


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}')









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





