



'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase_     :       Optional[int]								=						{'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_     :       Optional[int]								=						[
           'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
           'SwinForImageClassification',
           'SwinForMaskedImageModeling',
           'SwinModel',
           'SwinPreTrainedModel',
           'SwinBackbone',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_     :       str								=						[
           'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFSwinForImageClassification',
           'TFSwinForMaskedImageModeling',
           'TFSwinModel',
           'TFSwinPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_swin import (
                  SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SwinBackbone,
                  SwinForImageClassification,
                  SwinForMaskedImageModeling,
                  SwinModel,
                  SwinPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_swin import (
                  TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFSwinForImageClassification,
                  TFSwinForMaskedImageModeling,
                  TFSwinModel,
                  TFSwinPreTrainedModel,
              )

else:
       import sys

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









'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase_     :       Tuple								=						{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Dict								=						[
							    '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:
							lowerCAmelCase_     :       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

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




