'''simple docstring'''





from collections import deque





class       lowercase__		:


						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							process_name  # process name
												SCREAMING_SNAKE_CASE							:					Tuple           =							arrival_time  # arrival time of the process
												# completion time of finished process or last interrupted time
												SCREAMING_SNAKE_CASE							:					Any           =							arrival_time
												SCREAMING_SNAKE_CASE							:					Dict           =							burst_time  # remaining burst time
												SCREAMING_SNAKE_CASE							:					int           =							0  # total time of the process wait in ready queue
												SCREAMING_SNAKE_CASE							:					str           =							0  # time from arrival time to completion time






class       lowercase__		:


						def __init__( self			:       int					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							number_of_queues
												# time slice of queues that round robin algorithm applied
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							time_slices
												# unfinished process is in this ready_queue
												SCREAMING_SNAKE_CASE							:					List[str]           =							queue
												# current time
												SCREAMING_SNAKE_CASE							:					List[Any]           =							current_time
												# finished process is in this sequence queue
												SCREAMING_SNAKE_CASE							:					deque[Process]           =							deque()


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[]
												for i in range(len(UpperCamelCase__		)		):
																		waiting_times.append(queue[i].waiting_time		)
												return waiting_times


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							[]
												for i in range(len(UpperCamelCase__		)		):
																		turnaround_times.append(queue[i].turnaround_time		)
												return turnaround_times


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							[]
												for i in range(len(UpperCamelCase__		)		):
																		completion_times.append(queue[i].stop_time		)
												return completion_times


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												return [q.burst_time for q in queue]


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					deque[Process]           =							deque()  # sequence deque of finished process
												while len(UpperCamelCase__		) != 0:
																		SCREAMING_SNAKE_CASE							:					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 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
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							0
																		# set the process's turnaround time because it is finished
																		SCREAMING_SNAKE_CASE							:					List[str]           =							self.current_time - cp.arrival_time
																		# set the completion time
																		SCREAMING_SNAKE_CASE							:					Tuple           =							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			:       str					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_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(UpperCamelCase__		)		):
																		SCREAMING_SNAKE_CASE							:					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 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
																								SCREAMING_SNAKE_CASE							:					List[str]           =							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
																								SCREAMING_SNAKE_CASE							:					str           =							0
																								# set the finish time
																								SCREAMING_SNAKE_CASE							:					Tuple           =							self.current_time
																								# update the process' turnaround time because it is finished
																								SCREAMING_SNAKE_CASE							:					str           =							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			:       Dict		):





												'''simple docstring'''





												for i in range(self.number_of_queues - 1		):
																		SCREAMING_SNAKE_CASE							:					str           =							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	: int    		=  Process('P1', 0, 53)
					__UpperCamelCase	: str    		=  Process('P2', 0, 17)
					__UpperCamelCase	: List[Any]    		=  Process('P3', 0, 68)
					__UpperCamelCase	: int    		=  Process('P4', 0, 24)
					__UpperCamelCase	: Optional[int]    		=  3
					__UpperCamelCase	: List[Any]    		=  [17, 25]
					__UpperCamelCase	: Union[str, 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	: int    		=  Process('P1', 0, 53)
					__UpperCamelCase	: Optional[int]    		=  Process('P2', 0, 17)
					__UpperCamelCase	: str    		=  Process('P3', 0, 68)
					__UpperCamelCase	: List[str]    		=  Process('P4', 0, 24)
					__UpperCamelCase	: str    		=  3
					__UpperCamelCase	: Union[str, Any]    		=  [17, 25]
					__UpperCamelCase	: int    		=  deque([Pa, Pa, Pa, Pa])
					__UpperCamelCase	: Dict    		=  MLFQ(number_of_queues, time_slices, queue, 0)
					__UpperCamelCase	: Optional[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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase	: Tuple    		=  {
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Dict    		=  [
					    'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CTRLForSequenceClassification',
					    'CTRLLMHeadModel',
					    'CTRLModel',
					    'CTRLPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Any    		=  [
					    'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCTRLForSequenceClassification',
					    'TFCTRLLMHeadModel',
					    'TFCTRLModel',
					    'TFCTRLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
					from .tokenization_ctrl import CTRLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ctrl import (
										    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CTRLForSequenceClassification,
										    CTRLLMHeadModel,
										    CTRLModel,
										    CTRLPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ctrl import (
										    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCTRLForSequenceClassification,
										    TFCTRLLMHeadModel,
										    TFCTRLModel,
										    TFCTRLPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase	: Optional[Any]    		=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





