from ... import PretrainedConfig


lowerCAmelCase_ : Union[str, Any]						  =      {
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
   lowerCamelCase_    					='nezha'


   def __init__(   self				:	str   ,		__lowerCAmelCase				:	Tuple=2_1128   ,		__lowerCAmelCase				:	List[str]=768   ,		__lowerCAmelCase				:	Dict=12   ,		__lowerCAmelCase				:	Optional[Any]=12   ,		__lowerCAmelCase				:	Optional[Any]=3072   ,		__lowerCAmelCase				:	str="gelu"   ,		__lowerCAmelCase				:	str=0.1   ,		__lowerCAmelCase				:	int=0.1   ,		__lowerCAmelCase				:	int=512   ,		__lowerCAmelCase				:	int=64   ,		__lowerCAmelCase				:	str=2   ,		__lowerCAmelCase				:	str=0.02   ,		__lowerCAmelCase				:	List[Any]=1e-12   ,		__lowerCAmelCase				:	int=0.1   ,		__lowerCAmelCase				:	Dict=0   ,		__lowerCAmelCase				:	str=2   ,		__lowerCAmelCase				:	Union[str, Any]=3   ,		__lowerCAmelCase				:	List[str]=True   ,		**__lowerCAmelCase				:	int   ,		)      ->					Union[str, Any]:
         super().__init__(pad_token_id=__lowerCAmelCase   ,		bos_token_id=__lowerCAmelCase   ,		eos_token_id=__lowerCAmelCase   ,		**__lowerCAmelCase)

         lowercase_     =      vocab_size
         lowercase_     =      hidden_size
         lowercase_     =      num_hidden_layers
         lowercase_     =      num_attention_heads
         lowercase_     =      hidden_act
         lowercase_     =      intermediate_size
         lowercase_     =      hidden_dropout_prob
         lowercase_     =      attention_probs_dropout_prob
         lowercase_     =      max_position_embeddings
         lowercase_     =      max_relative_position
         lowercase_     =      type_vocab_size
         lowercase_     =      initializer_range
         lowercase_     =      layer_norm_eps
         lowercase_     =      classifier_dropout
         lowercase_     =      use_cache







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_ : str						  =      {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Union[str, Any]						  =      [
           "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Wav2Vec2ForAudioFrameClassification",
           "Wav2Vec2ForCTC",
           "Wav2Vec2ForMaskedLM",
           "Wav2Vec2ForPreTraining",
           "Wav2Vec2ForSequenceClassification",
           "Wav2Vec2ForXVector",
           "Wav2Vec2Model",
           "Wav2Vec2PreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : int						  =      [
           "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFWav2Vec2ForCTC",
           "TFWav2Vec2Model",
           "TFWav2Vec2PreTrainedModel",
           "TFWav2Vec2ForSequenceClassification",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Dict						  =      [
           "FlaxWav2Vec2ForCTC",
           "FlaxWav2Vec2ForPreTraining",
           "FlaxWav2Vec2Model",
           "FlaxWav2Vec2PreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
       from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
       from .processing_wavaveca import WavaVecaProcessor
       from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavaveca import (
                  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavaVecaForAudioFrameClassification,
                  WavaVecaForCTC,
                  WavaVecaForMaskedLM,
                  WavaVecaForPreTraining,
                  WavaVecaForSequenceClassification,
                  WavaVecaForXVector,
                  WavaVecaModel,
                  WavaVecaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFWavaVecaForCTC,
                  TFWavaVecaForSequenceClassification,
                  TFWavaVecaModel,
                  TFWavaVecaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  FlaxWavaVecaForCTC,
                  FlaxWavaVecaForPreTraining,
                  FlaxWavaVecaModel,
                  FlaxWavaVecaPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_ : Optional[int]						  =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






