

"""simple docstring"""
from ...processing_utils import ProcessorMixin

class 	lowerCamelCase     (    __lowercase					):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE 	=   "SpeechT5FeatureExtractor"
  SCREAMING_SNAKE_CASE 	=   "SpeechT5Tokenizer"



  def __init__(self				,    _lowerCamelCase				,    _lowerCamelCase     ):


        """simple docstring"""

        super().__init__(_a				,    _a     )



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


        """simple docstring"""

        UpperCAmelCase__       :				Dict						 =  kwargs.pop("""audio"""				,    _a     )
        UpperCAmelCase__       :				Dict						 =  kwargs.pop("""text"""				,    _a     )
        UpperCAmelCase__       :				Union[str, Any]						 =  kwargs.pop("""text_target"""				,    _a     )
        UpperCAmelCase__       :				Tuple						 =  kwargs.pop("""audio_target"""				,    _a     )
        UpperCAmelCase__       :				Tuple						 =  kwargs.pop("""sampling_rate"""				,    _a     )

        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:
              UpperCAmelCase__       :				Optional[int]						 =  self.feature_extractor(_a				,    *_a				,    sampling_rate=_a				,    **_a     )
        elif text is not None:
              UpperCAmelCase__       :				Any						 =  self.tokenizer(_a				,    **_a     )
        else:
              UpperCAmelCase__       :				int						 =  None

        if audio_target is not None:
              UpperCAmelCase__       :				Dict						 =  self.feature_extractor(audio_target=_a				,    *_a				,    sampling_rate=_a				,    **_a     )
              UpperCAmelCase__       :				Optional[int]						 =  targets['''input_values''']
        elif text_target is not None:
              UpperCAmelCase__       :				int						 =  self.tokenizer(_a				,    **_a     )
              UpperCAmelCase__       :				Union[str, Any]						 =  targets['''input_ids''']
        else:
              UpperCAmelCase__       :				Any						 =  None

        if inputs is None:
              return targets

        if targets is not None:
              UpperCAmelCase__       :				int						 =  labels

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

        return inputs



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


        """simple docstring"""

        UpperCAmelCase__       :				Optional[Any]						 =  kwargs.pop("""input_values"""				,    _a     )
        UpperCAmelCase__       :				Dict						 =  kwargs.pop("""input_ids"""				,    _a     )
        UpperCAmelCase__       :				int						 =  kwargs.pop("""labels"""				,    _a     )

        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:
              UpperCAmelCase__       :				Union[str, Any]						 =  self.feature_extractor.pad(_a				,    *_a				,    **_a     )
        elif input_ids is not None:
              UpperCAmelCase__       :				int						 =  self.tokenizer.pad(_a				,    **_a     )
        else:
              UpperCAmelCase__       :				Dict						 =  None

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

        if inputs is None:
              return targets

        if targets is not None:
              UpperCAmelCase__       :				Union[str, Any]						 =  labels

              UpperCAmelCase__       :				int						 =  targets.get("""attention_mask"""     )
              if decoder_attention_mask is not None:
                    UpperCAmelCase__       :				Optional[int]						 =  decoder_attention_mask

        return inputs



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


        """simple docstring"""

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




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


        """simple docstring"""

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




from math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



