





'''simple docstring'''







import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			_lowercase						(       __a				,		unittest.TestCase					):
     _UpperCAmelCase										=					LEDTokenizer
     _UpperCAmelCase										=					LEDTokenizerFast
     _UpperCAmelCase										=					True







     def     UpperCamelCase			( self    ) ->	int:
      super().setUp()
      snake_case				=			[
          '''l''',
          '''o''',
          '''w''',
          '''e''',
          '''r''',
          '''s''',
          '''t''',
          '''i''',
          '''d''',
          '''n''',
          '''\u0120''',
          '''\u0120l''',
          '''\u0120n''',
          '''\u0120lo''',
          '''\u0120low''',
          '''er''',
          '''\u0120lowest''',
          '''\u0120newer''',
          '''\u0120wider''',
          '''<unk>''',
      ]
      snake_case				=			dict(zip(A__    ,   range(len(A__    )    )    )    )
      snake_case				=			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
      snake_case				=			{'''unk_token''': '''<unk>'''}

      snake_case				=			os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES['''vocab_file''']    )
      snake_case				=			os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES['''merges_file''']    )
      with open(self.vocab_file    ,   '''w'''    ,   encoding='''utf-8'''    ) as fp:
       fp.write(json.dumps(A__    ) + '''\n'''    )
      with open(self.merges_file    ,   '''w'''    ,   encoding='''utf-8'''    ) as fp:
       fp.write('''\n'''.join(A__    )    )







     def     UpperCamelCase			( self    ,   **A__    ) ->	int:
      kwargs.update(self.special_tokens_map    )
      return self.tokenizer_class.from_pretrained(self.tmpdirname    ,   **A__    )







     def     UpperCamelCase			( self    ,   **A__    ) ->	List[Any]:
      kwargs.update(self.special_tokens_map    )
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,   **A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Dict:
      return "lower newer", "lower newer"







     @cached_property
     def     UpperCamelCase			( self    ) ->	str:
      return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''    )







     @cached_property
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	str:
      snake_case				=			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      snake_case				=			[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       snake_case				=			tokenizer(A__    ,   max_length=len(A__    )    ,   padding=A__    ,   return_tensors='''pt'''    )
       self.assertIsInstance(A__    ,   A__    )

       self.assertEqual((2, 9)    ,   batch.input_ids.shape    )
       self.assertEqual((2, 9)    ,   batch.attention_mask.shape    )
       snake_case				=			batch.input_ids.tolist()[0]
       self.assertListEqual(A__    ,   A__    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       snake_case				=			tokenizer(A__    ,   padding=A__    ,   return_tensors='''pt'''    )
       self.assertIn('''input_ids'''    ,   A__    )
       self.assertIn('''attention_mask'''    ,   A__    )
       self.assertNotIn('''labels'''    ,   A__    )
       self.assertNotIn('''decoder_attention_mask'''    ,   A__    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			[
          '''Summary of the text.''',
          '''Another summary.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       snake_case				=			tokenizer(text_target=A__    ,   max_length=32    ,   padding='''max_length'''    ,   return_tensors='''pt'''    )
       self.assertEqual(32    ,   targets['''input_ids'''].shape[1]    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	List[str]:
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       snake_case				=			tokenizer(
           ['''I am a small frog''' * 10_24, '''I am a small frog''']    ,   padding=A__    ,   truncation=A__    ,   return_tensors='''pt'''    )
       self.assertIsInstance(A__    ,   A__    )
       self.assertEqual(batch.input_ids.shape    ,   (2, 51_22)    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			['''A long paragraph for summarization.''']
      snake_case				=			[
          '''Summary of the text.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       snake_case				=			tokenizer(A__    ,   return_tensors='''pt'''    )
       snake_case				=			tokenizer(text_target=A__    ,   return_tensors='''pt'''    )
       snake_case				=			inputs['''input_ids''']
       snake_case				=			targets['''input_ids''']
       self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
       self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
       self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
       self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	int:
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       snake_case				=			['''Summary of the text.''', '''Another summary.''']
       snake_case				=			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

       snake_case				=			tokenizer(A__    ,   padding=A__    )
       snake_case				=			[[0] * len(A__    ) for x in encoded_output['''input_ids''']]
       snake_case				=			tokenizer.pad(A__    )
       self.assertSequenceEqual(outputs['''global_attention_mask''']    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Tuple:
      pass







     def     UpperCamelCase			( self    ) ->	Dict:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
        snake_case				=			self.rust_tokenizer_class.from_pretrained(A__    ,   **A__    )
        snake_case				=			self.tokenizer_class.from_pretrained(A__    ,   **A__    )
        snake_case				=			'''A, <mask> AllenNLP sentence.'''
        snake_case				=			tokenizer_r.encode_plus(A__    ,   add_special_tokens=A__    ,   return_token_type_ids=A__    )
        snake_case				=			tokenizer_p.encode_plus(A__    ,   add_special_tokens=A__    ,   return_token_type_ids=A__    )
        self.assertEqual(sum(tokens_r['''token_type_ids''']    )    ,   sum(tokens_p['''token_type_ids''']    )    )
        self.assertEqual(
            sum(tokens_r['''attention_mask''']    ) / len(tokens_r['''attention_mask''']    )    ,   sum(tokens_p['''attention_mask''']    ) / len(tokens_p['''attention_mask''']    )    ,   )

        snake_case				=			tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']    )
        snake_case				=			tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']    )
        self.assertSequenceEqual(tokens_p['''input_ids''']    ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )
        self.assertSequenceEqual(tokens_r['''input_ids''']    ,   [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )

        self.assertSequenceEqual(
            A__    ,   ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )
        self.assertSequenceEqual(
            A__    ,   ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )








'''simple docstring'''







import argparse
import copy

def    __UpperCamelCase	(	a    : Union[str, Any]     ) ->Tuple:
 snake_case				=			{}

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

 return dict_of_neighbours

def    __UpperCamelCase	(	a    : Dict					,   a    : Tuple     ) ->int:

 with open(a     ) as f:
  snake_case				=			f.read(1     )
 snake_case				=			start_node

 snake_case				=			[]

 snake_case				=			start_node

 snake_case				=			0
 while visiting not in first_solution:
  snake_case				=			1_0000
  for k in dict_of_neighbours[visiting]:
   if int(k[1]     ) < int(a     ) and k[0] not in first_solution:
    snake_case				=			k[1]
    snake_case				=			k[0]

  first_solution.append(a     )
  snake_case				=			distance_of_first_solution + int(a     )
  snake_case				=			best_node

 first_solution.append(a     )

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

 snake_case				=			(
     distance_of_first_solution
     + int(dict_of_neighbours[first_solution[-2]][position][1]     )
     - 1_0000
 )
 return first_solution, distance_of_first_solution

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : str     ) ->str:
 snake_case				=			[]

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

   snake_case				=			copy.deepcopy(a     )
   snake_case				=			kn
   snake_case				=			n

   snake_case				=			0

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

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

 snake_case				=			len(neighborhood_of_solution[0]     ) - 1

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

def    __UpperCamelCase	(	a    : Any					,   a    : Optional[Any]					,   a    : int					,   a    : Optional[int]					,   a    : Union[str, Any]     ) ->List[Any]:
 snake_case				=			1
 snake_case				=			first_solution
 snake_case				=			[]
 snake_case				=			distance_of_first_solution
 snake_case				=			solution

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

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

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

  snake_case				=			count + 1

 return best_solution_ever, best_cost

def    __UpperCamelCase	(	a    : Union[str, Any]=None     ) ->Optional[Any]:
 snake_case				=			generate_neighbours(args.File     )

 snake_case				,		snake_case				=			generate_first_solution(
     args.File					,   a     )

 snake_case				,		snake_case				=			tabu_search(
     a					,   a					,   a					,   args.Iterations					,   args.Size					,   )

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


if __name__ == "__main__":
   _lowercase       						=			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())

