


'''simple docstring'''



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class 						_snake_case   (       a__		):
					lowerCAmelCase :int						=   (UnCLIPScheduler,)




					def 			snake_case__  ( self							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Tuple       				=       {
								    """num_train_timesteps""": 1000,
								    """variance_type""": """fixed_small_log""",
								    """clip_sample""": True,
								    """clip_sample_range""": 1.0,
								    """prediction_type""": """epsilon""",
								}

								config.update(**_lowerCamelCase)
								return config




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




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




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




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




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




					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=_lowerCamelCase							,							prev_timestep=_lowerCamelCase)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Tuple       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   List[str]       				=       self.get_scheduler_config(variance_type="""fixed_small_log""")
								UpperCAmelCase__					:   Optional[int]       				=       scheduler_class(**_lowerCamelCase)

								assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0_0_0_0e-1_0)) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0549625)) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9994987)) < 1e-5




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   Tuple       				=       self.get_scheduler_config(variance_type="""learned_range""")
								UpperCAmelCase__					:   Tuple       				=       scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__					:   Union[str, Any]       				=       0.5

								assert scheduler._get_variance(1							,							predicted_variance=_lowerCamelCase) - -10.1712790 < 1e-5
								assert scheduler._get_variance(487							,							predicted_variance=_lowerCamelCase) - -5.7998052 < 1e-5
								assert scheduler._get_variance(999							,							predicted_variance=_lowerCamelCase) - -0.0010011 < 1e-5




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   Tuple       				=       self.get_scheduler_config()
								UpperCAmelCase__					:   Any       				=       scheduler_class(**_lowerCamelCase)

								UpperCAmelCase__					:   List[Any]       				=       scheduler.timesteps

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

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

											# 2. predict previous mean of sample x_t-1
											UpperCAmelCase__					:   Tuple       				=       scheduler.step(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							generator=_lowerCamelCase).prev_sample

											UpperCAmelCase__					:   Dict       				=       pred_prev_sample

								UpperCAmelCase__					:   Union[str, Any]       				=       torch.sum(torch.abs(_lowerCamelCase))
								UpperCAmelCase__					:   Dict       				=       torch.mean(torch.abs(_lowerCamelCase))

								assert abs(result_sum.item() - 252.2682495) < 1e-2
								assert abs(result_mean.item() - 0.3284743) < 1e-3




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       self.scheduler_classes[0]
								UpperCAmelCase__					:   str       				=       self.get_scheduler_config()
								UpperCAmelCase__					:   List[str]       				=       scheduler_class(**_lowerCamelCase)

								scheduler.set_timesteps(25)

								UpperCAmelCase__					:   Tuple       				=       scheduler.timesteps

								UpperCAmelCase__					:   List[Any]       				=       self.dummy_model()
								UpperCAmelCase__					:   Dict       				=       self.dummy_sample_deter
								UpperCAmelCase__					:   List[Any]       				=       torch.manual_seed(0)

								for i, t in enumerate(_lowerCamelCase):
											# 1. predict noise residual
											UpperCAmelCase__					:   Union[str, Any]       				=       model(_lowerCamelCase							,							_lowerCamelCase)

											if i + 1 == timesteps.shape[0]:
														UpperCAmelCase__					:   Dict       				=       None
											else:
														UpperCAmelCase__					:   Union[str, Any]       				=       timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											UpperCAmelCase__					:   Optional[Any]       				=       scheduler.step(
											    _lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							prev_timestep=_lowerCamelCase							,							generator=_lowerCamelCase).prev_sample

											UpperCAmelCase__					:   Tuple       				=       pred_prev_sample

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

								assert abs(result_sum.item() - 258.2044983) < 1e-2
								assert abs(result_mean.item() - 0.3362038) < 1e-3




					def 			snake_case__  ( self):
								pass




					def 			snake_case__  ( self):
								pass



'''simple docstring'''



import colorsys

from PIL import Image  # type: ignore







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Optional[Any]       				=       x
			UpperCAmelCase__					:   Optional[int]       				=       y
			for step in range(UpperCamelCase__   ):  # noqa: B007
						UpperCAmelCase__					:   List[str]       				=       a * a - b * b + x
						UpperCAmelCase__					:   Optional[int]       				=       2 * a * b + y
						UpperCAmelCase__					:   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       _UpperCamelCase (						UpperCamelCase__   ):
			if distance == 1:
						return (0, 0, 0)
			else:
						return (2_5_5, 2_5_5, 2_5_5)







def       _UpperCamelCase (						UpperCamelCase__   ):
			if distance == 1:
						return (0, 0, 0)
			else:
						return tuple(round(i * 2_5_5   ) for i in colorsys.hsv_to_rgb(UpperCamelCase__	, 1	, 1   )   )







def       _UpperCamelCase (						UpperCamelCase__ = 8_0_0	, UpperCamelCase__ = 6_0_0	, UpperCamelCase__ = -0.6	, UpperCamelCase__ = 0	, UpperCamelCase__ = 3.2	, UpperCamelCase__ = 5_0	, UpperCamelCase__ = True	, ):
			UpperCAmelCase__					:   str       				=       Image.new("""RGB"""	, (image_width, image_height)   )
			UpperCAmelCase__					:   Optional[int]       				=       img.load()

			# loop through the image-coordinates
			for image_x in range(UpperCamelCase__   ):
						for image_y in range(UpperCamelCase__   ):
									# determine the figure-coordinates based on the image-coordinates
									UpperCAmelCase__					:   Union[str, Any]       				=       figure_width / image_width * image_height
									UpperCAmelCase__					:   Union[str, Any]       				=       figure_center_x + (image_x / image_width - 0.5) * figure_width
									UpperCAmelCase__					:   Tuple       				=       figure_center_y + (image_y / image_height - 0.5) * figure_height

									UpperCAmelCase__					:   List[str]       				=       get_distance(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )

									# color the corresponding pixel based on the selected coloring-function
									if use_distance_color_coding:
												UpperCAmelCase__					:   Any       				=       get_color_coded_rgb(UpperCamelCase__   )
									else:
												UpperCAmelCase__					:   Tuple       				=       get_black_and_white_rgb(UpperCamelCase__   )

			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()