from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__      		=					{'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		=					[
        'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
        'SEWDForCTC',
        'SEWDForSequenceClassification',
        'SEWDModel',
        'SEWDPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew_d import (
            SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWDForCTC,
            SEWDForSequenceClassification,
            SEWDModel,
            SEWDPreTrainedModel,
        )


else:
    import sys

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





"""simple docstring"""
import doctest

import projectq
from projectq.ops import H, Measure
def   A_    (     _lowercase      ):







						'''simple docstring'''


						snake_case_					:Union[str, Any]		=     quantum_engine.allocate_qubit()
						H | qubit
						Measure | qubit
						return int(_lowercase      )


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

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

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

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

						print("Random numbers", random_numbers_list)
