





import argparse
import copy


def 	UpperCamelCase__(       UpperCamelCase__			:       List[Any]  )->Optional[Any]:
       A__											=						{}

       with open(UpperCamelCase__  ) as f:
              for line in f:
                     if line.split()[0] not in dict_of_neighbours:
                            A__											=						[]
                            _list.append([line.split()[1], line.split()[2]]  )
                            A__											=						_list
                     else:
                            dict_of_neighbours[line.split()[0]].append(
                                [line.split()[1], line.split()[2]]  )
                     if line.split()[1] not in dict_of_neighbours:
                            A__											=						[]
                            _list.append([line.split()[0], line.split()[2]]  )
                            A__											=						_list
                     else:
                            dict_of_neighbours[line.split()[1]].append(
                                [line.split()[0], line.split()[2]]  )

       return dict_of_neighbours


def 	UpperCamelCase__(       UpperCamelCase__			:       int ,     UpperCamelCase__			:       List[str]  )->Dict:

       with open(UpperCamelCase__  ) as f:
              A__											=						f.read(1  )
       A__											=						start_node

       A__											=						[]

       A__											=						start_node

       A__											=						0
       while visiting not in first_solution:
              A__											=						1_00_00
              for k in dict_of_neighbours[visiting]:
                     if int(k[1]  ) < int(UpperCamelCase__  ) and k[0] not in first_solution:
                            A__											=						k[1]
                            A__											=						k[0]

              first_solution.append(UpperCamelCase__  )
              A__											=						distance_of_first_solution + int(UpperCamelCase__  )
              A__											=						best_node

       first_solution.append(UpperCamelCase__  )

       A__											=						0
       for k in dict_of_neighbours[first_solution[-2]]:
              if k[0] == start_node:
                     break
              position += 1

       A__											=						(
           distance_of_first_solution
           + int(dict_of_neighbours[first_solution[-2]][position][1]  )
           - 1_00_00
       )
       return first_solution, distance_of_first_solution


def 	UpperCamelCase__(       UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       Union[str, Any]  )->Union[str, Any]:
       A__											=						[]

       for n in solution[1:-1]:
              A__											=						solution.index(UpperCamelCase__  )
              for kn in solution[1:-1]:
                     A__											=						solution.index(UpperCamelCase__  )
                     if n == kn:
                            continue

                     A__											=						copy.deepcopy(UpperCamelCase__  )
                     A__											=						kn
                     A__											=						n

                     A__											=						0

                     for k in _tmp[:-1]:
                            A__											=						_tmp[_tmp.index(UpperCamelCase__  ) + 1]
                            for i in dict_of_neighbours[k]:
                                   if i[0] == next_node:
                                          A__											=						distance + int(i[1]  )
                     _tmp.append(UpperCamelCase__  )

                     if _tmp not in neighborhood_of_solution:
                            neighborhood_of_solution.append(_tmp  )

       A__											=						len(neighborhood_of_solution[0]  ) - 1

       neighborhood_of_solution.sort(key=lambda UpperCamelCase__  : x[index_of_last_item_in_the_list]  )
       return neighborhood_of_solution


def 	UpperCamelCase__(       UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       List[str] ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       List[str] ,     UpperCamelCase__			:       int  )->Optional[int]:
       A__											=						1
       A__											=						first_solution
       A__											=						[]
       A__											=						distance_of_first_solution
       A__											=						solution

       while count <= iters:
              A__											=						find_neighborhood(UpperCamelCase__ ,     UpperCamelCase__  )
              A__											=						0
              A__											=						neighborhood[index_of_best_solution]
              A__											=						len(UpperCamelCase__  ) - 1

              A__											=						False
              while not found:
                     A__											=						0
                     while i < len(UpperCamelCase__  ):
                            if best_solution[i] != solution[i]:
                                   A__											=						best_solution[i]
                                   A__											=						solution[i]
                                   break
                            A__											=						i + 1

                     if [first_exchange_node, second_exchange_node] not in tabu_list and [
                         second_exchange_node,
                         first_exchange_node,
                     ] not in tabu_list:
                            tabu_list.append([first_exchange_node, second_exchange_node]  )
                            A__											=						True
                            A__											=						best_solution[:-1]
                            A__											=						neighborhood[index_of_best_solution][best_cost_index]
                            if cost < best_cost:
                                   A__											=						cost
                                   A__											=						solution
                     else:
                            A__											=						index_of_best_solution + 1
                            A__											=						neighborhood[index_of_best_solution]

              if len(UpperCamelCase__  ) >= size:
                     tabu_list.pop(0  )

              A__											=						count + 1

       return best_solution_ever, best_cost


def 	UpperCamelCase__(       UpperCamelCase__			:       Union[str, Any]=None  )->Optional[Any]:
       A__											=						generate_neighbours(args.File  )

       A__					,	A__											=						generate_first_solution(
           args.File ,     UpperCamelCase__  )

       A__					,	A__											=						tabu_search(
           UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     args.Iterations ,     args.Size ,     )

       print(f"Best solution: {best_sol}, with total distance: {best_cost}."  )


if __name__ == "__main__":
       a__:      List[str]									=		argparse.ArgumentParser(description='Tabu Search')
       parser.add_argument(
           '-f',
           '--File',
           type=str,
           help='Path to the file containing the data',
           required=True,
       )
       parser.add_argument(
           '-i',
           '--Iterations',
           type=int,
           help='How many iterations the algorithm should perform',
           required=True,
       )
       parser.add_argument(
           '-s', '--Size', type=int, help='Size of the tabu list', required=True
       )

       # Pass the arguments to main method
       main(parser.parse_args())












import os
import re
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__:      Union[str, Any]									=		logging.get_logger(__name__)

a__:      Union[str, Any]									=		{'vocab_file': 'spiece.model'}

a__:      Tuple									=		{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

a__:      Any									=		{
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}




class     SCREAMING_SNAKE_CASE__ (						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''']
 __SCREAMING_SNAKE_CASE							       =	[]



 def __init__(							self,__lowerCamelCase,__lowerCamelCase="<unk>",__lowerCamelCase="<s>",__lowerCamelCase="</s>",__lowerCamelCase="<pad>",__lowerCamelCase="[SEP]",__lowerCamelCase="[MASK]",__lowerCamelCase="[CLS]",__lowerCamelCase = None,**__lowerCamelCase,):
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else bos_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else eos_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else unk_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else pad_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else cls_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else mask_token

        A__											=						{} if sp_model_kwargs is None else sp_model_kwargs

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

        A__											=						vocab_file

        A__											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(__lowerCamelCase )



 @property
 def 				UpperCamelCase			(							self ):
        return self.sp_model.get_piece_size()



 def 				UpperCamelCase			(							self ):
        A__											=						{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 ):
        A__											=						self.__dict__.copy()
        A__											=						None
        return state



 def __setstate__(							self,__lowerCamelCase ):
        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 				UpperCamelCase			(							self,__lowerCamelCase ):
        return self.sp_model.encode(__lowerCamelCase,out_type=__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        return self.sp_model.piece_to_id(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						self.sp_model.IdToPiece(__lowerCamelCase )
        return token



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						[]
        A__											=						''''''
        A__											=						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(__lowerCamelCase ) + token
                      A__											=						True
                      A__											=						[]
               else:
                      current_sub_tokens.append(__lowerCamelCase )
                      A__											=						False
        out_string += self.sp_model.decode(__lowerCamelCase )
        return out_string.strip()



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = False,__lowerCamelCase = None,__lowerCamelCase = True,**__lowerCamelCase,):
        A__											=						kwargs.pop('''use_source_tokenizer''',__lowerCamelCase )

        A__											=						self.convert_ids_to_tokens(__lowerCamelCase,skip_special_tokens=__lowerCamelCase )

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        A__											=						[]
        A__											=						[]
        for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                      continue
               if token in self.added_tokens_encoder:
                      if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase ) )
                             A__											=						[]
                      sub_texts.append(__lowerCamelCase )
               else:
                      current_sub_text.append(__lowerCamelCase )
        if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase ) )

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
               A__											=						re.sub(r''' (\[(MASK|SEP)\])''',r'''\1''',''' '''.join(__lowerCamelCase ) )
        else:
               A__											=						''''''.join(__lowerCamelCase )

        A__											=						(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
               A__											=						self.clean_up_tokenization(__lowerCamelCase )
               return clean_text
        else:
               return text



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        if not os.path.isdir(__lowerCamelCase ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
               return
        A__											=						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 ) and os.path.isfile(self.vocab_file ):
               copyfile(self.vocab_file,__lowerCamelCase )
        elif not os.path.isfile(self.vocab_file ):
               with open(__lowerCamelCase,'''wb''' ) as fi:
                      A__											=						self.sp_model.serialized_model_proto()
                      fi.write(__lowerCamelCase )

        return (out_vocab_file,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        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 + token_ids_a + sep



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = False ):
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__lowerCamelCase,token_ids_a=__lowerCamelCase,already_has_special_tokens=__lowerCamelCase )

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




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        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 ) * [0] + len(token_ids_a + sep ) * [1]





