
import warnings

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




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""image_processor""", """tokenizer"""]
  a__   	=		"""VideoMAEImageProcessor"""
  a__   	=		("""CLIPTokenizer""", """CLIPTokenizerFast""")
  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			Dict=None      ,							UpperCamelCase__      :			Union[str, Any]=None      ,							**UpperCamelCase__      :			Tuple   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__    					=	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__      ,							)
      __magic_name__    					=	kwargs.pop("""feature_extractor"""   )

    __magic_name__    					=	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__   )
    __magic_name__    					=	self.image_processor
  def __call__(			self      :			Tuple      ,							UpperCamelCase__      :			Optional[Any]=None      ,							UpperCamelCase__      :			Union[str, Any]=None      ,							UpperCamelCase__      :			Dict=None      ,							**UpperCamelCase__      :			str   )      ->     List[str]:







    """simple docstring"""






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

    if text is not None:
      __magic_name__    					=	self.tokenizer(UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__   )

    if videos is not None:
      __magic_name__    					=	self.image_processor(UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__   )

    if text is not None and videos is not None:
      __magic_name__    					=	image_features.pixel_values
      return encoding
    elif text is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**UpperCamelCase__   )      ,							tensor_type=UpperCamelCase__   )
  def 							_lowercase							(			self      :			List[Any]      ,							*UpperCamelCase__      :			Optional[Any]      ,							**UpperCamelCase__      :			List[Any]   )      ->     str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[Any]      ,							*UpperCamelCase__      :			Optional[Any]      ,							**UpperCamelCase__      :			Tuple   )      ->     Optional[Any]:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  @property
  def 							_lowercase							(			self      :			str   )      ->     int:







    """simple docstring"""






    return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
  @property
  def 							_lowercase							(			self      :			Any   )      ->     str:







    """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 							_lowercase							(			self      :			List[Any]   )      ->     str:







    """simple docstring"""






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



def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	str(A_ ).strip()
  if not bin_string:
    raise ValueError("""Empty string was passed to the function""" )
  __magic_name__    					=	bin_string[0] == """-"""
  if is_negative:
    __magic_name__    					=	bin_string[1:]
  if not all(char in """01""" for char in bin_string ):
    raise ValueError("""Non-binary value was passed to the function""" )
  __magic_name__    					=	0
  for char in bin_string:
    __magic_name__    					=	2 * decimal_number + int(A_ )
  return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()

