


"""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__      (    SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]=1_0_2_4    ,				SCREAMING_SNAKE_CASE   :   List[str]=1_0_2_4    ,				SCREAMING_SNAKE_CASE   :   Dict=False    ,				**SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Optional[int]								=							SeqaSeqDataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				type_path="train"    ,				**SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[Any]								=							tok.pad_token_id

  def get_lens(SCREAMING_SNAKE_CASE   :   Dict    ):
    lowerCAmelCase					:					Optional[Any]								=							tqdm(
        DataLoader(SCREAMING_SNAKE_CASE    ,				batch_size=5_1_2    ,				num_workers=8    ,				shuffle=SCREAMING_SNAKE_CASE    ,				collate_fn=ds.collate_fn    )    ,				desc=str(ds.len_file    )    ,				)
    lowerCAmelCase					:					Tuple								=							[]
    for batch in dl:
      lowerCAmelCase					:					Dict								=							batch["input_ids"].ne(SCREAMING_SNAKE_CASE    ).sum(1    ).tolist()
      lowerCAmelCase					:					Optional[int]								=							batch["labels"].ne(SCREAMING_SNAKE_CASE    ).sum(1    ).tolist()
      if consider_target:
        for src, tgt in zip(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ):
          max_lens.append(max(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )    )
      else:
        max_lens.extend(SCREAMING_SNAKE_CASE    )
    return max_lens

  lowerCAmelCase					:					Tuple								=							get_lens(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Union[str, Any]								=							SeqaSeqDataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				type_path="val"    ,				**SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					List[str]								=							get_lens(SCREAMING_SNAKE_CASE    )
  pickle_save(SCREAMING_SNAKE_CASE    ,				train_ds.len_file    )
  pickle_save(SCREAMING_SNAKE_CASE    ,				val_ds.len_file    )


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









"""simple docstring"""







import re

from filelock import FileLock


try:
     import nltk

     lowerCAmelCase__				    =       True
except (ImportError, ModuleNotFoundError):
     lowerCAmelCase__				    =       False

if NLTK_AVAILABLE:
     with FileLock('''.lock''') as lock:
          nltk.download('''punkt''', quiet=True)
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  re.sub("<n>"    ,				""    ,				SCREAMING_SNAKE_CASE    )  # remove pegasus newline char
  assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
  return "\n".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE    )    )





