import gc
import threading
import time

import psutil
import torch






class   __lowercase       :

						def __init__(						self					:   Dict )				->		Any:


									'''simple docstring'''

									lowercase       		=		psutil.Process()
									lowercase       		=		False

						def 							__a		(						self					:   List[str] )				->		Union[str, Any]:


									'''simple docstring'''

									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 							__a		(						self					:   Union[str, Any] )				->		Union[str, Any]:


									'''simple docstring'''

									lowercase       		=		True
									lowercase       		=		threading.Thread(target=self.peak_monitor )
									lowercase       		=		True
									self.thread.start()



						def 							__a		(						self					:   Union[str, Any] )				->		Tuple:


									'''simple docstring'''

									lowercase       		=		False
									self.thread.join()
									return self.cpu_memory_peak





A_	     =   PeakCPUMemory()






def 							__UpperCAmelCase     (							)->							Union[str, Any]:



			"""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(__lowercase							)
			torch.cuda.reset_peak_memory_stats()

			return measures






def 							__UpperCAmelCase     (							UpperCAmelCase							)->							Optional[Any]:



			"""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(__lowercase							) - start_measures[str(__lowercase							)]) / 2**20
						lowercase       		=		(torch.cuda.max_memory_allocated(__lowercase							) - start_measures[str(__lowercase							)]) / 2**20

			return measures






def 							__UpperCAmelCase     (							UpperCAmelCase,		UpperCAmelCase							)->							Any:



			"""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'							)
						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'							)













import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__a   :							Any						= logging.get_logger(__name__)


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Tuple			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         warnings.warn(
             """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use DeiTImageProcessor instead."""			,						UpperCamelCase_			,						)
         super().__init__(*UpperCamelCase_			,						**UpperCamelCase_					)




