


import os
from datetime import datetime as dt

from github import Github


__UpperCamelCase       :       List[Any]						       =				[
    "good first issue",
    "feature request",
    "wip",
]

def 				_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Union[str, Any]          =   Github(os.environ['''GITHUB_TOKEN''']     )
 UpperCamelCase__					:    List[Any]          =   g.get_repo('''huggingface/accelerate'''     )
 UpperCamelCase__					:    Tuple          =   repo.get_issues(state='''open'''     )

 for issue in open_issues:
  UpperCamelCase__					:    str          =   sorted([comment for comment in issue.get_comments()]    , key=lambda SCREAMING_SNAKE_CASE     : i.created_at    , reverse=SCREAMING_SNAKE_CASE     )
  UpperCamelCase__					:    str          =   comments[0] if len(SCREAMING_SNAKE_CASE     ) > 0 else None
  UpperCamelCase__					:    Optional[int]          =   dt.utcnow()
  UpperCamelCase__					:    Optional[Any]          =   (current_time - issue.updated_at).days
  UpperCamelCase__					:    List[Any]          =   (current_time - issue.created_at).days
  if (
      last_comment is not None
      and last_comment.user.login == "github-actions[bot]"
      and days_since_updated > 7
      and days_since_creation >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
  ):
   # Close issue since it has been 7 days of inactivity since bot mention.
   issue.edit(state='''closed'''     )
  elif (
      days_since_updated > 23
      and days_since_creation >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
  ):
   # Add stale comment
   issue.create_comment(
       '''This issue has been automatically marked as stale because it has not had '''
       '''recent activity. If you think this still needs to be addressed '''
       '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
       '''[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '''
       '''are likely to be ignored.'''     )


if __name__ == "__main__":
       main()






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase       :       Tuple						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Union[str, Any]						       =				{"vocab_file": "sentencepiece.bpe.model"}

__UpperCamelCase       :       List[Any]						       =				{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

__UpperCamelCase       :       int						       =				{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

__UpperCamelCase       :       int						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		Tuple			=      VOCAB_FILES_NAMES
     A:		Optional[int]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		str			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		Union[str, Any]			=      ["input_ids", "attention_mask"]


     def __init__(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Tuple="<s>"							,	lowerCamelCase__							:     Tuple="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     Optional[Any]="<s>"							,	lowerCamelCase__							:     List[str]="<unk>"							,	lowerCamelCase__							:     str="<pad>"							,	lowerCamelCase__							:     int="<mask>"							,	lowerCamelCase__							:     Optional[Dict[str, Any]] = None							,	**lowerCamelCase__							:     Any							,	)  ->      None:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      UpperCamelCase__					:    Optional[int]          =   {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=lowerCamelCase__							,	eos_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	sp_model_kwargs=self.sp_model_kwargs							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Any          =   vocab_file
      UpperCamelCase__					:    str          =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.Load(str(lowerCamelCase__			)			)

      UpperCamelCase__					:    Any          =   {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

      UpperCamelCase__					:    Tuple          =   len(self.sp_model			) - 1
      UpperCamelCase__					:    str          =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__					:    List[str]          =   [self.cls_token_id]
      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None							,	lowerCamelCase__							:     bool = False			)  ->      List[int]:





      '''simple docstring'''






      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=lowerCamelCase__							,	token_ids_a=lowerCamelCase__							,	already_has_special_tokens=lowerCamelCase__			)

      if token_ids_a is None:
       return [1] + ([0] * len(lowerCamelCase__			)) + [1]
      return [1] + ([0] * len(lowerCamelCase__			)) + [1, 1] + ([0] * len(lowerCamelCase__			)) + [1]


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   [self.sep_token_id]
      UpperCamelCase__					:    List[str]          =   [self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep			) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]


     @property
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      int:





      '''simple docstring'''






      return len(self.sp_model			)


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   {self.convert_ids_to_tokens(lowerCamelCase__			): i for i in range(self.vocab_size			)}
      vocab.update(self.added_tokens_encoder			)
      return vocab


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     str			)  ->      List[str]:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     List[str]			)  ->      Union[str, Any]:





      '''simple docstring'''






      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      UpperCamelCase__					:    List[str]          =   self.sp_model.PieceToId(lowerCamelCase__			)

      return spm_id if spm_id else self.unk_token_id


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     int			)  ->      List[str]:





      '''simple docstring'''






      if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     Tuple			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   []
      UpperCamelCase__					:    Any          =   ''''''
      UpperCamelCase__					:    str          =   False
      for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
        if not prev_is_special:
         out_string += " "
        out_string += self.sp_model.decode(lowerCamelCase__			) + token
        UpperCamelCase__					:    str          =   True
        UpperCamelCase__					:    Tuple          =   []
       else:
        current_sub_tokens.append(lowerCamelCase__			)
        UpperCamelCase__					:    Any          =   False
      out_string += self.sp_model.decode(lowerCamelCase__			)
      return out_string.strip()


     def __getstate__(      self							:     Tuple			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   self.__dict__.copy()
      UpperCamelCase__					:    int          =   None
      return state


     def __setstate__(      self							:     Tuple							,	lowerCamelCase__							:     Any			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   d

      # for backward compatibility
      if not hasattr(self							,	'''sp_model_kwargs'''			):
       UpperCamelCase__					:    Optional[Any]          =   {}

      UpperCamelCase__					:    Optional[int]          =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.Load(self.vocab_file			)


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






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

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

      return (out_vocab_file,)


