






"""simple docstring"""




import math
from typing import Optional

import numpy as np

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


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    '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			(	snake_case_      ):
 UpperCamelCase					:       Optional[int]			=					'''encodec'''


 def __init__(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						Optional[int]=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]							,						UpperCAmelCase__		:						Tuple=24000							,						UpperCAmelCase__		:						Dict=1							,						UpperCAmelCase__		:						str=False							,						UpperCAmelCase__		:						List[str]=None							,						UpperCAmelCase__		:						str=None							,						UpperCAmelCase__		:						str=128							,						UpperCAmelCase__		:						Dict=32							,						UpperCAmelCase__		:						List[Any]=1							,						UpperCAmelCase__		:						int=[8, 5, 4, 2]							,						UpperCAmelCase__		:						int="weight_norm"							,						UpperCAmelCase__		:						List[str]=7							,						UpperCAmelCase__		:						Tuple=7							,						UpperCAmelCase__		:						Optional[Any]=3							,						UpperCAmelCase__		:						Optional[Any]=2							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						str="reflect"							,						UpperCAmelCase__		:						str=2							,						UpperCAmelCase__		:						Union[str, Any]=2							,						UpperCAmelCase__		:						Union[str, Any]=1.0							,						UpperCAmelCase__		:						int=1024							,						UpperCAmelCase__		:						int=None							,						UpperCAmelCase__		:						Tuple=True							,						**UpperCAmelCase__		:						Optional[int]							,						)					->       Optional[Any]:
   _a  :       Optional[Any]             =	target_bandwidths
   _a  :       str             =	sampling_rate
   _a  :       Any             =	audio_channels
   _a  :       Union[str, Any]             =	normalize
   _a  :       Optional[Any]             =	chunk_length_s
   _a  :       Dict             =	overlap
   _a  :       int             =	hidden_size
   _a  :       List[str]             =	num_filters
   _a  :       Any             =	num_residual_layers
   _a  :       Tuple             =	upsampling_ratios
   _a  :       List[str]             =	norm_type
   _a  :       List[str]             =	kernel_size
   _a  :       List[str]             =	last_kernel_size
   _a  :       int             =	residual_kernel_size
   _a  :       Tuple             =	dilation_growth_rate
   _a  :       Union[str, Any]             =	use_causal_conv
   _a  :       List[str]             =	pad_mode
   _a  :       Dict             =	compress
   _a  :       str             =	num_lstm_layers
   _a  :       Any             =	trim_right_ratio
   _a  :       Optional[Any]             =	codebook_size
   _a  :       Dict             =	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__(**UpperCAmelCase__							)


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







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









"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case          =   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AdaptiveEmbedding',
     'TransfoXLForSequenceClassification',
     'TransfoXLLMHeadModel',
     'TransfoXLModel',
     'TransfoXLPreTrainedModel',
     'load_tf_weights_in_transfo_xl',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFAdaptiveEmbedding',
     'TFTransfoXLForSequenceClassification',
     'TFTransfoXLLMHeadModel',
     'TFTransfoXLMainLayer',
     'TFTransfoXLModel',
     'TFTransfoXLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_transfo_xl import (
      TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      AdaptiveEmbedding,
      TransfoXLForSequenceClassification,
      TransfoXLLMHeadModel,
      TransfoXLModel,
      TransfoXLPreTrainedModel,
      load_tf_weights_in_transfo_xl,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_transfo_xl import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAdaptiveEmbedding,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLMainLayer,
      TFTransfoXLModel,
      TFTransfoXLPreTrainedModel,
  )

else:
 import sys

 _snake_case          =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

