





'''simple docstring'''




import math
from typing import Optional

import numpy as np

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


__SCREAMING_SNAKE_CASE     :Optional[int]       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :str       =				{
    '''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_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Tuple           =							"""encodec"""




 def __init__(					self       :    Tuple	,			snake_case_       :    Optional[int]=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]	,			snake_case_       :    Union[str, Any]=2_4_0_0_0	,			snake_case_       :    List[Any]=1	,			snake_case_       :    Optional[int]=False	,			snake_case_       :    Any=None	,			snake_case_       :    Dict=None	,			snake_case_       :    str=1_2_8	,			snake_case_       :    Union[str, Any]=3_2	,			snake_case_       :    Optional[int]=1	,			snake_case_       :    Optional[int]=[8, 5, 4, 2]	,			snake_case_       :    Dict="weight_norm"	,			snake_case_       :    List[str]=7	,			snake_case_       :    Optional[Any]=7	,			snake_case_       :    Dict=3	,			snake_case_       :    Dict=2	,			snake_case_       :    Tuple=True	,			snake_case_       :    Tuple="reflect"	,			snake_case_       :    Tuple=2	,			snake_case_       :    Tuple=2	,			snake_case_       :    Union[str, Any]=1.0	,			snake_case_       :    List[str]=1_0_2_4	,			snake_case_       :    Union[str, Any]=None	,			snake_case_       :    List[Any]=True	,			**snake_case_       :    List[Any]	,			):
      _UpperCAmelCase     							=   target_bandwidths
      _UpperCAmelCase     							=   sampling_rate
      _UpperCAmelCase     							=   audio_channels
      _UpperCAmelCase     							=   normalize
      _UpperCAmelCase     							=   chunk_length_s
      _UpperCAmelCase     							=   overlap
      _UpperCAmelCase     							=   hidden_size
      _UpperCAmelCase     							=   num_filters
      _UpperCAmelCase     							=   num_residual_layers
      _UpperCAmelCase     							=   upsampling_ratios
      _UpperCAmelCase     							=   norm_type
      _UpperCAmelCase     							=   kernel_size
      _UpperCAmelCase     							=   last_kernel_size
      _UpperCAmelCase     							=   residual_kernel_size
      _UpperCAmelCase     							=   dilation_growth_rate
      _UpperCAmelCase     							=   use_causal_conv
      _UpperCAmelCase     							=   pad_mode
      _UpperCAmelCase     							=   compress
      _UpperCAmelCase     							=   num_lstm_layers
      _UpperCAmelCase     							=   trim_right_ratio
      _UpperCAmelCase     							=   codebook_size
      _UpperCAmelCase     							=   codebook_dim if codebook_dim is not None else hidden_size
      _UpperCAmelCase     							=   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__(**snake_case_    )




 @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       :    Tuple    ):
      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       :    Dict    ):
      _UpperCAmelCase     							=   np.prod(self.upsampling_ratios    )
      return math.ceil(self.sampling_rate / hop_length    )




 @property
 def      lowercase	(					self       :    Union[str, Any]    ):
      return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)    )











"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_     :						List[str]						=					{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						str						=					['XLNetTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						List[str]						=					['XLNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						int						=					[
       'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLNetForMultipleChoice',
       'XLNetForQuestionAnswering',
       'XLNetForQuestionAnsweringSimple',
       'XLNetForSequenceClassification',
       'XLNetForTokenClassification',
       'XLNetLMHeadModel',
       'XLNetModel',
       'XLNetPreTrainedModel',
       'load_tf_weights_in_xlnet',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Union[str, Any]						=					[
       'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLNetForMultipleChoice',
       'TFXLNetForQuestionAnsweringSimple',
       'TFXLNetForSequenceClassification',
       'TFXLNetForTokenClassification',
       'TFXLNetLMHeadModel',
       'TFXLNetMainLayer',
       'TFXLNetModel',
       'TFXLNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_     :						List[Any]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)