


import gc
import threading
import time

import psutil
import torch

class 							lowercase  :







     def __init__(						self :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		psutil.Process()
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		False







     def 							lowercase__       (						self :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		-1

           while True:
                 SCREAMING_SNAKE_CASE__       :  str						     =		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 							lowercase__       (						self :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		True
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		threading.Thread(target=self.peak_monitor      )
           SCREAMING_SNAKE_CASE__       :  Any						     =		True
           self.thread.start()







     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		False
           self.thread.join()
           return self.cpu_memory_peak




a_ :Dict  							=			PeakCPUMemory()




def    a   (							)	-> Optional[Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Any						     =		{"""time""": time.time()}

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

      # CPU mem
      SCREAMING_SNAKE_CASE__       :  Dict						     =		psutil.Process().memory_info().rss
      cpu_peak_tracker.start()

      # GPU mem
      for i in range(torch.cuda.device_count()							):
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		torch.cuda.memory_allocated(A__							)
      torch.cuda.reset_peak_memory_stats()

      return measures




def    a   (							A__							)	-> Any:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		{"""time""": time.time() - start_measures["""time"""]}

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

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

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

      return measures





def    a   (							A__      ,    A__							)	-> Dict:




      '''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"""							)
            SCREAMING_SNAKE_CASE__       :  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'''






from __future__ import annotations

import numpy as np







def 							UpperCamelCase_     (       A__   :  np.ndarray   ):
  '''simple docstring'''





  lowerCAmelCase_,				lowerCAmelCase_     : List[str]     =  np.shape(A__   )
  if rows != columns:
    lowerCAmelCase_     : List[str]     =  (
        """'table' has to be of square shaped array but got a """
        f'{rows}x{columns} array:\n{table}'
    )
    raise ValueError(A__   )

  lowerCAmelCase_     : Optional[int]     =  np.zeros((rows, columns)   )
  lowerCAmelCase_     : Tuple     =  np.zeros((rows, columns)   )
  for i in range(A__   ):
    for j in range(A__   ):
      lowerCAmelCase_     : str     =  sum(lower[i][k] * upper[k][j] for k in range(A__   )   )
      if upper[j][j] == 0:
        raise ArithmeticError("""No LU decomposition exists"""   )
      lowerCAmelCase_     : Union[str, Any]     =  (table[i][j] - total) / upper[j][j]
    lowerCAmelCase_     : Dict     =  1
    for j in range(A__				,       A__   ):
      lowerCAmelCase_     : str     =  sum(lower[i][k] * upper[k][j] for k in range(A__   )   )
      lowerCAmelCase_     : List[str]     =  table[i][j] - total
  return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()



