



import warnings

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

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      ["""image_processor""", """tokenizer"""]
     lowerCAmelCase       =      """AutoImageProcessor"""
     lowerCAmelCase       =      """AutoTokenizer"""





     def __init__(							self			:				int  ,	UpperCamelCase			:				int=None  ,	UpperCamelCase			:				Any=None  ,	**UpperCamelCase			:				int					)->Optional[int]:
      __SCREAMING_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."  ,	UpperCamelCase  ,	)
       __SCREAMING_SNAKE_CASE							:     Tuple							=						kwargs.pop("feature_extractor"					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						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__(UpperCamelCase  ,	UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.image_processor





     def __call__(							self			:				Union[str, Any]  ,	UpperCamelCase			:				Optional[Any]=None  ,	UpperCamelCase			:				List[Any]=None  ,	UpperCamelCase			:				Union[str, Any]=None  ,	**UpperCamelCase			:				List[Any]					)->List[str]:

      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:
       __SCREAMING_SNAKE_CASE							:     str							=						self.tokenizer(UpperCamelCase  ,	return_tensors=UpperCamelCase  ,	**UpperCamelCase					)

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

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





     def 						__snake_case     (							self			:				Tuple  ,	*UpperCamelCase			:				Union[str, Any]  ,	**UpperCamelCase			:				str					)->int:
      return self.tokenizer.batch_decode(*UpperCamelCase  ,	**UpperCamelCase					)





     def 						__snake_case     (							self			:				Dict  ,	*UpperCamelCase			:				Any  ,	**UpperCamelCase			:				List[Any]					)->str:
      return self.tokenizer.decode(*UpperCamelCase  ,	**UpperCamelCase					)





     @property
     def 						__snake_case     (							self			:				Any					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





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









"""simple docstring"""
def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     str:



       """simple docstring"""







       return " ".join([word[::-1] for word in input_str.split()]   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
