




from typing import TYPE_CHECKING

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


UpperCamelCase__						=		{
    'configuration_markuplm': ['MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarkupLMConfig'],
    'feature_extraction_markuplm': ['MarkupLMFeatureExtractor'],
    'processing_markuplm': ['MarkupLMProcessor'],
    'tokenization_markuplm': ['MarkupLMTokenizer'],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MarkupLMForQuestionAnswering',
							    'MarkupLMForSequenceClassification',
							    'MarkupLMForTokenClassification',
							    'MarkupLMModel',
							    'MarkupLMPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase__						=		_LazyModule(__name__, globals()['__file__'], _import_structure)






def    snake_case   (				A     )				->				str:




	'''simple docstring'''


	if not isinstance(A		,  A     ):
		lowerCamelCase_				:						int				  =				(
		    'operation can not be conducted on a object of type '
		    f"{type(A     ).__name__}"
		)
		raise TypeError(A     )
	lowerCamelCase_				:						List[Any]				  =				''
	for _ in range(0		,  32     ):
		bit_string += str(number % 2     )
		lowerCamelCase_				:						int				  =				number >> 1
	return bit_string






def    snake_case   (				A     )				->				str:




	'''simple docstring'''


	if number < 0:
		raise ValueError('the value of input must be positive'     )
	elif isinstance(A		,  A     ):
		raise TypeError('Input value must be a \'int\' type'     )
	elif isinstance(A		,  A     ):
		raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''     )
	lowerCamelCase_				:						Any				  =				0
	# iterator over [1 to 32],since we are dealing with 32 bit integer
	for _ in range(1		,  33     ):
		# left shift the bits by unity
		lowerCamelCase_				:						Any				  =				result << 1
		# get the end bit
		lowerCamelCase_				:						Any				  =				number % 2
		# right shift the bits by unity
		lowerCamelCase_				:						Optional[Any]				  =				number >> 1
		# add that bit to our ans
		lowerCamelCase_				:						Dict				  =				result | end_bit
	return get_reverse_bit_string(A     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
