

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
					import torch

					from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

					if is_vision_available():
										from transformers import MaskaFormerImageProcessor

if is_vision_available():
					from PIL import Image






class        A_		:
							def __init__(		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			str=2   ,SCREAMING_SNAKE_CASE__		:			Dict=True   ,SCREAMING_SNAKE_CASE__		:			List[Any]=False   ,SCREAMING_SNAKE_CASE__		:			Dict=1_0   ,SCREAMING_SNAKE_CASE__		:			str=3   ,SCREAMING_SNAKE_CASE__		:			Dict=3_2 * 8   ,SCREAMING_SNAKE_CASE__		:			Dict=3_2 * 8   ,SCREAMING_SNAKE_CASE__		:			List[str]=4   ,SCREAMING_SNAKE_CASE__		:			Dict=6_4   ,):
								__lowerCamelCase				:				List[Any]					       = parent
								__lowerCamelCase				:				str					       = batch_size
								__lowerCamelCase				:				Optional[int]					       = is_training
								__lowerCamelCase				:				List[str]					       = use_auxiliary_loss
								__lowerCamelCase				:				List[Any]					       = num_queries
								__lowerCamelCase				:				Tuple					       = num_channels
								__lowerCamelCase				:				Union[str, Any]					       = min_size
								__lowerCamelCase				:				str					       = max_size
								__lowerCamelCase				:				List[Any]					       = num_labels
								__lowerCamelCase				:				str					       = hidden_dim
								__lowerCamelCase				:				Optional[int]					       = hidden_dim
							def     lowerCAmelCase     (		self		:			List[str]):
								__lowerCamelCase				:				Dict					       = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
								    SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				List[str]					       = torch.ones([self.batch_size, self.min_size, self.max_size]   ,device=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				str					       = (
								    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]   ,device=SCREAMING_SNAKE_CASE__) > 0.5
								).float()
								__lowerCamelCase				:				Optional[int]					       = (torch.rand((self.batch_size, self.num_labels)   ,device=SCREAMING_SNAKE_CASE__) > 0.5).long()

								__lowerCamelCase				:				Tuple					       = self.get_config()
								return config, pixel_values, pixel_mask, mask_labels, class_labels
							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				Optional[Any]					       = MaskaFormerConfig(
								    hidden_size=self.hidden_dim   ,)
								__lowerCamelCase				:				str					       = self.num_queries
								__lowerCamelCase				:				List[str]					       = self.num_labels

								__lowerCamelCase				:				Tuple					       = [1, 1, 1, 1]
								__lowerCamelCase				:				Dict					       = self.num_channels

								__lowerCamelCase				:				List[Any]					       = 6_4
								__lowerCamelCase				:				str					       = 1_2_8
								__lowerCamelCase				:				int					       = self.hidden_dim
								__lowerCamelCase				:				Any					       = self.hidden_dim
								__lowerCamelCase				:				List[Any]					       = self.hidden_dim
								return config
							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				str					       = self.prepare_config_and_inputs()
								__lowerCamelCase				:				Optional[Any]					       = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
								return config, inputs_dict
							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			str):
								__lowerCamelCase				:				Dict					       = output.encoder_hidden_states
								__lowerCamelCase				:				List[Any]					       = output.pixel_decoder_hidden_states
								__lowerCamelCase				:				Optional[Any]					       = output.transformer_decoder_hidden_states

								self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__)   ,len(config.backbone_config.depths))
								self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__)   ,len(config.backbone_config.depths))
								self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__)   ,config.decoder_layers)
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			Any   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			int=False):
								with torch.no_grad():
									__lowerCamelCase				:				Optional[int]					       = MaskaFormerModel(config=SCREAMING_SNAKE_CASE__)
									model.to(SCREAMING_SNAKE_CASE__)
									model.eval()

									__lowerCamelCase				:				List[str]					       = model(pixel_values=SCREAMING_SNAKE_CASE__   ,pixel_mask=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase				:				str					       = model(SCREAMING_SNAKE_CASE__   ,output_hidden_states=SCREAMING_SNAKE_CASE__)

								self.parent.assertEqual(
								    output.transformer_decoder_last_hidden_state.shape   ,(self.batch_size, self.num_queries, self.hidden_dim)   ,)
								# let's ensure the other two hidden state exists
								self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None)
								self.parent.assertTrue(output.encoder_last_hidden_state is not None)

								if output_hidden_states:
									self.check_output_hidden_state(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			str):
								__lowerCamelCase				:				int					       = MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()

								def comm_check_on_output(SCREAMING_SNAKE_CASE__		:			Optional[int]):
									# let's still check that all the required stuff is there
									self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None)
									self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None)
									self.parent.assertTrue(result.encoder_last_hidden_state is not None)
									# okay, now we need to check the logits shape
									# due to the encoder compression, masks have a //4 spatial size
									self.parent.assertEqual(
									    result.masks_queries_logits.shape   ,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)   ,)
									# + 1 for null class
									self.parent.assertEqual(
									    result.class_queries_logits.shape   ,(self.batch_size, self.num_queries, self.num_labels + 1))

								with torch.no_grad():
									__lowerCamelCase				:				int					       = model(pixel_values=SCREAMING_SNAKE_CASE__   ,pixel_mask=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase				:				int					       = model(SCREAMING_SNAKE_CASE__)

									comm_check_on_output(SCREAMING_SNAKE_CASE__)

									__lowerCamelCase				:				Any					       = model(
									    pixel_values=SCREAMING_SNAKE_CASE__   ,pixel_mask=SCREAMING_SNAKE_CASE__   ,mask_labels=SCREAMING_SNAKE_CASE__   ,class_labels=SCREAMING_SNAKE_CASE__)

								comm_check_on_output(SCREAMING_SNAKE_CASE__)

								self.parent.assertTrue(result.loss is not None)
								self.parent.assertEqual(result.loss.shape   ,torch.Size([1]))






@require_torch
class        A_		(       SCREAMING_SNAKE_CASE			,							SCREAMING_SNAKE_CASE			,							unittest.TestCase				):
							_UpperCAmelCase    :  Optional[Any]						      =      (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							_UpperCAmelCase    :  Optional[int]						      =      {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

							_UpperCAmelCase    :  Any						      =      False
							_UpperCAmelCase    :  Any						      =      False
							_UpperCAmelCase    :  Union[str, Any]						      =      False
							_UpperCAmelCase    :  Optional[Any]						      =      False
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				List[str]					       = MaskaFormerModelTester(self)
								__lowerCamelCase				:				int					       = ConfigTester(self   ,config_class=SCREAMING_SNAKE_CASE__   ,has_text_modality=SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Tuple):
								self.config_tester.run_common_tests()
							def     lowerCAmelCase     (		self		:			Tuple):
								__lowerCamelCase       , __lowerCamelCase				:				Tuple					       = self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__   ,output_hidden_states=SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				Union[str, Any]					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE__)
							@unittest.skip(reason='Mask2Former does not use inputs_embeds')
							def     lowerCAmelCase     (		self		:			List[str]):
								pass
							@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method')
							def     lowerCAmelCase     (		self		:			Tuple):
								pass
							@unittest.skip(reason='Mask2Former is not a generative model')
							def     lowerCAmelCase     (		self		:			Dict):
								pass
							@unittest.skip(reason='Mask2Former does not use token embeddings')
							def     lowerCAmelCase     (		self		:			Tuple):
								pass
							@require_torch_multi_gpu
							@unittest.skip(
							    reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`')
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								pass
							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
							def     lowerCAmelCase     (		self		:			Tuple):
								pass
							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase       , __lowerCamelCase				:				str					       = self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase				:				List[str]					       = ['pixel_values']
									self.assertListEqual(arg_names[:1]   ,SCREAMING_SNAKE_CASE__)
							@slow
							def     lowerCAmelCase     (		self		:			Dict):
								for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
									__lowerCamelCase				:				List[Any]					       = MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				Dict					       = (self.model_tester.min_size,) * 2
								__lowerCamelCase				:				Any					       = {
								    'pixel_values': torch.randn((2, 3, *size)   ,device=SCREAMING_SNAKE_CASE__),
								    'mask_labels': torch.randn((2, 1_0, *size)   ,device=SCREAMING_SNAKE_CASE__),
								    'class_labels': torch.zeros(2   ,1_0   ,device=SCREAMING_SNAKE_CASE__).long(),
								}
								__lowerCamelCase				:				int					       = self.model_tester.get_config()

								__lowerCamelCase				:				List[Any]					       = MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[int]					       = model(**SCREAMING_SNAKE_CASE__)
								self.assertTrue(outputs.loss is not None)
							def     lowerCAmelCase     (		self		:			List[str]):
								__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__   ,output_hidden_states=SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			str):
								__lowerCamelCase       , __lowerCamelCase				:				int					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase				:				Optional[Any]					       = model_class(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase				:				Optional[Any]					       = model(**SCREAMING_SNAKE_CASE__   ,output_attentions=SCREAMING_SNAKE_CASE__)
									self.assertTrue(outputs.attentions is not None)
							def     lowerCAmelCase     (		self		:			Dict):
								if not self.model_tester.is_training:
									return

								__lowerCamelCase				:				List[str]					       = self.all_model_classes[1]
								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				Optional[int]					       = self.model_tester.prepare_config_and_inputs()

								__lowerCamelCase				:				Union[str, Any]					       = model_class(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.train()

								__lowerCamelCase				:				Optional[Any]					       = model(SCREAMING_SNAKE_CASE__   ,mask_labels=SCREAMING_SNAKE_CASE__   ,class_labels=SCREAMING_SNAKE_CASE__).loss
								loss.backward()


							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								__lowerCamelCase				:				Optional[int]					       = self.all_model_classes[1]
								__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				List[Any]					       = self.model_tester.prepare_config_and_inputs()
								__lowerCamelCase				:				Tuple					       = True
								__lowerCamelCase				:				str					       = True

								__lowerCamelCase				:				Tuple					       = model_class(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
								model.train()

								__lowerCamelCase				:				Union[str, Any]					       = model(SCREAMING_SNAKE_CASE__   ,mask_labels=SCREAMING_SNAKE_CASE__   ,class_labels=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Optional[Any]					       = outputs.encoder_hidden_states[0]
								encoder_hidden_states.retain_grad()

								__lowerCamelCase				:				Dict					       = outputs.pixel_decoder_hidden_states[0]
								pixel_decoder_hidden_states.retain_grad()

								__lowerCamelCase				:				Union[str, Any]					       = outputs.transformer_decoder_hidden_states[0]
								transformer_decoder_hidden_states.retain_grad()

								__lowerCamelCase				:				Optional[int]					       = outputs.attentions[0]
								attentions.retain_grad()

								outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE__)

								self.assertIsNotNone(encoder_hidden_states.grad)
								self.assertIsNotNone(pixel_decoder_hidden_states.grad)
								self.assertIsNotNone(transformer_decoder_hidden_states.grad)
								self.assertIsNotNone(attentions.grad)
a		=1E-4






def    SCREAMING_SNAKE_CASE__       (  )  ->     Optional[Any]:
	__lowerCamelCase				:				Optional[Any]					       = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
	return image






@require_vision
@slow
class        A_		(       unittest.TestCase				):
							@cached_property
							def     lowerCAmelCase     (		self		:			Optional[int]):
								return "facebook/mask2former-swin-small-coco-instance"
							@cached_property
							def     lowerCAmelCase     (		self		:			str):
								return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None
							def     lowerCAmelCase     (		self		:			Any):
								__lowerCamelCase				:				Union[str, Any]					       = MaskaFormerModel.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				int					       = self.default_image_processor
								__lowerCamelCase				:				Dict					       = prepare_img()
								__lowerCamelCase				:				Optional[int]					       = image_processor(SCREAMING_SNAKE_CASE__   ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[str]					       = inputs['pixel_values'].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
								# check size
								self.assertEqual(SCREAMING_SNAKE_CASE__   ,(1, 3, 3_8_4, 3_8_4))

								with torch.no_grad():
									__lowerCamelCase				:				Tuple					       = model(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Any					       = torch.tensor(
								    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]).to(SCREAMING_SNAKE_CASE__)
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]   ,SCREAMING_SNAKE_CASE__   ,atol=SCREAMING_SNAKE_CASE__))

								__lowerCamelCase				:				str					       = torch.tensor(
								    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]).to(SCREAMING_SNAKE_CASE__)
								self.assertTrue(
								    torch.allclose(
								        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]   ,SCREAMING_SNAKE_CASE__   ,atol=SCREAMING_SNAKE_CASE__))

								__lowerCamelCase				:				List[str]					       = torch.tensor(
								    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]).to(SCREAMING_SNAKE_CASE__)
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]   ,SCREAMING_SNAKE_CASE__   ,atol=SCREAMING_SNAKE_CASE__))
							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase				:				List[Any]					       = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE__).eval()
								__lowerCamelCase				:				int					       = self.default_image_processor
								__lowerCamelCase				:				Tuple					       = prepare_img()
								__lowerCamelCase				:				Tuple					       = image_processor(SCREAMING_SNAKE_CASE__   ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Union[str, Any]					       = inputs['pixel_values'].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0)
								# check size
								self.assertEqual(SCREAMING_SNAKE_CASE__   ,(1, 3, 3_8_4, 3_8_4))

								with torch.no_grad():
									__lowerCamelCase				:				Optional[Any]					       = model(**SCREAMING_SNAKE_CASE__)
								# masks_queries_logits
								__lowerCamelCase				:				Dict					       = outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape   ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
								__lowerCamelCase				:				List[Any]					       = [
								    [-8.7839, -9.0056, -8.8121],
								    [-7.4104, -7.0313, -6.5401],
								    [-6.6105, -6.3427, -6.4675],
								]
								__lowerCamelCase				:				Tuple					       = torch.tensor(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,SCREAMING_SNAKE_CASE__   ,atol=SCREAMING_SNAKE_CASE__))
								# class_queries_logits
								__lowerCamelCase				:				List[Any]					       = outputs.class_queries_logits
								self.assertEqual(class_queries_logits.shape   ,(1, model.config.num_queries, model.config.num_labels + 1))
								__lowerCamelCase				:				Optional[int]					       = torch.tensor(
								    [
								        [1.8324, -8.0835, -4.1922],
								        [0.8450, -9.0050, -3.6053],
								        [0.3045, -7.7293, -3.0275],
								    ]).to(SCREAMING_SNAKE_CASE__)
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,SCREAMING_SNAKE_CASE__   ,atol=SCREAMING_SNAKE_CASE__))


							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				List[Any]					       = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(SCREAMING_SNAKE_CASE__).eval()
								__lowerCamelCase				:				int					       = self.default_image_processor

								__lowerCamelCase				:				Any					       = image_processor(
								    [np.zeros((3, 8_0_0, 1_3_3_3)), np.zeros((3, 8_0_0, 1_3_3_3))]   ,segmentation_maps=[np.zeros((3_8_4, 3_8_4)).astype(np.floataa), np.zeros((3_8_4, 3_8_4)).astype(np.floataa)]   ,return_tensors='pt'   ,)

								__lowerCamelCase				:				Tuple					       = inputs['pixel_values'].to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = [el.to(SCREAMING_SNAKE_CASE__) for el in inputs['mask_labels']]
								__lowerCamelCase				:				Union[str, Any]					       = [el.to(SCREAMING_SNAKE_CASE__) for el in inputs['class_labels']]

								with torch.no_grad():
									__lowerCamelCase				:				Optional[Any]					       = model(**SCREAMING_SNAKE_CASE__)

								self.assertTrue(outputs.loss is not None)








import heapq as hq
import math
from collections.abc import Iterator







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase       )     ->   Any:
      _lowercase							      =str(id_       )
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =[]
      _lowercase							      ={}  # {vertex:distance}





     def __lt__(self		,     UpperCAmelCase       )     ->   List[str]:
      return self.key < other.key





     def __repr__(self       )     ->   str:
      return self.id





     def      __A					(self		,     UpperCAmelCase       )     ->   Dict:
      self.neighbors.append(UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =weight



def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[str]:







 """simple docstring"""


 graph[a - 1].add_neighbor(graph[b - 1]						)
 graph[b - 1].add_neighbor(graph[a - 1]						)
 # add the edges:
 graph[a - 1].add_edge(graph[b - 1]     ,						__snake_case						)
 graph[b - 1].add_edge(graph[a - 1]     ,						__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			list:







 """simple docstring"""


 _lowercase							      =[]
 for u in graph:
  _lowercase							      =math.inf
  _lowercase							      =None
 _lowercase							      =0
 _lowercase							      =graph[:]
 while q:
  _lowercase							      =min(__snake_case						)
  q.remove(__snake_case						)
  for v in u.neighbors:
   if (v in q) and (u.edges[v.id] < v.key):
    _lowercase							      =u
    _lowercase							      =u.edges[v.id]
 for i in range(1     ,						len(__snake_case						)						):
  a.append((int(graph[i].id						) + 1, int(graph[i].pi.id						) + 1)						)
 return a
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Iterator[tuple]:







 """simple docstring"""


 for u in graph:
  _lowercase							      =math.inf
  _lowercase							      =None
 _lowercase							      =0

 _lowercase							      =list(__snake_case						)
 hq.heapify(__snake_case						)

 while h:
  _lowercase							      =hq.heappop(__snake_case						)
  for v in u.neighbors:
   if (v in h) and (u.edges[v.id] < v.key):
    _lowercase							      =u
    _lowercase							      =u.edges[v.id]
    hq.heapify(__snake_case						)

 for i in range(1     ,						len(__snake_case						)						):
  yield (int(graph[i].id						) + 1, int(graph[i].pi.id						) + 1)
def 				UpperCAmelCase_					(							)     ->			None:







 """simple docstring"""




if __name__ == "__main__":
  import doctest

  doctest.testmod()

