




UpperCAmelCase_									=	[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages






'''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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							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     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							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)




