






'''simple docstring'''



from collections import deque
class 							__SCREAMING_SNAKE_CASE       :




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->None:



         '''simple docstring'''
         __a				     =    process_name  # process name
         __a				     =    arrival_time  # arrival time of the process
         # completion time of finished process or last interrupted time
         __a				     =    arrival_time
         __a				     =    burst_time  # remaining burst time
         __a				     =    0  # total time of the process wait in ready queue
         __a				     =    0  # time from arrival time to completion time







class 							__SCREAMING_SNAKE_CASE       :




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->None:



         '''simple docstring'''
         __a				     =    number_of_queues
         # time slice of queues that round robin algorithm applied
         __a				     =    time_slices
         # unfinished process is in this ready_queue
         __a				     =    queue
         # current time
         __a				     =    current_time
         # finished process is in this sequence queue
         __a				     =    deque()




      def  __UpperCamelCase							(  self      )							->list[str]:



         '''simple docstring'''
         __a				     =    []
         for i in range(len(self.finish_queue      )      ):
            sequence.append(self.finish_queue[i].process_name      )
         return sequence




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->list[int]:



         '''simple docstring'''
         __a				     =    []
         for i in range(len(lowerCamelCase      )      ):
            waiting_times.append(queue[i].waiting_time      )
         return waiting_times




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->list[int]:



         '''simple docstring'''
         __a				     =    []
         for i in range(len(lowerCamelCase      )      ):
            turnaround_times.append(queue[i].turnaround_time      )
         return turnaround_times




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->list[int]:



         '''simple docstring'''
         __a				     =    []
         for i in range(len(lowerCamelCase      )      ):
            completion_times.append(queue[i].stop_time      )
         return completion_times




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->list[int]:



         '''simple docstring'''
         return [q.burst_time for q in queue]




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->int:



         '''simple docstring'''
         process.waiting_time += self.current_time - process.stop_time
         return process.waiting_time




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->deque[Process]:



         '''simple docstring'''
         __a				     =    deque()  # sequence deque of finished process
         while len(lowerCamelCase      ) != 0:
            __a				     =    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(lowerCamelCase      )
            # update current time
            self.current_time += cp.burst_time
            # finish the process and set the process's burst-time 0
            __a				     =    0
            # set the process's turnaround time because it is finished
            __a				     =    self.current_time - cp.arrival_time
            # set the completion time
            __a				     =    self.current_time
            # add the process to queue that has finished queue
            finished.append(lowerCamelCase      )

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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->tuple[deque[Process], deque[Process]]:



         '''simple docstring'''
         __a				     =    deque()  # sequence deque of terminated process
         # just for 1 cycle and unfinished processes will go back to queue
         for _ in range(len(lowerCamelCase      )      ):
            __a				     =    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(lowerCamelCase      )
            # 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
               __a				     =    self.current_time
               # locate the process behind the queue because it is not finished
               ready_queue.append(lowerCamelCase      )
            else:
               # use CPU for remaining burst time
               self.current_time += cp.burst_time
               # set burst time 0 because the process is finished
               __a				     =    0
               # set the finish time
               __a				     =    self.current_time
               # update the process' turnaround time because it is finished
               __a				     =    self.current_time - cp.arrival_time
               # add the process to queue that has finished queue
               finished.append(lowerCamelCase      )

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






      def  __UpperCamelCase							(  self      )							->deque[Process]:



         '''simple docstring'''

         #  all queues except last one have round_robin algorithm
         for i in range(self.number_of_queues - 1      ):
            __a     ,			__a				     =    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      :       str      			=      Process("""P1""", 0, 53)
 __UpperCamelCase      :       Union[str, Any]      			=      Process("""P2""", 0, 17)
 __UpperCamelCase      :       Union[str, Any]      			=      Process("""P3""", 0, 68)
 __UpperCamelCase      :       Optional[Any]      			=      Process("""P4""", 0, 24)
 __UpperCamelCase      :       int      			=      3
 __UpperCamelCase      :       Any      			=      [17, 25]
 __UpperCamelCase      :       Tuple      			=      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[int]      			=      Process("""P1""", 0, 53)
 __UpperCamelCase      :       Union[str, Any]      			=      Process("""P2""", 0, 17)
 __UpperCamelCase      :       Union[str, Any]      			=      Process("""P3""", 0, 68)
 __UpperCamelCase      :       Optional[int]      			=      Process("""P4""", 0, 24)
 __UpperCamelCase      :       Optional[Any]      			=      3
 __UpperCamelCase      :       Union[str, Any]      			=      [17, 25]
 __UpperCamelCase      :       Optional[Any]      			=      deque([Pa, Pa, Pa, Pa])
 __UpperCamelCase      :       int      			=      MLFQ(number_of_queues, time_slices, queue, 0)
 __UpperCamelCase      :       Optional[Any]      			=      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 inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Dict:

   """simple docstring"""
   __a     ,			__a				     =    image.size
   __a     ,			__a				     =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
   __a				     =    image.resize((w, h),					resample=PIL_INTERPOLATION['lanczos']    )
   __a				     =    np.array(SCREAMING_SNAKE_CASE__    ).astype(np.floataa    ) / 2_5_5.0
   __a				     =    image[None].transpose(0,					3,					1,					2    )
   __a				     =    torch.from_numpy(SCREAMING_SNAKE_CASE__    )
   return 2.0 * image - 1.0



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->Dict:



         '''simple docstring'''
         super().__init__()
         self.register_modules(vqvae=lowerCamelCase ,   unet=lowerCamelCase ,   scheduler=lowerCamelCase      )






      @torch.no_grad()
      def __call__(  self ,   lowerCamelCase = None ,   lowerCamelCase = 1 ,   lowerCamelCase = 100 ,   lowerCamelCase = 0.0 ,   lowerCamelCase = None ,   lowerCamelCase = "pil" ,   lowerCamelCase = True ,   )							->Union[Tuple, ImagePipelineOutput]:



         '''simple docstring'''
         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    1
         elif isinstance(lowerCamelCase ,   torch.Tensor      ):
            __a				     =    image.shape[0]
         else:
            raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase      )}"""      )

         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    preprocess(lowerCamelCase      )

         __a     ,			__a				     =    image.shape[-2:]

         # in_channels should be 6: 3 for latents, 3 for low resolution image
         __a				     =    (batch_size, self.unet.config.in_channels // 2, height, width)
         __a				     =    next(self.unet.parameters()      ).dtype

         __a				     =    randn_tensor(lowerCamelCase ,   generator=lowerCamelCase ,   device=self.device ,   dtype=lowerCamelCase      )

         __a				     =    image.to(device=self.device ,   dtype=lowerCamelCase      )

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(lowerCamelCase ,   device=self.device      )
         __a				     =    self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         __a				     =    latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         __a				     =    'eta' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
         __a				     =    {}
         if accepts_eta:
            __a				     =    eta

         for t in self.progress_bar(lowerCamelCase      ):
            # concat latents and low resolution image in the channel dimension.
            __a				     =    torch.cat([latents, image] ,   dim=1      )
            __a				     =    self.scheduler.scale_model_input(lowerCamelCase ,   lowerCamelCase      )
            # predict the noise residual
            __a				     =    self.unet(lowerCamelCase ,   lowerCamelCase      ).sample
            # compute the previous noisy sample x_t -> x_t-1
            __a				     =    self.scheduler.step(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   **lowerCamelCase      ).prev_sample

         # decode the image latents with the VQVAE
         __a				     =    self.vqvae.decode(lowerCamelCase      ).sample
         __a				     =    torch.clamp(lowerCamelCase ,   -1.0 ,   1.0      )
         __a				     =    image / 2 + 0.5
         __a				     =    image.cpu().permute(0 ,   2 ,   3 ,   1      ).numpy()

         if output_type == "pil":
            __a				     =    self.numpy_to_pil(lowerCamelCase      )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=lowerCamelCase      )