
"""simple docstring"""
import warnings

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






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Union[str, Any]	=       ["""image_processor""", """tokenizer"""]
    __lowercase				:			Dict	=       """AutoImageProcessor"""
    __lowercase				:			int	=       """AutoTokenizer"""
    def __init__(     self    ,     snake_case_=None    ,     snake_case_=None    ,     **snake_case_  ):
      _snake_case  :    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."    ,     snake_case_    ,     )
        _snake_case  :    Union[str, Any]            = kwargs.pop("feature_extractor"  )

      _snake_case  :    List[str]            = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError("You have to specify an image_processor."  )
      if tokenizer is None:
        raise ValueError("You have to specify a tokenizer."  )

      super().__init__(snake_case_    ,     snake_case_  )
      _snake_case  :    List[str]            = self.image_processor
    def __call__(     self    ,     snake_case_=None    ,     snake_case_=None    ,     snake_case_=None    ,     **snake_case_  ):

      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:
        _snake_case  :    List[str]            = self.tokenizer(snake_case_    ,     return_tensors=snake_case_    ,     **snake_case_  )

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

      if text is not None and images is not None:
        _snake_case  :    Dict            = image_features.pixel_values
        return encoding
      elif text is not None:
        return encoding
      else:
        return BatchEncoding(data=dict(**snake_case_  )    ,     tensor_type=snake_case_  )
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      return self.tokenizer.batch_decode(*snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      return self.tokenizer.decode(*snake_case_    ,     **snake_case_  )
    @property
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = self.tokenizer.model_input_names
      _snake_case  :    int            = self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
    @property
    def        lowerCamelCase__						(     self  ):
      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,     snake_case_    ,     )
      return self.image_processor_class


    @property
    def        lowerCamelCase__						(     self  ):
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,     snake_case_    ,     )
      return self.image_processor








from math import pi




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						float						):
     return degree / (180 / pi)


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

    testmod()