




'''simple docstring'''




import math
from typing import Optional

import numpy as np

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


lowercase__   :				Tuple      =   logging.get_logger(__name__)

lowercase__   :				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 			SCREAMING_SNAKE_CASE   (lowerCAmelCase_							):
     lowerCAmelCase		      =	'encodec'


     def __init__(						self						,     _UpperCAmelCase=[1.5, 3.0, 6.0, 12.0, 24.0]						,     _UpperCAmelCase=2_4000						,     _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						,     ):

          '''simple docstring'''






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

          '''simple docstring'''






          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):

          '''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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	np.prod(self.upsampling_ratios)
          return math.ceil(self.sampling_rate / hop_length)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10))






'''simple docstring'''




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )