

'''simple docstring'''
from ...processing_utils import ProcessorMixin


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					str     	=							'SpeechT5FeatureExtractor'
   _SCREAMING_SNAKE_CASE   :					Any     	=							'SpeechT5Tokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )


   def __call__(			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       kwargs.pop("audio"      ,    _UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       kwargs.pop("text"      ,    _UpperCamelCase  )
      _lowercase      :				List[Any]      					=       kwargs.pop("text_target"      ,    _UpperCamelCase  )
      _lowercase      :				str      					=       kwargs.pop("audio_target"      ,    _UpperCamelCase  )
      _lowercase      :				str      					=       kwargs.pop("sampling_rate"      ,    _UpperCamelCase  )

      if audio is not None and text is not None:
         raise ValueError(
             "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"  )
      if audio_target is not None and text_target is not None:
         raise ValueError(
             "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"  )
      if audio is None and audio_target is None and text is None and text_target is None:
         raise ValueError(
             "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."  )

      if audio is not None:
         _lowercase      :				Optional[Any]      					=       self.feature_extractor(_UpperCamelCase      ,    *_UpperCamelCase      ,    sampling_rate=_UpperCamelCase      ,    **_UpperCamelCase  )
      elif text is not None:
         _lowercase      :				Optional[Any]      					=       self.tokenizer(_UpperCamelCase      ,    **_UpperCamelCase  )
      else:
         _lowercase      :				Dict      					=       None

      if audio_target is not None:
         _lowercase      :				Any      					=       self.feature_extractor(audio_target=_UpperCamelCase      ,    *_UpperCamelCase      ,    sampling_rate=_UpperCamelCase      ,    **_UpperCamelCase  )
         _lowercase      :				Tuple      					=       targets["input_values"]
      elif text_target is not None:
         _lowercase      :				str      					=       self.tokenizer(_UpperCamelCase      ,    **_UpperCamelCase  )
         _lowercase      :				Any      					=       targets["input_ids"]
      else:
         _lowercase      :				Optional[int]      					=       None

      if inputs is None:
         return targets

      if targets is not None:
         _lowercase      :				Any      					=       labels

         _lowercase      :				Tuple      					=       targets.get("attention_mask"  )
         if decoder_attention_mask is not None:
            _lowercase      :				Any      					=       decoder_attention_mask

      return inputs


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       kwargs.pop("input_values"      ,    _UpperCamelCase  )
      _lowercase      :				List[Any]      					=       kwargs.pop("input_ids"      ,    _UpperCamelCase  )
      _lowercase      :				int      					=       kwargs.pop("labels"      ,    _UpperCamelCase  )

      if input_values is not None and input_ids is not None:
         raise ValueError("Cannot process both `input_values` and `input_ids` inputs."  )
      if input_values is None and input_ids is None and labels is None:
         raise ValueError(
             "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."  )

      if input_values is not None:
         _lowercase      :				List[Any]      					=       self.feature_extractor.pad(_UpperCamelCase      ,    *_UpperCamelCase      ,    **_UpperCamelCase  )
      elif input_ids is not None:
         _lowercase      :				List[Any]      					=       self.tokenizer.pad(_UpperCamelCase      ,    **_UpperCamelCase  )
      else:
         _lowercase      :				List[Any]      					=       None

      if labels is not None:
         if "input_ids" in labels or (isinstance(_UpperCamelCase      ,    _UpperCamelCase  ) and "input_ids" in labels[0]):
            _lowercase      :				List[Any]      					=       self.tokenizer.pad(_UpperCamelCase      ,    **_UpperCamelCase  )
            _lowercase      :				Dict      					=       targets["input_ids"]
         else:
            _lowercase      :				Optional[int]      					=       self.feature_extractor.feature_size
            _lowercase      :				str      					=       self.feature_extractor.num_mel_bins
            _lowercase      :				Union[str, Any]      					=       self.feature_extractor.pad(_UpperCamelCase      ,    *_UpperCamelCase      ,    **_UpperCamelCase  )
            _lowercase      :				Tuple      					=       feature_size_hack
            _lowercase      :				List[str]      					=       targets["input_values"]
      else:
         _lowercase      :				str      					=       None

      if inputs is None:
         return targets

      if targets is not None:
         _lowercase      :				str      					=       labels

         _lowercase      :				List[Any]      					=       targets.get("attention_mask"  )
         if decoder_attention_mask is not None:
            _lowercase      :				Union[str, Any]      					=       decoder_attention_mask

      return inputs


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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






   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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









'''simple docstring'''
import logging

from transformers import PretrainedConfig


_snake_case         =  logging.getLogger(__name__)


_snake_case         =  {
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'bertabs'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=512      ,    _UpperCamelCase=6      ,    _UpperCamelCase=512      ,    _UpperCamelCase=8      ,    _UpperCamelCase=512      ,    _UpperCamelCase=0.2      ,    _UpperCamelCase=6      ,    _UpperCamelCase=768      ,    _UpperCamelCase=8      ,    _UpperCamelCase=2048      ,    _UpperCamelCase=0.2      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      super().__init__(**_UpperCamelCase  )

      _lowercase      :				List[str]      					=       vocab_size
      _lowercase      :				Tuple      					=       max_pos

      _lowercase      :				int      					=       enc_layers
      _lowercase      :				str      					=       enc_hidden_size
      _lowercase      :				Optional[Any]      					=       enc_heads
      _lowercase      :				Union[str, Any]      					=       enc_ff_size
      _lowercase      :				Tuple      					=       enc_dropout

      _lowercase      :				Dict      					=       dec_layers
      _lowercase      :				List[str]      					=       dec_hidden_size
      _lowercase      :				List[str]      					=       dec_heads
      _lowercase      :				str      					=       dec_ff_size
      _lowercase      :				str      					=       dec_dropout






