


'''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 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase=1_0_2_4, lowerCamelCase=1_0_2_4, lowerCamelCase=False, **lowerCamelCase):
						__lowerCAmelCase											= AutoTokenizer.from_pretrained(a__)
						__lowerCAmelCase											= SeqaSeqDataset(a__, a__, a__, a__, type_path='''train''', **a__)
						__lowerCAmelCase											= tok.pad_token_id

						def get_lens(lowerCamelCase):
												__lowerCAmelCase											= tqdm(
												    DataLoader(a__, batch_size=5_1_2, num_workers=8, shuffle=a__, collate_fn=ds.collate_fn), desc=str(ds.len_file), )
												__lowerCAmelCase											= []
												for batch in dl:
																		__lowerCAmelCase											= batch['''input_ids'''].ne(a__).sum(1).tolist()
																		__lowerCAmelCase											= 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

						__lowerCAmelCase											= get_lens(a__)
						__lowerCAmelCase											= SeqaSeqDataset(a__, a__, a__, a__, type_path='''val''', **a__)
						__lowerCAmelCase											= 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)



# flake8: noqa
# Lint as: python3
A     :	Optional[Any]    = [
    'VerificationMode',
    'Version',
    'disable_progress_bar',
    'enable_progress_bar',
    'is_progress_bar_enabled',
    'experimental',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental