




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase      : Optional[Any]					      =   {
    "configuration_fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig"],
    "tokenization_fsmt": ["FSMTTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase      : Dict					      =   ["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]


if TYPE_CHECKING:
		from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
		from .tokenization_fsmt import FSMTTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
		import sys

		__UpperCamelCase      : Optional[int]					      =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







import doctest

import projectq
from projectq.ops import H, Measure





def 	__A	(			__lowerCamelCase					)   -> int:
	a		       =							quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(__lowerCamelCase					)


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

		# initialises a new quantum backend
		__UpperCamelCase      : List[Any]					      =   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)

