

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      List[str]          =					['image_processor', 'tokenizer']
      lowerCamelCase_	:      Dict          =					'AutoImageProcessor'
      lowerCamelCase_	:      Union[str, Any]          =					'AutoTokenizer'







      def __init__(     self							,  lowerCamelCase=None							,  lowerCamelCase=None							,  **lowerCamelCase   )						->       Optional[Any]:
        snake_case_       						=      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							,  )
          snake_case_       						=      kwargs.pop("""feature_extractor"""   )

        snake_case_       						=      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   )
        snake_case_       						=      self.image_processor
        snake_case_       						=      False







      def __call__(     self							,  *lowerCamelCase							,  **lowerCamelCase   )						->       List[Any]:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor(*lowerCamelCase							,  **lowerCamelCase   )

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

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







      def 	lowerCAmelCase_    (     self							,  *lowerCamelCase							,  **lowerCamelCase   )						->       str:
        return self.tokenizer.batch_decode(*lowerCamelCase							,  **lowerCamelCase   )







      def 	lowerCAmelCase_    (     self							,  *lowerCamelCase							,  **lowerCamelCase   )						->       List[Any]:
        return self.tokenizer.decode(*lowerCamelCase							,  **lowerCamelCase   )







      @contextmanager
      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        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."""   )
        snake_case_       						=      True
        snake_case_       						=      self.tokenizer
        yield
        snake_case_       						=      self.image_processor
        snake_case_       						=      False







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


from __future__ import annotations

from collections.abc import Generator



def    UpperCamelCase(					lowercase_							)	->				Generator[int, None, None]:





  '''simple docstring'''





  if not isinstance(lowercase_			,					lowercase_							) or n < 1:
    raise Exception("""Sequence only defined for positive integers"""							)

  yield n
  while n != 1:
    if n % 2 == 0:
      n //= 2
    else:
      snake_case_       						=      3 * n + 1
    yield n



def    UpperCamelCase(					)	->				Optional[int]:





  '''simple docstring'''





  snake_case_       						=      43
  snake_case_       						=      tuple(collatz_sequence(lowercase_							)							)
  print(lowercase_							)
  print(f'''Collatz sequence from {n} took {len(lowercase_							)} steps.'''							)


if __name__ == "__main__":
    main()