"""simple docstring"""
from statistics import mean

import numpy as np







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

  # Sort by arrival time.
  a__:     List[Any]										=	[burst_time[i] for i in np.argsort(_SCREAMING_SNAKE_CASE   )]
  a__:     Optional[int]										=	[process_name[i] for i in np.argsort(_SCREAMING_SNAKE_CASE   )]
  arrival_time.sort()

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

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

        # Calculate the turn around time
    a__:     Dict										=	current_time + burst_time[loc] - arrival_time[loc]
    current_time += burst_time[loc]
    # Indicates that the process has been performed.
    a__:     Any										=	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   )	->list:
  a__:     Dict										=	[0] * no_of_process
  for i in range(0		,     _SCREAMING_SNAKE_CASE   ):
    a__:     Optional[int]										=	turn_around_time[i] - burst_time[i]
  return waiting_time


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

 lowercase__					       =	calculate_turn_around_time(
     process_name, arrival_time, burst_time, no_of_process
 )
 lowercase__					       =	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}")



"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE = 1000000   )	->int:
  a__:     int										=	limit + 1
  a__:     Optional[int]										=	[0] * limit
  for first_term in range(1		,     _SCREAMING_SNAKE_CASE   ):
    for n in range(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      a__:     List[Any]										=	first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  a__:     Any										=	sum(1 for x in frequency[1:limit] if x == 10   )

  return count


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


