


'''simple docstring'''

from collections import deque


class       a__				:







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				process_name  # process name
										__lowerCAmelCase							       =				arrival_time  # arrival time of the process
										# completion time of finished process or last interrupted time
										__lowerCAmelCase							       =				arrival_time
										__lowerCAmelCase							       =				burst_time  # remaining burst time
										__lowerCAmelCase							       =				0  # total time of the process wait in ready queue
										__lowerCAmelCase							       =				0  # time from arrival time to completion time


class       a__				:







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			):
										# total number of mlfq's queues
										__lowerCAmelCase							       =				number_of_queues
										# time slice of queues that round robin algorithm applied
										__lowerCAmelCase							       =				time_slices
										# unfinished process is in this ready_queue
										__lowerCAmelCase							       =				queue
										# current time
										__lowerCAmelCase							       =				current_time
										# finished process is in this sequence queue
										__lowerCAmelCase							       =				deque()




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




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				[]
										for i in range(len(__lowercase		)		):
																waiting_times.append(queue[i].waiting_time		)
										return waiting_times




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				[]
										for i in range(len(__lowercase		)		):
																turnaround_times.append(queue[i].turnaround_time		)
										return turnaround_times




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				[]
										for i in range(len(__lowercase		)		):
																completion_times.append(queue[i].stop_time		)
										return completion_times




				def 					_snake_case  (self       ,			__lowercase		):
										return [q.burst_time for q in queue]




				def 					_snake_case  (self       ,			__lowercase		):
										process.waiting_time += self.current_time - process.stop_time
										return process.waiting_time




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				deque()  # sequence deque of finished process
										while len(__lowercase		) != 0:
																__lowerCAmelCase							       =				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
																__lowerCAmelCase							       =				0
																# set the process's turnaround time because it is finished
																__lowerCAmelCase							       =				self.current_time - cp.arrival_time
																# set the completion time
																__lowerCAmelCase							       =				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 					_snake_case  (self       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				deque()  # sequence deque of terminated process
										# just for 1 cycle and unfinished processes will go back to queue
										for _ in range(len(__lowercase		)		):
																__lowerCAmelCase							       =				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
																						__lowerCAmelCase							       =				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
																						__lowerCAmelCase							       =				0
																						# set the finish time
																						__lowerCAmelCase							       =				self.current_time
																						# update the process' turnaround time because it is finished
																						__lowerCAmelCase							       =				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 					_snake_case  (self		):

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





'''simple docstring'''

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_UpperCAmelCase				: Tuple							    =				transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def 							__magic_name__(						lowerCamelCase):
						if isinstance(lowerCamelCase,				torch.Tensor):
												return image
						elif isinstance(lowerCamelCase,				PIL.Image.Image):
												__lowerCAmelCase							       =				[image]

						__lowerCAmelCase							       =				[trans(img.convert('''RGB''')) for img in image]
						__lowerCAmelCase							       =				torch.stack(lowerCamelCase)
						return image




class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase       ,			__lowercase		):
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										__lowerCAmelCase							       =				DDIMScheduler.from_config(scheduler.config		)

										self.register_modules(unet=__lowercase       ,			scheduler=__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										if strength < 0 or strength > 1:
																raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase		):
										# get the original timestep using init_timestep
										__lowerCAmelCase							       =				min(int(num_inference_steps * strength		)       ,			__lowercase		)

										__lowerCAmelCase							       =				max(num_inference_steps - init_timestep       ,			0		)
										__lowerCAmelCase							       =				self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase       ,			__lowercase=None		):
										if not isinstance(__lowercase       ,			(torch.Tensor, PIL.Image.Image, list)		):
																raise ValueError(
																    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowercase		)}"""		)

										__lowerCAmelCase							       =				image.to(device=__lowercase       ,			dtype=__lowercase		)

										if isinstance(__lowercase       ,			__lowercase		) and len(__lowercase		) != batch_size:
																raise ValueError(
																    F"""You have passed a list of generators of length {len(__lowercase		)}, but requested an effective batch"""
																    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""		)

										__lowerCAmelCase							       =				init_latents.shape
										__lowerCAmelCase							       =				randn_tensor(__lowercase       ,			generator=__lowercase       ,			device=__lowercase       ,			dtype=__lowercase		)

										# get latents
										print('''add noise to latents at timestep'''       ,			__lowercase		)
										__lowerCAmelCase							       =				self.scheduler.add_noise(__lowercase       ,			__lowercase       ,			__lowercase		)
										__lowerCAmelCase							       =				init_latents

										return latents




				@torch.no_grad()
				def __call__(self       ,			__lowercase = None       ,			__lowercase = 0.8       ,			__lowercase = 1       ,			__lowercase = None       ,			__lowercase = 0.0       ,			__lowercase = 50       ,			__lowercase = None       ,			__lowercase = "pil"       ,			__lowercase = True       ,			):
										self.check_inputs(__lowercase		)

										# 2. Preprocess image
										__lowerCAmelCase							       =				preprocess(__lowercase		)

										# 3. set timesteps
										self.scheduler.set_timesteps(__lowercase       ,			device=self.device		)
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.get_timesteps(__lowercase       ,			__lowercase       ,			self.device		)
										__lowerCAmelCase							       =				timesteps[:1].repeat(__lowercase		)

										# 4. Prepare latent variables
										__lowerCAmelCase							       =				self.prepare_latents(__lowercase       ,			__lowercase       ,			__lowercase       ,			self.unet.dtype       ,			self.device       ,			__lowercase		)
										__lowerCAmelCase							       =				latents

										# 5. Denoising loop
										for t in self.progress_bar(__lowercase		):
																# 1. predict noise model_output
																__lowerCAmelCase							       =				self.unet(__lowercase       ,			__lowercase		).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																__lowerCAmelCase							       =				self.scheduler.step(
																    __lowercase       ,			__lowercase       ,			__lowercase       ,			eta=__lowercase       ,			use_clipped_model_output=__lowercase       ,			generator=__lowercase       ,			).prev_sample

										__lowerCAmelCase							       =				(image / 2 + 0.5).clamp(0       ,			1		)
										__lowerCAmelCase							       =				image.cpu().permute(0       ,			2       ,			3       ,			1		).numpy()
										if output_type == "pil":
																__lowerCAmelCase							       =				self.numpy_to_pil(__lowercase		)

										if not return_dict:
																return (image, latent_timestep.item())

										return ImagePipelineOutput(images=__lowercase		)

