

"""simple docstring"""




from typing import TYPE_CHECKING

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


__A     :    int    =		{
    'configuration_flava': [
        'FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'FlavaConfig',
        'FlavaImageCodebookConfig',
        'FlavaImageConfig',
        'FlavaMultimodalConfig',
        'FlavaTextConfig',
    ],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Optional[int]    =		['FlavaFeatureExtractor']
	__A     :    Tuple    =		['FlavaImageProcessor']
	__A     :    Optional[Any]    =		['FlavaProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Dict    =		[
	    'FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FlavaForPreTraining',
	    'FlavaImageCodebook',
	    'FlavaImageModel',
	    'FlavaModel',
	    'FlavaMultimodalModel',
	    'FlavaPreTrainedModel',
	    'FlavaTextModel',
	]

if TYPE_CHECKING:
	from .configuration_flava import (
	    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    FlavaConfig,
	    FlavaImageCodebookConfig,
	    FlavaImageConfig,
	    FlavaMultimodalConfig,
	    FlavaTextConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_flava import FlavaFeatureExtractor
		from .image_processing_flava import FlavaImageProcessor
		from .processing_flava import FlavaProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flava import (
		    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlavaForPreTraining,
		    FlavaImageCodebook,
		    FlavaImageModel,
		    FlavaModel,
		    FlavaMultimodalModel,
		    FlavaPreTrainedModel,
		    FlavaTextModel,
		)

else:
	import sys

	__A     :    Any    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""




import doctest

import projectq
from projectq.ops import H, Measure
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					quantum_engine.allocate_qubit()
					H | qubit
					Measure | qubit
					return int(lowercase__      )


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

	# initialises a new quantum backend
	__A     :    Dict    =		projectq.MainEngine()

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

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

	print('Random numbers', random_numbers_list)





