

'''simple docstring'''






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)




'''simple docstring'''






import random
from typing import Any


def 	_A  (					A__   ):






 """simple docstring"""



 for _ in range(len(A__   )   ):
  __lowercase													=				random.randint(0  ,       len(A__   ) - 1   )
  __lowercase													=				random.randint(0  ,       len(A__   ) - 1   )
  __lowercase						,			__lowercase													=				data[b], data[a]
 return data


if __name__ == "__main__":
 lowerCAmelCase__      		=			[0, 1, 2, 3, 4, 5, 6, 7]
 lowerCAmelCase__      		=			['''python''', '''says''', '''hello''', '''!''']
 print('''Fisher-Yates Shuffle:''')
 print('''List''', integers, strings)
 print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))

