
"""simple docstring"""

import math
from typing import Optional

import numpy as np

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


_A						=			logging.get_logger(__name__)

_A						=			{
    '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		):
	lowercase_				=     'encodec'




	def __init__(			self		,			UpperCAmelCase_=[1.5, 3.0, 6.0, 12.0, 24.0]		,			UpperCAmelCase_=24000		,			UpperCAmelCase_=1		,			UpperCAmelCase_=False		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=128		,			UpperCAmelCase_=32		,			UpperCAmelCase_=1		,			UpperCAmelCase_=[8, 5, 4, 2]		,			UpperCAmelCase_="weight_norm"		,			UpperCAmelCase_=7		,			UpperCAmelCase_=7		,			UpperCAmelCase_=3		,			UpperCAmelCase_=2		,			UpperCAmelCase_=True		,			UpperCAmelCase_="reflect"		,			UpperCAmelCase_=2		,			UpperCAmelCase_=2		,			UpperCAmelCase_=1.0		,			UpperCAmelCase_=1024		,			UpperCAmelCase_=None		,			UpperCAmelCase_=True		,			**UpperCAmelCase_		,			)		->							Tuple:
			lowerCamelCase					:      Any          =   target_bandwidths
			lowerCamelCase					:      Any          =   sampling_rate
			lowerCamelCase					:      Any          =   audio_channels
			lowerCamelCase					:      int          =   normalize
			lowerCamelCase					:      Any          =   chunk_length_s
			lowerCamelCase					:      Any          =   overlap
			lowerCamelCase					:      Dict          =   hidden_size
			lowerCamelCase					:      str          =   num_filters
			lowerCamelCase					:      Optional[Any]          =   num_residual_layers
			lowerCamelCase					:      Optional[int]          =   upsampling_ratios
			lowerCamelCase					:      int          =   norm_type
			lowerCamelCase					:      Any          =   kernel_size
			lowerCamelCase					:      List[Any]          =   last_kernel_size
			lowerCamelCase					:      Optional[Any]          =   residual_kernel_size
			lowerCamelCase					:      Optional[Any]          =   dilation_growth_rate
			lowerCamelCase					:      Optional[Any]          =   use_causal_conv
			lowerCamelCase					:      Dict          =   pad_mode
			lowerCamelCase					:      Any          =   compress
			lowerCamelCase					:      Dict          =   num_lstm_layers
			lowerCamelCase					:      Any          =   trim_right_ratio
			lowerCamelCase					:      Any          =   codebook_size
			lowerCamelCase					:      Tuple          =   codebook_dim if codebook_dim is not None else hidden_size
			lowerCamelCase					:      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__(**UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[int]:
			if self.chunk_length_s is None:
					return None
			else:
					return int(self.chunk_length_s * self.sampling_rate )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[int]:
			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       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      Any          =   np.prod(self.upsampling_ratios )
			return math.ceil(self.sampling_rate / hop_length )




	@property
	def       _UpperCamelCase   (			self )		->							int:
			return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )

"""simple docstring"""

import copy

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


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encoder-decoder'
	lowercase_				=     True




	def __init__(			self		,			**UpperCAmelCase_ )		->							str:
			super().__init__(**UpperCAmelCase_ )
			assert (
			    "encoder" in kwargs and "decoder" in kwargs
			), "Config has to be initialized with encoder and decoder config"
			lowerCamelCase					:      List[Any]          =   kwargs.pop('encoder' )
			lowerCamelCase					:      Optional[int]          =   encoder_config.pop('model_type' )
			lowerCamelCase					:      str          =   kwargs.pop('decoder' )
			lowerCamelCase					:      Dict          =   decoder_config.pop('model_type' )

			from ..auto.configuration_auto import AutoConfig

			lowerCamelCase					:      int          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   True




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							PretrainedConfig:
			logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
			lowerCamelCase					:      str          =   True
			lowerCamelCase					:      Optional[Any]          =   True

			return cls(encoder=encoder_config.to_dict()		,			decoder=decoder_config.to_dict()		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder.to_dict()
			lowerCamelCase					:      List[Any]          =   self.decoder.to_dict()
			lowerCamelCase					:      Tuple          =   self.__class__.model_type
			return output






