




"""simple docstring"""


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     A_      			=					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''')
     A_      			=					parser.parse_args()

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

     A_      			=					model.state_dict()
     A_      			=					{}

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

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

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

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

     A_      			=					state_dict['''cls.predictions.decoder.weight''']
     A_      			=					state_dict['''cls.predictions.bias''']
     if args.vocab_transform:
          for w in ["weight", "bias"]:
               A_      			=					state_dict[F'''cls.predictions.transform.dense.{w}''']
               A_      			=					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       UpperCAmelCase__ (snake_case__							:      list[int]	,     snake_case__							:      str					):
  """simple docstring"""

  _snake_case							:	str			=						int(snake_case__					)

  # Initialize Result
  _snake_case							:	str			=						[]

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

  return answer


# Driver Code
if __name__ == "__main__":
     A_      			=       []
     A_      			=       '''0'''

     if (
         input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
         == "y"
     ):
          A_      			=       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()))
          A_      			=       input('''Enter the change you want to make in Indian Currency: ''').strip()
     else:
          # All denominations of Indian Currency if user does not enter
          A_      			=       [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
          A_      			=       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}: ''')
          A_      			=       find_minimum_change(denominations, value)
          # Print result
          for i in range(len(answer)):
               print(answer[i], end=''' ''')


