





from statistics import mean

import numpy as np







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Any				       =   0
					# Number of processes finished
					_snake_case       :					Optional[int]				       =   0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					_snake_case       :					Union[str, Any]				       =   [0] * no_of_process
					# List to include calculation results
					_snake_case       :					List[str]				       =   [0] * no_of_process

					# Sort by arrival time.
					_snake_case       :					Optional[Any]				       =   [burst_time[i] for i in np.argsort(UpperCAmelCase				)]
					_snake_case       :					Union[str, Any]				       =   [process_name[i] for i in np.argsort(UpperCAmelCase				)]
					arrival_time.sort()

					while no_of_process > finished_process_count:
										_snake_case       :					int				       =   0
										while finished_process[i] == 1:
															i += 1
										if current_time < arrival_time[i]:
															_snake_case       :					Optional[int]				       =   arrival_time[i]

										_snake_case       :					Any				       =   0
										# Index showing the location of the process being performed
										_snake_case       :					Optional[int]				       =   0
										# Saves the current response ratio.
										_snake_case       :					List[str]				       =   0
										for i in range(0  ,  UpperCAmelCase				):
															if finished_process[i] == 0 and arrival_time[i] <= current_time:
																				_snake_case       :					List[Any]				       =   (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																				    i
																				]
															if response_ratio < temp:
																				_snake_case       :					Any				       =   temp
																				_snake_case       :					Dict				       =   i

        # Calculate the turn around time
										_snake_case       :					int				       =   current_time + burst_time[loc] - arrival_time[loc]
										current_time += burst_time[loc]
										# Indicates that the process has been performed.
										_snake_case       :					int				       =   1
										# Increase finished_process_count by 1
										finished_process_count += 1

					return turn_around_time







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   [0] * no_of_process
					for i in range(0  ,  UpperCAmelCase				):
										_snake_case       :					Tuple				       =   turn_around_time[i] - burst_time[i]
					return waiting_time


if __name__ == "__main__":
							__lowerCAmelCase :Union[str, Any]     =    5
							__lowerCAmelCase :int     =    ['A', 'B', 'C', 'D', 'E']
							__lowerCAmelCase :Union[str, Any]     =    [1, 2, 3, 4, 5]
							__lowerCAmelCase :Optional[int]     =    [1, 2, 3, 4, 5]

							__lowerCAmelCase :List[str]     =    calculate_turn_around_time(
							    process_name, arrival_time, burst_time, no_of_process
							)
							__lowerCAmelCase :str     =    calculate_waiting_time(
							    process_name, turn_around_time, burst_time, no_of_process
							)

							print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
							for i in range(0, no_of_process):
														print(
														    F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
														    F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
														)

							print(F'''average waiting time : {mean(waiting_time):.5f}''')
							print(F'''average turn around time : {mean(turn_around_time):.5f}''')






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__lowerCAmelCase :Union[str, Any]     =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__lowerCAmelCase :Optional[Any]     =    typing.Union[np.floataa, int, float]  # noqa: UP007







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return np.sqrt(np.sum((np.asarray(UpperCAmelCase				) - np.asarray(UpperCAmelCase				)) ** 2				)				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return sum((va - va) ** 2 for va, va in zip(UpperCAmelCase  ,  UpperCAmelCase				)				) ** (1 / 2)


if __name__ == "__main__":







							def    A				(							):
												from timeit import timeit

												print("Without Numpy"				)
												print(
												    timeit(
												        "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)
												print("With Numpy"				)
												print(
												    timeit(
												        "euclidean_distance([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)

							benchmark()