from typing import TYPE_CHECKING

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


a_							=      {
    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["MaskFormerFeatureExtractor"]
       a_							=      ["MaskFormerImageProcessor"]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MaskFormerForInstanceSegmentation",
           "MaskFormerModel",
           "MaskFormerPreTrainedModel",
       ]
       a_							=      [
           "MaskFormerSwinBackbone",
           "MaskFormerSwinModel",
           "MaskFormerSwinPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
       from .configuration_maskformer_swin import MaskFormerSwinConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_maskformer import MaskFormerFeatureExtractor
              from .image_processing_maskformer import MaskFormerImageProcessor
       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_maskformer import (
                  MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MaskFormerForInstanceSegmentation,
                  MaskFormerModel,
                  MaskFormerPreTrainedModel,
              )
              from .modeling_maskformer_swin import (
                  MaskFormerSwinBackbone,
                  MaskFormerSwinModel,
                  MaskFormerSwinPreTrainedModel,
              )


else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_							=      {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

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

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""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

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



