




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


_snake_case            = logging.get_logger(__name__)

_snake_case            = {"vocab_file": "sentencepiece.bpe.model"}

_snake_case            = {
    "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"
        ),
    },
}

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

_snake_case            = "▁"







class 		lowercase							(     UpperCamelCase__	):
     _a											=		VOCAB_FILES_NAMES
     _a											=		PRETRAINED_VOCAB_FILES_MAP
     _a											=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _a											=		["input_ids", "attention_mask"]

     def __init__(						self	,  _a	,  _a="<s>"	,  _a="</s>"	,  _a="</s>"	,  _a="<s>"	,  _a="<unk>"	,  _a="<pad>"	,  _a="<mask>"	,  _a = None	,  **_a	,  )		->							None:
           # Mask token behave like a normal word, i.e. include the space before it
           _A							:  List[Any]    =		AddedToken(_a	,  lstrip=_a	,  rstrip=_a       ) if isinstance(_a	,  _a       ) else mask_token

           _A							:  List[str]    =		{} if sp_model_kwargs is None else sp_model_kwargs

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

           _A							:  int    =		vocab_file
           _A							:  Dict    =		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(str(_a       )       )

           _A							:  Union[str, Any]    =		{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

           _A							:  Any    =		len(self.sp_model       ) - 1
           _A							:  Optional[int]    =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

     def       a__			(						self	,  _a	,  _a = None       )		->							List[int]:

           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _A							:  Optional[int]    =		[self.cls_token_id]
           _A							:  Optional[Any]    =		[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep

     def       a__			(						self	,  _a	,  _a = None	,  _a = False       )		->							List[int]:
           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=_a	,  token_ids_a=_a	,  already_has_special_tokens=_a       )

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

     def       a__			(						self	,  _a	,  _a = None       )		->							List[int]:
           _A							:  int    =		[self.sep_token_id]
           _A							:  Any    =		[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       a__			(						self       )		->							Tuple:
           return len(self.sp_model       )

     def       a__			(						self       )		->							Any:
           _A							:  Union[str, Any]    =		{self.convert_ids_to_tokens(_a       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab

     def       a__			(						self	,  _a       )		->							List[str]:
           return self.sp_model.encode(_a	,  out_type=_a       )

     def       a__			(						self	,  _a       )		->							List[Any]:
           if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
           _A							:  Optional[int]    =		self.sp_model.PieceToId(_a       )

           return spm_id if spm_id else self.unk_token_id

     def       a__			(						self	,  _a       )		->							Any:
           if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(_a       )

     def       a__			(						self	,  _a       )		->							Optional[int]:
           _A							:  Union[str, Any]    =		[]
           _A							:  str    =		""""""
           _A							:  List[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(_a       ) + token
                       _A							:  Tuple    =		True
                       _A							:  Dict    =		[]
                 else:
                       current_sub_tokens.append(_a       )
                       _A							:  Optional[Any]    =		False
           out_string += self.sp_model.decode(_a       )
           return out_string.strip()

     def __getstate__(						self       )		->							Dict:
           _A							:  List[Any]    =		self.__dict__.copy()
           _A							:  Union[str, Any]    =		None
           return state

     def __setstate__(						self	,  _a       )		->							int:
           _A							:  Optional[int]    =		d

           # for backward compatibility
           if not hasattr(self	,  """sp_model_kwargs"""       ):
                 _A							:  Tuple    =		{}

           _A							:  Union[str, Any]    =		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(self.vocab_file       )



     def       a__			(						self	,  _a	,  _a = None       )		->							Tuple[str]:
           if not os.path.isdir(_a       ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
                 return
           _A							:  Optional[Any]    =		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							:  Union[str, Any]    =		self.sp_model.serialized_model_proto()
                       fi.write(_a       )

           return (out_vocab_file,)







from datetime import datetime as dt
import os

from github import Github


_snake_case            = [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def        lowerCAmelCase_			(  ):
      _A							:  int    =		Github(os.environ["""GITHUB_TOKEN"""]  )
      _A							:  Tuple    =		g.get_repo("""huggingface/transformers"""  )
      _A							:  Dict    =		repo.get_issues(state="""open"""  )

      for issue in open_issues:
            _A							:  Optional[Any]    =		sorted([comment for comment in issue.get_comments()],key=lambda snake_case_  : i.created_at,reverse=snake_case_  )
            _A							:  Dict    =		comments[0] if len(snake_case_  ) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()  )
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state="""closed"""  )
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()  )
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  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/transformers/blob/main/CONTRIBUTING.md) """
                      """are likely to be ignored."""  )


if __name__ == "__main__":
  main()

