



"""simple docstring"""





import argparse
import copy
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				{}

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

      return dict_of_neighbours
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):

      with open(__UpperCAmelCase ) as f:
            UpperCAmelCase_     					=				f.read(1 )
      UpperCAmelCase_     					=				start_node

      UpperCAmelCase_     					=				[]

      UpperCAmelCase_     					=				start_node

      UpperCAmelCase_     					=				0
      while visiting not in first_solution:
            UpperCAmelCase_     					=				10000
            for k in dict_of_neighbours[visiting]:
                  if int(k[1] ) < int(__UpperCAmelCase ) and k[0] not in first_solution:
                        UpperCAmelCase_     					=				k[1]
                        UpperCAmelCase_     					=				k[0]

            first_solution.append(__UpperCAmelCase )
            UpperCAmelCase_     					=				distance_of_first_solution + int(__UpperCAmelCase )
            UpperCAmelCase_     					=				best_node

      first_solution.append(__UpperCAmelCase )

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

      UpperCAmelCase_     					=				(
          distance_of_first_solution
          + int(dict_of_neighbours[first_solution[-2]][position][1] )
          - 10000
      )
      return first_solution, distance_of_first_solution
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				[]

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

                  UpperCAmelCase_     					=				copy.deepcopy(__UpperCAmelCase )
                  UpperCAmelCase_     					=				kn
                  UpperCAmelCase_     					=				n

                  UpperCAmelCase_     					=				0

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

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

      UpperCAmelCase_     					=				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__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				1
      UpperCAmelCase_     					=				first_solution
      UpperCAmelCase_     					=				[]
      UpperCAmelCase_     					=				distance_of_first_solution
      UpperCAmelCase_     					=				solution

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

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

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

            UpperCAmelCase_     					=				count + 1

      return best_solution_ever, best_cost
def   a__      ( lowerCAmelCase__=None ):
      UpperCAmelCase_     					=				generate_neighbours(args.File )

      UpperCAmelCase_					,       UpperCAmelCase_     					=				generate_first_solution(
          args.File      ,     __UpperCAmelCase )

      UpperCAmelCase_					,       UpperCAmelCase_     					=				tabu_search(
          __UpperCAmelCase      ,     __UpperCAmelCase      ,     __UpperCAmelCase      ,     args.Iterations      ,     args.Size      ,     )

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


if __name__ == "__main__":
  lowerCamelCase  			=		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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Optional[Any]      						=					['''image_processor''', '''tokenizer''']
 _lowercase   :    Optional[int]      						=					'''ChineseCLIPImageProcessor'''
 _lowercase   :    List[Any]      						=					('''BertTokenizer''', '''BertTokenizerFast''')

 def __init__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= None
  if "feature_extractor" in kwargs:
   warnings.warn(
       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
       ''' instead.'''		,				SCREAMING_SNAKE_CASE		,				)
   a__												= kwargs.pop('''feature_extractor'''      )

  a__												= image_processor if image_processor is not None else feature_extractor
  if image_processor is None:
   raise ValueError('''You need to specify an `image_processor`.'''      )
  if tokenizer is None:
   raise ValueError('''You need to specify a `tokenizer`.'''      )

  super().__init__(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  a__												= self.image_processor

 def __call__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			List[str]:

  if text is None and images is None:
   raise ValueError('''You have to specify either text or images. Both cannot be none.'''      )

  if text is not None:
   a__												= self.tokenizer(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  if images is not None:
   a__												= self.image_processor(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  if text is not None and images is not None:
   a__												= image_features.pixel_values
   return encoding
  elif text is not None:
   return encoding
  else:
   return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE      )		,				tensor_type=SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			List[str]:
  return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  return self.tokenizer.decode(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

 @property
 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= self.tokenizer.model_input_names
  a__												= self.image_processor.model_input_names
  return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )



 @property
 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  warnings.warn(
      '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,				SCREAMING_SNAKE_CASE		,				)
  return self.image_processor_class





