






'''simple docstring'''





import argparse
import copy







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				A	 =      {}

				with open(UpperCAmelCase   ) 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      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Optional[int]   ):

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

				A	 =      []

				A	 =      start_node

				A	 =      0
				while visiting not in first_solution:
								A	 =      1_00_00
								for k in dict_of_neighbours[visiting]:
												if int(k[1]   ) < int(UpperCAmelCase   ) and k[0] not in first_solution:
																A	 =      k[1]
																A	 =      k[0]

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

				first_solution.append(UpperCAmelCase   )

				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]   )
				    - 1_00_00
				)
				return first_solution, distance_of_first_solution







def      snake_case		(							UpperCAmelCase	:       Any,						UpperCAmelCase	:       Optional[int]   ):
				A	 =      []

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

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

												A	 =      0

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

												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 UpperCAmelCase   : x[index_of_last_item_in_the_list]   )
				return neighborhood_of_solution







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Dict,						UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       List[str],						UpperCAmelCase	:       Dict   ):
				A	 =      1
				A	 =      first_solution
				A	 =      []
				A	 =      distance_of_first_solution
				A	 =      solution

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

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

								A	 =      count + 1

				return best_solution_ever, best_cost







def      snake_case		(							UpperCAmelCase	:       Optional[int]=None   ):
				A	 =      generate_neighbours(args.File   )

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

				A ,		A	 =      tabu_search(
				    UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						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())













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





