from collections import deque


class 						a__							:



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   None:
								__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__							:



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)			->   None:
								# total number of mlfq's queues
								__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 __SCREAMING_SNAKE_CASE							(						self   )			->   list[str]:
								__a		 =       []
								for i in range(len(self.finish_queue   )   ):
													sequence.append(self.finish_queue[i].process_name   )
								return sequence



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   list[int]:
								__a		 =       []
								for i in range(len(UpperCAmelCase   )   ):
													waiting_times.append(queue[i].waiting_time   )
								return waiting_times



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   list[int]:
								__a		 =       []
								for i in range(len(UpperCAmelCase   )   ):
													turnaround_times.append(queue[i].turnaround_time   )
								return turnaround_times



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   list[int]:
								__a		 =       []
								for i in range(len(UpperCAmelCase   )   ):
													completion_times.append(queue[i].stop_time   )
								return completion_times



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   list[int]:
								return [q.burst_time for q in queue]



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   int:
								process.waiting_time += self.current_time - process.stop_time
								return process.waiting_time



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   deque[Process]:
								__a		 =       deque()  # sequence deque of finished process
								while len(UpperCAmelCase   ) != 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(UpperCAmelCase   )
													# 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(UpperCAmelCase   )

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



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   tuple[deque[Process], deque[Process]]:
								__a		 =       deque()  # sequence deque of terminated process
								# just for 1 cycle and unfinished processes will go back to queue
								for _ in range(len(UpperCAmelCase   )   ):
													__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(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
																		__a		 =       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
																		__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(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 __SCREAMING_SNAKE_CASE							(						self   )			->   deque[Process]:

								#  all queues except last one have round_robin algorithm
								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

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

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :		Tuple							=	logging.get_logger(__name__)

lowerCamelCase_    :		List[Any]							=	{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}


class 						a__							(       __snake_case		):
			A__    :       Dict        =      'fnet'



			def __init__(						self				,						UpperCAmelCase=3_2_0_0_0				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase="gelu_new"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=4				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=False				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=3				,						UpperCAmelCase=1				,						UpperCAmelCase=2				,						**UpperCAmelCase				,						)			->   Dict:
								super().__init__(pad_token_id=UpperCAmelCase				,						bos_token_id=UpperCAmelCase				,						eos_token_id=UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       vocab_size
								__a		 =       max_position_embeddings
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       intermediate_size
								__a		 =       hidden_act
								__a		 =       hidden_dropout_prob
								__a		 =       initializer_range
								__a		 =       type_vocab_size
								__a		 =       layer_norm_eps
								__a		 =       use_tpu_fourier_optimizations
								__a		 =       tpu_short_seq_length




