



import argparse
import copy






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Union[str, Any]	)  ->						Dict:
					__lowerCAmelCase	   ={}

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

					return dict_of_neighbours






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    Any	)  ->						Tuple:

					with open(__lowerCamelCase	) as f:
										__lowerCAmelCase	   =f.read(1	)
					__lowerCAmelCase	   =start_node

					__lowerCAmelCase	   =[]

					__lowerCAmelCase	   =start_node

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

										first_solution.append(__lowerCamelCase	)
										__lowerCAmelCase	   =distance_of_first_solution + int(__lowerCamelCase	)
										__lowerCAmelCase	   =best_node

					first_solution.append(__lowerCamelCase	)

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

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






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int				,   __lowerCamelCase	:    str	)  ->						Optional[int]:
					__lowerCAmelCase	   =[]

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

															__lowerCAmelCase	   =copy.deepcopy(__lowerCamelCase	)
															__lowerCAmelCase	   =kn
															__lowerCAmelCase	   =n

															__lowerCAmelCase	   =0

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

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

					__lowerCAmelCase	   =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	:    List[str]				,   __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    Any				,   __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    List[Any]	)  ->						Any:
					__lowerCAmelCase	   =1
					__lowerCAmelCase	   =first_solution
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =distance_of_first_solution
					__lowerCAmelCase	   =solution

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

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

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

										__lowerCAmelCase	   =count + 1

					return best_solution_ever, best_cost






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Any=None	)  ->						List[Any]:
					__lowerCAmelCase	   =generate_neighbours(args.File	)

					__lowerCAmelCase   ,	__lowerCAmelCase	   =generate_first_solution(
					    args.File				,   __lowerCamelCase	)

					__lowerCAmelCase   ,	__lowerCAmelCase	   =tabu_search(
					    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   args.Iterations				,   args.Size				,   )

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


if __name__ == "__main__":
				lowercase_	       =			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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase_	       =			{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_	       =			[
				    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''EncodecModel''',
				    '''EncodecPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				lowercase_	       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




