





'''simple docstring'''

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							=logging.get_logger(__name__)

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

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

UpperCamelCase							="▁"




class     A    (      __snake_case						):






 """simple docstring"""





 __a			:						int      =						VOCAB_FILES_NAMES
 __a			:						Any      =						PRETRAINED_VOCAB_FILES_MAP
 __a			:						Any      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __a			:						Any      =						['''input_ids''', '''attention_mask''']
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase="<s>"			,     __lowerCAmelCase="</s>"			,     __lowerCAmelCase="</s>"			,     __lowerCAmelCase="<s>"			,     __lowerCAmelCase="<unk>"			,     __lowerCAmelCase="<pad>"			,     __lowerCAmelCase="<mask>"			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
       UpperCamelCase_				:   List[str]					    =							AddedToken(__UpperCamelCase			,     lstrip=__UpperCamelCase			,     rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase			,     __UpperCamelCase ) else mask_token

       UpperCamelCase_				:   Union[str, Any]					    =							{} if sp_model_kwargs is None else sp_model_kwargs

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

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

       UpperCamelCase_				:   Tuple					    =							{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

       UpperCamelCase_				:   List[str]					    =							len(self.sp_model ) - 1
       UpperCamelCase_				:   str					    =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):


       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase_				:   Dict					    =							[self.cls_token_id]
       UpperCamelCase_				:   int					    =							[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = False ):


       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 [1] + ([0] * len(__UpperCamelCase )) + [1]
       return [1] + ([0] * len(__UpperCamelCase )) + [1, 1] + ([0] * len(__UpperCamelCase )) + [1]
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       UpperCamelCase_				:   List[Any]					    =							[self.sep_token_id]
       UpperCamelCase_				:   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   _UpperCAmelCase       (							self ):
       return len(self.sp_model )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							{self.convert_ids_to_tokens(__UpperCamelCase ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       return self.sp_model.encode(__UpperCamelCase			,     out_type=__UpperCamelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):


       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       UpperCamelCase_				:   int					    =							self.sp_model.PieceToId(__UpperCamelCase )

       return spm_id if spm_id else self.unk_token_id
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):


       if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(__UpperCamelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Dict					    =							[]
       UpperCamelCase_				:   Optional[Any]					    =							""""""
       UpperCamelCase_				:   Dict					    =							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(__UpperCamelCase ) + token
                   UpperCamelCase_				:   Optional[int]					    =							True
                   UpperCamelCase_				:   Dict					    =							[]
             else:
                   current_sub_tokens.append(__UpperCamelCase )
                   UpperCamelCase_				:   str					    =							False
       out_string += self.sp_model.decode(__UpperCamelCase )
       return out_string.strip()
 def __getstate__(							self ):
       UpperCamelCase_				:   Dict					    =							self.__dict__.copy()
       UpperCamelCase_				:   List[Any]					    =							None
       return state
 def __setstate__(							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   int					    =							d

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

       UpperCamelCase_				:   List[Any]					    =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(self.vocab_file )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):


       if not os.path.isdir(__UpperCamelCase ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
       UpperCamelCase_				:   Optional[int]					    =							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:
                   UpperCamelCase_				:   int					    =							self.sp_model.serialized_model_proto()
                   fi.write(__UpperCamelCase )

       return (out_vocab_file,)














'''simple docstring'''

import random



def 							snake_case					(     a_							: int    ,   a_							: float    ,   a_							: bool = False	)       ->				dict:





      """simple docstring"""

      UpperCamelCase_				:   dict					    =							{i: [] for i in range(a_	)}

      # if probability is greater or equal than 1, then generate a complete graph
      if probability >= 1:
            return complete_graph(a_	)
      # if probability is lower or equal than 0, then return a graph without edges
      if probability <= 0:
            return graph

      # for each couple of nodes, add an edge from u to v
      # if the number randomly generated is greater than probability probability
      for i in range(a_	):
            for j in range(i + 1    ,   a_	):
                  if random.random() < probability:
                        graph[i].append(a_	)
                        if not directed:
                              # if the graph is undirected, add an edge in from j to i, either
                              graph[j].append(a_	)
      return graph



def 							snake_case					(     a_							: int	)       ->				dict:





      """simple docstring"""

      return {
          i: [j for j in range(a_	) if i != j] for i in range(a_	)
      }


if __name__ == "__main__":
      import doctest

      doctest.testmod()






