



from typing import TYPE_CHECKING

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


UpperCAmelCase__     = {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "RobertaPreLayerNormForCausalLM",
							    "RobertaPreLayerNormForMaskedLM",
							    "RobertaPreLayerNormForMultipleChoice",
							    "RobertaPreLayerNormForQuestionAnswering",
							    "RobertaPreLayerNormForSequenceClassification",
							    "RobertaPreLayerNormForTokenClassification",
							    "RobertaPreLayerNormModel",
							    "RobertaPreLayerNormPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFRobertaPreLayerNormForCausalLM",
							    "TFRobertaPreLayerNormForMaskedLM",
							    "TFRobertaPreLayerNormForMultipleChoice",
							    "TFRobertaPreLayerNormForQuestionAnswering",
							    "TFRobertaPreLayerNormForSequenceClassification",
							    "TFRobertaPreLayerNormForTokenClassification",
							    "TFRobertaPreLayerNormMainLayer",
							    "TFRobertaPreLayerNormModel",
							    "TFRobertaPreLayerNormPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "FlaxRobertaPreLayerNormForCausalLM",
							    "FlaxRobertaPreLayerNormForMaskedLM",
							    "FlaxRobertaPreLayerNormForMultipleChoice",
							    "FlaxRobertaPreLayerNormForQuestionAnswering",
							    "FlaxRobertaPreLayerNormForSequenceClassification",
							    "FlaxRobertaPreLayerNormForTokenClassification",
							    "FlaxRobertaPreLayerNormModel",
							    "FlaxRobertaPreLayerNormPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import qiskit





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     int					)       ->				qiskit.result.counts.Counts:







			'''simple docstring'''
			_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=1_000					)

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


if __name__ == "__main__":
							UpperCAmelCase__     = single_qubit_measure(2, 2)
							print(f"""Total count for various states are: {counts}""")
