


'''simple docstring'''



import argparse
import copy







def     _UpperCamelCase      (		UpperCamelCase__					):

			'''simple docstring'''





			UpperCAmelCase__  :					List[Any]        =     {}

			with open(__UpperCamelCase					) as f:
						for line in f:
									if line.split()[0] not in dict_of_neighbours:
												UpperCAmelCase__  :					Dict        =     []
												_list.append([line.split()[1], line.split()[2]]					)
												UpperCAmelCase__  :					int        =     _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__  :					Optional[Any]        =     []
												_list.append([line.split()[0], line.split()[2]]					)
												UpperCAmelCase__  :					Optional[Any]        =     _list
									else:
												dict_of_neighbours[line.split()[1]].append(
												    [line.split()[0], line.split()[2]]					)

			return dict_of_neighbours







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):

			'''simple docstring'''





			with open(__UpperCamelCase					) as f:
						UpperCAmelCase__  :					str        =     f.read(1					)
			UpperCAmelCase__  :					Any        =     start_node

			UpperCAmelCase__  :					Dict        =     []

			UpperCAmelCase__  :					Union[str, Any]        =     start_node

			UpperCAmelCase__  :					List[Any]        =     0
			while visiting not in first_solution:
						UpperCAmelCase__  :					List[Any]        =     1_0_0_0_0
						for k in dict_of_neighbours[visiting]:
									if int(k[1]					) < int(__UpperCamelCase					) and k[0] not in first_solution:
												UpperCAmelCase__  :					Dict        =     k[1]
												UpperCAmelCase__  :					Optional[int]        =     k[0]

						first_solution.append(__UpperCamelCase					)
						UpperCAmelCase__  :					List[Any]        =     distance_of_first_solution + int(__UpperCamelCase					)
						UpperCAmelCase__  :					Dict        =     best_node

			first_solution.append(__UpperCamelCase					)

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

			UpperCAmelCase__  :					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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):

			'''simple docstring'''





			UpperCAmelCase__  :					List[Any]        =     []

			for n in solution[1:-1]:
						UpperCAmelCase__  :					Optional[int]        =     solution.index(__UpperCamelCase					)
						for kn in solution[1:-1]:
									UpperCAmelCase__  :					Tuple        =     solution.index(__UpperCamelCase					)
									if n == kn:
												continue

									UpperCAmelCase__  :					List[Any]        =     copy.deepcopy(__UpperCamelCase					)
									UpperCAmelCase__  :					Union[str, Any]        =     kn
									UpperCAmelCase__  :					Dict        =     n

									UpperCAmelCase__  :					List[str]        =     0

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

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

			UpperCAmelCase__  :					Optional[int]        =     len(neighborhood_of_solution[0]					) - 1

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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):

			'''simple docstring'''





			UpperCAmelCase__  :					List[Any]        =     1
			UpperCAmelCase__  :					int        =     first_solution
			UpperCAmelCase__  :					Tuple        =     []
			UpperCAmelCase__  :					Tuple        =     distance_of_first_solution
			UpperCAmelCase__  :					Dict        =     solution

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

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

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

						UpperCAmelCase__  :					Optional[Any]        =     count + 1

			return best_solution_ever, best_cost







def     _UpperCamelCase      (		UpperCamelCase__=None					):

			'''simple docstring'''





			UpperCAmelCase__  :					str        =     generate_neighbours(args.File					)

			UpperCAmelCase__  :					str        =     generate_first_solution(
			    args.File ,      __UpperCamelCase					)

			UpperCAmelCase__  :					Tuple        =     tabu_search(
			    __UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase ,      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())



'''simple docstring'''



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class 		_snake_case					(     yaml.SafeLoader	):




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     [self.constructed_objects[key_node] for key_node, _ in node.value]
								UpperCAmelCase__  :					List[str]        =     [tuple(_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else key for key in keys]
								UpperCAmelCase__  :					List[str]        =     Counter(_lowerCamelCase)
								UpperCAmelCase__  :					Any        =     [key for key in counter if counter[key] > 1]
								if duplicate_keys:
											raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}''')




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=False):
								UpperCAmelCase__  :					List[str]        =     super().construct_mapping(_lowerCamelCase							,							deep=_lowerCamelCase)
								self._check_no_duplicates_on_constructed_node(_lowerCamelCase)
								return mapping







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Tuple        =     list(readme_content.splitlines()					)
			if full_content and full_content[0] == "---" and "---" in full_content[1:]:
						UpperCAmelCase__  :					Dict        =     full_content[1:].index("""---"""					) + 1
						UpperCAmelCase__  :					List[Any]        =     """\n""".join(full_content[1:sep_idx]					)
						return yamlblock, "\n".join(full_content[sep_idx + 1 :]					)

			return None, "\n".join(UpperCamelCase__					)


class 		_snake_case					(     a__	):
					# class attributes
					lowerCAmelCase      :str   =					{'''train_eval_index'''}  # train-eval-index in the YAML metadata




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase):
								with open(_lowerCamelCase							,							encoding="""utf-8""") as readme_file:
											UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     _split_yaml_from_readme(readme_file.read())
								if yaml_string is not None:
											return cls.from_yaml_string(_lowerCamelCase)
								else:
											return cls()




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if path.exists():
											with open(_lowerCamelCase							,							encoding="""utf-8""") as readme_file:
														UpperCAmelCase__  :					int        =     readme_file.read()
								else:
											UpperCAmelCase__  :					List[str]        =     None
								UpperCAmelCase__  :					Union[str, Any]        =     self._to_readme(_lowerCamelCase)
								with open(_lowerCamelCase							,							"""w"""							,							encoding="""utf-8""") as readme_file:
											readme_file.write(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase = None):
								if readme_content is not None:
											UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     _split_yaml_from_readme(_lowerCamelCase)
											UpperCAmelCase__  :					str        =     """---\n""" + self.to_yaml_string() + """---\n""" + content
								else:
											UpperCAmelCase__  :					Optional[int]        =     """---\n""" + self.to_yaml_string() + """---\n"""
								return full_content




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     yaml.load(_lowerCamelCase							,							Loader=_NoDuplicateSafeLoader) or {}

								# Convert the YAML keys to DatasetMetadata fields
								UpperCAmelCase__  :					List[str]        =     {
								    (key.replace("""-"""							,							"""_""") if key.replace("""-"""							,							"""_""") in cls._FIELDS_WITH_DASHES else key): value
								    for key, value in metadata_dict.items()
								}
								return cls(**_lowerCamelCase)




					def 				snake_case__						(      self):
								return yaml.safe_dump(
								    {
								        (key.replace("""_"""							,							"""-""") if key in self._FIELDS_WITH_DASHES else key): value
								        for key, value in self.items()
								    }							,							sort_keys=_lowerCamelCase							,							allow_unicode=_lowerCamelCase							,							encoding="""utf-8"""							,							).decode("""utf-8""")

__A	={
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
		from argparse import ArgumentParser

		__A	=ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
		ap.add_argument('readme_filepath')
		__A	=ap.parse_args()

		__A	=Path(args.readme_filepath)
		__A	=DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)