

"""simple docstring"""




import argparse
import copy
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Dict					   =					{}

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

 return dict_of_neighbours
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





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

 UpperCAmelCase__					:  Optional[Any]					   =					[]

 UpperCAmelCase__					:  Dict					   =					start_node

 UpperCAmelCase__					:  Dict					   =					0
 while visiting not in first_solution:
  UpperCAmelCase__					:  int					   =					10000
  for k in dict_of_neighbours[visiting]:
   if int(k[1]      ) < int(__UpperCamelCase      ) and k[0] not in first_solution:
    UpperCAmelCase__					:  Tuple					   =					k[1]
    UpperCAmelCase__					:  Union[str, Any]					   =					k[0]

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

 first_solution.append(__UpperCamelCase      )

 UpperCAmelCase__					:  Optional[int]					   =					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]      )
     - 10000
 )
 return first_solution, distance_of_first_solution
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Union[str, Any]					   =					[]

 for n in solution[1:-1]:
  UpperCAmelCase__					:  Dict					   =					solution.index(__UpperCamelCase      )
  for kn in solution[1:-1]:
   UpperCAmelCase__					:  Any					   =					solution.index(__UpperCamelCase      )
   if n == kn:
    continue

   UpperCAmelCase__					:  str					   =					copy.deepcopy(__UpperCamelCase      )
   UpperCAmelCase__					:  int					   =					kn
   UpperCAmelCase__					:  List[str]					   =					n

   UpperCAmelCase__					:  Tuple					   =					0

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

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

 UpperCAmelCase__					:  Any					   =					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   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[Any]					   =					1
 UpperCAmelCase__					:  Tuple					   =					first_solution
 UpperCAmelCase__					:  Tuple					   =					[]
 UpperCAmelCase__					:  Optional[int]					   =					distance_of_first_solution
 UpperCAmelCase__					:  Union[str, Any]					   =					solution

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

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

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

  UpperCAmelCase__					:  str					   =					count + 1

 return best_solution_ever, best_cost
def   lowerCAmelCase						(					__UpperCamelCase=None      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					generate_neighbours(args.File      )

 UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[Any]					   =					generate_first_solution(
     args.File							,    __UpperCamelCase      )

 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__":
      __UpperCAmelCase =				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"""




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





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

 return orig_key
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 for key in orig_state_dict.copy().keys():
  UpperCAmelCase__					:  Optional[Any]					   =					orig_state_dict.pop(__UpperCamelCase      )

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

 UpperCAmelCase__					:  Dict					   =					orig_state_dict["""cls.predictions.decoder.bias"""]
 UpperCAmelCase__					:  Optional[Any]					   =					torch.arange(__UpperCamelCase      ).expand((1, -1)      ) + 2

 return orig_state_dict
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  int					   =					torch.load(__UpperCamelCase							,    map_location="""cpu"""      )["""model_state_dict"""]
 UpperCAmelCase__					:  List[str]					   =					YosoConfig.from_json_file(__UpperCamelCase      )
 UpperCAmelCase__					:  Union[str, Any]					   =					YosoForMaskedLM(__UpperCamelCase      )

 UpperCAmelCase__					:  Optional[int]					   =					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__":
      __UpperCAmelCase =				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.'
      )
      __UpperCAmelCase =				parser.parse_args()
      convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





