"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


lowerCAmelCase__  :  Optional[int]						     =	logging.get_logger(__name__)
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "upernet"







					def __init__(				self   :	int    ,lowerCamelCase__   :	Any=None    ,lowerCamelCase__   :	List[Any]=512    ,lowerCamelCase__   :	str=0.0_2    ,lowerCamelCase__   :	List[Any]=[1, 2, 3, 6]    ,lowerCamelCase__   :	Optional[Any]=True    ,lowerCamelCase__   :	Optional[Any]=0.4    ,lowerCamelCase__   :	int=384    ,lowerCamelCase__   :	int=256    ,lowerCamelCase__   :	int=1    ,lowerCamelCase__   :	Optional[int]=False    ,lowerCamelCase__   :	Union[str, Any]=255    ,**lowerCamelCase__   :	str    ,):
									super().__init__(**lowerCamelCase__			)

									if backbone_config is None:
													logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'			)
													UpperCAmelCase__             =							CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']			)
									elif isinstance(lowerCamelCase__    ,lowerCamelCase__			):
													UpperCAmelCase__             =							backbone_config.get('model_type'			)
													UpperCAmelCase__             =							CONFIG_MAPPING[backbone_model_type]
													UpperCAmelCase__             =							config_class.from_dict(lowerCamelCase__			)

									UpperCAmelCase__             =							backbone_config
									UpperCAmelCase__             =							hidden_size
									UpperCAmelCase__             =							initializer_range
									UpperCAmelCase__             =							pool_scales
									UpperCAmelCase__             =							use_auxiliary_head
									UpperCAmelCase__             =							auxiliary_loss_weight
									UpperCAmelCase__             =							auxiliary_in_channels
									UpperCAmelCase__             =							auxiliary_channels
									UpperCAmelCase__             =							auxiliary_num_convs
									UpperCAmelCase__             =							auxiliary_concat_input
									UpperCAmelCase__             =							loss_ignore_index







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							copy.deepcopy(self.__dict__			)
									UpperCAmelCase__             =							self.backbone_config.to_dict()
									UpperCAmelCase__             =							self.__class__.model_type
									return output








"""simple docstring"""






import gc
import threading
import time

import psutil
import torch



class 			A__   :




	'''simple docstring'''





	def __init__(     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       psutil.Process()
				__lowerCAmelCase      :  str												=       False

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       -1

				while True:
							__lowerCAmelCase      :  str												=       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:		Tuple)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       True
				__lowerCAmelCase      :  str												=       threading.Thread(target=self.peak_monitor)
				__lowerCAmelCase      :  Tuple												=       True
				self.thread.start()







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       False
				self.thread.join()
				return self.cpu_memory_peak






__snake_case							:   Tuple      =    PeakCPUMemory()


def 			_lowercase				(  )						->   str:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time()}

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

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

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       torch.cuda.memory_allocated(__snake_case	)
			torch.cuda.reset_peak_memory_stats()

			return measures


def 			_lowercase				(  __snake_case	)						->   Optional[Any]:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time() - start_measures["time"]}

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

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

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       (torch.cuda.memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20
						__lowerCAmelCase      :  Any												=       (torch.cuda.max_memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20

			return measures





def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			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(__snake_case	)]:.2f}MiB"""	)
						__lowerCAmelCase      :  Optional[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"""	)