



'''simple docstring'''





import argparse
import copy




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  {}

     with open(__A) 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 lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     with open(__A) as f:
          _a      =  f.read(1)
     _a      =  start_node

     _a      =  []

     _a      =  start_node

     _a      =  0
     while visiting not in first_solution:
          _a      =  10_000
          for k in dict_of_neighbours[visiting]:
               if int(k[1]) < int(__A) and k[0] not in first_solution:
                    _a      =  k[1]
                    _a      =  k[0]

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

     first_solution.append(__A)

     _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])
         - 10_000
     )
     return first_solution, distance_of_first_solution




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  []

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

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

               _a      =  0

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

               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 __A: x[index_of_last_item_in_the_list])
     return neighborhood_of_solution




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  1
     _a      =  first_solution
     _a      =  []
     _a      =  distance_of_first_solution
     _a      =  solution

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

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

          _a      =  count + 1

     return best_solution_ever, best_cost




def lowerCAmelCase  (__A=None):



     """simple docstring"""



     _a      =  generate_neighbours(args.File)

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

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

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


if __name__ == "__main__":
  lowercase_		       =  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())








'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase_		       =  {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_		       =  {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       List[Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Any          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       str          =       ['input_ids', 'attention_mask']
 __lowerCamelCase    :       Union[str, Any]          =       RobertaTokenizer



 def __init__(self ,   A=None ,   A=None ,   A=None ,   A="replace" ,   A="<s>" ,   A="</s>" ,   A="</s>" ,   A="<s>" ,   A="<unk>" ,   A="<pad>" ,   A="<mask>" ,   A=False ,   A=True ,   **A ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(
          A ,   A ,   tokenizer_file=A ,   errors=A ,   bos_token=A ,   eos_token=A ,   sep_token=A ,   cls_token=A ,   unk_token=A ,   pad_token=A ,   mask_token=A ,   add_prefix_space=A ,   trim_offsets=A ,   **A ,   )

      _a      =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
      if pre_tok_state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
           _a      =  getattr(A ,   pre_tok_state.pop('''type'''       )       )
           _a      =  add_prefix_space
           _a      =  pre_tok_class(**A       )

      _a      =  add_prefix_space

      _a      =  '''post_processor'''
      _a      =  getattr(self.backend_tokenizer ,   A ,   A       )
      if tokenizer_component_instance:
           _a      =  json.loads(tokenizer_component_instance.__getstate__()       )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
                _a      =  tuple(state['''sep''']       )
           if "cls" in state:
                _a      =  tuple(state['''cls''']       )

           _a      =  False

           if state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
                _a      =  add_prefix_space
                _a      =  True

           if state.get('''trim_offsets''' ,   A       ) != trim_offsets:
                _a      =  trim_offsets
                _a      =  True

           if changes_to_apply:
                _a      =  getattr(A ,   state.pop('''type'''       )       )
                _a      =  component_class(**A       )
                setattr(self.backend_tokenizer ,   A ,   A       )



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      if self._mask_token is None:
           if self.verbose:
                logger.error('''Using mask_token, but it is not set yet.'''       )
           return None
      return str(self._mask_token       )



 @mask_token.setter
 def 					a__  (self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  AddedToken(A ,   lstrip=A ,   rstrip=A       ) if isinstance(A ,   A       ) else value
      _a      =  value



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  kwargs.get('''is_split_into_words''' ,   A       )
      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*A ,   **A       )



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  kwargs.get('''is_split_into_words''' ,   A       )

      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*A ,   **A       )



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      _a      =  self._tokenizer.model.save(A ,   name=A       )
      return tuple(A       )



 def 					a__  (self ,   A ,   A=None       )    ->     List[str]:


      """simple docstring"""



      _a      =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
           return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



 def 					a__  (self ,   A ,   A = None       )    ->     List[int]:


      """simple docstring"""



      _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 + sep + token_ids_a + sep       ) * [0]



