





'''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   : Any             =			logging.get_logger(__name__)

A   : int             =			{'vocab_file': 'sentencepiece.bpe.model'}

A   : Optional[Any]             =			{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

A   : Any             =			{
    'camembert-base': 512,
}

A   : List[str]             =			'▁'







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE							   =    PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE							   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE							   =    ["input_ids", "attention_mask"]


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





											'''simple docstring'''





											_snake_case:   Tuple  					=		AddedToken(__snake_case       ,     lstrip=__snake_case       ,     rstrip=__snake_case       ) if isinstance(__snake_case       ,     __snake_case       ) else mask_token

											_snake_case:   Dict  					=		{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    bos_token=__snake_case       ,     eos_token=__snake_case       ,     unk_token=__snake_case       ,     sep_token=__snake_case       ,     cls_token=__snake_case       ,     pad_token=__snake_case       ,     mask_token=__snake_case       ,     additional_special_tokens=__snake_case       ,     sp_model_kwargs=self.sp_model_kwargs       ,     **__snake_case       ,     )
											_snake_case:   List[str]  					=		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(str(__snake_case       )       )
											_snake_case:   Optional[Any]  					=		vocab_file
											# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
											# sentencepiece vocabulary (this is the case for <s> and </s>
											_snake_case:   str  					=		{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
											_snake_case:   Tuple  					=		len(self.fairseq_tokens_to_ids       )
											_snake_case:   Dict  					=		len(self.sp_model       ) + len(self.fairseq_tokens_to_ids       )
											_snake_case:   List[str]  					=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  List[int]       ,     __snake_case	:  Optional[List[int]] = None       ):





											'''simple docstring'''





											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_snake_case:   List[Any]  					=		[self.cls_token_id]
											_snake_case:   str  					=		[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep


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





											'''simple docstring'''





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

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ,     __snake_case	:  List[int]       ,     __snake_case	:  Optional[List[int]] = None       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		[self.sep_token_id]
											_snake_case:   str  					=		[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    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											return len(self.fairseq_tokens_to_ids       ) + len(self.sp_model       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		{self.convert_ids_to_tokens(__snake_case       ): i for i in range(self.vocab_size       )}
											vocab.update(self.added_tokens_encoder       )
											return vocab


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  str       ):





											'''simple docstring'''





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


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  int       ):





											'''simple docstring'''





											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											elif self.sp_model.PieceToId(__snake_case       ) == 0:
																	# Convert sentence piece unk token to fairseq unk token index
																	return self.unk_token_id
											return self.fairseq_offset + self.sp_model.PieceToId(__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  List[Any]       ):





											'''simple docstring'''





											if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   str  					=		[]
											_snake_case:   Dict  					=		''
											_snake_case:   Any  					=		False
											for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																							if not prev_is_special:
																													out_string += " "
																							out_string += self.sp_model.decode(__snake_case       ) + token
																							_snake_case:   Tuple  					=		True
																							_snake_case:   Dict  					=		[]
																	else:
																							current_sub_tokens.append(__snake_case       )
																							_snake_case:   Tuple  					=		False
											out_string += self.sp_model.decode(__snake_case       )
											return out_string.strip()


					def __getstate__(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   int  					=		self.__dict__.copy()
											_snake_case:   List[Any]  					=		None
											return state


					def __setstate__(					self	:  List[str]       ,     __snake_case	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		d

											# for backward compatibility
											if not hasattr(self       ,     'sp_model_kwargs'       ):
																	_snake_case:   Optional[Any]  					=		{}

											_snake_case:   Any  					=		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(self.vocab_file       )




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





											'''simple docstring'''





											if not os.path.isdir(__snake_case       ):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
																	return
											_snake_case:   int  					=		os.path.join(
											    __snake_case       ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

											return (out_vocab_file,)












'''simple docstring'''






from __future__ import annotations

import os
from collections.abc import Mapping

A   : Optional[Any]             =			tuple[int, int]







class 			lowerCamelCase					:


					def __init__(					self	:  Tuple       ,     __snake_case	:  set[int]       ,     __snake_case	:  Mapping[EdgeT, int]       ):





											'''simple docstring'''





											_snake_case:   set[int]  					=		vertices
											_snake_case:   dict[EdgeT, int]  					=		{
											    (min(__snake_case       ), max(__snake_case       )): weight for edge, weight in edges.items()
											}


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  EdgeT       ,     __snake_case	:  int       ):





											'''simple docstring'''





											self.vertices.add(edge[0]       )
											self.vertices.add(edge[1]       )
											_snake_case:   Dict  					=		weight




					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Graph  					=		Graph({min(self.vertices       )}       ,     {}       )
											_snake_case:   EdgeT
											_snake_case:   int
											_snake_case:   EdgeT
											_snake_case:   int

											while len(subgraph.vertices       ) < len(self.vertices       ):
																	_snake_case:   List[str]  					=		max(self.edges.values()       ) + 1
																	for edge, weight in self.edges.items():
																							if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																													if weight < min_weight:
																																			_snake_case:   Optional[Any]  					=		edge
																																			_snake_case:   Optional[int]  					=		weight

																	subgraph.add_edge(__snake_case       ,     __snake_case       )

											return subgraph





def       lowercase_      (		lowercase__ = "p107_network.txt"						)   ->int:
						_snake_case:   str  					=		os.path.abspath(os.path.dirname(lowercase__						)						)
						_snake_case:   str  					=		os.path.join(lowercase__				,  lowercase__						)
						_snake_case:   dict[EdgeT, int]  					=		{}
						_snake_case:   list[str]
						_snake_case:   int
						_snake_case:   int

						with open(lowercase__						) as f:
												_snake_case:   Tuple  					=		f.read().strip().split('\n'						)

						_snake_case:   Tuple  					=		[line.split(','						) for line in data]

						for edgea in range(1				,  len(lowercase__						)						):
												for edgea in range(lowercase__						):
																		if adjaceny_matrix[edgea][edgea] != "-":
																								_snake_case:   int  					=		int(adjaceny_matrix[edgea][edgea]						)

						_snake_case:   Graph  					=		Graph(set(range(len(lowercase__						)						)						)				,  lowercase__						)

						_snake_case:   Graph  					=		graph.prims_algorithm()

						_snake_case:   int  					=		sum(graph.edges.values()						)
						_snake_case:   int  					=		sum(subgraph.edges.values()						)

						return initial_total - optimal_total


if __name__ == "__main__":
		print(F'{solution() = }')





