






"""simple docstring"""







import qiskit





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  qiskit.Aer.get_backend('''aer_simulator''')

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

					# 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
					a__          =  qiskit.execute(lowerCamelCase_ ,     lowerCamelCase_ ,     shots=1000)

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


if __name__ == "__main__":
	__a       :	List[Any]             = 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       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    '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       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


