





"""simple docstring"""


import gc
import threading
import time

import psutil
import torch



class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							Optional[int]						)		->					List[str]:
        _UpperCamelCase   :						Dict    =							psutil.Process()
        _UpperCamelCase   :						List[str]    =							False

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					str:
        _UpperCamelCase   :						Union[str, Any]    =							-1

        while True:
              _UpperCamelCase   :						List[Any]    =							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  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Dict:
        _UpperCamelCase   :						List[str]    =							True
        _UpperCamelCase   :						List[Any]    =							threading.Thread(target=self.peak_monitor						)
        _UpperCamelCase   :						Dict    =							True
        self.thread.start()


  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Optional[int]:
        _UpperCamelCase   :						Union[str, Any]    =							False
        self.thread.join()
        return self.cpu_memory_peak





lowerCamelCase__						= PeakCPUMemory()




def        lowercase__     (       )       ->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							{"time": time.time()}

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

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

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

      return measures




def        lowercase__     (       lowercase_      )       ->		Dict:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							{"time": time.time() - start_measures["time"]}

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

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

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

      return measures




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Optional[Any]:




      """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(lowercase_      )]:.2f}MiB'''      )
            _UpperCamelCase   :						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"""


import warnings


warnings.warn(
    "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
    "`from accelerate import find_executable_batch_size` to avoid this warning.",
    FutureWarning,
)
