



'''simple docstring'''




lowerCamelCase							:Dict				    =					'''Input must be a string of 8 numbers plus letter'''
lowerCamelCase							:int				    =					'''TRWAGMYFPDXBNJZSQVHLCKE'''



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if not isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
						A_	:						Union[str, Any]	=     f'Expected string as input, found {type(lowerCamelCase__    ).__name__}'
						raise TypeError(lowerCamelCase__    )

			A_	:						Any	=     spanish_id.replace("""-"""						,      """"""    ).upper()
			if len(lowerCamelCase__    ) != 9:
						raise ValueError(lowerCamelCase__    )

			try:
						A_	:						List[Any]	=     int(spanish_id_clean[0:8]    )
						A_	:						List[Any]	=     spanish_id_clean[8]
			except ValueError as ex:
						raise ValueError(lowerCamelCase__    ) from ex

			if letter.isdigit():
						raise ValueError(lowerCamelCase__    )

			return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




'''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 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=10_24						,      lowerCamelCase__=10_24						,      lowerCamelCase__=False						,      **lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""train"""						,      **lowerCamelCase__    )
			A_	:						Optional[Any]	=     tok.pad_token_id

			def get_lens(lowerCamelCase__    ):
						A_	:						int	=     tqdm(
						    DataLoader(lowerCamelCase__						,      batch_size=5_12						,      num_workers=8						,      shuffle=lowerCamelCase__						,      collate_fn=ds.collate_fn    )						,      desc=str(ds.len_file    )						,      )
						A_	:						int	=     []
						for batch in dl:
									A_	:						str	=     batch["""input_ids"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									A_	:						Tuple	=     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_	:						Dict	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""val"""						,      **lowerCamelCase__    )
			A_	:						List[Any]	=     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)