"""simple docstring"""







from collections import deque

class 		UpperCamelCase      :



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







class 		UpperCamelCase      :



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



				def  _lowercase					(self    :				Any)   ->     Tuple:
					__snake_case      :							str						=			[]
					for i in range(len(self.finish_queue)):
						sequence.append(self.finish_queue[i].process_name)
					return sequence



				def  _lowercase					(self    :				str     ,  _A    :				list[Process])   ->     Dict:
					__snake_case      :							Tuple						=			[]
					for i in range(len(lowercase_)):
						waiting_times.append(queue[i].waiting_time)
					return waiting_times



				def  _lowercase					(self    :				List[str]     ,  _A    :				list[Process])   ->     Dict:
					__snake_case      :							Union[str, Any]						=			[]
					for i in range(len(lowercase_)):
						turnaround_times.append(queue[i].turnaround_time)
					return turnaround_times



				def  _lowercase					(self    :				Union[str, Any]     ,  _A    :				list[Process])   ->     Union[str, Any]:
					__snake_case      :							str						=			[]
					for i in range(len(lowercase_)):
						completion_times.append(queue[i].stop_time)
					return completion_times



				def  _lowercase					(self    :				str     ,  _A    :				deque[Process])   ->     Optional[Any]:
					return [q.burst_time for q in queue]



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				Process)   ->     int:
					process.waiting_time += self.current_time - process.stop_time
					return process.waiting_time



				def  _lowercase					(self    :				Optional[int]     ,  _A    :				deque[Process])   ->     str:
					__snake_case      :							deque[Process]						=			deque()  # sequence deque of finished process
					while len(lowercase_) != 0:
						__snake_case      :							Union[str, 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(lowercase_)
						# update current time
						self.current_time += cp.burst_time
						# finish the process and set the process's burst-time 0
						__snake_case      :							int						=			0
						# set the process's turnaround time because it is finished
						__snake_case      :							Optional[Any]						=			self.current_time - cp.arrival_time
						# set the completion time
						__snake_case      :							Optional[Any]						=			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  _lowercase					(self    :				Optional[Any]     ,  _A    :				deque[Process]     ,  _A    :				int)   ->     str:
					__snake_case      :							deque[Process]						=			deque()  # sequence deque of terminated process
					# just for 1 cycle and unfinished processes will go back to queue
					for _ in range(len(lowercase_)):
						__snake_case      :							List[str]						=			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
							__snake_case      :							List[Any]						=			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
							__snake_case      :							str						=			0
							# set the finish time
							__snake_case      :							List[str]						=			self.current_time
							# update the process' turnaround time because it is finished
							__snake_case      :							Dict						=			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  _lowercase					(self    :				int)   ->     Optional[Any]:

					#  all queues except last one have round_robin algorithm
					for i in range(self.number_of_queues - 1):
						__snake_case      :							Dict						=			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					:					Optional[int]=			Process("P1", 0, 53)
		_a					:					Any=			Process("P2", 0, 17)
		_a					:					Optional[Any]=			Process("P3", 0, 68)
		_a					:					Optional[Any]=			Process("P4", 0, 24)
		_a					:					Optional[Any]=			3
		_a					:					List[str]=			[17, 25]
		_a					:					int=			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					:					Dict=			Process("P1", 0, 53)
		_a					:					List[str]=			Process("P2", 0, 17)
		_a					:					Any=			Process("P3", 0, 68)
		_a					:					List[Any]=			Process("P4", 0, 24)
		_a					:					str=			3
		_a					:					List[str]=			[17, 25]
		_a					:					Optional[Any]=			deque([Pa, Pa, Pa, Pa])
		_a					:					Dict=			MLFQ(number_of_queues, time_slices, queue, 0)
		_a					:					Union[str, 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()}'''
		)






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_a					:					Tuple=			{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				str   =		"""albert"""



				def __init__(self    :				Union[str, Any]     ,  _A    :				int=3_00_00     ,  _A    :				Any=1_28     ,  _A    :				Tuple=40_96     ,  _A    :				int=12     ,  _A    :				Tuple=1     ,  _A    :				int=64     ,  _A    :				Optional[Any]=1_63_84     ,  _A    :				Optional[Any]=1     ,  _A    :				List[str]="gelu_new"     ,  _A    :				Any=0     ,  _A    :				Optional[Any]=0     ,  _A    :				List[Any]=5_12     ,  _A    :				List[Any]=2     ,  _A    :				Dict=0.02     ,  _A    :				Union[str, Any]=1E-12     ,  _A    :				Tuple=0.1     ,  _A    :				int="absolute"     ,  _A    :				List[Any]=0     ,  _A    :				str=2     ,  _A    :				int=3     ,  **_A    :				Tuple     ,  )   ->     Union[str, Any]:
					super().__init__(pad_token_id=_A     ,  bos_token_id=_A     ,  eos_token_id=_A     ,  **_A)

					__snake_case      :							str						=			vocab_size
					__snake_case      :							List[Any]						=			embedding_size
					__snake_case      :							Tuple						=			hidden_size
					__snake_case      :							Optional[Any]						=			num_hidden_layers
					__snake_case      :							List[str]						=			num_hidden_groups
					__snake_case      :							Dict						=			num_attention_heads
					__snake_case      :							Any						=			inner_group_num
					__snake_case      :							Union[str, Any]						=			hidden_act
					__snake_case      :							List[Any]						=			intermediate_size
					__snake_case      :							List[str]						=			hidden_dropout_prob
					__snake_case      :							int						=			attention_probs_dropout_prob
					__snake_case      :							Any						=			max_position_embeddings
					__snake_case      :							Optional[int]						=			type_vocab_size
					__snake_case      :							Optional[Any]						=			initializer_range
					__snake_case      :							List[Any]						=			layer_norm_eps
					__snake_case      :							Optional[Any]						=			classifier_dropout_prob
					__snake_case      :							List[str]						=			position_embedding_type







class 		UpperCamelCase      (   lowercase						):



				@property
				def  _lowercase					(self    :				Tuple)   ->     Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						__snake_case      :							int						=			{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						__snake_case      :							Optional[Any]						=			{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					        ('token_type_ids', dynamic_axis),
					    ])





