




'''simple docstring'''



import gc
import threading
import time

import psutil
import torch







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				psutil.Process()
      _SCREAMING_SNAKE_CASE	=				False



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				-1

      while True:
         _SCREAMING_SNAKE_CASE	=				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 :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				True
      _SCREAMING_SNAKE_CASE	=				threading.Thread(target=self.peak_monitor   )
      _SCREAMING_SNAKE_CASE	=				True
      self.thread.start()



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				False
      self.thread.join()
      return self.cpu_memory_peak


lowerCamelCase_			=						PeakCPUMemory()
def 			SCREAMING_SNAKE_CASE_			(							)			->			List[str]:
   # Time
   _SCREAMING_SNAKE_CASE	=				{"time": time.time()}

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

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

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

   return measures
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]  )			->			Optional[Any]:
   # Time
   _SCREAMING_SNAKE_CASE	=				{"time": time.time() - start_measures["time"]}

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

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

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

   return measures






def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]	,					__A    :			Dict  )			->			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"""  )
      _SCREAMING_SNAKE_CASE	=				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'''



lowerCamelCase_			=						'Tobias Carryer'

from time import time







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Dict=int(time()   )   ):  # noqa: B008

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				multiplier
      _SCREAMING_SNAKE_CASE	=				increment
      _SCREAMING_SNAKE_CASE	=				modulo
      _SCREAMING_SNAKE_CASE	=				seed



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				(self.multiplier * self.seed + self.increment) % self.modulo
      return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 lowerCamelCase_			=						LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
 while True:
  print(lcg.next_number())

