



'''simple docstring'''




import math
from typing import Optional

import numpy as np

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


a     :    int      						=					logging.get_logger(__name__)

a     :    Union[str, 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     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'encodec'





	def __init__(						self	,	A=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]	,	A=24000	,	A=1	,	A=False	,	A=None	,	A=None	,	A=128	,	A=32	,	A=1	,	A=[8, 5, 4, 2]	,	A="weight_norm"	,	A=7	,	A=7	,	A=3	,	A=2	,	A=True	,	A="reflect"	,	A=2	,	A=2	,	A=1.0	,	A=1024	,	A=None	,	A=True	,	**A	,	)    ->    Optional[Any]:
								UpperCAmelCase  :	int							  =	target_bandwidths
								UpperCAmelCase  :	str							  =	sampling_rate
								UpperCAmelCase  :	Union[str, Any]							  =	audio_channels
								UpperCAmelCase  :	Optional[int]							  =	normalize
								UpperCAmelCase  :	Optional[Any]							  =	chunk_length_s
								UpperCAmelCase  :	Any							  =	overlap
								UpperCAmelCase  :	int							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_filters
								UpperCAmelCase  :	Optional[int]							  =	num_residual_layers
								UpperCAmelCase  :	Optional[Any]							  =	upsampling_ratios
								UpperCAmelCase  :	Optional[int]							  =	norm_type
								UpperCAmelCase  :	Tuple							  =	kernel_size
								UpperCAmelCase  :	str							  =	last_kernel_size
								UpperCAmelCase  :	Optional[Any]							  =	residual_kernel_size
								UpperCAmelCase  :	Optional[int]							  =	dilation_growth_rate
								UpperCAmelCase  :	List[str]							  =	use_causal_conv
								UpperCAmelCase  :	Dict							  =	pad_mode
								UpperCAmelCase  :	Optional[Any]							  =	compress
								UpperCAmelCase  :	int							  =	num_lstm_layers
								UpperCAmelCase  :	Any							  =	trim_right_ratio
								UpperCAmelCase  :	Optional[Any]							  =	codebook_size
								UpperCAmelCase  :	Any							  =	codebook_dim if codebook_dim is not None else hidden_size
								UpperCAmelCase  :	List[str]							  =	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__(**A					)





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





	@property
	def 				_lowercase(						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 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	List[str]							  =	np.prod(self.upsampling_ratios					)
								return math.ceil(self.sampling_rate / hop_length					)






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






'''simple docstring'''




from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
							from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

