


import numpy as np




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Dict  ,			__lowerCamelCase:		Any  ):







	'''simple docstring'''



	lowercase_			 = np.array(__lowerCamelCase  )
	lowercase_			 = np.array(__lowerCamelCase  )

	lowercase_			 = abs(predict - actual  )
	lowercase_			 = difference.mean()

	return score




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



	lowercase_			 = np.array(__lowerCamelCase  )
	lowercase_			 = np.array(__lowerCamelCase  )

	lowercase_			 = predict - actual
	lowercase_			 = np.square(__lowerCamelCase  )

	lowercase_			 = square_diff.mean()
	return score




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		Dict  ):







	'''simple docstring'''



	lowercase_			 = np.array(__lowerCamelCase  )
	lowercase_			 = np.array(__lowerCamelCase  )

	lowercase_			 = predict - actual
	lowercase_			 = np.square(__lowerCamelCase  )
	lowercase_			 = square_diff.mean()
	lowercase_			 = np.sqrt(__lowerCamelCase  )
	return score




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		int  ):







	'''simple docstring'''



	lowercase_			 = np.array(__lowerCamelCase  )
	lowercase_			 = np.array(__lowerCamelCase  )

	lowercase_			 = np.log(predict + 1  )
	lowercase_			 = np.log(actual + 1  )

	lowercase_			 = log_predict - log_actual
	lowercase_			 = np.square(__lowerCamelCase  )
	lowercase_			 = square_diff.mean()

	lowercase_			 = np.sqrt(__lowerCamelCase  )

	return score




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Tuple  ,			__lowerCamelCase:		Union[str, Any]  ):







	'''simple docstring'''



	lowercase_			 = np.array(__lowerCamelCase  )
	lowercase_			 = np.array(__lowerCamelCase  )

	lowercase_			 = predict - actual
	lowercase_			 = np.sum(__lowerCamelCase  ) / len(__lowerCamelCase  )
	lowercase_			 = np.sum(__lowerCamelCase  ) / len(__lowerCamelCase  )
	# print(numerator, denumerator)
	lowercase_			 = float(__lowerCamelCase  ) / denumerator * 100

	return score




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		Any  ):







	'''simple docstring'''



	return np.mean(np.array(__lowerCamelCase  ) == np.array(__lowerCamelCase  )  )





from __future__ import annotations

from random import randrange




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list  ):







	'''simple docstring'''



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

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

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

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


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

