



from typing import TYPE_CHECKING

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


UpperCAmelCase :				List[str]							={"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				int							=["""ViltFeatureExtractor"""]
	UpperCAmelCase :				Union[str, Any]							=["""ViltImageProcessor"""]
	UpperCAmelCase :				Optional[int]							=["""ViltProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				int							=[
	    """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """ViltForImageAndTextRetrieval""",
	    """ViltForImagesAndTextClassification""",
	    """ViltForTokenClassification""",
	    """ViltForMaskedLM""",
	    """ViltForQuestionAnswering""",
	    """ViltLayer""",
	    """ViltModel""",
	    """ViltPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_vilt import ViltFeatureExtractor
		from .image_processing_vilt import ViltImageProcessor
		from .processing_vilt import ViltProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_vilt import (
		    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ViltForImageAndTextRetrieval,
		    ViltForImagesAndTextClassification,
		    ViltForMaskedLM,
		    ViltForQuestionAnswering,
		    ViltForTokenClassification,
		    ViltLayer,
		    ViltModel,
		    ViltPreTrainedModel,
		)


else:
	import sys

	UpperCAmelCase :				Optional[Any]							=_LazyModule(__name__, globals()["""__file__"""], _import_structure)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :				List[Any]							={"""configuration_qdqbert""": ["""QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """QDQBertConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase :				Dict							=[
	    """QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """QDQBertForMaskedLM""",
	    """QDQBertForMultipleChoice""",
	    """QDQBertForNextSentencePrediction""",
	    """QDQBertForQuestionAnswering""",
	    """QDQBertForSequenceClassification""",
	    """QDQBertForTokenClassification""",
	    """QDQBertLayer""",
	    """QDQBertLMHeadModel""",
	    """QDQBertModel""",
	    """QDQBertPreTrainedModel""",
	    """load_tf_weights_in_qdqbert""",
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

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