




'''simple docstring'''

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


a__         =     logging.get_logger(__name__)

a__         =     {
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class __magic_name__(						__lowerCAmelCase      ):
						UpperCAmelCase_		:					int    =  """nystromformer"""






						def __init__(   self						:						int					,   __UpperCamelCase						:						Optional[int]=3_0_0_0_0					,   __UpperCamelCase						:						int=7_6_8					,   __UpperCamelCase						:						Union[str, Any]=1_2					,   __UpperCamelCase						:						Optional[int]=1_2					,   __UpperCamelCase						:						List[Any]=3_0_7_2					,   __UpperCamelCase						:						Dict="gelu_new"					,   __UpperCamelCase						:						Optional[Any]=0.1					,   __UpperCamelCase						:						List[Any]=0.1					,   __UpperCamelCase						:						int=5_1_0					,   __UpperCamelCase						:						List[Any]=2					,   __UpperCamelCase						:						List[str]=6_4					,   __UpperCamelCase						:						Tuple=6_4					,   __UpperCamelCase						:						Optional[int]=6_5					,   __UpperCamelCase						:						int=False					,   __UpperCamelCase						:						Optional[int]=0.02					,   __UpperCamelCase						:						str=1E-5					,   __UpperCamelCase						:						Optional[Any]=1					,   __UpperCamelCase						:						Dict=0					,   __UpperCamelCase						:						Tuple=2					,   **__UpperCamelCase						:						Any					,   ):



									'''simple docstring'''






									snake_case__          =						vocab_size
									snake_case__          =						max_position_embeddings
									snake_case__          =						hidden_size
									snake_case__          =						num_hidden_layers
									snake_case__          =						num_attention_heads
									snake_case__          =						intermediate_size
									snake_case__          =						hidden_act
									snake_case__          =						hidden_dropout_prob
									snake_case__          =						attention_probs_dropout_prob
									snake_case__          =						initializer_range
									snake_case__          =						type_vocab_size
									snake_case__          =						segment_means_seq_len
									snake_case__          =						num_landmarks
									snake_case__          =						conv_kernel_size
									snake_case__          =						inv_coeff_init_option
									snake_case__          =						layer_norm_eps
									super().__init__(pad_token_id=__UpperCamelCase					,   bos_token_id=__UpperCamelCase					,   eos_token_id=__UpperCamelCase					,   **__UpperCamelCase       )


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : Dict  			=   'AutoFeatureExtractor'
      lowerCamelCase_      : Optional[int]  			=   'Speech2Text2Tokenizer'







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase  )		->			List[str]:
        super().__init__(lowerCamelCase				,    lowerCamelCase  )
        snake_case_					= self.feature_extractor
        snake_case_					= False







      def __call__(	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			Dict:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor(*lowerCamelCase				,    **lowerCamelCase  )

        if "raw_speech" in kwargs:
          warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""  )
          snake_case_					= kwargs.pop("""raw_speech"""  )
        else:
          snake_case_					= kwargs.pop("""audio"""				,    lowerCamelCase  )
        snake_case_					= kwargs.pop("""sampling_rate"""				,    lowerCamelCase  )
        snake_case_					= kwargs.pop("""text"""				,    lowerCamelCase  )
        if len(lowerCamelCase  ) > 0:
          snake_case_					= args[0]
          snake_case_					= args[1:]

        if audio is None and text is None:
          raise ValueError("""You need to specify either an `audio` or `text` input to process."""  )

        if audio is not None:
          snake_case_					= self.feature_extractor(lowerCamelCase				,    *lowerCamelCase				,    sampling_rate=lowerCamelCase				,    **lowerCamelCase  )
        if text is not None:
          snake_case_					= self.tokenizer(lowerCamelCase				,    **lowerCamelCase  )

        if text is None:
          return inputs
        elif audio is None:
          return encodings
        else:
          snake_case_					= encodings["""input_ids"""]
          return inputs







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			int:
        return self.tokenizer.batch_decode(*lowerCamelCase				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			Union[str, Any]:
        return self.tokenizer.decode(*lowerCamelCase				,    **lowerCamelCase  )







      @contextmanager
      def       lowerCAmelCase_       (	self  )		->			Any:
        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your audio inputs, or in a separate call."""  )
        snake_case_					= True
        snake_case_					= self.tokenizer
        yield
        snake_case_					= self.feature_extractor
        snake_case_					= False