






"""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    (__a					,       __a					,       __a=10_24					,       __a=10_24					,       __a=False					,       **__a							)				-> Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						int							=	AutoTokenizer.from_pretrained(__a							)
    SCREAMING_SNAKE_CASE_   :						List[str]							=	SeqaSeqDataset(__a					,       __a					,       __a					,       __a					,       type_path='''train'''					,       **__a							)
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	tok.pad_token_id

    def get_lens(__a							):
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tqdm(
            DataLoader(__a					,       batch_size=5_12					,       num_workers=8					,       shuffle=__a					,       collate_fn=ds.collate_fn							)					,       desc=str(ds.len_file							)					,       )
        SCREAMING_SNAKE_CASE_   :						List[str]							=	[]
        for batch in dl:
            SCREAMING_SNAKE_CASE_   :						Dict							=	batch['''input_ids'''].ne(__a							).sum(1							).tolist()
            SCREAMING_SNAKE_CASE_   :						Tuple							=	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

    SCREAMING_SNAKE_CASE_   :						List[Any]							=	get_lens(__a							)
    SCREAMING_SNAKE_CASE_   :						str							=	SeqaSeqDataset(__a					,       __a					,       __a					,       __a					,       type_path='''val'''					,       **__a							)
    SCREAMING_SNAKE_CASE_   :						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 math import isqrt



def       _A    (__a							)				-> list[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[True] * max_number
    for i in range(2					,       isqrt(max_number - 1							) + 1							):
        if is_prime[i]:
            for j in range(i**2					,       __a					,       __a							):
                SCREAMING_SNAKE_CASE_   :						Tuple							=	False

    return [i for i in range(2					,       __a							) if is_prime[i]]



def       _A    (__a = 10**8							)				-> int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	calculate_prime_numbers(max_number // 2							)

    SCREAMING_SNAKE_CASE_   :						List[str]							=	0
    SCREAMING_SNAKE_CASE_   :						Any							=	0
    SCREAMING_SNAKE_CASE_   :						List[str]							=	len(__a							) - 1
    while left <= right:
        while prime_numbers[left] * prime_numbers[right] >= max_number:
            right -= 1
        semiprimes_count += right - left + 1
        left += 1

    return semiprimes_count


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


