






'''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)











'''simple docstring'''



def 							lowercase_				(    __A :       int = 6_0_0_8_5_1_4_7_5_1_4_3			)       ->      int:



 """simple docstring"""




 try:
  lowercase					:   Any				  =int(__A			)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''			)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''			)
 lowercase					:   Optional[Any]				  =1
 lowercase					:   Dict				  =2
 while i * i <= n:
  while n % i == 0:
   lowercase					:   Optional[int]				  =i
   n //= i
  i += 1
 if n > 1:
  lowercase					:   Dict				  =n
 return int(__A			)


if __name__ == "__main__":
      print(f"""{solution() = }""")



