

import gc
import threading
import time

import psutil
import torch

class 					a__						:
			"""simple docstring"""







			def __init__(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			psutil.Process()
							A__        =			False


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			-1

							while True:
											A__        =			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       )  ->						Union[str, Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






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

lowerCAmelCase__	   = PeakCPUMemory()



def 					lowerCAmelCase__							(	)			->      List[Any]:




				'''simple docstring'''





				A__        =			{"time": time.time()}

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

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

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

				return measures



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[Any]      )			->      Optional[Any]:




				'''simple docstring'''





				A__        =			{"time": time.time() - start_measures["time"]}

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

				# CPU mem
				A__        =			(psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**2_0
				A__        =			(cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**2_0

				# GPU mem
				for i in range(torch.cuda.device_count()      ):
								A__        =			(torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_      ) - start_measures[str(SCREAMING_SNAKE_CASE_      )]) / 2**2_0
								A__        =			(torch.cuda.max_memory_allocated(SCREAMING_SNAKE_CASE_      ) - start_measures[str(SCREAMING_SNAKE_CASE_      )]) / 2**2_0

				return measures





def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int	,       SCREAMING_SNAKE_CASE_:							str      )			->      Tuple:




				'''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(SCREAMING_SNAKE_CASE_      )]:.2f}MiB'      )
								A__        =			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'      )




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
	from .autoencoder_kl import AutoencoderKL
	from .controlnet import ControlNetModel
	from .dual_transformer_ad import DualTransformeraDModel
	from .modeling_utils import ModelMixin
	from .prior_transformer import PriorTransformer
	from .ta_film_transformer import TaFilmDecoder
	from .transformer_ad import TransformeraDModel
	from .unet_ad import UNetaDModel
	from .unet_ad import UNetaDModel
	from .unet_ad_condition import UNetaDConditionModel
	from .unet_ad_condition import UNetaDConditionModel
	from .vq_model import VQModel

if is_flax_available():
	from .controlnet_flax import FlaxControlNetModel
	from .unet_ad_condition_flax import FlaxUNetaDConditionModel
	from .vae_flax import FlaxAutoencoderKL

