






'''simple docstring'''

import qiskit







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			qiskit.result.counts.Counts:
      UpperCamelCase      = qiskit.Aer.get_backend("""aer_simulator"""   )

      # Create a Quantum Circuit acting on the q register
      UpperCamelCase      = qiskit.QuantumCircuit(__UpperCamelCase		,						__UpperCamelCase   )

      # Apply X (NOT) Gate to Qubits 0 & 1
      circuit.x(0   )
      circuit.x(1   )

      # Map the quantum measurement to the classical bits
      circuit.measure([0, 1]		,						[0, 1]   )

      # Execute the circuit on the qasm simulator
      UpperCamelCase      = 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__":
     SCREAMING_SNAKE_CASE__     =       single_qubit_measure(2, 2)
     print(f'Total count for various states are: {counts}')








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__     =       {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'NllbMoeForConditionalGeneration',
         'NllbMoeModel',
         'NllbMoePreTrainedModel',
         'NllbMoeTop2Router',
         'NllbMoeSparseMLP',
     ]


if TYPE_CHECKING:
     from .configuration_nllb_moe import (
         NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
         NllbMoeConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nllb_moe import (
              NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
              NllbMoeForConditionalGeneration,
              NllbMoeModel,
              NllbMoePreTrainedModel,
              NllbMoeSparseMLP,
              NllbMoeTopaRouter,
          )


else:
     import sys

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