



import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm

def       A_   (						__a  :					List[Any]			,		__a  :					Tuple		):

				"""simple docstring"""
				if isinstance(__a			,		(list, tuple)		):
								return honor_type(__a			,		(move_to_device(__a			,		__a		) for t in state)		)
				elif isinstance(__a			,		__a		):
								return type(__a		)({k: move_to_device(__a			,		__a		) for k, v in state.items()}		)
				elif isinstance(__a			,		torch.Tensor		):
								return state.to(__a		)
				return state




class 			__snake_case   ( torch.optim.Optimizer):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_=True	,	a_=None		):
											a__					   =		optimizer
											a__					   =		scaler
											a__					   =		AcceleratorState()
											a__					   =		GradientState()
											a__					   =		device_placement
											a__					   =		False
											a__					   =		None

											# Handle device placement
											if device_placement:
															a__					   =		self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(a_	,	self.accelerator_state.device		)
															else:
																			a__					   =		move_to_device(a_	,	self.accelerator_state.device		)
															self.optimizer.load_state_dict(a_		)


							@property
							def _a   (				self		):
											return self.optimizer.state


							@state.setter
							def _a   (				self	,	a_		):
											a__					   =		state


							@property
							def _a   (				self		):
											return self.optimizer.param_groups


							@param_groups.setter
							def _a   (				self	,	a_		):
											a__					   =		param_groups


							@property
							def _a   (				self		):
											return self.optimizer.defaults


							@defaults.setter
							def _a   (				self	,	a_		):
											a__					   =		defaults


							def _a   (				self	,	a_		):
											self.optimizer.add_param_group(a_		)


							def _a   (				self	,	a_		):
											if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
															xm.send_cpu_data_to_device(a_	,	self.accelerator_state.device		)
											self.optimizer.load_state_dict(a_		)


							def _a   (				self		):
											return self.optimizer.state_dict()


							def _a   (				self	,	a_=None		):
											if self.gradient_state.sync_gradients:
															a__					   =		"""set_to_none""" in inspect.signature(self.optimizer.zero_grad		).parameters
															if accept_arg:
																			if set_to_none is None:
																							a__					   =		False
																			self.optimizer.zero_grad(set_to_none=a_		)
															else:
																			if set_to_none is not None:
																							raise ValueError("""`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."""		)
																			self.optimizer.zero_grad()


							def _a   (				self	,	a_=None		):
											if self.gradient_state.sync_gradients:
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			a__					   =		{"""closure""": closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer	,	optimizer_args=a_		)
															elif self.scaler is not None:
																			a__					   =		False
																			if self._last_scale is None:
																							# `get_scale` is an async operation requiring full synchronization
																							# on CPU and GPUs before finishing. As a result, we store away
																							# the prior one to reduce the call overhead
																							a__					   =		self.scaler.get_scale()
																							a__					   =		True
																			self.scaler.step(self.optimizer	,	a_		)
																			self.scaler.update()
																			a__					   =		self.scaler.get_scale()
																			if not new_scale:
																							# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																							a__					   =		scale_after < self._last_scale
																			a__					   =		scale_after

															else:
																			self.optimizer.step(a_		)


							def _a   (				self	,	a_		):
											for param_group in self.optimizer.param_groups:
															a__					   =		[parameters_map.get(a_	,	a_		) for p in param_group["""params"""]]


							@property
							def _a   (				self		):
											warnings.warn(
											    """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
											    """`optimizer.step_was_skipped` instead."""	,	a_	,	)
											return self._is_overflow


							@property
							def _a   (				self		):
											return self._is_overflow


							def __getstate__(				self		):
											return self.__dict__.copy()







							def __setstate__(				self	,	a_		):
											self.__dict__.update(a_		)






from __future__ import annotations

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_ = 0		):
											a__					   =		value
											a__					   =		None
											a__					   =		None




def       A_   (						__a  :					Node | None			,		__a  :					Node | None		):

				"""simple docstring"""
				if treea is None:
								return treea
				if treea is None:
								return treea

				a__					   =		treea.value + treea.value
				a__					   =		merge_two_binary_trees(treea.left			,		treea.left		)
				a__					   =		merge_two_binary_trees(treea.right			,		treea.right		)
				return treea

def       A_   (						__a  :					Node | None		):

				"""simple docstring"""
				if root:
								print(root.value		)
								print_preorder(root.left		)
								print_preorder(root.right		)


if __name__ == "__main__":
	UpperCAmelCase							=       Node(1)
	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(3)
	UpperCAmelCase							=       Node(4)

	UpperCAmelCase							=       Node(2)
	UpperCAmelCase							=       Node(4)
	UpperCAmelCase							=       Node(6)
	UpperCAmelCase							=       Node(9)
	UpperCAmelCase							=       Node(5)

	print("""Tree1 is: """)
	print_preorder(treea)
	print("""Tree2 is: """)
	print_preorder(treea)
	UpperCAmelCase							=       merge_two_binary_trees(treea, treea)
	print("""Merged Tree is: """)
	print_preorder(merged_tree)

