


"""simple docstring"""






from collections import deque
class        snake_case :




					"""simple docstring"""

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






class        snake_case :




					"""simple docstring"""

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







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							[]
									for i in range(len(self.finish_queue    )    ):
													sequence.append(self.finish_queue[i].process_name    )
									return sequence







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			Tuple    ):
									UpperCAmelCase__    =							[]
									for i in range(len(_lowercase    )    ):
													waiting_times.append(queue[i].waiting_time    )
									return waiting_times







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			Any    ):
									UpperCAmelCase__    =							[]
									for i in range(len(_lowercase    )    ):
													turnaround_times.append(queue[i].turnaround_time    )
									return turnaround_times







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							[]
									for i in range(len(_lowercase    )    ):
													completion_times.append(queue[i].stop_time    )
									return completion_times







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			Dict    ):
									return [q.burst_time for q in queue]







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			Union[str, Any]    ):
									process.waiting_time += self.current_time - process.stop_time
									return process.waiting_time







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

									#  all queues except last one have round_robin algorithm
									for i in range(self.number_of_queues - 1    ):
													UpperCAmelCase__    =							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__						:						List[str]		  =			Process('P1', 0, 53)
						lowerCAmelCase__						:						List[str]		  =			Process('P2', 0, 17)
						lowerCAmelCase__						:						Dict		  =			Process('P3', 0, 68)
						lowerCAmelCase__						:						Union[str, Any]		  =			Process('P4', 0, 24)
						lowerCAmelCase__						:						Any		  =			3
						lowerCAmelCase__						:						Optional[Any]		  =			[17, 25]
						lowerCAmelCase__						:						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])})

						lowerCAmelCase__						:						Optional[Any]		  =			Process('P1', 0, 53)
						lowerCAmelCase__						:						Tuple		  =			Process('P2', 0, 17)
						lowerCAmelCase__						:						Optional[int]		  =			Process('P3', 0, 68)
						lowerCAmelCase__						:						int		  =			Process('P4', 0, 24)
						lowerCAmelCase__						:						int		  =			3
						lowerCAmelCase__						:						Any		  =			[17, 25]
						lowerCAmelCase__						:						Any		  =			deque([Pa, Pa, Pa, Pa])
						lowerCAmelCase__						:						Tuple		  =			MLFQ(number_of_queues, time_slices, queue, 0)
						lowerCAmelCase__						:						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 unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCAmelCase__						:						Optional[int]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






@slow
@require_torch_gpu
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe(
									    image=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2



