



"""simple docstring"""







from collections import deque







class  lowerCamelCase__ :






    def __init__(			self     ,A     ,A     ,A ):
          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  lowerCamelCase__ :






    def __init__(			self     ,A     ,A     ,A     ,A     ,):
          # 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     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	[]
          for i in range(len(self.finish_queue ) ):
                sequence.append(self.finish_queue[i].process_name )
          return sequence






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	[]
          for i in range(len(__snake_case ) ):
                waiting_times.append(queue[i].waiting_time )
          return waiting_times






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	[]
          for i in range(len(__snake_case ) ):
                turnaround_times.append(queue[i].turnaround_time )
          return turnaround_times






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	[]
          for i in range(len(__snake_case ) ):
                completion_times.append(queue[i].stop_time )
          return completion_times






    def     _UpperCamelCase							(			self     ,A ):
          return [q.burst_time for q in queue]






    def     _UpperCamelCase							(			self     ,A ):
          process.waiting_time += self.current_time - process.stop_time
          return process.waiting_time






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	deque()  # sequence deque of finished process
          while len(__snake_case ) != 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(__snake_case )
                # 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(__snake_case )

          self.finish_queue.extend(__snake_case )  # add finished process to finish queue
          # FCFS will finish all remaining processes
          return finished






    def     _UpperCamelCase							(			self     ,A     ,A ):
          UpperCAmelCase          =	deque()  # sequence deque of terminated process
          # just for 1 cycle and unfinished processes will go back to queue
          for _ in range(len(__snake_case ) ):
                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(__snake_case )
                # 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(__snake_case )
                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(__snake_case )

          self.finish_queue.extend(__snake_case )  # add finished process to finish queue
          # return finished processes queue and remaining processes queue
          return finished, ready_queue






    def     _UpperCamelCase							(			self ):

          #  all queues except last one have round_robin algorithm
          for i in range(self.number_of_queues - 1 ):
                UpperCAmelCase      , 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

  _UpperCamelCase					      =  Process("""P1""", 0, 53)
  _UpperCamelCase					      =  Process("""P2""", 0, 17)
  _UpperCamelCase					      =  Process("""P3""", 0, 68)
  _UpperCamelCase					      =  Process("""P4""", 0, 24)
  _UpperCamelCase					      =  3
  _UpperCamelCase					      =  [17, 25]
  _UpperCamelCase					      =  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					      =  Process("""P1""", 0, 53)
  _UpperCamelCase					      =  Process("""P2""", 0, 17)
  _UpperCamelCase					      =  Process("""P3""", 0, 68)
  _UpperCamelCase					      =  Process("""P4""", 0, 24)
  _UpperCamelCase					      =  3
  _UpperCamelCase					      =  [17, 25]
  _UpperCamelCase					      =  deque([Pa, Pa, Pa, Pa])
  _UpperCamelCase					      =  MLFQ(number_of_queues, time_slices, queue, 0)
  _UpperCamelCase					      =  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 torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                UpperCAmelCase          =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,device=self.device )

                # correction step
                for _ in range(self.scheduler.config.correct_steps ):
                      UpperCAmelCase          =	self.unet(A     ,A ).sample
                      UpperCAmelCase          =	self.scheduler.step_correct(A     ,A     ,generator=A ).prev_sample

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

          UpperCAmelCase          =	sample_mean.clamp(0     ,1 )
          UpperCAmelCase          =	sample.cpu().permute(0     ,2     ,3     ,1 ).numpy()
          if output_type == "pil":
                UpperCAmelCase          =	self.numpy_to_pil(A )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



