





"""simple docstring"""




import argparse
import copy





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  {}

  with open(_snake_case					) as f:
    for line in f:
      if line.split()[0] not in dict_of_neighbours:
        A_  :       str										=  []
        _list.append([line.split()[1], line.split()[2]]					)
        A_  :       Optional[Any]										=  _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_  :       Union[str, Any]										=  []
        _list.append([line.split()[0], line.split()[2]]					)
        A_  :       str										=  _list
      else:
        dict_of_neighbours[line.split()[1]].append(
            [line.split()[0], line.split()[2]]					)

  return dict_of_neighbours





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  with open(_snake_case					) as f:
    A_  :       str										=  f.read(1					)
  A_  :       Tuple										=  start_node

  A_  :       Union[str, Any]										=  []

  A_  :       List[str]										=  start_node

  A_  :       Optional[int]										=  0
  while visiting not in first_solution:
    A_  :       List[str]										=  10000
    for k in dict_of_neighbours[visiting]:
      if int(k[1]					) < int(_snake_case					) and k[0] not in first_solution:
        A_  :       Tuple										=  k[1]
        A_  :       str										=  k[0]

    first_solution.append(_snake_case					)
    A_  :       int										=  distance_of_first_solution + int(_snake_case					)
    A_  :       List[str]										=  best_node

  first_solution.append(_snake_case					)

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

  A_  :       str										=  (
      distance_of_first_solution
      + int(dict_of_neighbours[first_solution[-2]][position][1]					)
      - 10000
  )
  return first_solution, distance_of_first_solution





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  []

  for n in solution[1:-1]:
    A_  :       Any										=  solution.index(_snake_case					)
    for kn in solution[1:-1]:
      A_  :       Any										=  solution.index(_snake_case					)
      if n == kn:
        continue

      A_  :       List[Any]										=  copy.deepcopy(_snake_case					)
      A_  :       str										=  kn
      A_  :       Dict										=  n

      A_  :       List[Any]										=  0

      for k in _tmp[:-1]:
        A_  :       Dict										=  _tmp[_tmp.index(_snake_case					) + 1]
        for i in dict_of_neighbours[k]:
          if i[0] == next_node:
            A_  :       Any										=  distance + int(i[1]					)
      _tmp.append(_snake_case					)

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

  A_  :       Optional[Any]										=  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 ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  1
  A_  :       List[str]										=  first_solution
  A_  :       str										=  []
  A_  :       Tuple										=  distance_of_first_solution
  A_  :       Optional[int]										=  solution

  while count <= iters:
    A_  :       List[str]										=  find_neighborhood(_snake_case ,					_snake_case					)
    A_  :       str										=  0
    A_  :       Optional[int]										=  neighborhood[index_of_best_solution]
    A_  :       Dict										=  len(_snake_case					) - 1

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

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

    A_  :       str										=  count + 1

  return best_solution_ever, best_cost





def 						UpperCAmelCase__		(     _UpperCAmelCase=None					):






  """simple docstring"""





  A_  :       Tuple										=  generate_neighbours(args.File					)

  A_						,       A_  :       Tuple										=  generate_first_solution(
      args.File ,					_snake_case					)

  A_						,       A_  :       Optional[Any]										=  tabu_search(
      _snake_case ,					_snake_case ,					_snake_case ,					args.Iterations ,					args.Size ,					)

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


if __name__ == "__main__":
   lowerCamelCase_     :						Dict						=					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())


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						['image_processor', 'tokenizer']
			SCREAMING_SNAKE_CASE							   =						'BridgeTowerImageProcessor'
			SCREAMING_SNAKE_CASE							   =						('RobertaTokenizer', 'RobertaTokenizerFast')





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						super().__init__(__snake_case					,  __snake_case					)





			def __call__(		self					,  __snake_case					,  __snake_case = None					,  __snake_case = True					,  __snake_case = False					,  __snake_case = None					,  __snake_case = None					,  __snake_case = 0					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = True					,  __snake_case = None					,  **__snake_case					,  )   -> BatchEncoding:




						'''simple docstring'''
						__a        =self.tokenizer(
						    text=__snake_case					,  add_special_tokens=__snake_case					,  padding=__snake_case					,  truncation=__snake_case					,  max_length=__snake_case					,  stride=__snake_case					,  pad_to_multiple_of=__snake_case					,  return_token_type_ids=__snake_case					,  return_attention_mask=__snake_case					,  return_overflowing_tokens=__snake_case					,  return_special_tokens_mask=__snake_case					,  return_offsets_mapping=__snake_case					,  return_length=__snake_case					,  verbose=__snake_case					,  return_tensors=__snake_case					,  **__snake_case					,  )
						# add pixel_values + pixel_mask
						__a        =self.image_processor(
						    __snake_case					,  return_tensors=__snake_case					,  do_normalize=__snake_case					,  do_center_crop=__snake_case					,  **__snake_case					)
						encoding.update(__snake_case					)

						return encoding





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> str:




						'''simple docstring'''
						return self.tokenizer.batch_decode(*__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> Optional[Any]:




						'''simple docstring'''
						return self.tokenizer.decode(*__snake_case					,  **__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.tokenizer.model_input_names
						__a        =self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






