'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class        SCREAMING_SNAKE_CASE   (				__a ):
  """simple docstring"""







  __A     =		"AutoFeatureExtractor"
  __A     =		"Speech2Text2Tokenizer"



  def __init__(  self   :					int					,					__lowerCAmelCase   :					List[str]					,					__lowerCAmelCase   :					Optional[Any]					):


        """simple docstring"""



        super().__init__(__lowerCAmelCase					,					__lowerCAmelCase					)
        _lowerCAmelCase		   =   self.feature_extractor
        _lowerCAmelCase		   =   False



  def __call__(  self   :					Tuple					,					*__lowerCAmelCase   :					List[str]					,					**__lowerCAmelCase   :					Optional[int]					):


        """simple docstring"""



        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.'					)
              _lowerCAmelCase		   =   kwargs.pop('raw_speech'					)
        else:
              _lowerCAmelCase		   =   kwargs.pop('audio'					,					__lowerCAmelCase					)
        _lowerCAmelCase		   =   kwargs.pop('sampling_rate'					,					__lowerCAmelCase					)
        _lowerCAmelCase		   =   kwargs.pop('text'					,					__lowerCAmelCase					)
        if len(__lowerCAmelCase					) > 0:
              _lowerCAmelCase		   =   args[0]
              _lowerCAmelCase		   =   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:
              _lowerCAmelCase		   =   self.feature_extractor(__lowerCAmelCase					,					*__lowerCAmelCase					,					sampling_rate=__lowerCAmelCase					,					**__lowerCAmelCase					)
        if text is not None:
              _lowerCAmelCase		   =   self.tokenizer(__lowerCAmelCase					,					**__lowerCAmelCase					)

        if text is None:
              return inputs
        elif audio is None:
              return encodings
        else:
              _lowerCAmelCase		   =   encodings['input_ids']
              return inputs



  def   a   (  self   :					List[Any]					,					*__lowerCAmelCase   :					Tuple					,					**__lowerCAmelCase   :					Optional[int]					):


        """simple docstring"""



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



  def   a   (  self   :					Tuple					,					*__lowerCAmelCase   :					Optional[Any]					,					**__lowerCAmelCase   :					Optional[Any]					):


        """simple docstring"""



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



  @contextmanager
  def   a   (  self   :					Union[str, Any]					):


        """simple docstring"""



        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.'					)
        _lowerCAmelCase		   =   True
        _lowerCAmelCase		   =   self.tokenizer
        yield
        _lowerCAmelCase		   =   self.feature_extractor
        _lowerCAmelCase		   =   False






'''simple docstring'''




import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def 			A_   ( _lowerCamelCase			:							float = np.pi / 2  ,  _lowerCamelCase			:							float = np.pi / 2  ,  _lowerCamelCase			:							float = np.pi / 2    ):
      _lowerCAmelCase		   =   QuantumRegister(3  ,  'qr'    )  # Define the number of quantum bits
      _lowerCAmelCase		   =   ClassicalRegister(1  ,  'cr'    )  # Define the number of classical bits

      _lowerCAmelCase		   =   QuantumCircuit(_lowerCamelCase  ,  _lowerCamelCase    )  # Define the quantum circuit.

      # Build the circuit
      quantum_circuit.u(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase  ,  0    )  # Quantum State to teleport
      quantum_circuit.h(1    )  # add hadamard gate
      quantum_circuit.cx(
          1  ,  2    )  # add control gate with qubit 1 as control and 2 as target.
      quantum_circuit.cx(0  ,  1    )
      quantum_circuit.h(0    )
      quantum_circuit.cz(0  ,  2    )  # add control z gate.
      quantum_circuit.cx(1  ,  2    )

      quantum_circuit.measure([2]  ,  [0]    )  # measure the qubit.

      # Simulate the circuit using qasm simulator
      _lowerCAmelCase		   =   Aer.get_backend('aer_simulator'    )
      _lowerCAmelCase		   =   execute(_lowerCamelCase  ,  _lowerCamelCase  ,  shots=1_000    )

      return job.result().get_counts(_lowerCamelCase    )


if __name__ == "__main__":
 print(
     '''Total count for teleported state is: '''
     f'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
 )





