





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


snake_case__					:							str													=     logging.getLogger(__name__)





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = None
   lowerCAmelCase__        = None





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = """train"""
   lowerCAmelCase__        = """dev"""
   lowerCAmelCase__        = """test"""





class A_					:




   @staticmethod
   def _lowerCAmelCase      (_UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Union[Split, str] )->					List[InputExample]:
         raise NotImplementedError




   @staticmethod
   def _lowerCAmelCase      (_UpperCamelCase	:str )->					List[str]:
         raise NotImplementedError






   @staticmethod
   def _lowerCAmelCase      (_UpperCamelCase	:List[InputExample]    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:PreTrainedTokenizer    ,   _UpperCamelCase	:Dict=False    ,   _UpperCamelCase	:Any="[CLS]"    ,   _UpperCamelCase	:Dict=1    ,   _UpperCamelCase	:str="[SEP]"    ,   _UpperCamelCase	:List[Any]=False    ,   _UpperCamelCase	:int=False    ,   _UpperCamelCase	:Union[str, Any]=0    ,   _UpperCamelCase	:Optional[int]=0    ,   _UpperCamelCase	:Optional[int]=-100    ,   _UpperCamelCase	:List[str]=0    ,   _UpperCamelCase	:Union[str, Any]=True    ,   )->					List[InputFeatures]:
         __A          =				{label: i for i, label in enumerate(_UpperCamelCase )}

         __A          =				[]
         for ex_index, example in enumerate(_UpperCamelCase ):
               if ex_index % 1_0000 == 0:
                     logger.info('''Writing example %d of %d'''    ,   _UpperCamelCase    ,   len(_UpperCamelCase ) )

               __A          =				[]
               __A          =				[]
               for word, label in zip(example.words    ,   example.labels ):
                     __A          =				tokenizer.tokenize(_UpperCamelCase )

                     # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                     if len(_UpperCamelCase ) > 0:
                           tokens.extend(_UpperCamelCase )
                           # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                           label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_UpperCamelCase ) - 1) )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
               __A          =				tokenizer.num_special_tokens_to_add()
               if len(_UpperCamelCase ) > max_seq_length - special_tokens_count:
                     __A          =				tokens[: (max_seq_length - special_tokens_count)]
                     __A          =				label_ids[: (max_seq_length - special_tokens_count)]

               # The convention in BERT is:
               # (a) For sequence pairs:
               #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
               #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
               # (b) For single sequences:
               #  tokens:   [CLS] the dog is hairy . [SEP]
               #  type_ids:   0   0   0   0  0     0   0
               #
               # Where "type_ids" are used to indicate whether this is the first
               # sequence or the second sequence. The embedding vectors for `type=0` and
               # `type=1` were learned during pre-training and are added to the wordpiece
               # embedding vector (and position vector). This is not *strictly* necessary
               # since the [SEP] token unambiguously separates the sequences, but it makes
               # it easier for the model to learn the concept of sequences.
               #
               # For classification tasks, the first vector (corresponding to [CLS]) is
               # used as the "sentence vector". Note that this only makes sense because
               # the entire model is fine-tuned.
               tokens += [sep_token]
               label_ids += [pad_token_label_id]
               if sep_token_extra:
                     # roberta uses an extra separator b/w pairs of sentences
                     tokens += [sep_token]
                     label_ids += [pad_token_label_id]
               __A          =				[sequence_a_segment_id] * len(_UpperCamelCase )

               if cls_token_at_end:
                     tokens += [cls_token]
                     label_ids += [pad_token_label_id]
                     segment_ids += [cls_token_segment_id]
               else:
                     __A          =				[cls_token] + tokens
                     __A          =				[pad_token_label_id] + label_ids
                     __A          =				[cls_token_segment_id] + segment_ids

               __A          =				tokenizer.convert_tokens_to_ids(_UpperCamelCase )

               # The mask has 1 for real tokens and 0 for padding tokens. Only real
               # tokens are attended to.
               __A          =				[1 if mask_padding_with_zero else 0] * len(_UpperCamelCase )

               # Zero-pad up to the sequence length.
               __A          =				max_seq_length - len(_UpperCamelCase )
               if pad_on_left:
                     __A          =				([pad_token] * padding_length) + input_ids
                     __A          =				([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                     __A          =				([pad_token_segment_id] * padding_length) + segment_ids
                     __A          =				([pad_token_label_id] * padding_length) + label_ids
               else:
                     input_ids += [pad_token] * padding_length
                     input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                     segment_ids += [pad_token_segment_id] * padding_length
                     label_ids += [pad_token_label_id] * padding_length

               assert len(_UpperCamelCase ) == max_seq_length
               assert len(_UpperCamelCase ) == max_seq_length
               assert len(_UpperCamelCase ) == max_seq_length
               assert len(_UpperCamelCase ) == max_seq_length

               if ex_index < 5:
                     logger.info('''*** Example ***''' )
                     logger.info('''guid: %s'''    ,   example.guid )
                     logger.info('''tokens: %s'''    ,   ''' '''.join([str(_UpperCamelCase ) for x in tokens] ) )
                     logger.info('''input_ids: %s'''    ,   ''' '''.join([str(_UpperCamelCase ) for x in input_ids] ) )
                     logger.info('''input_mask: %s'''    ,   ''' '''.join([str(_UpperCamelCase ) for x in input_mask] ) )
                     logger.info('''segment_ids: %s'''    ,   ''' '''.join([str(_UpperCamelCase ) for x in segment_ids] ) )
                     logger.info('''label_ids: %s'''    ,   ''' '''.join([str(_UpperCamelCase ) for x in label_ids] ) )

               if "token_type_ids" not in tokenizer.model_input_names:
                     __A          =				None

               features.append(
                   InputFeatures(
                       input_ids=_UpperCamelCase    ,   attention_mask=_UpperCamelCase    ,   token_type_ids=_UpperCamelCase    ,   label_ids=_UpperCamelCase ) )
         return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset





       class A_					(			_lowerCamelCase   ):
          lowerCAmelCase__        = 42
          lowerCAmelCase__        = nn.CrossEntropyLoss().ignore_index




          def __init__(self	:Optional[int]    ,   _UpperCamelCase	:TokenClassificationTask    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:PreTrainedTokenizer    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Optional[int] = None    ,   _UpperCamelCase	:List[str]=False    ,   _UpperCamelCase	:Split = Split.train    ,   )->					int:
                # Load data features from cache or dataset file
                __A          =				os.path.join(
                    _UpperCamelCase    ,   '''cached_{}_{}_{}'''.format(mode.value    ,   tokenizer.__class__.__name__    ,   str(_UpperCamelCase ) )    ,   )

                # Make sure only the first process in distributed training processes the dataset,
                # and the others will use the cache.
                __A          =				cached_features_file + '''.lock'''
                with FileLock(_UpperCamelCase ):
                      if os.path.exists(_UpperCamelCase ) and not overwrite_cache:
                            logger.info(f"""Loading features from cached file {cached_features_file}""" )
                            __A          =				torch.load(_UpperCamelCase )
                      else:
                            logger.info(f"""Creating features from dataset file at {data_dir}""" )
                            __A          =				token_classification_task.read_examples_from_file(_UpperCamelCase    ,   _UpperCamelCase )
                            # TODO clean up all this to leverage built-in features of tokenizers
                            __A          =				token_classification_task.convert_examples_to_features(
                                _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   cls_token_at_end=bool(model_type in ['''xlnet'''] )    ,   cls_token=tokenizer.cls_token    ,   cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0    ,   sep_token=tokenizer.sep_token    ,   sep_token_extra=_UpperCamelCase    ,   pad_on_left=bool(tokenizer.padding_side == '''left''' )    ,   pad_token=tokenizer.pad_token_id    ,   pad_token_segment_id=tokenizer.pad_token_type_id    ,   pad_token_label_id=self.pad_token_label_id    ,   )
                            logger.info(f"""Saving features into cached file {cached_features_file}""" )
                            torch.save(self.features    ,   _UpperCamelCase )




          def __len__(self	:List[str] )->					Optional[int]:
                return len(self.features )






          def __getitem__(self	:Optional[Any]    ,   _UpperCamelCase	:Optional[int] )->					InputFeatures:
                return self.features[i]


if is_tf_available():
       import tensorflow as tf





       class A_					:
          lowerCAmelCase__        = 42
          lowerCAmelCase__        = -100




          def __init__(self	:List[str]    ,   _UpperCamelCase	:TokenClassificationTask    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:PreTrainedTokenizer    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Optional[int] = None    ,   _UpperCamelCase	:Tuple=False    ,   _UpperCamelCase	:Split = Split.train    ,   )->					Tuple:
                __A          =				token_classification_task.read_examples_from_file(_UpperCamelCase    ,   _UpperCamelCase )
                # TODO clean up all this to leverage built-in features of tokenizers
                __A          =				token_classification_task.convert_examples_to_features(
                    _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   cls_token_at_end=bool(model_type in ['''xlnet'''] )    ,   cls_token=tokenizer.cls_token    ,   cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0    ,   sep_token=tokenizer.sep_token    ,   sep_token_extra=_UpperCamelCase    ,   pad_on_left=bool(tokenizer.padding_side == '''left''' )    ,   pad_token=tokenizer.pad_token_id    ,   pad_token_segment_id=tokenizer.pad_token_type_id    ,   pad_token_label_id=self.pad_token_label_id    ,   )

                def gen():
                      for ex in self.features:
                            if ex.token_type_ids is None:
                                  yield (
                                      {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                      ex.label_ids,
                                  )
                            else:
                                  yield (
                                      {
                                          "input_ids": ex.input_ids,
                                          "attention_mask": ex.attention_mask,
                                          "token_type_ids": ex.token_type_ids,
                                      },
                                      ex.label_ids,
                                  )

                if "token_type_ids" not in tokenizer.model_input_names:
                      __A          =				tf.data.Dataset.from_generator(
                          _UpperCamelCase    ,   ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)    ,   (
                              {'''input_ids''': tf.TensorShape([None] ), '''attention_mask''': tf.TensorShape([None] )},
                              tf.TensorShape([None] ),
                          )    ,   )
                else:
                      __A          =				tf.data.Dataset.from_generator(
                          _UpperCamelCase    ,   ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)    ,   (
                              {
                                  '''input_ids''': tf.TensorShape([None] ),
                                  '''attention_mask''': tf.TensorShape([None] ),
                                  '''token_type_ids''': tf.TensorShape([None] ),
                              },
                              tf.TensorShape([None] ),
                          )    ,   )




          def _lowerCAmelCase      (self	:Dict )->					Optional[int]:
                __A          =				self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )

                return self.dataset




          def __len__(self	:Optional[int] )->					Dict:
                return len(self.features )






          def __getitem__(self	:Any    ,   _UpperCamelCase	:Any )->					InputFeatures:
                return self.features[i]












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())





