




from __future__ import annotations

import queue


class  A		:



		def __init__(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  data
								lowerCAmelCase_      			=  None
								lowerCAmelCase_      			=  None







def __UpperCamelCase			(       ):
						print('''\n********Press N to stop entering at any point of time********\n'''  )
						lowerCAmelCase_      			=  input('''Enter the value of the root node: '''  ).strip().lower()
						lowerCAmelCase_      			=  queue.Queue()
						lowerCAmelCase_      			=  TreeNode(int(_UpperCAmelCase  )  )
						q.put(_UpperCAmelCase  )
						while not q.empty():
												lowerCAmelCase_      			=  q.get()
												lowerCAmelCase_      			=  f"Enter the left node of {node_found.data}: "
												lowerCAmelCase_      			=  input(_UpperCAmelCase  ).strip().lower() or '''n'''
												if check == "n":
																		return tree_node
												lowerCAmelCase_      			=  TreeNode(int(_UpperCAmelCase  )  )
												lowerCAmelCase_      			=  left_node
												q.put(_UpperCAmelCase  )
												lowerCAmelCase_      			=  f"Enter the right node of {node_found.data}: "
												lowerCAmelCase_      			=  input(_UpperCAmelCase  ).strip().lower() or '''n'''
												if check == "n":
																		return tree_node
												lowerCAmelCase_      			=  TreeNode(int(_UpperCAmelCase  )  )
												lowerCAmelCase_      			=  right_node
												q.put(_UpperCAmelCase  )
						raise





def __UpperCamelCase			(       _A  ):





						if not isinstance(_UpperCAmelCase ,  _UpperCAmelCase  ) or not node:
												return
						print(node.data ,  end=''','''  )
						pre_order(node.left  )
						pre_order(node.right  )





def __UpperCamelCase			(       _A  ):





						if not isinstance(_UpperCAmelCase ,  _UpperCAmelCase  ) or not node:
												return
						in_order(node.left  )
						print(node.data ,  end=''','''  )
						in_order(node.right  )





def __UpperCamelCase			(       _A  ):





						if not isinstance(_UpperCAmelCase ,  _UpperCAmelCase  ) or not node:
												return
						post_order(node.left  )
						post_order(node.right  )
						print(node.data ,  end=''','''  )





def __UpperCamelCase			(       _A  ):





						if not isinstance(_UpperCAmelCase ,  _UpperCAmelCase  ) or not node:
												return
						lowerCAmelCase_      			=  queue.Queue()
						q.put(_UpperCAmelCase  )
						while not q.empty():
												lowerCAmelCase_      			=  q.get()
												print(node_dequeued.data ,  end=''','''  )
												if node_dequeued.left:
																		q.put(node_dequeued.left  )
												if node_dequeued.right:
																		q.put(node_dequeued.right  )





def __UpperCamelCase			(       _A  ):





						if not isinstance(_UpperCAmelCase ,  _UpperCAmelCase  ) or not node:
												return
						lowerCAmelCase_      			=  queue.Queue()
						q.put(_UpperCAmelCase  )
						while not q.empty():
												lowerCAmelCase_      			=  []
												while not q.empty():
																		lowerCAmelCase_      			=  q.get()
																		print(node_dequeued.data ,  end=''','''  )
																		if node_dequeued.left:
																								list_.append(node_dequeued.left  )
																		if node_dequeued.right:
																								list_.append(node_dequeued.right  )
												print()
												for node in list_:
																		q.put(_UpperCAmelCase  )





def __UpperCamelCase			(       _A  ):





						if not isinstance(_UpperCAmelCase ,  _UpperCAmelCase  ) or not node:
												return
						lowerCAmelCase_      			=  []
						lowerCAmelCase_      			=  node
						while n or stack:
												while n:  # start from root node, find its left child
																		print(n.data ,  end=''','''  )
																		stack.append(_UpperCAmelCase  )
																		lowerCAmelCase_      			=  n.left
												# end of while means current node doesn't have left child
												lowerCAmelCase_      			=  stack.pop()
												# start to traverse its right child
												lowerCAmelCase_      			=  n.right





def __UpperCamelCase			(       _A  ):





						if not isinstance(_UpperCAmelCase ,  _UpperCAmelCase  ) or not node:
												return
						lowerCAmelCase_      			=  []
						lowerCAmelCase_      			=  node
						while n or stack:
												while n:
																		stack.append(_UpperCAmelCase  )
																		lowerCAmelCase_      			=  n.left
												lowerCAmelCase_      			=  stack.pop()
												print(n.data ,  end=''','''  )
												lowerCAmelCase_      			=  n.right





def __UpperCamelCase			(       _A  ):





						if not isinstance(_UpperCAmelCase ,  _UpperCAmelCase  ) or not node:
												return
						lowerCAmelCase_      			=  [], []
						lowerCAmelCase_      			=  node
						stacka.append(_UpperCAmelCase  )
						while stacka:  # to find the reversed order of post order, store it in stack2
												lowerCAmelCase_      			=  stacka.pop()
												if n.left:
																		stacka.append(n.left  )
												if n.right:
																		stacka.append(n.right  )
												stacka.append(_UpperCAmelCase  )
						while stacka:  # pop up from stack2 will be the post order
												print(stacka.pop().data ,  end=''','''  )





def __UpperCamelCase			(       _A = "" ,  _A=50 ,  _A="*"  ):





						if not s:
												return "\n" + width * char
						lowerCAmelCase_      			=  divmod(width - len(_UpperCAmelCase  ) - 2 ,  2  )
						return f"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(prompt('''Binary Tree Traversals'''))

						_A     =			build_tree()
						print(prompt('''Pre Order Traversal'''))
						pre_order(node)
						print(prompt() + '''\n''')

						print(prompt('''In Order Traversal'''))
						in_order(node)
						print(prompt() + '''\n''')

						print(prompt('''Post Order Traversal'''))
						post_order(node)
						print(prompt() + '''\n''')

						print(prompt('''Level Order Traversal'''))
						level_order(node)
						print(prompt() + '''\n''')

						print(prompt('''Actual Level Order Traversal'''))
						level_order_actual(node)
						print('''*''' * 50 + '''\n''')

						print(prompt('''Pre Order Traversal - Iteration Version'''))
						pre_order_iter(node)
						print(prompt() + '''\n''')

						print(prompt('''In Order Traversal - Iteration Version'''))
						in_order_iter(node)
						print(prompt() + '''\n''')

						print(prompt('''Post Order Traversal - Iteration Version'''))
						post_order_iter(node)
						print(prompt())









#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist

def        a						(      *_UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		with open(_UpperCAmelCase ,   '''r'''						) as fh:
				fcntl.flock(_UpperCAmelCase ,   fcntl.LOCK_EX						)
				try:
						print(*_UpperCAmelCase						)
				finally:
						fcntl.flock(_UpperCAmelCase ,   fcntl.LOCK_UN						)


__A   			=int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
__A   			=torch.device("cuda", local_rank)
__A   			=socket.gethostname()

__A   			=f'''[{hostname}-{local_rank}]'''

try:
				# test distributed
				dist.init_process_group("nccl")
				dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
				dist.barrier()

				# test cuda is available and can allocate memory
				torch.cuda.is_available()
				torch.ones(1).cuda(local_rank)

				# global rank
				__A   			=dist.get_rank()
				__A   			=dist.get_world_size()

				printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')

				dist.barrier()
				if rank == 0:
								printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
				printflock(f'''{gpu} is broken''')
				raise





