
"""simple docstring"""






import gc
import threading
import time

import psutil
import torch




class 				lowercase__:
	'''simple docstring'''

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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= psutil.Process()
			SCREAMING_SNAKE_CASE       :     Any				= False





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= -1

			while True:
					SCREAMING_SNAKE_CASE       :     List[str]				= 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   __lowerCAmelCase (						self    :List[str]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= True
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= threading.Thread(target=self.peak_monitor					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= True
			self.thread.start()







	def   __lowerCAmelCase (						self    :str					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= False
			self.thread.join()
			return self.cpu_memory_peak





lowerCamelCase__ :       List[Any]						     =			PeakCPUMemory()





def __A						(   )->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= {'''time''': time.time()}

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

		# CPU mem
		SCREAMING_SNAKE_CASE       :     str				= psutil.Process().memory_info().rss
		cpu_peak_tracker.start()

		# GPU mem
		for i in range(torch.cuda.device_count()     ):
				SCREAMING_SNAKE_CASE       :     Optional[int]				= torch.cuda.memory_allocated(a_     )
		torch.cuda.reset_peak_memory_stats()

		return measures





def __A						(   a_ :							List[Any]     )->							Any:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[Any]				= {'''time''': time.time() - start_measures['''time''']}

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

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

		# GPU mem
		for i in range(torch.cuda.device_count()     ):
				SCREAMING_SNAKE_CASE       :     str				= (torch.cuda.memory_allocated(a_     ) - start_measures[str(a_     )]) / 2**20
				SCREAMING_SNAKE_CASE       :     List[str]				= (torch.cuda.max_memory_allocated(a_     ) - start_measures[str(a_     )]) / 2**20

		return measures





def __A						(   a_ :							str		,		a_ :							int     )->							Optional[int]:




		'''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"     )
				SCREAMING_SNAKE_CASE       :     Optional[int]				= 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"""






import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Any						     =			{
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """encodec"""





	def __init__(						self    :List[str]		, lowerCamelCase_    :Tuple=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]		, lowerCamelCase_    :str=2_40_00		, lowerCamelCase_    :Any=1		, lowerCamelCase_    :List[Any]=False		, lowerCamelCase_    :Optional[int]=None		, lowerCamelCase_    :Optional[Any]=None		, lowerCamelCase_    :str=1_28		, lowerCamelCase_    :Any=32		, lowerCamelCase_    :int=1		, lowerCamelCase_    :Dict=[8, 5, 4, 2]		, lowerCamelCase_    :List[Any]="weight_norm"		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :Tuple=7		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :int=2		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :Optional[int]="reflect"		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Dict=1.0		, lowerCamelCase_    :Any=10_24		, lowerCamelCase_    :str=None		, lowerCamelCase_    :Union[str, Any]=True		, **lowerCamelCase_    :Optional[int]		, )							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= target_bandwidths
			SCREAMING_SNAKE_CASE       :     List[str]				= sampling_rate
			SCREAMING_SNAKE_CASE       :     Tuple				= audio_channels
			SCREAMING_SNAKE_CASE       :     Tuple				= normalize
			SCREAMING_SNAKE_CASE       :     str				= chunk_length_s
			SCREAMING_SNAKE_CASE       :     List[str]				= overlap
			SCREAMING_SNAKE_CASE       :     int				= hidden_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_filters
			SCREAMING_SNAKE_CASE       :     Tuple				= num_residual_layers
			SCREAMING_SNAKE_CASE       :     List[Any]				= upsampling_ratios
			SCREAMING_SNAKE_CASE       :     Optional[int]				= norm_type
			SCREAMING_SNAKE_CASE       :     Any				= kernel_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= last_kernel_size
			SCREAMING_SNAKE_CASE       :     Tuple				= residual_kernel_size
			SCREAMING_SNAKE_CASE       :     Any				= dilation_growth_rate
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_causal_conv
			SCREAMING_SNAKE_CASE       :     str				= pad_mode
			SCREAMING_SNAKE_CASE       :     List[Any]				= compress
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_lstm_layers
			SCREAMING_SNAKE_CASE       :     Dict				= trim_right_ratio
			SCREAMING_SNAKE_CASE       :     List[Any]				= codebook_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= codebook_dim if codebook_dim is not None else hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= use_conv_shortcut

			if self.norm_type not in ["weight_norm", "time_group_norm"]:
					raise ValueError(
					    f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"					)

			super().__init__(**lowerCamelCase_					)





	@property
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			if self.chunk_length_s is None:
					return None
			else:
					return int(self.chunk_length_s * self.sampling_rate					)





	@property
	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[int]:







			'''simple docstring'''




			if self.chunk_length_s is None or self.overlap is None:
					return None
			else:
					return max(1		, int((1.0 - self.overlap) * self.chunk_length					)					)





	@property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= np.prod(self.upsampling_ratios					)
			return math.ceil(self.sampling_rate / hop_length					)







	@property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)





