'''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							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase=1_0_2_4     ,UpperCAmelCase=1_0_2_4     ,UpperCAmelCase=False     ,**UpperCAmelCase					)     ->			Dict:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[Any]   			=						AutoTokenizer.from_pretrained(UpperCAmelCase					)
						_UpperCamelCase			:			int   			=						SeqaSeqDataset(UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,type_path="train"     ,**UpperCAmelCase					)
						_UpperCamelCase			:			int   			=						tok.pad_token_id

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

						_UpperCamelCase			:			Any   			=						get_lens(UpperCAmelCase					)
						_UpperCamelCase			:			List[str]   			=						SeqaSeqDataset(UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,type_path="val"     ,**UpperCAmelCase					)
						_UpperCamelCase			:			str   			=						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'''





def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase=False					)     ->			Optional[int]:




						'''simple docstring'''

						if isinstance(UpperCAmelCase     ,UpperCAmelCase					) and isinstance(UpperCAmelCase     ,UpperCAmelCase					):
												_UpperCamelCase			:			Any   			=						len(set_a.intersection(UpperCAmelCase					)					)

												if alternative_union:
																		_UpperCamelCase			:			List[str]   			=						len(UpperCAmelCase					) + len(UpperCAmelCase					)
												else:
																		_UpperCamelCase			:			List[str]   			=						len(set_a.union(UpperCAmelCase					)					)

												return intersection / union

						if isinstance(UpperCAmelCase     ,(list, tuple)					) and isinstance(UpperCAmelCase     ,(list, tuple)					):
												_UpperCamelCase			:			List[Any]   			=						[element for element in set_a if element in set_b]

												if alternative_union:
																		_UpperCamelCase			:			str   			=						len(UpperCAmelCase					) + len(UpperCAmelCase					)
																		return len(UpperCAmelCase					) / union
												else:
																		_UpperCamelCase			:			int   			=						set_a + [element for element in set_b if element not in set_a]
																		return len(UpperCAmelCase					) / len(UpperCAmelCase					)

												return len(UpperCAmelCase					) / len(UpperCAmelCase					)
						return None


if __name__ == "__main__":
			lowerCAmelCase_	:		List[str]			=     {"""a""", """b""", """c""", """d""", """e"""}
			lowerCAmelCase_	:		Dict			=     {"""c""", """d""", """e""", """f""", """h""", """i"""}
			print(jaccard_similarity(set_a, set_b))


