





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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def A_	(		A__		,      A__		,      A__=1024		,      A__=1024		,      A__=False		,      **A__				)  ->		List[str]:
       a__						:	int =				AutoTokenizer.from_pretrained(A__				)
       a__						:	Union[str, Any] =				SeqaSeqDataset(A__		,      A__		,      A__		,      A__		,      type_path='train'		,      **A__				)
       a__						:	Optional[int] =				tok.pad_token_id

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

       a__						:	Any =				get_lens(A__				)
       a__						:	List[str] =				SeqaSeqDataset(A__		,      A__		,      A__		,      A__		,      type_path='val'		,      **A__				)
       a__						:	Union[str, Any] =				get_lens(A__				)
       pickle_save(A__		,      train_ds.len_file				)
       pickle_save(A__		,      val_ds.len_file				)


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




"""simple docstring"""





from __future__ import annotations



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		float:


					"""simple docstring"""





					_UpperCamelCase							 =  sorted(numsa + numsa	)
					_UpperCamelCase ,  _UpperCamelCase							 =  divmod(len(__snake_case	),     2	)
					if mod == 1:
										return all_numbers[div]
					else:
										return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				_a														=		[float(x) for x in input("""Enter the elements of first array: """).split()]
				_a														=		[float(x) for x in input("""Enter the elements of second array: """).split()]
				print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")






