
"""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 __lowerCAmelCase		(      _snake_case			):
     lowercase						   =						""
     lowercase						   =						"hf-legacy"  # "hf://"" is reserved for hffs





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

      '''simple docstring'''




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





     def 						UpperCAmelCase					(	self      ):

      '''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(_lowerCamelCase      ): {'name': str(_lowerCamelCase      ), 'size': None, 'type': 'directory'}
                for d in list(PurePosixPath(hf_file.rfilename      ).parents      )[:-1]
            }      )





     def 						UpperCAmelCase					(	self	,     __UpperCAmelCase	,     __UpperCAmelCase = "rb"	,     **__UpperCAmelCase	,     ):

      '''simple docstring'''




      if not isinstance(self.repo_info	,     _lowerCamelCase      ):
       raise NotImplementedError(F'Open is only implemented for dataset repositories, but got {self.repo_info}'      )
      __UpperCamelCase				  =       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	,     __UpperCAmelCase	,     **__UpperCAmelCase      ):

      '''simple docstring'''




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







     def 						UpperCAmelCase					(	self	,     __UpperCAmelCase	,     __UpperCAmelCase=False	,     **__UpperCAmelCase      ):

      '''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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'ClapFeatureExtractor'
 SCREAMING_SNAKE_CASE__      =       ('RobertaTokenizer', 'RobertaTokenizerFast')




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       super().__init__(_lowerCamelCase					,  _lowerCamelCase    )




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  **_lowerCamelCase    ):
       a						:Dict 							=       kwargs.pop('''sampling_rate'''					,  _lowerCamelCase    )

       if text is None and audios is None:
             raise ValueError('''You have to specify either text or audios. Both cannot be none.'''    )

       if text is not None:
             a						:Optional[int] 							=       self.tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if audios is not None:
             a						:Tuple 							=       self.feature_extractor(
                 _lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if text is not None and audios is not None:
             a						:Union[str, Any] 							=       audio_features.input_features
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**_lowerCamelCase    )					,  tensor_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.tokenizer.model_input_names
       a						:str 							=       self.feature_extractor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )





