





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_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		(DPMSolverSDEScheduler,)
					lowerCAmelCase_							=		10




					def 		lowerCAmelCase     (						self			,  **lowerCAmelCase_					)						->							Union[str, Any]:
										_snake_case													=			{
										    'num_train_timesteps': 1100,
										    'beta_start': 0.00_01,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										    'noise_sampler_seed': 0,
										}

										config.update(**lowerCAmelCase_					)
										return config




					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										for timesteps in [10, 50, 100, 1000]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							Optional[int]:
										for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,  [0.00_02, 0.0_02, 0.02]					):
															self.check_over_configs(beta_start=lowerCAmelCase_			,  beta_end=lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self					)						->							int:
										for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=lowerCAmelCase_					)




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




					def 		lowerCAmelCase     (						self					)						->							List[str]:
										_snake_case													=			self.scheduler_classes[0]
										_snake_case													=			self.get_scheduler_config()
										_snake_case													=			scheduler_class(**lowerCAmelCase_					)

										scheduler.set_timesteps(self.num_inference_steps					)

										_snake_case													=			self.dummy_model()
										_snake_case													=			self.dummy_sample_deter * scheduler.init_noise_sigma
										_snake_case													=			sample.to(lowerCAmelCase_					)

										for i, t in enumerate(scheduler.timesteps					):
															_snake_case													=			scheduler.scale_model_input(lowerCAmelCase_			,  lowerCAmelCase_					)

															_snake_case													=			model(lowerCAmelCase_			,  lowerCAmelCase_					)

															_snake_case													=			scheduler.step(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)
															_snake_case													=			output.prev_sample

										_snake_case													=			torch.sum(torch.abs(lowerCAmelCase_					)					)
										_snake_case													=			torch.mean(torch.abs(lowerCAmelCase_					)					)

										if torch_device in ["mps"]:
															assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75					) < 1E-2
															assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77					) < 1E-3
										elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06					) < 1E-2
															assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52					) < 1E-3
										else:
															assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62					) < 1E-2
															assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26					) < 1E-3




					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										_snake_case													=			self.scheduler_classes[0]
										_snake_case													=			self.get_scheduler_config(prediction_type='v_prediction'					)
										_snake_case													=			scheduler_class(**lowerCAmelCase_					)

										scheduler.set_timesteps(self.num_inference_steps					)

										_snake_case													=			self.dummy_model()
										_snake_case													=			self.dummy_sample_deter * scheduler.init_noise_sigma
										_snake_case													=			sample.to(lowerCAmelCase_					)

										for i, t in enumerate(scheduler.timesteps					):
															_snake_case													=			scheduler.scale_model_input(lowerCAmelCase_			,  lowerCAmelCase_					)

															_snake_case													=			model(lowerCAmelCase_			,  lowerCAmelCase_					)

															_snake_case													=			scheduler.step(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)
															_snake_case													=			output.prev_sample

										_snake_case													=			torch.sum(torch.abs(lowerCAmelCase_					)					)
										_snake_case													=			torch.mean(torch.abs(lowerCAmelCase_					)					)

										if torch_device in ["mps"]:
															assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53					) < 1E-2
															assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84					) < 1E-3
										elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03					) < 1E-2
															assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97					) < 1E-3
										else:
															assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25					) < 1E-2
															assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21					) < 1E-3




					def 		lowerCAmelCase     (						self					)						->							Optional[int]:
										_snake_case													=			self.scheduler_classes[0]
										_snake_case													=			self.get_scheduler_config()
										_snake_case													=			scheduler_class(**lowerCAmelCase_					)

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

										_snake_case													=			self.dummy_model()
										_snake_case													=			self.dummy_sample_deter.to(lowerCAmelCase_					) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
															_snake_case													=			scheduler.scale_model_input(lowerCAmelCase_			,  lowerCAmelCase_					)

															_snake_case													=			model(lowerCAmelCase_			,  lowerCAmelCase_					)

															_snake_case													=			scheduler.step(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)
															_snake_case													=			output.prev_sample

										_snake_case													=			torch.sum(torch.abs(lowerCAmelCase_					)					)
										_snake_case													=			torch.mean(torch.abs(lowerCAmelCase_					)					)

										if torch_device in ["mps"]:
															assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38					) < 1E-2
															assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35					) < 1E-3
										elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12					) < 1E-2
															assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71					) < 1E-3
										else:
															assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62					) < 1E-2
															assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26					) < 1E-3






					def 		lowerCAmelCase     (						self					)						->							Optional[int]:
										_snake_case													=			self.scheduler_classes[0]
										_snake_case													=			self.get_scheduler_config()
										_snake_case													=			scheduler_class(**lowerCAmelCase_			,  use_karras_sigmas=lowerCAmelCase_					)

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

										_snake_case													=			self.dummy_model()
										_snake_case													=			self.dummy_sample_deter.to(lowerCAmelCase_					) * scheduler.init_noise_sigma
										_snake_case													=			sample.to(lowerCAmelCase_					)

										for t in scheduler.timesteps:
															_snake_case													=			scheduler.scale_model_input(lowerCAmelCase_			,  lowerCAmelCase_					)

															_snake_case													=			model(lowerCAmelCase_			,  lowerCAmelCase_					)

															_snake_case													=			scheduler.step(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)
															_snake_case													=			output.prev_sample

										_snake_case													=			torch.sum(torch.abs(lowerCAmelCase_					)					)
										_snake_case													=			torch.mean(torch.abs(lowerCAmelCase_					)					)

										if torch_device in ["mps"]:
															assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88					) < 1E-2
															assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11					) < 1E-2
										elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25					) < 1E-2
															assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11					) < 1E-2
										else:
															assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72					) < 1E-2
															assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11					) < 1E-2










from cva import destroyAllWindows, imread, imshow, waitKey





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict  )     ->	Optional[Any]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			img.shape[0], img.shape[1]

					# converting each pixel's color to its negative
					for i in range(UpperCamelCase__  ):
										for j in range(UpperCamelCase__  ):
															_snake_case													=			[255, 255, 255] - img[i][j]

					return img


if __name__ == "__main__":
					# read original image
					UpperCAmelCase_ 							=       imread("""image_data/lena.jpg""", 1)

					# convert to its negative
					UpperCAmelCase_ 							=       convert_to_negative(img)

					# show result image
					imshow("""negative of original image""", img)
					waitKey(0)
					destroyAllWindows()



