





from __future__ import annotations

import pandas as pd



def A_	(		A__		,      A__		,      A__				)  ->		list[int]:
       a__						:	Optional[Any] =				[0] * no_of_processes
       a__						:	Tuple =				[0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(A__				):
              a__						:	Optional[Any] =				burst_time[i]

       a__						:	Optional[int] =				0
       a__						:	int =				0
       a__						:	int =				9_9999_9999
       a__						:	Optional[int] =				0
       a__						:	Tuple =				False

       # Process until all processes are completed
       while complete != no_of_processes:
              for j in range(A__				):
                     if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                            if remaining_time[j] < minm:
                                   a__						:	Dict =				remaining_time[j]
                                   a__						:	int =				j
                                   a__						:	Tuple =				True

              if not check:
                     increment_time += 1
                     continue
              remaining_time[short] -= 1

              a__						:	Tuple =				remaining_time[short]
              if minm == 0:
                     a__						:	Optional[Any] =				9_9999_9999

              if remaining_time[short] == 0:
                     complete += 1
                     a__						:	Optional[int] =				False

                     # Find finish time of current process
                     a__						:	Optional[Any] =				increment_time + 1

                     # Calculate waiting time
                     a__						:	Optional[Any] =				finish_time - arrival_time[short]
                     a__						:	Any =				finar - burst_time[short]

                     if waiting_time[short] < 0:
                            a__						:	int =				0

        # Increment time
              increment_time += 1
       return waiting_time



def A_	(		A__		,      A__		,      A__				)  ->		list[int]:
       a__						:	List[str] =				[0] * no_of_processes
       for i in range(A__				):
              a__						:	Dict =				burst_time[i] + waiting_time[i]
       return turn_around_time



def A_	(		A__		,      A__		,      A__				)  ->		None:
       a__						:	List[Any] =				0
       a__						:	Any =				0
       for i in range(A__				):
              a__						:	str =				total_waiting_time + waiting_time[i]
              a__						:	Tuple =				total_turn_around_time + turn_around_time[i]
       print(F'Average waiting time = {total_waiting_time / no_of_processes:.5f}'				)
       print('Average turn around time ='		,      total_turn_around_time / no_of_processes				)


if __name__ == "__main__":
    print("""Enter how many process you want to analyze""")
    lowercase      :       str									=  int(input())
    lowercase      :       List[str]									=  [0] * no_of_processes
    lowercase      :       int									=  [0] * no_of_processes
    lowercase      :       List[Any]									=  list(range(1, no_of_processes + 1))

    for i in range(no_of_processes):
        print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
        lowercase						,					lowercase      :       Optional[int]									=  map(int, input().split())

    lowercase      :       Optional[int]									=  calculate_waitingtime(arrival_time, burst_time, no_of_processes)

    lowercase      :       Dict									=  burst_time
    lowercase      :       int									=  no_of_processes
    lowercase      :       Any									=  waiting_time
    lowercase      :       Union[str, Any]									=  calculate_turnaroundtime(bt, n, wt)

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    lowercase      :       Any									=  pd.DataFrame(
        list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
        columns=[
            """Process""",
            """BurstTime""",
            """ArrivalTime""",
            """WaitingTime""",
            """TurnAroundTime""",
        ],
    )

    # Printing the dataFrame
    pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
    print(fcfs)









from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
    from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


