



import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
      _lowerCamelCase						    =  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						    =  parser.parse_args()

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

      _lowerCamelCase						    =  model.state_dict()
      _lowerCamelCase						    =  {}

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

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

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

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

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



 """simple docstring"""





 if height >= 1:
  move_tower(height - 1		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
  move_disk(__lowerCamelCase		,	__lowerCamelCase     )
  move_tower(height - 1		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     Optional[int]     ):



 """simple docstring"""





 print("moving disk from"		,	__lowerCamelCase		,	"to"		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	int(input("Height of hanoi: "     ).strip()     )
 move_tower(__lowerCamelCase		,	"A"		,	"B"		,	"C"     )


if __name__ == "__main__":
      main()






