





import gc
import threading
import time

import psutil
import torch
class     UpperCamelCase__					:






		'''simple docstring'''






		def __init__(							self  )       -> str:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     psutil.Process()
									lowercase_	:   Optional[Any]  				=     False






		def 						snake_case__				(							self  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   Any  				=     -1

									while True:
																lowercase_	:   Optional[Any]  				=     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 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   Any  				=     True
									lowercase_	:   Optional[Any]  				=     threading.Thread(target=self.peak_monitor  )
									lowercase_	:   Tuple  				=     True
									self.thread.start()






		def 						snake_case__				(							self  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   Tuple  				=     False
									self.thread.join()
									return self.cpu_memory_peak




UpperCAmelCase_							=				PeakCPUMemory()





def  __magic_name__  (				)				-> Optional[Any]:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     {"""time""": time.time()}

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

							# CPU mem
							lowercase_	:   Dict  				=     psutil.Process().memory_info().rss
							cpu_peak_tracker.start()

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

							return measures





def  __magic_name__  (				lowercase				)				-> Tuple:

							"""simple docstring"""



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

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

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

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

							return measures





def  __magic_name__  (				lowercase				,       lowercase				)				-> Optional[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(__a				)]:.2f}MiB"""				)
														lowercase_	:   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 ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_pt_objects import *  # noqa F403
else:
						from .scheduling_consistency_models import CMStochasticIterativeScheduler
						from .scheduling_ddim import DDIMScheduler
						from .scheduling_ddim_inverse import DDIMInverseScheduler
						from .scheduling_ddim_parallel import DDIMParallelScheduler
						from .scheduling_ddpm import DDPMScheduler
						from .scheduling_ddpm_parallel import DDPMParallelScheduler
						from .scheduling_deis_multistep import DEISMultistepScheduler
						from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
						from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
						from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
						from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
						from .scheduling_euler_discrete import EulerDiscreteScheduler
						from .scheduling_heun_discrete import HeunDiscreteScheduler
						from .scheduling_ipndm import IPNDMScheduler
						from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
						from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
						from .scheduling_karras_ve import KarrasVeScheduler
						from .scheduling_pndm import PNDMScheduler
						from .scheduling_repaint import RePaintScheduler
						from .scheduling_sde_ve import ScoreSdeVeScheduler
						from .scheduling_sde_vp import ScoreSdeVpScheduler
						from .scheduling_unclip import UnCLIPScheduler
						from .scheduling_unipc_multistep import UniPCMultistepScheduler
						from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
						from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_flax_objects import *  # noqa F403
else:
						from .scheduling_ddim_flax import FlaxDDIMScheduler
						from .scheduling_ddpm_flax import FlaxDDPMScheduler
						from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
						from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
						from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
						from .scheduling_pndm_flax import FlaxPNDMScheduler
						from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
						from .scheduling_utils_flax import (
						    FlaxKarrasDiffusionSchedulers,
						    FlaxSchedulerMixin,
						    FlaxSchedulerOutput,
						    broadcast_to_shape_from_left,
						)


try:
						if not (is_torch_available() and is_scipy_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
						from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
						if not (is_torch_available() and is_torchsde_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
						from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler