




import doctest

import projectq
from projectq.ops import H, Measure

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							int:




       '''simple docstring'''






       _lowercase					:				Optional[Any]           =			quantum_engine.allocate_qubit()
       H | qubit
       Measure | qubit
       return int(UpperCAmelCase_			)


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   UpperCamelCase__				       =			projectq.MainEngine()

   # Generate a list of 10 random numbers
   UpperCamelCase__				       =			[get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print('Random numbers', random_numbers_list)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__				       =			{
    'configuration_wav2vec2_conformer': [
        'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Wav2Vec2ConformerConfig',
    ],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			[
       'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Wav2Vec2ConformerForAudioFrameClassification',
       'Wav2Vec2ConformerForCTC',
       'Wav2Vec2ConformerForPreTraining',
       'Wav2Vec2ConformerForSequenceClassification',
       'Wav2Vec2ConformerForXVector',
       'Wav2Vec2ConformerModel',
       'Wav2Vec2ConformerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_wavaveca_conformer import (
       WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       WavaVecaConformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca_conformer import (
          WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaConformerForAudioFrameClassification,
          WavaVecaConformerForCTC,
          WavaVecaConformerForPreTraining,
          WavaVecaConformerForSequenceClassification,
          WavaVecaConformerForXVector,
          WavaVecaConformerModel,
          WavaVecaConformerPreTrainedModel,
      )

else:
   import sys

   UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)