





class       __A     :


				def __init__(		self   , UpperCAmelCase_							):
								lowerCamelCase	 =val
								lowerCamelCase	 =None
								lowerCamelCase	 =None


				def _snake_case       (		self   , UpperCAmelCase_							):
								if self.val:
												if val < self.val:
																if self.left is None:
																				lowerCamelCase	 =Node(UpperCAmelCase_							)
																else:
																				self.left.insert(UpperCAmelCase_							)
												elif val > self.val:
																if self.right is None:
																				lowerCamelCase	 =Node(UpperCAmelCase_							)
																else:
																				self.right.insert(UpperCAmelCase_							)
								else:
												lowerCamelCase	 =val





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[str]:
				# Recursive traversal
				if root:
								inorder(root.left			,      _UpperCAmelCase				)
								res.append(root.val				)
								inorder(root.right			,      _UpperCAmelCase				)





def      _lowercase					(						_UpperCAmelCase				)						->  str:
				# Build BST
				if len(_UpperCAmelCase				) == 0:
								return arr
				lowerCamelCase	 =Node(arr[0]				)
				for i in range(1			,      len(_UpperCAmelCase				)				):
								root.insert(arr[i]				)
				# Traverse BST in order.
				lowerCamelCase	 =[]
				inorder(_UpperCAmelCase			,      _UpperCAmelCase				)
				return res


if __name__ == "__main__":
							print(tree_sort([10, 1, 3, 2, 9, 14, 13]))












import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=False			,      **_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =AutoTokenizer.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""train"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =tok.pad_token_id

				def get_lens(_UpperCAmelCase				):
								lowerCamelCase	 =tqdm(
								    DataLoader(_UpperCAmelCase			,      batch_size=5_12			,      num_workers=8			,      shuffle=_UpperCAmelCase			,      collate_fn=ds.collate_fn				)			,      desc=str(ds.len_file				)			,      )
								lowerCamelCase	 =[]
								for batch in dl:
												lowerCamelCase	 =batch["""input_ids"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												lowerCamelCase	 =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

				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""val"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =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)





