





import gc
import threading
import time

import psutil
import torch


class   A      :
			'''simple docstring'''




			def __init__(      self       :   Tuple       )     ->     List[Any]:







					"""simple docstring"""






					A__  				=       psutil.Process()
					A__  				=       False






			def        a_	(      self       :   Optional[Any]       )     ->     Tuple:







					"""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        a_	(      self       :   Optional[Any]       )     ->     List[Any]:







					"""simple docstring"""






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







			def        a_	(      self       :   Any       )     ->     str:







					"""simple docstring"""






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

A   : Any			=				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(__a   )
		torch.cuda.reset_peak_memory_stats()

		return measures







def        __lowerCamelCase (							__a       :List[str]   )		->    Tuple:






		"""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(__a   ) - start_measures[str(__a   )]) / 2**2_0
				A__  				=       (torch.cuda.max_memory_allocated(__a   ) - start_measures[str(__a   )]) / 2**2_0

		return measures







def        __lowerCamelCase (							__a       :str   ,					__a       :Optional[Any]   )		->    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'   )
				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_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .notes_encoder import SpectrogramNotesEncoder
					from .continous_encoder import SpectrogramContEncoder
					from .pipeline_spectrogram_diffusion import (
					    SpectrogramContEncoder,
					    SpectrogramDiffusionPipeline,
					    TaFilmDecoder,
					)

try:
					if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
					from .midi_utils import MidiProcessor



