


'''simple docstring'''

class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    str   ,						UpperCamelCase    :    List[str]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= name
             lowercase__      			= value
             lowercase__      			= weight



      def __repr__(self    :    Union[str, Any] ):

             '''simple docstring'''







             return f"{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             return self.value



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             return self.name



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             return self.weight






      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             return self.value / self.weight






def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       Dict:






       """simple docstring"""







       lowercase__      			= []
       for i in range(len(A       )       ):
              menu.append(Things(name[i]  , value[i]  , weight[i]       )       )
       return menu

def _SCREAMING_SNAKE_CASE      (A  , A  , A       )       ->       List[Any]:






       """simple docstring"""







       lowercase__      			= sorted(A  , key=A  , reverse=A       )
       lowercase__      			= []
       lowercase__			,lowercase__      			= 0.0, 0.0
       for i in range(len(A       )       ):
              if (total_cost + items_copy[i].get_weight()) <= max_cost:
                     result.append(items_copy[i]       )
                     total_cost += items_copy[i].get_weight()
                     total_value += items_copy[i].get_value()
       return (result, total_value)

def _SCREAMING_SNAKE_CASE      ()       ->       Dict:






       """simple docstring"""









if __name__ == "__main__":
 import doctest

 doctest.testmod()








'''simple docstring'''

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 lowerCamelCase							:   List[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')
 lowerCamelCase							:   str  						=	parser.parse_args()

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

 lowerCamelCase							:   int  						=	model.state_dict()
 lowerCamelCase							:   int  						=	{}

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

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

   lowerCamelCase							:   Tuple  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
   ]
   lowerCamelCase							:   Optional[int]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
   ]

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

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




