






from typing import TYPE_CHECKING

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


lowerCAmelCase_           = {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_           = [
					    'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'RobertaPreLayerNormForCausalLM',
					    'RobertaPreLayerNormForMaskedLM',
					    'RobertaPreLayerNormForMultipleChoice',
					    'RobertaPreLayerNormForQuestionAnswering',
					    'RobertaPreLayerNormForSequenceClassification',
					    'RobertaPreLayerNormForTokenClassification',
					    'RobertaPreLayerNormModel',
					    'RobertaPreLayerNormPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_           = [
					    'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFRobertaPreLayerNormForCausalLM',
					    'TFRobertaPreLayerNormForMaskedLM',
					    'TFRobertaPreLayerNormForMultipleChoice',
					    'TFRobertaPreLayerNormForQuestionAnswering',
					    'TFRobertaPreLayerNormForSequenceClassification',
					    'TFRobertaPreLayerNormForTokenClassification',
					    'TFRobertaPreLayerNormMainLayer',
					    'TFRobertaPreLayerNormModel',
					    'TFRobertaPreLayerNormPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_           = [
					    'FlaxRobertaPreLayerNormForCausalLM',
					    'FlaxRobertaPreLayerNormForMaskedLM',
					    'FlaxRobertaPreLayerNormForMultipleChoice',
					    'FlaxRobertaPreLayerNormForQuestionAnswering',
					    'FlaxRobertaPreLayerNormForSequenceClassification',
					    'FlaxRobertaPreLayerNormForTokenClassification',
					    'FlaxRobertaPreLayerNormModel',
					    'FlaxRobertaPreLayerNormPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

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













from itertools import permutations







def      snake_case		(							UpperCAmelCase	:       tuple   ):
				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

				A	 =      [7, 11, 13, 17]
				for i, test in enumerate(UpperCAmelCase   ):
								if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
												return False
				return True







def      snake_case		(							UpperCAmelCase	:       int = 10   ):
				return sum(
				    int(''.join(map(UpperCAmelCase,						UpperCAmelCase   )   )   )
				    for num in permutations(range(UpperCAmelCase   )   )
				    if is_substring_divisible(UpperCAmelCase   )   )


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





