





"""simple docstring"""



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 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					""""""
 _lowerCamelCase				=					"""hf-legacy"""  # "hf://"" is reserved for hffs

 def __init__(   self ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(self ,						**__lowerCamelCase  )
     __A   :						int									=					repo_info
     __A   :						Optional[int]									=					token
     __A   :						int									=					None

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if self.dir_cache is None:
         __A   :						int									=					{}
         for hf_file in self.repo_info.siblings:
             # TODO(QL): add sizes
             __A   :						Tuple									=					{
                 '''name''': hf_file.rfilename,
                 '''size''': None,
                 '''type''': '''file''',
             }
             self.dir_cache.update(
                 {
                     str(__lowerCamelCase  ): {'''name''': str(__lowerCamelCase  ), '''size''': None, '''type''': '''directory'''}
                     for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
                 }  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = "rb" ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     if not isinstance(self.repo_info ,						__lowerCamelCase  ):
         raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""  )
     __A   :						Union[str, Any]									=					hf_hub_url(self.repo_info.id ,						__lowerCamelCase ,						revision=self.repo_info.sha  )
     return fsspec.open(
         __lowerCamelCase ,						mode=__lowerCamelCase ,						headers=get_authentication_headers_for_url(__lowerCamelCase ,						use_auth_token=self.token  ) ,						client_kwargs={'''trust_env''': True} ,						).open()

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




     self._get_dirs()
     __A   :						Optional[Any]									=					self._strip_protocol(__lowerCamelCase  )
     if path in self.dir_cache:
         return self.dir_cache[path]
     else:
         raise FileNotFoundError(__lowerCamelCase  )







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase=False ,						**__lowerCamelCase  ):



     '''simple docstring'''




     self._get_dirs()
     __A   :						Any									=					PurePosixPath(path.strip('''/'''  )  )
     __A   :						Any									=					{}
     for p, f in self.dir_cache.items():
         __A   :						List[Any]									=					PurePosixPath(p.strip('''/'''  )  )
         __A   :						Dict									=					p.parent
         if root == path:
             __A   :						Union[str, Any]									=					f
     __A   :						List[str]									=					list(paths.values()  )
     if detail:
         return out
     else:
         return sorted(f['''name'''] for f in out  )











"""simple docstring"""



from typing import TYPE_CHECKING

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


a_			=      {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

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

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




