






import sys
from collections import defaultdict






class 		lowercase_					:






      '''simple docstring'''




      def __init__(      self      :						Dict  )    ->Tuple:




       """simple docstring"""


       a		  =      []







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					__UpperCAmelCase      :						str  )    ->Dict:




       """simple docstring"""


       return self.node_position[vertex]







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




       """simple docstring"""


       a		  =      pos







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




       """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      :						Any ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Union[str, Any] ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						str  )    ->Union[str, Any]:




       """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      :						Any ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						Optional[int]  )    ->List[Any]:




       """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      :						Tuple ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						Any  )    ->Any:




       """simple docstring"""


       a		  =      positions[0]
       a		  =      sys.maxsize
       self.top_to_bottom(lowerCamelCase__ ,					0 ,					len(lowerCamelCase__  ) ,					lowerCamelCase__  )
       return temp
def 						_a    (      a :str )							->			List[str]:
 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 --------- >
     UpperCAmelCase__				   =		int(input("Enter number of edges: ").strip())
     UpperCAmelCase__				   =		defaultdict(list)
     for _ in range(edges_number):
          UpperCAmelCase__				   =		[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 unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    DebertaForMaskedLM,
					    DebertaForQuestionAnswering,
					    DebertaForSequenceClassification,
					    DebertaForTokenClassification,
					    DebertaModel,
					)
					from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__=13			,						lowerCamelCase__=7			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=99			,						lowerCamelCase__=32			,						lowerCamelCase__=5			,						lowerCamelCase__=4			,						lowerCamelCase__=37			,						lowerCamelCase__="gelu"			,						lowerCamelCase__=0.1			,						lowerCamelCase__=0.1			,						lowerCamelCase__=512			,						lowerCamelCase__=16			,						lowerCamelCase__=2			,						lowerCamelCase__=0.02			,						lowerCamelCase__=False			,						lowerCamelCase__=True			,						lowerCamelCase__="None"			,						lowerCamelCase__=3			,						lowerCamelCase__=4			,						lowerCamelCase__=None			,						)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    parent
							__lowerCamelCase		       =    batch_size
							__lowerCamelCase		       =    seq_length
							__lowerCamelCase		       =    is_training
							__lowerCamelCase		       =    use_input_mask
							__lowerCamelCase		       =    use_token_type_ids
							__lowerCamelCase		       =    use_labels
							__lowerCamelCase		       =    vocab_size
							__lowerCamelCase		       =    hidden_size
							__lowerCamelCase		       =    num_hidden_layers
							__lowerCamelCase		       =    num_attention_heads
							__lowerCamelCase		       =    intermediate_size
							__lowerCamelCase		       =    hidden_act
							__lowerCamelCase		       =    hidden_dropout_prob
							__lowerCamelCase		       =    attention_probs_dropout_prob
							__lowerCamelCase		       =    max_position_embeddings
							__lowerCamelCase		       =    type_vocab_size
							__lowerCamelCase		       =    type_sequence_label_size
							__lowerCamelCase		       =    initializer_range
							__lowerCamelCase		       =    num_labels
							__lowerCamelCase		       =    num_choices
							__lowerCamelCase		       =    relative_attention
							__lowerCamelCase		       =    position_biased_input
							__lowerCamelCase		       =    pos_att_type
							__lowerCamelCase		       =    scope


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size							)

							__lowerCamelCase		       =    None
							if self.use_input_mask:
											__lowerCamelCase		       =    ids_tensor([self.batch_size, self.seq_length]			,						vocab_size=2							)

							__lowerCamelCase		       =    None
							if self.use_token_type_ids:
											__lowerCamelCase		       =    ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size							)

							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    None
							if self.use_labels:
											__lowerCamelCase		       =    ids_tensor([self.batch_size]			,						self.type_sequence_label_size							)
											__lowerCamelCase		       =    ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels							)
											__lowerCamelCase		       =    ids_tensor([self.batch_size]			,						self.num_choices							)

							__lowerCamelCase		       =    self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							return DebertaConfig(
							    vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						initializer_range=self.initializer_range			,						relative_attention=self.relative_attention			,						position_biased_input=self.position_biased_input			,						pos_att_type=self.pos_att_type			,						)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.get_config()
							__lowerCamelCase		       =    300
							return config


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							self.parent.assertListEqual(list(result.loss.size()							)			,						[]							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    DebertaModel(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__							)[0]
							__lowerCamelCase		       =    model(lowerCamelCase__			,						token_type_ids=lowerCamelCase__							)[0]
							__lowerCamelCase		       =    model(lowerCamelCase__							)[0]

							self.parent.assertListEqual(list(sequence_output.size()							)			,						[self.batch_size, self.seq_length, self.hidden_size]							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    DebertaForMaskedLM(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						labels=lowerCamelCase__							)

							self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.num_labels
							__lowerCamelCase		       =    DebertaForSequenceClassification(lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						labels=lowerCamelCase__							)
							self.parent.assertListEqual(list(result.logits.size()							)			,						[self.batch_size, self.num_labels]							)
							self.check_loss_output(lowerCamelCase__							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.num_labels
							__lowerCamelCase		       =    DebertaForTokenClassification(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						labels=lowerCamelCase__							)
							self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    DebertaForQuestionAnswering(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(
							    lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						start_positions=lowerCamelCase__			,						end_positions=lowerCamelCase__			,						)
							self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length)							)
							self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length)							)






			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.prepare_config_and_inputs()
							(
							    (
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						(
							    __lowerCamelCase
							)       ,						
							)		       =    config_and_inputs
							__lowerCamelCase		       =    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_torch
class     __lowerCAmelCase (       __magic_name__			,				__magic_name__			,				unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       (
			    (
			        DebertaModel,
			        DebertaForMaskedLM,
			        DebertaForSequenceClassification,
			        DebertaForTokenClassification,
			        DebertaForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			snake_case_   		=       (
			    {
			        '''feature-extraction''': DebertaModel,
			        '''fill-mask''': DebertaForMaskedLM,
			        '''question-answering''': DebertaForQuestionAnswering,
			        '''text-classification''': DebertaForSequenceClassification,
			        '''token-classification''': DebertaForTokenClassification,
			        '''zero-shot''': DebertaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)

			snake_case_   		=       True
			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    DebertaModelTester(self							)
							__lowerCamelCase		       =    ConfigTester(self			,						config_class=lowerCamelCase__			,						hidden_size=37							)


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							self.config_tester.run_common_tests()


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase__							)






			@slow
			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase		       =    DebertaModel.from_pretrained(lowerCamelCase__							)
											self.assertIsNotNone(lowerCamelCase__							)







@require_torch
@require_sentencepiece
@require_tokenizers
class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			@unittest.skip(reason='Model not available yet'							)
			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							pass






			@slow
			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    DebertaModel.from_pretrained('microsoft/deberta-base'							)

							__lowerCamelCase		       =    torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]							)
							__lowerCamelCase		       =    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
							with torch.no_grad():
											__lowerCamelCase		       =    model(lowerCamelCase__			,						attention_mask=lowerCamelCase__							)[0]
							# compare the actual values for a slice.
							__lowerCamelCase		       =    torch.tensor(
							    [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]							)
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,						lowerCamelCase__			,						atol=1e-4							)			,						f"""{output[:, 1:4, 1:4]}"""							)


