


from statistics import mean

import numpy as np





def __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A			)						->					list:



       '''simple docstring'''





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

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

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

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

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

       return turn_around_time





def __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A			)						->					list:



       '''simple docstring'''





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


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

    A      = calculate_turn_around_time(
        process_name, arrival_time, burst_time, no_of_process
    )
    A      = 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 .imports import is_tqdm_available


if is_tqdm_available():
    from tqdm.auto import tqdm as _tqdm

from ..state import PartialState





def __UpperCAmelCase       (		__A = True ,						*__A ,						**__A			)						->					Any:



       '''simple docstring'''





       if not is_tqdm_available():
              raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."			)
       UpperCAmelCase__										=     False
       if main_process_only:
              UpperCAmelCase__										=     PartialState().local_process_index == 0
       return _tqdm(*__A ,						**__A ,						disable=__A			)





