



'''simple docstring'''





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {"vocab_file": "spiece.model"}

lowercase_		       =  {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

lowercase_		       =  {"bert_for_seq_generation": 512}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Dict          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Dict          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       List[int]          =       []
 __lowerCamelCase    :       str          =       ['input_ids', 'attention_mask']



 def __init__(self ,   A ,   A="<s>" ,   A="</s>" ,   A="<unk>" ,   A="<pad>" ,   A="<::::>" ,   A = None ,   **A ,   )    ->     None:


      """simple docstring"""



      _a      =  {} if sp_model_kwargs is None else sp_model_kwargs

      # Add extra_ids to the special token list
      super().__init__(
          bos_token=A ,   eos_token=A ,   unk_token=A ,   pad_token=A ,   sep_token=A ,   sp_model_kwargs=self.sp_model_kwargs ,   **A ,   )

      _a      =  vocab_file

      _a      =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(A       )



 @property
 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      return self.sp_model.get_piece_size()



 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  {self.convert_ids_to_tokens(A       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab



 def __getstate__(self       )    ->     List[str]:


      """simple docstring"""



      _a      =  self.__dict__.copy()
      _a      =  None
      return state



 def __setstate__(self ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  d

      # for backward compatibility
      if not hasattr(self ,   '''sp_model_kwargs'''       ):
           _a      =  {}

      _a      =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(self.vocab_file       )



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      return self.sp_model.encode(A ,   out_type=A       )



 def 					a__  (self ,   A       )    ->     Union[str, Any]:


      """simple docstring"""



      return self.sp_model.piece_to_id(A       )



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      _a      =  self.sp_model.IdToPiece(A       )
      return token



 def 					a__  (self ,   A       )    ->     List[Any]:


      """simple docstring"""



      _a      =  []
      _a      =  ''''''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(A       ) + token
                _a      =  []
           else:
                current_sub_tokens.append(A       )
      out_string += self.sp_model.decode(A       )
      return out_string.strip()



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      if not os.path.isdir(A       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
      _a      =  os.path.join(
          A ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

      if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ) and os.path.isfile(self.vocab_file       ):
           copyfile(self.vocab_file ,   A       )
      elif not os.path.isfile(self.vocab_file       ):
           with open(A ,   '''wb'''       ) as fi:
                _a      =  self.sp_model.serialized_model_proto()
                fi.write(A       )

      return (out_vocab_file,)








'''simple docstring'''





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


lowercase_		       =  datasets.utils.logging.get_logger(__name__)






class __A			(  folder_based_builder.FolderBasedBuilderConfig						):



 '''simple docstring'''







 __lowerCamelCase    :       bool          =       None
 __lowerCamelCase    :       bool          =       None






class __A			(  folder_based_builder.FolderBasedBuilder						):



 '''simple docstring'''







 __lowerCamelCase    :       int          =       datasets.Audio()
 __lowerCamelCase    :       str          =       'audio'
 __lowerCamelCase    :       Optional[Any]          =       AudioFolderConfig
 __lowerCamelCase    :       List[str]  # definition at the bottom of the script
 __lowerCamelCase    :       Union[str, Any]          =       AudioClassification(audio_column='audio'   ,   label_column='label'						)





lowercase_		       =  [
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
lowercase_		       =  AUDIO_EXTENSIONS



