

"""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


SCREAMING_SNAKE_CASE_					:	int							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Dict							  =						'▁'

SCREAMING_SNAKE_CASE_					:	Tuple							  =						{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

SCREAMING_SNAKE_CASE_					:	List[Any]							  =						{'vinai/bartpho-syllable': 1_0_2_4}



class 					a						(       _lowerCamelCase				):



				"""simple docstring"""



				UpperCAmelCase						 =					VOCAB_FILES_NAMES
				UpperCAmelCase						 =					PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase						 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase						 =					["input_ids", "attention_mask"]

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






									"""simple docstring"""


									A__										=  AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase    ,						UpperCamelCase       ) else mask_token

									A__										=  {} 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    ,						)

									A__										=  vocab_file
									A__										=  monolingual_vocab_file
									A__										=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
									self.sp_model.Load(str(UpperCamelCase       )       )

									# Load the reduced vocab

									# Keep order of special tokens for backward compatibility
									A__										=  {}
									A__										=  0
									for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
														if str(UpperCamelCase       ) not in self.fairseq_tokens_to_ids:
																			A__										=  cnt
																			cnt += 1
									with open(UpperCamelCase    ,						"""r"""    ,						encoding="""utf-8"""       ) as f:
														for line in f.readlines():
																			A__										=  line.strip().split()[0]
																			A__										=  len(self.fairseq_tokens_to_ids       )
									if str(UpperCamelCase       ) not in self.fairseq_tokens_to_ids:
														A__										=  len(self.fairseq_tokens_to_ids       )

									A__										=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}

				def __getstate__(     self: List[str]       ):






									"""simple docstring"""


									A__										=  self.__dict__.copy()
									A__										=  None
									A__										=  self.sp_model.serialized_model_proto()
									return state

				def __setstate__(     self: Optional[Any]    ,						UpperCamelCase: Union[str, 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.LoadFromSerializedProto(self.sp_model_proto       )

				def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: List[int]    ,						UpperCamelCase: Optional[List[int]] = None       ):






									"""simple docstring"""


									if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									A__										=  [self.cls_token_id]
									A__										=  [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep

				def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: List[int]    ,						UpperCamelCase: Optional[List[int]] = None    ,						UpperCamelCase: bool = False       ):






									"""simple docstring"""


									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: Dict    ,						UpperCamelCase: List[int]    ,						UpperCamelCase: Optional[List[int]] = None       ):






									"""simple docstring"""


									A__										=  [self.sep_token_id]
									A__										=  [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: Dict       ):






									"""simple docstring"""


									return len(self.fairseq_ids_to_tokens       )

				def 				UpperCamelCase	(     self: Dict       ):






									"""simple docstring"""


									A__										=  {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: Optional[Any]    ,						UpperCamelCase: str       ):






									"""simple docstring"""


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

				def 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: Any       ):






									"""simple docstring"""


									if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
									else:
														return self.unk_token_id

				def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: int       ):






									"""simple docstring"""


									return self.fairseq_ids_to_tokens[index]

				def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Tuple       ):






									"""simple docstring"""


									A__										=  """""".join(UpperCamelCase       ).replace(UpperCamelCase    ,						""" """       ).strip()
									return out_string





				def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[str] = None       ):






									"""simple docstring"""


									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"""]       )
									A__										=  os.path.join(
									    UpperCamelCase    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_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       )

									if os.path.abspath(self.monolingual_vocab_file       ) != os.path.abspath(
									    UpperCamelCase       ) and os.path.isfile(self.monolingual_vocab_file       ):
														copyfile(self.monolingual_vocab_file    ,						UpperCamelCase       )
									elif not os.path.isfile(self.monolingual_vocab_file       ):
														with open(UpperCamelCase    ,						"""w"""    ,						encoding="""utf-8"""       ) as fp:
																			for token in self.fairseq_tokens_to_ids:
																								if token not in self.all_special_tokens:
																													fp.write(f"""{str(UpperCamelCase       )} \n"""       )

									return out_vocab_file, out_monolingual_vocab_file









"""simple docstring"""



from collections import defaultdict





def  _snake_case     (    UpperCAmelCase_  :					int			):
     A__										=  1
     A__										=  True
     for v in tree[start]:
          if v not in visited:
               ret += dfs(UpperCAmelCase_			)
     if ret % 2 == 0:
          cuts.append(UpperCAmelCase_			)
     return ret





def  _snake_case     (    ):
     dfs(1			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_  ,SCREAMING_SNAKE_CASE_					:	Any							  =						1_0, 9
 SCREAMING_SNAKE_CASE_					:	Any							  =						defaultdict(list)
 SCREAMING_SNAKE_CASE_					:	dict[int, bool]							  =						{}
 SCREAMING_SNAKE_CASE_					:	list[int]							  =						[]
 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						0
 SCREAMING_SNAKE_CASE_					:	Any							  =						[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (1_0, 8)]
 for u, v in edges:
  tree[u].append(v)
  tree[v].append(u)
 even_tree()
 print(len(cuts) - 1)






