


import os
import unicodedata
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 SPIECE_UNDERLINE, logging


SCREAMING_SNAKE_CASE__					:     Any       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     {'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE__					:     str       =     {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):


     def __init__(  self							,					A_							,					A_=False							,					A_=True							,					A_=False							,					A_="<s>"							,					A_="</s>"							,					A_="<unk>"							,					A_="<sep>"							,					A_="<pad>"							,					A_="<cls>"							,					A_="<mask>"							,					A_=["<eop>", "<eod>"]							,					A_ = None							,					**A_							,					):
       _UpperCAmelCase		:							List[Any]							=     AddedToken(A_							,					lstrip=A_							,					rstrip=A_    ) if isinstance(A_							,					A_    ) else mask_token

       _UpperCAmelCase		:							int							=     {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=A_							,					remove_space=A_							,					keep_accents=A_							,					bos_token=A_							,					eos_token=A_							,					unk_token=A_							,					sep_token=A_							,					pad_token=A_							,					cls_token=A_							,					mask_token=A_							,					additional_special_tokens=A_							,					sp_model_kwargs=self.sp_model_kwargs							,					**A_							,					)

       _UpperCAmelCase		:							str							=     3

       _UpperCAmelCase		:							int							=     do_lower_case
       _UpperCAmelCase		:							Dict							=     remove_space
       _UpperCAmelCase		:							str							=     keep_accents
       _UpperCAmelCase		:							Dict							=     vocab_file

       _UpperCAmelCase		:							str							=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.Load(A_    )

       try:
         import jieba
       except ModuleNotFoundError as error:
         raise error.__class__(
             """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
             """See https://pypi.org/project/jieba/ for installation."""    )
       _UpperCAmelCase		:							int							=     jieba
       _UpperCAmelCase		:							List[Any]							=     str.maketrans(""" \n"""							,					"""\u2582\u2583"""    )


     @property
     # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
     def        __snake_case(  self    ):
       return len(self.sp_model    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     {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    ):
       _UpperCAmelCase		:							Optional[int]							=     self.__dict__.copy()
       _UpperCAmelCase		:							int							=     None
       return state


     def __setstate__(  self							,					A_    ):
       _UpperCAmelCase		:							List[Any]							=     d

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

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


     def        __snake_case(  self							,					A_    ):
       if self.remove_space:
         _UpperCAmelCase		:							str							=     """ """.join(inputs.strip().split()    )
       else:
         _UpperCAmelCase		:							str							=     inputs
       _UpperCAmelCase		:							List[Any]							=     outputs.replace("""``"""							,					"""\""""    ).replace("""''"""							,					"""\""""    )

       if not self.keep_accents:
         _UpperCAmelCase		:							Dict							=     unicodedata.normalize("""NFKD"""							,					A_    )
         _UpperCAmelCase		:							str							=     """""".join([c for c in outputs if not unicodedata.combining(A_    )]    )
       if self.do_lower_case:
         _UpperCAmelCase		:							Tuple							=     outputs.lower()

       return outputs


     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							Any							=     self.preprocess_text(A_    )
       _UpperCAmelCase		:							Dict							=     self.sp_model.encode(A_							,					out_type=A_    )
       _UpperCAmelCase		:							Union[str, Any]							=     []
       for piece in pieces:
         if len(A_    ) > 1 and piece[-1] == str(""","""    ) and piece[-2].isdigit():
           _UpperCAmelCase		:							int							=     self.sp_model.EncodeAsPieces(piece[:-1].replace(A_							,					""""""    )    )
           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
             if len(cur_pieces[0]    ) == 1:
               _UpperCAmelCase		:							Dict							=     cur_pieces[1:]
             else:
               _UpperCAmelCase		:							List[str]							=     cur_pieces[0][1:]
           cur_pieces.append(piece[-1]    )
           new_pieces.extend(A_    )
         else:
           new_pieces.append(A_    )

       return new_pieces


     def        __snake_case(  self							,					A_    ):
       return self.sp_model.PieceToId(A_    )


     def        __snake_case(  self							,					A_    ):
       return self.sp_model.IdToPiece(A_    )


     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							List[Any]							=     """""".join(A_    ).replace(A_							,					""" """    ).strip()
       return out_string


     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							List[Any]							=     [self.sep_token_id]
       _UpperCAmelCase		:							Tuple							=     [self.cls_token_id]
       if token_ids_a is None:
         return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls


     def        __snake_case(  self							,					A_							,					A_ = None							,					A_ = False    ):

       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 not None:
         return ([0] * len(A_    )) + [1] + ([0] * len(A_    )) + [1, 1]
       return ([0] * len(A_    )) + [1, 1]


     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							Any							=     [self.sep_token_id]
       _UpperCAmelCase		:							List[Any]							=     [2]

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


     def        __snake_case(  self							,					A_							,					A_ = None    ):
       if not os.path.isdir(A_    ):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
         return
       _UpperCAmelCase		:							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:
           _UpperCAmelCase		:							Optional[int]							=     self.sp_model.serialized_model_proto()
           fi.write(A_    )

       return (out_vocab_file,)




     def        __snake_case(  self							,					*A_							,					**A_    ):
       _UpperCAmelCase		:							Optional[Any]							=     super()._decode(*A_							,					**A_    )
       _UpperCAmelCase		:							Dict							=     text.replace(""" """							,					""""""    ).replace("""\u2582"""							,					""" """    ).replace("""\u2583"""							,					"""\n"""    )
       return text









def    a__    (		snake_case__					:							Tuple     ):  # noqa: E741
  _UpperCAmelCase		:							Dict							=     len(snake_case__     )
  _UpperCAmelCase		:							Tuple							=     0
  _UpperCAmelCase		:							Union[str, Any]							=     [0] * n
  _UpperCAmelCase		:							Union[str, Any]							=     [False] * n
  _UpperCAmelCase		:							Tuple							=     [False] * n

  def dfs(snake_case__					:							List[str]						,   snake_case__					:							int						,   snake_case__					:							str						,   snake_case__					:							int     ):
    if parent == root:
      out_edge_count += 1
    _UpperCAmelCase		:							List[Any]							=     True
    _UpperCAmelCase		:							List[str]							=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _UpperCAmelCase		:							Optional[Any]							=     dfs(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__     )
        _UpperCAmelCase		:							Optional[Any]							=     min(low[at]						,   low[to]     )

        # AP found via bridge
        if at < low[to]:
          _UpperCAmelCase		:							List[str]							=     True
        # AP found via cycle
        if at == low[to]:
          _UpperCAmelCase		:							Optional[Any]							=     True
      else:
        _UpperCAmelCase		:							str							=     min(low[at]						,   snake_case__     )
    return out_edge_count

  for i in range(snake_case__     ):
    if not visited[i]:
      _UpperCAmelCase		:							List[Any]							=     0
      _UpperCAmelCase		:							List[str]							=     dfs(snake_case__						,   snake_case__						,   -1						,   snake_case__     )
      _UpperCAmelCase		:							Optional[Any]							=     out_edge_count > 1

  for x in range(len(snake_case__     )     ):
    if is_art[x] is True:
      print(snake_case__     )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}





compute_ap(data)





