






"""simple docstring"""



import gc
import threading
import time

import psutil
import torch







class        lowercase__      :


			def __init__(				self					:  Any ):
										lowerCamelCase_	:							List[str]									=psutil.Process()
										lowerCamelCase_	:							int									=False


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Union[str, Any]									=-1

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


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Any									=False
										self.thread.join()
										return self.cpu_memory_peak


A__	:    List[str]			 = PeakCPUMemory()

def 			_snake_case    (					)    ->     List[Any]:
							# Time
							lowerCamelCase_	:							Optional[int]									={"time": time.time()}

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

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

							# GPU mem
							for i in range(torch.cuda.device_count()  ):
														lowerCamelCase_	:							List[Any]									=torch.cuda.memory_allocated(_lowerCAmelCase  )
							torch.cuda.reset_peak_memory_stats()

							return measures

def 			_snake_case    (					lowerCamelCase__     :   Dict  )    ->     int:
							# Time
							lowerCamelCase_	:							Optional[int]									={"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_	:							Optional[int]									=(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(_lowerCAmelCase  ) - start_measures[str(_lowerCAmelCase  )]) / 2**20
														lowerCamelCase_	:							List[Any]									=(torch.cuda.max_memory_allocated(_lowerCAmelCase  ) - start_measures[str(_lowerCAmelCase  )]) / 2**20

							return measures

def 			_snake_case    (					lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   List[Any]  )    ->     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(_lowerCAmelCase  )]:.2f}MiB"""  )
														lowerCamelCase_	:							Dict									=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


A__	:    List[Any]			 = {
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    List[str]			 = [
						    'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'LiltForQuestionAnswering',
						    'LiltForSequenceClassification',
						    'LiltForTokenClassification',
						    'LiltModel',
						    'LiltPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

						A__	:    Optional[Any]			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



