






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			:List[str]	    =		{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_			:Tuple	    =		[
							    '''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

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









from typing import Callable






def 				A	(			a_   )	->		List[str]:

					def wrapper(a_   ):
										__UpperCamelCase :   Dict									=function_with_docstring.__doc__
										return target_function

					return wrapper

