


"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase_       :       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_       :       Union[str, Any]      						=  [
           """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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_       :       Union[str, Any]      						=  {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Optional[int]      						=  ["""LayoutXLMTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       lowerCamelCase_       :       Union[str, Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)