



"""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__=1024			,    lowerCAmelCase__=1024			,    lowerCAmelCase__=False			,    **lowerCAmelCase__   ):
      UpperCAmelCase_    				=							AutoTokenizer.from_pretrained(lowerCAmelCase__   )
      UpperCAmelCase_    				=							SeqaSeqDataset(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    type_path="train"			,    **lowerCAmelCase__   )
      UpperCAmelCase_    				=							tok.pad_token_id

      def get_lens(lowerCAmelCase__   ):
            UpperCAmelCase_    				=							tqdm(
                DataLoader(lowerCAmelCase__			,    batch_size=512			,    num_workers=8			,    shuffle=lowerCAmelCase__			,    collate_fn=ds.collate_fn   )			,    desc=str(ds.len_file   )			,    )
            UpperCAmelCase_    				=							[]
            for batch in dl:
                  UpperCAmelCase_    				=							batch["input_ids"].ne(lowerCAmelCase__   ).sum(1   ).tolist()
                  UpperCAmelCase_    				=							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

      UpperCAmelCase_    				=							get_lens(lowerCAmelCase__   )
      UpperCAmelCase_    				=							SeqaSeqDataset(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    type_path="val"			,    **lowerCAmelCase__   )
      UpperCAmelCase_    				=							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"""





from __future__ import annotations
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) == 0:
            return []
      UpperCAmelCase_				,					UpperCAmelCase_    				=							min(lowerCAmelCase__   ), max(lowerCAmelCase__   )
      UpperCAmelCase_    				=							int(max_value - min_value   ) + 1
      UpperCAmelCase_    				=							[[] for _ in range(lowerCAmelCase__   )]

      for i in my_list:
            buckets[int(i - min_value   )].append(lowerCAmelCase__   )

      return [v for bucket in buckets for v in sorted(lowerCAmelCase__   )]


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]




