






from typing import TYPE_CHECKING

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


lowerCAmelCase_           = {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_           = ['RobertaTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_           = [
					    'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'RobertaForCausalLM',
					    'RobertaForMaskedLM',
					    'RobertaForMultipleChoice',
					    'RobertaForQuestionAnswering',
					    'RobertaForSequenceClassification',
					    'RobertaForTokenClassification',
					    'RobertaModel',
					    'RobertaPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_           = [
					    'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFRobertaForCausalLM',
					    'TFRobertaForMaskedLM',
					    'TFRobertaForMultipleChoice',
					    'TFRobertaForQuestionAnswering',
					    'TFRobertaForSequenceClassification',
					    'TFRobertaForTokenClassification',
					    'TFRobertaMainLayer',
					    'TFRobertaModel',
					    'TFRobertaPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_           = [
					    'FlaxRobertaForCausalLM',
					    'FlaxRobertaForMaskedLM',
					    'FlaxRobertaForMultipleChoice',
					    'FlaxRobertaForQuestionAnswering',
					    'FlaxRobertaForSequenceClassification',
					    'FlaxRobertaForTokenClassification',
					    'FlaxRobertaModel',
					    'FlaxRobertaPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
					from .tokenization_roberta import RobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roberta_fast import RobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta import (
										    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaForCausalLM,
										    RobertaForMaskedLM,
										    RobertaForMultipleChoice,
										    RobertaForQuestionAnswering,
										    RobertaForSequenceClassification,
										    RobertaForTokenClassification,
										    RobertaModel,
										    RobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta import (
										    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaForCausalLM,
										    TFRobertaForMaskedLM,
										    TFRobertaForMultipleChoice,
										    TFRobertaForQuestionAnswering,
										    TFRobertaForSequenceClassification,
										    TFRobertaForTokenClassification,
										    TFRobertaMainLayer,
										    TFRobertaModel,
										    TFRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta import (
										    FlaxRobertaForCausalLM,
										    FlaxRobertaForMaskedLM,
										    FlaxRobertaForMultipleChoice,
										    FlaxRobertaForQuestionAnswering,
										    FlaxRobertaForSequenceClassification,
										    FlaxRobertaForTokenClassification,
										    FlaxRobertaModel,
										    FlaxRobertaPreTrainedModel,
										)

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() = }''')





