




'''simple docstring'''


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 lowerCAmelCase_      :  Tuple				     =  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_      :  Any				     =  parser.parse_args()

 if args.model_type == "bert":
  lowerCAmelCase_      :  int				     =  BertForMaskedLM.from_pretrained(args.model_name)
  lowerCAmelCase_      :  Tuple				     =  'bert'
 else:
  raise ValueError('args.model_type should be "bert".')

 lowerCAmelCase_      :  Optional[Any]				     =  model.state_dict()
 lowerCAmelCase_      :  Any				     =  {}

 for w in ["word_embeddings", "position_embeddings"]:
  lowerCAmelCase_      :  Tuple				     =  state_dict[f"{prefix}.embeddings.{w}.weight"]
 for w in ["weight", "bias"]:
  lowerCAmelCase_      :  Optional[Any]				     =  state_dict[f"{prefix}.embeddings.LayerNorm.{w}"]

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

   lowerCAmelCase_      :  int				     =  state_dict[
       f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
   ]
   lowerCAmelCase_      :  Union[str, Any]				     =  state_dict[
       f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
   ]

   lowerCAmelCase_      :  Union[str, Any]				     =  state_dict[
       f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
   ]
   lowerCAmelCase_      :  Optional[int]				     =  state_dict[
       f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
   ]
   lowerCAmelCase_      :  Dict				     =  state_dict[
       f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
   ]
  std_idx += 1

 lowerCAmelCase_      :  Any				     =  state_dict['cls.predictions.decoder.weight']
 lowerCAmelCase_      :  Tuple				     =  state_dict['cls.predictions.bias']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   lowerCAmelCase_      :  Any				     =  state_dict[f"cls.predictions.transform.dense.{w}"]
   lowerCAmelCase_      :  List[Any]				     =  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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					list[int] ,    UpperCamelCase__    :					str				):




     """simple docstring"""


     a_     :							Tuple     						=  int(UpperCamelCase__				)

     # Initialize Result
     a_     :							Tuple     						=  []

     # Traverse through all denomination
     for denomination in reversed(UpperCamelCase__				):
          # Find denominations
          while int(UpperCamelCase__				) >= int(UpperCamelCase__				):
               total_value -= int(UpperCamelCase__				)
               answer.append(UpperCamelCase__				)  # Append the "answers" array

     return answer


# Driver Code
if __name__ == "__main__":
 lowerCAmelCase_      :  List[str]				     =  []
 lowerCAmelCase_      :  Union[str, Any]				     =  '0'

 if (
     input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
     == "y"
 ):
  lowerCAmelCase_      :  str				     =  int(input('Enter the number of denominations you want to add: ').strip())

  for i in range(0, n):
   denominations.append(int(input(f"Denomination {i}: ").strip()))
  lowerCAmelCase_      :  Union[str, Any]				     =  input('Enter the change you want to make in Indian Currency: ').strip()
 else:
  # All denominations of Indian Currency if user does not enter
  lowerCAmelCase_      :  List[Any]				     =  [1, 2, 5, 10, 20, 50, 100, 500, 2000]
  lowerCAmelCase_      :  str				     =  input('Enter the change you want to make: ').strip()

 if int(value) == 0 or int(value) < 0:
  print('The total value cannot be zero or negative.')

 else:
  print(f"Following is minimal change for {value}: ")
  lowerCAmelCase_      :  Optional[int]				     =  find_minimum_change(denominations, value)
  # Print result
  for i in range(len(answer)):
   print(answer[i], end=' ')
