'''simple docstring'''

import gc
import threading
import time

import psutil
import torch
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






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






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=-1

								while True:
														_lowerCAmelCase										=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]:
								_lowerCAmelCase										=True
								_lowerCAmelCase										=threading.Thread(target=self.peak_monitor				)
								_lowerCAmelCase										=True
								self.thread.start()






		def 		UpperCamelCase__       (   self				) ->     int:
								_lowerCAmelCase										=False
								self.thread.join()
								return self.cpu_memory_peak

lowercase_     		= PeakCPUMemory()



def 							UpperCamelCase__			(			):


						'''simple docstring'''






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

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

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

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

						return measures



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






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

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

						# CPU mem
						_lowerCAmelCase										=(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**2_0
						_lowerCAmelCase										=(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**2_0

						# GPU mem
						for i in range(torch.cuda.device_count()      ):
												_lowerCAmelCase										=(torch.cuda.memory_allocated(a__      ) - start_measures[str(a__      )]) / 2**2_0
												_lowerCAmelCase										=(torch.cuda.max_memory_allocated(a__      ) - start_measures[str(a__      )]) / 2**2_0

						return measures



def 							UpperCamelCase__			(			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'''      )
												_lowerCAmelCase										=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'''

from __future__ import annotations



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=len(a__      ) // 2

						# choose the middle 3 elements
						_lowerCAmelCase										=lst[m - 1 : m + 2]

						# if middle element is peak
						if three[1] > three[0] and three[1] > three[2]:
												return three[1]

						# if increasing, recurse on right
						elif three[0] < three[2]:
												if len(lst[:m]      ) == 2:
																		m -= 1
												return peak(lst[m:]      )

						# decreasing
						else:
												if len(lst[:m]      ) == 2:
																		m += 1
												return peak(lst[:m]      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





