


import argparse
import copy







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Tuple										=       {}

							with open(__lowerCamelCase     ) as f:
														for line in f:
																					if line.split()[0] not in dict_of_neighbours:
																												__snake_case		:      Tuple										=       []
																												_list.append([line.split()[1], line.split()[2]]     )
																												__snake_case		:      List[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:
																												__snake_case		:      Union[str, Any]										=       []
																												_list.append([line.split()[0], line.split()[2]]     )
																												__snake_case		:      str										=       _list
																					else:
																												dict_of_neighbours[line.split()[1]].append(
																												    [line.split()[0], line.split()[2]]     )

							return dict_of_neighbours







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):

							with open(__lowerCamelCase     ) as f:
														__snake_case		:      Dict										=       f.read(1     )
							__snake_case		:      Optional[int]										=       start_node

							__snake_case		:      List[Any]										=       []

							__snake_case		:      Any										=       start_node

							__snake_case		:      int										=       0
							while visiting not in first_solution:
														__snake_case		:      Tuple										=       1_0_0_0_0
														for k in dict_of_neighbours[visiting]:
																					if int(k[1]     ) < int(__lowerCamelCase     ) and k[0] not in first_solution:
																												__snake_case		:      Any										=       k[1]
																												__snake_case		:      List[Any]										=       k[0]

														first_solution.append(__lowerCamelCase     )
														__snake_case		:      Dict										=       distance_of_first_solution + int(__lowerCamelCase     )
														__snake_case		:      List[Any]										=       best_node

							first_solution.append(__lowerCamelCase     )

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

							__snake_case		:      Tuple										=       (
							    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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       []

							for n in solution[1:-1]:
														__snake_case		:      Optional[Any]										=       solution.index(__lowerCamelCase     )
														for kn in solution[1:-1]:
																					__snake_case		:      str										=       solution.index(__lowerCamelCase     )
																					if n == kn:
																												continue

																					__snake_case		:      List[str]										=       copy.deepcopy(__lowerCamelCase     )
																					__snake_case		:      int										=       kn
																					__snake_case		:      Optional[Any]										=       n

																					__snake_case		:      Optional[Any]										=       0

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

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

							__snake_case		:      Tuple										=       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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Any										=       1
							__snake_case		:      str										=       first_solution
							__snake_case		:      Optional[int]										=       []
							__snake_case		:      List[str]										=       distance_of_first_solution
							__snake_case		:      Optional[Any]										=       solution

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

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

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

														__snake_case		:      Optional[int]										=       count + 1

							return best_solution_ever, best_cost







def     lowerCAmelCase_					( __lowerCamelCase=None     ):
							__snake_case		:      Tuple										=       generate_neighbours(args.File     )

							__snake_case		:      Tuple										=       generate_first_solution(
							    args.File							,     __lowerCamelCase     )

							__snake_case		:      Optional[Any]										=       tabu_search(
							    __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     args.Iterations							,     args.Size							,     )

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


if __name__ == "__main__":
			_snake_case					:		List[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 logging

from transformers.configuration_utils import PretrainedConfig


_snake_case					:		Optional[Any]      =    logging.getLogger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "masked_bert"

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Any=30522   ,     lowerCamelCase	:  Tuple=768   ,     lowerCamelCase	:  str=12   ,     lowerCamelCase	:  Dict=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  List[str]="gelu"   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  List[Any]=512   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  str=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  Dict="topK"   ,     lowerCamelCase	:  List[Any]="constant"   ,     lowerCamelCase	:  Dict=0.0   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Dict:
														super().__init__(pad_token_id=lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       vocab_size
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      Union[str, Any]										=       num_attention_heads
														__snake_case		:      Optional[Any]										=       hidden_act
														__snake_case		:      str										=       intermediate_size
														__snake_case		:      Tuple										=       hidden_dropout_prob
														__snake_case		:      Dict										=       attention_probs_dropout_prob
														__snake_case		:      List[str]										=       max_position_embeddings
														__snake_case		:      Union[str, Any]										=       type_vocab_size
														__snake_case		:      Any										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps
														__snake_case		:      str										=       pruning_method
														__snake_case		:      int										=       mask_init
														__snake_case		:      Any										=       mask_scale

