






"""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	(		snake_case__   ,   snake_case__   ,   snake_case__=1_024   ,   snake_case__=1_024   ,   snake_case__=False   ,   **snake_case__					):
	A_		:						Any	    =   AutoTokenizer.from_pretrained(snake_case__					)
	A_		:						Tuple	    =   SeqaSeqDataset(snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   type_path="""train"""   ,   **snake_case__					)
	A_		:						List[str]	    =   tok.pad_token_id

	def get_lens(snake_case__					):
		A_		:						Optional[int]	    =   tqdm(
		    DataLoader(snake_case__   ,   batch_size=512   ,   num_workers=8   ,   shuffle=snake_case__   ,   collate_fn=ds.collate_fn					)   ,   desc=str(ds.len_file					)   ,   )
		A_		:						List[str]	    =   []
		for batch in dl:
			A_		:						str	    =   batch["""input_ids"""].ne(snake_case__					).sum(1					).tolist()
			A_		:						Any	    =   batch["""labels"""].ne(snake_case__					).sum(1					).tolist()
			if consider_target:
				for src, tgt in zip(snake_case__   ,   snake_case__					):
					max_lens.append(max(snake_case__   ,   snake_case__					)					)
			else:
				max_lens.extend(snake_case__					)
		return max_lens

	A_		:						int	    =   get_lens(snake_case__					)
	A_		:						List[Any]	    =   SeqaSeqDataset(snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   type_path="""val"""   ,   **snake_case__					)
	A_		:						Optional[Any]	    =   get_lens(snake_case__					)
	pickle_save(snake_case__   ,   train_ds.len_file					)
	pickle_save(snake_case__   ,   val_ds.len_file					)


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









"""simple docstring"""
import math






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	if (
	    not isinstance(snake_case__   ,   (int, float)					)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("""power_factor must be a valid float value between -1 and 1."""					)
	return apparent_power * power_factor






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	if (
	    not isinstance(snake_case__   ,   (int, float)					)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("""power_factor must be a valid float value between -1 and 1."""					)
	return apparent_power * math.sqrt(1 - power_factor**2					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

