
"""simple docstring"""







import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class 					A__			(      unittest.TestCase):
		# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
		A_		:			str							  =      (ScoreSdeVeScheduler,)
		A_		:			str							  =      ()

		@property
		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = 4
			__lowerCAmelCase     :		Dict		     = 3
			__lowerCAmelCase     :		Optional[int]		     = 8
			__lowerCAmelCase     :		Optional[int]		     = 8

			__lowerCAmelCase     :		str		     = torch.rand((batch_size, num_channels, height, width)					)

			return sample

		@property
		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Tuple		     = 4
			__lowerCAmelCase     :		List[str]		     = 3
			__lowerCAmelCase     :		Tuple		     = 8
			__lowerCAmelCase     :		Dict		     = 8

			__lowerCAmelCase     :		List[str]		     = batch_size * num_channels * height * width
			__lowerCAmelCase     :		Dict		     = torch.arange(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Any		     = sample.reshape(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Union[str, Any]		     = sample / num_elems
			__lowerCAmelCase     :		Optional[Any]		     = sample.permute(3			,						0			,						1			,						2					)

			return sample

		def 				__lowerCamelCase				( self					):
			def model(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						*_SCREAMING_SNAKE_CASE					):
				return sample * t / (t + 1)

			return model

		def 				__lowerCamelCase				( self			,						**_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Optional[int]		     = {
			    'num_train_timesteps': 20_00,
			    'snr': 0.15,
			    'sigma_min': 0.01,
			    'sigma_max': 13_48,
			    'sampling_eps': 1E-5,
			}

			config.update(**_SCREAMING_SNAKE_CASE					)
			return config

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE=0			,						**_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		Optional[Any]		     = dict(self.forward_default_kwargs					)

			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase     :		Any		     = self.dummy_sample
				__lowerCAmelCase     :		Dict		     = 0.1 * sample

				__lowerCAmelCase     :		Any		     = self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase     :		Dict		     = scheduler_class(**_SCREAMING_SNAKE_CASE					)

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(_SCREAMING_SNAKE_CASE					)
					__lowerCAmelCase     :		List[Any]		     = scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

				__lowerCAmelCase     :		Dict		     = scheduler.step_pred(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						generator=torch.manual_seed(0					)			,						**_SCREAMING_SNAKE_CASE					).prev_sample
				__lowerCAmelCase     :		Any		     = new_scheduler.step_pred(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						generator=torch.manual_seed(0					)			,						**_SCREAMING_SNAKE_CASE					).prev_sample

				assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

				__lowerCAmelCase     :		List[Any]		     = scheduler.step_correct(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						generator=torch.manual_seed(0					)			,						**_SCREAMING_SNAKE_CASE					).prev_sample
				__lowerCAmelCase     :		List[Any]		     = new_scheduler.step_correct(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						generator=torch.manual_seed(0					)			,						**_SCREAMING_SNAKE_CASE					).prev_sample

				assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE=0			,						**_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		int		     = dict(self.forward_default_kwargs					)
			kwargs.update(_SCREAMING_SNAKE_CASE					)

			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase     :		int		     = self.dummy_sample
				__lowerCAmelCase     :		Union[str, Any]		     = 0.1 * sample

				__lowerCAmelCase     :		str		     = self.get_scheduler_config()
				__lowerCAmelCase     :		Tuple		     = scheduler_class(**_SCREAMING_SNAKE_CASE					)

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(_SCREAMING_SNAKE_CASE					)
					__lowerCAmelCase     :		List[str]		     = scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

				__lowerCAmelCase     :		Tuple		     = scheduler.step_pred(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						generator=torch.manual_seed(0					)			,						**_SCREAMING_SNAKE_CASE					).prev_sample
				__lowerCAmelCase     :		Any		     = new_scheduler.step_pred(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						generator=torch.manual_seed(0					)			,						**_SCREAMING_SNAKE_CASE					).prev_sample

				assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

				__lowerCAmelCase     :		Optional[int]		     = scheduler.step_correct(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						generator=torch.manual_seed(0					)			,						**_SCREAMING_SNAKE_CASE					).prev_sample
				__lowerCAmelCase     :		str		     = new_scheduler.step_correct(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						generator=torch.manual_seed(0					)			,						**_SCREAMING_SNAKE_CASE					).prev_sample

				assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"

		def 				__lowerCamelCase				( self					):
			for timesteps in [10, 1_00, 10_00]:
				self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]			,						[1, 1_00, 10_00]					):
				self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE			,						sigma_max=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			for t in [0.1, 0.5, 0.75]:
				self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		List[str]		     = dict(self.forward_default_kwargs					)

			__lowerCAmelCase     :		str		     = self.scheduler_classes[0]
			__lowerCAmelCase     :		Dict		     = self.get_scheduler_config()
			__lowerCAmelCase     :		Optional[Any]		     = scheduler_class(**_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		Tuple		     = 3

			__lowerCAmelCase     :		Any		     = self.dummy_model()
			__lowerCAmelCase     :		Any		     = self.dummy_sample_deter

			scheduler.set_sigmas(_SCREAMING_SNAKE_CASE					)
			scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		str		     = torch.manual_seed(0					)

			for i, t in enumerate(scheduler.timesteps					):
				__lowerCAmelCase     :		List[Any]		     = scheduler.sigmas[i]

				for _ in range(scheduler.config.correct_steps					):
					with torch.no_grad():
						__lowerCAmelCase     :		Optional[Any]		     = model(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
					__lowerCAmelCase     :		List[Any]		     = scheduler.step_correct(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						generator=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					).prev_sample

				with torch.no_grad():
					__lowerCAmelCase     :		int		     = model(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)

				__lowerCAmelCase     :		Dict		     = scheduler.step_pred(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						generator=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase     ,  __lowerCAmelCase     :		List[Any]		     = output.prev_sample, output.prev_sample_mean

			__lowerCAmelCase     :		Tuple		     = torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
			__lowerCAmelCase     :		List[str]		     = torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

			assert np.isclose(result_sum.item()			,						143_7275_8528.0					)
			assert np.isclose(result_mean.item()			,						1871_4530.0					)







		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = dict(self.forward_default_kwargs					)

			__lowerCAmelCase     :		Optional[int]		     = kwargs.pop('num_inference_steps'			,						_SCREAMING_SNAKE_CASE					)

			for scheduler_class in self.scheduler_classes:
				__lowerCAmelCase     :		str		     = self.get_scheduler_config()
				__lowerCAmelCase     :		Dict		     = scheduler_class(**_SCREAMING_SNAKE_CASE					)

				__lowerCAmelCase     :		List[str]		     = self.dummy_sample
				__lowerCAmelCase     :		int		     = 0.1 * sample

				if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE			,						'set_timesteps'					):
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
				elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE			,						'set_timesteps'					):
					__lowerCAmelCase     :		Union[str, Any]		     = num_inference_steps

				__lowerCAmelCase     :		Dict		     = scheduler.step_pred(_SCREAMING_SNAKE_CASE			,						0			,						_SCREAMING_SNAKE_CASE			,						generator=torch.manual_seed(0					)			,						**_SCREAMING_SNAKE_CASE					).prev_sample
				__lowerCAmelCase     :		Tuple		     = scheduler.step_pred(_SCREAMING_SNAKE_CASE			,						1			,						_SCREAMING_SNAKE_CASE			,						generator=torch.manual_seed(0					)			,						**_SCREAMING_SNAKE_CASE					).prev_sample

				self.assertEqual(output_a.shape			,						sample.shape					)
				self.assertEqual(output_a.shape			,						output_a.shape					)

"""simple docstring"""







def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Any		     = sorted(_UpperCamelCase			,    key=lambda _UpperCamelCase						: edge[2]						)

	__lowerCAmelCase     :		Union[str, Any]		     = list(range(_UpperCamelCase						)						)

	def find_parent(_UpperCamelCase						):
		if i != parent[i]:
			__lowerCAmelCase     :		int		     = find_parent(parent[i]						)
		return parent[i]

	__lowerCAmelCase     :		Optional[Any]		     = 0
	__lowerCAmelCase     :		List[str]		     = []

	for edge in edges:
		__lowerCAmelCase     :		List[str]		     = find_parent(edge[0]						)
		__lowerCAmelCase     :		Optional[Any]		     = find_parent(edge[1]						)
		if parent_a != parent_b:
			minimum_spanning_tree_cost += edge[2]
			minimum_spanning_tree.append(_UpperCamelCase						)
			__lowerCAmelCase     :		Optional[Any]		     = parent_b

	return minimum_spanning_tree


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

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

							kruskal(num_nodes, edges)