






"""simple docstring"""

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						A_							(      snake_case_  ):







				def __init__(							self							:						List[Any]						,    __lowerCamelCase							:						VQModel						,    __lowerCamelCase							:						UNetaDModel						,    __lowerCamelCase							:						DDIMScheduler	)				->					Any:
										super().__init__()
										self.register_modules(vqvae=__lowerCamelCase						,    unet=__lowerCamelCase						,    scheduler=__lowerCamelCase	)







				@torch.no_grad()
				def __call__(							self							:						Dict						,    __lowerCamelCase							:						int = 1						,    __lowerCamelCase							:						Optional[Union[torch.Generator, List[torch.Generator]]] = None						,    __lowerCamelCase							:						float = 0.0						,    __lowerCamelCase							:						int = 5_0						,    __lowerCamelCase							:						Optional[str] = "pil"						,    __lowerCamelCase							:						bool = True						,    **__lowerCamelCase							:						List[Any]						,    )				->					Union[Tuple, ImagePipelineOutput]:
										__magic_name__            =     randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,    generator=__lowerCamelCase						,    )
										__magic_name__            =     latents.to(self.device	)

										# scale the initial noise by the standard deviation required by the scheduler
										__magic_name__            =     latents * self.scheduler.init_noise_sigma

										self.scheduler.set_timesteps(__lowerCamelCase	)

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										__magic_name__            =     "eta" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)

										__magic_name__            =     {}
										if accepts_eta:
																__magic_name__            =     eta

										for t in self.progress_bar(self.scheduler.timesteps	):
																__magic_name__            =     self.scheduler.scale_model_input(__lowerCamelCase						,    __lowerCamelCase	)
																# predict the noise residual
																__magic_name__            =     self.unet(__lowerCamelCase						,    __lowerCamelCase	).sample
																# compute the previous noisy sample x_t -> x_t-1
																__magic_name__            =     self.scheduler.step(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    **__lowerCamelCase	).prev_sample

										# decode the image latents with the VAE
										__magic_name__            =     self.vqvae.decode(__lowerCamelCase	).sample

										__magic_name__            =     (image / 2 + 0.5).clamp(0						,    1	)
										__magic_name__            =     image.cpu().permute(0						,    2						,    3						,    1	).numpy()
										if output_type == "pil":
																__magic_name__            =     self.numpy_to_pil(__lowerCamelCase	)

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=__lowerCamelCase	)











"""simple docstring"""

import colorsys

from PIL import Image  # type: ignore






def _lowerCAmelCase	(  __lowerCamelCase:float ,						__lowerCamelCase:float ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     x
						__magic_name__            =     y
						for step in range(__lowerCamelCase   ):  # noqa: B007
												__magic_name__            =     a * a - b * b + x
												__magic_name__            =     2 * a * b + y
												__magic_name__            =     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 (2_5_5, 2_5_5, 2_5_5)






def _lowerCAmelCase	(  __lowerCamelCase:float   ):

						'''simple docstring'''






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






def _lowerCAmelCase	(  __lowerCamelCase:int = 8_0_0 ,						__lowerCamelCase:int = 6_0_0 ,						__lowerCamelCase:float = -0.6 ,						__lowerCamelCase:float = 0 ,						__lowerCamelCase:float = 3.2 ,						__lowerCamelCase:int = 5_0 ,						__lowerCamelCase:bool = True ,						):

						'''simple docstring'''






						__magic_name__            =     Image.new("RGB" ,						(image_width, image_height)   )
						__magic_name__            =     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
																		__magic_name__            =     figure_width / image_width * image_height
																		__magic_name__            =     figure_center_x + (image_x / image_width - 0.5) * figure_width
																		__magic_name__            =     figure_center_y + (image_y / image_height - 0.5) * figure_height

																		__magic_name__            =     get_distance(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								__magic_name__            =     get_color_coded_rgb(__lowerCamelCase   )
																		else:
																								__magic_name__            =     get_black_and_white_rgb(__lowerCamelCase   )

						return img


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# colored version, full figure
			lowercase    			=       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()



