

'''simple docstring'''





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  UpperCAmelCase__ :     List[Any]				    =    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""")
  UpperCAmelCase__ :     Dict				    =    parser.parse_args()

  if args.model_type == "bert":
    UpperCAmelCase__ :     Union[str, Any]				    =    BertForMaskedLM.from_pretrained(args.model_name)
    UpperCAmelCase__ :     Optional[Any]				    =    """bert"""
  else:
    raise ValueError("""args.model_type should be \"bert\".""")

  UpperCAmelCase__ :     List[Any]				    =    model.state_dict()
  UpperCAmelCase__ :     Optional[Any]				    =    {}

  for w in ["word_embeddings", "position_embeddings"]:
    UpperCAmelCase__ :     Union[str, Any]				    =    state_dict[F"""{prefix}.embeddings.{w}.weight"""]
  for w in ["weight", "bias"]:
    UpperCAmelCase__ :     Optional[Any]				    =    state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

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

      UpperCAmelCase__ :     List[str]				    =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
      ]
      UpperCAmelCase__ :     str				    =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
      ]

      UpperCAmelCase__ :     List[Any]				    =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
      ]
      UpperCAmelCase__ :     Optional[int]				    =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
      ]
      UpperCAmelCase__ :     Dict				    =    state_dict[
          F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
      ]
    std_idx += 1

  UpperCAmelCase__ :     Union[str, Any]				    =    state_dict["""cls.predictions.decoder.weight"""]
  UpperCAmelCase__ :     Tuple				    =    state_dict["""cls.predictions.bias"""]
  if args.vocab_transform:
    for w in ["weight", "bias"]:
      UpperCAmelCase__ :     int				    =    state_dict[F"""cls.predictions.transform.dense.{w}"""]
      UpperCAmelCase__ :     str				    =    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)







from __future__ import annotations






def 					__lowercase      (     _A						,    _A			)     ->						list[str]:
      if nth_term == "":
            return [""]
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   int(_A			)
      SCREAMING_SNAKE_CASE :      list[str]       =   []
      for temp in range(int(_A			)			):
            series.append(F"1 / {pow(temp + 1						,    int(_A			)			)}" if series else """1"""			)
      return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase__ :     Dict				    =    int(input("""Enter the last number (nth term) of the P-Series"""))
  UpperCAmelCase__ :     Optional[int]				    =    int(input("""Enter the power for  P-Series"""))
  print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
  print(p_series(nth_term, power))




