
"""simple docstring"""







import qiskit
def 	__UpperCamelCase (       SCREAMING_SNAKE_CASE				,     SCREAMING_SNAKE_CASE			)	->  qiskit.result.counts.Counts:







							"""simple docstring"""


							__snake_case        =							qiskit.Aer.get_backend("aer_simulator"			)

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

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

							# Execute the circuit on the simulator
							__snake_case        =							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__":
		print(F"""Total count for various states are: {single_qubit_measure(1, 1)}""")








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__   :  Tuple        =							{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Optional[Any]        =							['''PLBartTokenizer''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Tuple        =							[
           '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''PLBartForCausalLM''',
           '''PLBartForConditionalGeneration''',
           '''PLBartForSequenceClassification''',
           '''PLBartModel''',
           '''PLBartPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_plbart import PLBartTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_plbart import (
                  PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PLBartForCausalLM,
                  PLBartForConditionalGeneration,
                  PLBartForSequenceClassification,
                  PLBartModel,
                  PLBartPreTrainedModel,
              )


else:
       import sys

       __magic_name__   :  Optional[int]        =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)



