


import sys
from collections import defaultdict




class       lowercase_					:





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





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





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> Any:
											a__      =pos





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> List[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]       ,  lowercase_)

																									self.top_to_bottom(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> str:
											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]       ,  lowercase_)
																		else:
																									a__      =val
																									a__      =temp
																									self.set_position(lowercase_       ,  lowercase_)
																									break
																		a__      =parent
											else:
																		a__      =val
																		a__      =temp
																		self.set_position(lowercase_       ,  0)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =len(lowercase_) // 2 - 1
											for i in range(lowercase_       ,  -1       ,  -1):
																		self.top_to_bottom(lowercase_       ,  lowercase_       ,  len(lowercase_)       ,  lowercase_)





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







def   _lowercase(					__a				:						Any     ):
							a__      =Heap()

							a__      =[0] * len(__a     )
							a__      =[-1] * len(__a     )  # 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(__a     )     ):
														distance_tv.append(sys.maxsize     )
														positions.append(__a     )
														heap.node_position.append(__a     )

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

							for _ in range(1    ,     len(__a     )     ):
														a__      =heap.delete_minimum(__a    ,     __a     )
														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(__a     )]
																												):
																																			a__      =distance
																																			heap.bottom_to_top(
																																			    __a    ,     heap.get_position(__a     )    ,     __a    ,     __a     )
																																			a__      =vertex
							return tree_edges


if __name__ == "__main__":  # pragma: no cover
	# < --------- Prims Algorithm --------- >
	_lowerCAmelCase:						str   				=       int(input('Enter number of edges: ').strip())
	_lowerCAmelCase:						Optional[int]   				=       defaultdict(list)
	for _ in range(edges_number):
		_lowerCAmelCase:						str   				=       [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
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase:						List[str]   				=       logging.get_logger()






def   _lowercase(					__a				:						int    ,     __a				:						str    ,     __a				:						LevitConfig    ,     __a				:						Path    ,     __a				:						bool = True     ):
							print(f"""Converting {name}..."""     )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												a__      =timm.create_model('levit_128s'    ,     pretrained=__a     )
																					else:
																												a__      =timm.create_model('levit_128'    ,     pretrained=__a     )
														if hidden_sizes == 192:
																					a__      =timm.create_model('levit_192'    ,     pretrained=__a     )
														if hidden_sizes == 256:
																					a__      =timm.create_model('levit_256'    ,     pretrained=__a     )
														if hidden_sizes == 384:
																					a__      =timm.create_model('levit_384'    ,     pretrained=__a     )

														from_model.eval()
														a__      =LevitForImageClassificationWithTeacher(__a     ).eval()
														a__      =OrderedDict()

														a__      =from_model.state_dict()
														a__      =list(from_model.state_dict().keys()     )
														a__      =list(our_model.state_dict().keys()     )
														print(len(__a     )    ,     len(__a     )     )
														for i in range(len(__a     )     ):
																					a__      =weights[og_keys[i]]
														our_model.load_state_dict(__a     )

														a__      =torch.randn((2, 3, 224, 224)     )
														a__      =from_model(__a     )
														a__      =our_model(__a     ).logits

							assert torch.allclose(__a    ,     __a     ), "The model logits don't match the original one."

							a__      =name
							print(__a     )

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name     )
														a__      =LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name     )

														print(f"""Pushed {checkpoint_name}"""     )






def   _lowercase(					__a				:						Path    ,     __a				:						str = None    ,     __a				:						bool = True     ):
							a__      ='imagenet-1k-id2label.json'
							a__      =1000
							a__      =(1, num_labels)

							a__      ='huggingface/label-files'
							a__      =num_labels
							a__      =json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
							a__      ={int(__a     ): v for k, v in idalabel.items()}

							a__      =idalabel
							a__      ={v: k for k, v in idalabel.items()}

							a__      =partial(__a    ,     num_labels=__a    ,     idalabel=__a    ,     labelaid=__a     )

							a__      ={
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							a__      ={
							    'levit-128S': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]    ,     num_attention_heads=[4, 6, 8]    ,     depths=[2, 3, 4]    ,     key_dim=[16, 16, 16]    ,     drop_path_rate=0    ,     ),
							    'levit-128': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]    ,     num_attention_heads=[4, 8, 12]    ,     depths=[4, 4, 4]    ,     key_dim=[16, 16, 16]    ,     drop_path_rate=0    ,     ),
							    'levit-192': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]    ,     num_attention_heads=[3, 5, 6]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0    ,     ),
							    'levit-256': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]    ,     num_attention_heads=[4, 6, 8]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0    ,     ),
							    'levit-384': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]    ,     num_attention_heads=[6, 9, 12]    ,     depths=[4, 4, 4]    ,     key_dim=[32, 32, 32]    ,     drop_path_rate=0.1    ,     ),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]    ,     __a    ,     names_to_config[model_name]    ,     __a    ,     __a     )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]    ,     __a    ,     __a    ,     __a    ,     __a     )
							return config, expected_shape


if __name__ == "__main__":
	_lowerCAmelCase:						Any   				=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default=None,
	    type=str,
	    help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='levit-dump-folder/',
	    type=Path,
	    required=False,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
	parser.add_argument(
	    '--no-push_to_hub',
	    dest='push_to_hub',
	    action='store_false',
	    help='Do not push model and image processor to the hub',
	)

	_lowerCAmelCase:						Union[str, Any]   				=       parser.parse_args()
	_lowerCAmelCase:						Path   				=       args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

