




'''simple docstring'''
import gc
import threading
import time

import psutil
import torch
class   __UpperCAmelCase				:



					'''simple docstring'''





					def __init__(	self     :						Optional[Any])    ->							List[str]:
								A_									= psutil.Process()
								A_									= False



					def     __snake_case						(	self     :						Union[str, Any])    ->							int:
								A_									= -1

								while True:
											A_									= max(self.process.memory_info().rss      ,       self.cpu_memory_peak)

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											if not self.peak_monitoring:
														break



					def     __snake_case						(	self     :						List[str])    ->							Dict:
								A_									= True
								A_									= threading.Thread(target=self.peak_monitor)
								A_									= True
								self.thread.start()



					def     __snake_case						(	self     :						Tuple)    ->							Tuple:
								A_									= False
								self.thread.join()
								return self.cpu_memory_peak





_SCREAMING_SNAKE_CASE			=						PeakCPUMemory()





def 			lowerCamelCase(							)		->		Tuple:
			# Time
			A_									= {'time': time.time()}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			A_									= psutil.Process().memory_info().rss
			cpu_peak_tracker.start()

			# GPU mem
			for i in range(torch.cuda.device_count()     ):
						A_									= torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_     )
			torch.cuda.reset_peak_memory_stats()

			return measures





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Optional[int]:
			# Time
			A_									= {'time': time.time() - start_measures['time']}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			A_									= (psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
			A_									= (cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count()     ):
						A_									= (torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_     ) - start_measures[str(SCREAMING_SNAKE_CASE_     )]) / 2**20
						A_									= (torch.cuda.max_memory_allocated(SCREAMING_SNAKE_CASE_     ) - start_measures[str(SCREAMING_SNAKE_CASE_     )]) / 2**20

			return measures






def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			print(F'{description}:'     )
			print(F'- Time: {measures["time"]:.2f}s'     )
			for i in range(torch.cuda.device_count()     ):
						print(F'- GPU {i} allocated: {measures[str(SCREAMING_SNAKE_CASE_     )]:.2f}MiB'     )
						A_									= measures[F'{i}-peak']
						print(F'- GPU {i} peak: {peak:.2f}MiB'     )
			print(F'- CPU RAM allocated: {measures["cpu"]:.2f}MiB'     )
			print(F'- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB'     )










'''simple docstring'''
from PIL import Image





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Image:

			def brightness(SCREAMING_SNAKE_CASE_     ) -> float:
						return 128 + level + (c - 128)

			if not -255.0 <= level <= 255.0:
						raise ValueError('level must be between -255.0 (black) and 255.0 (white)'     )
			return img.point(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change brightness to 100
														_SCREAMING_SNAKE_CASE			=						change_brightness(img, 1_00)
														brigt_img.save("image_data/lena_brightness.png", format="png")




