
"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_												= 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_												= parser.parse_args()

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

 SCREAMING_SNAKE_CASE_												= model.state_dict()
 SCREAMING_SNAKE_CASE_												= {}

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

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

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

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

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



import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   str      ,					**UpperCAmelCase__					:   List[str]							)	->     Tuple:
							lowerCamelCase_					=       AutoConfig.from_pretrained(UpperCAmelCase__      ,					**UpperCAmelCase__							)
							lowerCamelCase_					=       AutoModelForSeqaSeqLM.from_config(UpperCAmelCase__							)
							model.save_pretrained(UpperCAmelCase__							)
							AutoTokenizer.from_pretrained(UpperCAmelCase__							).save_pretrained(UpperCAmelCase__							)
							return model


if __name__ == "__main__":
		fire.Fire(save_randomly_initialized_version)





