





from ...processing_utils import ProcessorMixin





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """SpeechT5FeatureExtractor"""
  __UpperCamelCase	     =  """SpeechT5Tokenizer"""







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







  def __call__(	self    ,						*_lowercase    ,						**_lowercase						):
       lowerCAmelCase_					:       Optional[Any]    =		kwargs.pop("""audio"""    ,						_lowercase						)
       lowerCAmelCase_					:       List[Any]    =		kwargs.pop("""text"""    ,						_lowercase						)
       lowerCAmelCase_					:       Optional[int]    =		kwargs.pop("""text_target"""    ,						_lowercase						)
       lowerCAmelCase_					:       int    =		kwargs.pop("""audio_target"""    ,						_lowercase						)
       lowerCAmelCase_					:       Optional[Any]    =		kwargs.pop("""sampling_rate"""    ,						_lowercase						)

       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:
            lowerCAmelCase_					:       Optional[int]    =		self.feature_extractor(_lowercase    ,						*_lowercase    ,						sampling_rate=_lowercase    ,						**_lowercase						)
       elif text is not None:
            lowerCAmelCase_					:       Union[str, Any]    =		self.tokenizer(_lowercase    ,						**_lowercase						)
       else:
            lowerCAmelCase_					:       Dict    =		None

       if audio_target is not None:
            lowerCAmelCase_					:       Dict    =		self.feature_extractor(audio_target=_lowercase    ,						*_lowercase    ,						sampling_rate=_lowercase    ,						**_lowercase						)
            lowerCAmelCase_					:       str    =		targets["""input_values"""]
       elif text_target is not None:
            lowerCAmelCase_					:       str    =		self.tokenizer(_lowercase    ,						**_lowercase						)
            lowerCAmelCase_					:       List[str]    =		targets["""input_ids"""]
       else:
            lowerCAmelCase_					:       Dict    =		None

       if inputs is None:
            return targets

       if targets is not None:
            lowerCAmelCase_					:       int    =		labels

            lowerCAmelCase_					:       List[Any]    =		targets.get("""attention_mask"""						)
            if decoder_attention_mask is not None:
                 lowerCAmelCase_					:       Tuple    =		decoder_attention_mask

       return inputs







  def 	UpperCAmelCase__		(	self    ,						*_lowercase    ,						**_lowercase						):
       lowerCAmelCase_					:       List[Any]    =		kwargs.pop("""input_values"""    ,						_lowercase						)
       lowerCAmelCase_					:       Tuple    =		kwargs.pop("""input_ids"""    ,						_lowercase						)
       lowerCAmelCase_					:       str    =		kwargs.pop("""labels"""    ,						_lowercase						)

       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:
            lowerCAmelCase_					:       Optional[int]    =		self.feature_extractor.pad(_lowercase    ,						*_lowercase    ,						**_lowercase						)
       elif input_ids is not None:
            lowerCAmelCase_					:       Tuple    =		self.tokenizer.pad(_lowercase    ,						**_lowercase						)
       else:
            lowerCAmelCase_					:       Optional[Any]    =		None

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

       if inputs is None:
            return targets

       if targets is not None:
            lowerCAmelCase_					:       Dict    =		labels

            lowerCAmelCase_					:       str    =		targets.get("""attention_mask"""						)
            if decoder_attention_mask is not None:
                 lowerCAmelCase_					:       Any    =		decoder_attention_mask

       return inputs







  def 	UpperCAmelCase__		(	self    ,						*_lowercase    ,						**_lowercase						):
       return self.tokenizer.batch_decode(*_lowercase    ,						**_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						*_lowercase    ,						**_lowercase						):
       return self.tokenizer.decode(*_lowercase    ,						**_lowercase						)












from collections.abc import Sequence






def 							_lowerCAmelCase (  _a   :					Sequence[float]					,   _a   :					bool = False			)	->			float:
     if not arr:
          return 0

     lowerCAmelCase_					:       List[str]    =		0 if allow_empty_subarrays else float("""-inf"""			)
     lowerCAmelCase_					:       List[Any]    =		0.0
     for num in arr:
          lowerCAmelCase_					:       int    =		max(0 if allow_empty_subarrays else num					,   curr_sum + num			)
          lowerCAmelCase_					:       List[str]    =		max(_a					,   _a			)

     return max_sum


if __name__ == "__main__":
      from doctest import testmod

      testmod()

      UpperCAmelCase_					:   int 							=						[-2, 1, -3, 4, -1, 2, 1, -5, 4]
      print(F"""{max_subarray_sum(nums) = }""")
