



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 				__A					( A_      ):
			UpperCamelCase					:Optional[Any]     =			'''ClapFeatureExtractor'''
			UpperCamelCase					:Dict     =			('''RobertaTokenizer''', '''RobertaTokenizerFast''')





			def __init__(self							,    __magic_name__							,    __magic_name__				):
						super().__init__(__magic_name__							,    __magic_name__				)





			def __call__(self							,    __magic_name__=None							,    __magic_name__=None							,    __magic_name__=None							,    **__magic_name__				):
						lowerCamelCase__ :      List[Any]								=		kwargs.pop("""sampling_rate"""							,    __magic_name__				)

						if text is None and audios is None:
									raise ValueError("""You have to specify either text or audios. Both cannot be none."""				)

						if text is not None:
									lowerCamelCase__ :      str								=		self.tokenizer(__magic_name__							,    return_tensors=__magic_name__							,    **__magic_name__				)

						if audios is not None:
									lowerCamelCase__ :      str								=		self.feature_extractor(
									    __magic_name__							,    sampling_rate=__magic_name__							,    return_tensors=__magic_name__							,    **__magic_name__				)

						if text is not None and audios is not None:
									lowerCamelCase__ :      int								=		audio_features.input_features
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**__magic_name__				)							,    tensor_type=__magic_name__				)





			def        _snake_case    (self							,    *__magic_name__							,    **__magic_name__				):
						return self.tokenizer.batch_decode(*__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    *__magic_name__							,    **__magic_name__				):
						return self.tokenizer.decode(*__magic_name__							,    **__magic_name__				)





			@property
			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		self.tokenizer.model_input_names
						lowerCamelCase__ :      List[str]								=		self.feature_extractor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names				)				)








from ... import PretrainedConfig


_lowercase     							=						{
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class 				__A					( A_      ):
			UpperCamelCase					:Optional[Any]     =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			UpperCamelCase					:int     =			'''nezha'''





			def __init__(self							,    __magic_name__=21128							,    __magic_name__=768							,    __magic_name__=12							,    __magic_name__=12							,    __magic_name__=3072							,    __magic_name__="gelu"							,    __magic_name__=0.1							,    __magic_name__=0.1							,    __magic_name__=512							,    __magic_name__=64							,    __magic_name__=2							,    __magic_name__=0.02							,    __magic_name__=1E-12							,    __magic_name__=0.1							,    __magic_name__=0							,    __magic_name__=2							,    __magic_name__=3							,    __magic_name__=True							,    **__magic_name__							,    ):
						super().__init__(pad_token_id=__magic_name__							,    bos_token_id=__magic_name__							,    eos_token_id=__magic_name__							,    **__magic_name__				)

						lowerCamelCase__ :      List[str]								=		vocab_size
						lowerCamelCase__ :      int								=		hidden_size
						lowerCamelCase__ :      int								=		num_hidden_layers
						lowerCamelCase__ :      Optional[int]								=		num_attention_heads
						lowerCamelCase__ :      Dict								=		hidden_act
						lowerCamelCase__ :      str								=		intermediate_size
						lowerCamelCase__ :      Optional[Any]								=		hidden_dropout_prob
						lowerCamelCase__ :      Dict								=		attention_probs_dropout_prob
						lowerCamelCase__ :      str								=		max_position_embeddings
						lowerCamelCase__ :      Optional[Any]								=		max_relative_position
						lowerCamelCase__ :      Optional[int]								=		type_vocab_size
						lowerCamelCase__ :      Optional[Any]								=		initializer_range
						lowerCamelCase__ :      Any								=		layer_norm_eps
						lowerCamelCase__ :      Union[str, Any]								=		classifier_dropout
						lowerCamelCase__ :      Union[str, Any]								=		use_cache



