code: 
  
  
  from __future__ import annotations
  
  from statistics import mean
  
  
  
  
  def       a__						(		snake_case__				:       list[int]    ):
    _UpperCAmelCase		:       Dict       =    2
    _UpperCAmelCase		:       Optional[int]       =    list(snake_case__    )
    _UpperCAmelCase		:       Optional[Any]       =    [0] * len(snake_case__    )
    _UpperCAmelCase		:       Optional[int]       =    0
    while True:
      _UpperCAmelCase		:       Optional[int]       =    True
      for i, burst_time in enumerate(snake_case__    ):
        if rem_burst_times[i] > 0:
          _UpperCAmelCase		:       List[Any]       =    False
          if rem_burst_times[i] > quantum:
            t += quantum
            rem_burst_times[i] -= quantum
          else:
            t += rem_burst_times[i]
            _UpperCAmelCase		:       int       =    t - burst_time
            _UpperCAmelCase		:       List[Any]       =    0
      if done is True:
        return waiting_times
  
  
  
  
  def       a__						(		snake_case__				:       list[int]	,      snake_case__				:       list[int]    ):
    return [burst + waiting for burst, waiting in zip(snake_case__	,      snake_case__    )]
  
  
  if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__				:    List[str]         =    [3, 5, 7]
   SCREAMING_SNAKE_CASE__				:    int         =    calculate_waiting_times(burst_times)
   SCREAMING_SNAKE_CASE__				:    List[Any]         =    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}')
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  @dataclass
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
       __SCREAMING_SNAKE_CASE    				=						{
           '''sld_warmup_steps''': 1_5,
           '''sld_guidance_scale''': 2_0,
           '''sld_threshold''': 0.0,
           '''sld_momentum_scale''': 0.0,
           '''sld_mom_beta''': 0.0,
       }
       __SCREAMING_SNAKE_CASE    				=						{
           '''sld_warmup_steps''': 1_0,
           '''sld_guidance_scale''': 1_0_0_0,
           '''sld_threshold''': 0.01,
           '''sld_momentum_scale''': 0.3,
           '''sld_mom_beta''': 0.4,
       }
       __SCREAMING_SNAKE_CASE    				=						{
           '''sld_warmup_steps''': 7,
           '''sld_guidance_scale''': 2_0_0_0,
           '''sld_threshold''': 0.025,
           '''sld_momentum_scale''': 0.5,
           '''sld_mom_beta''': 0.7,
       }
       __SCREAMING_SNAKE_CASE    				=						{
           '''sld_warmup_steps''': 0,
           '''sld_guidance_scale''': 5_0_0_0,
           '''sld_threshold''': 1.0,
           '''sld_momentum_scale''': 0.5,
           '''sld_mom_beta''': 0.7,
       }
  
  
  @dataclass
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
       __SCREAMING_SNAKE_CASE    				=						42
       __SCREAMING_SNAKE_CASE    				=						42
       __SCREAMING_SNAKE_CASE    				=						42
       __SCREAMING_SNAKE_CASE    				=						42
  
  
  if is_transformers_available() and is_torch_available():
   from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
   from .safety_checker import SafeStableDiffusionSafetyChecker
  
  
  
  
  
  