

"""simple docstring"""




from __future__ import annotations
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * len(lowercase__ )
					for i in range(1     ,				len(lowercase__ ) ):
										A     =   duration_times[i - 1] + waiting_times[i - 1]
					return waiting_times
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return [
					    duration_time + waiting_times[i]
					    for i, duration_time in enumerate(lowercase__ )
					]
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					return sum(lowercase__ ) / len(lowercase__ )
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					return sum(lowercase__ ) / len(lowercase__ )


if __name__ == "__main__":
	# process id's
	__A	:       int        =						[1, 2, 3]

	# ensure that we actually have processes
	if len(processes) == 0:
		print('Zero amount of processes')
		raise SystemExit(0)

	# duration time of all processes
	__A	:       Optional[Any]        =						[19, 8, 9]

	# ensure we can match each id to a duration time
	if len(duration_times) != len(processes):
		print('Unable to match all id\'s with their duration time')
		raise SystemExit(0)

	# get the waiting times and the turnaround times
	__A	:       Any        =						calculate_waiting_times(duration_times)
	__A	:       Dict        =						calculate_turnaround_times(duration_times, waiting_times)

	# get the average times
	__A	:       str        =						calculate_average_waiting_time(waiting_times)
	__A	:       Any        =						calculate_average_turnaround_time(turnaround_times)

	# print all the results
	print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
	for i, process in enumerate(processes):
		print(
		    f"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
		    f"""{turnaround_times[i]}"""
		)
	print(f"""Average waiting time = {average_waiting_time}""")
	print(f"""Average turn around time = {average_turnaround_time}""")









'''simple docstring'''

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    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				__UpperCamelCase     :		Optional[Any]						=						{
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1_000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2_000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				__UpperCamelCase     :		str						=						{
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5_000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Union[List[PIL.Image.Image], np.ndarray]
				__UpperCamelCase     :		Optional[List[bool]]
				__UpperCamelCase     :		Optional[Union[List[PIL.Image.Image], np.ndarray]]
				__UpperCamelCase     :		Optional[str]


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker

