





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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save







def   __lowerCamelCase   (				__a  :Any       ,	__a  :Optional[Any]       ,	__a  :int=1_0_2_4       ,	__a  :Union[str, Any]=1_0_2_4       ,	__a  :Tuple=False       ,	**__a  :List[Any]       )						->							Optional[int]:






		"""simple docstring"""

		A__     =  AutoTokenizer.from_pretrained(__a       )
		A__     =  SeqaSeqDataset(__a       ,	__a       ,	__a       ,	__a       ,	type_path="""train"""       ,	**__a       )
		A__     =  tok.pad_token_id

		def get_lens(__a  :List[Any]       ):
				A__     =  tqdm(
				    DataLoader(__a       ,	batch_size=5_1_2       ,	num_workers=8       ,	shuffle=__a       ,	collate_fn=ds.collate_fn       )       ,	desc=str(ds.len_file       )       ,	)
				A__     =  []
				for batch in dl:
						A__     =  batch["""input_ids"""].ne(__a       ).sum(1       ).tolist()
						A__     =  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

		A__     =  get_lens(__a       )
		A__     =  SeqaSeqDataset(__a       ,	__a       ,	__a       ,	__a       ,	type_path="""val"""       ,	**__a       )
		A__     =  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)










import numpy as np







def   __lowerCamelCase   (				__a  :np.array       )						->							np.array:






		"""simple docstring"""

		return 1 / (1 + np.exp(-vector       ))







def   __lowerCamelCase   (				__a  :np.array       )						->							np.array:






		"""simple docstring"""

		return vector * sigmoid(1.702 * vector       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



