






"""simple docstring"""
import argparse
import copy






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Optional[int]	    =   {}

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

	return dict_of_neighbours






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):

	with open(snake_case__					) as f:
		A_		:						Any	    =   f.read(1					)
	A_		:						Optional[int]	    =   start_node

	A_		:						Any	    =   []

	A_		:						Union[str, Any]	    =   start_node

	A_		:						List[str]	    =   0
	while visiting not in first_solution:
		A_		:						Optional[Any]	    =   10_000
		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_		:						Optional[int]	    =   k[0]

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

	first_solution.append(snake_case__					)

	A_		:						str	    =   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]					)
	    - 10_000
	)
	return first_solution, distance_of_first_solution






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						Optional[int]	    =   []

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

			A_		:						Optional[Any]	    =   copy.deepcopy(snake_case__					)
			A_		:						Optional[int]	    =   kn
			A_		:						List[Any]	    =   n

			A_		:						int	    =   0

			for k in _tmp[:-1]:
				A_		:						List[Any]	    =   _tmp[_tmp.index(snake_case__					) + 1]
				for i in dict_of_neighbours[k]:
					if i[0] == next_node:
						A_		:						Tuple	    =   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 snake_case__					: x[index_of_last_item_in_the_list]					)
	return neighborhood_of_solution






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Any	    =   1
	A_		:						List[Any]	    =   first_solution
	A_		:						Tuple	    =   []
	A_		:						Any	    =   distance_of_first_solution
	A_		:						Optional[int]	    =   solution

	while count <= iters:
		A_		:						List[Any]	    =   find_neighborhood(snake_case__   ,   snake_case__					)
		A_		:						Any	    =   0
		A_		:						Optional[int]	    =   neighborhood[index_of_best_solution]
		A_		:						Tuple	    =   len(snake_case__					) - 1

		A_		:						Optional[Any]	    =   False
		while not found:
			A_		:						Any	    =   0
			while i < len(snake_case__					):
				if best_solution[i] != solution[i]:
					A_		:						Any	    =   best_solution[i]
					A_		:						Dict	    =   solution[i]
					break
				A_		:						List[Any]	    =   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_		:						Dict	    =   True
				A_		:						str	    =   best_solution[:-1]
				A_		:						Optional[int]	    =   neighborhood[index_of_best_solution][best_cost_index]
				if cost < best_cost:
					A_		:						List[str]	    =   cost
					A_		:						Union[str, Any]	    =   solution
			else:
				A_		:						List[Any]	    =   index_of_best_solution + 1
				A_		:						Tuple	    =   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	(		snake_case__=None					):
	A_		:						List[Any]	    =   generate_neighbours(args.File					)

	A_    ,     A_		:						Optional[int]	    =   generate_first_solution(
	    args.File   ,   snake_case__					)

	A_    ,     A_		:						str	    =   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         =							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"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				str    =				"""AutoImageProcessor"""
	_A       :				Union[str, Any]    =				"""AutoTokenizer"""





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   self.image_processor





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):

		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_		:						Optional[int]	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						List[Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

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





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return ["input_ids", "attention_mask", "pixel_values"]

