




'''simple docstring'''


import sys
from collections import defaultdict

class      A       :







							def __init__(    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														_a      	=							[]







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    str   )     ->				int:


														"""simple docstring"""


														return self.node_position[vertex]







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Union[str, Any]   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							pos







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    int   )     ->				Union[str, Any]:


														"""simple docstring"""


														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]				,     lowerCAmelCase_   )

																												self.top_to_bottom(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    Any   )     ->				Tuple:


														"""simple docstring"""


														_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]				,     lowerCAmelCase_   )
																					else:
																												_a      	=							val
																												_a      	=							temp
																												self.set_position(lowerCAmelCase_				,     lowerCAmelCase_   )
																												break
																					_a      	=							parent
														else:
																					_a      	=							val
																					_a      	=							temp
																					self.set_position(lowerCAmelCase_				,     0   )







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    int   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							len(lowerCAmelCase_   ) // 2 - 1
														for i in range(lowerCAmelCase_				,     -1				,     -1   ):
																					self.top_to_bottom(lowerCAmelCase_				,     lowerCAmelCase_				,     len(lowerCAmelCase_   )				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    str   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							positions[0]
														_a      	=							sys.maxsize
														self.top_to_bottom(lowerCAmelCase_				,     0				,     len(lowerCAmelCase_   )				,     lowerCAmelCase_   )
														return temp



def 						snake_case_       (UpperCamelCase			:    Union[str, Any] ):





							'''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 --------- >
				_snake_case : Optional[Any]  				=						int(input('Enter number of edges: ').strip())
				_snake_case : List[str]  				=						defaultdict(list)
				for _ in range(edges_number):
								_snake_case : List[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 os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE:     int									= ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE:     Tuple									= {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
						raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= ''' Hello world! cécé herlolip'''

SCREAMING_SNAKE_CASE:     Dict									= [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]


def 					_a							(					lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    '_float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							dct.pop(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							val


def 					_a							(					lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
		SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						'bart.large.cnn' ).eval()
		hub_interface.model.load_state_dict(sd['model'] )
		return hub_interface


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							emb.weight.shape
		SCREAMING_SNAKE_CASE_					      =							nn.Linear(lowerCAmelCase    ,						lowerCAmelCase    ,						bias=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							emb.weight.data
		return lin_layer


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		if not os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						lowerCAmelCase ).eval()
		else:
				SCREAMING_SNAKE_CASE_					      =							load_xsum_checkpoint(lowerCAmelCase )

		bart.model.upgrade_state_dict(bart.model.state_dict() )
		if hf_checkpoint_name is None:
				SCREAMING_SNAKE_CASE_					      =							checkpoint_path.replace('.'    ,						'-' )
		SCREAMING_SNAKE_CASE_					      =							BartConfig.from_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							bart.encode(lowerCAmelCase ).unsqueeze(0 )
		SCREAMING_SNAKE_CASE_					      =							BartTokenizer.from_pretrained(lowerCAmelCase ).encode(lowerCAmelCase    ,						return_tensors='pt' ).unsqueeze(0 )
		if not torch.eq(lowerCAmelCase    ,						lowerCAmelCase ).all():
				raise ValueError(
				    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )

		if checkpoint_path == "bart.large.mnli":
				SCREAMING_SNAKE_CASE_					      =							bart.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['model.decoder.embed_tokens.weight']
				for src, dest in mnli_rename_keys:
						rename_key(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							BartForSequenceClassification(lowerCAmelCase ).eval()
				model.load_state_dict(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							bart.predict('mnli'    ,						lowerCAmelCase    ,						return_logits=lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase )[0]  # logits
		else:  # no classification heads to worry about
				SCREAMING_SNAKE_CASE_					      =							bart.model.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['decoder.embed_tokens.weight']
				SCREAMING_SNAKE_CASE_					      =							bart.extract_features(lowerCAmelCase )
				if hf_checkpoint_name == "facebook/bart-large":
						SCREAMING_SNAKE_CASE_					      =							BartModel(lowerCAmelCase ).eval()
						model.load_state_dict(lowerCAmelCase )
						SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase ).model[0]
				else:
						SCREAMING_SNAKE_CASE_					      =							BartForConditionalGeneration(lowerCAmelCase ).eval()  # an existing summarization ckpt
						model.model.load_state_dict(lowerCAmelCase )
						if hasattr(lowerCAmelCase    ,						'lm_head' ):
								SCREAMING_SNAKE_CASE_					      =							make_linear_from_emb(model.model.shared )
						SCREAMING_SNAKE_CASE_					      =							model.model(lowerCAmelCase )[0]

    # Check results
		if fairseq_output.shape != new_model_outputs.shape:
				raise ValueError(
				    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
		if (fairseq_output != new_model_outputs).any().item():
				raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
		Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Dict									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
						)
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
						)
						SCREAMING_SNAKE_CASE:     List[str]									= parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)