

def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> tuple[int, int]:



    '''simple docstring'''






    if len(_UpperCAmelCase				) == 1:
        return a[0], a[0]
    else:
        # split A into half.
        lowerCAmelCase				:			str    			=      a[: len(_UpperCAmelCase				) // 2]
        lowerCAmelCase				:			List[str]    			=      a[len(_UpperCAmelCase				) // 2 :]

        # 2 sub problems, 1/2 of original size.
        lowerCAmelCase			,  lowerCAmelCase				:			Tuple    			=      max_difference(_UpperCAmelCase				)
        lowerCAmelCase			,  lowerCAmelCase				:			Optional[Any]    			=      max_difference(_UpperCAmelCase				)

        # get min of first and max of second
        # linear time
        lowerCAmelCase				:			List[str]    			=      min(_UpperCAmelCase				)
        lowerCAmelCase				:			Tuple    			=      max(_UpperCAmelCase				)

        # 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()








__A   :     List[Any]      =			'''
# Installation de Transformers
! pip install transformers datasets
# Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

__A   :     Dict      =			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__A   :     Tuple      =			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





