




"""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"""







from collections import defaultdict
from math import ceil, sqrt




def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000  ,						lowerCAmelCase_ = 10   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				defaultdict(lowerCAmelCase_   )

					for outer_width in range(3  ,						(t_limit // 4) + 2   ):
										if outer_width * outer_width > t_limit:
															__SCREAMING_SNAKE_CASE						   =				max(
															    ceil(sqrt(outer_width * outer_width - t_limit   )   )  ,						1   )
										else:
															__SCREAMING_SNAKE_CASE						   =				1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(lowerCAmelCase_  ,						outer_width - 1  ,						2   ):
															count[outer_width * outer_width - hole_width * hole_width] += 1

					return sum(1 for n in count.values() if 1 <= n <= 10   )


if __name__ == "__main__":
		print(F"{solution() = }")
