


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase    :						Union[str, Any]     =							{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[str]     =							['LayoutLMv2TokenizerFast']

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[str]     =							['LayoutLMv2FeatureExtractor']
    __UpperCamelCase    :						Union[str, Any]     =							['LayoutLMv2ImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						Dict     =							[
        'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'LayoutLMv2ForQuestionAnswering',
        'LayoutLMv2ForSequenceClassification',
        'LayoutLMv2ForTokenClassification',
        'LayoutLMv2Layer',
        'LayoutLMv2Model',
        'LayoutLMv2PreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
    from .processing_layoutlmva import LayoutLMvaProcessor
    from .tokenization_layoutlmva import LayoutLMvaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlmva import (
            LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMvaForQuestionAnswering,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaLayer,
            LayoutLMvaModel,
            LayoutLMvaPreTrainedModel,
        )
else:
    import sys

    __UpperCamelCase    :						Optional[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







import qiskit





def   _UpperCAmelCase       (     UpperCAmelCase      :			int						,	UpperCAmelCase      :			int      ):
      """simple docstring"""




      __lowerCamelCase     :      Dict       						=						qiskit.Aer.get_backend("""aer_simulator"""      )

      __lowerCamelCase     :      Dict       						=						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
      __lowerCamelCase     :      str       						=						qiskit.execute(UpperCAmelCase						,	UpperCAmelCase						,	shots=1_000      )

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


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



