"""simple docstring"""





import gc
import threading
import time

import psutil
import torch


class 	UpperCAmelCase_      :


      def __init__(  self				)			->   List[Any]:
             lowercase__     :						int         =      psutil.Process()
             lowercase__     :						int         =      False


      def 						_UpperCAmelCase			(  self				)			->   List[str]:
             lowercase__     :						Dict         =      -1

             while True:
                    lowercase__     :						Tuple         =      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 						_UpperCAmelCase			(  self				)			->   Optional[int]:
             lowercase__     :						List[Any]         =      True
             lowercase__     :						Tuple         =      threading.Thread(target=self.peak_monitor				)
             lowercase__     :						Union[str, Any]         =      True
             self.thread.start()


      def 						_UpperCAmelCase			(  self				)			->   List[str]:
             lowercase__     :						List[str]         =      False
             self.thread.join()
             return self.cpu_memory_peak





_UpperCamelCase  :		Optional[int]           =       PeakCPUMemory()





def 							a_	( ):





       '''simple docstring'''

       lowercase__     :						Optional[int]         =      {"time": time.time()}

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

       # CPU mem
       lowercase__     :						List[Any]         =      psutil.Process().memory_info().rss
       cpu_peak_tracker.start()

       # GPU mem
       for i in range(torch.cuda.device_count()	):
              lowercase__     :						Dict         =      torch.cuda.memory_allocated(_a	)
       torch.cuda.reset_peak_memory_stats()

       return measures





def 							a_	( _lowerCAmelCase    :					int	):





       '''simple docstring'''

       lowercase__     :						Optional[int]         =      {"time": time.time() - start_measures["time"]}

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

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

       # GPU mem
       for i in range(torch.cuda.device_count()	):
              lowercase__     :						Tuple         =      (torch.cuda.memory_allocated(_a	) - start_measures[str(_a	)]) / 2**20
              lowercase__     :						Optional[Any]         =      (torch.cuda.max_memory_allocated(_a	) - start_measures[str(_a	)]) / 2**20

       return measures






def 							a_	( _lowerCAmelCase    :					int  ,					_lowerCAmelCase    :					str	):





       '''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__     :						Tuple         =      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"""	)




from __future__ import annotations

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							2
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							[]
	while i * i <= n:
		if n % i:
			i += 1
		else:
			n //= i
			factors.append(_a)
	if n > 1:
		factors.append(_a)
	return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()