





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_  (  snake_case__							):
					_a      :						Union[str, Any]						    =					(DPMSolverSDEScheduler,)
					_a      :						List[Any]						    =					1_0



					def   __a     (		self     :     Any       ,      **lowerCamelCase     :     str						):
							lowerCamelCase_	:     Any 						=      {
							    'num_train_timesteps': 11_00,
							    'beta_start': 0.0_001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'noise_sampler_seed': 0,
							}

							config.update(**lowerCamelCase						)
							return config



					def   __a     (		self     :     Tuple						):
							for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase						)



					def   __a     (		self     :     int						):
							for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,      [0.0_002, 0.002, 0.02]						):
									self.check_over_configs(beta_start=lowerCamelCase       ,      beta_end=lowerCamelCase						)



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



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



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     int 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     str 						=      self.get_scheduler_config()
							lowerCamelCase_	:     Optional[Any] 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowerCamelCase_	:     Optional[Any] 						=      self.dummy_model()
							lowerCamelCase_	:     Optional[Any] 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_	:     Union[str, Any] 						=      sample.to(lowerCamelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowerCamelCase_	:     str 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Tuple 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Optional[int] 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Tuple 						=      output.prev_sample

							lowerCamelCase_	:     List[str] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     Optional[Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.47_821_044_921_875						) < 1E-2
									assert abs(result_mean.item() - 0.2_178_705_964_565_277						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59_352_111_816_406						) < 1E-2
									assert abs(result_mean.item() - 0.22_342_906_892_299_652						) < 1E-3
							else:
									assert abs(result_sum.item() - 162.52_383_422_851_562						) < 1E-2
									assert abs(result_mean.item() - 0.211_619_570_851_326						) < 1E-3



					def   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     List[str] 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     Any 						=      self.get_scheduler_config(prediction_type='v_prediction'						)
							lowerCamelCase_	:     Tuple 						=      scheduler_class(**lowerCamelCase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							lowerCamelCase_	:     Any 						=      self.dummy_model()
							lowerCamelCase_	:     Tuple 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_	:     List[Any] 						=      sample.to(lowerCamelCase						)

							for i, t in enumerate(scheduler.timesteps						):
									lowerCamelCase_	:     Dict 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Union[str, Any] 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Dict 						=      output.prev_sample

							lowerCamelCase_	:     Optional[Any] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     List[Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 124.77_149_200_439_453						) < 1E-2
									assert abs(result_mean.item() - 0.16_226_289_014_816_284						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 128.1_663_360_595_703						) < 1E-2
									assert abs(result_mean.item() - 0.16_688_326_001_167_297						) < 1E-3
							else:
									assert abs(result_sum.item() - 119.8_487_548_828_125						) < 1E-2
									assert abs(result_mean.item() - 0.1_560_530_662_536_621						) < 1E-3



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Union[str, Any] 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     List[Any] 						=      self.get_scheduler_config()
							lowerCamelCase_	:     List[str] 						=      scheduler_class(**lowerCamelCase						)

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

							lowerCamelCase_	:     Dict 						=      self.dummy_model()
							lowerCamelCase_	:     Dict 						=      self.dummy_sample_deter.to(lowerCamelCase						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
									lowerCamelCase_	:     Tuple 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     List[str] 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     int 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Optional[int] 						=      output.prev_sample

							lowerCamelCase_	:     List[Any] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     str 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.46_957_397_460_938						) < 1E-2
									assert abs(result_mean.item() - 0.21_805_934_607_982_635						) < 1E-3
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59_353_637_695_312						) < 1E-2
									assert abs(result_mean.item() - 0.22_342_908_382_415_771						) < 1E-3
							else:
									assert abs(result_sum.item() - 162.52_383_422_851_562						) < 1E-2
									assert abs(result_mean.item() - 0.211_619_570_851_326						) < 1E-3





					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     Dict 						=      self.scheduler_classes[0]
							lowerCamelCase_	:     List[Any] 						=      self.get_scheduler_config()
							lowerCamelCase_	:     Union[str, Any] 						=      scheduler_class(**lowerCamelCase       ,      use_karras_sigmas=lowerCamelCase						)

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

							lowerCamelCase_	:     int 						=      self.dummy_model()
							lowerCamelCase_	:     str 						=      self.dummy_sample_deter.to(lowerCamelCase						) * scheduler.init_noise_sigma
							lowerCamelCase_	:     Dict 						=      sample.to(lowerCamelCase						)

							for t in scheduler.timesteps:
									lowerCamelCase_	:     Optional[Any] 						=      scheduler.scale_model_input(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Dict 						=      model(lowerCamelCase       ,      lowerCamelCase						)

									lowerCamelCase_	:     Dict 						=      scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)
									lowerCamelCase_	:     Any 						=      output.prev_sample

							lowerCamelCase_	:     Optional[int] 						=      torch.sum(torch.abs(lowerCamelCase						)						)
							lowerCamelCase_	:     Union[str, Any] 						=      torch.mean(torch.abs(lowerCamelCase						)						)

							if torch_device in ["mps"]:
									assert abs(result_sum.item() - 176.66_974_135_742_188						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
							elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 177.63_653_564_453_125						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2
							else:
									assert abs(result_sum.item() - 170.3_135_223_388_672						) < 1E-2
									assert abs(result_mean.item() - 0.23_003_872_730_981_811						) < 1E-2







_lowercase		:  Any			       ={"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
_lowercase		:  Union[str, Any]			       =["""a""", """b""", """c""", """d""", """e"""]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      start
		# add current to visited
		visited.append(lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      edges[current]
		for neighbor in neighbors:
				# if neighbor not in visited, visit
				if neighbor not in visited:
						lowerCamelCase_	:     Dict 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # if all neighbors visited add current to sort
		sort.append(lowerCAmelCase__						)
		# if all vertices haven't been visited select a new one to visit
		if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
				for vertice in vertices:
						if vertice not in visited:
								lowerCamelCase_	:     Any 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # return sort
		return sort


if __name__ == "__main__":
		_lowercase		:  str			       =topological_sort("""a""", [], [])
		print(sort)
