13, 17]
				for i, test in enumerate(UpperCamelCase__      ):
								if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
												return False
				return True







def 			lowerCamelCase_		(       UpperCamelCase__				:      int = 10      )     ->       int:





				"""simple docstring"""


				return sum(
				    int(''.join(map(UpperCamelCase__ ,     UpperCamelCase__      )      )      )
				    for num in permutations(range(UpperCamelCase__      )      )
				    if is_substring_divisible(UpperCamelCase__      )      )


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








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__     						=						{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """IBertForMaskedLM""",
			    """IBertForMultipleChoice""",
			    """IBertForQuestionAnswering""",
			    """IBertForSequenceClassification""",
			    """IBertForTokenClassification""",
			    """IBertModel""",
			    """IBertPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ibert import (
						    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    IBertForMaskedLM,
						    IBertForMultipleChoice,
						    IBertForQuestionAnswering,
						    IBertForSequenceClassification,
						    IBertForTokenClassification,
						    IBertModel,
						    IBertPreTrainedModel,
						)

else:
			import sys

			lowercase__     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)