





import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
       from ...tokenization_utils_base import TextInput
from ...utils import logging


snake_case__					:							List[str]													=     logging.get_logger(__name__)

snake_case__					:							List[str]													=     {'vocab_file': 'spiece.model'}

snake_case__					:							Tuple													=     {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
snake_case__					:							Optional[int]													=     {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

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





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = VOCAB_FILES_NAMES
   lowerCAmelCase__        = PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase__        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase__        = ["""input_ids""", """attention_mask"""]




   def __init__(self	:str    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Optional[int]="</s>"    ,   _UpperCamelCase	:int="<unk>"    ,   _UpperCamelCase	:Union[str, Any]="<pad>"    ,   _UpperCamelCase	:Any=100    ,   _UpperCamelCase	:Optional[Any]=None    ,   _UpperCamelCase	:Optional[Dict[str, Any]] = None    ,   _UpperCamelCase	:Optional[int]=True    ,   **_UpperCamelCase	:Tuple    ,   )->					None:
         # Add extra_ids to the special token list
         if extra_ids > 0 and additional_special_tokens is None:
               __A          =				[f"""<extra_id_{i}>""" for i in range(_UpperCamelCase )]
         elif extra_ids > 0 and additional_special_tokens is not None:
               # Check that we have the right number of extra_id special tokens
               __A          =				len(set(filter(lambda _UpperCamelCase : bool('''extra_id''' in str(_UpperCamelCase ) )    ,   _UpperCamelCase ) ) )
               if extra_tokens != extra_ids:
                     raise ValueError(
                         f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                         ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                         ''' tokens''' )
         if legacy:
               logger.warning_once(
                   f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
                   ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )

         __A          =				legacy
         __A          =				{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=_UpperCamelCase    ,   unk_token=_UpperCamelCase    ,   pad_token=_UpperCamelCase    ,   extra_ids=_UpperCamelCase    ,   additional_special_tokens=_UpperCamelCase    ,   sp_model_kwargs=self.sp_model_kwargs    ,   legacy=_UpperCamelCase    ,   **_UpperCamelCase    ,   )

         __A          =				vocab_file
         __A          =				extra_ids

         __A          =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(_UpperCamelCase )




   @staticmethod
   def _lowerCAmelCase      (_UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:str )->					Tuple:
         if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
               __A          =				TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
               if init_max_model_length is not None and init_max_model_length != max_model_length:
                     return init_max_model_length
               elif init_max_model_length is None:
                     warnings.warn(
                         '''This tokenizer was incorrectly instantiated with a model max length of'''
                         f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                         ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                         ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                         f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                         f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                         f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                         ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                         ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''    ,   _UpperCamelCase    ,   )

         return max_model_length




   @property
   def _lowerCAmelCase      (self	:List[str] )->					Dict:
         return self.sp_model.get_piece_size() + self._extra_ids




   def _lowerCAmelCase      (self	:Optional[int] )->					int:
         __A          =				{self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:List[int]    ,   _UpperCamelCase	:Optional[List[int]] = None    ,   _UpperCamelCase	:bool = False )->					List[int]:
         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_UpperCamelCase    ,   token_ids_a=_UpperCamelCase    ,   already_has_special_tokens=_UpperCamelCase )

         # normal case: some special tokens
         if token_ids_a is None:
               return ([0] * len(_UpperCamelCase )) + [1]
         return ([0] * len(_UpperCamelCase )) + [1] + ([0] * len(_UpperCamelCase )) + [1]




   def _lowerCAmelCase      (self	:int )->					List[str]:
         return list(
             set(filter(lambda _UpperCamelCase : bool(re.search(R'''<extra_id_\d+>'''    ,   _UpperCamelCase ) ) is not None    ,   self.additional_special_tokens ) ) )




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Any:
         return [self._convert_token_to_id(_UpperCamelCase ) for token in self.get_sentinel_tokens()]




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:List[int] )->					List[int]:
         if len(_UpperCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
               warnings.warn(
                   f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                   ''' eos tokens being added.''' )
               return token_ids
         else:
               return token_ids + [self.eos_token_id]




   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:List[int]    ,   _UpperCamelCase	:Optional[List[int]] = None )->					List[int]:
         __A          =				[self.eos_token_id]

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




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:List[int]    ,   _UpperCamelCase	:Optional[List[int]] = None )->					List[int]:
         __A          =				self._add_eos_if_not_present(_UpperCamelCase )
         if token_ids_a is None:
               return token_ids_a
         else:
               __A          =				self._add_eos_if_not_present(_UpperCamelCase )
               return token_ids_a + token_ids_a




   def __getstate__(self	:Union[str, Any] )->					List[str]:
         __A          =				self.__dict__.copy()
         __A          =				None
         return state




   def __setstate__(self	:str    ,   _UpperCamelCase	:Optional[int] )->					int:
         __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 _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:"TextInput"    ,   **_UpperCamelCase	:List[str] )->					List[str]:
         # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
         # the beginning of the text
         if not self.legacy:
               __A          =				SPIECE_UNDERLINE + text.replace(_UpperCamelCase    ,   ''' ''' )
         return super().tokenize(_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:Any    ,   **_UpperCamelCase	:Optional[Any] )->					Any:
         if not self.legacy:
               __A          =				text.startswith(_UpperCamelCase )
               if is_first:
                     __A          =				text[1:]

         __A          =				self.sp_model.encode(_UpperCamelCase    ,   out_type=_UpperCamelCase )

         if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_UpperCamelCase ):
               __A          =				([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
         return tokens




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:str )->					List[Any]:
         if token.startswith('''<extra_id_''' ):
               __A          =				re.match(R'''<extra_id_(\d+)>'''    ,   _UpperCamelCase )
               __A          =				int(match.group(1 ) )
               return self.vocab_size - num - 1
         return self.sp_model.piece_to_id(_UpperCamelCase )




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Union[str, Any] )->					Dict:
         if index < self.sp_model.get_piece_size():
               __A          =				self.sp_model.IdToPiece(_UpperCamelCase )
         else:
               __A          =				f"""<extra_id_{self.vocab_size - 1 - index}>"""
         return token




   def _lowerCAmelCase      (self	:Union[str, Any]    ,   _UpperCamelCase	:List[str] )->					Dict:
         __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(_UpperCamelCase ) + token
                     __A          =				True
                     __A          =				[]
               else:
                     current_sub_tokens.append(_UpperCamelCase )
                     __A          =				False
         out_string += self.sp_model.decode(_UpperCamelCase )
         return out_string.strip()






   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Optional[str] = None )->					Tuple[str]:
         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'''] )

         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 )

         return (out_vocab_file,)












import argparse
import copy



def _a				(	lowerCamelCase:   List[Any] )	->						List[str]:







      '''simple docstring'''






      __A          =				{}

      with open(lowerCamelCase ) 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 _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   Optional[Any] )	->						Dict:







      '''simple docstring'''






      with open(lowerCamelCase ) 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(lowerCamelCase ) and k[0] not in first_solution:
                        __A          =				k[1]
                        __A          =				k[0]

            first_solution.append(lowerCamelCase )
            __A          =				distance_of_first_solution + int(lowerCamelCase )
            __A          =				best_node

      first_solution.append(lowerCamelCase )

      __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 _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Any )	->						Any:







      '''simple docstring'''






      __A          =				[]

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

                  __A          =				copy.deepcopy(lowerCamelCase )
                  __A          =				kn
                  __A          =				n

                  __A          =				0

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

                  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 lowerCamelCase : x[index_of_last_item_in_the_list] )
      return neighborhood_of_solution



def _a				(	lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Dict					,				lowerCamelCase:   Any					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Union[str, Any] )	->						Any:







      '''simple docstring'''






      __A          =				1
      __A          =				first_solution
      __A          =				[]
      __A          =				distance_of_first_solution
      __A          =				solution

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

            __A          =				False
            while not found:
                  __A          =				0
                  while i < len(lowerCamelCase ):
                        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(lowerCamelCase ) >= size:
                  tabu_list.pop(0 )

            __A          =				count + 1

      return best_solution_ever, best_cost



def _a				(	lowerCamelCase:   List[str]=None )	->						str:







      '''simple docstring'''






      __A          =				generate_neighbours(args.File )

      __A   ,						__A          =				generate_first_solution(
          args.File					,				lowerCamelCase )

      __A   ,						__A          =				tabu_search(
          lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				args.Iterations					,				args.Size					,				)

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


if __name__ == "__main__":
       snake_case__					:							Tuple													=     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())





