




'''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


a  :				Optional[Any]									=  logging.get_logger(__name__)

a  :				Union[str, Any]									=  '▁'

a  :				int									=  {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

a  :				str									=  {
    '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',
    },
}

a  :				Optional[int]									=  {'vinai/bartpho-syllable': 1024}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		VOCAB_FILES_NAMES
				snake_case_      =		PRETRAINED_VOCAB_FILES_MAP
				snake_case_      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_      =		["input_ids", "attention_mask"]


				def __init__(		self    :					Optional[int]       ,				lowercase_    :					int       ,				lowercase_    :					Tuple       ,				lowercase_    :					Optional[int]="<s>"       ,				lowercase_    :					Dict="</s>"       ,				lowercase_    :					str="</s>"       ,				lowercase_    :					Union[str, Any]="<s>"       ,				lowercase_    :					Optional[int]="<unk>"       ,				lowercase_    :					Optional[Any]="<pad>"       ,				lowercase_    :					Optional[Any]="<mask>"       ,				lowercase_    :					Optional[Dict[str, Any]] = None       ,				**lowercase_    :					List[Any]       ,				):
									# Mask token behave like a normal word, i.e. include the space before it
									snake_case_									=				AddedToken(lowercase_       ,				lstrip=lowercase_       ,				rstrip=lowercase_					) if isinstance(lowercase_       ,				lowercase_					) else mask_token

									snake_case_									=				{} if sp_model_kwargs is None else sp_model_kwargs

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

									snake_case_									=				vocab_file
									snake_case_									=				monolingual_vocab_file
									snake_case_									=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(str(lowercase_					)					)

									# Load the reduced vocab

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

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


				def __getstate__(		self    :					Tuple					):
									snake_case_									=				self.__dict__.copy()
									snake_case_									=				None
									snake_case_									=				self.sp_model.serialized_model_proto()
									return state


				def __setstate__(		self    :					Optional[Any]       ,				lowercase_    :					Optional[Any]					):
									snake_case_									=				d

									# for backward compatibility
									if not hasattr(self       ,				'''sp_model_kwargs'''					):
														snake_case_									=				{}

									snake_case_									=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)


				def   A_ (		self    :					Tuple       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):

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


				def   A_ (		self    :					List[str]       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None       ,				lowercase_    :					bool = False					):

									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=lowercase_       ,				token_ids_a=lowercase_       ,				already_has_special_tokens=lowercase_					)

									if token_ids_a is None:
														return [1] + ([0] * len(lowercase_					)) + [1]
									return [1] + ([0] * len(lowercase_					)) + [1, 1] + ([0] * len(lowercase_					)) + [1]


				def   A_ (		self    :					str       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				[self.sep_token_id]
									snake_case_									=				[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   A_ (		self    :					Any					):
									return len(self.fairseq_ids_to_tokens					)


				def   A_ (		self    :					Dict					):
									snake_case_									=				{self.convert_ids_to_tokens(lowercase_					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab


				def   A_ (		self    :					Dict       ,				lowercase_    :					str					):
									return self.sp_model.encode(lowercase_       ,				out_type=lowercase_					)


				def   A_ (		self    :					str       ,				lowercase_    :					List[Any]					):
									if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
									else:
														return self.unk_token_id


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					int					):
									return self.fairseq_ids_to_tokens[index]


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					Optional[Any]					):
									snake_case_									=				''''''.join(lowercase_					).replace(lowercase_       ,				''' '''					).strip()
									return out_string


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					str       ,				lowercase_    :					Optional[str] = None					):
									if not os.path.isdir(lowercase_					):
														logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
														return
									snake_case_									=				os.path.join(
									    lowercase_       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
									snake_case_									=				os.path.join(
									    lowercase_       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']       ,				)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase_					) and os.path.isfile(self.vocab_file					):
														copyfile(self.vocab_file       ,				lowercase_					)
									elif not os.path.isfile(self.vocab_file					):
														with open(lowercase_       ,				'''wb'''					) as fi:
																			snake_case_									=				self.sp_model.serialized_model_proto()
																			fi.write(lowercase_					)

									if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
									    lowercase_					) and os.path.isfile(self.monolingual_vocab_file					):
														copyfile(self.monolingual_vocab_file       ,				lowercase_					)
									elif not os.path.isfile(self.monolingual_vocab_file					):
														with open(lowercase_       ,				'''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(lowercase_					)} \n"					)

									return out_vocab_file, out_monolingual_vocab_file







'''simple docstring'''

from collections import defaultdict


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				1
					snake_case_									=				True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(__UpperCAmelCase	)
					if ret % 2 == 0:
										cuts.append(__UpperCAmelCase	)
					return ret


def 		__magic_name__    (	)		->					Union[str, Any]:

					'''simple docstring'''

					dfs(1	)


if __name__ == "__main__":
							a		,a  :				Dict									=  10, 9
							a  :				Dict									=  defaultdict(list)
							a  :				dict[int, bool]									=  {}
							a  :				list[int]									=  []
							a  :				Tuple									=  0
							a  :				str									=  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)

