






"""simple docstring"""



import argparse
import copy



def 						lowercase    (				__snake_case				: Any	):
		lowercase_ : Any							       =   {}

		with open(__snake_case	) as f:
				for line in f:
						if line.split()[0] not in dict_of_neighbours:
								lowercase_ : Any							       =   []
								_list.append([line.split()[1], line.split()[2]]	)
								lowercase_ : List[Any]							       =   _list
						else:
								dict_of_neighbours[line.split()[0]].append(
								    [line.split()[1], line.split()[2]]	)
						if line.split()[1] not in dict_of_neighbours:
								lowercase_ : int							       =   []
								_list.append([line.split()[0], line.split()[2]]	)
								lowercase_ : List[str]							       =   _list
						else:
								dict_of_neighbours[line.split()[1]].append(
								    [line.split()[0], line.split()[2]]	)

		return dict_of_neighbours



def 						lowercase    (				__snake_case				: Optional[Any]		,					__snake_case				: Optional[int]	):

		with open(__snake_case	) as f:
				lowercase_ : int							       =   f.read(1	)
		lowercase_ : str							       =   start_node

		lowercase_ : List[Any]							       =   []

		lowercase_ : str							       =   start_node

		lowercase_ : List[str]							       =   0
		while visiting not in first_solution:
				lowercase_ : List[str]							       =   1_0_0_0_0
				for k in dict_of_neighbours[visiting]:
						if int(k[1]	) < int(__snake_case	) and k[0] not in first_solution:
								lowercase_ : Dict							       =   k[1]
								lowercase_ : int							       =   k[0]

				first_solution.append(__snake_case	)
				lowercase_ : Optional[int]							       =   distance_of_first_solution + int(__snake_case	)
				lowercase_ : Tuple							       =   best_node

		first_solution.append(__snake_case	)

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

		lowercase_ : List[str]							       =   (
		    distance_of_first_solution
		    + int(dict_of_neighbours[first_solution[-2]][position][1]	)
		    - 1_0_0_0_0
		)
		return first_solution, distance_of_first_solution



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: Optional[int]	):
		lowercase_ : List[str]							       =   []

		for n in solution[1:-1]:
				lowercase_ : List[Any]							       =   solution.index(__snake_case	)
				for kn in solution[1:-1]:
						lowercase_ : List[Any]							       =   solution.index(__snake_case	)
						if n == kn:
								continue

						lowercase_ : List[Any]							       =   copy.deepcopy(__snake_case	)
						lowercase_ : int							       =   kn
						lowercase_ : Union[str, Any]							       =   n

						lowercase_ : int							       =   0

						for k in _tmp[:-1]:
								lowercase_ : List[str]							       =   _tmp[_tmp.index(__snake_case	) + 1]
								for i in dict_of_neighbours[k]:
										if i[0] == next_node:
												lowercase_ : Any							       =   distance + int(i[1]	)
						_tmp.append(__snake_case	)

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

		lowercase_ : str							       =   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 						lowercase    (				__snake_case				: Optional[Any]		,					__snake_case				: int		,					__snake_case				: Dict		,					__snake_case				: List[str]		,					__snake_case				: Union[str, Any]	):
		lowercase_ : Any							       =   1
		lowercase_ : Optional[int]							       =   first_solution
		lowercase_ : Any							       =   []
		lowercase_ : List[Any]							       =   distance_of_first_solution
		lowercase_ : List[Any]							       =   solution

		while count <= iters:
				lowercase_ : Optional[Any]							       =   find_neighborhood(__snake_case		,					__snake_case	)
				lowercase_ : Dict							       =   0
				lowercase_ : Tuple							       =   neighborhood[index_of_best_solution]
				lowercase_ : Union[str, Any]							       =   len(__snake_case	) - 1

				lowercase_ : Any							       =   False
				while not found:
						lowercase_ : List[str]							       =   0
						while i < len(__snake_case	):
								if best_solution[i] != solution[i]:
										lowercase_ : str							       =   best_solution[i]
										lowercase_ : Optional[Any]							       =   solution[i]
										break
								lowercase_ : Dict							       =   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]	)
								lowercase_ : List[Any]							       =   True
								lowercase_ : int							       =   best_solution[:-1]
								lowercase_ : str							       =   neighborhood[index_of_best_solution][best_cost_index]
								if cost < best_cost:
										lowercase_ : str							       =   cost
										lowercase_ : Optional[int]							       =   solution
						else:
								lowercase_ : List[str]							       =   index_of_best_solution + 1
								lowercase_ : str							       =   neighborhood[index_of_best_solution]

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

				lowercase_ : int							       =   count + 1

		return best_solution_ever, best_cost



def 						lowercase    (				__snake_case				: Dict=None	):
		lowercase_ : Optional[int]							       =   generate_neighbours(args.File	)

		lowercase_       ,					lowercase_ : Union[str, Any]							       =   generate_first_solution(
		    args.File		,					__snake_case	)

		lowercase_       ,					lowercase_ : Union[str, Any]							       =   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__":
						__A		:       List[str]  				=		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())











"""simple docstring"""



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
						import torch





class 					_UpperCAmelCase       ( TensorFormatter[Mapping, "torch.Tensor", Mapping]							):




					def __init__(				self     :						Any    ,						A     :						int=None    ,						**A     :						str   )  ->  Union[str, Any]:
							super().__init__(features=A   )
							lowercase_ : Union[str, Any]							       =   torch_tensor_kwargs
							import torch  # noqa import torch at initialization




					def   A  (				self     :						Dict    ,						A     :						int   )  ->  List[Any]:
							import torch

							if isinstance(A    ,						A   ) and column:
									if all(
									    isinstance(A    ,						torch.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype
									    for x in column   ):
											return torch.stack(A   )
							return column




					def   A  (				self     :						int    ,						A     :						Any   )  ->  Optional[Any]:
							import torch

							if isinstance(A    ,						(str, bytes, type(A   ))   ):
									return value
							elif isinstance(A    ,						(np.character, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.character   ):
									return value.tolist()

							lowercase_ : Any							       =   {}

							if isinstance(A    ,						(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.integer   ):
									lowercase_ : Any							       =   {'''dtype''': torch.intaa}
							elif isinstance(A    ,						(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.floating   ):
									lowercase_ : Dict							       =   {'''dtype''': torch.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

									if isinstance(A    ,						PIL.Image.Image   ):
											lowercase_ : Dict							       =   np.asarray(A   )
							return torch.tensor(A    ,						**{**default_dtype, **self.torch_tensor_kwargs}   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						Optional[int]   )  ->  str:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(A    ,						'''__array__'''   ) and not isinstance(A    ,						torch.Tensor   ):
									lowercase_ : Optional[int]							       =   data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(A    ,						np.ndarray   ):
									if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
											return self._consolidate([self.recursive_tensorize(A   ) for substruct in data_struct]   )
							elif isinstance(A    ,						(list, tuple)   ):
									return self._consolidate([self.recursive_tensorize(A   ) for substruct in data_struct]   )
							return self._tensorize(A   )




					def   A  (				self     :						Dict    ,						A     :						dict   )  ->  Tuple:
							return map_nested(self._recursive_tensorize    ,						A    ,						map_list=A   )




					def   A  (				self     :						str    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[Any]							       =   self.numpy_arrow_extractor().extract_row(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_row(A   )
							return self.recursive_tensorize(A   )




					def   A  (				self     :						List[Any]    ,						A     :						pa.Table   )  ->  "torch.Tensor":
							lowercase_ : List[str]							       =   self.numpy_arrow_extractor().extract_column(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_column(A    ,						pa_table.column_names[0]   )
							lowercase_ : Optional[int]							       =   self.recursive_tensorize(A   )
							lowercase_ : Any							       =   self._consolidate(A   )
							return column






					def   A  (				self     :						List[str]    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[int]							       =   self.numpy_arrow_extractor().extract_batch(A   )
							lowercase_ : int							       =   self.python_features_decoder.decode_batch(A   )
							lowercase_ : Dict							       =   self.recursive_tensorize(A   )
							for column_name in batch:
									lowercase_ : Optional[Any]							       =   self._consolidate(batch[column_name]   )
							return batch



