



'''simple docstring'''

from typing import TYPE_CHECKING

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


_A					:     List[Any]      ={
    '''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					:     Tuple      =['''FlavaFeatureExtractor''']
		_A					:     int      =['''FlavaImageProcessor''']
		_A					:     Optional[int]      =['''FlavaProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A					:     Tuple      =[
		    '''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					:     Optional[Any]      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:     List[Any]      ={'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A					:     Optional[Any]      =[
		    '''UMT5EncoderModel''',
		    '''UMT5ForConditionalGeneration''',
		    '''UMT5ForQuestionAnswering''',
		    '''UMT5Model''',
		    '''UMT5PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_umta import UMTaConfig, UMTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_umta import (
				    UMTaEncoderModel,
				    UMTaForConditionalGeneration,
				    UMTaForQuestionAnswering,
				    UMTaModel,
				    UMTaPreTrainedModel,
				)
else:
		import sys

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