"""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       (					UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       Optional[Any]    ,							UpperCAmelCase_					:       str=10_24    ,							UpperCAmelCase_					:       int=10_24    ,							UpperCAmelCase_					:       Optional[int]=False    ,							**UpperCAmelCase_					:       Any						)							->    List[Any]:

	'''simple docstring'''





	__snake_case    :							Optional[Any]   	=				AutoTokenizer.from_pretrained(__UpperCamelCase						)
	__snake_case    :							Tuple   	=				SeqaSeqDataset(__UpperCamelCase    ,							__UpperCamelCase    ,							__UpperCamelCase    ,							__UpperCamelCase    ,							type_path='train'    ,							**__UpperCamelCase						)
	__snake_case    :							Any   	=				tok.pad_token_id

	def get_lens(UpperCAmelCase_					:       Optional[Any]						):
		__snake_case    :							int   	=				tqdm(
		    DataLoader(__UpperCamelCase    ,							batch_size=5_12    ,							num_workers=8    ,							shuffle=__UpperCamelCase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
		__snake_case    :							List[str]   	=				[]
		for batch in dl:
			__snake_case    :							str   	=				batch['''input_ids'''].ne(__UpperCamelCase						).sum(1						).tolist()
			__snake_case    :							Any   	=				batch['''labels'''].ne(__UpperCamelCase						).sum(1						).tolist()
			if consider_target:
				for src, tgt in zip(__UpperCamelCase    ,							__UpperCamelCase						):
					max_lens.append(max(__UpperCamelCase    ,							__UpperCamelCase						)						)
			else:
				max_lens.extend(__UpperCamelCase						)
		return max_lens

	__snake_case    :							Dict   	=				get_lens(__UpperCamelCase						)
	__snake_case    :							Tuple   	=				SeqaSeqDataset(__UpperCamelCase    ,							__UpperCamelCase    ,							__UpperCamelCase    ,							__UpperCamelCase    ,							type_path='val'    ,							**__UpperCamelCase						)
	__snake_case    :							Any   	=				get_lens(__UpperCamelCase						)
	pickle_save(__UpperCamelCase    ,							train_ds.len_file						)
	pickle_save(__UpperCamelCase    ,							val_ds.len_file						)


if __name__ == "__main__":
	fire.Fire(save_len_file)
"""simple docstring"""







import numpy as np
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       np.array						)							->    np.array:

	'''simple docstring'''





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


if __name__ == "__main__":
	import doctest

	doctest.testmod()





