






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 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Dict				    =						(DPMSolverSDEScheduler,)
		UpperCAmelCase__      :				Optional[int]				    =						10


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {
						    'num_train_timesteps': 1100,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'noise_sampler_seed': 0,
						}

						config.update(**UpperCamelCase__       )
						return config


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


		def   __snake_case			(						self       ):
						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=UpperCamelCase__ ,    beta_end=UpperCamelCase__       )


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


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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   scheduler_class(**UpperCamelCase__       )

						scheduler.set_timesteps(self.num_inference_steps       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	Any													=   self.dummy_sample_deter * scheduler.init_noise_sigma
						SCREAMING_SNAKE_CASE_   :	Any													=   sample.to(UpperCamelCase__       )

						for i, t in enumerate(scheduler.timesteps       ):
										SCREAMING_SNAKE_CASE_   :	Any													=   scheduler.scale_model_input(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	List[str]													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   scheduler.step(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   output.prev_sample

						SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.sum(torch.abs(UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.mean(torch.abs(UpperCamelCase__       )       )

						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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_scheduler_config(prediction_type='v_prediction'       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   scheduler_class(**UpperCamelCase__       )

						scheduler.set_timesteps(self.num_inference_steps       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.dummy_sample_deter * scheduler.init_noise_sigma
						SCREAMING_SNAKE_CASE_   :	Tuple													=   sample.to(UpperCamelCase__       )

						for i, t in enumerate(scheduler.timesteps       ):
										SCREAMING_SNAKE_CASE_   :	Tuple													=   scheduler.scale_model_input(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	str													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	List[Any]													=   scheduler.step(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   output.prev_sample

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.sum(torch.abs(UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.mean(torch.abs(UpperCamelCase__       )       )

						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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_   :	int													=   scheduler_class(**UpperCamelCase__       )

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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.dummy_sample_deter.to(UpperCamelCase__       ) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   scheduler.scale_model_input(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	Any													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	List[str]													=   scheduler.step(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   output.prev_sample

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.sum(torch.abs(UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   torch.mean(torch.abs(UpperCamelCase__       )       )

						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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_scheduler_config()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   scheduler_class(**UpperCamelCase__ ,    use_karras_sigmas=UpperCamelCase__       )

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

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

						for t in scheduler.timesteps:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   scheduler.scale_model_input(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	List[str]													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										SCREAMING_SNAKE_CASE_   :	Dict													=   scheduler.step(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   output.prev_sample

						SCREAMING_SNAKE_CASE_   :	int													=   torch.sum(torch.abs(UpperCamelCase__       )       )
						SCREAMING_SNAKE_CASE_   :	int													=   torch.mean(torch.abs(UpperCamelCase__       )       )

						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







import colorsys

from PIL import Image  # type: ignore



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Any													=   x
				SCREAMING_SNAKE_CASE_   :	Tuple													=   y
				for step in range(lowerCAmelCase__      ):  # noqa: B007
								SCREAMING_SNAKE_CASE_   :	str													=   a * a - b * b + x
								SCREAMING_SNAKE_CASE_   :	Any													=   2 * a * b + y
								SCREAMING_SNAKE_CASE_   :	int													=   a_new

								# divergence happens for all complex number with an absolute value
								# greater than 4
								if a * a + b * b > 4:
												break
				return step / (max_step - 1)



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				if distance == 1:
								return (0, 0, 0)
				else:
								return (255, 255, 255)



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				if distance == 1:
								return (0, 0, 0)
				else:
								return tuple(round(i * 255      ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__    ,    1    ,    1      )      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int = 800    ,    lowerCAmelCase__				:		int = 600    ,    lowerCAmelCase__				:		float = -0.6    ,    lowerCAmelCase__				:		float = 0    ,    lowerCAmelCase__				:		float = 3.2    ,    lowerCAmelCase__				:		int = 50    ,    lowerCAmelCase__				:		bool = True    ,    ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   Image.new('RGB'    ,    (image_width, image_height)      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   img.load()

				# loop through the image-coordinates
				for image_x in range(lowerCAmelCase__      ):
								for image_y in range(lowerCAmelCase__      ):
												# determine the figure-coordinates based on the image-coordinates
												SCREAMING_SNAKE_CASE_   :	List[str]													=   figure_width / image_width * image_height
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   figure_center_x + (image_x / image_width - 0.5) * figure_width
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   figure_center_y + (image_y / image_height - 0.5) * figure_height

												SCREAMING_SNAKE_CASE_   :	str													=   get_distance(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

												# color the corresponding pixel based on the selected coloring-function
												if use_distance_color_coding:
																SCREAMING_SNAKE_CASE_   :	int													=   get_color_coded_rgb(lowerCAmelCase__      )
												else:
																SCREAMING_SNAKE_CASE_   :	Dict													=   get_black_and_white_rgb(lowerCAmelCase__      )

				return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				A        =			get_image()

				# uncomment for colored version, different section, zoomed in
				# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
				# figure_width = 0.8)

				# uncomment for black and white version, full figure
				# img = get_image(use_distance_color_coding = False)

				# uncomment to save the image
				# img.save("mandelbrot.png")

				img.show()