



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



class 						lowerCamelCase   (    snake_case_	):







					'''simple docstring'''







					lowerCAmelCase_					:     Union[str, Any]					       =	'''ClapFeatureExtractor'''
					lowerCAmelCase_					:     List[str]					       =	('''RobertaTokenizer''', '''RobertaTokenizerFast''')



					def __init__(      self     ,						lowerCAmelCase     ,						lowerCAmelCase  ):
							super().__init__(__lowercase     ,						__lowercase  )



					def __call__(      self     ,						lowerCAmelCase=None     ,						lowerCAmelCase=None     ,						lowerCAmelCase=None     ,						**lowerCAmelCase  ):
							UpperCAmelCase_						       =					kwargs.pop("sampling_rate"     ,						__lowercase  )

							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_						       =					self.tokenizer(__lowercase     ,						return_tensors=__lowercase     ,						**__lowercase  )

							if audios is not None:
									UpperCAmelCase_						       =					self.feature_extractor(
									    __lowercase     ,						sampling_rate=__lowercase     ,						return_tensors=__lowercase     ,						**__lowercase  )

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



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



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



					@property
					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.tokenizer.model_input_names
							UpperCAmelCase_						       =					self.feature_extractor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names  )  )





'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_			:  Optional[int]										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[str]										=   {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		List[Any]     				=    '''fnet'''


 def __init__(   self   ,   __lowercase=32_000   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu_new"   ,   __lowercase=0.1   ,   __lowercase=512   ,   __lowercase=4   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=False   ,   __lowercase=512   ,   __lowercase=3   ,   __lowercase=1   ,   __lowercase=2   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(pad_token_id=__lowercase   ,   bos_token_id=__lowercase   ,   eos_token_id=__lowercase   ,   **__lowercase    )

  __A	:		Union[str, Any]	  =      vocab_size
  __A	:		Dict	  =      max_position_embeddings
  __A	:		List[str]	  =      hidden_size
  __A	:		Tuple	  =      num_hidden_layers
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		Dict	  =      hidden_act
  __A	:		List[str]	  =      hidden_dropout_prob
  __A	:		str	  =      initializer_range
  __A	:		Dict	  =      type_vocab_size
  __A	:		int	  =      layer_norm_eps
  __A	:		Tuple	  =      use_tpu_fourier_optimizations
  __A	:		Optional[Any]	  =      tpu_short_seq_length






