

import math
from typing import Optional

import numpy as np

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


_A		:				Tuple									=       logging.get_logger(__name__)

_A		:				Optional[int]									=       {
    '''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        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  List[Any]						      =	"encodec"





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





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        if self.chunk_length_s is None:
           return None
        else:
           return int(self.chunk_length_s * self.sampling_rate							)





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        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  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				str     =	np.prod(self.upsampling_ratios							)
        return math.ceil(self.sampling_rate / hop_length							)







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






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


