


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:str			=  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''')
   SCREAMING_SNAKE_CASE						:int			=  parser.parse_args()

   if args.model_type == "bert":
      SCREAMING_SNAKE_CASE						:Optional[int]			=  BertForMaskedLM.from_pretrained(args.model_name)
      SCREAMING_SNAKE_CASE						:List[Any]			=  '''bert'''
   else:
      raise ValueError('''args.model_type should be "bert".''')

   SCREAMING_SNAKE_CASE						:List[str]			=  model.state_dict()
   SCREAMING_SNAKE_CASE						:Tuple			=  {}

   for w in ["word_embeddings", "position_embeddings"]:
      SCREAMING_SNAKE_CASE						:Optional[int]			=  state_dict[F'''{prefix}.embeddings.{w}.weight''']
   for w in ["weight", "bias"]:
      SCREAMING_SNAKE_CASE						:Optional[Any]			=  state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

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

         SCREAMING_SNAKE_CASE						:int			=  state_dict[
             F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
         ]
         SCREAMING_SNAKE_CASE						:Union[str, Any]			=  state_dict[
             F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
         ]

         SCREAMING_SNAKE_CASE						:List[Any]			=  state_dict[
             F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
         ]
         SCREAMING_SNAKE_CASE						:Union[str, Any]			=  state_dict[
             F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
         ]
         SCREAMING_SNAKE_CASE						:List[str]			=  state_dict[
             F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
         ]
      std_idx += 1

   SCREAMING_SNAKE_CASE						:Dict			=  state_dict['''cls.predictions.decoder.weight''']
   SCREAMING_SNAKE_CASE						:Tuple			=  state_dict['''cls.predictions.bias''']
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         SCREAMING_SNAKE_CASE						:Dict			=  state_dict[F'''cls.predictions.transform.dense.{w}''']
         SCREAMING_SNAKE_CASE						:Tuple			=  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)








def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:int				)->Optional[int]:
   '''simple docstring'''






   snake_case_            =       0
   while b > 0:
      if b & 1:
         res += a

      a += a
      b >>= 1

   return res



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Dict				)->Dict:
   '''simple docstring'''






   snake_case_            =       0
   while b > 0:
      if b & 1:
         snake_case_            =       ((res % c) + (a % c)) % c

      a += a
      b >>= 1

   return res




