
"""simple docstring"""





import warnings

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


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		["""image_processor""", """tokenizer"""]
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		"""CLIPImageProcessor"""
  SCREAMING_SNAKE_CASE__			:						str       =		("""CLIPTokenizer""", """CLIPTokenizerFast""")







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

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

     UpperCAmelCase_					:					Tuple			     =    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__(lowercase_				,   lowercase_  )







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

     """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:
        UpperCAmelCase_					:					Tuple			     =    self.tokenizer(lowercase_				,   return_tensors=lowercase_				,   **lowercase_  )

     if images is not None:
        UpperCAmelCase_					:					Optional[int]			     =    self.image_processor(lowercase_				,   return_tensors=lowercase_				,   **lowercase_  )

     if text is not None and images is not None:
        UpperCAmelCase_					:					str			     =    image_features.pixel_values
        return encoding
     elif text is not None:
        return encoding
     else:
        return BatchEncoding(data=dict(**lowercase_  )				,   tensor_type=lowercase_  )







  def        UpperCamelCase__					(		self				,   *lowercase_				,   **lowercase_  ):

     """simple docstring"""
     return self.tokenizer.batch_decode(*lowercase_				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   *lowercase_				,   **lowercase_  ):

     """simple docstring"""
     return self.tokenizer.decode(*lowercase_				,   **lowercase_  )







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.tokenizer.model_input_names
     UpperCAmelCase_					:					str			     =    self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







  @property
  def        UpperCamelCase__					(		self  ):

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







  @property
  def        UpperCamelCase__					(		self  ):

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




"""simple docstring"""





from __future__ import annotations







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   if (voltage, current, resistance).count(0  ) != 1:
      raise ValueError("One and only one argument must be 0"  )
   if resistance < 0:
      raise ValueError("Resistance cannot be negative"  )
   if voltage == 0:
      return {"voltage": float(current * resistance  )}
   elif current == 0:
      return {"current": voltage / resistance}
   elif resistance == 0:
      return {"resistance": voltage / current}
   else:
      raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


