

from collections import deque


class  A						:







    """simple docstring"""





    def __init__(      self :       str,lowercase_ :       str,lowercase_ :       int,lowercase_ :       int		)->    None:





        '''simple docstring'''







        A__          =   process_name  # process name
        A__          =   arrival_time  # arrival time of the process
        # completion time of finished process or last interrupted time
        A__          =   arrival_time
        A__          =   burst_time  # remaining burst time
        A__          =   0  # total time of the process wait in ready queue
        A__          =   0  # time from arrival time to completion time




class  A						:







    """simple docstring"""





    def __init__(      self :       List[Any],lowercase_ :       int,lowercase_ :       list[int],lowercase_ :       deque[Process],lowercase_ :       int,)->    None:





        '''simple docstring'''







        A__          =   number_of_queues
        # time slice of queues that round robin algorithm applied
        A__          =   time_slices
        # unfinished process is in this ready_queue
        A__          =   queue
        # current time
        A__          =   current_time
        # finished process is in this sequence queue
        A__          =   deque()


    def 						snake_case__     (      self :       Any		)->    list[str]:





        '''simple docstring'''







        A__          =   []
        for i in range(len(self.finish_queue		)		):
            sequence.append(self.finish_queue[i].process_name		)
        return sequence


    def 						snake_case__     (      self :       List[Any],lowercase_ :       list[Process]		)->    list[int]:





        '''simple docstring'''







        A__          =   []
        for i in range(len(lowercase_		)		):
            waiting_times.append(queue[i].waiting_time		)
        return waiting_times


    def 						snake_case__     (      self :       List[Any],lowercase_ :       list[Process]		)->    list[int]:





        '''simple docstring'''







        A__          =   []
        for i in range(len(lowercase_		)		):
            turnaround_times.append(queue[i].turnaround_time		)
        return turnaround_times


    def 						snake_case__     (      self :       Dict,lowercase_ :       list[Process]		)->    list[int]:





        '''simple docstring'''







        A__          =   []
        for i in range(len(lowercase_		)		):
            completion_times.append(queue[i].stop_time		)
        return completion_times


    def 						snake_case__     (      self :       Dict,lowercase_ :       deque[Process]		)->    list[int]:





        '''simple docstring'''







        return [q.burst_time for q in queue]


    def 						snake_case__     (      self :       List[str],lowercase_ :       Process		)->    int:





        '''simple docstring'''







        process.waiting_time += self.current_time - process.stop_time
        return process.waiting_time


    def 						snake_case__     (      self :       int,lowercase_ :       deque[Process]		)->    deque[Process]:





        '''simple docstring'''







        A__          =   deque()  # sequence deque of finished process
        while len(lowercase_		) != 0:
            A__          =   ready_queue.popleft()  # current process

            # if process's arrival time is later than current time, update current time
            if self.current_time < cp.arrival_time:
                self.current_time += cp.arrival_time

            # update waiting time of current process
            self.update_waiting_time(lowercase_		)
            # update current time
            self.current_time += cp.burst_time
            # finish the process and set the process's burst-time 0
            A__          =   0
            # set the process's turnaround time because it is finished
            A__          =   self.current_time - cp.arrival_time
            # set the completion time
            A__          =   self.current_time
            # add the process to queue that has finished queue
            finished.append(lowercase_		)

        self.finish_queue.extend(lowercase_		)  # add finished process to finish queue
        # FCFS will finish all remaining processes
        return finished


    def 						snake_case__     (      self :       Tuple,lowercase_ :       deque[Process],lowercase_ :       int		)->    tuple[deque[Process], deque[Process]]:





        '''simple docstring'''







        A__          =   deque()  # sequence deque of terminated process
        # just for 1 cycle and unfinished processes will go back to queue
        for _ in range(len(lowercase_		)		):
            A__          =   ready_queue.popleft()  # current process

            # if process's arrival time is later than current time, update current time
            if self.current_time < cp.arrival_time:
                self.current_time += cp.arrival_time

            # update waiting time of unfinished processes
            self.update_waiting_time(lowercase_		)
            # if the burst time of process is bigger than time-slice
            if cp.burst_time > time_slice:
                # use CPU for only time-slice
                self.current_time += time_slice
                # update remaining burst time
                cp.burst_time -= time_slice
                # update end point time
                A__          =   self.current_time
                # locate the process behind the queue because it is not finished
                ready_queue.append(lowercase_		)
            else:
                # use CPU for remaining burst time
                self.current_time += cp.burst_time
                # set burst time 0 because the process is finished
                A__          =   0
                # set the finish time
                A__          =   self.current_time
                # update the process' turnaround time because it is finished
                A__          =   self.current_time - cp.arrival_time
                # add the process to queue that has finished queue
                finished.append(lowercase_		)

        self.finish_queue.extend(lowercase_		)  # add finished process to finish queue
        # return finished processes queue and remaining processes queue
        return finished, ready_queue


    def 						snake_case__     (      self :       List[Any]		)->    deque[Process]:





        '''simple docstring'''







        for i in range(self.number_of_queues - 1		):
            A__      ,						A__          =   self.round_robin(
                self.ready_queue,self.time_slices[i]		)
        #  the last queue has first_come_first_served algorithm
        self.first_come_first_served(self.ready_queue		)

        return self.finish_queue


if __name__ == "__main__":
     import doctest

     lowercase_       		=  Process("P1", 0, 53)
     lowercase_       		=  Process("P2", 0, 17)
     lowercase_       		=  Process("P3", 0, 68)
     lowercase_       		=  Process("P4", 0, 24)
     lowercase_       		=  3
     lowercase_       		=  [17, 25]
     lowercase_       		=  deque([Pa, Pa, Pa, Pa])

     if len(time_slices) != number_of_queues - 1:
          raise SystemExit(0)

     doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})

     lowercase_       		=  Process("P1", 0, 53)
     lowercase_       		=  Process("P2", 0, 17)
     lowercase_       		=  Process("P3", 0, 68)
     lowercase_       		=  Process("P4", 0, 24)
     lowercase_       		=  3
     lowercase_       		=  [17, 25]
     lowercase_       		=  deque([Pa, Pa, Pa, Pa])
     lowercase_       		=  MLFQ(number_of_queues, time_slices, queue, 0)
     lowercase_       		=  mlfq.multi_level_feedback_queue()

     # print total waiting times of processes(P1, P2, P3, P4)
     print(
         f"""waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"""
     )
     # print completion times of processes(P1, P2, P3, P4)
     print(
         f"""completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"""
     )
     # print total turnaround times of processes(P1, P2, P3, P4)
     print(
         f"""turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"""
     )
     # print sequence of finished processes
     print(
         f"""sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}"""
     )




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    global f  # a global dp table for knapsack
    if f[i][j] < 0:
        if j < wt[i - 1]:
            A__          =   mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            A__          =   max(
                mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )    ,			mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			j - wt[i - 1]      ) + val[i - 1]    ,			)
        A__          =   val
    return f[i][j]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Tuple:





    '''simple docstring'''






    A__          =   [[0] * (w + 1) for _ in range(n + 1      )]

    for i in range(1    ,			n + 1      ):
        for w_ in range(1    ,			w + 1      ):
            if wt[i - 1] <= w_:
                A__          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]    ,			dp[i - 1][w_]      )
            else:
                A__          =   dp[i - 1][w_]

    return dp[n][w_], dp
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list      )							->      Union[str, Any]:





    '''simple docstring'''






    if not (isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      ) and isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      )):
        raise ValueError(
            'Both the weights and values vectors must be either lists or tuples'      )

    A__          =   len(SCREAMING_SNAKE_CASE__      )
    if num_items != len(SCREAMING_SNAKE_CASE__      ):
        A__          =   (
            'The number of weights must be the same as the number of values.\n'
            f'But got {num_items} weights and {len(SCREAMING_SNAKE_CASE__      )} values'
        )
        raise ValueError(SCREAMING_SNAKE_CASE__      )
    for i in range(SCREAMING_SNAKE_CASE__      ):
        if not isinstance(wt[i]    ,			SCREAMING_SNAKE_CASE__      ):
            A__          =   (
                'All weights must be integers but got weight of '
                f'type {type(wt[i]      )} at index {i}'
            )
            raise TypeError(SCREAMING_SNAKE_CASE__      )

    A__      ,						A__          =   knapsack(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   set()
    _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return optimal_val, example_optional_set
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	set      )							->      Optional[int]:





    '''simple docstring'''






    if i > 0 and j > 0:
        if dp[i - 1][j] == dp[i][j]:
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            optimal_set.add(SCREAMING_SNAKE_CASE__      )
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			j - wt[i - 1]    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     lowercase_       		=  [3, 2, 4, 4]
     lowercase_       		=  [4, 3, 2, 3]
     lowercase_       		=  4
     lowercase_       		=  6
     lowercase_       		=  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowercase_		,			lowercase_       		=  knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     lowercase_		,			lowercase_       		=  knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("optimal_value = ", optimal_solution)
     print("An optimal subset corresponding to the optimal value", optimal_subset)

