

'''simple docstring'''





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







class 	lowercase    (				snake_case_							):


		"""simple docstring"""




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


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


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       **__snake_case):
			_UpperCamelCase   :		Optional[Any]			=						kwargs.pop('sampling_rate'	,       __snake_case)

			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:
				_UpperCamelCase   :		Union[str, Any]			=						self.tokenizer(__snake_case	,       return_tensors=__snake_case	,       **__snake_case)

			if audios is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.feature_extractor(
				    __snake_case	,       sampling_rate=__snake_case	,       return_tensors=__snake_case	,       **__snake_case)

			if text is not None and audios is not None:
				_UpperCamelCase   :		Optional[int]			=						audio_features.input_features
				return encoding
			elif text is not None:
				return encoding
			else:
				return BatchEncoding(data=dict(**__snake_case)	,       tensor_type=__snake_case)


		def 		A__					(				self	,       *__snake_case	,       **__snake_case):
			return self.tokenizer.batch_decode(*__snake_case	,       **__snake_case)


		def 		A__					(				self	,       *__snake_case	,       **__snake_case):
			return self.tokenizer.decode(*__snake_case	,       **__snake_case)






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						self.tokenizer.model_input_names
			_UpperCamelCase   :		Tuple			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))







import logging

from transformers.configuration_utils import PretrainedConfig


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "masked_bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="topK"	,       __snake_case="constant"	,       __snake_case=0.0	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		List[Any]			=						vocab_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Any			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		str			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						max_position_embeddings
			_UpperCamelCase   :		Dict			=						type_vocab_size
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		List[Any]			=						layer_norm_eps
			_UpperCamelCase   :		Tuple			=						pruning_method
			_UpperCamelCase   :		Tuple			=						mask_init
			_UpperCamelCase   :		Dict			=						mask_scale




