


from typing import TYPE_CHECKING

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


a : Optional[int]		=				{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a : Optional[int]		=				[
	    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TapasForMaskedLM',
	    'TapasForQuestionAnswering',
	    'TapasForSequenceClassification',
	    'TapasModel',
	    'TapasPreTrainedModel',
	    'load_tf_weights_in_tapas',
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a : List[str]		=				[
	    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFTapasForMaskedLM',
	    'TFTapasForQuestionAnswering',
	    'TFTapasForSequenceClassification',
	    'TFTapasModel',
	    'TFTapasPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
	from .tokenization_tapas import TapasTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tapas import (
		    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TapasForMaskedLM,
		    TapasForQuestionAnswering,
		    TapasForSequenceClassification,
		    TapasModel,
		    TapasPreTrainedModel,
		    load_tf_weights_in_tapas,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_tapas import (
		    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTapasForMaskedLM,
		    TFTapasForQuestionAnswering,
		    TFTapasForSequenceClassification,
		    TFTapasModel,
		    TFTapasPreTrainedModel,
		)


else:
	import sys

	a : str		=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






from ..utils import DummyObject, requires_backends
class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["onnx"]






					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								requires_backends(self ,       ['''onnx''']						)






					@classmethod
					def A      (				cls ,       *snake_case_ ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								requires_backends(cls ,       ['''onnx''']						)






					@classmethod
					def A      (				cls ,       *snake_case_ ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								requires_backends(cls ,       ['''onnx''']						)


