



def      lowerCAmelCase__		(   lowerCamelCase_		:		int = 50000000):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					set()
				lowerCAmelCase__					:    str												=					int((limit - 24) ** (1 / 2))

				lowerCAmelCase__					:    Dict												=					set(range(3	,prime_square_limit + 1	,2))
				primes.add(2)
				for p in range(3	,prime_square_limit + 1	,2):
								if p not in primes:
												continue
								primes.difference_update(set(range(p * p	,prime_square_limit + 1	,lowerCamelCase_)))

				for primea in primes:
								lowerCAmelCase__					:    Optional[int]												=					primea * primea
								for primea in primes:
												lowerCAmelCase__					:    Any												=					primea * primea * primea
												if square + cube >= limit - 16:
																break
												for primea in primes:
																lowerCAmelCase__					:    Tuple												=					primea * primea * primea * primea
																lowerCAmelCase__					:    Tuple												=					square + cube + tetr
																if total >= limit:
																				break
																ret.add(lowerCamelCase_)

				return len(lowerCamelCase_)


if __name__ == "__main__":
							print(f"""{solution() = }""")






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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]=1024	,lowerCamelCase_		:		int=1024	,lowerCamelCase_		:		Dict=False	,**lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[Any]												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''train'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    int												=					tok.pad_token_id

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

				lowerCAmelCase__					:    str												=					get_lens(lowerCamelCase_)
				lowerCAmelCase__					:    Tuple												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''val'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					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)

