



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						(UnCLIPScheduler,)


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {
						    'num_train_timesteps': 1000,
						    'variance_type': 'fixed_small_log',
						    'clip_sample': True,
						    'clip_sample_range': 1.0,
						    'prediction_type': 'epsilon',
						}

						config.update(**UpperCamelCase__       )
						return config


		def   __snake_case			(						self       ):
						for timesteps in [1, 5, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCamelCase__       )


		def   __snake_case			(						self       ):
						for variance in ["fixed_small_log", "learned_range"]:
										self.check_over_configs(variance_type=UpperCamelCase__       )


		def   __snake_case			(						self       ):
						for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=UpperCamelCase__       )


		def   __snake_case			(						self       ):
						for clip_sample_range in [1, 5, 10, 20]:
										self.check_over_configs(clip_sample_range=UpperCamelCase__       )


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


		def   __snake_case			(						self       ):
						for time_step in [0, 500, 999]:
										for prev_timestep in [None, 5, 100, 250, 500, 750]:
														if prev_timestep is not None and prev_timestep >= time_step:
																		continue

														self.check_over_forward(time_step=UpperCamelCase__ ,    prev_timestep=UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_scheduler_config(variance_type='fixed_small_log'       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   scheduler_class(**UpperCamelCase__       )

						assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0_000E-10       )       ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_54_96_25       )       ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9_99_49_87       )       ) < 1E-5


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.scheduler_classes[0]
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_scheduler_config(variance_type='learned_range'       )
						SCREAMING_SNAKE_CASE_   :	int													=   scheduler_class(**UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   0.5

						assert scheduler._get_variance(1 ,    predicted_variance=UpperCamelCase__       ) - -10.1_71_27_90 < 1E-5
						assert scheduler._get_variance(487 ,    predicted_variance=UpperCamelCase__       ) - -5.7_99_80_52 < 1E-5
						assert scheduler._get_variance(999 ,    predicted_variance=UpperCamelCase__       ) - -0.0_01_00_11 < 1E-5


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

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   scheduler.timesteps

						SCREAMING_SNAKE_CASE_   :	Any													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	str													=   self.dummy_sample_deter
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.manual_seed(0       )

						for i, t in enumerate(UpperCamelCase__       ):
										# 1. predict noise residual
										SCREAMING_SNAKE_CASE_   :	Tuple													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										# 2. predict previous mean of sample x_t-1
										SCREAMING_SNAKE_CASE_   :	Any													=   scheduler.step(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    generator=UpperCamelCase__       ).prev_sample

										SCREAMING_SNAKE_CASE_   :	str													=   pred_prev_sample

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

						assert abs(result_sum.item() - 252.2682495       ) < 1E-2
						assert abs(result_mean.item() - 0.3_28_47_43       ) < 1E-3


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

						scheduler.set_timesteps(25       )

						SCREAMING_SNAKE_CASE_   :	str													=   scheduler.timesteps

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.dummy_model()
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.dummy_sample_deter
						SCREAMING_SNAKE_CASE_   :	Dict													=   torch.manual_seed(0       )

						for i, t in enumerate(UpperCamelCase__       ):
										# 1. predict noise residual
										SCREAMING_SNAKE_CASE_   :	Any													=   model(UpperCamelCase__ ,    UpperCamelCase__       )

										if i + 1 == timesteps.shape[0]:
														SCREAMING_SNAKE_CASE_   :	Dict													=   None
										else:
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   timesteps[i + 1]

										# 2. predict previous mean of sample x_t-1
										SCREAMING_SNAKE_CASE_   :	Any													=   scheduler.step(
										    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    prev_timestep=UpperCamelCase__ ,    generator=UpperCamelCase__       ).prev_sample

										SCREAMING_SNAKE_CASE_   :	List[str]													=   pred_prev_sample

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

						assert abs(result_sum.item() - 258.2044983       ) < 1E-2
						assert abs(result_mean.item() - 0.3_36_20_38       ) < 1E-3


		def   __snake_case			(						self       ):
						pass






		def   __snake_case			(						self       ):
						pass







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()