



"""simple docstring"""







from math import pow

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Any    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				int    ,   )						->   List[str]:

  """simple docstring"""




  if current_sum == needed_sum:
    # If the sum of the powers is equal to needed_sum, then we have a solution.
    solutions_count += 1
    return current_sum, solutions_count

  lowerCAmelCase_   :				Optional[int]								= int(pow(lowerCamelCase__    ,   lowerCamelCase__						)						)
  if current_sum + i_to_n <= needed_sum:
    # If the sum of the powers is less than needed_sum, then continue adding powers.
    current_sum += i_to_n
    lowerCAmelCase_   :				int								= backtrack(
        lowerCamelCase__    ,   lowerCamelCase__    ,   current_number + 1    ,   lowerCamelCase__    ,   lowerCamelCase__						)
    current_sum -= i_to_n
  if i_to_n < needed_sum:
    # If the power of i is less than needed_sum, then try with the next power.
    lowerCAmelCase_   :				int								= backtrack(
        lowerCamelCase__    ,   lowerCamelCase__    ,   current_number + 1    ,   lowerCamelCase__    ,   lowerCamelCase__						)
  return current_sum, solutions_count

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
    raise ValueError(
        'Invalid input\n'
        'needed_sum must be between 1 and 1000, power between 2 and 10.'						)

  return backtrack(lowerCamelCase__    ,   lowerCamelCase__    ,   1    ,   0    ,   0						)[1]  # Return the solutions_count


if __name__ == "__main__":
     import doctest

     doctest.testmod()










'''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__=10_24						,      lowerCamelCase__=10_24						,      lowerCamelCase__=False						,      **lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""train"""						,      **lowerCamelCase__    )
			A_	:						Optional[Any]	=     tok.pad_token_id

			def get_lens(lowerCamelCase__    ):
						A_	:						int	=     tqdm(
						    DataLoader(lowerCamelCase__						,      batch_size=5_12						,      num_workers=8						,      shuffle=lowerCamelCase__						,      collate_fn=ds.collate_fn    )						,      desc=str(ds.len_file    )						,      )
						A_	:						int	=     []
						for batch in dl:
									A_	:						str	=     batch["""input_ids"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									A_	:						Tuple	=     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

			A_	:						str	=     get_lens(lowerCamelCase__    )
			A_	:						Dict	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""val"""						,      **lowerCamelCase__    )
			A_	:						List[Any]	=     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)