import sys
from collections import defaultdict
class     lowerCAmelCase_     :







							def __init__(   self      :     Optional[int]		):
												_UpperCamelCase			     =     []







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     str		):
												return self.node_position[vertex]







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     List[str] ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     pos







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     List[str] ,			_A      :     int ,			_A      :     Optional[Any] ,			_A      :     Union[str, Any]		):
												if start > size // 2 - 1:
																	return
												else:
																	if 2 * start + 2 >= size:
																						_UpperCamelCase			     =     2 * start + 1
																	else:
																						if heap[2 * start + 1] < heap[2 * start + 2]:
																											_UpperCamelCase			     =     2 * start + 1
																						else:
																											_UpperCamelCase			     =     2 * start + 2
																	if heap[smallest_child] < heap[start]:
																						_UpperCamelCase     ,    _UpperCamelCase			     =     heap[smallest_child], positions[smallest_child]
																						_UpperCamelCase     ,    _UpperCamelCase			     =     (
																						    heap[start],
																						    positions[start],
																						)
																						_UpperCamelCase     ,    _UpperCamelCase			     =     temp, tempa

																						_UpperCamelCase			     =     self.get_position(positions[smallest_child]		)
																						self.set_position(
																						    positions[smallest_child] ,			self.get_position(positions[start]		)		)
																						self.set_position(positions[start] ,			_A		)

																						self.top_to_bottom(_A ,			_A ,			_A ,			_A		)







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Tuple ,			_A      :     Optional[Any] ,			_A      :     int ,			_A      :     Optional[int]		):
												_UpperCamelCase			     =     position[index]

												while index != 0:
																	_UpperCamelCase			     =     int((index - 2) / 2		) if index % 2 == 0 else int((index - 1) / 2		)

																	if val < heap[parent]:
																						_UpperCamelCase			     =     heap[parent]
																						_UpperCamelCase			     =     position[parent]
																						self.set_position(position[parent] ,			_A		)
																	else:
																						_UpperCamelCase			     =     val
																						_UpperCamelCase			     =     temp
																						self.set_position(_A ,			_A		)
																						break
																	_UpperCamelCase			     =     parent
												else:
																	_UpperCamelCase			     =     val
																	_UpperCamelCase			     =     temp
																	self.set_position(_A ,			0		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     Tuple ,			_A      :     int		):
												_UpperCamelCase			     =     len(_A		) // 2 - 1
												for i in range(_A ,			-1 ,			-1		):
																	self.top_to_bottom(_A ,			_A ,			len(_A		) ,			_A		)







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     int ,			_A      :     List[str]		):
												_UpperCamelCase			     =     positions[0]
												_UpperCamelCase			     =     sys.maxsize
												self.top_to_bottom(_A ,			0 ,			len(_A		) ,			_A		)
												return temp




def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     Heap()

					_UpperCamelCase			     =     [0] * len(__snake_case						)
					_UpperCamelCase			     =     [-1] * len(__snake_case						)  # Neighboring Tree Vertex of selected vertex
					# Minimum Distance of explored vertex with neighboring vertex of partial tree
					# formed in graph
					_UpperCamelCase			     =     []  # Heap of Distance of vertices from their neighboring vertex
					_UpperCamelCase			     =     []

					for vertex in range(len(__snake_case						)						):
										distance_tv.append(sys.maxsize						)
										positions.append(__snake_case						)
										heap.node_position.append(__snake_case						)

					_UpperCamelCase			     =     []
					_UpperCamelCase			     =     1
					_UpperCamelCase			     =     sys.maxsize
					for neighbor, distance in adjacency_list[0]:
										_UpperCamelCase			     =     0
										_UpperCamelCase			     =     distance
					heap.heapify(__snake_case  ,     __snake_case						)

					for _ in range(1  ,     len(__snake_case						)						):
										_UpperCamelCase			     =     heap.delete_minimum(__snake_case  ,     __snake_case						)
										if visited[vertex] == 0:
															tree_edges.append((nbr_tv[vertex], vertex)						)
															_UpperCamelCase			     =     1
															for neighbor, distance in adjacency_list[vertex]:
																				if (
																				    visited[neighbor] == 0
																				    and distance < distance_tv[heap.get_position(__snake_case						)]
																				):
																									_UpperCamelCase			     =     distance
																									heap.bottom_to_top(
																									    __snake_case  ,     heap.get_position(__snake_case						)  ,     __snake_case  ,     __snake_case						)
																									_UpperCamelCase			     =     vertex
					return tree_edges


if __name__ == "__main__":  # pragma: no cover
					# < --------- Prims Algorithm --------- >
					_lowerCAmelCase       =		int(input("Enter number of edges: ").strip())
					_lowerCAmelCase       =		defaultdict(list)
					for _ in range(edges_number):
										_lowerCAmelCase       =		[int(x) for x in input().strip().split()]
										adjacency_list[edge[0]].append([edge[1], edge[2]])
										adjacency_list[edge[1]].append([edge[0], edge[2]])
					print(prisms_algorithm(adjacency_list))








import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase__							  =						version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 		_a       ( a					:Union[str, Any]      ,				a					:tuple      ,				a					:Path      ,				a					:List[Any]      ,				a					:Union[str, Any]      ,				a					:Optional[int]      ,				a					:List[Any]      ,				a					:Union[str, Any]=False      ,				)				->     Any:
 output_path.parent.mkdir(parents=a      ,				exist_ok=a					)
 # 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(
      a      ,				a      ,				f=output_path.as_posix()      ,				input_names=a      ,				output_names=a      ,				dynamic_axes=a      ,				do_constant_folding=a      ,				use_external_data_format=a      ,				enable_onnx_checker=a      ,				opset_version=a      ,				)
 else:
  export(
      a      ,				a      ,				f=output_path.as_posix()      ,				input_names=a      ,				output_names=a      ,				dynamic_axes=a      ,				do_constant_folding=a      ,				opset_version=a      ,				)
@torch.no_grad()
def 		_a       ( a					:str      ,				a					:str      ,				a					:int      ,				a					:bool = False					)				->     Union[str, Any]:
 a						    = torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  a						    = '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''					)
 else:
  a						    = '''cpu'''
 a						    = Path(a					)

 # VAE DECODER
 a						    = AutoencoderKL.from_pretrained(model_path + '''/vae'''					)
 a						    = vae_decoder.config.latent_channels
 # forward only through the decoder part
 a						    = vae_decoder.decode
 onnx_export(
     a      ,				model_args=(
         torch.randn(1      ,				a      ,				25      ,				25					).to(device=a      ,				dtype=a					),
         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=a      ,				)
 del vae_decoder


if __name__ == "__main__":
     UpperCAmelCase__							  =						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__							  =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")


