



'''simple docstring'''




def    __lowerCamelCase						(      _lowercase = 1			,       _lowercase = 1_0_0_0	)   ->    int:
							UpperCAmelCase  :	Union[str, Any]							  =	1
							UpperCAmelCase  :	str							  =	0

							for divide_by_number in range(_lowercase			,       digit + 1	):
														UpperCAmelCase  :	list[int]							  =	[]
														UpperCAmelCase  :	Any							  =	numerator
														for _ in range(1			,       digit + 1	):
																					if now_divide in has_been_divided:
																												if longest_list_length < len(_lowercase	):
																																			UpperCAmelCase  :	Optional[int]							  =	len(_lowercase	)
																																			UpperCAmelCase  :	Any							  =	divide_by_number
																					else:
																												has_been_divided.append(_lowercase	)
																												UpperCAmelCase  :	Tuple							  =	now_divide * 1_0 % divide_by_number

							return the_digit


# Tests
if __name__ == "__main__":
							import doctest

							doctest.testmod()






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a     :    Any      						=					{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Any      						=					[
							    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NezhaForNextSentencePrediction""",
							    """NezhaForMaskedLM""",
							    """NezhaForPreTraining""",
							    """NezhaForMultipleChoice""",
							    """NezhaForQuestionAnswering""",
							    """NezhaForSequenceClassification""",
							    """NezhaForTokenClassification""",
							    """NezhaModel""",
							    """NezhaPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nezha import (
														    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NezhaForMaskedLM,
														    NezhaForMultipleChoice,
														    NezhaForNextSentencePrediction,
														    NezhaForPreTraining,
														    NezhaForQuestionAnswering,
														    NezhaForSequenceClassification,
														    NezhaForTokenClassification,
														    NezhaModel,
														    NezhaPreTrainedModel,
														)


else:
							import sys

							a     :    Union[str, Any]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

