





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

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


snake_case__					:							Optional[int]													=     logging.get_logger(__name__)

snake_case__					:							Dict													=     '▁'

snake_case__					:							List[str]													=     {'vocab_file': 'prophetnet.tokenizer'}

snake_case__					:							Optional[Any]													=     {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

snake_case__					:							Dict													=     {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

snake_case__					:							Any													=     {
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}



def _a				(	lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






      __A          =				collections.OrderedDict()
      with open(lowerCamelCase					,				'''r'''					,				encoding='''utf-8''' ) as reader:
            __A          =				reader.readlines()
      for index, token in enumerate(lowerCamelCase ):
            __A          =				token.rstrip('''\n''' )
            __A          =				index
      return vocab







class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = VOCAB_FILES_NAMES
   lowerCAmelCase__        = PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase__        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase__        = ["""input_ids""", """attention_mask"""]




   def __init__(self	:Optional[Any]    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Optional[Any]="[SEP]"    ,   _UpperCamelCase	:List[str]="[SEP]"    ,   _UpperCamelCase	:str="[SEP]"    ,   _UpperCamelCase	:Tuple="[UNK]"    ,   _UpperCamelCase	:Union[str, Any]="[PAD]"    ,   _UpperCamelCase	:List[str]="[CLS]"    ,   _UpperCamelCase	:List[Any]="[MASK]"    ,   _UpperCamelCase	:Optional[Dict[str, Any]] = None    ,   **_UpperCamelCase	:List[str]    ,   )->					None:
         __A          =				{} if sp_model_kwargs is None else sp_model_kwargs

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

         try:
               import sentencepiece as spm
         except ImportError:
               logger.warning(
                   '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                   ''' pip install sentencepiece''' )
               raise

         __A          =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(str(_UpperCamelCase ) )
         __A          =				vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # put special tokens and [unused] tokens into the vocab
         __A          =				{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

         for i in range(10 ):
               __A          =				f"""[unused{i}]"""
               __A          =				5 + i

         # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
         __A          =				12
         __A          =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
         for k in self.fairseq_tokens_to_ids.keys():
               self.unique_no_split_tokens.append(_UpperCamelCase )




   def __getstate__(self	:Any )->					Tuple:
         __A          =				self.__dict__.copy()
         __A          =				None
         return state




   def __setstate__(self	:int    ,   _UpperCamelCase	:Tuple )->					int:
         __A          =				d
         try:
               import sentencepiece as spm
         except ImportError:
               logger.warning(
                   '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                   ''' pip install sentencepiece''' )
               raise

         # 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 _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:List[int]    ,   _UpperCamelCase	:Optional[List[int]] = None    ,   _UpperCamelCase	:bool = False )->					List[int]:

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

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




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:List[int]    ,   _UpperCamelCase	:Optional[List[int]] = None )->					List[int]:
         __A          =				[self.sep_token_id]

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




   @property
   def _lowerCAmelCase      (self	:List[str] )->					Optional[int]:
         return len(self.sp_model ) + self.fairseq_offset




   def _lowerCAmelCase      (self	:Dict )->					Optional[Any]:
         __A          =				{self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:str )->					str:
         return self.sp_model.encode(_UpperCamelCase    ,   out_type=_UpperCamelCase )




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:Union[str, Any] )->					str:
         if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
         __A          =				self.sp_model.PieceToId(_UpperCamelCase )

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:Dict )->					Dict:
         if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset )




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:List[str] )->					Any:
         __A          =				''''''.join(_UpperCamelCase ).replace(_UpperCamelCase    ,   ''' ''' ).strip()
         return out_string




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Optional[str] = None )->					Tuple[str]:
         if not os.path.isdir(_UpperCamelCase ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
               return
         __A          =				os.path.join(
             _UpperCamelCase    ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

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

         return (out_vocab_file,)






   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:List[int]    ,   _UpperCamelCase	:Optional[List[int]] = None )->					List[int]:

         if token_ids_a is None:
               return token_ids_a + [self.sep_token_id]
         __A          =				[self.sep_token_id]
         return token_ids_a + sep + token_ids_a + sep












import os
from datetime import datetime as dt

from github import Github


snake_case__					:							Union[str, Any]													=     [
    'good first issue',
    'feature request',
    'wip',
]



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				Github(os.environ['''GITHUB_TOKEN'''] )
      __A          =				g.get_repo('''huggingface/accelerate''' )
      __A          =				repo.get_issues(state='''open''' )

      for issue in open_issues:
            __A          =				sorted([comment for comment in issue.get_comments()]					,				key=lambda lowerCamelCase : i.created_at					,				reverse=lowerCamelCase )
            __A          =				comments[0] if len(lowerCamelCase ) > 0 else None
            __A          =				dt.utcnow()
            __A          =				(current_time - issue.updated_at).days
            __A          =				(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()





