

"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   '''Speech2TextFeatureExtractor'''
    _lowerCamelCase					=   '''Speech2TextTokenizer'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      Tuple:
          super().__init__(lowerCamelCase_ ,lowerCamelCase_    )
          A				  =		self.feature_extractor
          A				  =		False




    def __call__( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      Dict:
          # For backward compatibility
          if self._in_target_context_manager:
                return self.current_processor(*lowerCamelCase_ ,**lowerCamelCase_    )

          if "raw_speech" in kwargs:
                warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""    )
                A				  =		kwargs.pop("""raw_speech"""    )
          else:
                A				  =		kwargs.pop("""audio""" ,lowerCamelCase_    )
          A				  =		kwargs.pop("""sampling_rate""" ,lowerCamelCase_    )
          A				  =		kwargs.pop("""text""" ,lowerCamelCase_    )
          if len(lowerCamelCase_    ) > 0:
                A				  =		args[0]
                A				  =		args[1:]

          if audio is None and text is None:
                raise ValueError("""You need to specify either an `audio` or `text` input to process."""    )

          if audio is not None:
                A				  =		self.feature_extractor(lowerCamelCase_ ,*lowerCamelCase_ ,sampling_rate=lowerCamelCase_ ,**lowerCamelCase_    )
          if text is not None:
                A				  =		self.tokenizer(lowerCamelCase_ ,**lowerCamelCase_    )

          if text is None:
                return inputs
          elif audio is None:
                return encodings
          else:
                A				  =		encodings["""input_ids"""]
                return inputs




    def UpperCamelCase__		( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      Union[str, Any]:
          return self.tokenizer.batch_decode(*lowerCamelCase_ ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      Optional[int]:
          return self.tokenizer.decode(*lowerCamelCase_ ,**lowerCamelCase_    )




    @contextmanager
    def UpperCamelCase__		( self    )    ->      Dict:
          warnings.warn(
              """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
              """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
              """your audio inputs, or in a separate call."""    )
          A				  =		True
          A				  =		self.tokenizer
          yield
          A				  =		self.feature_extractor
          A				  =		False







"""simple docstring"""


import qiskit



def   _A					(	_a	:						int     ,   _a	:						int				):



      """simple docstring"""






      A				  =		qiskit.Aer.get_backend("""aer_simulator"""				)

      # Create a Quantum Circuit acting on the q register
      A				  =		qiskit.QuantumCircuit(_a     ,   _a				)

      # Apply X (NOT) Gate to Qubits 0 & 1
      circuit.x(0				)
      circuit.x(1				)

      # Map the quantum measurement to the classical bits
      circuit.measure([0, 1]     ,   [0, 1]				)

      # Execute the circuit on the qasm simulator
      A				  =		qiskit.execute(_a     ,   _a     ,   shots=1_0_0_0				)

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


if __name__ == "__main__":
  UpperCAmelCase													=single_qubit_measure(2, 2)
  print(f"""Total count for various states are: {counts}""")




