

'''simple docstring'''





import argparse

import torch

from transformers import BertForMaskedLM


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

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

       _SCREAMING_SNAKE_CASE  :						List[str]         =    model.state_dict()
       _SCREAMING_SNAKE_CASE  :						Any         =    {}

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

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

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

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

       _SCREAMING_SNAKE_CASE  :						List[str]         =    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  :						List[Any]         =    state_dict[F"cls.predictions.transform.dense.{w}"]
                     _SCREAMING_SNAKE_CASE  :						List[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)





def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]
     for data in source_data:
          for i, el in enumerate(_A       ):
               if len(_A       ) < i + 1:
                    data_lists.append([]       )
               data_lists[i].append(float(_A       )       )
     return data_lists
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]
     for dlist, weight in zip(_A   ,	_A       ):
          SCREAMING_SNAKE_CASE__					=					min(_A       )
          SCREAMING_SNAKE_CASE__					=					max(_A       )

          SCREAMING_SNAKE_CASE__					=					[]
          # for weight 0 score is 1 - actual score
          if weight == 0:
               for item in dlist:
                    try:
                         score.append(1 - ((item - mind) / (maxd - mind))       )
                    except ZeroDivisionError:
                         score.append(1       )

          elif weight == 1:
               for item in dlist:
                    try:
                         score.append((item - mind) / (maxd - mind)       )
                    except ZeroDivisionError:
                         score.append(0       )

        # weight not 0 or 1
          else:
               SCREAMING_SNAKE_CASE__					=					F'''Invalid weight of {weight:f} provided'''
               raise ValueError(_A       )

          score_lists.append(_A       )

     return score_lists
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[0 for i in range(len(score_lists[0]       )       )]

     for slist in score_lists:
          for j, ele in enumerate(_A       ):
               SCREAMING_SNAKE_CASE__					=					final_scores[j] + ele

     return final_scores







def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					get_data(_A       )
     SCREAMING_SNAKE_CASE__					=					calculate_each_score(_A   ,	_A       )
     SCREAMING_SNAKE_CASE__					=					generate_final_scores(_A       )

     # append scores to source data
     for i, ele in enumerate(_A       ):
          source_data[i].append(_A       )

     return source_data


