






"""simple docstring"""





import gc
import threading
import time

import psutil
import torch






class      a						:






    def __init__(       self     :							List[Any]					)     ->		Union[str, Any]:
      lowerCamelCase_         =						psutil.Process()
      lowerCamelCase_         =						False






    def    UpperCamelCase       (       self     :							Dict					)     ->		Union[str, Any]:
      lowerCamelCase_         =						-1

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






    def    UpperCamelCase       (       self     :							int					)     ->		Any:
      lowerCamelCase_         =						False
      self.thread.join()
      return self.cpu_memory_peak
_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						PeakCPUMemory()







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

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

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

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

  return measures







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple )      ->				Optional[Any]:
  # Time
  lowerCamelCase_         =						{'time': time.time() - start_measures['time']}

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

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

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

  return measures







def      lowerCamelCase__       (							_lowerCamelCase       :		Any		,				_lowerCamelCase       :		Optional[int] )      ->				List[Any]:
  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''' )
    lowerCamelCase_         =						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_torch_available


lowercase__     						=						{"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """ViTMSNModel""",
			    """ViTMSNForImageClassification""",
			    """ViTMSNPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit_msn import (
						    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTMSNForImageClassification,
						    ViTMSNModel,
						    ViTMSNPreTrainedModel,
						)

else:
			import sys

			lowercase__     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)