






import argparse
import copy

def   snake_case (UpperCAmelCase__  )	->    Any:
  UpperCamelCase_: int   				=    {}

  with open(UpperCAmelCase__  ) as f:
    for line in f:
      if line.split()[0] not in dict_of_neighbours:
        UpperCamelCase_: List[str]   				=    []
        _list.append([line.split()[1], line.split()[2]]  )
        UpperCamelCase_: List[str]   				=    _list
      else:
        dict_of_neighbours[line.split()[0]].append(
            [line.split()[1], line.split()[2]]  )
      if line.split()[1] not in dict_of_neighbours:
        UpperCamelCase_: Optional[int]   				=    []
        _list.append([line.split()[0], line.split()[2]]  )
        UpperCamelCase_: Optional[int]   				=    _list
      else:
        dict_of_neighbours[line.split()[1]].append(
            [line.split()[0], line.split()[2]]  )

  return dict_of_neighbours

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Optional[int]:

  with open(UpperCAmelCase__  ) as f:
    UpperCamelCase_: List[str]   				=    f.read(1  )
  UpperCamelCase_: Optional[Any]   				=    start_node

  UpperCamelCase_: Optional[Any]   				=    []

  UpperCamelCase_: int   				=    start_node

  UpperCamelCase_: Union[str, Any]   				=    0
  while visiting not in first_solution:
    UpperCamelCase_: str   				=    1_0_0_0_0
    for k in dict_of_neighbours[visiting]:
      if int(k[1]  ) < int(UpperCAmelCase__  ) and k[0] not in first_solution:
        UpperCamelCase_: Optional[Any]   				=    k[1]
        UpperCamelCase_: Optional[int]   				=    k[0]

    first_solution.append(UpperCAmelCase__  )
    UpperCamelCase_: List[str]   				=    distance_of_first_solution + int(UpperCAmelCase__  )
    UpperCamelCase_: List[str]   				=    best_node

  first_solution.append(UpperCAmelCase__  )

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

  UpperCamelCase_: List[Any]   				=    (
      distance_of_first_solution
      + int(dict_of_neighbours[first_solution[-2]][position][1]  )
      - 1_0_0_0_0
  )
  return first_solution, distance_of_first_solution

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Tuple:
  UpperCamelCase_: str   				=    []

  for n in solution[1:-1]:
    UpperCamelCase_: List[str]   				=    solution.index(UpperCAmelCase__  )
    for kn in solution[1:-1]:
      UpperCamelCase_: List[str]   				=    solution.index(UpperCAmelCase__  )
      if n == kn:
        continue

      UpperCamelCase_: str   				=    copy.deepcopy(UpperCAmelCase__  )
      UpperCamelCase_: Optional[int]   				=    kn
      UpperCamelCase_: int   				=    n

      UpperCamelCase_: Optional[Any]   				=    0

      for k in _tmp[:-1]:
        UpperCamelCase_: List[str]   				=    _tmp[_tmp.index(UpperCAmelCase__  ) + 1]
        for i in dict_of_neighbours[k]:
          if i[0] == next_node:
            UpperCamelCase_: List[str]   				=    distance + int(i[1]  )
      _tmp.append(UpperCAmelCase__  )

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

  UpperCamelCase_: int   				=    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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Tuple:
  UpperCamelCase_: Optional[Any]   				=    1
  UpperCamelCase_: Optional[Any]   				=    first_solution
  UpperCamelCase_: int   				=    []
  UpperCamelCase_: Optional[Any]   				=    distance_of_first_solution
  UpperCamelCase_: List[str]   				=    solution

  while count <= iters:
    UpperCamelCase_: Any   				=    find_neighborhood(UpperCAmelCase__     ,   UpperCAmelCase__  )
    UpperCamelCase_: Dict   				=    0
    UpperCamelCase_: Union[str, Any]   				=    neighborhood[index_of_best_solution]
    UpperCamelCase_: Optional[Any]   				=    len(UpperCAmelCase__  ) - 1

    UpperCamelCase_: Tuple   				=    False
    while not found:
      UpperCamelCase_: List[Any]   				=    0
      while i < len(UpperCAmelCase__  ):
        if best_solution[i] != solution[i]:
          UpperCamelCase_: List[str]   				=    best_solution[i]
          UpperCamelCase_: str   				=    solution[i]
          break
        UpperCamelCase_: List[str]   				=    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]  )
        UpperCamelCase_: List[str]   				=    True
        UpperCamelCase_: List[Any]   				=    best_solution[:-1]
        UpperCamelCase_: Any   				=    neighborhood[index_of_best_solution][best_cost_index]
        if cost < best_cost:
          UpperCamelCase_: Union[str, Any]   				=    cost
          UpperCamelCase_: Union[str, Any]   				=    solution
      else:
        UpperCamelCase_: int   				=    index_of_best_solution + 1
        UpperCamelCase_: List[str]   				=    neighborhood[index_of_best_solution]

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

    UpperCamelCase_: Optional[Any]   				=    count + 1

  return best_solution_ever, best_cost

def   snake_case (UpperCAmelCase__=None  )	->    Dict:
  UpperCamelCase_: str   				=    generate_neighbours(args.File  )

  UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    generate_first_solution(
      args.File     ,   UpperCAmelCase__  )

  UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    tabu_search(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   args.Iterations     ,   args.Size     ,   )

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


if __name__ == "__main__":
   A_		:		Any		     =			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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @slow
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  ,      return_dict=_lowerCamelCase							).to(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    AutoTokenizer.from_pretrained('google/mt5-small'							)

         UpperCamelCase_: Dict   				=    tokenizer('Hello there'  ,      return_tensors='pt'							).input_ids
         UpperCamelCase_: Optional[Any]   				=    tokenizer('Hi I am'  ,      return_tensors='pt'							).input_ids

         UpperCamelCase_: int   				=    model(input_ids.to(_lowerCamelCase							)  ,      labels=labels.to(_lowerCamelCase							)							).loss
         UpperCamelCase_: Tuple   				=    -(labels.shape[-1] * loss.item())

         UpperCamelCase_: Any   				=    -8_4.9_1_2_7
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)