






import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= (DPMSolverSDEScheduler,)
				lowercase__       							= 10




				def        lowercase__     (  self		:					Optional[int],						**lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    {
								    '''num_train_timesteps''': 1_100,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''noise_sampler_seed''': 0,
								}

								config.update(**__lowerCAmelCase						)
								return config




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''







								for timesteps in [10, 50, 100, 1_000]:
												self.check_over_configs(num_train_timesteps=__lowerCAmelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''







								for beta_start, beta_end in zip([0.00001, 0.0001, 0.001],						[0.0002, 0.002, 0.02]						):
												self.check_over_configs(beta_start=__lowerCAmelCase,						beta_end=__lowerCAmelCase						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''







								for schedule in ["linear", "scaled_linear"]:
												self.check_over_configs(beta_schedule=__lowerCAmelCase						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''







								for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__lowerCAmelCase						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.scheduler_classes[0]
								lowercase__     				=    self.get_scheduler_config()
								lowercase__     				=    scheduler_class(**__lowerCAmelCase						)

								scheduler.set_timesteps(self.num_inference_steps						)

								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter * scheduler.init_noise_sigma
								lowercase__     				=    sample.to(__lowerCAmelCase						)

								for i, t in enumerate(scheduler.timesteps						):
												lowercase__     				=    scheduler.scale_model_input(__lowerCAmelCase,						__lowerCAmelCase						)

												lowercase__     				=    model(__lowerCAmelCase,						__lowerCAmelCase						)

												lowercase__     				=    scheduler.step(__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase						)
												lowercase__     				=    output.prev_sample

								lowercase__     				=    torch.sum(torch.abs(__lowerCAmelCase						)						)
								lowercase__     				=    torch.mean(torch.abs(__lowerCAmelCase						)						)

								if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.47821044921875						) < 1E-2
												assert abs(result_mean.item() - 0.2178705964565277						) < 1E-3
								elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59352111816406						) < 1E-2
												assert abs(result_mean.item() - 0.22342906892299652						) < 1E-3
								else:
												assert abs(result_sum.item() - 162.52383422851562						) < 1E-2
												assert abs(result_mean.item() - 0.211619570851326						) < 1E-3




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.scheduler_classes[0]
								lowercase__     				=    self.get_scheduler_config(prediction_type='''v_prediction'''						)
								lowercase__     				=    scheduler_class(**__lowerCAmelCase						)

								scheduler.set_timesteps(self.num_inference_steps						)

								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter * scheduler.init_noise_sigma
								lowercase__     				=    sample.to(__lowerCAmelCase						)

								for i, t in enumerate(scheduler.timesteps						):
												lowercase__     				=    scheduler.scale_model_input(__lowerCAmelCase,						__lowerCAmelCase						)

												lowercase__     				=    model(__lowerCAmelCase,						__lowerCAmelCase						)

												lowercase__     				=    scheduler.step(__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase						)
												lowercase__     				=    output.prev_sample

								lowercase__     				=    torch.sum(torch.abs(__lowerCAmelCase						)						)
								lowercase__     				=    torch.mean(torch.abs(__lowerCAmelCase						)						)

								if torch_device in ["mps"]:
												assert abs(result_sum.item() - 124.77149200439453						) < 1E-2
												assert abs(result_mean.item() - 0.16226289014816284						) < 1E-3
								elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 128.1663360595703						) < 1E-2
												assert abs(result_mean.item() - 0.16688326001167297						) < 1E-3
								else:
												assert abs(result_sum.item() - 119.8487548828125						) < 1E-2
												assert abs(result_mean.item() - 0.1560530662536621						) < 1E-3




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.scheduler_classes[0]
								lowercase__     				=    self.get_scheduler_config()
								lowercase__     				=    scheduler_class(**__lowerCAmelCase						)

								scheduler.set_timesteps(self.num_inference_steps,						device=__lowerCAmelCase						)

								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter.to(__lowerCAmelCase						) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
												lowercase__     				=    scheduler.scale_model_input(__lowerCAmelCase,						__lowerCAmelCase						)

												lowercase__     				=    model(__lowerCAmelCase,						__lowerCAmelCase						)

												lowercase__     				=    scheduler.step(__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase						)
												lowercase__     				=    output.prev_sample

								lowercase__     				=    torch.sum(torch.abs(__lowerCAmelCase						)						)
								lowercase__     				=    torch.mean(torch.abs(__lowerCAmelCase						)						)

								if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.46957397460938						) < 1E-2
												assert abs(result_mean.item() - 0.21805934607982635						) < 1E-3
								elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59353637695312						) < 1E-2
												assert abs(result_mean.item() - 0.22342908382415771						) < 1E-3
								else:
												assert abs(result_sum.item() - 162.52383422851562						) < 1E-2
												assert abs(result_mean.item() - 0.211619570851326						) < 1E-3




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.scheduler_classes[0]
								lowercase__     				=    self.get_scheduler_config()
								lowercase__     				=    scheduler_class(**__lowerCAmelCase,						use_karras_sigmas=__lowerCAmelCase						)

								scheduler.set_timesteps(self.num_inference_steps,						device=__lowerCAmelCase						)

								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter.to(__lowerCAmelCase						) * scheduler.init_noise_sigma
								lowercase__     				=    sample.to(__lowerCAmelCase						)

								for t in scheduler.timesteps:
												lowercase__     				=    scheduler.scale_model_input(__lowerCAmelCase,						__lowerCAmelCase						)

												lowercase__     				=    model(__lowerCAmelCase,						__lowerCAmelCase						)

												lowercase__     				=    scheduler.step(__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase						)
												lowercase__     				=    output.prev_sample

								lowercase__     				=    torch.sum(torch.abs(__lowerCAmelCase						)						)
								lowercase__     				=    torch.mean(torch.abs(__lowerCAmelCase						)						)

								if torch_device in ["mps"]:
												assert abs(result_sum.item() - 176.66974135742188						) < 1E-2
												assert abs(result_mean.item() - 0.23003872730981811						) < 1E-2
								elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 177.63653564453125						) < 1E-2
												assert abs(result_mean.item() - 0.23003872730981811						) < 1E-2
								else:
												assert abs(result_sum.item() - 170.3135223388672						) < 1E-2
												assert abs(result_mean.item() - 0.23003872730981811						) < 1E-2







from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    [[] for _ in range(lowerCamelCase						)]
								lowercase__     				=    size




				def __getitem__(  self		:					Optional[Any],						lowerCamelCase		:					int						):

								'''simple docstring'''
								return iter(self._graph[vertex]						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return self._size




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								if weight not in (0, 1):
												raise ValueError('''Edge weight must be either 0 or 1.'''						)

								if to_vertex < 0 or to_vertex >= self.size:
												raise ValueError('''Vertex indexes must be in [0; size).'''						)

								self._graph[from_vertex].append(Edge(lowerCamelCase,						lowerCamelCase						)						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    deque([start_vertex]						)
								lowercase__     				=    [None] * self.size
								lowercase__     				=    0

								while queue:
												lowercase__     				=    queue.popleft()
												lowercase__     				=    distances[current_vertex]
												if current_distance is None:
																continue

												for edge in self[current_vertex]:
																lowercase__     				=    current_distance + edge.weight
																lowercase__     				=    distances[edge.destination_vertex]
																if (
																    isinstance(lowerCamelCase,						lowerCamelCase						)
																    and new_distance >= dest_vertex_distance
																):
																				continue
																lowercase__     				=    new_distance
																if edge.weight == 0:
																				queue.appendleft(edge.destination_vertex						)
																else:
																				queue.append(edge.destination_vertex						)

								if distances[finish_vertex] is None:
												raise ValueError('''No path from start_vertex to finish_vertex.'''						)

								return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




