



"""simple docstring"""

from ...processing_utils import ProcessorMixin







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """feature_extractor"""]
						_A		:							List[str]							       =		"""TvltImageProcessor"""
						_A		:							Optional[Any]							       =		"""TvltFeatureExtractor"""







						def __init__(self   ,		lowercase__   ,		lowercase__       ):
											super().__init__(image_processor=lowercase__   ,		feature_extractor=lowercase__       )

											snake_case_					:		str 					=					image_processor
											snake_case_					:		Dict 					=					feature_extractor







						def __call__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=False   ,		lowercase__=False   ,		*lowercase__   ,		**lowercase__   ,		):

											if images is None and audio is None:
																raise ValueError("""You need to specify either an `images` or `audio` input to process."""       )

											snake_case_					:		Optional[Any] 					=					None
											if images is not None:
																snake_case_					:		str 					=					self.image_processor(lowercase__   ,		mask_pixel=lowercase__   ,		*lowercase__   ,		**lowercase__       )
											if images_mixed is not None:
																snake_case_					:		Union[str, Any] 					=					self.image_processor(lowercase__   ,		is_mixed=lowercase__   ,		*lowercase__   ,		**lowercase__       )
											if audio is not None:
																snake_case_					:		Tuple 					=					self.feature_extractor(
																    lowercase__   ,		*lowercase__   ,		sampling_rate=lowercase__   ,		mask_audio=lowercase__   ,		**lowercase__       )

											snake_case_					:		int 					=					{}
											if audio is not None:
																output_dict.update(lowercase__       )
											if images is not None:
																output_dict.update(lowercase__       )
											if images_mixed_dict is not None:
																output_dict.update(lowercase__       )
											return output_dict







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					self.image_processor.model_input_names
											snake_case_					:		Union[str, Any] 					=					self.feature_extractor.model_input_names
											return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names       )       )









"""simple docstring"""

import argparse
import copy







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					{}

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

					return dict_of_neighbours







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					with open(SCREAMING_SNAKE_CASE__			) as f:
										snake_case_					:		Optional[Any] 					=					f.read(1			)
					snake_case_					:		Union[str, Any] 					=					start_node

					snake_case_					:		Dict 					=					[]

					snake_case_					:		Union[str, Any] 					=					start_node

					snake_case_					:		Tuple 					=					0
					while visiting not in first_solution:
										snake_case_					:		int 					=					1_0_0_0_0
										for k in dict_of_neighbours[visiting]:
															if int(k[1]			) < int(SCREAMING_SNAKE_CASE__			) and k[0] not in first_solution:
																				snake_case_					:		Union[str, Any] 					=					k[1]
																				snake_case_					:		Any 					=					k[0]

										first_solution.append(SCREAMING_SNAKE_CASE__			)
										snake_case_					:		Tuple 					=					distance_of_first_solution + int(SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[str] 					=					best_node

					first_solution.append(SCREAMING_SNAKE_CASE__			)

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

					snake_case_					:		int 					=					(
					    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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					[]

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

															snake_case_					:		Optional[Any] 					=					copy.deepcopy(SCREAMING_SNAKE_CASE__			)
															snake_case_					:		int 					=					kn
															snake_case_					:		Dict 					=					n

															snake_case_					:		Optional[int] 					=					0

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

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

					snake_case_					:		Optional[Any] 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  List[Any]			):



					"""simple docstring"""





					snake_case_					:		Dict 					=					1
					snake_case_					:		List[Any] 					=					first_solution
					snake_case_					:		List[Any] 					=					[]
					snake_case_					:		Optional[Any] 					=					distance_of_first_solution
					snake_case_					:		Dict 					=					solution

					while count <= iters:
										snake_case_					:		List[str] 					=					find_neighborhood(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					0
										snake_case_					:		List[Any] 					=					neighborhood[index_of_best_solution]
										snake_case_					:		Union[str, Any] 					=					len(SCREAMING_SNAKE_CASE__			) - 1

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

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

										snake_case_					:		List[str] 					=					count + 1

					return best_solution_ever, best_cost







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]=None			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					generate_neighbours(args.File			)

					snake_case_					,			snake_case_					:		Optional[Any] 					=					generate_first_solution(
					    args.File				,    SCREAMING_SNAKE_CASE__			)

					snake_case_					,			snake_case_					:		Dict 					=					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())




