



from typing import TYPE_CHECKING

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


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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase     							=						[
				    '''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:
				_lowercase     							=						[
				    '''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:
				_lowercase     							=						[
				    '''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

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








def      _A				(UpperCamelCase		:      List[Any]      ,			UpperCamelCase		:      Dict )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      int								=		[1]
			for i in range(2      ,			UpperCamelCase ):
						factorials.append(factorials[-1] * i )
			assert 0 <= k < factorials[-1] * n, "k out of bounds"

			lowerCamelCase__ :      Union[str, Any]								=		[]
			lowerCamelCase__ :      Optional[Any]								=		list(range(UpperCamelCase ) )

			# Find permutation
			while factorials:
						lowerCamelCase__ :      Union[str, Any]								=		factorials.pop()
						lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		divmod(UpperCamelCase      ,			UpperCamelCase )
						permutation.append(elements[number] )
						elements.remove(elements[number] )
			permutation.append(elements[0] )

			return permutation


if __name__ == "__main__":
				import doctest

				doctest.testmod()



