

'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  Any			 =							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE					:  str			 =							'AutoImageProcessor'
   _SCREAMING_SNAKE_CASE					:  Optional[Any]			 =							'AutoTokenizer'


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





      """simple docstring"""





      _lowercase    :   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."    ,       _UpperCamelCase    ,       )
         _lowercase    :   List[str]      =     kwargs.pop("feature_extractor"						)

      _lowercase    :   Union[str, Any]      =     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						)
      _lowercase    :   Union[str, Any]      =     self.image_processor
      _lowercase    :   Any      =     False


   def __call__(				self    ,       *_UpperCamelCase    ,       **_UpperCamelCase						):





      """simple docstring"""





      if self._in_target_context_manager:
         return self.current_processor(*_UpperCamelCase    ,       **_UpperCamelCase						)

      _lowercase    :   Optional[Any]      =     kwargs.pop("images"    ,       _UpperCamelCase						)
      _lowercase    :   Union[str, Any]      =     kwargs.pop("text"    ,       _UpperCamelCase						)
      if len(_UpperCamelCase						) > 0:
         _lowercase    :   Any      =     args[0]
         _lowercase    :   List[Any]      =     args[1:]

      if images is None and text is None:
         raise ValueError("You need to specify either an `images` or `text` input to process."						)

      if images is not None:
         _lowercase    :   Dict      =     self.image_processor(_UpperCamelCase    ,       *_UpperCamelCase    ,       **_UpperCamelCase						)
      if text is not None:
         _lowercase    :   List[str]      =     self.tokenizer(_UpperCamelCase    ,       **_UpperCamelCase						)

      if text is None:
         return inputs
      elif images is None:
         return encodings
      else:
         _lowercase    :   str      =     encodings["input_ids"]
         return inputs


   def 		_lowerCamelCase     (				self    ,       *_UpperCamelCase    ,       **_UpperCamelCase						):





      """simple docstring"""





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


   def 		_lowerCamelCase     (				self    ,       *_UpperCamelCase    ,       **_UpperCamelCase						):





      """simple docstring"""





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


   @contextmanager
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      warnings.warn(
          "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
          "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
          "your images inputs, or in a separate call."						)
      _lowercase    :   Any      =     True
      _lowercase    :   Any      =     self.tokenizer
      yield
      _lowercase    :   List[Any]      =     self.image_processor
      _lowercase    :   Dict      =     False


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      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 		_lowerCamelCase     (				self						):





      """simple docstring"""





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









'''simple docstring'''
def       _A	(			snake_case  )    ->			int:

   # validate:
   if not isinstance(snake_case	,							snake_case  ) or not all(
       isinstance(snake_case	,							snake_case  ) and item >= 0 for item in citations  ):
      raise ValueError("The citations should be a list of non negative integers."  )

   citations.sort()
   _lowercase    :   Tuple      =     len(snake_case  )

   for i in range(snake_case  ):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
    import doctest

    doctest.testmod()






