
'''simple docstring'''





from __future__ import annotations

from statistics import mean
def 	snake_case_							(				_a  :				list[int]      ):
      snake_case_    							=				2
      snake_case_    							=				list(_a      )
      snake_case_    							=				[0] * len(_a      )
      snake_case_    							=				0
      while True:
            snake_case_    							=				True
            for i, burst_time in enumerate(_a      ):
                  if rem_burst_times[i] > 0:
                        snake_case_    							=				False
                        if rem_burst_times[i] > quantum:
                              t += quantum
                              rem_burst_times[i] -= quantum
                        else:
                              t += rem_burst_times[i]
                              snake_case_    							=				t - burst_time
                              snake_case_    							=				0
            if done is True:
                  return waiting_times
def 	snake_case_							(				_a  :				list[int]			,       _a  :				list[int]      ):
      return [burst + waiting for burst, waiting in zip(_a			,       _a      )]


if __name__ == "__main__":
     A							     = [3, 5, 7]
     A							     = calculate_waiting_times(burst_times)
     A							     = calculate_turn_around_times(burst_times, waiting_times)
     print("Process ID \tBurst Time \tWaiting Time \tTurnaround Time")
     for i, burst_time in enumerate(burst_times):
          print(
              F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
              F"""{turn_around_times[i]}"""
          )
     print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
     print(F"""Average turn around time = {mean(turn_around_times):.5f}""")





from typing import List, Optional, Tuple, Union

import torch

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







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      super().__init__()

      _lowercase				 =PNDMScheduler.from_config(scheduler.config			)

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





     @torch.no_grad()
     def __call__(self						,	UpperCAmelCase = 1						,	UpperCAmelCase = 5_0						,	UpperCAmelCase = None						,	UpperCAmelCase = "pil"						,	UpperCAmelCase = True						,	**UpperCAmelCase						,	)	->  Union[ImagePipelineOutput, Tuple]:
      _lowercase				 =randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,	generator=UpperCAmelCase						,	device=self.device						,	)

      self.scheduler.set_timesteps(UpperCAmelCase			)
      for t in self.progress_bar(self.scheduler.timesteps			):
       _lowercase				 =self.unet(UpperCAmelCase						,	UpperCAmelCase			).sample

       _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).prev_sample

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase			)

