





"""simple docstring"""



from __future__ import annotations

import pandas as pd







def 		__lowercase     (   snake_case_  :  list[int]				,snake_case_  :  list[int]				,snake_case_  :  int						)   ->list[int]:



    '''simple docstring'''







    __A    :						Any						 =     [0] * no_of_processes
    __A    :						int						 =     [0] * no_of_processes
    # Copy the burst time into remaining_time[]
    for i in range(snake_case_						):
        __A    :						Any						 =     burst_time[i]

    __A    :						Tuple						 =     0
    __A    :						Tuple						 =     0
    __A    :						Optional[int]						 =     999999999
    __A    :						Optional[Any]						 =     0
    __A    :						Dict						 =     False

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

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

        __A    :						Any						 =     remaining_time[short]
        if minm == 0:
            __A    :						Optional[int]						 =     999999999

        if remaining_time[short] == 0:
            complete += 1
            __A    :						Optional[Any]						 =     False

            # Find finish time of current process
            __A    :						Union[str, Any]						 =     increment_time + 1

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

            if waiting_time[short] < 0:
                __A    :						Dict						 =     0

        # Increment time
        increment_time += 1
    return waiting_time







def 		__lowercase     (   snake_case_  :  list[int]				,snake_case_  :  int				,snake_case_  :  list[int]						)   ->list[int]:



    '''simple docstring'''







    __A    :						Dict						 =     [0] * no_of_processes
    for i in range(snake_case_						):
        __A    :						Dict						 =     burst_time[i] + waiting_time[i]
    return turn_around_time







def 		__lowercase     (   snake_case_  :  list[int]				,snake_case_  :  list[int]				,snake_case_  :  int						)   ->None:



    '''simple docstring'''







    __A    :						List[str]						 =     0
    __A    :						Union[str, Any]						 =     0
    for i in range(snake_case_						):
        __A    :						Optional[Any]						 =     total_waiting_time + waiting_time[i]
        __A    :						List[str]						 =     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""")
       a_ =			int(input())
       a_ =			[0] * no_of_processes
       a_ =			[0] * no_of_processes
       a_ =			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))
              a_ ,			a_ =			map(int, input().split())

       a_ =			calculate_waitingtime(arrival_time, burst_time, no_of_processes)

       a_ =			burst_time
       a_ =			no_of_processes
       a_ =			waiting_time
       a_ =			calculate_turnaroundtime(bt, n, wt)

       calculate_average_times(waiting_time, turn_around_time, no_of_processes)

       a_ =			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)











"""simple docstring"""



def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						List[str]						 =     set()
    # edges = list of graph's edges
    __A    :						Optional[int]						 =     get_edges(snake_case_						)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        __A ,       __A    :						str						 =     edges.pop()
        chosen_vertices.add(snake_case_						)
        chosen_vertices.add(snake_case_						)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case_						)
    return chosen_vertices







def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						Tuple						 =     set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)						)
    return edges


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
       # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")




