



"""simple docstring"""





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   ['image_processor', 'tokenizer']
   __UpperCamelCase									=   'AutoImageProcessor'
   __UpperCamelCase									=   'AutoTokenizer'







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




      '''simple docstring'''




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

      _lowerCAmelCase					     =		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__(lowerCamelCase       ,	lowerCamelCase				)
      _lowerCAmelCase					     =		self.image_processor
      _lowerCAmelCase					     =		False







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




      '''simple docstring'''




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

      _lowerCAmelCase					     =		kwargs.pop("""images"""       ,	lowerCamelCase				)
      _lowerCAmelCase					     =		kwargs.pop("""text"""       ,	lowerCamelCase				)
      if len(lowerCamelCase				) > 0:
         _lowerCAmelCase					     =		args[0]
         _lowerCAmelCase					     =		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:
         _lowerCAmelCase					     =		self.image_processor(lowerCamelCase       ,	*lowerCamelCase       ,	**lowerCamelCase				)
      if text is not None:
         _lowerCAmelCase					     =		self.tokenizer(lowerCamelCase       ,	**lowerCamelCase				)

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







   def 							A__						(self       ,	*lowerCamelCase       ,	**lowerCamelCase				):




      '''simple docstring'''




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







   def 							A__						(self       ,	*lowerCamelCase       ,	**lowerCamelCase				):




      '''simple docstring'''




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







   @contextmanager
   def 							A__						(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."""				)
      _lowerCAmelCase					     =		True
      _lowerCAmelCase					     =		self.tokenizer
      yield
      _lowerCAmelCase					     =		self.image_processor
      _lowerCAmelCase					     =		False







   @property
   def 							A__						(self				):




      '''simple docstring'''




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







   @property
   def 							A__						(self				):




      '''simple docstring'''




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




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  float   )							->	int:






   """simple docstring"""


   return int(snake_case_   ) if x - int(snake_case_   ) <= 0 else int(snake_case_   ) + 1


if __name__ == "__main__":
  import doctest

  doctest.testmod()