

def a(lowercase__	):





	'''simple docstring'''

	if isinstance(lowercase__			, lowercase__	):
		raise TypeError('\'float\' object cannot be interpreted as an integer'	)
	if isinstance(lowercase__			, lowercase__	):
		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(lowercase__	) for e in binary	)

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A											=     {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['PerceiverFeatureExtractor']
		A											=     ['PerceiverImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PerceiverForImageClassificationConvProcessing',
		    'PerceiverForImageClassificationFourier',
		    'PerceiverForImageClassificationLearned',
		    'PerceiverForMaskedLM',
		    'PerceiverForMultimodalAutoencoding',
		    'PerceiverForOpticalFlow',
		    'PerceiverForSequenceClassification',
		    'PerceiverLayer',
		    'PerceiverModel',
		    'PerceiverPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

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