






"""simple docstring"""






from collections import deque




class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						str		,			lowercase_  :						str		,			lowercase_  :						int		,			lowercase_  :						int):




          '''simple docstring'''


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




class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						Dict		,			lowercase_  :						int		,			lowercase_  :						list[int]		,			lowercase_  :						deque[Process]		,			lowercase_  :						int		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	number_of_queues
          # time slice of queues that round robin algorithm applied
          SCREAMING_SNAKE_CASE_   :						Tuple							=	time_slices
          # unfinished process is in this ready_queue
          SCREAMING_SNAKE_CASE_   :						Dict							=	queue
          # current time
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	current_time
          # finished process is in this sequence queue
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	deque()
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	[]
          for i in range(len(self.finish_queue)):
              sequence.append(self.finish_queue[i].process_name)
          return sequence
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]		,			lowercase_  :						list[Process]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	[]
          for i in range(len(__UpperCamelCase)):
              waiting_times.append(queue[i].waiting_time)
          return waiting_times
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						list[Process]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	[]
          for i in range(len(__UpperCamelCase)):
              turnaround_times.append(queue[i].turnaround_time)
          return turnaround_times
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						list[Process]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	[]
          for i in range(len(__UpperCamelCase)):
              completion_times.append(queue[i].stop_time)
          return completion_times
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						deque[Process]):




          '''simple docstring'''


          return [q.burst_time for q in queue]
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						Process):




          '''simple docstring'''


          process.waiting_time += self.current_time - process.stop_time
          return process.waiting_time
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						deque[Process]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	deque()  # sequence deque of finished process
          while len(__UpperCamelCase) != 0:
              SCREAMING_SNAKE_CASE_   :						Optional[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(__UpperCamelCase)
              # update current time
              self.current_time += cp.burst_time
              # finish the process and set the process's burst-time 0
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	0
              # set the process's turnaround time because it is finished
              SCREAMING_SNAKE_CASE_   :						str							=	self.current_time - cp.arrival_time
              # set the completion time
              SCREAMING_SNAKE_CASE_   :						str							=	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 _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						deque[Process]		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	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_   :						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(__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[Any]							=	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_   :						Union[str, Any]							=	0
                  # set the finish time
                  SCREAMING_SNAKE_CASE_   :						List[Any]							=	self.current_time
                  # update the process' turnaround time because it is finished
                  SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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 _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          for i in range(self.number_of_queues - 1):
              SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						List[Any]							=	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_     :				Any         =				Process("""P1""", 0, 53)
  UpperCAmelCase_     :				str         =				Process("""P2""", 0, 17)
  UpperCAmelCase_     :				List[str]         =				Process("""P3""", 0, 68)
  UpperCAmelCase_     :				Dict         =				Process("""P4""", 0, 24)
  UpperCAmelCase_     :				Any         =				3
  UpperCAmelCase_     :				Union[str, Any]         =				[17, 25]
  UpperCAmelCase_     :				Dict         =				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_     :				Dict         =				Process("""P1""", 0, 53)
  UpperCAmelCase_     :				Any         =				Process("""P2""", 0, 17)
  UpperCAmelCase_     :				Optional[Any]         =				Process("""P3""", 0, 68)
  UpperCAmelCase_     :				Dict         =				Process("""P4""", 0, 24)
  UpperCAmelCase_     :				Tuple         =				3
  UpperCAmelCase_     :				Optional[Any]         =				[17, 25]
  UpperCAmelCase_     :				Any         =				deque([Pa, Pa, Pa, Pa])
  UpperCAmelCase_     :				List[Any]         =				MLFQ(number_of_queues, time_slices, queue, 0)
  UpperCAmelCase_     :				Tuple         =				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 ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	UNetaDModel(
              block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,			)
          return model



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.dummy_uncond_unet
          SCREAMING_SNAKE_CASE_   :						int							=	ScoreSdeVeScheduler()

          SCREAMING_SNAKE_CASE_   :						List[str]							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Dict							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_		,			return_dict=lowercase_)[
              0
          ]

          SCREAMING_SNAKE_CASE_   :						Dict							=	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_   :						str							=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

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




@slow
@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''google/ncsnpp-church-256'''
          SCREAMING_SNAKE_CASE_   :						str							=	UNetaDModel.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVeScheduler.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	sde_ve(num_inference_steps=10		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Any							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          SCREAMING_SNAKE_CASE_   :						int							=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

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


