





from collections import deque

class      lowerCAmelCase							:
 def __init__(   self						:   Optional[int]    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   int			)    ->			None:
     lowerCamelCase__					:   Optional[int]      				=	process_name  # process name
     lowerCamelCase__					:   Union[str, Any]      				=	arrival_time  # arrival time of the process
     # completion time of finished process or last interrupted time
     lowerCamelCase__					:   Any      				=	arrival_time
     lowerCamelCase__					:   Dict      				=	burst_time  # remaining burst time
     lowerCamelCase__					:   Any      				=	0  # total time of the process wait in ready queue
     lowerCamelCase__					:   List[Any]      				=	0  # time from arrival time to completion time


class      lowerCAmelCase							:
 def __init__(   self						:   str    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   list[int]    ,  UpperCAmelCase						:   deque[Process]    ,  UpperCAmelCase						:   int    ,  )    ->			None:
     # total number of mlfq's queues
     lowerCamelCase__					:   List[Any]      				=	number_of_queues
     # time slice of queues that round robin algorithm applied
     lowerCamelCase__					:   Dict      				=	time_slices
     # unfinished process is in this ready_queue
     lowerCamelCase__					:   Optional[int]      				=	queue
     # current time
     lowerCamelCase__					:   str      				=	current_time
     # finished process is in this sequence queue
     lowerCamelCase__					:   deque[Process]      				=	deque()
 def   A_			(   self						:   Union[str, Any]			)    ->			list[str]:
     lowerCamelCase__					:   Optional[Any]      				=	[]
     for i in range(len(self.finish_queue			)			):
         sequence.append(self.finish_queue[i].process_name			)
     return sequence
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   list[Process]			)    ->			list[int]:
     lowerCamelCase__					:   str      				=	[]
     for i in range(len(UpperCAmelCase			)			):
         waiting_times.append(queue[i].waiting_time			)
     return waiting_times
 def   A_			(   self						:   Any    ,  UpperCAmelCase						:   list[Process]			)    ->			list[int]:
     lowerCamelCase__					:   Dict      				=	[]
     for i in range(len(UpperCAmelCase			)			):
         turnaround_times.append(queue[i].turnaround_time			)
     return turnaround_times
 def   A_			(   self						:   Optional[Any]    ,  UpperCAmelCase						:   list[Process]			)    ->			list[int]:
     lowerCamelCase__					:   str      				=	[]
     for i in range(len(UpperCAmelCase			)			):
         completion_times.append(queue[i].stop_time			)
     return completion_times
 def   A_			(   self						:   str    ,  UpperCAmelCase						:   deque[Process]			)    ->			list[int]:
     return [q.burst_time for q in queue]
 def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   Process			)    ->			int:
     process.waiting_time += self.current_time - process.stop_time
     return process.waiting_time
 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   deque[Process]			)    ->			deque[Process]:
     lowerCamelCase__					:   deque[Process]      				=	deque()  # sequence deque of finished process
     while len(UpperCAmelCase			) != 0:
         lowerCamelCase__					:   Any      				=	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(UpperCAmelCase			)
         # update current time
         self.current_time += cp.burst_time
         # finish the process and set the process's burst-time 0
         lowerCamelCase__					:   Optional[int]      				=	0
         # set the process's turnaround time because it is finished
         lowerCamelCase__					:   Any      				=	self.current_time - cp.arrival_time
         # set the completion time
         lowerCamelCase__					:   Any      				=	self.current_time
         # add the process to queue that has finished queue
         finished.append(UpperCAmelCase			)

     self.finish_queue.extend(UpperCAmelCase			)  # add finished process to finish queue
     # FCFS will finish all remaining processes
     return finished
 def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   deque[Process]    ,  UpperCAmelCase						:   int			)    ->			tuple[deque[Process], deque[Process]]:
     lowerCamelCase__					:   deque[Process]      				=	deque()  # sequence deque of terminated process
     # just for 1 cycle and unfinished processes will go back to queue
     for _ in range(len(UpperCAmelCase			)			):
         lowerCamelCase__					:   Dict      				=	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(UpperCAmelCase			)
         # 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
             lowerCamelCase__					:   int      				=	self.current_time
             # locate the process behind the queue because it is not finished
             ready_queue.append(UpperCAmelCase			)
         else:
             # use CPU for remaining burst time
             self.current_time += cp.burst_time
             # set burst time 0 because the process is finished
             lowerCamelCase__					:   List[Any]      				=	0
             # set the finish time
             lowerCamelCase__					:   int      				=	self.current_time
             # update the process' turnaround time because it is finished
             lowerCamelCase__					:   Dict      				=	self.current_time - cp.arrival_time
             # add the process to queue that has finished queue
             finished.append(UpperCAmelCase			)

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







 def   A_			(   self						:   Tuple			)    ->			deque[Process]:

     #  all queues except last one have round_robin algorithm
     for i in range(self.number_of_queues - 1			):
         lowerCamelCase__				,							lowerCamelCase__					:   Tuple      				=	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

    _UpperCAmelCase       : Optional[Any]			     =       Process("""P1""", 0, 53)
    _UpperCAmelCase       : List[str]			     =       Process("""P2""", 0, 17)
    _UpperCAmelCase       : Dict			     =       Process("""P3""", 0, 68)
    _UpperCAmelCase       : Optional[int]			     =       Process("""P4""", 0, 24)
    _UpperCAmelCase       : Optional[int]			     =       3
    _UpperCAmelCase       : Any			     =       [17, 25]
    _UpperCAmelCase       : List[Any]			     =       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])})

    _UpperCAmelCase       : Tuple			     =       Process("""P1""", 0, 53)
    _UpperCAmelCase       : List[str]			     =       Process("""P2""", 0, 17)
    _UpperCAmelCase       : Dict			     =       Process("""P3""", 0, 68)
    _UpperCAmelCase       : List[str]			     =       Process("""P4""", 0, 24)
    _UpperCAmelCase       : List[Any]			     =       3
    _UpperCAmelCase       : List[Any]			     =       [17, 25]
    _UpperCAmelCase       : Tuple			     =       deque([Pa, Pa, Pa, Pa])
    _UpperCAmelCase       : Optional[int]			     =       MLFQ(number_of_queues, time_slices, queue, 0)
    _UpperCAmelCase       : Any			     =       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()}"""
    )












import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_UpperCAmelCase       : Union[str, Any]			     =       logging.get_logger(__name__)

class      lowerCAmelCase							:
 UpperCAmelCase__									=	None


@experimental
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    if ParallelBackendConfig.backend_name is None:
        return _map_with_multiprocessing_pool(
            _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)

    return _map_with_joblib(_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   int      				=	num_proc if num_proc <= len(_UpperCAmelCase				) else len(_UpperCAmelCase				)
    lowerCamelCase__					:   Optional[int]      				=	[]  # We organize the splits ourselve (contiguous splits)
    for index in range(_UpperCAmelCase				):
        lowerCamelCase__					:   str      				=	len(_UpperCAmelCase				) // num_proc
        lowerCamelCase__					:   List[str]      				=	len(_UpperCAmelCase				) % num_proc
        lowerCamelCase__					:   Tuple      				=	div * index + min(_UpperCAmelCase		,			_UpperCAmelCase				)
        lowerCamelCase__					:   int      				=	start + div + (1 if index < mod else 0)
        split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)				)

    if len(_UpperCAmelCase				) != sum(len(i[1]				) for i in split_kwds				):
        raise ValueError(
            F"""Error dividing inputs iterable among processes. """
            F"""Total number of objects {len(_UpperCAmelCase				)}, """
            F"""length: {sum(len(i[1]				) for i in split_kwds				)}"""				)

    logger.info(
        F"""Spawning {num_proc} processes for {len(_UpperCAmelCase				)} objects in slices of {[len(i[1]				) for i in split_kwds]}"""				)
    lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	None, None
    if not disable_tqdm:
        lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	(RLock(),), tqdm.set_lock
    with Pool(_UpperCAmelCase		,			initargs=_UpperCAmelCase		,			initializer=_UpperCAmelCase				) as pool:
        lowerCamelCase__					:   Any      				=	pool.map(_UpperCAmelCase		,			_UpperCAmelCase				)
    logger.info(F"""Finished {num_proc} processes"""				)
    lowerCamelCase__					:   Union[str, Any]      				=	[obj for proc_res in mapped for obj in proc_res]
    logger.info(F"""Unpacked {len(_UpperCAmelCase				)} objects"""				)

    return mapped


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  int:
    # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
    # and it requires monkey-patching joblib internal classes which is subject to change
    import joblib

    with joblib.parallel_backend(ParallelBackendConfig.backend_name		,			n_jobs=_UpperCAmelCase				):
        return joblib.Parallel()(
            joblib.delayed(_UpperCAmelCase				)((function, obj, types, None, True, None)				) for obj in iterable				)


@experimental
@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Tuple:
    lowerCamelCase__					:   List[str]      				=	backend_name

    if backend_name == "spark":
        from joblibspark import register_spark

        register_spark()

        # TODO: call create_cache_and_write_probe if "download" in steps
        # TODO: raise NotImplementedError when Dataset.map etc is called

    try:
        yield
    finally:
        lowerCamelCase__					:   str      				=	None





