





from __future__ import annotations

import queue

class       __A     :


				def __init__(		self   , UpperCAmelCase_							):
								lowerCamelCase	 =data
								lowerCamelCase	 =None
								lowerCamelCase	 =None





def      _lowercase					(						)						->  TreeNode:
				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      _lowercase					(						_UpperCAmelCase				)						->  None:
				if not isinstance(_UpperCAmelCase			,      _UpperCAmelCase				) or not node:
								return
				print(node.data			,      end=""","""				)
				pre_order(node.left				)
				pre_order(node.right				)





def      _lowercase					(						_UpperCAmelCase				)						->  None:
				if not isinstance(_UpperCAmelCase			,      _UpperCAmelCase				) or not node:
								return
				in_order(node.left				)
				print(node.data			,      end=""","""				)
				in_order(node.right				)





def      _lowercase					(						_UpperCAmelCase				)						->  None:
				if not isinstance(_UpperCAmelCase			,      _UpperCAmelCase				) or not node:
								return
				post_order(node.left				)
				post_order(node.right				)
				print(node.data			,      end=""","""				)





def      _lowercase					(						_UpperCAmelCase				)						->  None:
				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      _lowercase					(						_UpperCAmelCase				)						->  None:
				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      _lowercase					(						_UpperCAmelCase				)						->  None:
				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      _lowercase					(						_UpperCAmelCase				)						->  None:
				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      _lowercase					(						_UpperCAmelCase				)						->  None:
				if not isinstance(_UpperCAmelCase			,      _UpperCAmelCase				) or not node:
								return
				lowerCamelCase ,  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      _lowercase					(						_UpperCAmelCase = ""			,      _UpperCAmelCase=50			,      _UpperCAmelCase="*"				)						->  str:
				if not s:
								return "\n" + width * char
				lowerCamelCase ,  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'''))

							UpperCAmelCase__						:			TreeNode									=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())












import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCAmelCase__						:			Tuple									=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False			,      )						->  int:
				output_path.parent.mkdir(parents=_UpperCAmelCase			,      exist_ok=_UpperCAmelCase				)
				# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
				# so we check the torch version for backwards compatibility
				if is_torch_less_than_1_11:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      use_external_data_format=_UpperCAmelCase			,      enable_onnx_checker=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )
				else:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = False				)						->  Tuple:
				lowerCamelCase	 =torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								lowerCamelCase	 ="""cuda"""
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""				)
				else:
								lowerCamelCase	 ="""cpu"""
				lowerCamelCase	 =Path(_UpperCAmelCase				)

				# VAE DECODER
				lowerCamelCase	 =AutoencoderKL.from_pretrained(model_path + """/vae"""				)
				lowerCamelCase	 =vae_decoder.config.latent_channels
				# forward only through the decoder part
				lowerCamelCase	 =vae_decoder.decode
				onnx_export(
				    _UpperCAmelCase			,      model_args=(
				        torch.randn(1			,      _UpperCAmelCase			,      25			,      25				).to(device=_UpperCAmelCase			,      dtype=_UpperCAmelCase				),
				        False,
				    )			,      output_path=output_path / """vae_decoder""" / """model.onnx"""			,      ordered_input_names=["""latent_sample""", """return_dict"""]			,      output_names=["""sample"""]			,      dynamic_axes={
				        """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
				    }			,      opset=_UpperCAmelCase			,      )
				del vae_decoder


if __name__ == "__main__":
							UpperCAmelCase__						:			Union[str, Any]									=argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_path''',
							    type=str,
							    required=True,
							    help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
							)

							parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
							parser.add_argument(
							    '''--opset''',
							    default=14,
							    type=int,
							    help='''The version of the ONNX operator set to use.''',
							)
							parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

							UpperCAmelCase__						:			Union[str, Any]									=parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print('''SD: Done: ONNX''')





