




from typing import Union

import fire
import torch
from tqdm import tqdm







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str = "cpu"				,       UpperCamelCase__							:			Union[str, None] = None				)    -> Any:
							'''simple docstring'''


							__lowercase      =    torch.load(UpperCamelCase__				,       map_location=UpperCamelCase__				)
							for k, v in tqdm(state_dict.items()				):
														if not isinstance(UpperCamelCase__				,       torch.Tensor				):
																					raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''				)
														__lowercase      =    v.half()
							if save_path is None:  # overwrite src_path
														__lowercase      =    src_path
							torch.save(UpperCamelCase__				,       UpperCamelCase__				)


if __name__ == "__main__":
			fire.Fire(convert)












import argparse
import copy







def 		_A(       UpperCamelCase__							:			Union[str, Any]				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    {}

							with open(UpperCamelCase__				) 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 		_A(       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Optional[Any]				)    -> Dict:
							'''simple docstring'''


							with open(UpperCamelCase__				) as f:
														__lowercase      =    f.read(1				)
							__lowercase      =    start_node

							__lowercase      =    []

							__lowercase      =    start_node

							__lowercase      =    0
							while visiting not in first_solution:
														__lowercase      =    1_0000
														for k in dict_of_neighbours[visiting]:
																					if int(k[1]				) < int(UpperCamelCase__				) and k[0] not in first_solution:
																												__lowercase      =    k[1]
																												__lowercase      =    k[0]

														first_solution.append(UpperCamelCase__				)
														__lowercase      =    distance_of_first_solution + int(UpperCamelCase__				)
														__lowercase      =    best_node

							first_solution.append(UpperCamelCase__				)

							__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]				)
							    - 1_0000
							)
							return first_solution, distance_of_first_solution







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Optional[Any]				)    -> Optional[int]:
							'''simple docstring'''


							__lowercase      =    []

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

																					__lowercase      =    copy.deepcopy(UpperCamelCase__				)
																					__lowercase      =    kn
																					__lowercase      =    n

																					__lowercase      =    0

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

																					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 UpperCamelCase__				: x[index_of_last_item_in_the_list]				)
							return neighborhood_of_solution







def 		_A(       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			List[str]				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Optional[int]				)    -> List[str]:
							'''simple docstring'''


							__lowercase      =    1
							__lowercase      =    first_solution
							__lowercase      =    []
							__lowercase      =    distance_of_first_solution
							__lowercase      =    solution

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

														__lowercase      =    False
														while not found:
																					__lowercase      =    0
																					while i < len(UpperCamelCase__				):
																												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(UpperCamelCase__				) >= size:
																					tabu_list.pop(0				)

														__lowercase      =    count + 1

							return best_solution_ever, best_cost







def 		_A(       UpperCamelCase__							:			List[Any]=None				)    -> int:
							'''simple docstring'''


							__lowercase      =    generate_neighbours(args.File				)

							__lowercase     ,     __lowercase      =    generate_first_solution(
							    args.File				,       UpperCamelCase__				)

							__lowercase     ,     __lowercase      =    tabu_search(
							    UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       args.Iterations				,       args.Size				,       )

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


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






