


'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCAmelCase__  =     {
    """configuration_unispeech_sat""": ["""UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechSatConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__  =     [
         """UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """UniSpeechSatForAudioFrameClassification""",
         """UniSpeechSatForCTC""",
         """UniSpeechSatForPreTraining""",
         """UniSpeechSatForSequenceClassification""",
         """UniSpeechSatForXVector""",
         """UniSpeechSatModel""",
         """UniSpeechSatPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_unispeech_sat import (
              UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
              UniSpeechSatForAudioFrameClassification,
              UniSpeechSatForCTC,
              UniSpeechSatForPreTraining,
              UniSpeechSatForSequenceClassification,
              UniSpeechSatForXVector,
              UniSpeechSatModel,
              UniSpeechSatPreTrainedModel,
          )

else:
     import sys

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








import doctest

import projectq
from projectq.ops import H, Measure



def       lowerCAmelCase			(		lowerCAmelCase_			)->			int:
				lowerCAmelCase_			:   List[Any]     					=					quantum_engine.allocate_qubit()
				H | qubit
				Measure | qubit
				return int(lowerCAmelCase_			)


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

						# initialises a new quantum backend
						_UpperCAmelCase      :	str  =projectq.MainEngine()

						# Generate a list of 10 random numbers
						_UpperCAmelCase      :	Optional[int]  =[get_random_number(quantum_engine) for _ in range(10)]

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

						print("""Random numbers""", random_numbers_list)