

'''simple docstring'''






import gc
import threading
import time

import psutil
import torch






class     lowercase_			:




		"""simple docstring"""




		def __init__(   self     :  List[Any]    ):
			__lowercase													=				psutil.Process()
			__lowercase													=				False






		def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
			__lowercase													=				-1

			while True:
				__lowercase													=				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     :  Optional[int]    ):
			__lowercase													=				True
			__lowercase													=				threading.Thread(target=self.peak_monitor    )
			__lowercase													=				True
			self.thread.start()






		def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
			__lowercase													=				False
			self.thread.join()
			return self.cpu_memory_peak
lowerCAmelCase__      		=			PeakCPUMemory()


def 	_A  (					):






	"""simple docstring"""



	__lowercase													=				{'''time''': time.time()}

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

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

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

	return measures


def 	_A  (					A__   ):






	"""simple docstring"""



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

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

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

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

	return measures






def 	_A  (					A__  ,       A__   ):






	"""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(A__   )]:.2f}MiB"   )
		__lowercase													=				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'''






def 	_A  (					A__   ):






 """simple docstring"""



 if not nums:  # Makes sure that the list is not empty
  raise ValueError('''List is empty'''   )

 __lowercase													=				sum(A__   ) / len(A__   )  # Calculate the average
 return sum(abs(x - average   ) for x in nums   ) / len(A__   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

