



'''simple docstring'''
import argparse
import copy



def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   Tuple					):


			'''simple docstring'''







			A:   List[str]								= {}

			with open(UpperCamelCase__					) as f:
						for line in f:
									if line.split()[0] not in dict_of_neighbours:
												A:   int								= []
												_list.append([line.split()[1], line.split()[2]]					)
												A:   Optional[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:
												A:   str								= []
												_list.append([line.split()[0], line.split()[2]]					)
												A:   Dict								= _list
									else:
												dict_of_neighbours[line.split()[1]].append(
												    [line.split()[0], line.split()[2]]					)

			return dict_of_neighbours



def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   Any				,				lowerCamelCase__ :   Union[str, Any]					):


			'''simple docstring'''







			with open(UpperCamelCase__					) as f:
						A:   List[str]								= f.read(1					)
			A:   Any								= start_node

			A:   List[str]								= []

			A:   Tuple								= start_node

			A:   Any								= 0
			while visiting not in first_solution:
						A:   int								= 10000
						for k in dict_of_neighbours[visiting]:
									if int(k[1]					) < int(UpperCamelCase__					) and k[0] not in first_solution:
												A:   Any								= k[1]
												A:   Optional[int]								= k[0]

						first_solution.append(UpperCamelCase__					)
						A:   Any								= distance_of_first_solution + int(UpperCamelCase__					)
						A:   Dict								= best_node

			first_solution.append(UpperCamelCase__					)

			A:   Union[str, Any]								= 0
			for k in dict_of_neighbours[first_solution[-2]]:
						if k[0] == start_node:
									break
						position += 1

			A:   Union[str, Any]								= (
			    distance_of_first_solution
			    + int(dict_of_neighbours[first_solution[-2]][position][1]					)
			    - 10000
			)
			return first_solution, distance_of_first_solution



def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   Any				,				lowerCamelCase__ :   List[Any]					):


			'''simple docstring'''







			A:   int								= []

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

									A:   Dict								= copy.deepcopy(UpperCamelCase__					)
									A:   List[Any]								= kn
									A:   Dict								= n

									A:   Any								= 0

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

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

			A:   Any								= 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 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   Optional[int]				,				lowerCamelCase__ :   Any				,				lowerCamelCase__ :   Tuple				,				lowerCamelCase__ :   str				,				lowerCamelCase__ :   str					):


			'''simple docstring'''







			A:   Dict								= 1
			A:   Tuple								= first_solution
			A:   int								= []
			A:   Any								= distance_of_first_solution
			A:   List[Any]								= solution

			while count <= iters:
						A:   Union[str, Any]								= find_neighborhood(UpperCamelCase__				,				UpperCamelCase__					)
						A:   Tuple								= 0
						A:   List[str]								= neighborhood[index_of_best_solution]
						A:   str								= len(UpperCamelCase__					) - 1

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

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

						A:   Tuple								= count + 1

			return best_solution_ever, best_cost



def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   int=None					):


			'''simple docstring'''







			A:   str								= generate_neighbours(args.File					)

			A				,		A:   Optional[Any]								= generate_first_solution(
			    args.File				,				UpperCamelCase__					)

			A				,		A:   str								= tabu_search(
			    UpperCamelCase__				,				UpperCamelCase__				,				UpperCamelCase__				,				args.Iterations				,				args.Size				,				)

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


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE			:   str							   =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 unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											snake_case_     							=       10




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4]
											snake_case_     							=       [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       ''
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       (
											    'It was the year of Our Lord one thousand seven hundred and '
											    'seventy-five\n\nSpiritual revelations were conceded to England '
											    'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
											)
											snake_case_							,       snake_case_     							=       process_story(snake_case			)

											snake_case_     							=       [
											    'It was the year of Our Lord one thousand seven hundred and seventy-five.',
											    'Spiritual revelations were conceded to England at that favoured period, as at this.',
											]
											self.assertEqual(snake_case ,  snake_case			)

											snake_case_     							=       ['It was the best of times.']
											self.assertEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  0			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4, 23, 23, 23]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  23			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([8, 2, 3, 4, 1, 1, 1]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  1			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       101
											snake_case_     							=       torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]			)
											snake_case_     							=       torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]			)

											snake_case_     							=       compute_token_type_ids(snake_case ,  snake_case			)
											np.testing.assert_array_equal(snake_case ,  snake_case			)




