



from typing import TYPE_CHECKING

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


a_								=   {
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_								=   [
		    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MPNetForMaskedLM''',
		    '''MPNetForMultipleChoice''',
		    '''MPNetForQuestionAnswering''',
		    '''MPNetForSequenceClassification''',
		    '''MPNetForTokenClassification''',
		    '''MPNetLayer''',
		    '''MPNetModel''',
		    '''MPNetPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_								=   [
		    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFMPNetEmbeddings''',
		    '''TFMPNetForMaskedLM''',
		    '''TFMPNetForMultipleChoice''',
		    '''TFMPNetForQuestionAnswering''',
		    '''TFMPNetForSequenceClassification''',
		    '''TFMPNetForTokenClassification''',
		    '''TFMPNetMainLayer''',
		    '''TFMPNetModel''',
		    '''TFMPNetPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
		from .tokenization_mpnet import MPNetTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mpnet_fast import MPNetTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mpnet import (
				    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MPNetForMaskedLM,
				    MPNetForMultipleChoice,
				    MPNetForQuestionAnswering,
				    MPNetForSequenceClassification,
				    MPNetForTokenClassification,
				    MPNetLayer,
				    MPNetModel,
				    MPNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mpnet import (
				    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMPNetEmbeddings,
				    TFMPNetForMaskedLM,
				    TFMPNetForMultipleChoice,
				    TFMPNetForQuestionAnswering,
				    TFMPNetForSequenceClassification,
				    TFMPNetForTokenClassification,
				    TFMPNetMainLayer,
				    TFMPNetModel,
				    TFMPNetPreTrainedModel,
				)

else:
		import sys

		a_								=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











def 						a__     (						_UpperCamelCase     :	int							):
   if not isinstance(_UpperCamelCase	,_UpperCamelCase							):
      __lowerCamelCase  							=      (
          '''operation can not be conducted on a object of type '''
          F"""{type(_UpperCamelCase							).__name__}"""
      )
      raise TypeError(_UpperCamelCase							)
   __lowerCamelCase  							=      ''''''
   for _ in range(0	,32							):
      bit_string += str(number % 2							)
      __lowerCamelCase  							=      number >> 1
   return bit_string





def 						a__     (						_UpperCamelCase     :	int							):
   if number < 0:
      raise ValueError('''the value of input must be positive'''							)
   elif isinstance(_UpperCamelCase	,_UpperCamelCase							):
      raise TypeError('''Input value must be a \'int\' type'''							)
   elif isinstance(_UpperCamelCase	,_UpperCamelCase							):
      raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''							)
   __lowerCamelCase  							=      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  							=      result << 1
      # get the end bit
      __lowerCamelCase  							=      number % 2
      # right shift the bits by unity
      __lowerCamelCase  							=      number >> 1
      # add that bit to our ans
      __lowerCamelCase  							=      result | end_bit
   return get_reverse_bit_string(_UpperCamelCase							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






