


"""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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=1024  ,						lowerCAmelCase_=1024  ,						lowerCAmelCase_=False  ,						**lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    AutoTokenizer.from_pretrained(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    SeqaSeqDataset(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						type_path="train"  ,						**lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    tok.pad_token_id

					def get_lens(lowerCAmelCase_       ):
										__SCREAMING_SNAKE_CASE						       =    tqdm(
										    DataLoader(lowerCAmelCase_  ,						batch_size=512  ,						num_workers=8  ,						shuffle=lowerCAmelCase_  ,						collate_fn=ds.collate_fn       )  ,						desc=str(ds.len_file       )  ,						)
										__SCREAMING_SNAKE_CASE						       =    []
										for batch in dl:
															__SCREAMING_SNAKE_CASE						       =    batch["input_ids"].ne(lowerCAmelCase_       ).sum(1       ).tolist()
															__SCREAMING_SNAKE_CASE						       =    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

					__SCREAMING_SNAKE_CASE						       =    get_lens(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    SeqaSeqDataset(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						type_path="val"  ,						**lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    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)




"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					return " ".join(
					    "".join(word[::-1]       ) if len(lowerCAmelCase_       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(reverse_long_words('''Hey wollef sroirraw'''))
