import argparse
import copy




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= {}

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

					return dict_of_neighbours




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					with open(_lowercase ) as f:
										_A								= f.read(1 )
					_A								= start_node

					_A								= []

					_A								= start_node

					_A								= 0
					while visiting not in first_solution:
										_A								= 1_00_00
										for k in dict_of_neighbours[visiting]:
															if int(k[1] ) < int(_lowercase ) and k[0] not in first_solution:
																				_A								= k[1]
																				_A								= k[0]

										first_solution.append(_lowercase )
										_A								= distance_of_first_solution + int(_lowercase )
										_A								= best_node

					first_solution.append(_lowercase )

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

					_A								= (
					    distance_of_first_solution
					    + int(dict_of_neighbours[first_solution[-2]][position][1] )
					    - 1_00_00
					)
					return first_solution, distance_of_first_solution




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= []

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

															_A								= copy.deepcopy(_lowercase )
															_A								= kn
															_A								= n

															_A								= 0

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

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

					_A								= len(neighborhood_of_solution[0] ) - 1

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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= 1
					_A								= first_solution
					_A								= []
					_A								= distance_of_first_solution
					_A								= solution

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

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

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

										_A								= count + 1

					return best_solution_ever, best_cost




def      __A					(     _lowercase=None ):

					'''simple docstring'''







					_A								= generate_neighbours(args.File )

					_A							,_A								= generate_first_solution(
					    args.File					,			_lowercase )

					_A							,_A								= tabu_search(
					    _lowercase					,			_lowercase					,			_lowercase					,			args.Iterations					,			args.Size					,			)

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


if __name__ == "__main__":
				__A      =				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 math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case					):





							"""simple docstring"""

							A_						  =					1

							@register_to_config
							def __init__(  self:   Any		,     __A:   int = 10_00		,     __A:   Optional[Union[np.ndarray, List[float]]] = None   )    ->				List[str]:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__A   )

												# standard deviation of the initial noise distribution
												_A								= 1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												_A								= 4

												# running values
												_A								= []

							def       __A (  self:   str		,     __A:   int		,     __A:   Union[str, torch.device] = None   )    ->				int:
												_A								= num_inference_steps
												_A								= torch.linspace(1		,     0		,     num_inference_steps + 1   )[:-1]
												_A								= torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																	_A								= torch.tensor(self.config.trained_betas		,     dtype=torch.floataa   )
												else:
																	_A								= torch.sin(steps * math.pi / 2   ) ** 2

												_A								= (1.0 - self.betas**2) ** 0.5

												_A								= (torch.atana(self.betas		,     self.alphas   ) / math.pi * 2)[:-1]
												_A								= timesteps.to(__A   )

												_A								= []

							def       __A (  self:   Tuple		,     __A:   torch.FloatTensor		,     __A:   int		,     __A:   torch.FloatTensor		,     __A:   bool = True		,     )    ->				Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																	raise ValueError(
																	    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''   )

												_A								= (self.timesteps == timestep).nonzero().item()
												_A								= timestep_index + 1

												_A								= sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__A   )

												if len(self.ets   ) == 1:
																	_A								= self.ets[-1]
												elif len(self.ets   ) == 2:
																	_A								= (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																	_A								= (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																	_A								= (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												_A								= self._get_prev_sample(__A		,     __A		,     __A		,     __A   )

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=__A   )

							def       __A (  self:   Optional[int]		,     __A:   torch.FloatTensor		,     *__A:   Tuple		,     **__A:   List[Any]   )    ->				torch.FloatTensor:
												return sample

							def       __A (  self:   List[str]		,     __A:   Optional[Any]		,     __A:   Optional[Any]		,     __A:   Any		,     __A:   List[Any]   )    ->				List[Any]:
												_A								= self.alphas[timestep_index]
												_A								= self.betas[timestep_index]

												_A								= self.alphas[prev_timestep_index]
												_A								= self.betas[prev_timestep_index]

												_A								= (sample - sigma * ets) / max(__A		,     1e-8   )
												_A								= next_alpha * pred + ets * next_sigma

												return prev_sample



							def __len__(  self:   List[str]   )    ->				Dict:
												return self.config.num_train_timesteps



