

'''simple docstring'''
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     lowerCamelCase			:				Tuple 			=					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			:				Any 			=					parser.parse_args()

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

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

     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			:				Dict 			=					0
     for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
          for w in ["weight", "bias"]:
               lowerCamelCase			:				Any 			=					state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
               ]
               lowerCamelCase			:				Any 			=					state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
               ]
               lowerCamelCase			:				Tuple 			=					state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
               ]

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

               lowerCamelCase			:				int 			=					state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
               ]
               lowerCamelCase			:				Tuple 			=					state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
               ]
               lowerCamelCase			:				List[Any] 			=					state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
               ]
          std_idx += 1

     lowerCamelCase			:				Optional[Any] 			=					state_dict["cls.predictions.decoder.weight"]
     lowerCamelCase			:				Union[str, Any] 			=					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			:				int 			=					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)



'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					float							,			_UpperCamelCase						:					float    )				->      float:
     """simple docstring"""




     if mass < 0:
          raise ValueError('The mass of a body cannot be negative'    )
     return 0.5 * mass * abs(_UpperCamelCase    ) * abs(_UpperCamelCase    )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
