


import argparse
import copy

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __UpperCamelCase   							={}

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

     return dict_of_neighbours

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Tuple    ):

     with open(SCREAMING_SNAKE_CASE__    ) as f:
          __UpperCamelCase   							=f.read(1    )
     __UpperCamelCase   							=start_node

     __UpperCamelCase   							=[]

     __UpperCamelCase   							=start_node

     __UpperCamelCase   							=0
     while visiting not in first_solution:
          __UpperCamelCase   							=1_00_00
          for k in dict_of_neighbours[visiting]:
               if int(k[1]    ) < int(SCREAMING_SNAKE_CASE__    ) and k[0] not in first_solution:
                    __UpperCamelCase   							=k[1]
                    __UpperCamelCase   							=k[0]

          first_solution.append(SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=distance_of_first_solution + int(SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=best_node

     first_solution.append(SCREAMING_SNAKE_CASE__    )

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

     __UpperCamelCase   							=(
         distance_of_first_solution
         + int(dict_of_neighbours[first_solution[-2]][position][1]    )
         - 1_00_00
     )
     return first_solution, distance_of_first_solution

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : Dict    ):
     __UpperCamelCase   							=[]

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

               __UpperCamelCase   							=copy.deepcopy(SCREAMING_SNAKE_CASE__    )
               __UpperCamelCase   							=kn
               __UpperCamelCase   							=n

               __UpperCamelCase   							=0

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

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

     __UpperCamelCase   							=len(neighborhood_of_solution[0]    ) - 1

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]      ,					SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase   							=1
     __UpperCamelCase   							=first_solution
     __UpperCamelCase   							=[]
     __UpperCamelCase   							=distance_of_first_solution
     __UpperCamelCase   							=solution

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

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

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

          __UpperCamelCase   							=count + 1

     return best_solution_ever, best_cost

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int=None    ):
     __UpperCamelCase   							=generate_neighbours(args.File    )

     __UpperCamelCase							,				__UpperCamelCase   							=generate_first_solution(
         args.File      ,					SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase							,				__UpperCamelCase   							=tabu_search(
         SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					args.Iterations      ,					args.Size      ,					)

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


if __name__ == "__main__":
   _A			=						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 os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Features    ):
     __UpperCamelCase   							=np.inf

     def set_batch_size(SCREAMING_SNAKE_CASE__      : FeatureType    ) -> None:
          nonlocal batch_size
          if isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS    )
          elif isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS    )
          elif isinstance(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ) and feature.dtype == "binary":
               __UpperCamelCase   							=min(SCREAMING_SNAKE_CASE__      ,					config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS    )

     _visit(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     return None if batch_size is np.inf else batch_size






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = False ,       A_ = False ,       A_ = None ,       **A_ ,       )  ->	Dict:
         super().__init__(
             A_ ,       split=A_ ,       features=A_ ,       cache_dir=A_ ,       keep_in_memory=A_ ,       streaming=A_ ,       num_proc=A_ ,       **A_ ,       )
         __UpperCamelCase   							=path_or_paths if isinstance(A_ ,       A_   ) else {self.split: path_or_paths}
         __UpperCamelCase   							=_PACKAGED_DATASETS_MODULES['parquet'][1]
         __UpperCamelCase   							=Parquet(
             cache_dir=A_ ,       data_files=A_ ,       features=A_ ,       hash=A_ ,       **A_ ,       )






    def _a (      self   )  ->	List[Any]:
         # Build iterable dataset
         if self.streaming:
              __UpperCamelCase   							=self.builder.as_streaming_dataset(split=self.split   )
         # Build regular (map-style) dataset
         else:
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None
              __UpperCamelCase   							=None

              self.builder.download_and_prepare(
                  download_config=A_ ,       download_mode=A_ ,       verification_mode=A_ ,       base_path=A_ ,       num_proc=self.num_proc ,       )
              __UpperCamelCase   							=self.builder.as_dataset(
                  split=self.split ,       verification_mode=A_ ,       in_memory=self.keep_in_memory   )
         return dataset






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	List[Any]:
         __UpperCamelCase   							=dataset
         __UpperCamelCase   							=path_or_buf
         __UpperCamelCase   							=batch_size or get_writer_batch_size(dataset.features   )
         __UpperCamelCase   							=parquet_writer_kwargs






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf ,       (str, bytes, os.PathLike)   ):
              with open(self.path_or_buf ,       'wb+'   ) as buffer:
                   __UpperCamelCase   							=self._write(file_obj=A_ ,       batch_size=A_ ,       **self.parquet_writer_kwargs   )
         else:
              __UpperCamelCase   							=self._write(file_obj=self.path_or_buf ,       batch_size=A_ ,       **self.parquet_writer_kwargs   )
         return written






    def _a (      self ,       A_ ,       A_ ,       **A_   )  ->	int:
         __UpperCamelCase   							=0
         __UpperCamelCase   							=parquet_writer_kwargs.pop('path_or_buf' ,       A_   )
         __UpperCamelCase   							=self.dataset.features.arrow_schema

         __UpperCamelCase   							=pq.ParquetWriter(A_ ,       schema=A_ ,       **A_   )

         for offset in logging.tqdm(
             range(0 ,       len(self.dataset   ) ,       A_   ) ,       unit='ba' ,       disable=not logging.is_progress_bar_enabled() ,       desc='Creating parquet from Arrow format' ,       ):
              __UpperCamelCase   							=query_table(
                  table=self.dataset._data ,       key=slice(A_ ,       offset + batch_size   ) ,       indices=self.dataset._indices if self.dataset._indices is not None else None ,       )
              writer.write_table(A_   )
              written += batch.nbytes
         writer.close()
         return written





