




import qiskit



def 						lowerCAmelCase		(		_lowerCAmelCase							:   int						,							_lowerCAmelCase							:   int	)     ->   Optional[Any]:




 """simple docstring"""







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

 # Create a Quantum Circuit acting on the q register
 UpperCAmelCase__							 =						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
 UpperCAmelCase__							 =						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__":
  _lowerCAmelCase						:   int		=   single_qubit_measure(2, 2)
  print(F'''Total count for various states are: {counts}''')











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





