


'''simple docstring'''






import argparse
import copy







def 							UpperCamelCase_     (       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  {}

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

  return dict_of_neighbours







def 							UpperCamelCase_     (       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  with open(A__   ) as f:
    lowerCAmelCase_     : int     =  f.read(1   )
  lowerCAmelCase_     : Any     =  start_node

  lowerCAmelCase_     : Union[str, Any]     =  []

  lowerCAmelCase_     : str     =  start_node

  lowerCAmelCase_     : Tuple     =  0
  while visiting not in first_solution:
    lowerCAmelCase_     : str     =  1_00_00
    for k in dict_of_neighbours[visiting]:
      if int(k[1]   ) < int(A__   ) and k[0] not in first_solution:
        lowerCAmelCase_     : List[str]     =  k[1]
        lowerCAmelCase_     : Union[str, Any]     =  k[0]

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

  first_solution.append(A__   )

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

  lowerCAmelCase_     : Optional[int]     =  (
      distance_of_first_solution
      + int(dict_of_neighbours[first_solution[-2]][position][1]   )
      - 1_00_00
  )
  return first_solution, distance_of_first_solution







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Tuple   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  []

  for n in solution[1:-1]:
    lowerCAmelCase_     : Union[str, Any]     =  solution.index(A__   )
    for kn in solution[1:-1]:
      lowerCAmelCase_     : Any     =  solution.index(A__   )
      if n == kn:
        continue

      lowerCAmelCase_     : Tuple     =  copy.deepcopy(A__   )
      lowerCAmelCase_     : int     =  kn
      lowerCAmelCase_     : int     =  n

      lowerCAmelCase_     : Optional[int]     =  0

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

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

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

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







def 							UpperCamelCase_     (       A__   :  List[Any]				,       A__   :  List[str]				,       A__   :  Optional[Any]				,       A__   :  int				,       A__   :  Optional[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : int     =  1
  lowerCAmelCase_     : Dict     =  first_solution
  lowerCAmelCase_     : int     =  []
  lowerCAmelCase_     : int     =  distance_of_first_solution
  lowerCAmelCase_     : Tuple     =  solution

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

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

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

    lowerCAmelCase_     : Optional[int]     =  count + 1

  return best_solution_ever, best_cost







def 							UpperCamelCase_     (       A__   :  Any=None   ):
  '''simple docstring'''





  lowerCAmelCase_     : Union[str, Any]     =  generate_neighbours(args.File   )

  lowerCAmelCase_,				lowerCAmelCase_     : Dict     =  generate_first_solution(
      args.File				,       A__   )

  lowerCAmelCase_,				lowerCAmelCase_     : Any     =  tabu_search(
      A__				,       A__				,       A__				,       args.Iterations				,       args.Size				,       )

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


if __name__ == "__main__":
  __A   :    str											= 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 (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__A   :    str											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  WavaVecaForSequenceClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : str     =  downstream_dict["""projector.weight"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""projector.bias"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""model.post_net.linear.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.post_net.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  WavaVecaForAudioFrameClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.linear.weight"""]
  lowerCAmelCase_     : List[str]     =  downstream_dict["""model.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[Any]     =  WavaVecaForXVector.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : List[Any]     =  downstream_dict["""connector.weight"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
    lowerCAmelCase_     : List[str]     =  downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowerCAmelCase_     : List[str]     =  downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  lowerCAmelCase_     : int     =  downstream_dict["""objective.W"""]
  return model







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  torch.load(A__				,       map_location="""cpu"""   )

  lowerCAmelCase_     : Optional[int]     =  checkpoint["""Downstream"""]

  lowerCAmelCase_     : int     =  WavaVecaConfig.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  WavaVecaFeatureExtractor.from_pretrained(
      A__				,       return_attention_mask=A__				,       do_normalize=A__   )

  lowerCAmelCase_     : Optional[int]     =  hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""   ):
    lowerCAmelCase_     : List[str]     =  convert_classification(A__				,       A__				,       A__   )
  elif arch.endswith("""ForAudioFrameClassification"""   ):
    lowerCAmelCase_     : Dict     =  convert_diarization(A__				,       A__				,       A__   )
  elif arch.endswith("""ForXVector"""   ):
    lowerCAmelCase_     : List[Any]     =  convert_xvector(A__				,       A__				,       A__   )
  else:
    raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'   )

  if hf_config.use_weighted_layer_sum:
    lowerCAmelCase_     : Optional[int]     =  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(A__   )
  hf_model.save_pretrained(A__   )


if __name__ == "__main__":
  __A   :    List[Any]											= argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __A   :    Tuple											= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



