





"""simple docstring"""


import gc
import threading
import time

import psutil
import torch



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Dict:
        _UpperCamelCase      :	Tuple						=       -1

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


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





lowerCamelCase__				  =   PeakCPUMemory()




def 					lowercase__    (					)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       {"time": time.time()}

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

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

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

      return measures




def 					lowercase__    (					lowercase_			)					->		int:




      """simple docstring"""

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

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

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

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

      return measures




def 					lowercase__    (					lowercase_		,lowercase_			)					->		int:




      """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      :	Union[str, Any]						=       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, is_vision_available


lowerCamelCase__				  =   {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   ["Mask2FormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   [
      "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Mask2FormerForUniversalSegmentation",
      "Mask2FormerModel",
      "Mask2FormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_maskaformer import MaskaFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskaformer import (
        MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskaFormerForUniversalSegmentation,
        MaskaFormerModel,
        MaskaFormerPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase__				  =   _LazyModule(__name__, globals()["__file__"], _import_structure)
