import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Dict							     =		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 :	Tuple							     =		parser.parse_args()

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

  SCREAMING_SNAKE_CASE :	int							     =		model.state_dict()
  SCREAMING_SNAKE_CASE :	List[Any]							     =		{}

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

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

      SCREAMING_SNAKE_CASE :	List[str]							     =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
      ]
      SCREAMING_SNAKE_CASE :	Optional[int]							     =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
      ]

      SCREAMING_SNAKE_CASE :	Optional[int]							     =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
      ]
      SCREAMING_SNAKE_CASE :	Optional[int]							     =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
      ]
      SCREAMING_SNAKE_CASE :	Dict							     =		state_dict[
          f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
      ]
    std_idx += 1

  SCREAMING_SNAKE_CASE :	Tuple							     =		state_dict["""cls.predictions.decoder.weight"""]
  SCREAMING_SNAKE_CASE :	Dict							     =		state_dict["""cls.predictions.bias"""]
  if args.vocab_transform:
    for w in ["weight", "bias"]:
      SCREAMING_SNAKE_CASE :	List[str]							     =		state_dict[f'''cls.predictions.transform.dense.{w}''']
      SCREAMING_SNAKE_CASE :	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)



from __future__ import annotations



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [True] * limit
 __a       =  False
 __a       =  False
 __a       =  True

 for i in range(3							,				int(limit**0.5 + 1					)							,				2					):
  __a       =  i * 2
  while index < limit:
   __a       =  False
   __a       =  index + i

 __a       =  [2]

 for i in range(3							,				_A							,				2					):
  if is_prime[i]:
   primes.append(_A					)

 return primes



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  prime_sieve(_A					)
 __a       =  0
 __a       =  0

 for i in range(len(_A					)					):
  for j in range(i + length							,				len(_A					)					):
   __a       =  sum(primes[i:j]					)
   if sol >= ceiling:
    break

   if sol in primes:
    __a       =  j - i
    __a       =  sol

 return largest


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


