





"""simple docstring"""


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  lowerCamelCase__				  =   argparse.ArgumentParser(
      description=(
          "Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
          " Distillation"
      )
  )
  parser.add_argument("--model_type", default="bert", choices=["bert"])
  parser.add_argument("--model_name", default="bert-base-uncased", type=str)
  parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
  parser.add_argument("--vocab_transform", action="store_true")
  lowerCamelCase__				  =   parser.parse_args()

  if args.model_type == "bert":
    lowerCamelCase__				  =   BertForMaskedLM.from_pretrained(args.model_name)
    lowerCamelCase__				  =   "bert"
  else:
    raise ValueError("args.model_type should be \"bert\".")

  lowerCamelCase__				  =   model.state_dict()
  lowerCamelCase__				  =   {}

  for w in ["word_embeddings", "position_embeddings"]:
    lowerCamelCase__				  =   state_dict[f"""{prefix}.embeddings.{w}.weight"""]
  for w in ["weight", "bias"]:
    lowerCamelCase__				  =   state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

  lowerCamelCase__				  =   0
  for teacher_idx in [0, 2, 4, 7, 9, 11]:
    for w in ["weight", "bias"]:
      lowerCamelCase__				  =   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
      ]
      lowerCamelCase__				  =   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
      ]
      lowerCamelCase__				  =   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
      ]

      lowerCamelCase__				  =   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
      ]
      lowerCamelCase__				  =   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
      ]

      lowerCamelCase__				  =   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
      ]
      lowerCamelCase__				  =   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
      ]
      lowerCamelCase__				  =   state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
      ]
    std_idx += 1

  lowerCamelCase__				  =   state_dict["cls.predictions.decoder.weight"]
  lowerCamelCase__				  =   state_dict["cls.predictions.bias"]
  if args.vocab_transform:
    for w in ["weight", "bias"]:
      lowerCamelCase__				  =   state_dict[f"""cls.predictions.transform.dense.{w}"""]
      lowerCamelCase__				  =   state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

  print(f"""N layers selected for distillation: {std_idx}""")
  print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

  print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
  torch.save(compressed_sd, args.dump_checkpoint)







"""simple docstring"""


import math



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       list[list[float]]					,   __a   :       list[int]	)		->				int:
        _UpperCamelCase      :	List[Any]						=       0.0
        _UpperCamelCase      :	Union[str, Any]						=       0.0
        for i in range(len(__a	)	):
              da += math.pow((sample[i] - weights[0][i])					,   2	)
              da += math.pow((sample[i] - weights[1][i])					,   2	)
              return 0 if da > da else 1
        return 0


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       list[list[int | float]]					,   __a   :       list[int]					,   __a   :       int					,   __a   :       float	)		->				list[list[int | float]]:
        for i in range(len(__a	)	):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights





def 					lowercase__    (					)					->		None:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

      # weight initialization ( n, C )
      _UpperCamelCase      :	List[str]						=       [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

      # training
      _UpperCamelCase      :	List[Any]						=       SelfOrganizingMap()
      _UpperCamelCase      :	int						=       3
      _UpperCamelCase      :	List[Any]						=       0.5

      for _ in range(lowercase_			):
            for j in range(len(lowercase_			)			):
                  # training sample
                  _UpperCamelCase      :	int						=       training_samples[j]

                  # Compute the winning vector
                  _UpperCamelCase      :	Tuple						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

                  # Update the winning vector
                  _UpperCamelCase      :	int						=       self_organizing_map.update(lowercase_		,lowercase_		,lowercase_		,lowercase_			)

    # classify test sample
      _UpperCamelCase      :	Optional[int]						=       [0, 0, 0, 1]
      _UpperCamelCase      :	Dict						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

      # results
      print(F'''Clusters that the test sample belongs to : {winner}'''			)
      print(F'''Weights that have been trained : {weights}'''			)


# running the main() function
if __name__ == "__main__":
  main()
