
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 		lowercase_       (       __SCREAMING_SNAKE_CASE				):
 A__						:				Optional[Any]         =    ["""image_processor""", """tokenizer"""]
 A__						:				Optional[Any]         =    """AutoImageProcessor"""
 A__						:				Dict         =    """AutoTokenizer"""

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




        """simple docstring"""


        UpperCamelCase_							       =       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     ,	)
               UpperCamelCase_							       =       kwargs.pop("""feature_extractor"""     )

        UpperCamelCase_							       =       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     )
        UpperCamelCase_							       =       self.image_processor
        UpperCamelCase_							       =       False

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




        """simple docstring"""


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

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

        if text is None:
               return inputs
        elif images is None:
               return encodings
        else:
               UpperCamelCase_							       =       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."""     )
        UpperCamelCase_							       =       True
        UpperCamelCase_							       =       self.tokenizer
        yield
        UpperCamelCase_							       =       self.image_processor
        UpperCamelCase_							       =       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





def 				lowerCamelCase__				(						a__  :  list     )		->	list:
       UpperCamelCase_							       =       1.3
       UpperCamelCase_							       =       len(a__     )
       UpperCamelCase_							       =       False

       while not completed:
              # Update the gap value for a next comb
              UpperCamelCase_							       =       int(gap / shrink_factor     )
              if gap <= 1:
                     UpperCamelCase_							       =       True

              UpperCamelCase_							       =       0
              while index + gap < len(a__     ):
                     if data[index] > data[index + gap]:
                            # Swap values
                            UpperCamelCase_       ,				UpperCamelCase_							       =       data[index + gap], data[index]
                            UpperCamelCase_							       =       False
                     index += 1

       return data


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      _A	  =					input('''Enter numbers separated by a comma:\n''').strip()
      _A	  =					[int(item) for item in user_input.split(''',''')]
      print(comb_sort(unsorted))



