






'''simple docstring'''
import qiskit




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  qiskit.Aer.get_backend("aer_simulator"				)

   lowercase_						:     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
   lowercase_						:     str							     =  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__":
 UpperCamelCase__			=      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,
)


UpperCamelCase__			=      {
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'FunnelBaseModel',
     'FunnelForMaskedLM',
     'FunnelForMultipleChoice',
     'FunnelForPreTraining',
     'FunnelForQuestionAnswering',
     'FunnelForSequenceClassification',
     'FunnelForTokenClassification',
     'FunnelModel',
     'FunnelPreTrainedModel',
     'load_tf_weights_in_funnel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFFunnelBaseModel',
     'TFFunnelForMaskedLM',
     'TFFunnelForMultipleChoice',
     'TFFunnelForPreTraining',
     'TFFunnelForQuestionAnswering',
     'TFFunnelForSequenceClassification',
     'TFFunnelForTokenClassification',
     'TFFunnelModel',
     'TFFunnelPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
 from .tokenization_funnel import FunnelTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_funnel_fast import FunnelTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_funnel import (
      FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
      FunnelBaseModel,
      FunnelForMaskedLM,
      FunnelForMultipleChoice,
      FunnelForPreTraining,
      FunnelForQuestionAnswering,
      FunnelForSequenceClassification,
      FunnelForTokenClassification,
      FunnelModel,
      FunnelPreTrainedModel,
      load_tf_weights_in_funnel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_funnel import (
      TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFunnelBaseModel,
      TFFunnelForMaskedLM,
      TFFunnelForMultipleChoice,
      TFFunnelForPreTraining,
      TFFunnelForQuestionAnswering,
      TFFunnelForSequenceClassification,
      TFFunnelForTokenClassification,
      TFFunnelModel,
      TFFunnelPreTrainedModel,
  )

else:
 import sys

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




