


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__      		= {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['SpeechT5Tokenizer']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= [
        'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
        'SpeechT5ForSpeechToText',
        'SpeechT5ForSpeechToSpeech',
        'SpeechT5ForTextToSpeech',
        'SpeechT5Model',
        'SpeechT5PreTrainedModel',
        'SpeechT5HifiGan',
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__      		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







"""simple docstring"""




import qiskit





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Tuple										=  qiskit.Aer.get_backend('aer_simulator'					)

  A_  :       str										=  qiskit.QuantumCircuit(4 ,					2					)
  # encode inputs in qubits 0 and 1
  if bita == 1:
    qc_ha.x(0					)
  if bita == 1:
    qc_ha.x(1					)
  qc_ha.barrier()

  # use cnots to write XOR of the inputs on qubit2
  qc_ha.cx(0 ,					2					)
  qc_ha.cx(1 ,					2					)

  # use ccx / toffoli gate to write AND of the inputs on qubit3
  qc_ha.ccx(0 ,					1 ,					3					)
  qc_ha.barrier()

  # extract outputs
  qc_ha.measure(2 ,					0					)  # extract XOR value
  qc_ha.measure(3 ,					1					)  # extract AND value

  # Execute the circuit on the qasm simulator
  A_  :       Optional[Any]										=  qiskit.execute(_UpperCAmelCase ,					_UpperCAmelCase ,					shots=1000					)

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


if __name__ == "__main__":
   lowerCamelCase_     :						List[str]						=					half_adder(1, 1)
   print(F"Half Adder Output Qubit Counts: {counts}")