




from typing import TYPE_CHECKING

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


UpperCAmelCase_									=	{
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase_									=	[
           'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'NezhaForNextSentencePrediction',
           'NezhaForMaskedLM',
           'NezhaForPreTraining',
           'NezhaForMultipleChoice',
           'NezhaForQuestionAnswering',
           'NezhaForSequenceClassification',
           'NezhaForTokenClassification',
           'NezhaModel',
           'NezhaPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_nezha import (
                  NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  NezhaForMaskedLM,
                  NezhaForMultipleChoice,
                  NezhaForNextSentencePrediction,
                  NezhaForPreTraining,
                  NezhaForQuestionAnswering,
                  NezhaForSequenceClassification,
                  NezhaForTokenClassification,
                  NezhaModel,
                  NezhaPreTrainedModel,
              )


else:
       import sys

       UpperCAmelCase_									=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase          =  {
    '''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''ResNetForImageClassification''',
						    '''ResNetModel''',
						    '''ResNetPreTrainedModel''',
						    '''ResNetBackbone''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFResNetForImageClassification''',
						    '''TFResNetModel''',
						    '''TFResNetPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''FlaxResNetForImageClassification''',
						    '''FlaxResNetModel''',
						    '''FlaxResNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_resnet import (
												    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ResNetBackbone,
												    ResNetForImageClassification,
												    ResNetModel,
												    ResNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_resnet import (
												    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFResNetForImageClassification,
												    TFResNetModel,
												    TFResNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure)




