






'''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							:							Optional[Any]					     =					42
			__a							:							List[str]					     =					None
			__a							:							Tuple					     =					None







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = Node(1					)
						UpperCamelCase__         = Node(2					)
						UpperCamelCase__         = Node(3					)
						UpperCamelCase__         = Node(4					)
						UpperCamelCase__         = Node(5					)
						return tree







def 	__magic_name__( _A					):
						'''simple docstring'''





						return [root.data, *preorder(root.left					), *preorder(root.right					)] if root else []







def 	__magic_name__( _A					):
						'''simple docstring'''





						return postorder(root.left					) + postorder(root.right					) + [root.data] if root else []







def 	__magic_name__( _A					):
						'''simple docstring'''





						return [*inorder(root.left					), root.data, *inorder(root.right					)] if root else []







def 	__magic_name__( _A					):
						'''simple docstring'''





						return (max(height(root.left					)						,      height(root.right					)					) + 1) if root else 0







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = []

						if root is None:
												return output

						UpperCamelCase__         = deque([root]					)

						while process_queue:
												UpperCamelCase__         = 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 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = []

						def populate_output(_A						,      _A					) -> 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(_A						,      _A					)
						return output







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = []

						def populate_output(_A						,      _A					) -> 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(_A						,      _A					)
						return output







def 	__magic_name__( _A					):
						'''simple docstring'''





						if root is None:
												return []

						UpperCamelCase__         = []

						UpperCamelCase__         = 0
						UpperCamelCase__         = height(_A					)

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

						return output







def 	__magic_name__( ):  # Main function for testing.
						'''simple docstring'''





						UpperCamelCase__         = make_tree()
						print(f"In-order Traversal: {inorder(_A					)}"					)
						print(f"Pre-order Traversal: {preorder(_A					)}"					)
						print(f"Post-order Traversal: {postorder(_A					)}"						,      """\n"""					)

						print(f"Height of Tree: {height(_A					)}"						,      """\n"""					)

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

						print("""Level-wise order Traversal: """					)

						for level in range(1						,      height(_A					) + 1					):
												print(f"Level {level}:"						,      get_nodes_from_left_to_right(_A						,      level=_A					)					)

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()








'''simple docstring'''


# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						Optional[int] ,      lowercase							:						Optional[int] ,      lowercase							:						Optional[Any] ,      lowercase							:						bool = True ,      lowercase							:						bool = False			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = scheduler
									UpperCamelCase__         = optimizers if isinstance(lowercase ,      (list, tuple)			) else [optimizers]
									UpperCamelCase__         = split_batches
									UpperCamelCase__         = step_with_optimizer
									UpperCamelCase__         = GradientState()






			def 				A      (   self							:						int ,      *lowercase							:						int ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									if not self.step_with_optimizer:
															# No link between scheduler and optimizer -> just step
															self.scheduler.step(*lowercase ,      **lowercase			)
															return

									# Otherwise, first make sure the optimizer was stepped.
									if not self.gradient_state.sync_gradients:
															if self.gradient_state.adjust_scheduler:
																					self.scheduler._step_count += 1
															return

									for opt in self.optimizers:
															if opt.step_was_skipped:
																					return
									if self.split_batches:
															# Split batches -> the training dataloader batch size is not changed so one step per training step
															self.scheduler.step(*lowercase ,      **lowercase			)
									else:
															# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
															# num_processes steps per training step
															UpperCamelCase__         = AcceleratorState().num_processes
															for _ in range(lowercase			):
																					# Special case when using OneCycle and `drop_last` was not used
																					if hasattr(self.scheduler ,      """total_steps"""			):
																											if self.scheduler._step_count <= self.scheduler.total_steps:
																																	self.scheduler.step(*lowercase ,      **lowercase			)
																					else:
																											self.scheduler.step(*lowercase ,      **lowercase			)






			def 				A      (   self							:						List[str]			)							->    Optional[Any]:

									'''simple docstring'''







									return self.scheduler.get_last_lr()






			def 				A      (   self							:						Any			)							->    int:

									'''simple docstring'''







									return self.scheduler.state_dict()






			def 				A      (   self							:						Any ,      lowercase							:						int			)							->    Optional[int]:

									'''simple docstring'''







									self.scheduler.load_state_dict(lowercase			)






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									return self.scheduler.get_lr()







			def 				A      (   self							:						Union[str, Any] ,      *lowercase							:						List[Any] ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									return self.scheduler.print_lr(*lowercase ,      **lowercase			)
