





'''simple docstring'''







from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				__a            =42
				__a            =None
				__a            =None






def  _lowerCamelCase				( )	->	Node | None:
				_a								= Node(1						)
				_a								= Node(2						)
				_a								= Node(3						)
				_a								= Node(4						)
				_a								= Node(5						)
				return tree






def  _lowerCamelCase				( lowercase						:      Node | None						)	->	list[int]:
				return [root.data, *preorder(root.left						), *preorder(root.right						)] if root else []






def  _lowerCamelCase				( lowercase						:      Node | None						)	->	list[int]:
				return postorder(root.left						) + postorder(root.right						) + [root.data] if root else []






def  _lowerCamelCase				( lowercase						:      Node | None						)	->	list[int]:
				return [*inorder(root.left						), root.data, *inorder(root.right						)] if root else []






def  _lowerCamelCase				( lowercase						:      Node | None						)	->	int:
				return (max(height(root.left						)    ,							height(root.right						)						) + 1) if root else 0






def  _lowerCamelCase				( lowercase						:      Node | None						)	->	Sequence[Node | None]:
				_a								= []

				if root is None:
								return output

				_a								= deque([root]						)

				while process_queue:
								_a								= process_queue.popleft()
								output.append(node.data						)

								if node.left:
												process_queue.append(node.left						)
								if node.right:
												process_queue.append(node.right						)
				return output






def  _lowerCamelCase				( lowercase						:      Node | None    ,							lowercase						:      int						)	->	Sequence[Node | None]:
				_a								= []

				def populate_output(lowercase						:      Node | None    ,							lowercase						:      int						) -> None:
								if not root:
												return
								if level == 1:
												output.append(root.data						)
								elif level > 1:
												populate_output(root.left    ,							level - 1						)
												populate_output(root.right    ,							level - 1						)

				populate_output(lowercase    ,							lowercase						)
				return output






def  _lowerCamelCase				( lowercase						:      Node | None    ,							lowercase						:      int						)	->	Sequence[Node | None]:
				_a								= []

				def populate_output(lowercase						:      Node | None    ,							lowercase						:      int						) -> None:
								if root is None:
												return
								if level == 1:
												output.append(root.data						)
								elif level > 1:
												populate_output(root.right    ,							level - 1						)
												populate_output(root.left    ,							level - 1						)

				populate_output(lowercase    ,							lowercase						)
				return output






def  _lowerCamelCase				( lowercase						:      Node | None						)	->	Sequence[Node | None] | list[Any]:
				if root is None:
								return []

				_a								= []

				_a								= 0
				_a								= height(lowercase						)

				for h in range(1    ,							height_tree + 1						):
								if not flag:
												output.append(get_nodes_from_left_to_right(lowercase    ,							lowercase						)						)
												_a								= 1
								else:
												output.append(get_nodes_from_right_to_left(lowercase    ,							lowercase						)						)
												_a								= 0

				return output






def  _lowerCamelCase				( )	->	None:  # Main function for testing.
				_a								= make_tree()
				print(F'In-order Traversal: {inorder(lowercase						)}'						)
				print(F'Pre-order Traversal: {preorder(lowercase						)}'						)
				print(F'Post-order Traversal: {postorder(lowercase						)}'    ,							"\n"						)

				print(F'Height of Tree: {height(lowercase						)}'    ,							"\n"						)

				print("Complete Level Order Traversal: "						)
				print(level_order(lowercase						)    ,							"\n"						)

				print("Level-wise order Traversal: "						)

				for level in range(1    ,							height(lowercase						) + 1						):
								print(F'Level {level}:'    ,							get_nodes_from_left_to_right(lowercase    ,							level=lowercase						)						)

				print("\nZigZag order Traversal: "						)
				print(zigzag(lowercase						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					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 	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)