


import gc
import threading
import time

import psutil
import torch







class 		_snake_case  :




	def __init__(		self   ):
							a						:Tuple										=	psutil.Process()
							a						:str										=	False




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:Union[str, Any]										=	-1

							while True:
													a						:Union[str, Any]										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:Tuple										=	True
							a						:List[str]										=	threading.Thread(target=self.peak_monitor   )
							a						:List[str]										=	True
							self.thread.start()




	def 	SCREAMING_SNAKE_CASE__    (		self   ):
							a						:List[str]										=	False
							self.thread.join()
							return self.cpu_memory_peak







snake_case    :   Any	    =					PeakCPUMemory()
def     __lowerCamelCase						(			):




						"""simple docstring"""





						a						:Tuple										=	{'''time''': time.time()}

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

						# CPU mem
						a						:Any										=	psutil.Process().memory_info().rss
						cpu_peak_tracker.start()

						# GPU mem
						for i in range(torch.cuda.device_count()						):
												a						:Optional[int]										=	torch.cuda.memory_allocated(UpperCAmelCase_						)
						torch.cuda.reset_peak_memory_stats()

						return measures
def     __lowerCamelCase						(			UpperCAmelCase_						:		int						):




						"""simple docstring"""





						a						:str										=	{'''time''': time.time() - start_measures['''time''']}

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

						# CPU mem
						a						:int										=	(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
						a						:Optional[Any]										=	(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

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

						return measures






def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		Dict						):




						"""simple docstring"""





						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(UpperCAmelCase_						)]:.2f}MiB'''						)
												a						:Union[str, Any]										=	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'''						)









def     __lowerCamelCase						(			UpperCAmelCase_						:		int = 100						):




      """simple docstring"""





      a						:List[Any]										=	0
      a						:List[Any]										=	0
      for i in range(1			,  n + 1						):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
     print(F"""{solution() = }""")





