'''simple docstring'''

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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							(DPMSolverSDEScheduler,)
		lowercase   :      Dict       =							10






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Union[str, Any]:
								_lowerCAmelCase										={
								    'num_train_timesteps': 1100,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'noise_sampler_seed': 0,
								}

								config.update(**__A				)
								return config






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								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=__A		,						beta_end=__A				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=__A				)






		def 		UpperCamelCase__       (   self				) ->     str:
								for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=__A				)






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config()
								_lowerCAmelCase										=scheduler_class(**__A				)

								scheduler.set_timesteps(self.num_inference_steps				)

								_lowerCAmelCase										=self.dummy_model()
								_lowerCAmelCase										=self.dummy_sample_deter * scheduler.init_noise_sigma
								_lowerCAmelCase										=sample.to(__A				)

								for i, t in enumerate(scheduler.timesteps				):
														_lowerCAmelCase										=scheduler.scale_model_input(__A		,						__A				)

														_lowerCAmelCase										=model(__A		,						__A				)

														_lowerCAmelCase										=scheduler.step(__A		,						__A		,						__A				)
														_lowerCAmelCase										=output.prev_sample

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

								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 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config(prediction_type='v_prediction'				)
								_lowerCAmelCase										=scheduler_class(**__A				)

								scheduler.set_timesteps(self.num_inference_steps				)

								_lowerCAmelCase										=self.dummy_model()
								_lowerCAmelCase										=self.dummy_sample_deter * scheduler.init_noise_sigma
								_lowerCAmelCase										=sample.to(__A				)

								for i, t in enumerate(scheduler.timesteps				):
														_lowerCAmelCase										=scheduler.scale_model_input(__A		,						__A				)

														_lowerCAmelCase										=model(__A		,						__A				)

														_lowerCAmelCase										=scheduler.step(__A		,						__A		,						__A				)
														_lowerCAmelCase										=output.prev_sample

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

								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 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config()
								_lowerCAmelCase										=scheduler_class(**__A				)

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

								_lowerCAmelCase										=self.dummy_model()
								_lowerCAmelCase										=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
														_lowerCAmelCase										=scheduler.scale_model_input(__A		,						__A				)

														_lowerCAmelCase										=model(__A		,						__A				)

														_lowerCAmelCase										=scheduler.step(__A		,						__A		,						__A				)
														_lowerCAmelCase										=output.prev_sample

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

								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 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										=self.scheduler_classes[0]
								_lowerCAmelCase										=self.get_scheduler_config()
								_lowerCAmelCase										=scheduler_class(**__A		,						use_karras_sigmas=__A				)

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

								_lowerCAmelCase										=self.dummy_model()
								_lowerCAmelCase										=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma
								_lowerCAmelCase										=sample.to(__A				)

								for t in scheduler.timesteps:
														_lowerCAmelCase										=scheduler.scale_model_input(__A		,						__A				)

														_lowerCAmelCase										=model(__A		,						__A				)

														_lowerCAmelCase										=scheduler.step(__A		,						__A		,						__A				)
														_lowerCAmelCase										=output.prev_sample

								_lowerCAmelCase										=torch.sum(torch.abs(__A				)				)
								_lowerCAmelCase										=torch.mean(torch.abs(__A				)				)

								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






'''simple docstring'''

def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=set()
						# To detect a back edge, keep track of vertices currently in the recursion stack
						_lowerCAmelCase										=set()
						return any(
						    node not in visited and depth_first_search(a__			,      a__			,      a__			,      a__      )
						    for node in graph      )



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__      ):


						'''simple docstring'''






						visited.add(a__      )
						rec_stk.add(a__      )

						for node in graph[vertex]:
												if node not in visited:
																		if depth_first_search(a__			,      a__			,      a__			,      a__      ):
																								return True
												elif node in rec_stk:
																		return True

    # The node needs to be removed from recursion stack before function ends
						rec_stk.remove(a__      )
						return False


if __name__ == "__main__":
							from doctest import testmod

							testmod()





