


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


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "roc_bert"


     def __init__(						self ,						__lowerCamelCase=3_0_5_2_2 ,						__lowerCamelCase=7_6_8 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=3_0_7_2 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=5_1_2 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-12 ,						__lowerCamelCase=True ,						__lowerCamelCase=0 ,						__lowerCamelCase="absolute" ,						__lowerCamelCase=None ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=7_6_8 ,						__lowerCamelCase=9_1_0 ,						__lowerCamelCase=5_1_2 ,						__lowerCamelCase=2_4_8_5_8 ,						__lowerCamelCase=True ,						**__lowerCamelCase ,						)			->				Dict:
            _A      :       int				    = vocab_size
            _A      :       List[str]				    = max_position_embeddings
            _A      :       Union[str, Any]				    = hidden_size
            _A      :       str				    = num_hidden_layers
            _A      :       Dict				    = num_attention_heads
            _A      :       List[Any]				    = intermediate_size
            _A      :       List[Any]				    = hidden_act
            _A      :       Dict				    = hidden_dropout_prob
            _A      :       List[str]				    = attention_probs_dropout_prob
            _A      :       str				    = initializer_range
            _A      :       str				    = type_vocab_size
            _A      :       Dict				    = layer_norm_eps
            _A      :       Union[str, Any]				    = use_cache
            _A      :       Optional[Any]				    = enable_pronunciation
            _A      :       Tuple				    = enable_shape
            _A      :       str				    = pronunciation_embed_dim
            _A      :       Tuple				    = pronunciation_vocab_size
            _A      :       Union[str, Any]				    = shape_embed_dim
            _A      :       List[str]				    = shape_vocab_size
            _A      :       List[str]				    = concat_input
            _A      :       Tuple				    = position_embedding_type
            _A      :       Union[str, Any]				    = classifier_dropout
            super().__init__(pad_token_id=__lowerCamelCase ,						**__lowerCamelCase)




from typing import TYPE_CHECKING

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


lowerCAmelCase__      		= {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['SpeechT5Tokenizer']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= [
        'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
        'SpeechT5ForSpeechToText',
        'SpeechT5ForSpeechToSpeech',
        'SpeechT5ForTextToSpeech',
        'SpeechT5Model',
        'SpeechT5PreTrainedModel',
        'SpeechT5HifiGan',
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

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