
import argparse
import copy

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      {}

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

				return dict_of_neighbours

def  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''simple docstring'''


				with open(__magic_name__						) as f:
								lowercase         =      f.read(1						)
				lowercase         =      start_node

				lowercase         =      []

				lowercase         =      start_node

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

								first_solution.append(__magic_name__						)
								lowercase         =      distance_of_first_solution + int(__magic_name__						)
								lowercase         =      best_node

				first_solution.append(__magic_name__						)

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

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

def  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      []

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

												lowercase         =      copy.deepcopy(__magic_name__						)
												lowercase         =      kn
												lowercase         =      n

												lowercase         =      0

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

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

				lowercase         =      len(neighborhood_of_solution[0]						) - 1

				neighborhood_of_solution.sort(key=lambda __magic_name__						: x[index_of_last_item_in_the_list]						)
				return neighborhood_of_solution

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      1
				lowercase         =      first_solution
				lowercase         =      []
				lowercase         =      distance_of_first_solution
				lowercase         =      solution

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

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

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

								lowercase         =      count + 1

				return best_solution_ever, best_cost

def  __snake_case							(	__magic_name__=None						):







				'''simple docstring'''


				lowercase         =      generate_neighbours(args.File						)

				lowercase     ,	lowercase         =      generate_first_solution(
				    args.File		,    __magic_name__						)

				lowercase     ,	lowercase         =      tabu_search(
				    __magic_name__		,    __magic_name__		,    __magic_name__		,    args.Iterations		,    args.Size		,    )

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


if __name__ == "__main__":
						_snake_case      :							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())


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      ArgumentParser(
				    description=(
				        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
				    )						)

				# Optional arguments for the launch helper
				parser.add_argument("--num_cores"		,    type=__magic_name__		,    default=1		,    help="Number of TPU cores to use (1 or 8)."						)

				# positional
				parser.add_argument(
				    "training_script"		,    type=__magic_name__		,    help=(
				        "The full path to the single TPU training "
				        "program/script to be launched in parallel, "
				        "followed by all the arguments for the "
				        "training script"
				    )		,    )

				# rest from the training program
				parser.add_argument("training_script_args"		,    nargs=__magic_name__						)

				return parser.parse_args()

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      parse_args()

				# Import training_script as a module.
				lowercase         =      Path(args.training_script						)
				sys.path.append(str(script_fpath.parent.resolve()						)						)
				lowercase         =      script_fpath.stem
				lowercase         =      importlib.import_module(__magic_name__						)

				# Patch sys.argv
				lowercase         =      [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores						)]

				xmp.spawn(mod._mp_fn		,    args=()		,    nprocs=args.num_cores						)


if __name__ == "__main__":
						main()
