






import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


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






def 				A	(			a_				,a_ = False   )	->		Optional[int]:
					if device_specific:
										seed += AcceleratorState().process_index
					random.seed(a_   )
					np.random.seed(a_   )
					torch.manual_seed(a_   )
					if is_xpu_available():
										torch.xpu.manual_seed_all(a_   )
					elif is_npu_available():
										torch.npu.manual_seed_all(a_   )
					else:
										torch.cuda.manual_seed_all(a_   )
					# ^^ safe to call this function even if cuda is not available
					if is_tpu_available():
										xm.set_rng_state(a_   )






def 				A	(			a_ = None				,a_ = None   )	->		Tuple:
					# Get the proper rng state
					if rng_type == RNGType.TORCH:
										__UpperCamelCase :   Any									=torch.get_rng_state()
					elif rng_type == RNGType.CUDA:
										__UpperCamelCase :   int									=torch.cuda.get_rng_state()
					elif rng_type == RNGType.XLA:
										assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
										__UpperCamelCase :   Optional[Any]									=torch.tensor(xm.get_rng_state()   )
					elif rng_type == RNGType.NPU:
										assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
										__UpperCamelCase :   str									=torch.npu.get_rng_state()
					elif rng_type == RNGType.XPU:
										assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
										__UpperCamelCase :   Dict									=torch.xpu.get_rng_state()
					elif rng_type == RNGType.GENERATOR:
										assert generator is not None, "Need a generator to synchronize its seed."
										__UpperCamelCase :   List[str]									=generator.get_state()

					# Broadcast the rng state from device 0 to other devices
					__UpperCamelCase :   str									=AcceleratorState()
					if state.distributed_type == DistributedType.TPU:
										__UpperCamelCase :   int									=rng_state.to(xm.xla_device()   )
										xm.collective_broadcast([rng_state]   )
										xm.mark_step()
										__UpperCamelCase :   int									=rng_state.cpu()
					elif (
					    state.distributed_type in CUDA_DISTRIBUTED_TYPES
					    or state.distributed_type == DistributedType.MULTI_NPU
					    or state.distributed_type == DistributedType.MULTI_XPU
					):
										__UpperCamelCase :   Dict									=rng_state.to(state.device   )
										torch.distributed.broadcast(a_				,0   )
										__UpperCamelCase :   Dict									=rng_state.cpu()
					elif state.distributed_type == DistributedType.MULTI_CPU:
										torch.distributed.broadcast(a_				,0   )

					# Set the broadcast rng state
					if rng_type == RNGType.TORCH:
										torch.set_rng_state(a_   )
					elif rng_type == RNGType.CUDA:
										torch.cuda.set_rng_state(a_   )
					elif rng_type == RNGType.NPU:
										torch.npu.set_rng_state(a_   )
					elif rng_type == RNGType.XPU:
										torch.xpu.set_rng_state(a_   )
					elif rng_type == RNGType.XLA:
										xm.set_rng_state(rng_state.item()   )
					elif rng_type == RNGType.GENERATOR:
										generator.set_state(a_   )






def 				A	(			a_				,a_ = None   )	->		Union[str, Any]:
					for rng_type in rng_types:
										synchronize_rng_state(RNGType(a_   )				,generator=a_   )









from __future__ import annotations




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__ = 0       ):




									"""simple docstring"""

									__UpperCamelCase :   str									=value
									__UpperCamelCase :   Node | None									=None
									__UpperCamelCase :   Node | None									=None






def 				A	(			a_				,a_   )	->		Node | None:
					if treea is None:
										return treea
					if treea is None:
										return treea

					__UpperCamelCase :   Dict									=treea.value + treea.value
					__UpperCamelCase :   Any									=merge_two_binary_trees(treea.left				,treea.left   )
					__UpperCamelCase :   Union[str, Any]									=merge_two_binary_trees(treea.right				,treea.right   )
					return treea






def 				A	(			a_   )	->		None:
					if root:
										print(root.value   )
										print_preorder(root.left   )
										print_preorder(root.right   )


if __name__ == "__main__":
							A_			:List[str]	    =		Node(1)
							A_			:Dict	    =		Node(2)
							A_			:str	    =		Node(3)
							A_			:Union[str, Any]	    =		Node(4)

							A_			:Any	    =		Node(2)
							A_			:Tuple	    =		Node(4)
							A_			:Optional[Any]	    =		Node(6)
							A_			:Optional[Any]	    =		Node(9)
							A_			:str	    =		Node(5)

							print('''Tree1 is: ''')
							print_preorder(treea)
							print('''Tree2 is: ''')
							print_preorder(treea)
							A_			:Optional[Any]	    =		merge_two_binary_trees(treea, treea)
							print('''Merged Tree is: ''')
							print_preorder(merged_tree)

