




import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
       from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
       from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

SCREAMING_SNAKE_CASE__       :      Tuple  =   128022
SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   128028




@require_sentencepiece
class 		UpperCamelCase__	(lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[Any]	     =      MaMaaaTokenizer
    lowerCamelCase_      :	List[str]	     =      False
    lowerCamelCase_      :	Any	     =      False
    lowerCamelCase_      :	List[str]	     =      True







    def     _lowercase     (    self     )     ->      List[str]:
           super().setUp()

           lowerCamelCase   :      Union[str, Any]			 =					["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
           lowerCamelCase   :      str			 =					dict(zip(UpperCamelCase__     ,  range(len(UpperCamelCase__     )     )     )     )
           lowerCamelCase   :      List[str]			 =					Path(self.tmpdirname     )
           save_json(UpperCamelCase__     ,  save_dir / VOCAB_FILES_NAMES["vocab_file"]     )
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                  copyfile(UpperCamelCase__     ,  save_dir / VOCAB_FILES_NAMES["spm_file"]     )

           lowerCamelCase   :      Tuple			 =					MaMaaaTokenizer.from_pretrained(self.tmpdirname     )
           tokenizer.save_pretrained(self.tmpdirname     )







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      Union[str, Any]:
           return MaMaaaTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[Any]:
           return (
               "This is a test",
               "This is a test",
           )







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      str			 =					"</s>"
           lowerCamelCase   :      int			 =					0

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







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      Tuple			 =					self.get_tokenizer()
           lowerCamelCase   :      int			 =					list(tokenizer.get_vocab().keys()     )

           self.assertEqual(vocab_keys[0]     ,  "</s>"     )
           self.assertEqual(vocab_keys[1]     ,  "<unk>"     )
           self.assertEqual(vocab_keys[-1]     ,  "<s>"     )
           self.assertEqual(len(UpperCamelCase__     )     ,  tokenizer.vocab_size + len(tokenizer.get_added_vocab()     )     )







    @unittest.skip("Skip this test while all models are still to be uploaded."     )
    def     _lowercase     (    self     )     ->      int:
           pass







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      List[Any]			 =					self.get_tokenizer()

           lowerCamelCase   :      Optional[Any]			 =					tokenizer.tokenize("This is a test"     )
           self.assertListEqual(UpperCamelCase__     ,  ["▁This", "▁is", "▁a", "▁t", "est"]     )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(UpperCamelCase__     )     ,  [2, 3, 4, 5, 6]     ,  )

           lowerCamelCase   :      List[Any]			 =					tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]     )
           self.assertListEqual(UpperCamelCase__     ,  ["▁This", "▁is", "▁a", "▁t", "est"]     )

           lowerCamelCase   :      str			 =					tokenizer.convert_tokens_to_string(UpperCamelCase__     )
           self.assertEqual(UpperCamelCase__     ,  "This is a test"     )







    @slow
    def     _lowercase     (    self     )     ->      Optional[int]:
           # fmt: off
           lowerCamelCase   :      Optional[int]			 =					{"input_ids": [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 5, 2, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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, 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]], "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, 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, 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, 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, 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], [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, 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]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCamelCase__     ,  model_name="facebook/m2m100_418M"     ,  revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"     ,  )




@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[int]	     =      """facebook/m2m100_418M"""
    lowerCamelCase_      :	Optional[int]	     =      [
        """In my opinion, there are two levels of response from the French government.""",
        """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
    ]
    lowerCamelCase_      :	List[Any]	     =      [
        """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
        """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
    ]

    # fmt: off
    lowerCamelCase_      :	List[str]	     =      [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]







    @classmethod
    def     _lowercase     (    cls     )     ->      List[Any]:
           lowerCamelCase   :      MaMaaaTokenizer			 =					MaMaaaTokenizer.from_pretrained(
               cls.checkpoint_name     ,  src_lang="en"     ,  tgt_lang="fr"     )
           lowerCamelCase   :      Dict			 =					1
           return cls







    def     _lowercase     (    self     )     ->      Tuple:
           self.assertEqual(self.tokenizer.get_lang_id("ar"     )     ,  12_8006     )
           self.assertEqual(self.tokenizer.get_lang_id("en"     )     ,  12_8022     )
           self.assertEqual(self.tokenizer.get_lang_id("ro"     )     ,  12_8076     )
           self.assertEqual(self.tokenizer.get_lang_id("mr"     )     ,  12_8063     )







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      List[str]			 =					self.tokenizer.get_vocab()
           self.assertEqual(len(UpperCamelCase__     )     ,  self.tokenizer.vocab_size     )
           self.assertEqual(vocab["<unk>"]     ,  3     )
           self.assertIn(self.tokenizer.get_lang_token("en"     )     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      Any			 =					"en"
           lowerCamelCase   :      Optional[Any]			 =					self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      int:
           self.assertIn(UpperCamelCase__     ,  self.tokenizer.all_special_ids     )
           # fmt: off
           lowerCamelCase   :      Any			 =					[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
           # fmt: on
           lowerCamelCase   :      List[str]			 =					self.tokenizer.decode(UpperCamelCase__     ,  skip_special_tokens=UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					self.tokenizer.decode(generated_ids[1:]     ,  skip_special_tokens=UpperCamelCase__     )
           self.assertEqual(UpperCamelCase__     ,  UpperCamelCase__     )
           self.assertNotIn(self.tokenizer.eos_token     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      List[Any]			 =					tempfile.mkdtemp()
           lowerCamelCase   :      List[Any]			 =					self.tokenizer.lang_token_to_id
           self.tokenizer.save_pretrained(UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					MaMaaaTokenizer.from_pretrained(UpperCamelCase__     )
           self.assertDictEqual(new_tok.lang_token_to_id     ,  UpperCamelCase__     )







    @require_torch
    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      Any			 =					"en"
           lowerCamelCase   :      Optional[Any]			 =					"fr"

           lowerCamelCase   :      List[Any]			 =					self.tokenizer(self.src_text     ,  text_target=self.tgt_text     ,  padding=UpperCamelCase__     ,  return_tensors="pt"     )

           lowerCamelCase   :      Dict			 =					shift_tokens_right(
               batch["labels"]     ,  self.tokenizer.pad_token_id     ,  self.tokenizer.eos_token_id     )

           for k in batch:
                  lowerCamelCase   :      Any			 =					batch[k].tolist()
           # batch = {k: v.tolist() for k,v in batch.items()}
           # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
           # batch.decoder_inputs_ids[0][0] ==
           assert batch.input_ids[1][0] == EN_CODE
           assert batch.input_ids[1][-1] == 2
           assert batch.labels[1][0] == FR_CODE
           assert batch.labels[1][-1] == 2
           assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]







    @require_torch
    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      Union[str, Any]			 =					"mr"
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id("mr"     )]     )
           self.assertListEqual(self.tokenizer.suffix_tokens     ,  [self.tokenizer.eos_token_id]     )

           lowerCamelCase   :      List[str]			 =					"zh"
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id("zh"     )]     )
           self.assertListEqual(self.tokenizer.suffix_tokens     ,  [self.tokenizer.eos_token_id]     )







    @require_torch
    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      List[Any]			 =					"mr"
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id("mr"     )]     )
           self.assertListEqual(self.tokenizer.suffix_tokens     ,  [self.tokenizer.eos_token_id]     )
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id(self.tokenizer.src_lang     )]     )

           lowerCamelCase   :      Dict			 =					"zh"
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id("zh"     )]     )
           self.assertListEqual(self.tokenizer.suffix_tokens     ,  [self.tokenizer.eos_token_id]     )
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens     ,  [self.tokenizer.get_lang_id(self.tokenizer.src_lang     )]     )







    @require_torch
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      List[Any]			 =					self.tokenizer._build_translation_inputs("A test"     ,  return_tensors="pt"     ,  src_lang="en"     ,  tgt_lang="ar"     )

           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  {
                   # en_XX, A, test, EOS
                   "input_ids": [[12_8022, 58, 4183, 2]],
                   "attention_mask": [[1, 1, 1, 1]],
                   # ar_AR
                   "forced_bos_token_id": 12_8006,
               }     ,  )













import argparse
import copy



def _a				(	lowerCamelCase:   List[Any] )	->						List[str]:







      '''simple docstring'''






      __A          =				{}

      with open(lowerCamelCase ) 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				(	lowerCamelCase:   Any					,				lowerCamelCase:   Optional[Any] )	->						Dict:







      '''simple docstring'''






      with open(lowerCamelCase ) 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(lowerCamelCase ) and k[0] not in first_solution:
                        __A          =				k[1]
                        __A          =				k[0]

            first_solution.append(lowerCamelCase )
            __A          =				distance_of_first_solution + int(lowerCamelCase )
            __A          =				best_node

      first_solution.append(lowerCamelCase )

      __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				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Any )	->						Any:







      '''simple docstring'''






      __A          =				[]

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

                  __A          =				copy.deepcopy(lowerCamelCase )
                  __A          =				kn
                  __A          =				n

                  __A          =				0

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

                  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 lowerCamelCase : x[index_of_last_item_in_the_list] )
      return neighborhood_of_solution



def _a				(	lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Dict					,				lowerCamelCase:   Any					,				lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Union[str, Any] )	->						Any:







      '''simple docstring'''






      __A          =				1
      __A          =				first_solution
      __A          =				[]
      __A          =				distance_of_first_solution
      __A          =				solution

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

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

            __A          =				count + 1

      return best_solution_ever, best_cost



def _a				(	lowerCamelCase:   List[str]=None )	->						str:







      '''simple docstring'''






      __A          =				generate_neighbours(args.File )

      __A   ,						__A          =				generate_first_solution(
          args.File					,				lowerCamelCase )

      __A   ,						__A          =				tabu_search(
          lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				args.Iterations					,				args.Size					,				)

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


if __name__ == "__main__":
       snake_case__					:							Tuple													=     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())





