

'''simple docstring'''






import gc
import threading
import time

import psutil
import torch






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  List[Any]    ):
   __lowercase													=				psutil.Process()
   __lowercase													=				False






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				-1

   while True:
    __lowercase													=				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     :  Optional[int]    ):
   __lowercase													=				True
   __lowercase													=				threading.Thread(target=self.peak_monitor    )
   __lowercase													=				True
   self.thread.start()






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				False
   self.thread.join()
   return self.cpu_memory_peak
lowerCAmelCase__      		=			PeakCPUMemory()


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				{'''time''': time.time()}

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

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

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

 return measures


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				{'''time''': time.time() - start_measures['''time''']}

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

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

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

 return measures






def 	_A  (					A__  ,       A__   ):






 """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"   )
  __lowercase													=				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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__      		=			{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      		=			[
     '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTMAEForPreTraining''',
     '''ViTMAELayer''',
     '''ViTMAEModel''',
     '''ViTMAEPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      		=			[
     '''TFViTMAEForPreTraining''',
     '''TFViTMAEModel''',
     '''TFViTMAEPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_mae import (
      VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTMAEForPreTraining,
      ViTMAELayer,
      ViTMAEModel,
      ViTMAEPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
 import sys

 lowerCAmelCase__      		=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

