



from statistics import mean

import numpy as np






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= 0
 # Number of processes finished
 a__												= 0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 a__												= [0] * no_of_process
 # List to include calculation results
 a__												= [0] * no_of_process

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

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

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

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

 return turn_around_time






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= [0] * no_of_process
 for i in range(0							,  __UpperCAmelCase       ):
  a__												= turn_around_time[i] - burst_time[i]
 return waiting_time


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

     a_      :      List[Any]    =     calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     a_      :      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 math
from collections.abc import Iterator
from itertools import takewhile






def     __a					(		__UpperCAmelCase       ):

 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5							,  int(math.sqrt(__UpperCAmelCase       ) + 1       )							,  6       ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def     __a					(		):
 a__												= 2
 while True:
  if is_prime(__UpperCAmelCase       ):
   yield num
  num += 1






def     __a					(		__UpperCAmelCase = 200_0000       ):
 return sum(takewhile(lambda __UpperCAmelCase       : x < n							,  prime_generator()       )       )


if __name__ == "__main__":
     print(f'{solution() = }')





