





from typing import TYPE_CHECKING

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


snake_case__					:							Optional[Any]													=     {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							Any													=     [
           '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:
       snake_case__					:							int													=     [
           '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:
       snake_case__					:							List[str]													=     [
           '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

       snake_case__					:							Optional[int]													=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












from itertools import permutations



def _a				(	lowerCamelCase:   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

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



def _a				(	lowerCamelCase:   int = 10 )	->						int:







      '''simple docstring'''






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


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





