






'''simple docstring'''



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						=	{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecAudioForAudioFrameClassification',
          'Data2VecAudioForCTC',
          'Data2VecAudioForSequenceClassification',
          'Data2VecAudioForXVector',
          'Data2VecAudioModel',
          'Data2VecAudioPreTrainedModel',
      ]
      SCREAMING_SNAKE_CASE						=	[
          'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecTextForCausalLM',
          'Data2VecTextForMaskedLM',
          'Data2VecTextForMultipleChoice',
          'Data2VecTextForQuestionAnswering',
          'Data2VecTextForSequenceClassification',
          'Data2VecTextForTokenClassification',
          'Data2VecTextModel',
          'Data2VecTextPreTrainedModel',
      ]
      SCREAMING_SNAKE_CASE						=	[
          'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecVisionForImageClassification',
          'Data2VecVisionForMaskedImageModeling',
          'Data2VecVisionForSemanticSegmentation',
          'Data2VecVisionModel',
          'Data2VecVisionPreTrainedModel',
      ]

if is_tf_available():
      SCREAMING_SNAKE_CASE						=	[
          'TFData2VecVisionForImageClassification',
          'TFData2VecVisionForSemanticSegmentation',
          'TFData2VecVisionModel',
          'TFData2VecVisionPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
      from .configuration_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecTextConfig,
          DataaVecTextOnnxConfig,
      )
      from .configuration_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecVisionConfig,
          DataaVecVisionOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dataavec_audio import (
                DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecAudioForAudioFrameClassification,
                DataaVecAudioForCTC,
                DataaVecAudioForSequenceClassification,
                DataaVecAudioForXVector,
                DataaVecAudioModel,
                DataaVecAudioPreTrainedModel,
            )
            from .modeling_dataavec_text import (
                DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecTextForCausalLM,
                DataaVecTextForMaskedLM,
                DataaVecTextForMultipleChoice,
                DataaVecTextForQuestionAnswering,
                DataaVecTextForSequenceClassification,
                DataaVecTextForTokenClassification,
                DataaVecTextModel,
                DataaVecTextPreTrainedModel,
            )
            from .modeling_dataavec_vision import (
                DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecVisionForImageClassification,
                DataaVecVisionForMaskedImageModeling,
                DataaVecVisionForSemanticSegmentation,
                DataaVecVisionModel,
                DataaVecVisionPreTrainedModel,
            )
      if is_tf_available():
            from .modeling_tf_dataavec_vision import (
                TFDataaVecVisionForImageClassification,
                TFDataaVecVisionForSemanticSegmentation,
                TFDataaVecVisionModel,
                TFDataaVecVisionPreTrainedModel,
            )

else:
      import sys

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











'''simple docstring'''



def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   List[Any]				  =str(bin(__A			)			)
 binary_number += "0" * shift_amount
 return binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   Union[str, Any]				  =str(bin(__A			)			)[2:]
 if shift_amount >= len(__A			):
  return "0b0"
 lowercase					:   Any				  =binary_number[: len(__A			) - shift_amount]
 return "0b" + shifted_binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number >= 0:  # Get binary representation of positive number
  lowercase					:   str				  ='''0''' + str(bin(__A			)			).strip('''-'''			)[2:]
 else:  # Get binary (2's complement) representation of negative number
  lowercase					:   Dict				  =len(bin(__A			)[3:]			)  # Find 2's complement of number
  lowercase					:   Optional[Any]				  =bin(abs(__A			) - (1 << binary_number_length)			)[3:]
  lowercase					:   int				  =(
      '''1''' + '''0''' * (binary_number_length - len(__A			)) + binary_number
  )

 if shift_amount >= len(__A			):
  return "0b" + binary_number[0] * len(__A			)
 return (
     "0b"
     + binary_number[0] * shift_amount
     + binary_number[: len(__A			) - shift_amount]
 )


if __name__ == "__main__":
      import doctest

      doctest.testmod()



