




import gc
import threading
import time

import psutil
import torch






class  _lowerCAmelCase     :

      def __init__(   self		):
        lowerCAmelCase__				:  int		       =     psutil.Process()
        lowerCAmelCase__				:  Optional[int]		       =     False

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     -1

        while True:
          lowerCAmelCase__				:  List[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 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     True
        lowerCAmelCase__				:  int		       =     threading.Thread(target=self.peak_monitor		)
        lowerCAmelCase__				:  Tuple		       =     True
        self.thread.start()







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     False
        self.thread.join()
        return self.cpu_memory_peak
lowerCAmelCase_					    = PeakCPUMemory()



def      __lowerCAmelCase			(					)     ->	Union[str, Any]:
  # Time
  lowerCAmelCase__				:  Dict		       =     {'''time''': time.time()}

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

  # CPU mem
  lowerCAmelCase__				:  str		       =     psutil.Process().memory_info().rss
  cpu_peak_tracker.start()

  # GPU mem
  for i in range(torch.cuda.device_count()      ):
    lowerCAmelCase__				:  Union[str, Any]		       =     torch.cuda.memory_allocated(UpperCamelCase      )
  torch.cuda.reset_peak_memory_stats()

  return measures



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Dict:
  # Time
  lowerCAmelCase__				:  Dict		       =     {'''time''': time.time() - start_measures['''time''']}

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

  # CPU mem
  lowerCAmelCase__				:  List[Any]		       =     (psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
  lowerCAmelCase__				:  Any		       =     (cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

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

  return measures



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	str:
  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(UpperCamelCase      )]:.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"""      )







lowerCAmelCase_					    = 6_55_21



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	int:
  lowerCAmelCase__				:  List[str]		       =     1
  lowerCAmelCase__				:  List[Any]		       =     0
  for plain_chr in plain_text:
    lowerCAmelCase__				:  Union[str, Any]		       =     (a + ord(UpperCamelCase      )) % MOD_ADLER
    lowerCAmelCase__				:  Optional[Any]		       =     (b + a) % MOD_ADLER
  return (b << 16) | a

