

from ...processing_utils import ProcessorMixin






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "SpeechT5FeatureExtractor"
  a									= "SpeechT5Tokenizer"






  def __init__(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		Dict )		->							Dict:
       super().__init__(__lowerCamelCase     ,       __lowerCamelCase )






  def __call__(							self						:		Optional[int]     ,       *__lowerCamelCase						:		Optional[Any]     ,       **__lowerCamelCase						:		List[str] )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''audio'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''text'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''text_target'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''audio_target'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''sampling_rate'''     ,       __lowerCamelCase )

       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:
            SCREAMING_SNAKE_CASE__   =   self.feature_extractor(__lowerCamelCase     ,       *__lowerCamelCase     ,       sampling_rate=__lowerCamelCase     ,       **__lowerCamelCase )
       elif text is not None:
            SCREAMING_SNAKE_CASE__   =   self.tokenizer(__lowerCamelCase     ,       **__lowerCamelCase )
       else:
            SCREAMING_SNAKE_CASE__   =   None

       if audio_target is not None:
            SCREAMING_SNAKE_CASE__   =   self.feature_extractor(audio_target=__lowerCamelCase     ,       *__lowerCamelCase     ,       sampling_rate=__lowerCamelCase     ,       **__lowerCamelCase )
            SCREAMING_SNAKE_CASE__   =   targets['''input_values''']
       elif text_target is not None:
            SCREAMING_SNAKE_CASE__   =   self.tokenizer(__lowerCamelCase     ,       **__lowerCamelCase )
            SCREAMING_SNAKE_CASE__   =   targets['''input_ids''']
       else:
            SCREAMING_SNAKE_CASE__   =   None

       if inputs is None:
            return targets

       if targets is not None:
            SCREAMING_SNAKE_CASE__   =   labels

            SCREAMING_SNAKE_CASE__   =   targets.get('''attention_mask''' )
            if decoder_attention_mask is not None:
                 SCREAMING_SNAKE_CASE__   =   decoder_attention_mask

       return inputs






  def 				lowercase_		(							self						:		Union[str, Any]     ,       *__lowerCamelCase						:		Dict     ,       **__lowerCamelCase						:		Union[str, Any] )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''input_values'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''input_ids'''     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''labels'''     ,       __lowerCamelCase )

       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:
            SCREAMING_SNAKE_CASE__   =   self.feature_extractor.pad(__lowerCamelCase     ,       *__lowerCamelCase     ,       **__lowerCamelCase )
       elif input_ids is not None:
            SCREAMING_SNAKE_CASE__   =   self.tokenizer.pad(__lowerCamelCase     ,       **__lowerCamelCase )
       else:
            SCREAMING_SNAKE_CASE__   =   None

       if labels is not None:
            if "input_ids" in labels or (isinstance(__lowerCamelCase     ,       __lowerCamelCase ) and "input_ids" in labels[0]):
                 SCREAMING_SNAKE_CASE__   =   self.tokenizer.pad(__lowerCamelCase     ,       **__lowerCamelCase )
                 SCREAMING_SNAKE_CASE__   =   targets['''input_ids''']
            else:
                 SCREAMING_SNAKE_CASE__   =   self.feature_extractor.feature_size
                 SCREAMING_SNAKE_CASE__   =   self.feature_extractor.num_mel_bins
                 SCREAMING_SNAKE_CASE__   =   self.feature_extractor.pad(__lowerCamelCase     ,       *__lowerCamelCase     ,       **__lowerCamelCase )
                 SCREAMING_SNAKE_CASE__   =   feature_size_hack
                 SCREAMING_SNAKE_CASE__   =   targets['''input_values''']
       else:
            SCREAMING_SNAKE_CASE__   =   None

       if inputs is None:
            return targets

       if targets is not None:
            SCREAMING_SNAKE_CASE__   =   labels

            SCREAMING_SNAKE_CASE__   =   targets.get('''attention_mask''' )
            if decoder_attention_mask is not None:
                 SCREAMING_SNAKE_CASE__   =   decoder_attention_mask

       return inputs






  def 				lowercase_		(							self						:		str     ,       *__lowerCamelCase						:		Optional[Any]     ,       **__lowerCamelCase						:		str )		->							Union[str, Any]:
       return self.tokenizer.batch_decode(*__lowerCamelCase     ,       **__lowerCamelCase )







  def 				lowercase_		(							self						:		Tuple     ,       *__lowerCamelCase						:		List[str]     ,       **__lowerCamelCase						:		Tuple )		->							List[str]:
       return self.tokenizer.decode(*__lowerCamelCase     ,       **__lowerCamelCase )





from __future__ import annotations
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   len(_A    ) // 2

     # choose the middle 3 elements
     SCREAMING_SNAKE_CASE__   =   lst[m - 1 : m + 2]

     # if middle element is peak
     if three[1] > three[0] and three[1] > three[2]:
          return three[1]

     # if increasing, recurse on right
     elif three[0] < three[2]:
          if len(lst[:m]    ) == 2:
               m -= 1
          return peak(lst[m:]    )

     # decreasing
     else:
          if len(lst[:m]    ) == 2:
               m += 1
          return peak(lst[:m]    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


