

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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Union[str, Any]=1_024    ,			UpperCAmelCase							:      Tuple=1_024    ,			UpperCAmelCase							:      int=False    ,			**UpperCAmelCase							:      List[str]      )  ->						Any:
  UpperCAmelCase	:							Optional[Any]						=			AutoTokenizer.from_pretrained(UpperCAmelCase      )
  UpperCAmelCase	:							str						=			SeqaSeqDataset(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			type_path='''train'''    ,			**UpperCAmelCase      )
  UpperCAmelCase	:							str						=			tok.pad_token_id

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

  UpperCAmelCase	:							Optional[Any]						=			get_lens(UpperCAmelCase      )
  UpperCAmelCase	:							int						=			SeqaSeqDataset(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			type_path='''val'''    ,			**UpperCAmelCase      )
  UpperCAmelCase	:							Dict						=			get_lens(UpperCAmelCase      )
  pickle_save(UpperCAmelCase    ,			train_ds.len_file      )
  pickle_save(UpperCAmelCase    ,			val_ds.len_file      )


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








def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      int      )  ->						str:
  if a < 0 or b < 0:
    raise ValueError('''the value of both inputs must be positive'''      )
  UpperCAmelCase	:							List[Any]						=			str(bin(UpperCAmelCase      )      )[2:]  # remove the leading "0b"
  UpperCAmelCase	:							List[str]						=			str(bin(UpperCAmelCase      )      )[2:]
  UpperCAmelCase	:							Optional[Any]						=			max(len(UpperCAmelCase      )    ,			len(UpperCAmelCase      )      )
  return "0b" + "".join(
      str(int('''1''' in (char_a, char_b)      )      )
      for char_a, char_b in zip(a_binary.zfill(UpperCAmelCase      )    ,			b_binary.zfill(UpperCAmelCase      )      )      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





