





'''simple docstring'''






from typing import TYPE_CHECKING

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


A__							:  List[str]    ={'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Dict    =['''BeitFeatureExtractor''']
      A__							:  Tuple    =['''BeitImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Any    =[
          '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BeitForImageClassification''',
          '''BeitForMaskedImageModeling''',
          '''BeitForSemanticSegmentation''',
          '''BeitModel''',
          '''BeitPreTrainedModel''',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Tuple    =[
          '''FlaxBeitForImageClassification''',
          '''FlaxBeitForMaskedImageModeling''',
          '''FlaxBeitModel''',
          '''FlaxBeitPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

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













'''simple docstring'''






import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A__							:  Optional[int]    ='''.'''


if __name__ == "__main__":
      A__							:  int    =os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
      A__							:  Optional[int]    =[]
      A__							:  Tuple    =[]
      with open(doctest_file_path) as fp:
            for line in fp:
                  A__							:  Optional[Any]    =line.strip()
                  A__							:  Union[str, Any]    =os.path.join(REPO_PATH, line)
                  if not (os.path.isfile(path) or os.path.isdir(path)):
                        non_existent_paths.append(line)
                  all_paths.append(path)
      if len(non_existent_paths) > 0:
            A__							:  List[Any]    ='''\n'''.join(non_existent_paths)
            raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
      if all_paths != sorted(all_paths):
            raise ValueError('''Files in `utils/documentation_tests.txt` are not in alphabetical order.''')






