


SCREAMING_SNAKE_CASE       :      dict[tuple[int, int, int], int]												= {}



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						int:

    # if we are absent twice, or late 3 consecutive days,
    # no further prize strings are possible
    if late == 3 or absent == 2:
        return 0

    # if we have no days left, and have not failed any other rules,
    # we have a prize string
    if days == 0:
        return 1

    # No easy solution, so now we need to do the recursive calculation

    # First, check if the combination is already in the cache, and
    # if yes, return the stored value from there since we already
    # know the number of possible prize strings from this point on
    _lowercase       :							Dict      		=		(days, absent, late)
    if key in cache:
        return cache[key]

    # now we calculate the three possible ways that can unfold from
    # this point on, depending on our attendance today

    # 1) if we are late (but not absent), the "absent" counter stays as
    # it is, but the "late" counter increases by one
    _lowercase       :							str      		=		_calculate(days - 1							, lowerCamelCase_							, late + 1 )

    # 2) if we are absent, the "absent" counter increases by 1, and the
    # "late" counter resets to 0
    _lowercase       :							int      		=		_calculate(days - 1							, absent + 1							, 0 )

    # 3) if we are on time, this resets the "late" counter and keeps the
    # absent counter
    _lowercase       :							Tuple      		=		_calculate(days - 1							, lowerCamelCase_							, 0 )

    _lowercase       :							Tuple      		=		state_late + state_absent + state_ontime

    _lowercase       :							str      		=		prizestrings
    return prizestrings



def 				UpperCamelCase_(      lowerCamelCase_ = 30 )    ->						int:
    return _calculate(lowerCamelCase_							, absent=0							, late=0 )


if __name__ == "__main__":
 print(solution())









import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       snake_case__					:							Optional[int]													=     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__					:							Optional[int]													=     parser.parse_args()

       if args.model_type == "bert":
              snake_case__					:							Dict													=     BertForMaskedLM.from_pretrained(args.model_name)
              snake_case__					:							Union[str, Any]													=     'bert'
       else:
              raise ValueError('args.model_type should be "bert".')

       snake_case__					:							Optional[int]													=     model.state_dict()
       snake_case__					:							List[Any]													=     {}

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

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

                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
                     ]
                     snake_case__					:							Optional[int]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
                     ]

                     snake_case__					:							Optional[Any]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
                     ]
                     snake_case__					:							List[str]													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
                     ]
                     snake_case__					:							int													=     state_dict[
                         f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
                     ]
              std_idx += 1

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





