






'''simple docstring'''
import qiskit





def 	_a(					UpperCamelCase__			:  int,		UpperCamelCase__			:  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			int													=qiskit.Aer.get_backend('''aer_simulator'''							)

      SCREAMING_SNAKE_CASE__       :			Tuple													=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
      SCREAMING_SNAKE_CASE__       :			Any													=qiskit.execute(UpperCamelCase__,		UpperCamelCase__,		shots=1_0_0_0							)

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


if __name__ == "__main__":
  a_					=	half_adder(1, 1)
  print(F'''Half Adder Output Qubit Counts: {counts}''')







'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					=	{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	['LxmertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

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