




import argparse
import copy




def 			__magic_name__				( A		:					int   ):






			'''simple docstring'''







			a    =			{}

			with open(A   ) 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 			__magic_name__				( A		:					Optional[int],  A		:					Dict   ):






			'''simple docstring'''







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

			a    =			[]

			a    =			start_node

			a    =			0
			while visiting not in first_solution:
						a    =			10000
						for k in dict_of_neighbours[visiting]:
									if int(k[1]   ) < int(A   ) and k[0] not in first_solution:
												a    =			k[1]
												a    =			k[0]

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

			first_solution.append(A   )

			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]   )
			    - 10000
			)
			return first_solution, distance_of_first_solution




def 			__magic_name__				( A		:					Tuple,  A		:					List[str]   ):






			'''simple docstring'''







			a    =			[]

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

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

									a    =			0

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

									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 A   : x[index_of_last_item_in_the_list]   )
			return neighborhood_of_solution




def 			__magic_name__				( A		:					Any,  A		:					List[str],  A		:					Optional[int],  A		:					Dict,  A		:					List[Any]   ):






			'''simple docstring'''







			a    =			1
			a    =			first_solution
			a    =			[]
			a    =			distance_of_first_solution
			a    =			solution

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

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

						a    =			count + 1

			return best_solution_ever, best_cost




def 			__magic_name__				( A		:					Optional[Any]=None   ):






			'''simple docstring'''







			a    =			generate_neighbours(args.File   )

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

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

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


if __name__ == "__main__":
		__lowerCAmelCase		:				Any       			=		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())












# 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     snake_case__							(TensorFormatter[Mapping, """torch.Tensor""", Mapping]   ):
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     Union[str, Any]=None					,      **__lowerCamelCase				:     Any							)					->     Optional[Any]:
							super().__init__(features=__lowerCamelCase							)
							a    =			torch_tensor_kwargs
							import torch  # noqa import torch at initialization





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Dict							)					->     Dict:
							import torch

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





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]							)					->     str:
							import torch

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

							a    =			{}

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

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





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Tuple							)					->     List[str]:
							import torch

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





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     dict							)					->     str:
							return map_nested(self._recursive_tensorize					,      __lowerCamelCase					,      map_list=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_row(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_row(__lowerCamelCase							)
							return self.recursive_tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     pa.Table							)					->     "torch.Tensor":
							a    =			self.numpy_arrow_extractor().extract_column(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_column(__lowerCamelCase					,      pa_table.column_names[0]							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							a    =			self._consolidate(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_batch(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_batch(__lowerCamelCase							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							for column_name in batch:
										a    =			self._consolidate(batch[column_name]							)
							return batch






