



'''simple docstring'''






from ..utils import DummyObject, requires_backends

class        SCREAMING_SNAKE_CASE(							metaclass=A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= ["""transformers""", """torch""", """note_seq"""]







	def __init__(		self    :  str	,    *__snake_case    :  Any	,    **__snake_case    :  Optional[Any]	)    ->	Union[str, Any]:
								requires_backends(self	,    ['''transformers''', '''torch''', '''note_seq''']	)







	@classmethod
	def   A			(		cls    :  List[Any]	,    *__snake_case    :  Union[str, Any]	,    **__snake_case    :  Tuple	)    ->	Tuple:
								requires_backends(cls	,    ['''transformers''', '''torch''', '''note_seq''']	)







	@classmethod
	def   A			(		cls    :  Optional[int]	,    *__snake_case    :  Any	,    **__snake_case    :  List[str]	)    ->	List[str]:
								requires_backends(cls	,    ['''transformers''', '''torch''', '''note_seq''']	)










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__:     Union[str, Any]										=    {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:     List[str]										=    [
						    "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LiltForQuestionAnswering",
						    "LiltForSequenceClassification",
						    "LiltForTokenClassification",
						    "LiltModel",
						    "LiltPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

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





