

def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list[tuple[int, int, int]]:



    """simple docstring"""






    UpperCamelCase_          =		sorted(SCREAMING_SNAKE_CASE_       ,		key=lambda SCREAMING_SNAKE_CASE_       : edge[2]       )

    UpperCamelCase_          =		list(range(SCREAMING_SNAKE_CASE_       )       )

    def find_parent(SCREAMING_SNAKE_CASE_       ):
        if i != parent[i]:
            UpperCamelCase_          =		find_parent(parent[i]       )
        return parent[i]

    UpperCamelCase_          =		0
    UpperCamelCase_          =		[]

    for edge in edges:
        UpperCamelCase_          =		find_parent(edge[0]       )
        UpperCamelCase_          =		find_parent(edge[1]       )
        if parent_a != parent_b:
            minimum_spanning_tree_cost += edge[2]
            minimum_spanning_tree.append(SCREAMING_SNAKE_CASE_       )
            UpperCamelCase_          =		parent_b

    return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
 SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE						:str      =			list(map(int, input().strip().split()))
 SCREAMING_SNAKE_CASE						:Dict      =			[]

 for _ in range(num_edges):
  SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE						:Dict      =			(int(x) for x in input().strip().split())
  edges.append((nodea, nodea, cost))

 kruskal(num_nodes, edges)



import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import DonutSwinModel
 from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST






class 		__magic_name__ :






     def __init__(							self						,		_lowercase						,		_lowercase=13						,		_lowercase=32						,		_lowercase=2						,		_lowercase=3						,		_lowercase=16						,		_lowercase=[1, 2, 1]						,		_lowercase=[2, 2, 4]						,		_lowercase=2						,		_lowercase=2.0						,		_lowercase=True						,		_lowercase=0.0						,		_lowercase=0.0						,		_lowercase=0.1						,		_lowercase="gelu"						,		_lowercase=False						,		_lowercase=True						,		_lowercase=0.02						,		_lowercase=1e-5						,		_lowercase=True						,		_lowercase=None						,		_lowercase=True						,		_lowercase=10						,		_lowercase=8						,		)->			Tuple:
         UpperCamelCase_          =		parent
         UpperCamelCase_          =		batch_size
         UpperCamelCase_          =		image_size
         UpperCamelCase_          =		patch_size
         UpperCamelCase_          =		num_channels
         UpperCamelCase_          =		embed_dim
         UpperCamelCase_          =		depths
         UpperCamelCase_          =		num_heads
         UpperCamelCase_          =		window_size
         UpperCamelCase_          =		mlp_ratio
         UpperCamelCase_          =		qkv_bias
         UpperCamelCase_          =		hidden_dropout_prob
         UpperCamelCase_          =		attention_probs_dropout_prob
         UpperCamelCase_          =		drop_path_rate
         UpperCamelCase_          =		hidden_act
         UpperCamelCase_          =		use_absolute_embeddings
         UpperCamelCase_          =		patch_norm
         UpperCamelCase_          =		layer_norm_eps
         UpperCamelCase_          =		initializer_range
         UpperCamelCase_          =		is_training
         UpperCamelCase_          =		scope
         UpperCamelCase_          =		use_labels
         UpperCamelCase_          =		type_sequence_label_size
         UpperCamelCase_          =		encoder_stride






     def      UpperCAmelCase_							(							self     )->			Dict:
         UpperCamelCase_          =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

         UpperCamelCase_          =		None
         if self.use_labels:
             UpperCamelCase_          =		ids_tensor([self.batch_size]						,		self.type_sequence_label_size     )

         UpperCamelCase_          =		self.get_config()

         return config, pixel_values, labels






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         return DonutSwinConfig(
             image_size=self.image_size						,		patch_size=self.patch_size						,		num_channels=self.num_channels						,		embed_dim=self.embed_dim						,		depths=self.depths						,		num_heads=self.num_heads						,		window_size=self.window_size						,		mlp_ratio=self.mlp_ratio						,		qkv_bias=self.qkv_bias						,		hidden_dropout_prob=self.hidden_dropout_prob						,		attention_probs_dropout_prob=self.attention_probs_dropout_prob						,		drop_path_rate=self.drop_path_rate						,		hidden_act=self.hidden_act						,		use_absolute_embeddings=self.use_absolute_embeddings						,		path_norm=self.patch_norm						,		layer_norm_eps=self.layer_norm_eps						,		initializer_range=self.initializer_range						,		encoder_stride=self.encoder_stride						,		)






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			Optional[Any]:
         UpperCamelCase_          =		DonutSwinModel(config=_lowercase     )
         model.to(_lowercase     )
         model.eval()
         UpperCamelCase_          =		model(_lowercase     )

         UpperCamelCase_          =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
         UpperCamelCase_          =		int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

         self.parent.assertEqual(result.last_hidden_state.shape						,		(self.batch_size, expected_seq_len, expected_dim)     )






     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		self.prepare_config_and_inputs()
         (
             (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    (
             UpperCamelCase_
         )   ,    
         )          =		config_and_inputs
         UpperCamelCase_          =		{"pixel_values": pixel_values}
         return config, inputs_dict






@require_torch
class 		__magic_name__ (				snake_case       ,	snake_case       ,	unittest.TestCase							):
     UpperCamelCase_     :List[str]									=	(DonutSwinModel,) if is_torch_available() else ()
     UpperCamelCase_     :Optional[Any]									=	{"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
     UpperCamelCase_     :Any									=	True

     UpperCamelCase_     :Tuple									=	False
     UpperCamelCase_     :List[str]									=	False
     UpperCamelCase_     :List[str]									=	False






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_          =		DonutSwinModelTester(self     )
         UpperCamelCase_          =		ConfigTester(self						,		config_class=_lowercase						,		embed_dim=37     )






     def      UpperCAmelCase_							(							self     )->			Optional[int]:
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()






     def      UpperCAmelCase_							(							self     )->			Optional[int]:
         return






     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase     )






     def      UpperCAmelCase_							(							self     )->			int:
         # DonutSwin does not use inputs_embeds
         pass






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             UpperCamelCase_          =		model_class(_lowercase     )
             self.assertIsInstance(model.get_input_embeddings()						,		(nn.Module)     )
             UpperCamelCase_          =		model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_lowercase						,		nn.Linear     )     )






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             UpperCamelCase_          =		model_class(_lowercase     )
             UpperCamelCase_          =		inspect.signature(model.forward     )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase_          =		[*signature.parameters.keys()]

             UpperCamelCase_          =		["pixel_values"]
             self.assertListEqual(arg_names[:1]						,		_lowercase     )






     def      UpperCAmelCase_							(							self     )->			List[Any]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_          =		True

         for model_class in self.all_model_classes:
             UpperCamelCase_          =		True
             UpperCamelCase_          =		False
             UpperCamelCase_          =		True
             UpperCamelCase_          =		model_class(_lowercase     )
             model.to(_lowercase     )
             model.eval()
             with torch.no_grad():
                 UpperCamelCase_          =		model(**self._prepare_for_class(_lowercase						,		_lowercase     )     )
             UpperCamelCase_          =		outputs.attentions
             UpperCamelCase_          =		len(self.model_tester.depths     )
             self.assertEqual(len(_lowercase     )						,		_lowercase     )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             UpperCamelCase_          =		True
             UpperCamelCase_          =		config.window_size**2
             UpperCamelCase_          =		model_class(_lowercase     )
             model.to(_lowercase     )
             model.eval()
             with torch.no_grad():
                 UpperCamelCase_          =		model(**self._prepare_for_class(_lowercase						,		_lowercase     )     )
             UpperCamelCase_          =		outputs.attentions
             self.assertEqual(len(_lowercase     )						,		_lowercase     )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]     )						,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,		)
             UpperCamelCase_          =		len(_lowercase     )

             # Check attention is always last and order is fine
             UpperCamelCase_          =		True
             UpperCamelCase_          =		True
             UpperCamelCase_          =		model_class(_lowercase     )
             model.to(_lowercase     )
             model.eval()
             with torch.no_grad():
                 UpperCamelCase_          =		model(**self._prepare_for_class(_lowercase						,		_lowercase     )     )

             if hasattr(self.model_tester						,		"num_hidden_states_types"     ):
                 UpperCamelCase_          =		self.model_tester.num_hidden_states_types
             else:
                 # also another +1 for reshaped_hidden_states
                 UpperCamelCase_          =		2
             self.assertEqual(out_len + added_hidden_states						,		len(_lowercase     )     )

             UpperCamelCase_          =		outputs.attentions

             self.assertEqual(len(_lowercase     )						,		_lowercase     )

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]     )						,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,		)






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		model_class(_lowercase     )
         model.to(_lowercase     )
         model.eval()

         with torch.no_grad():
             UpperCamelCase_          =		model(**self._prepare_for_class(_lowercase						,		_lowercase     )     )

         UpperCamelCase_          =		outputs.hidden_states

         UpperCamelCase_          =		getattr(
             self.model_tester						,		"expected_num_hidden_layers"						,		len(self.model_tester.depths     ) + 1     )
         self.assertEqual(len(_lowercase     )						,		_lowercase     )

         # DonutSwin has a different seq_length
         UpperCamelCase_          =		(
             config.patch_size
             if isinstance(config.patch_size						,		collections.abc.Iterable     )
             else (config.patch_size, config.patch_size)
         )

         UpperCamelCase_          =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]     )						,		[num_patches, self.model_tester.embed_dim]						,		)

         UpperCamelCase_          =		outputs.reshaped_hidden_states
         self.assertEqual(len(_lowercase     )						,		_lowercase     )

         UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		reshaped_hidden_states[0].shape
         UpperCamelCase_          =		(
             reshaped_hidden_states[0].view(_lowercase						,		_lowercase						,		height * width     ).permute(0						,		2						,		1     )
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:]     )						,		[num_patches, self.model_tester.embed_dim]						,		)






     def      UpperCAmelCase_							(							self     )->			List[str]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

         UpperCamelCase_          =		(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size						,		collections.abc.Iterable     )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
             UpperCamelCase_          =		True
             self.check_hidden_states_output(_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase_          =		True

             self.check_hidden_states_output(_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_          =		3

         UpperCamelCase_          =		(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size						,		collections.abc.Iterable     )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         UpperCamelCase_          =		(
             config.patch_size
             if isinstance(config.patch_size						,		collections.abc.Iterable     )
             else (config.patch_size, config.patch_size)
         )

         UpperCamelCase_          =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         UpperCamelCase_          =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
             UpperCamelCase_          =		True
             self.check_hidden_states_output(_lowercase						,		_lowercase						,		_lowercase						,		(padded_height, padded_width)     )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase_          =		True
             self.check_hidden_states_output(_lowercase						,		_lowercase						,		_lowercase						,		(padded_height, padded_width)     )






     @slow
     def      UpperCAmelCase_							(							self     )->			List[str]:
         for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_          =		DonutSwinModel.from_pretrained(_lowercase     )
             self.assertIsNotNone(_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

         UpperCamelCase_          =		_config_zero_init(_lowercase     )
         for model_class in self.all_model_classes:
             UpperCamelCase_          =		model_class(config=_lowercase     )
             for name, param in model.named_parameters():
                 if "embeddings" not in name and param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1e9).round() / 1e9).item()						,		[0.0, 1.0]						,		msg=F"Parameter {name} of model {model_class} seems not properly initialized"						,		)
