






"""simple docstring"""







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

import sentencepiece as spm

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


__a       :	Tuple             = logging.get_logger(__name__)

__a       :	Optional[int]             = {'vocab_file': 'spiece.model'}

__a       :	Any             = {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

__a       :	List[str]             = {'bert_for_seq_generation': 512}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE    =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE    =[]
							_SCREAMING_SNAKE_CASE    =['input_ids', 'attention_mask']




							def __init__(   self:     Any				,		__A:     Optional[Any]				,		__A:     Tuple="<s>"				,		__A:     int="</s>"				,		__A:     Optional[Any]="<unk>"				,		__A:     str="<pad>"				,		__A:     Optional[Any]="<::::>"				,		__A:     Optional[Dict[str, Any]] = None				,		**__A:     Union[str, Any]				,		):

												'''simple docstring'''

												a__          =  {} if sp_model_kwargs is None else sp_model_kwargs

												# Add extra_ids to the special token list
												super().__init__(
												    bos_token=__A				,		eos_token=__A				,		unk_token=__A				,		pad_token=__A				,		sep_token=__A				,		sp_model_kwargs=self.sp_model_kwargs				,		**__A				,		)

												a__          =  vocab_file

												a__          =  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
												self.sp_model.Load(__A    )




							@property
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												return self.sp_model.get_piece_size()




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  {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:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  self.__dict__.copy()
												a__          =  None
												return state




							def __setstate__(   self:     List[Any]				,		__A:     Any    ):

												'''simple docstring'''

												a__          =  d

												# 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  lowercase		(   self:     Optional[Any]				,		__A:     str    ):

												'''simple docstring'''

												return self.sp_model.encode(__A				,		out_type=__A    )




							def  lowercase		(   self:     Any				,		__A:     Any    ):

												'''simple docstring'''

												return self.sp_model.piece_to_id(__A    )




							def  lowercase		(   self:     Optional[Any]				,		__A:     int    ):

												'''simple docstring'''

												a__          =  self.sp_model.IdToPiece(__A    )
												return token




							def  lowercase		(   self:     Union[str, Any]				,		__A:     Tuple    ):

												'''simple docstring'''

												a__          =  []
												a__          =  ''''''
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(__A    ) + token
																						a__          =  []
																	else:
																						current_sub_tokens.append(__A    )
												out_string += self.sp_model.decode(__A    )
												return out_string.strip()





							def  lowercase		(   self:     Union[str, Any]				,		__A:     str				,		__A:     Optional[str] = None    ):

												'''simple docstring'''

												if not os.path.isdir(__A    ):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
																	return
												a__          =  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__          =  self.sp_model.serialized_model_proto()
																						fi.write(__A    )

												return (out_vocab_file,)










"""simple docstring"""







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Any    ):

												'''simple docstring'''

												a__          =  {}




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												print(self.vertex    )
												for i in self.vertex:
																	print(__A				,		''' -> '''				,		''' -> '''.join([str(__A    ) for j in self.vertex[i]]    )    )




							def  lowercase		(   self:     Union[str, Any]				,		__A:     int				,		__A:     int    ):

												'''simple docstring'''

												if from_vertex in self.vertex:
																	self.vertex[from_vertex].append(__A    )
												else:
																	# else make a new vertex
																	a__          =  [to_vertex]




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  [False] * len(self.vertex    )

												# call the recursive helper function
												for i in range(len(self.vertex    )    ):
																	if not visited[i]:
																						self.dfs_recursive(__A				,		__A    )





							def  lowercase		(   self:     Optional[int]				,		__A:     int				,		__A:     list    ):

												'''simple docstring'''

												a__          =  True

												print(__A				,		end=''' '''    )

												# Recur for all the vertices that are adjacent to this node
												for i in self.vertex:
																	if not visited[i]:
																						self.dfs_recursive(__A				,		__A    )


if __name__ == "__main__":
	__a       :	Any             = Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	print('DFS:')
	g.dfs()

	# OUTPUT:
	# 0  ->  1 -> 2
	# 1  ->  2
	# 2  ->  0 -> 3
	# 3  ->  3
	# DFS:
	#  0 1 2 3


