





'''simple docstring'''




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  lowercase__       : Optional[int]         =       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''')
  lowercase__       : Dict         =       parser.parse_args()

  if args.model_type == "bert":
    lowercase__       : Dict         =       BertForMaskedLM.from_pretrained(args.model_name)
    lowercase__       : int         =       '''bert'''
  else:
    raise ValueError('''args.model_type should be "bert".''')

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

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

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

      lowercase__       : Dict         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
      ]
      lowercase__       : List[Any]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
      ]

      lowercase__       : Dict         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
      ]
      lowercase__       : List[str]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
      ]
      lowercase__       : List[Any]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
      ]
    std_idx += 1

  lowercase__       : int         =       state_dict['''cls.predictions.decoder.weight''']
  lowercase__       : List[Any]         =       state_dict['''cls.predictions.bias''']
  if args.vocab_transform:
    for w in ["weight", "bias"]:
      lowercase__       : Optional[Any]         =       state_dict[f"""cls.predictions.transform.dense.{w}"""]
      lowercase__       : Union[str, Any]         =       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      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   _UpperCAmelCase           =   str(SCREAMING_SNAKE_CASE_						)
   return len(SCREAMING_SNAKE_CASE_						) == 9 and set(SCREAMING_SNAKE_CASE_						) == set('''123456789'''						)
def      A__       (    )  ->  int | None:






   """simple docstring"""







   for base_num in range(99_99				,	49_99				,	-1						):
      _UpperCAmelCase           =   10_00_02 * base_num
      if is_9_pandigital(SCREAMING_SNAKE_CASE_						):
         return candidate

   for base_num in range(3_33				,	99				,	-1						):
      _UpperCAmelCase           =   1_00_20_03 * base_num
      if is_9_pandigital(SCREAMING_SNAKE_CASE_						):
         return candidate

   return None


if __name__ == "__main__":
   print(f'''{solution() = }''')