from statistics import mean

import numpy as np
def  A      (       _lowercase						,    _lowercase						,    _lowercase						,    _lowercase		):
						SCREAMING_SNAKE_CASE				:       str     							=				0
						# Number of processes finished
						SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				0
						# Displays the finished process.
						# If it is 0, the performance is completed if it is 1, before the performance.
						SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				[0] * no_of_process
						# List to include calculation results
						SCREAMING_SNAKE_CASE				:       Any     							=				[0] * no_of_process

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

						while no_of_process > finished_process_count:
												SCREAMING_SNAKE_CASE				:       List[str]     							=				0
												while finished_process[i] == 1:
																		i += 1
												if current_time < arrival_time[i]:
																		SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				arrival_time[i]

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

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

						return turn_around_time
def  A      (       _lowercase						,    _lowercase						,    _lowercase						,    _lowercase		):
						SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				[0] * no_of_process
						for i in range(0						,    _lowercase		):
												SCREAMING_SNAKE_CASE				:       Tuple     							=				turn_around_time[i] - burst_time[i]
						return waiting_time


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

					__UpperCamelCase						:			Optional[Any]     		=	calculate_turn_around_time(
					    process_name, arrival_time, burst_time, no_of_process
					)
					__UpperCamelCase						:			int     		=	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}""")






import random




def    lowerCAmelCase_  (		lowercase:				int	,       lowercase:				float	,       lowercase:				bool = False		)     ->					dict:







 '''simple docstring'''
 _UpperCamelCase:  dict    						=			{i: [] for i in range(lowercase		)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(lowercase		)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(lowercase		):
  for j in range(i + 1	,       lowercase		):
   if random.random() < probability:
    graph[i].append(lowercase		)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(lowercase		)
 return graph




def    lowerCAmelCase_  (		lowercase:				int		)     ->					dict:







 '''simple docstring'''
 return {
     i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
 }


if __name__ == "__main__":
  import doctest

  doctest.testmod()