



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""ClapFeatureExtractor"""
		snake_case_     =("""RobertaTokenizer""", """RobertaTokenizerFast""")



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						super().__init__(__lowerCamelCase		,__lowerCamelCase			)



		def __call__(self		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,**__lowerCamelCase			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					kwargs.pop('''sampling_rate'''		,__lowerCamelCase			)

						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__					:    Tuple												=					self.tokenizer(__lowerCamelCase		,return_tensors=__lowerCamelCase		,**__lowerCamelCase			)

						if audios is not None:
										lowerCAmelCase__					:    List[str]												=					self.feature_extractor(
										    __lowerCamelCase		,sampling_rate=__lowerCamelCase		,return_tensors=__lowerCamelCase		,**__lowerCamelCase			)

						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(**__lowerCamelCase			)		,tensor_type=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      str:

						"""simple docstring"""






						return self.tokenizer.batch_decode(*__lowerCamelCase		,**__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      Any:

						"""simple docstring"""






						return self.tokenizer.decode(*__lowerCamelCase		,**__lowerCamelCase			)



		@property
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.tokenizer.model_input_names
						lowerCAmelCase__					:    Any												=					self.feature_extractor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names			)			)






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


__snake_case    :		Any  =logging.get_logger(__name__)

__snake_case    :		Tuple  ={
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""vit_msn"""



		def __init__(self		,__lowerCamelCase=7_68		,__lowerCamelCase=12		,__lowerCamelCase=12		,__lowerCamelCase=30_72		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.0		,__lowerCamelCase=0.0		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-06		,__lowerCamelCase=2_24		,__lowerCamelCase=16		,__lowerCamelCase=3		,__lowerCamelCase=True		,**__lowerCamelCase		,)					->      Any:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					hidden_size
						lowerCAmelCase__					:    str												=					num_hidden_layers
						lowerCAmelCase__					:    List[str]												=					num_attention_heads
						lowerCAmelCase__					:    Optional[int]												=					intermediate_size
						lowerCAmelCase__					:    List[Any]												=					hidden_act
						lowerCAmelCase__					:    Tuple												=					hidden_dropout_prob
						lowerCAmelCase__					:    List[Any]												=					attention_probs_dropout_prob
						lowerCAmelCase__					:    int												=					initializer_range
						lowerCAmelCase__					:    Union[str, Any]												=					layer_norm_eps
						lowerCAmelCase__					:    List[str]												=					image_size
						lowerCAmelCase__					:    str												=					patch_size
						lowerCAmelCase__					:    Optional[int]												=					num_channels
						lowerCAmelCase__					:    int												=					qkv_bias

