
'''simple docstring'''







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:

	if isinstance(a__    ,       a__			):
		raise TypeError('\'float\' object cannot be interpreted as an integer'			)
	if isinstance(a__    ,       a__			):
		raise TypeError('\'str\' object cannot be interpreted as an integer'			)

	if num == 0:
		return "0b0"

	_snake_case     		=	False

	if num < 0:
		_snake_case     		=	True
		_snake_case     		=	-num

	_snake_case     		=	[]
	while num > 0:
		binary.insert(0    ,       num % 2			)
		num >>= 1

	if negative:
		return "-0b" + "".join(str(a__			) for e in binary			)

	return "0b" + "".join(str(a__			) for e in binary			)


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


lowercase					:							str   =      {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							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

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

