def   a_					(_lowerCAmelCase  :   list[int]       )->				tuple[int, int]:
    # base case
    if len(_lowerCAmelCase       ) == 1:
        return a[0], a[0]
    else:
        # split A into half.
        snake_case:						Optional[int]         =   a[: len(_lowerCAmelCase       ) // 2]
        snake_case:						List[Any]         =   a[len(_lowerCAmelCase       ) // 2 :]

        # 2 sub problems, 1/2 of original size.
        snake_case    , snake_case:						str         =   max_difference(_lowerCAmelCase       )
        snake_case    , snake_case:						Tuple         =   max_difference(_lowerCAmelCase       )

        # get min of first and max of second
        # linear time
        snake_case:						List[Any]         =   min(_lowerCAmelCase       )
        snake_case:						List[str]         =   max(_lowerCAmelCase       )

        # 3 cases, either (small1, big1),
        # (min_first, max_second), (small2, big2)
        # constant comparisons
        if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
            return smalla, biga
        elif biga - smalla > max_second - min_first:
            return smalla, biga
        else:
            return min_first, max_second


if __name__ == "__main__":
    import doctest

    doctest.testmod()



import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


__lowerCAmelCase    :  Tuple										= logging.get_logger(__name__)
class 				lowerCamelCase			(							__snake_case					):






      def __init__(      self  ,		*__lowerCamelCase  ,		**__lowerCamelCase						)     ->				None:


          '''simple docstring'''







          warnings.warn(
              """The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
              """ Please use ConditionalDetrImageProcessor instead."""  ,		__lowerCamelCase  ,		)
          super().__init__(*__lowerCamelCase  ,		**__lowerCamelCase						)


