


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_lowercase       :      Dict	=	False
_lowercase       :      List[Any]	=	True
_lowercase       :      Optional[Any]	=	False


if __name__ == "__main__":
				_lowercase       :      Any	=	argparse.ArgumentParser()

				parser.add_argument(
				    "--repo_path",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the architecture.",
				)

				parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

				_lowercase       :      Dict	=	parser.parse_args()

				_lowercase       :      List[str]	=	{
				    "image_size": "sample_size",
				    "num_res_blocks": "layers_per_block",
				    "block_channels": "block_out_channels",
				    "down_blocks": "down_block_types",
				    "up_blocks": "up_block_types",
				    "downscale_freq_shift": "freq_shift",
				    "resnet_num_groups": "norm_num_groups",
				    "resnet_act_fn": "act_fn",
				    "resnet_eps": "norm_eps",
				    "num_head_channels": "attention_head_dim",
				}

				_lowercase       :      Union[str, Any]	=	{
				    "time_steps": "time_proj",
				    "mid": "mid_block",
				    "downsample_blocks": "down_blocks",
				    "upsample_blocks": "up_blocks",
				}

				_lowercase       :      Optional[int]	=	"" if has_file(args.repo_path, "config.json") else "unet"

				with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
								_lowercase       :      str	=	reader.read()
								_lowercase       :      Any	=	json.loads(text)

				if do_only_config:
								for key in config_parameters_to_change.keys():
												config.pop(key, None)

				if has_file(args.repo_path, "config.json"):
								_lowercase       :      Tuple	=	UNetaDModel(**config)
				else:
								_lowercase       :      Any	=	UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
								_lowercase       :      Dict	=	class_name(**config)

				if do_only_config:
								model.save_config(os.path.join(args.repo_path, subfolder))

				_lowercase       :      Dict	=	dict(model.config)

				if do_only_renaming:
								for key, value in config_parameters_to_change.items():
												if key in config:
																_lowercase       :      str	=	config[key]
																del config[key]

								_lowercase       :      str	=	[k.replace("UNetRes", "") for k in config["down_block_types"]]
								_lowercase       :      Tuple	=	[k.replace("UNetRes", "") for k in config["up_block_types"]]

				if do_only_weights:
								_lowercase       :      List[str]	=	torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

								_lowercase       :      List[str]	=	{}
								for param_key, param_value in state_dict.items():
												if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
																continue
												_lowercase       :      Optional[Any]	=	False
												for key, new_key in key_parameters_to_change.items():
																if not has_changed and param_key.split(".")[0] == key:
																				_lowercase       :      List[str]	=	param_value
																				_lowercase       :      Union[str, Any]	=	True
												if not has_changed:
																_lowercase       :      Any	=	param_value

								model.load_state_dict(new_state_dict)
								model.save_pretrained(os.path.join(args.repo_path, subfolder))







import sys
from collections import defaultdict






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self   )	->				Any:
					A								=				[]

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[str]:
					return self.node_position[vertex]

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Optional[int]:
					A								=				pos

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__   )	->				Any:
					if start > size // 2 - 1:
						return
					else:
						if 2 * start + 2 >= size:
							A								=				2 * start + 1
						else:
							if heap[2 * start + 1] < heap[2 * start + 2]:
								A								=				2 * start + 1
							else:
								A								=				2 * start + 2
						if heap[smallest_child] < heap[start]:
							A					,			A								=				heap[smallest_child], positions[smallest_child]
							A					,			A								=				(
							    heap[start],
							    positions[start],
							)
							A					,			A								=				temp, tempa

							A								=				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				,   a__				,   a__				,   a__				,   a__   )	->				Dict:
					A								=				position[index]

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

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

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Optional[int]:
					A								=				len(a__   ) // 2 - 1
					for i in range(a__				,   -1				,   -1   ):
						self.top_to_bottom(a__				,   a__				,   len(a__   )				,   a__   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Any:
					A								=				positions[0]
					A								=				sys.maxsize
					self.top_to_bottom(a__				,   0				,   len(a__   )				,   a__   )
					return temp





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]						) ->   List[str]:





	"""simple docstring"""







	A								=				Heap()

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

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

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

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


if __name__ == "__main__":  # pragma: no cover
				# < --------- Prims Algorithm --------- >
				_lowercase       :      Tuple	=	int(input("Enter number of edges: ").strip())
				_lowercase       :      int	=	defaultdict(list)
				for _ in range(edges_number):
								_lowercase       :      Optional[Any]	=	[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))



