



from typing import TYPE_CHECKING

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


A_		:     Optional[int]        =     {
    '''configuration_mobilenet_v2''': [
        '''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV2Config''',
        '''MobileNetV2OnnxConfig''',
    ],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:     List[str]        =     ['''MobileNetV2FeatureExtractor''']
     A_		:     Tuple        =     ['''MobileNetV2ImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:     Tuple        =     [
         '''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MobileNetV2ForImageClassification''',
         '''MobileNetV2ForSemanticSegmentation''',
         '''MobileNetV2Model''',
         '''MobileNetV2PreTrainedModel''',
         '''load_tf_weights_in_mobilenet_v2''',
     ]


if TYPE_CHECKING:
     from .configuration_mobilenet_va import (
         MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileNetVaConfig,
         MobileNetVaOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
          from .image_processing_mobilenet_va import MobileNetVaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilenet_va import (
              MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileNetVaForImageClassification,
              MobileNetVaForSemanticSegmentation,
              MobileNetVaModel,
              MobileNetVaPreTrainedModel,
              load_tf_weights_in_mobilenet_va,
          )


else:
     import sys

     A_		:     Any        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 if a < 0 or b < 0:
  raise ValueError("the value of both inputs must be positive"					)

 a						=		str(bin(UpperCAmelCase__					)					)[2:]  # remove the leading "0b"
 a						=		str(bin(UpperCAmelCase__					)					)[2:]  # remove the leading "0b"

 a						=		max(len(UpperCAmelCase__					) ,					len(UpperCAmelCase__					)					)

 return "0b" + "".join(
     str(int(char_a == "1" and char_b == "1"					)					)
     for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase__					) ,					b_binary.zfill(UpperCAmelCase__					)					)					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

