




"""simple docstring"""




import qiskit







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ):
			__lowercase       :   str        =    qiskit.Aer.get_backend('''aer_simulator'''     )

			# Create a Quantum Circuit acting on the q register
			__lowercase       :   Union[str, Any]        =    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
			__lowercase       :   int        =    qiskit.execute(__UpperCamelCase     ,							__UpperCamelCase     ,							shots=10_00     )

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


if __name__ == "__main__":
			a_	   =    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,
)


_A	:			Union[str, Any]         ={
    '''configuration_swiftformer''': [
        '''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwiftFormerConfig''',
        '''SwiftFormerOnnxConfig''',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			Optional[Any]         =[
			    '''SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SwiftFormerForImageClassification''',
			    '''SwiftFormerModel''',
			    '''SwiftFormerPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_swiftformer import (
			    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SwiftFormerConfig,
			    SwiftFormerOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swiftformer import (
						    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwiftFormerForImageClassification,
						    SwiftFormerModel,
						    SwiftFormerPreTrainedModel,
						)

else:
			import sys

			_A	:			Tuple         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

