

import argparse
import copy






def        UpperCamelCase_(							_snake_case   :  List[str]	):




			"""simple docstring"""






			__a        ={}

			with open(_snake_case	) 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        UpperCamelCase_(							_snake_case   :  Any       ,     _snake_case   :  str	):




			"""simple docstring"""






			with open(_snake_case	) as f:
						__a        =f.read(1	)
			__a        =start_node

			__a        =[]

			__a        =start_node

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

						first_solution.append(_snake_case	)
						__a        =distance_of_first_solution + int(_snake_case	)
						__a        =best_node

			first_solution.append(_snake_case	)

			__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]	)
			    - 10000
			)
			return first_solution, distance_of_first_solution






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Dict	):




			"""simple docstring"""






			__a        =[]

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

									__a        =copy.deepcopy(_snake_case	)
									__a        =kn
									__a        =n

									__a        =0

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

									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 _snake_case	: x[index_of_last_item_in_the_list]	)
			return neighborhood_of_solution






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Tuple       ,     _snake_case   :  Any       ,     _snake_case   :  List[str]       ,     _snake_case   :  List[Any]	):




			"""simple docstring"""






			__a        =1
			__a        =first_solution
			__a        =[]
			__a        =distance_of_first_solution
			__a        =solution

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

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

						__a        =count + 1

			return best_solution_ever, best_cost






def        UpperCamelCase_(							_snake_case   :  List[str]=None	):




			"""simple docstring"""






			__a        =generate_neighbours(args.File	)

			__a      ,    __a        =generate_first_solution(
			    args.File       ,     _snake_case	)

			__a      ,    __a        =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__":
						_lowerCAmelCase					:			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 unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase					:			List[Any]  				=			get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")






@require_sentencepiece
@require_tokenizers
class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						GPTSwaTokenizer
			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						True
			SCREAMING_SNAKE_CASE							   =						False





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						super().setUp()

						# We have a SentencePiece fixture for testing
						__a        =GPTSwaTokenizer(__snake_case					,  eos_token='<unk>'					,  bos_token='<unk>'					,  pad_token='<unk>'					)

						tokenizer.save_pretrained(self.tmpdirname					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> Tuple:




						'''simple docstring'''
						__a        ='This is a test'
						__a        ='This is a test'
						return input_text, output_text





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        ='<s>'
						__a        =1

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





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =list(self.get_tokenizer().get_vocab().keys()					)

						self.assertEqual(vocab_keys[0]					,  '<unk>'					)
						self.assertEqual(vocab_keys[1]					,  '<s>'					)
						self.assertEqual(vocab_keys[-1]					,  'j'					)
						self.assertEqual(len(__snake_case					)					,  2000					)





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						self.assertEqual(self.get_tokenizer().vocab_size					,  2000					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =GPTSwaTokenizer(__snake_case					)

						__a        =tokenizer.tokenize('This is a test'					)
						self.assertListEqual(__snake_case					,  ['▁This', '▁is', '▁a', '▁t', 'est']					)

						self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case					)					,  [465, 287, 265, 631, 842]					)

						__a        =tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
						# fmt: off
						self.assertListEqual(
						    __snake_case					,  ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					,  )
						# fmt: on

						__a        =tokenizer.convert_tokens_to_ids(__snake_case					)
						self.assertListEqual(
						    __snake_case					,  [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,  )

						__a        =tokenizer.convert_ids_to_tokens(__snake_case					)
						# fmt: off
						self.assertListEqual(
						    __snake_case					,  ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					)
						# fmt: on





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =GPTSwaTokenizer(__snake_case					)
						__a        =['This is a test', 'I was born in 92000, and this is falsé.']
						__a        =[
						    [465, 287, 265, 631, 842],
						    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
						]

						# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
						for text, expected_ids in zip(__snake_case					,  __snake_case					):
									self.assertListEqual(tokenizer.encode_fast(__snake_case					)					,  __snake_case					)

						# Test that decode_fast returns the input text
						for text, token_ids in zip(__snake_case					,  __snake_case					):
									self.assertEqual(tokenizer.decode_fast(__snake_case					)					,  __snake_case					)





			@slow
			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        =[
						    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
						    'Hey there, how are you doing this fine day?',
						    'This is a text with a trailing spaces followed by a dot     .',
						    'Häj sväjs lillebrör! =)',
						    'Det är inget fel på Mr. Cool',
						]

						# fmt: off
						__a        ={'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
						# fmt: on
						self.tokenizer_integration_test_util(
						    expected_encoding=__snake_case					,  model_name='AI-Sweden/gpt-sw3-126m'					,  sequences=__snake_case					,  )






