
"""simple docstring"""

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


UpperCamelCase	:				List[str]  	= logging.get_logger(__name__)

UpperCamelCase	:				Tuple  	= {
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}

class __lowerCAmelCase		(      _snake_case			):
     lowercase						   =						"biogpt"





     def __init__(	self	,     __UpperCAmelCase=4_2384	,     __UpperCAmelCase=1024	,     __UpperCAmelCase=24	,     __UpperCAmelCase=16	,     __UpperCAmelCase=4096	,     __UpperCAmelCase="gelu"	,     __UpperCAmelCase=0.1	,     __UpperCAmelCase=0.1	,     __UpperCAmelCase=1024	,     __UpperCAmelCase=0.0_2	,     __UpperCAmelCase=1E-12	,     __UpperCAmelCase=True	,     __UpperCAmelCase=True	,     __UpperCAmelCase=0.0	,     __UpperCAmelCase=0.0	,     __UpperCAmelCase=1	,     __UpperCAmelCase=0	,     __UpperCAmelCase=2	,     **__UpperCAmelCase	,     ):

      '''simple docstring'''




      __UpperCamelCase				  =       vocab_size
      __UpperCamelCase				  =       max_position_embeddings
      __UpperCamelCase				  =       hidden_size
      __UpperCamelCase				  =       num_hidden_layers
      __UpperCamelCase				  =       num_attention_heads
      __UpperCamelCase				  =       intermediate_size
      __UpperCamelCase				  =       hidden_act
      __UpperCamelCase				  =       hidden_dropout_prob
      __UpperCamelCase				  =       attention_probs_dropout_prob
      __UpperCamelCase				  =       initializer_range
      __UpperCamelCase				  =       layer_norm_eps
      __UpperCamelCase				  =       scale_embedding
      __UpperCamelCase				  =       use_cache
      __UpperCamelCase				  =       layerdrop
      __UpperCamelCase				  =       activation_dropout
      super().__init__(pad_token_id=_lowerCamelCase	,     bos_token_id=_lowerCamelCase	,     eos_token_id=_lowerCamelCase	,     **_lowerCamelCase      )







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







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'ClapFeatureExtractor'
 SCREAMING_SNAKE_CASE__      =       ('RobertaTokenizer', 'RobertaTokenizerFast')




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




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  **_lowerCamelCase    ):
       a						:Dict 							=       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:
             a						:Optional[int] 							=       self.tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if audios is not None:
             a						:Tuple 							=       self.feature_extractor(
                 _lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if text is not None and audios is not None:
             a						:Union[str, Any] 							=       audio_features.input_features
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**_lowerCamelCase    )					,  tensor_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.tokenizer.model_input_names
       a						:str 							=       self.feature_extractor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )





