



'''simple docstring'''

def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Optional[Any]:
		_a     :      Optional[Any]        =							[]
		_a     :      List[str]        =							set({'(', '[', '{'}  )
		_a     :      List[str]        =							set({')', ']', '}'}  )
		_a     :      Optional[int]        =							{'{': '}', '[': ']', '(': ')'}

		for i in range(len(lowerCAmelCase_  )  ):
				if s[i] in open_brackets:
						stack.append(s[i]  )

				elif s[i] in closed_brackets and (
				    len(lowerCAmelCase_  ) == 0 or (len(lowerCAmelCase_  ) > 0 and open_to_closed[stack.pop()] != s[i])
				):
						return False

		return len(lowerCAmelCase_  ) == 0



def      __lowerCamelCase     (      )			->      str:
		_a     :      Any        =							input('Enter sequence of brackets: '  )
		if is_balanced(lowerCAmelCase_  ):
				print(lowerCAmelCase_						,			'is balanced'  )
		else:
				print(lowerCAmelCase_						,			'is not balanced'  )


if __name__ == "__main__":
							main()









'''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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							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     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							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)




