






from ...processing_utils import ProcessorMixin


class  SCREAMING_SNAKE_CASE			(			a__					):
  '''simple docstring'''


  UpperCamelCase_							:		int			=	"SpeechT5FeatureExtractor"
  UpperCamelCase_							:		Optional[int]			=	"SpeechT5Tokenizer"


  def __init__(				self       :	int					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Union[str, Any]      ):
    super().__init__(lowerCamelCase_					, lowerCamelCase_      )


  def __call__(				self       :	Optional[int]					, *UpperCAmelCase_       :	str					, **UpperCAmelCase_       :	Tuple      ):
    SCREAMING_SNAKE_CASE       :      Tuple							=    kwargs.pop("audio"					, lowerCamelCase_      )
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    kwargs.pop("text"					, lowerCamelCase_      )
    SCREAMING_SNAKE_CASE       :      List[Any]							=    kwargs.pop("text_target"					, lowerCamelCase_      )
    SCREAMING_SNAKE_CASE       :      Tuple							=    kwargs.pop("audio_target"					, lowerCamelCase_      )
    SCREAMING_SNAKE_CASE       :      List[Any]							=    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       :      str							=    self.feature_extractor(lowerCamelCase_					, *lowerCamelCase_					, sampling_rate=lowerCamelCase_					, **lowerCamelCase_      )
    elif text is not None:
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.tokenizer(lowerCamelCase_					, **lowerCamelCase_      )
    else:
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    None

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

    if inputs is None:
      return targets

    if targets is not None:
      SCREAMING_SNAKE_CASE       :      Tuple							=    labels

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

    return inputs


  def 				_A       (				self       :	List[str]					, *UpperCAmelCase_       :	Optional[Any]					, **UpperCAmelCase_       :	Optional[Any]      ):
    SCREAMING_SNAKE_CASE       :      Tuple							=    kwargs.pop("input_values"					, lowerCamelCase_      )
    SCREAMING_SNAKE_CASE       :      List[str]							=    kwargs.pop("input_ids"					, lowerCamelCase_      )
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    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       :      str							=    self.feature_extractor.pad(lowerCamelCase_					, *lowerCamelCase_					, **lowerCamelCase_      )
    elif input_ids is not None:
      SCREAMING_SNAKE_CASE       :      str							=    self.tokenizer.pad(lowerCamelCase_					, **lowerCamelCase_      )
    else:
      SCREAMING_SNAKE_CASE       :      Any							=    None

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

    if inputs is None:
      return targets

    if targets is not None:
      SCREAMING_SNAKE_CASE       :      int							=    labels

      SCREAMING_SNAKE_CASE       :      Any							=    targets.get("attention_mask"      )
      if decoder_attention_mask is not None:
        SCREAMING_SNAKE_CASE       :      Dict							=    decoder_attention_mask

    return inputs


  def 				_A       (				self       :	Any					, *UpperCAmelCase_       :	List[str]					, **UpperCAmelCase_       :	str      ):
    return self.tokenizer.batch_decode(*lowerCamelCase_					, **lowerCamelCase_      )






  def 				_A       (				self       :	Optional[int]					, *UpperCAmelCase_       :	int					, **UpperCAmelCase_       :	Optional[int]      ):
    return self.tokenizer.decode(*lowerCamelCase_					, **lowerCamelCase_      )














import qiskit


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[str]							=    qiskit.Aer.get_backend("aer_simulator"				)

  # Create a Quantum Circuit acting on the q register
  SCREAMING_SNAKE_CASE       :      Dict							=    qiskit.QuantumCircuit(lowercase						,     lowercase				)

  # Map the quantum measurement to the classical bits
  circuit.measure([0]						,     [0]				)

  # Execute the circuit on the simulator
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    qiskit.execute(lowercase						,     lowercase						,     shots=1000				)

  # Return the histogram data of the results of the experiment.
  return job.result().get_counts(lowercase				)


if __name__ == "__main__":
   print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")






