

'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					int     	=							'CLIPImageProcessor'
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							('CLIPTokenizer', 'CLIPTokenizerFast')


   def __init__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       None
      if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."      ,    _UpperCamelCase      ,    )
         _lowercase      :				int      					=       kwargs.pop("feature_extractor"  )

      _lowercase      :				Any      					=       image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."  )
      if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."  )

      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )


   def __call__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."  )

      if text is not None:
         _lowercase      :				int      					=       self.tokenizer(_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )

      if images is not None:
         _lowercase      :				Dict      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase  )

      if text is not None and images is not None:
         _lowercase      :				Optional[Any]      					=       image_features.pixel_values
         return encoding
      elif text is not None:
         return encoding
      else:
         return BatchEncoding(data=dict(**_UpperCamelCase  )      ,    tensor_type=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Any      					=       self.tokenizer.model_input_names
      _lowercase      :				Tuple      					=       self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor_class






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,    _UpperCamelCase      ,    )
      return self.image_processor






'''simple docstring'''
from timeit import timeit







def  _A		(     snake_case					)      ->     int:
   if number < 0:
      raise ValueError("the value of input must not be negative"					)
   _lowercase      :				Union[str, Any]      					=       0
   while number:
      number &= number - 1
      result += 1
   return result







def  _A		(     snake_case					)      ->     int:
   if number < 0:
      raise ValueError("the value of input must not be negative"					)
   _lowercase      :				int      					=       0
   while number:
      if number % 2 == 1:
         result += 1
      number >>= 1
   return result







def  _A		(     )      ->     None:

   def do_benchmark(snake_case					) -> None:
      _lowercase      :				Optional[int]      					=       "import __main__ as z"
      print(F'''Benchmark when {number = }:'''					)
      print(F'''{get_set_bits_count_using_modulo_operator(snake_case					) = }'''					)
      _lowercase      :				int      					=       timeit("z.get_set_bits_count_using_modulo_operator(25)"		,							setup=snake_case					)
      print(F'''timeit() runs in {timing} seconds'''					)
      print(F'''{get_set_bits_count_using_brian_kernighans_algorithm(snake_case					) = }'''					)
      _lowercase      :				Optional[int]      					=       timeit(
          "z.get_set_bits_count_using_brian_kernighans_algorithm(25)"		,							setup=snake_case		,							)
      print(F'''timeit() runs in {timing} seconds'''					)

   for number in (25, 37, 58, 0):
      do_benchmark(snake_case					)
      print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()






