






'''simple docstring'''

from collections import deque






class 							__magic_name__     :







	"""simple docstring"""



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






class 							__magic_name__     :







	"""simple docstring"""



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

							#  all queues except last one have round_robin algorithm
							for i in range(self.number_of_queues - 1       ):
													lowercase_  ,      lowercase_      :   int        =			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, 5_3)
						A:      List[str]  				=				Process("P2", 0, 1_7)
						A:      Dict  				=				Process("P3", 0, 6_8)
						A:      Union[str, Any]  				=				Process("P4", 0, 2_4)
						A:      Any  				=				3
						A:      Optional[Any]  				=				[1_7, 2_5]
						A:      List[str]  				=				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:      Optional[Any]  				=				Process("P1", 0, 5_3)
						A:      Tuple  				=				Process("P2", 0, 1_7)
						A:      Optional[int]  				=				Process("P3", 0, 6_8)
						A:      int  				=				Process("P4", 0, 2_4)
						A:      int  				=				3
						A:      Any  				=				[1_7, 2_5]
						A:      Any  				=				deque([Pa, Pa, Pa, Pa])
						A:      Tuple  				=				MLFQ(number_of_queues, time_slices, queue, 0)
						A:      List[str]  				=				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'''

import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
A:      Tuple  				=				logging.getLogger(__name__)






def 	_UpperCAmelCase    (  a		:	str							)			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   List[str]        =			git.Repo(search_parent_directories=a							)
						lowercase_      :   Union[str, Any]        =			{
						    'repo_id': str(a							),
						    'repo_sha': str(repo.head.object.hexsha							),
						    'repo_branch': str(repo.active_branch							),
						}

						with open(os.path.join(a						,			'git_log.json'							)						,			'w'							) as f:
												json.dump(a						,			a						,			indent=4							)






def 	_UpperCAmelCase    (  a		:	str							)			->			Union[str, Any]:





						"""simple docstring"""
						if params.n_gpu <= 0:
												lowercase_      :   int        =			0
												lowercase_      :   Union[str, Any]        =			-1
												lowercase_      :   List[str]        =			True
												lowercase_      :   Optional[Any]        =			False
												return

						assert torch.cuda.is_available()

						logger.info('Initializing GPUs'							)
						if params.n_gpu > 1:
												assert params.local_rank != -1

												lowercase_      :   Dict        =			int(os.environ['WORLD_SIZE']							)
												lowercase_      :   Union[str, Any]        =			int(os.environ['N_GPU_NODE']							)
												lowercase_      :   Optional[int]        =			int(os.environ['RANK']							)

												# number of nodes / node ID
												lowercase_      :   int        =			params.world_size // params.n_gpu_per_node
												lowercase_      :   str        =			params.global_rank // params.n_gpu_per_node
												lowercase_      :   Dict        =			True

												assert params.n_nodes == int(os.environ['N_NODES']							)
												assert params.node_id == int(os.environ['NODE_RANK']							)

						# local job (single GPU)
						else:
												assert params.local_rank == -1

												lowercase_      :   str        =			1
												lowercase_      :   Dict        =			0
												lowercase_      :   Tuple        =			0
												lowercase_      :   List[Any]        =			0
												lowercase_      :   int        =			1
												lowercase_      :   Tuple        =			1
												lowercase_      :   str        =			False

						# sanity checks
						assert params.n_nodes >= 1
						assert 0 <= params.node_id < params.n_nodes
						assert 0 <= params.local_rank <= params.global_rank < params.world_size
						assert params.world_size == params.n_nodes * params.n_gpu_per_node

						# define whether this is the master process / if we are in multi-node distributed mode
						lowercase_      :   List[str]        =			params.node_id == 0 and params.local_rank == 0
						lowercase_      :   Optional[Any]        =			params.n_nodes > 1

						# summary
						lowercase_      :   int        =			f"--- Global rank: {params.global_rank} - "
						logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes							)
						logger.info(PREFIX + 'Node ID        : %i' % params.node_id							)
						logger.info(PREFIX + 'Local rank     : %i' % params.local_rank							)
						logger.info(PREFIX + 'World size     : %i' % params.world_size							)
						logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node							)
						logger.info(PREFIX + 'Master         : %s' % str(params.is_master							)							)
						logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node							)							)
						logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu							)							)
						logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()							)

						# set GPU device
						torch.cuda.set_device(params.local_rank							)

						# initialize multi-GPU
						if params.multi_gpu:
												logger.info('Initializing PyTorch distributed'							)
												torch.distributed.init_process_group(
												    init_method='env://'						,			backend='nccl'						,			)






def 	_UpperCAmelCase    (  a		:	Dict							)			->			Optional[int]:





						"""simple docstring"""
						np.random.seed(args.seed							)
						torch.manual_seed(args.seed							)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed							)
