


from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url






class    UpperCAmelCase__	( A_		):
    UpperCAmelCase__						:		List[Any]              =  ""
    UpperCAmelCase__						:		int              =  "hf-legacy"  # "hf://"" is reserved for hffs






    def __init__(      self ,       A_ = None ,       A_ = None ,       **A_ ,       )  ->	List[str]:
         super().__init__(self ,       **A_   )
         __UpperCamelCase   							=repo_info
         __UpperCamelCase   							=token
         __UpperCamelCase   							=None






    def _a (      self   )  ->	Union[str, Any]:
         if self.dir_cache is None:
              __UpperCamelCase   							={}
              for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   __UpperCamelCase   							={
                       'name': hf_file.rfilename,
                       'size': None,
                       'type': 'file',
                   }
                   self.dir_cache.update(
                       {
                           str(A_   ): {'name': str(A_   ), 'size': None, 'type': 'directory'}
                           for d in list(PurePosixPath(hf_file.rfilename   ).parents   )[:-1]
                       }   )






    def _a (      self ,       A_ ,       A_ = "rb" ,       **A_ ,       )  ->	Dict:
         if not isinstance(self.repo_info ,       A_   ):
              raise NotImplementedError(f'Open is only implemented for dataset repositories, but got {self.repo_info}'   )
         __UpperCamelCase   							=hf_hub_url(self.repo_info.id ,       A_ ,       revision=self.repo_info.sha   )
         return fsspec.open(
             A_ ,       mode=A_ ,       headers=get_authentication_headers_for_url(A_ ,       use_auth_token=self.token   ) ,       client_kwargs={'trust_env': True} ,       ).open()






    def _a (      self ,       A_ ,       **A_   )  ->	Optional[Any]:
         self._get_dirs()
         __UpperCamelCase   							=self._strip_protocol(A_   )
         if path in self.dir_cache:
              return self.dir_cache[path]
         else:
              raise FileNotFoundError(A_   )






    def _a (      self ,       A_ ,       A_=False ,       **A_   )  ->	str:
         self._get_dirs()
         __UpperCamelCase   							=PurePosixPath(path.strip('/'   )   )
         __UpperCamelCase   							={}
         for p, f in self.dir_cache.items():
              __UpperCamelCase   							=PurePosixPath(p.strip('/'   )   )
              __UpperCamelCase   							=p.parent
              if root == path:
                   __UpperCamelCase   							=f
         __UpperCamelCase   							=list(paths.values()   )
         if detail:
              return out
         else:
              return sorted(f['name'] for f in out   )









from typing import TYPE_CHECKING

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


_A			=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						['ViTFeatureExtractor']
   _A			=						['ViTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTForImageClassification',
       'ViTForMaskedImageModeling',
       'ViTModel',
       'ViTPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'TFViTForImageClassification',
       'TFViTModel',
       'TFViTPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'FlaxViTForImageClassification',
       'FlaxViTModel',
       'FlaxViTPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

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





