






import argparse
import copy

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   {}

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

				return dict_of_neighbours

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				with open(snake_case			) as f:
								__magic_name__		:Optional[Any]   =   f.read(1			)
				__magic_name__		:List[Any]   =   start_node

				__magic_name__		:int   =   []

				__magic_name__		:str   =   start_node

				__magic_name__		:Optional[Any]   =   0
				while visiting not in first_solution:
								__magic_name__		:List[str]   =   1_0_0_0_0
								for k in dict_of_neighbours[visiting]:
												if int(k[1]			) < int(snake_case			) and k[0] not in first_solution:
																__magic_name__		:Optional[Any]   =   k[1]
																__magic_name__		:int   =   k[0]

								first_solution.append(snake_case			)
								__magic_name__		:Dict   =   distance_of_first_solution + int(snake_case			)
								__magic_name__		:int   =   best_node

				first_solution.append(snake_case			)

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

				__magic_name__		:Optional[int]   =   (
				    distance_of_first_solution
				    + int(dict_of_neighbours[first_solution[-2]][position][1]			)
				    - 1_0_0_0_0
				)
				return first_solution, distance_of_first_solution

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   []

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

												__magic_name__		:Union[str, Any]   =   copy.deepcopy(snake_case			)
												__magic_name__		:List[str]   =   kn
												__magic_name__		:Optional[int]   =   n

												__magic_name__		:Any   =   0

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

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

				__magic_name__		:Optional[int]   =   len(neighborhood_of_solution[0]			) - 1

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

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   1
				__magic_name__		:Dict   =   first_solution
				__magic_name__		:Optional[Any]   =   []
				__magic_name__		:Optional[Any]   =   distance_of_first_solution
				__magic_name__		:str   =   solution

				while count <= iters:
								__magic_name__		:Dict   =   find_neighborhood(snake_case,	snake_case			)
								__magic_name__		:Any   =   0
								__magic_name__		:Union[str, Any]   =   neighborhood[index_of_best_solution]
								__magic_name__		:Union[str, Any]   =   len(snake_case			) - 1

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

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

								__magic_name__		:List[Any]   =   count + 1

				return best_solution_ever, best_cost

def   __lowercase       (			snake_case=None			):







				"""simple docstring"""






				__magic_name__		:str   =   generate_neighbours(args.File			)

				__magic_name__  ,					__magic_name__		:List[str]   =   generate_first_solution(
				    args.File,	snake_case			)

				__magic_name__  ,					__magic_name__		:List[Any]   =   tabu_search(
				    snake_case,	snake_case,	snake_case,	args.Iterations,	args.Size,	)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      List[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())










"""simple docstring"""




import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__						: Dict    =				get_tests_dir('fixtures/test_sentencepiece.model')
__magic_name__						: int    =				get_tests_dir('fixtures/test_sentencepiece_bpe.model')

__magic_name__						: Any    =				'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class 			__snake_case   (lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       CamembertTokenizer
							__a					 =       CamembertTokenizerFast
							__a					 =       True
							__a					 =       True




							def   __a   (     self:    List[Any]   ):
										super().setUp()

										# We have a SentencePiece fixture for testing
										__lowerCamelCase          =      CamembertTokenizer(A_   )
										tokenizer.save_pretrained(self.tmpdirname   )




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      """<pad>"""
										__lowerCamelCase          =      1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_   )     ,							A_   )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_   )     ,							A_   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      list(self.get_tokenizer().get_vocab().keys()   )

										self.assertEqual(vocab_keys[0]     ,							"""<s>NOTUSED"""   )
										self.assertEqual(vocab_keys[1]     ,							"""<pad>"""   )
										self.assertEqual(vocab_keys[-1]     ,							"""<mask>"""   )
										self.assertEqual(len(A_   )     ,							10_04   )




							def   __a   (     self:    Any   ):
										self.assertEqual(self.get_tokenizer().vocab_size     ,							10_05   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      CamembertTokenizer(A_   )
										tokenizer.save_pretrained(self.tmpdirname   )
										__lowerCamelCase          =      CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

										__lowerCamelCase          =      """I was born in 92000, and this is falsé."""

										__lowerCamelCase          =      tokenizer.encode(A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      tokenizer.encode(A_     ,							add_special_tokens=A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_     ,							add_special_tokens=A_   )
										self.assertListEqual(A_     ,							A_   )

										# <unk> tokens are not the same for `rust` than for `slow`.
										# Because spm gives back raw token instead of `unk` in EncodeAsPieces
										# tokens = tokenizer.tokenize(sequence)
										__lowerCamelCase          =      tokenizer.convert_ids_to_tokens(A_   )
										__lowerCamelCase          =      rust_tokenizer.tokenize(A_   )
										self.assertListEqual(A_     ,							A_   )




							def   __a   (     self:    List[str]   ):
										if not self.test_rust_tokenizer:
													return

										__lowerCamelCase          =      self.get_tokenizer()
										__lowerCamelCase          =      self.get_rust_tokenizer()

										__lowerCamelCase          =      """I was born in 92000, and this is falsé."""

										__lowerCamelCase          =      tokenizer.tokenize(A_   )
										__lowerCamelCase          =      rust_tokenizer.tokenize(A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      tokenizer.encode(A_     ,							add_special_tokens=A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_     ,							add_special_tokens=A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      self.get_rust_tokenizer()
										__lowerCamelCase          =      tokenizer.encode(A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_   )
										self.assertListEqual(A_     ,							A_   )




							@slow
							def   __a   (     self:    Optional[Any]   ):
										# fmt: off
										__lowerCamelCase          =      {"""input_ids""": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
										# fmt: on

										# camembert is a french model. So we also use french texts.
										__lowerCamelCase          =      [
										    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
										    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
										    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
										    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
										    """telles que la traduction et la synthèse de texte.""",
										]

										self.tokenizer_integration_test_util(
										    expected_encoding=A_     ,							model_name="""camembert-base"""     ,							revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""     ,							sequences=A_     ,							)




