



'''simple docstring'''

import argparse
import copy



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		_a					:				Any				=							{}

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

		return dict_of_neighbours



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[Any]:

		with open(lowerCAmelCase_  ) as f:
				_a					:				List[str]				=							f.read(1  )
		_a					:				Any				=							start_node

		_a					:				Any				=							[]

		_a					:				List[str]				=							start_node

		_a					:				str				=							0
		while visiting not in first_solution:
				_a					:				Optional[int]				=							10000
				for k in dict_of_neighbours[visiting]:
						if int(k[1]  ) < int(lowerCAmelCase_  ) and k[0] not in first_solution:
								_a					:				Dict				=							k[1]
								_a					:				Optional[int]				=							k[0]

				first_solution.append(lowerCAmelCase_  )
				_a					:				int				=							distance_of_first_solution + int(lowerCAmelCase_  )
				_a					:				Optional[int]				=							best_node

		first_solution.append(lowerCAmelCase_  )

		_a					:				Union[str, Any]				=							0
		for k in dict_of_neighbours[first_solution[-2]]:
				if k[0] == start_node:
						break
				position += 1

		_a					:				int				=							(
		    distance_of_first_solution
		    + int(dict_of_neighbours[first_solution[-2]][position][1]  )
		    - 10000
		)
		return first_solution, distance_of_first_solution



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		_a					:				Tuple				=							[]

		for n in solution[1:-1]:
				_a					:				int				=							solution.index(lowerCAmelCase_  )
				for kn in solution[1:-1]:
						_a					:				Any				=							solution.index(lowerCAmelCase_  )
						if n == kn:
								continue

						_a					:				Tuple				=							copy.deepcopy(lowerCAmelCase_  )
						_a					:				Optional[Any]				=							kn
						_a					:				Optional[int]				=							n

						_a					:				List[Any]				=							0

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

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

		_a					:				str				=							len(neighborhood_of_solution[0]  ) - 1

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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Any:
		_a					:				List[Any]				=							1
		_a					:				Optional[Any]				=							first_solution
		_a					:				Tuple				=							[]
		_a					:				Optional[Any]				=							distance_of_first_solution
		_a					:				Tuple				=							solution

		while count <= iters:
				_a					:				List[Any]				=							find_neighborhood(lowerCAmelCase_						,      lowerCAmelCase_  )
				_a					:				Optional[Any]				=							0
				_a					:				List[Any]				=							neighborhood[index_of_best_solution]
				_a					:				str				=							len(lowerCAmelCase_  ) - 1

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

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

				_a					:				Optional[int]				=							count + 1

		return best_solution_ever, best_cost



def 					__lowerCamelCase					(				lowerCAmelCase_=None  )      ->				Any:
		_a					:				str				=							generate_neighbours(args.File  )

		_a	, _a					:				str				=							generate_first_solution(
		    args.File						,      lowerCAmelCase_  )

		_a	, _a					:				Dict				=							tabu_search(
		    lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      args.Iterations						,      args.Size						,      )

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


if __name__ == "__main__":
							__lowerCAmelCase						      =       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 OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Optional[int]           = 'distilbert'
							lowerCAmelCase	:   Optional[int]           = {
							    'hidden_size': 'dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							}







							def __init__(							self :	str     ,_UpperCAmelCase :	Optional[Any]=30522     ,_UpperCAmelCase :	int=512     ,_UpperCAmelCase :	int=False     ,_UpperCAmelCase :	str=6     ,_UpperCAmelCase :	int=12     ,_UpperCAmelCase :	Union[str, Any]=768     ,_UpperCAmelCase :	Optional[Any]=4 * 768     ,_UpperCAmelCase :	int=0.1     ,_UpperCAmelCase :	Union[str, Any]=0.1     ,_UpperCAmelCase :	Tuple="gelu"     ,_UpperCAmelCase :	Any=0.02     ,_UpperCAmelCase :	List[Any]=0.1     ,_UpperCAmelCase :	Dict=0.2     ,_UpperCAmelCase :	Union[str, Any]=0     ,**_UpperCAmelCase :	List[Any]     ,):
									_a					:				Optional[Any]				=							vocab_size
									_a					:				Dict				=							max_position_embeddings
									_a					:				Optional[int]				=							sinusoidal_pos_embds
									_a					:				List[Any]				=							n_layers
									_a					:				Any				=							n_heads
									_a					:				int				=							dim
									_a					:				Optional[Any]				=							hidden_dim
									_a					:				str				=							dropout
									_a					:				Optional[Any]				=							attention_dropout
									_a					:				Any				=							activation
									_a					:				Tuple				=							initializer_range
									_a					:				Union[str, Any]				=							qa_dropout
									_a					:				List[Any]				=							seq_classif_dropout
									super().__init__(**_UpperCAmelCase     ,pad_token_id=_UpperCAmelCase		)

class      __magic_name__						(   _UpperCamelCase       ):







							@property
							def 							__lowercase      (							self :	Union[str, Any]		):
									if self.task == "multiple-choice":
											_a					:				Union[str, Any]				=							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_a					:				List[str]				=							{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)




