


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 	__lowerCamelCase			(_a						):
       _lowercase											=     """"""
       _lowercase											=     """hf-legacy"""  # "hf://"" is reserved for hffs







       def __init__(			self: Tuple,A_: Optional[DatasetInfo] = None,A_: Optional[str] = None,**A_: List[str],):






            '''simple docstring'''




            super().__init__(self,**A_					)
            __UpperCamelCase =			repo_info
            __UpperCamelCase =			token
            __UpperCamelCase =			None







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            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 		snake_case_	(			self: Any,A_: str,A_: str = "rb",**A_: str,):






            '''simple docstring'''




            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 		snake_case_	(			self: int,A_: Optional[int],**A_: Any					):






            '''simple docstring'''




            self._get_dirs()
            __UpperCamelCase =			self._strip_protocol(A_					)
            if path in self.dir_cache:
                 return self.dir_cache[path]
            else:
                 raise FileNotFoundError(A_					)







       def 		snake_case_	(			self: List[Any],A_: Union[str, Any],A_: Any=False,**A_: Any					):






            '''simple docstring'''




            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,
)


__snake_case   							=						{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''UniSpeechForCTC''',
        '''UniSpeechForPreTraining''',
        '''UniSpeechForSequenceClassification''',
        '''UniSpeechModel''',
        '''UniSpeechPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_unispeech import (
            UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
            UniSpeechForCTC,
            UniSpeechForPreTraining,
            UniSpeechForSequenceClassification,
            UniSpeechModel,
            UniSpeechPreTrainedModel,
        )

else:
    import sys

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


