




import warnings

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




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Any	     =      ["""image_processor""", """tokenizer"""]
    lowerCamelCase_      :	int	     =      """CLIPImageProcessor"""
    lowerCamelCase_      :	Any	     =      ("""CLIPTokenizer""", """CLIPTokenizerFast""")







    def __init__(    self     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Any:
           lowerCamelCase   :      Optional[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__     ,  )
                  lowerCamelCase   :      Optional[Any]			 =					kwargs.pop("feature_extractor"     )

           lowerCamelCase   :      Dict			 =					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__     )     ->      int:

           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:
                  lowerCamelCase   :      Tuple			 =					self.tokenizer(UpperCamelCase__     ,  return_tensors=UpperCamelCase__     ,  **UpperCamelCase__     )

           if images is not None:
                  lowerCamelCase   :      Any			 =					self.image_processor(UpperCamelCase__     ,  return_tensors=UpperCamelCase__     ,  **UpperCamelCase__     )

           if text is not None and images is not None:
                  lowerCamelCase   :      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     _lowercase     (    self     ,  *UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Dict:
           return self.tokenizer.batch_decode(*UpperCamelCase__     ,  **UpperCamelCase__     )







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







    @property
    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      Union[str, Any]			 =					self.tokenizer.model_input_names
           lowerCamelCase   :      Tuple			 =					self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )







    @property
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           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     )     ->      Dict:
           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,  UpperCamelCase__     ,  )
           return self.image_processor












import math

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> float:
       if (
           not isinstance(_SCREAMING_SNAKE_CASE ,(int, float)    )
           or power_factor < -1
           or power_factor > 1
       ):
              raise ValueError("power_factor must be a valid float value between -1 and 1."    )
       return apparent_power * power_factor

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> float:
       if (
           not isinstance(_SCREAMING_SNAKE_CASE ,(int, float)    )
           or power_factor < -1
           or power_factor > 1
       ):
              raise ValueError("power_factor must be a valid float value between -1 and 1."    )
       return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






