


'''simple docstring'''






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

import sentencepiece as spm

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


__UpperCAmelCase       							=				logging.get_logger(__name__)

__UpperCAmelCase       							=				{'''vocab_file''': '''sentencepiece.model'''}

__UpperCAmelCase       							=				{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__UpperCAmelCase       							=				{
    '''google/rembert''': 256,
}




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  str      		= VOCAB_FILES_NAMES
							lowercase__     :  Dict      		= PRETRAINED_VOCAB_FILES_MAP
							lowercase__     :  Optional[int]      		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=False   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_="[CLS]"   ,       lowerCamelCase_="[SEP]"   ,       lowerCamelCase_="[UNK]"   ,       lowerCamelCase_="[SEP]"   ,       lowerCamelCase_="[PAD]"   ,       lowerCamelCase_="[CLS]"   ,       lowerCamelCase_="[MASK]"   ,       **lowerCamelCase_   ,       )			->				Tuple:
														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_   ,       **lowerCamelCase_   ,       )

														lowerCAmelCase__			  =      do_lower_case
														lowerCAmelCase__			  =      remove_space
														lowerCAmelCase__			  =      keep_accents
														lowerCAmelCase__			  =      vocab_file

														lowerCAmelCase__			  =      spm.SentencePieceProcessor()
														self.sp_model.Load(lowerCamelCase_  )







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														return len(self.sp_model  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      {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  )			->				Dict:
														lowerCAmelCase__			  =      self.__dict__.copy()
														lowerCAmelCase__			  =      None
														return state







							def __setstate__(						self   ,       lowerCamelCase_  )			->				str:
														lowerCAmelCase__			  =      d
														lowerCAmelCase__			  =      spm.SentencePieceProcessor()
														self.sp_model.Load(self.vocab_file  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=False  )			->				int:
														lowerCAmelCase__			  =      self.sp_model.EncodeAsPieces(lowerCamelCase_  )
														return pieces







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Any:
														return self.sp_model.PieceToId(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Tuple:
														return self.sp_model.IdToPiece(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      self.sp_model.decode_pieces(lowerCamelCase_  )
														return out_string







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				List[int]:
														lowerCAmelCase__			  =      [self.sep_token_id]
														lowerCAmelCase__			  =      [self.cls_token_id]
														if token_ids_a is None:
																					return cls + token_ids_a + sep
														return cls + token_ids_a + sep + token_ids_a + sep







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = False  )			->				List[int]:

														if already_has_special_tokens:
																					if token_ids_a is not None:
																												raise ValueError(
																												    '''You should not supply a second sequence if the provided sequence of '''
																												    '''ids is already formatted with special tokens for the model.'''  )
																					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

														if token_ids_a is not None:
																					return [1] + ([0] * len(lowerCamelCase_  )) + [1] + ([0] * len(lowerCamelCase_  )) + [1]
														return [1] + ([0] * len(lowerCamelCase_  )) + [1]







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				List[int]:
														lowerCAmelCase__			  =      [self.sep_token_id]
														lowerCAmelCase__			  =      [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  ) * [0] + len(token_ids_a + sep  ) * [1]







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None  )			->				Tuple[str]:
														if not os.path.isdir(lowerCamelCase_  ):
																					logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_  )  )
																					return
														lowerCAmelCase__			  =      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_  ):
																					copyfile(self.vocab_file   ,       lowerCamelCase_  )

														return (out_vocab_file,)

"""simple docstring"""




from __future__ import annotations

import os
from collections.abc import Mapping

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







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         a__		=			vertices
         a__		=			{
             (min(__SCREAMING_SNAKE_CASE   ), max(__SCREAMING_SNAKE_CASE   )): weight for edge, weight in edges.items()
         }



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



         self.vertices.add(edge[0]   )
         self.vertices.add(edge[1]   )
         a__		=			weight




     def 			lowercase__  (							self   )		->     Graph:







         """simple docstring"""



         a__		=			Graph({min(self.vertices   )}     ,				{}   )
         a__		=			42
         a__		=			42
         a__		=			42
         a__		=			42

         while len(subgraph.vertices   ) < len(self.vertices   ):
             a__		=			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:
                         a__		=			edge
                         a__		=			weight

             subgraph.add_edge(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         return subgraph




def     __magic_name__		(					UpperCamelCase     :		str = "p107_network.txt"     )     ->						int:
    a__		=			os.path.abspath(os.path.dirname(UpperCamelCase     )     )
    a__		=			os.path.join(UpperCamelCase							,		UpperCamelCase     )
    a__		=			{}
    a__		=			42
    a__		=			42
    a__		=			42

    with open(UpperCamelCase     ) as f:
        a__		=			f.read().strip().split('\n'     )

    a__		=			[line.split(','     ) for line in data]

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

    a__		=			Graph(set(range(len(UpperCamelCase     )     )     )							,		UpperCamelCase     )

    a__		=			graph.prims_algorithm()

    a__		=			sum(graph.edges.values()     )
    a__		=			sum(subgraph.edges.values()     )

    return initial_total - optimal_total


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


