





'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=1_024			,      lowerCAmelCase_=1_024			,      lowerCAmelCase_=False			,      **lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     AutoTokenizer.from_pretrained(lowerCAmelCase_	)
      _snake_case		:	List[Any]					     =     SeqaSeqDataset(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      type_path='''train'''			,      **lowerCAmelCase_	)
      _snake_case		:	Dict					     =     tok.pad_token_id

      def get_lens(lowerCAmelCase_	):
            _snake_case		:	int					     =     tqdm(
                DataLoader(lowerCAmelCase_			,      batch_size=512			,      num_workers=8			,      shuffle=lowerCAmelCase_			,      collate_fn=ds.collate_fn	)			,      desc=str(ds.len_file	)			,      )
            _snake_case		:	Union[str, Any]					     =     []
            for batch in dl:
                  _snake_case		:	str					     =     batch['''input_ids'''].ne(lowerCAmelCase_	).sum(1	).tolist()
                  _snake_case		:	int					     =     batch['''labels'''].ne(lowerCAmelCase_	).sum(1	).tolist()
                  if consider_target:
                        for src, tgt in zip(lowerCAmelCase_			,      lowerCAmelCase_	):
                              max_lens.append(max(lowerCAmelCase_			,      lowerCAmelCase_	)	)
                  else:
                        max_lens.extend(lowerCAmelCase_	)
            return max_lens

      _snake_case		:	Optional[Any]					     =     get_lens(lowerCAmelCase_	)
      _snake_case		:	Optional[int]					     =     SeqaSeqDataset(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      type_path='''val'''			,      **lowerCAmelCase_	)
      _snake_case		:	Union[str, Any]					     =     get_lens(lowerCAmelCase_	)
      pickle_save(lowerCAmelCase_			,      train_ds.len_file	)
      pickle_save(lowerCAmelCase_			,      val_ds.len_file	)


if __name__ == "__main__":
   fire.Fire(save_len_file)









'''simple docstring'''

def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      if n == 1 or not isinstance(lowerCAmelCase_			,      lowerCAmelCase_	):
            return 0
      elif n == 2:
            return 1
      else:
            _snake_case		:	Union[str, Any]					     =     [0, 1]
            for i in range(2			,      n + 1	):
                  sequence.append(sequence[i - 1] + sequence[i - 2]	)

            return sequence[n]




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[int]					     =     0
      _snake_case		:	int					     =     2

      while digits < n:
            index += 1
            _snake_case		:	Tuple					     =     len(str(fibonacci(lowerCAmelCase_	)	)	)

      return index




def    _a    (							lowerCAmelCase_ = 1_000	):





      """simple docstring"""



      return fibonacci_digits_index(lowerCAmelCase_	)


if __name__ == "__main__":
   print(solution(int(str(input()).strip())))


