


import gc
import threading
import time

import psutil
import torch




class    UpperCAmelCase					:




			def __init__(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					psutil.Process()
						UpperCamelCase         =					False




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					-1

						while True:
									UpperCamelCase         =					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   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					True
						UpperCamelCase         =					threading.Thread(target=self.peak_monitor						)
						UpperCamelCase         =					True
						self.thread.start()





			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					False
						self.thread.join()
						return self.cpu_memory_peak


__snake_case            =   PeakCPUMemory()






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					{"""time""": time.time()}

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

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

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

			return measures






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any	):





			"""simple docstring"""




			UpperCamelCase         =					{"""time""": time.time() - start_measures["""time"""]}

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

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

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

			return measures






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""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(SCREAMING_SNAKE_CASE_	)]:.2f}MiB'	)
						UpperCamelCase         =					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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			assert (
			    isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) and number_of_steps > 0
			), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
			if number_of_steps == 1:
						return 1
			UpperCamelCase  ,  UpperCamelCase         =					1, 1
			for _ in range(number_of_steps - 1	):
						UpperCamelCase  ,  UpperCamelCase         =					current + previous, current
			return current


if __name__ == "__main__":
			import doctest

			doctest.testmod()




