
'''simple docstring'''

import argparse

import torch

from transformers import BertForMaskedLM


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

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

     snake_case									=    model.state_dict()
     snake_case									=    {}

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

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

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

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

     snake_case									=    state_dict["""cls.predictions.decoder.weight"""]
     snake_case									=    state_dict["""cls.predictions.bias"""]
     if args.vocab_transform:
          for w in ["weight", "bias"]:
               snake_case									=    state_dict[f'cls.predictions.transform.dense.{w}']
               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'''

import warnings
from functools import wraps
from typing import Callable


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 @wraps(lowerCamelCase_			)
 def _inner_fn(*lowerCamelCase_      ,			**lowerCamelCase_			):
  warnings.warn(
      (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,			lowerCamelCase_      ,			)
  return fn(*lowerCamelCase_      ,			**lowerCamelCase_			)

 return _inner_fn





