


'''simple docstring'''


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


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {'vocab_file': 'spiece.model'}

__magic_name__			=      {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

__magic_name__			=      {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
__magic_name__			=      0
__magic_name__			=      1
__magic_name__			=      2
__magic_name__			=      3
__magic_name__			=      4



class 	__lowerCAmelCase		(							a__    ):


  '''simple docstring'''







  a_  					=							VOCAB_FILES_NAMES
  a_  					=							PRETRAINED_VOCAB_FILES_MAP
  a_  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_  					=							"left"






  def __init__(							self :       Tuple	,_a :       List[Any]	,_a :       Tuple=False	,_a :       int=True	,_a :       str=False	,_a :       Dict="<s>"	,_a :       Tuple="</s>"	,_a :       str="<unk>"	,_a :       Tuple="<sep>"	,_a :       Optional[int]="<pad>"	,_a :       Union[str, Any]="<cls>"	,_a :       List[str]="<mask>"	,_a :       Tuple=["<eop>", "<eod>"]	,_a :       int = None	,**_a :       Dict	,):

     '''simple docstring'''




     A_						:  List[Any]          =				AddedToken(lowerCamelCase_	,lstrip=lowerCamelCase_	,rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_	,lowerCamelCase_ ) else mask_token

     A_						:  List[Any]          =				{} if sp_model_kwargs is None else sp_model_kwargs

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

     A_						:  List[str]          =				3

     A_						:  Any          =				do_lower_case
     A_						:  List[Any]          =				remove_space
     A_						:  List[Any]          =				keep_accents
     A_						:  Dict          =				vocab_file

     A_						:  Tuple          =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(lowerCamelCase_ )






  @property
  def        _a							(							self :       List[Any] ):

     '''simple docstring'''




     return len(self.sp_model )






  def        _a							(							self :       Optional[int] ):

     '''simple docstring'''




     A_						:  str          =				{self.convert_ids_to_tokens(lowerCamelCase_ ): i for i in range(self.vocab_size )}
     vocab.update(self.added_tokens_encoder )
     return vocab






  def __getstate__(							self :       str ):

     '''simple docstring'''




     A_						:  Dict          =				self.__dict__.copy()
     A_						:  Tuple          =				None
     return state






  def __setstate__(							self :       Any	,_a :       int ):

     '''simple docstring'''




     A_						:  List[Any]          =				d

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

     A_						:  Dict          =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(self.vocab_file )






  def        _a							(							self :       Dict	,_a :       Dict ):

     '''simple docstring'''




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

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

     return outputs






  def        _a							(							self :       Union[str, Any]	,_a :       Optional[int] ):

     '''simple docstring'''




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

     return new_pieces






  def        _a							(							self :       List[Any]	,_a :       Optional[Any] ):

     '''simple docstring'''




     return self.sp_model.PieceToId(lowerCamelCase_ )






  def        _a							(							self :       List[Any]	,_a :       Optional[int] ):

     '''simple docstring'''




     return self.sp_model.IdToPiece(lowerCamelCase_ )






  def        _a							(							self :       Optional[int]	,_a :       Union[str, Any] ):

     '''simple docstring'''




     A_						:  Optional[int]          =				"""""".join(lowerCamelCase_ ).replace(lowerCamelCase_	,""" """ ).strip()
     return out_string






  def        _a							(							self :       Union[str, Any]	,_a :       Union[str, Any]	,_a :       Dict = False	,_a :       int = None	,_a :       Optional[Any] = True	,**_a :       Dict	,):

     '''simple docstring'''




     A_						:  List[str]          =				kwargs.pop("""use_source_tokenizer"""	,lowerCamelCase_ )

     A_						:  List[Any]          =				self.convert_ids_to_tokens(lowerCamelCase_	,skip_special_tokens=lowerCamelCase_ )

     # To avoid mixing byte-level and unicode for byte-level BPT
     # we need to build string separately for added tokens and byte-level tokens
     # cf. https://github.com/huggingface/transformers/issues/1133
     A_						:  List[str]          =				[]
     A_						:  str          =				[]
     for token in filtered_tokens:
        if skip_special_tokens and token in self.all_special_ids:
           continue
        if token in self.added_tokens_encoder:
           if current_sub_text:
              sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_ ) )
              A_						:  Tuple          =				[]
           sub_texts.append(lowerCamelCase_ )
        else:
           current_sub_text.append(lowerCamelCase_ )
     if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_ ) )

     # Mimic the behavior of the Rust tokenizer:
     # By default, there are no spaces between special tokens
     A_						:  Tuple          =				"""""".join(lowerCamelCase_ )

     A_						:  str          =				(
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
        A_						:  List[Any]          =				self.clean_up_tokenization(lowerCamelCase_ )
        return clean_text
     else:
        return text






  def        _a							(							self :       Union[str, Any]	,_a :       List[str]	,_a :       Optional[Any] = None ):

     '''simple docstring'''




     A_						:  Optional[Any]          =				[self.sep_token_id]
     A_						:  Optional[int]          =				[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        _a							(							self :       int	,_a :       Union[str, Any]	,_a :       Optional[int] = None	,_a :       List[str] = False ):

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






  def        _a							(							self :       List[str]	,_a :       int	,_a :       int = None ):

     '''simple docstring'''




     A_						:  int          =				[self.sep_token_id]
     A_						:  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        _a							(							self :       int	,_a :       Optional[Any]	,_a :       Optional[int] = None ):

     '''simple docstring'''




     if not os.path.isdir(lowerCamelCase_ ):
        logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
        return
     A_						:  int          =				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:
           A_						:  Any          =				self.sp_model.serialized_model_proto()
           fi.write(lowerCamelCase_ )

     return (out_vocab_file,)




'''simple docstring'''


from __future__ import annotations




def      lowerCamelCase					(      lowerCamelCase  :      dict							,						lowerCamelCase  :      str):
      A_   ,					A_						:  List[Any]          =				set(lowerCamelCase), [start]

      while stack:
            A_						:  Optional[Any]          =				stack.pop()
            explored.add(lowerCamelCase)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]):
                  if adj not in explored:
                        stack.append(lowerCamelCase)
      return explored


__magic_name__			=      {
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, 'A'))
