

from collections import deque



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__   ,	A__      ):
						A__							:   str					       =		process_name  # process name
						A__							:   List[str]					       =		arrival_time  # arrival time of the process
						# completion time of finished process or last interrupted time
						A__							:   Optional[Any]					       =		arrival_time
						A__							:   int					       =		burst_time  # remaining burst time
						A__							:   List[Any]					       =		0  # total time of the process wait in ready queue
						A__							:   Any					       =		0  # time from arrival time to completion time



class    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						# total number of mlfq's queues
						A__							:   str					       =		number_of_queues
						# time slice of queues that round robin algorithm applied
						A__							:   Optional[int]					       =		time_slices
						# unfinished process is in this ready_queue
						A__							:   Any					       =		queue
						# current time
						A__							:   Dict					       =		current_time
						# finished process is in this sequence queue
						A__							:   deque[Process]					       =		deque()






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






					def   __A      (			self   ,	A__      ):
						A__							:   str					       =		[]
						for i in range(len(A__      )      ):
							waiting_times.append(queue[i].waiting_time      )
						return waiting_times






					def   __A      (			self   ,	A__      ):
						A__							:   Dict					       =		[]
						for i in range(len(A__      )      ):
							turnaround_times.append(queue[i].turnaround_time      )
						return turnaround_times






					def   __A      (			self   ,	A__      ):
						A__							:   Union[str, Any]					       =		[]
						for i in range(len(A__      )      ):
							completion_times.append(queue[i].stop_time      )
						return completion_times






					def   __A      (			self   ,	A__      ):
						return [q.burst_time for q in queue]






					def   __A      (			self   ,	A__      ):
						process.waiting_time += self.current_time - process.stop_time
						return process.waiting_time






					def   __A      (			self   ,	A__      ):
						A__							:   deque[Process]					       =		deque()  # sequence deque of finished process
						while len(A__      ) != 0:
							A__							:   Tuple					       =		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(A__      )
							# update current time
							self.current_time += cp.burst_time
							# finish the process and set the process's burst-time 0
							A__							:   Union[str, Any]					       =		0
							# set the process's turnaround time because it is finished
							A__							:   Dict					       =		self.current_time - cp.arrival_time
							# set the completion time
							A__							:   List[Any]					       =		self.current_time
							# add the process to queue that has finished queue
							finished.append(A__      )

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






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   deque[Process]					       =		deque()  # sequence deque of terminated process
						# just for 1 cycle and unfinished processes will go back to queue
						for _ in range(len(A__      )      ):
							A__							:   Optional[int]					       =		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(A__      )
							# 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__							:   Union[str, Any]					       =		self.current_time
								# locate the process behind the queue because it is not finished
								ready_queue.append(A__      )
							else:
								# use CPU for remaining burst time
								self.current_time += cp.burst_time
								# set burst time 0 because the process is finished
								A__							:   List[Any]					       =		0
								# set the finish time
								A__							:   List[Any]					       =		self.current_time
								# update the process' turnaround time because it is finished
								A__							:   Optional[Any]					       =		self.current_time - cp.arrival_time
								# add the process to queue that has finished queue
								finished.append(A__      )

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






					def   __A      (			self      ):

						#  all queues except last one have round_robin algorithm
						for i in range(self.number_of_queues - 1      ):
							A__       ,       A__							:   Optional[Any]					       =		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

	A_	:			List[str]  	=      Process('P1', 0, 53)
	A_	:			str  	=      Process('P2', 0, 17)
	A_	:			Union[str, Any]  	=      Process('P3', 0, 68)
	A_	:			List[Any]  	=      Process('P4', 0, 24)
	A_	:			List[str]  	=      3
	A_	:			str  	=      [17, 25]
	A_	:			Tuple  	=      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])})

	A_	:			Any  	=      Process('P1', 0, 53)
	A_	:			str  	=      Process('P2', 0, 17)
	A_	:			List[Any]  	=      Process('P3', 0, 68)
	A_	:			Tuple  	=      Process('P4', 0, 24)
	A_	:			Dict  	=      3
	A_	:			Optional[int]  	=      [17, 25]
	A_	:			List[Any]  	=      deque([Pa, Pa, Pa, Pa])
	A_	:			Optional[Any]  	=      MLFQ(number_of_queues, time_slices, queue, 0)
	A_	:			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 warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	'''MCTCTFeatureExtractor'''
					UpperCAmelCase__:       Optional[int]  							=	'''AutoTokenizer'''






					def __init__(			self   ,	A__   ,	A__      ):
						super().__init__(A__   ,	A__      )
						A__							:   List[str]					       =		self.feature_extractor
						A__							:   Optional[int]					       =		False






					def __call__(			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor(*A__   ,	**A__      )

						if "raw_speech" in kwargs:
							warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""      )
							A__							:   Dict					       =		kwargs.pop("""raw_speech"""      )
						else:
							A__							:   Tuple					       =		kwargs.pop("""audio"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""sampling_rate"""   ,	A__      )
						A__							:   int					       =		kwargs.pop("""text"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   Optional[int]					       =		args[0]
							A__							:   Dict					       =		args[1:]

						if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process."""      )

						if audio is not None:
							A__							:   List[str]					       =		self.feature_extractor(A__   ,	*A__   ,	sampling_rate=A__   ,	**A__      )
						if text is not None:
							A__							:   Optional[Any]					       =		self.tokenizer(A__   ,	**A__      )

						if text is None:
							return inputs
						elif audio is None:
							return encodings
						else:
							A__							:   List[Any]					       =		encodings["""input_ids"""]
							return inputs






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.batch_decode(*A__   ,	**A__      )






					def   __A      (			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor.pad(*A__   ,	**A__      )

						A__							:   Optional[Any]					       =		kwargs.pop("""input_features"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""labels"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   List[Any]					       =		args[0]
							A__							:   Optional[int]					       =		args[1:]

						if input_features is not None:
							A__							:   Union[str, Any]					       =		self.feature_extractor.pad(A__   ,	*A__   ,	**A__      )
						if labels is not None:
							A__							:   List[Any]					       =		self.tokenizer.pad(A__   ,	**A__      )

						if labels is None:
							return input_features
						elif input_features is None:
							return labels
						else:
							A__							:   Dict					       =		labels["""input_ids"""]
							return input_features






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.decode(*A__   ,	**A__      )






					@contextmanager
					def   __A      (			self      ):
						warnings.warn(
						    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
						    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
						    """your audio inputs, or in a separate call."""      )
						A__							:   int					       =		True
						A__							:   List[Any]					       =		self.tokenizer
						yield
						A__							:   Tuple					       =		self.feature_extractor
						A__							:   Dict					       =		False



