





'''simple docstring'''






import gc
import threading
import time

import psutil
import torch
class 		lowerCamelCase  :



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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   -1

			while True:
					lowerCAmelCase     :    Optional[int]						=   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	):
			lowerCAmelCase     :    List[str]						=   True
			lowerCAmelCase     :    Dict						=   threading.Thread(target=self.peak_monitor	)
			lowerCAmelCase     :    str						=   True
			self.thread.start()





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   False
			self.thread.join()
			return self.cpu_memory_peak






lowerCAmelCase       		=      PeakCPUMemory()






def      __A						(							):
		# Time
		lowerCAmelCase     :    List[str]						=   {"time": time.time()}

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

		# CPU mem
		lowerCAmelCase     :    Union[str, Any]						=   psutil.Process().memory_info().rss
		cpu_peak_tracker.start()

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

		return measures






def      __A						(							a_						:  Any	):
		# Time
		lowerCAmelCase     :    Optional[int]						=   {"time": time.time() - start_measures["time"]}

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

		# CPU mem
		lowerCAmelCase     :    str						=   (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**2_0
		lowerCAmelCase     :    Optional[int]						=   (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**2_0

		# GPU mem
		for i in range(torch.cuda.device_count()	):
				lowerCAmelCase     :    str						=   (torch.cuda.memory_allocated(a_	) - start_measures[str(a_	)]) / 2**2_0
				lowerCAmelCase     :    Union[str, Any]						=   (torch.cuda.max_memory_allocated(a_	) - start_measures[str(a_	)]) / 2**2_0

		return measures






def      __A						(							a_						:  List[Any]      ,a_						:  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(a_	)]:.2f}MiB'''	)
				lowerCAmelCase     :    List[str]						=   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_						:  int	):
		assert (
		    isinstance(a_      ,a_	) 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
		lowerCAmelCase	,		lowerCAmelCase     :    int						=   1, 1
		for _ in range(number_of_steps - 1	):
				lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   current + previous, current
		return current


if __name__ == "__main__":
					import doctest

					doctest.testmod()



