


"""simple docstring"""


import gc
import threading
import time

import psutil
import torch
class snake_case      :







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





      '''simple docstring'''







      _A					  =			psutil.Process()
      _A					  =			False







  def  a_ (					self      :       Tuple		)       ->						Union[str, Any]:





      '''simple docstring'''







      _A					  =			-1

      while True:
          _A					  =			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  a_ (					self      :       int		)       ->						Dict:





      '''simple docstring'''







      _A					  =			True
      _A					  =			threading.Thread(target=self.peak_monitor		)
      _A					  =			True
      self.thread.start()







  def  a_ (					self      :       Dict		)       ->						Union[str, Any]:





      '''simple docstring'''







      _A					  =			False
      self.thread.join()
      return self.cpu_memory_peak





a_       							=    PeakCPUMemory()






def      a__ (				)	->	str:
    _A					  =			{"time": time.time()}

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

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

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

    return measures






def      a__ (				__lowercase   )	->	Union[str, Any]:
    _A					  =			{"time": time.time() - start_measures["time"]}

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

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

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

    return measures






def      a__ (				__lowercase     ,			__lowercase   )	->	int:
    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"""   )
        _A					  =			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

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a_       							=    logging.get_logger(__name__)
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       str    ,   *a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						None:





						'''simple docstring'''







						warnings.warn(
						    "The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
						    " use GLPNImageProcessor instead."    ,   a__    ,   )
						super().__init__(*a__    ,   **a__		)