


from statistics import mean

import numpy as np
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      0
		# Number of processes finished
		lowercase__            =      0
		# Displays the finished process.
		# If it is 0, the performance is completed if it is 1, before the performance.
		lowercase__            =      [0] * no_of_process
		# List to include calculation results
		lowercase__            =      [0] * no_of_process

		# Sort by arrival time.
		lowercase__            =      [burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE     )]
		lowercase__            =      [process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE     )]
		arrival_time.sort()

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

				lowercase__            =      0
				# Index showing the location of the process being performed
				lowercase__            =      0
				# Saves the current response ratio.
				lowercase__            =      0
				for i in range(0						,				SCREAMING_SNAKE_CASE     ):
						if finished_process[i] == 0 and arrival_time[i] <= current_time:
								lowercase__            =      (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
								    i
								]
						if response_ratio < temp:
								lowercase__            =      temp
								lowercase__            =      i

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

		return turn_around_time
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      [0] * no_of_process
		for i in range(0						,				SCREAMING_SNAKE_CASE     ):
				lowercase__            =      turn_around_time[i] - burst_time[i]
		return waiting_time


if __name__ == "__main__":
			lowerCAmelCase       =							5
			lowerCAmelCase       =							['A', 'B', 'C', 'D', 'E']
			lowerCAmelCase       =							[1, 2, 3, 4, 5]
			lowerCAmelCase       =							[1, 2, 3, 4, 5]

			lowerCAmelCase       =							calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			lowerCAmelCase       =							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

from collections.abc import Iterator


class        _a							:

							def __init__(							self:	List[str]    ,						UpperCamelCase_:	int							)			->   None:





									"""simple docstring"""







									lowercase__            =      value
									lowercase__            =      None
									lowercase__            =      None


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	Node							)			->   None:





									"""simple docstring"""







									lowercase__            =      tree

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Node | None							)			->   int:





									"""simple docstring"""







									if node is None:
											return 0
									return node.value + (
									    self.depth_first_search(node.left							) + self.depth_first_search(node.right							)
									)


							def __iter__(							self:	List[str]							)			->   Iterator[int]:





									"""simple docstring"""







									yield self.depth_first_search(self.tree							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


