
'''simple docstring'''



import argparse
import copy
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				{}

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

   return dict_of_neighbours
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""

   with open(SCREAMING_SNAKE_CASE_						) as f:
      _SCREAMING_SNAKE_CASE      =				f.read(1						)
   _SCREAMING_SNAKE_CASE      =				start_node

   _SCREAMING_SNAKE_CASE      =				[]

   _SCREAMING_SNAKE_CASE      =				start_node

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

      first_solution.append(SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE      =				distance_of_first_solution + int(SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE      =				best_node

   first_solution.append(SCREAMING_SNAKE_CASE_						)

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

   _SCREAMING_SNAKE_CASE      =				(
       distance_of_first_solution
       + int(dict_of_neighbours[first_solution[-2]][position][1]						)
       - 1_00_00
   )
   return first_solution, distance_of_first_solution
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[]

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

         _SCREAMING_SNAKE_CASE      =				copy.deepcopy(SCREAMING_SNAKE_CASE_						)
         _SCREAMING_SNAKE_CASE      =				kn
         _SCREAMING_SNAKE_CASE      =				n

         _SCREAMING_SNAKE_CASE      =				0

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

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

   _SCREAMING_SNAKE_CASE      =				len(neighborhood_of_solution[0]						) - 1

   neighborhood_of_solution.sort(key=lambda SCREAMING_SNAKE_CASE_						: x[index_of_last_item_in_the_list]						)
   return neighborhood_of_solution
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				1
   _SCREAMING_SNAKE_CASE      =				first_solution
   _SCREAMING_SNAKE_CASE      =				[]
   _SCREAMING_SNAKE_CASE      =				distance_of_first_solution
   _SCREAMING_SNAKE_CASE      =				solution

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

      _SCREAMING_SNAKE_CASE      =				False
      while not found:
         _SCREAMING_SNAKE_CASE      =				0
         while i < len(SCREAMING_SNAKE_CASE_						):
            if best_solution[i] != solution[i]:
               _SCREAMING_SNAKE_CASE      =				best_solution[i]
               _SCREAMING_SNAKE_CASE      =				solution[i]
               break
            _SCREAMING_SNAKE_CASE      =				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]						)
            _SCREAMING_SNAKE_CASE      =				True
            _SCREAMING_SNAKE_CASE      =				best_solution[:-1]
            _SCREAMING_SNAKE_CASE      =				neighborhood[index_of_best_solution][best_cost_index]
            if cost < best_cost:
               _SCREAMING_SNAKE_CASE      =				cost
               _SCREAMING_SNAKE_CASE      =				solution
         else:
            _SCREAMING_SNAKE_CASE      =				index_of_best_solution + 1
            _SCREAMING_SNAKE_CASE      =				neighborhood[index_of_best_solution]

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

      _SCREAMING_SNAKE_CASE      =				count + 1

   return best_solution_ever, best_cost
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_=None						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				generate_neighbours(args.File						)

   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				generate_first_solution(
       args.File    ,      SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				tabu_search(
       SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      args.Iterations    ,      args.Size    ,      )

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


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








'''simple docstring'''



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

import sentencepiece as spm

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


UpperCamelCase__      :		List[Any]       							=  logging.get_logger(__name__)


UpperCamelCase__      :		Any       							=  "▁"

UpperCamelCase__      :		Any       							=  {"vocab_file": "spiece.model"}

UpperCamelCase__      :		int       							=  {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

UpperCamelCase__      :		Optional[int]       							=  {
    "google/reformer-crime-and-punishment": 524_288,
}

class    _a				(_lowerCamelCase):





       """simple docstring"""




       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       ,							A__       ,							A__="</s>"       ,							A__="<unk>"       ,							A__=[]       ,							A__ = None       ,							**A__       ,							)    ->			None:
          _SCREAMING_SNAKE_CASE      =				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=A__       ,							unk_token=A__       ,							additional_special_tokens=A__       ,							sp_model_kwargs=self.sp_model_kwargs       ,							**A__       ,							)

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






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






       def        UpperCamelCase				(							self      )    ->			Dict[str, int]:
          _SCREAMING_SNAKE_CASE      =				{self.convert_ids_to_tokens(A__      ): i for i in range(self.vocab_size      )}
          vocab.update(self.added_tokens_encoder      )
          return vocab






       def __getstate__(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				self.__dict__.copy()
          _SCREAMING_SNAKE_CASE      =				None
          return state






       def __setstate__(							self       ,							A__      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				d

          # for backward compatibility
          if not hasattr(self       ,							"""sp_model_kwargs"""      ):
             _SCREAMING_SNAKE_CASE      =				{}

          _SCREAMING_SNAKE_CASE      =				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
          self.sp_model.Load(self.vocab_file      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			List[str]:
          return self.sp_model.encode(A__       ,							out_type=A__      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			Union[str, Any]:
          return self.sp_model.piece_to_id(A__      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			List[Any]:
          if index < self.sp_model.get_piece_size():
             _SCREAMING_SNAKE_CASE      =				self.sp_model.IdToPiece(A__      )
          return token






       def        UpperCamelCase				(							self       ,							A__      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				[]
          _SCREAMING_SNAKE_CASE      =				""""""
          for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                out_string += self.sp_model.decode(A__      ) + token
                _SCREAMING_SNAKE_CASE      =				[]
             else:
                current_sub_tokens.append(A__      )
          out_string += self.sp_model.decode(A__      )
          return out_string.strip()






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = None      )    ->			Tuple[str]:
          if not os.path.isdir(A__      ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
             return
          _SCREAMING_SNAKE_CASE      =				os.path.join(
              A__       ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

          if os.path.abspath(self.vocab_file      ) != os.path.abspath(A__      ) and os.path.isfile(self.vocab_file      ):
             copyfile(self.vocab_file       ,							A__      )
          elif not os.path.isfile(self.vocab_file      ):
             with open(A__       ,							"""wb"""      ) as fi:
                _SCREAMING_SNAKE_CASE      =				self.sp_model.serialized_model_proto()
                fi.write(A__      )

          return (out_vocab_file,)






