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     lowercase     (  a  ):
							lowercase__    :							Any										=				(DPMSolverSDEScheduler,)
							lowercase__    :							int										=				10




							def 						__snake_case(    self :       Optional[int]						,				**_UpperCamelCase :       Tuple   )       ->					int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							{
												    "num_train_timesteps": 1_100,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												    "noise_sampler_seed": 0,
												}

												config.update(**__a   )
												return config




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



												'''simple docstring'''

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




							def 						__snake_case(    self :       Any   )       ->					Dict:



												'''simple docstring'''

												for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,				[0.0_0_0_2, 0.0_0_2, 0.0_2]   ):
																	self.check_over_configs(beta_start=__a						,				beta_end=__a   )




							def 						__snake_case(    self :       Union[str, Any]   )       ->					Optional[int]:



												'''simple docstring'''

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




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



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =							self.get_scheduler_config()
												SCREAMING_SNAKE_CASE           =							scheduler_class(**__a   )

												scheduler.set_timesteps(self.num_inference_steps   )

												SCREAMING_SNAKE_CASE           =							self.dummy_model()
												SCREAMING_SNAKE_CASE           =							self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE           =							sample.to(__a   )

												for i, t in enumerate(scheduler.timesteps   ):
																	SCREAMING_SNAKE_CASE           =							scheduler.scale_model_input(__a						,				__a   )

																	SCREAMING_SNAKE_CASE           =							model(__a						,				__a   )

																	SCREAMING_SNAKE_CASE           =							scheduler.step(__a						,				__a						,				__a   )
																	SCREAMING_SNAKE_CASE           =							output.prev_sample

												SCREAMING_SNAKE_CASE           =							torch.sum(torch.abs(__a   )   )
												SCREAMING_SNAKE_CASE           =							torch.mean(torch.abs(__a   )   )

												if torch_device in ["mps"]:
																	assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5   ) < 1e-2
																	assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7   ) < 1e-3
												elif torch_device in ["cuda"]:
																	assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6   ) < 1e-2
																	assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2   ) < 1e-3
												else:
																	assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2   ) < 1e-2
																	assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6   ) < 1e-3




							def 						__snake_case(    self :       Optional[int]   )       ->					List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =							self.get_scheduler_config(prediction_type="v_prediction"   )
												SCREAMING_SNAKE_CASE           =							scheduler_class(**__a   )

												scheduler.set_timesteps(self.num_inference_steps   )

												SCREAMING_SNAKE_CASE           =							self.dummy_model()
												SCREAMING_SNAKE_CASE           =							self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE           =							sample.to(__a   )

												for i, t in enumerate(scheduler.timesteps   ):
																	SCREAMING_SNAKE_CASE           =							scheduler.scale_model_input(__a						,				__a   )

																	SCREAMING_SNAKE_CASE           =							model(__a						,				__a   )

																	SCREAMING_SNAKE_CASE           =							scheduler.step(__a						,				__a						,				__a   )
																	SCREAMING_SNAKE_CASE           =							output.prev_sample

												SCREAMING_SNAKE_CASE           =							torch.sum(torch.abs(__a   )   )
												SCREAMING_SNAKE_CASE           =							torch.mean(torch.abs(__a   )   )

												if torch_device in ["mps"]:
																	assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3   ) < 1e-2
																	assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4   ) < 1e-3
												elif torch_device in ["cuda"]:
																	assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3   ) < 1e-2
																	assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7   ) < 1e-3
												else:
																	assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5   ) < 1e-2
																	assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1   ) < 1e-3




							def 						__snake_case(    self :       List[str]   )       ->					Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =							self.get_scheduler_config()
												SCREAMING_SNAKE_CASE           =							scheduler_class(**__a   )

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

												SCREAMING_SNAKE_CASE           =							self.dummy_model()
												SCREAMING_SNAKE_CASE           =							self.dummy_sample_deter.to(__a   ) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																	SCREAMING_SNAKE_CASE           =							scheduler.scale_model_input(__a						,				__a   )

																	SCREAMING_SNAKE_CASE           =							model(__a						,				__a   )

																	SCREAMING_SNAKE_CASE           =							scheduler.step(__a						,				__a						,				__a   )
																	SCREAMING_SNAKE_CASE           =							output.prev_sample

												SCREAMING_SNAKE_CASE           =							torch.sum(torch.abs(__a   )   )
												SCREAMING_SNAKE_CASE           =							torch.mean(torch.abs(__a   )   )

												if torch_device in ["mps"]:
																	assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8   ) < 1e-2
																	assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5   ) < 1e-3
												elif torch_device in ["cuda"]:
																	assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2   ) < 1e-2
																	assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1   ) < 1e-3
												else:
																	assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2   ) < 1e-2
																	assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6   ) < 1e-3





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =							self.get_scheduler_config()
												SCREAMING_SNAKE_CASE           =							scheduler_class(**__a						,				use_karras_sigmas=__a   )

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

												SCREAMING_SNAKE_CASE           =							self.dummy_model()
												SCREAMING_SNAKE_CASE           =							self.dummy_sample_deter.to(__a   ) * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE           =							sample.to(__a   )

												for t in scheduler.timesteps:
																	SCREAMING_SNAKE_CASE           =							scheduler.scale_model_input(__a						,				__a   )

																	SCREAMING_SNAKE_CASE           =							model(__a						,				__a   )

																	SCREAMING_SNAKE_CASE           =							scheduler.step(__a						,				__a						,				__a   )
																	SCREAMING_SNAKE_CASE           =							output.prev_sample

												SCREAMING_SNAKE_CASE           =							torch.sum(torch.abs(__a   )   )
												SCREAMING_SNAKE_CASE           =							torch.mean(torch.abs(__a   )   )

												if torch_device in ["mps"]:
																	assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8   ) < 1e-2
																	assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1e-2
												elif torch_device in ["cuda"]:
																	assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5   ) < 1e-2
																	assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1e-2
												else:
																	assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2   ) < 1e-2
																	assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1   ) < 1e-2






"""simple docstring"""





import heapq



def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					# for each node and his adjacency list add them and the rank of the node to queue
					# using heapq module the queue will be filled like a Priority Queue
					# heapq works with a min priority queue, so I used -1*len(v) to build it
					for key, value in graph.items():
										# O(log(n))
										heapq.heappush(__snake_case,	[-1 * len(__snake_case      ), (key, value)]      )

					# chosen_vertices = set of chosen vertices
					_UpperCamelCase									=    set()

					# while queue isn't empty and there are still edges
					#   (queue[0][0] is the rank of the node with max rank)
					while queue and queue[0][0] != 0:
										# extract vertex with max rank from queue and add it to chosen_vertices
										_UpperCamelCase									=    heapq.heappop(__snake_case      )[1][0]
										chosen_vertices.add(__snake_case      )

										# Remove all arcs adjacent to argmax
										for elem in queue:
															# if v haven't adjacent node, skip
															if elem[0] == 0:
																				continue
															# if argmax is reachable from elem
															# remove argmax from elem's adjacent list and update his rank
															if argmax in elem[1][1]:
																				_UpperCamelCase									=    elem[1][1].index(__snake_case      )
																				del elem[1][1][index]
																				elem[0] += 1
        # re-order the queue
										heapq.heapify(__snake_case      )
					return chosen_vertices


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a								=						{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
				print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")






