




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   :    Optional[int]   ,				__A   :    Any   ,				__A   :    str=1024   ,				__A   :    Tuple=1024   ,				__A   :    int=False   ,				**__A   :    Any       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							AutoTokenizer.from_pretrained(__A       )
     UpperCAmelCase_          =							SeqaSeqDataset(__A   ,				__A   ,				__A   ,				__A   ,				type_path='''train'''   ,				**__A       )
     UpperCAmelCase_          =							tok.pad_token_id

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

     UpperCAmelCase_          =							get_lens(__A       )
     UpperCAmelCase_          =							SeqaSeqDataset(__A   ,				__A   ,				__A   ,				__A   ,				type_path='''val'''   ,				**__A       )
     UpperCAmelCase_          =							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 collections.abc import Callable

import numpy as np

def       _snake_case						(						UpperCamelCase						:		Callable			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 UpperCAmelCase	:					Any	=	int(np.ceil((x_end - xa) / step_size						)						)
 UpperCAmelCase	:					Optional[Any]	=	np.zeros((n + 1,)						)
 UpperCAmelCase	:					Optional[int]	=	ya
 UpperCAmelCase	:					int	=	xa

 for k in range(UpperCamelCase						):
  UpperCAmelCase	:					Optional[int]	=	y[k] + step_size * ode_func(UpperCamelCase			,		y[k]						)
  UpperCAmelCase	:					Optional[int]	=	y[k] + (
      (step_size / 2) * (ode_func(UpperCamelCase			,		y[k]						) + ode_func(x + step_size			,		UpperCamelCase						))
  )
  x += step_size

 return y


if __name__ == "__main__":
      import doctest

      doctest.testmod()
