


"""simple docstring"""







import argparse

import torch

from transformers import BertForMaskedLM


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

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

 __lowercase    =				model.state_dict()
 __lowercase    =				{}

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

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

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

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

 __lowercase    =				state_dict["""cls.predictions.decoder.weight"""]
 __lowercase    =				state_dict["""cls.predictions.bias"""]
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   __lowercase    =				state_dict[f'''cls.predictions.transform.dense.{w}''']
   __lowercase    =				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_  ,       A_	)->				float:
  '''simple docstring'''







  def get_matched_characters(A_  ,       A_	) -> str:
    a						:		Optional[int]											=      []
    a						:		List[Any]											=      min(len(_stra	)  ,       len(_stra	)	) // 2
    for i, l in enumerate(_stra	):
      a						:		int											=      int(max(0  ,       i - limit	)	)
      a						:		Optional[int]											=      int(min(i + limit + 1  ,       len(_stra	)	)	)
      if l in _stra[left:right]:
        matched.append(A_	)
        a						:		int											=      F'''{_stra[0:_stra.index(A_	)]} {_stra[_stra.index(A_	) + 1:]}'''

    return "".join(A_	)

  # matching characters
  a						:		Tuple											=      get_matched_characters(A_  ,       A_	)
  a						:		str											=      get_matched_characters(A_  ,       A_	)
  a						:		List[str]											=      len(A_	)

  # transposition
  a						:		Union[str, Any]											=      (
      len([(ca, ca) for ca, ca in zip(A_  ,       A_	) if ca != ca]	) // 2
  )

  if not match_count:
    a						:		Tuple											=      0.0
  else:
    a						:		List[str]											=      (
        1
        / 3
        * (
            match_count / len(A_	)
            + match_count / len(A_	)
            + (match_count - transpositions) / match_count
        )
    )

  # common prefix up to 4 characters
  a						:		Union[str, Any]											=      0
  for ca, ca in zip(stra[:4]  ,       stra[:4]	):
    if ca == ca:
      prefix_len += 1
    else:
      break

  return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(jaro_winkler("""hello""", """world"""))






