

from __future__ import annotations

from random import randrange




def        __UpperCamelCase     (      _A					:							list		)				->list:



							"""simple docstring"""
							if len(_A		) < 2:
														return collection
							lowerCamelCase_								=randrange(len(_A		)		)  # Use random element as pivot
							lowerCamelCase_								=collection[pivot_index]
							lowerCamelCase_								=[]  # All elements greater than pivot
							lowerCamelCase_								=[]  # All elements less than or equal to pivot

							for element in collection[:pivot_index]:
														(greater if element > pivot else lesser).append(_A		)

							for element in collection[pivot_index + 1 :]:
														(greater if element > pivot else lesser).append(_A		)

							return [*quick_sort(_A		), pivot, *quick_sort(_A		)]


if __name__ == "__main__":
	__A	:   List[str]	=					input('Enter numbers separated by a comma:\n').strip()
	__A	:   Optional[int]	=					[int(item) for item in user_input.split(',')]
	print(quick_sort(unsorted))



from sklearn.neural_network import MLPClassifier

__A	:   Tuple	=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
__A	:   Optional[int]	=					[0, 1, 0, 0]


__A	:   int	=					MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


__A	:   str	=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
__A	:   str	=					clf.predict(test)




def        __UpperCamelCase     (      _A					:							int		)				->Union[str, Any]:



							"""simple docstring"""
							return list(_A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
