
'''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__							:							Tuple       ,							UpperCAmelCase__							:							List[str]       ,							UpperCAmelCase__							:							Dict=10_24       ,							UpperCAmelCase__							:							Tuple=10_24       ,							UpperCAmelCase__							:							str=False       ,							**UpperCAmelCase__							:							Tuple):
				lowerCamelCase					:			Tuple									= AutoTokenizer.from_pretrained(lowerCAmelCase__)
				lowerCamelCase					:			Optional[int]									= SeqaSeqDataset(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       ,							type_path='train'       ,							**lowerCAmelCase__)
				lowerCamelCase					:			List[Any]									= tok.pad_token_id

				def get_lens(UpperCAmelCase__							:							int):
								lowerCamelCase					:			str									= tqdm(
								    DataLoader(lowerCAmelCase__       ,							batch_size=5_12       ,							num_workers=8       ,							shuffle=lowerCAmelCase__       ,							collate_fn=ds.collate_fn)       ,							desc=str(ds.len_file)       ,							)
								lowerCamelCase					:			Optional[int]									= []
								for batch in dl:
												lowerCamelCase					:			Union[str, Any]									= batch['input_ids'].ne(lowerCAmelCase__).sum(1).tolist()
												lowerCamelCase					:			str									= 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

				lowerCamelCase					:			Dict									= get_lens(lowerCAmelCase__)
				lowerCamelCase					:			Any									= SeqaSeqDataset(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       ,							type_path='val'       ,							**lowerCAmelCase__)
				lowerCamelCase					:			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)


'''simple docstring'''






from __future__ import annotations





def    UpperCAmelCase							(  UpperCAmelCase__							:							int):
				lowerCamelCase					:			List[Any]									= str(UpperCAmelCase__)
				return len(UpperCAmelCase__) == 9 and set(UpperCAmelCase__) == set('123456789')





def    UpperCAmelCase							(  ):
				for base_num in range(99_99       ,							49_99       ,							-1):
								lowerCamelCase					:			Tuple									= 10_00_02 * base_num
								if is_9_pandigital(UpperCAmelCase__):
												return candidate

				for base_num in range(3_33       ,							99       ,							-1):
								lowerCamelCase					:			List[Any]									= 1_00_20_03 * base_num
								if is_9_pandigital(UpperCAmelCase__):
												return candidate

				return None


if __name__ == "__main__":
					print(f"""{solution() = }""")
