





'''simple docstring'''






from __future__ import annotations

from statistics import mean





def      lowercase  (  lowerCAmelCase :						List[str]):



				"""simple docstring"""


				_A :				Any				=  2
				_A :				Optional[Any]				=  list(_UpperCAmelCase)
				_A :				int				=  [0] * len(_UpperCAmelCase)
				_A :				Union[str, Any]				=  0
				while True:
								_A :				Union[str, Any]				=  True
								for i, burst_time in enumerate(_UpperCAmelCase):
												if rem_burst_times[i] > 0:
																_A :				List[Any]				=  False
																if rem_burst_times[i] > quantum:
																				t += quantum
																				rem_burst_times[i] -= quantum
																else:
																				t += rem_burst_times[i]
																				_A :				str				=  t - burst_time
																				_A :				Tuple				=  0
								if done is True:
												return waiting_times





def      lowercase  (  lowerCAmelCase :						Tuple   , lowerCAmelCase :						List[str]):



				"""simple docstring"""


				return [burst + waiting for burst, waiting in zip(_UpperCAmelCase   , _UpperCAmelCase)]


if __name__ == "__main__":
	__UpperCamelCase      :   Dict								=     [3, 5, 7]
	__UpperCamelCase      :   str								=     calculate_waiting_times(burst_times)
	__UpperCamelCase      :   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}')







'''simple docstring'''

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Any		):







							"""simple docstring"""
							super().__init__()

							self.register_buffer("watermark_image"		,	torch.zeros((62, 62, 4)		)		)
							__UpperCAmelCase	:							Optional[int]     =     None






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      List[PIL.Image.Image]		,	UpperCAmelCase_       :      int=None		):







							"""simple docstring"""
							# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

							__UpperCAmelCase	:							int     =     images[0].height
							__UpperCAmelCase	:							Any     =     images[0].width

							__UpperCAmelCase	:							Union[str, Any]     =     sample_size or h

							__UpperCAmelCase	:							Dict     =     min(h / sample_size		,	w / sample_size		)
							__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

							__UpperCAmelCase  ,			__UpperCAmelCase	:							Union[str, Any]     =     1_024**2, img_w * img_h
							__UpperCAmelCase	:							Optional[int]     =     (Sa / Sa) ** 0.5
							__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							str     =     int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

							if self.watermark_image_as_pil is None:
									__UpperCAmelCase	:							int     =     self.watermark_image.to(torch.uinta		).cpu().numpy()
									__UpperCAmelCase	:							Optional[int]     =     Image.fromarray(UpperCAmelCase_		,	mode="RGBA"		)
									__UpperCAmelCase	:							Optional[int]     =     watermark_image

							__UpperCAmelCase	:							List[str]     =     self.watermark_image_as_pil.resize(
							    (wm_size, wm_size)		,	PIL_INTERPOLATION["bicubic"]		,	reducing_gap=UpperCAmelCase_		)

							for pil_img in images:
									pil_img.paste(UpperCAmelCase_		,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)		,	mask=wm_img.split()[-1]		)

							return images




