





import warnings

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





class 					lowerCAmelCase							(   _lowerCAmelCase					):
  __lowerCamelCase		=			["image_processor", "tokenizer"]
  __lowerCamelCase		=			"AutoImageProcessor"
  __lowerCamelCase		=			"AutoTokenizer"







  def __init__(			self  :Optional[int]    ,							_lowercase  :Optional[int]=None    ,							_lowercase  :List[str]=None    ,							**_lowercase  :Dict					):






     '''simple docstring'''





     lowercase__    =  None
     if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."    ,							A_    ,							)
        lowercase__    =  kwargs.pop("feature_extractor"					)

     lowercase__    =  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__(A_    ,							A_					)
     lowercase__    =  self.image_processor







  def __call__(			self  :Any    ,							_lowercase  :Union[str, Any]=None    ,							_lowercase  :int=None    ,							_lowercase  :Dict=None    ,							**_lowercase  :List[Any]					):






     '''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:
        lowercase__    =  self.tokenizer(A_    ,							return_tensors=A_    ,							**A_					)

     if images is not None:
        lowercase__    =  self.image_processor(A_    ,							return_tensors=A_    ,							**A_					)

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







  def   UpperCAmelCase     (			self  :List[str]    ,							*_lowercase  :Any    ,							**_lowercase  :Optional[int]					):






     '''simple docstring'''





     return self.tokenizer.batch_decode(*A_    ,							**A_					)







  def   UpperCAmelCase     (			self  :Optional[int]    ,							*_lowercase  :Any    ,							**_lowercase  :Tuple					):






     '''simple docstring'''





     return self.tokenizer.decode(*A_    ,							**A_					)







  @property
  def   UpperCAmelCase     (			self  :str					):






     '''simple docstring'''





     lowercase__    =  self.tokenizer.model_input_names
     lowercase__    =  self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)







  @property
  def   UpperCAmelCase     (			self  :Dict					):






     '''simple docstring'''





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







  @property
  def   UpperCAmelCase     (			self  :Tuple					):






     '''simple docstring'''





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











'''simple docstring'''

from __future__ import annotations






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						list[int]							,		_SCREAMING_SNAKE_CASE     :						int							)			->						list[int]:







	"""simple docstring"""
	lowerCAmelCase    =	{}
	for index, val in enumerate(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase    =	target - val
		if compl in chk_map:
			return [chk_map[compl], index]
		lowerCAmelCase    =	index
	return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(F'''{two_sum([2, 7, 11, 15], 9) = }''')