use dyn_any::DynAny;
use fastnoise_lite;
use glam::{DAffine2, DVec2, Vec2};
use graphene_core::raster::bbox::Bbox;
use graphene_core::raster::image::{Image, ImageFrameTable};
use graphene_core::raster::{
	Alpha, AlphaMut, Bitmap, BitmapMut, CellularDistanceFunction, CellularReturnType, DomainWarpType, FractalType, Linear, LinearChannel, Luminance, NoiseType, Pixel, RGBMut, RedGreenBlue, Sample,
};
use graphene_core::transform::{Transform, TransformMut};
use graphene_core::{AlphaBlending, Color, Ctx, ExtractFootprint, GraphicElement, Node};
use rand::prelude::*;
use rand_chacha::ChaCha8Rng;
use std::fmt::Debug;
use std::hash::Hash;
use std::marker::PhantomData;

#[derive(Debug, DynAny)]
pub enum Error {
	IO(std::io::Error),
	Image(image::ImageError),
}

impl From<std::io::Error> for Error {
	fn from(e: std::io::Error) -> Self {
		Error::IO(e)
	}
}

#[node_macro::node(category("Debug: Raster"))]
fn sample_image(ctx: impl ExtractFootprint + Clone + Send, image_frame: ImageFrameTable<Color>) -> ImageFrameTable<Color> {
	let image_frame_transform = image_frame.transform();
	let image_frame_alpha_blending = image_frame.one_instance().alpha_blending;

	let image = image_frame.one_instance().instance;

	// Resize the image using the image crate
	let data = bytemuck::cast_vec(image.data.clone());

	let footprint = ctx.footprint();
	let viewport_bounds = footprint.viewport_bounds_in_local_space();
	let image_bounds = Bbox::from_transform(image_frame_transform).to_axis_aligned_bbox();
	let intersection = viewport_bounds.intersect(&image_bounds);
	let image_size = DAffine2::from_scale(DVec2::new(image.width as f64, image.height as f64));
	let size = intersection.size();
	let size_px = image_size.transform_vector2(size).as_uvec2();

	// If the image would not be visible, return an empty image
	if size.x <= 0. || size.y <= 0. {
		return ImageFrameTable::one_empty_image();
	}

	let image_buffer = image::Rgba32FImage::from_raw(image.width, image.height, data).expect("Failed to convert internal image format into image-rs data type.");

	let dynamic_image: image::DynamicImage = image_buffer.into();
	let offset = (intersection.start - image_bounds.start).max(DVec2::ZERO);
	let offset_px = image_size.transform_vector2(offset).as_uvec2();
	let cropped = dynamic_image.crop_imm(offset_px.x, offset_px.y, size_px.x, size_px.y);

	let viewport_resolution_x = footprint.transform.transform_vector2(DVec2::X * size.x).length();
	let viewport_resolution_y = footprint.transform.transform_vector2(DVec2::Y * size.y).length();
	let mut new_width = size_px.x;
	let mut new_height = size_px.y;

	// Only downscale the image for now
	let resized = if new_width < image.width || new_height < image.height {
		new_width = viewport_resolution_x as u32;
		new_height = viewport_resolution_y as u32;
		// TODO: choose filter based on quality requirements
		cropped.resize_exact(new_width, new_height, image::imageops::Triangle)
	} else {
		cropped
	};
	let buffer = resized.to_rgba32f();
	let buffer = buffer.into_raw();
	let vec = bytemuck::cast_vec(buffer);
	let image = Image {
		width: new_width,
		height: new_height,
		data: vec,
		base64_string: None,
	};
	// we need to adjust the offset if we truncate the offset calculation

	let new_transform = image_frame_transform * DAffine2::from_translation(offset) * DAffine2::from_scale(size);

	let mut result = ImageFrameTable::new(image);
	*result.transform_mut() = new_transform;
	*result.one_instance_mut().alpha_blending = *image_frame_alpha_blending;

	result
}

#[derive(Debug, Clone, Copy)]
pub struct MapImageNode<P, MapFn> {
	map_fn: MapFn,
	_p: PhantomData<P>,
}

#[node_macro::old_node_fn(MapImageNode<_P>)]
fn map_image<MapFn, _P, Img: BitmapMut<Pixel = _P>>(image: Img, map_fn: &'input MapFn) -> Img
where
	MapFn: for<'any_input> Node<'any_input, _P, Output = _P> + 'input,
{
	let mut image = image;

	image.map_pixels(|c| map_fn.eval(c));
	image
}

#[node_macro::node]
fn insert_channel<
	// _P is the color of the input image.
	_P: RGBMut,
	_S: Pixel + Luminance,
	// Input image
	Input: BitmapMut<Pixel = _P>,
	Insertion: Bitmap<Pixel = _S>,
>(
	_: impl Ctx,
	#[implementations(ImageFrameTable<Color>)] mut image: Input,
	#[implementations(ImageFrameTable<Color>)] insertion: Insertion,
	target_channel: RedGreenBlue,
) -> Input
where
	_P::ColorChannel: Linear,
{
	if insertion.width() == 0 {
		return image;
	}

	if insertion.width() != image.width() || insertion.height() != image.height() {
		log::warn!("Stencil and image have different sizes. This is not supported.");
		return image;
	}

	for y in 0..image.height() {
		for x in 0..image.width() {
			let image_pixel = image.get_pixel_mut(x, y).unwrap();
			let insertion_pixel = insertion.get_pixel(x, y).unwrap();
			match target_channel {
				RedGreenBlue::Red => image_pixel.set_red(insertion_pixel.l().cast_linear_channel()),
				RedGreenBlue::Green => image_pixel.set_green(insertion_pixel.l().cast_linear_channel()),
				RedGreenBlue::Blue => image_pixel.set_blue(insertion_pixel.l().cast_linear_channel()),
			}
		}
	}

	image
}
#[node_macro::node]
fn combine_channels<
	// _P is the color of the input image.
	_P: RGBMut + AlphaMut,
	_S: Pixel + Luminance,
	// Input image
	Input: BitmapMut<Pixel = _P>,
	Red: Bitmap<Pixel = _S>,
	Green: Bitmap<Pixel = _S>,
	Blue: Bitmap<Pixel = _S>,
	Alpha: Bitmap<Pixel = _S>,
>(
	_: impl Ctx,
	#[implementations(ImageFrameTable<Color>)] mut image: Input,
	#[implementations(ImageFrameTable<Color>)] red: Red,
	#[implementations(ImageFrameTable<Color>)] green: Green,
	#[implementations(ImageFrameTable<Color>)] blue: Blue,
	#[implementations(ImageFrameTable<Color>)] alpha: Alpha,
) -> Input
where
	_P::ColorChannel: Linear,
{
	let dimensions = [red.dim(), green.dim(), blue.dim(), alpha.dim()];
	if dimensions.iter().all(|&(x, _)| x == 0) {
		return image;
	}

	if dimensions.iter().any(|&(x, y)| x != image.width() || y != image.height()) {
		log::warn!("Stencil and image have different sizes. This is not supported.");
		return image;
	}

	for y in 0..image.height() {
		for x in 0..image.width() {
			let image_pixel = image.get_pixel_mut(x, y).unwrap();
			if let Some(r) = red.get_pixel(x, y) {
				image_pixel.set_red(r.l().cast_linear_channel());
			}
			if let Some(g) = green.get_pixel(x, y) {
				image_pixel.set_green(g.l().cast_linear_channel());
			}
			if let Some(b) = blue.get_pixel(x, y) {
				image_pixel.set_blue(b.l().cast_linear_channel());
			}
			if let Some(a) = alpha.get_pixel(x, y) {
				image_pixel.set_alpha(a.l().cast_linear_channel());
			}
		}
	}

	image
}

#[node_macro::node()]
fn mask_image<
	// _P is the color of the input image. It must have an alpha channel because that is going to
	// be modified by the mask
	_P: Alpha,
	// _S is the color of the stencil. It must have a luminance channel because that is used to
	// mask the input image
	_S: Luminance,
	// Input image
	Input: Transform + BitmapMut<Pixel = _P>,
	// Stencil
	Stencil: Transform + Sample<Pixel = _S>,
>(
	_: impl Ctx,
	#[implementations(ImageFrameTable<Color>)] mut image: Input,
	#[implementations(ImageFrameTable<Color>)] stencil: Stencil,
) -> Input {
	let image_size = DVec2::new(image.width() as f64, image.height() as f64);
	let mask_size = stencil.transform().decompose_scale();

	if mask_size == DVec2::ZERO {
		return image;
	}

	// Transforms a point from the background image to the foreground image
	let bg_to_fg = image.transform() * DAffine2::from_scale(1. / image_size);
	let stencil_transform_inverse = stencil.transform().inverse();

	let area = bg_to_fg.transform_vector2(DVec2::ONE);
	for y in 0..image.height() {
		for x in 0..image.width() {
			let image_point = DVec2::new(x as f64, y as f64);
			let mut mask_point = bg_to_fg.transform_point2(image_point);
			let local_mask_point = stencil_transform_inverse.transform_point2(mask_point);
			mask_point = stencil.transform().transform_point2(local_mask_point.clamp(DVec2::ZERO, DVec2::ONE));

			let image_pixel = image.get_pixel_mut(x, y).unwrap();
			if let Some(mask_pixel) = stencil.sample(mask_point, area) {
				*image_pixel = image_pixel.multiplied_alpha(mask_pixel.l().cast_linear_channel());
			}
		}
	}

	image
}

// #[derive(Debug, Clone, Copy)]
// pub struct BlendImageTupleNode<P, Fg, MapFn> {
// 	map_fn: MapFn,
// 	_p: PhantomData<P>,
// 	_fg: PhantomData<Fg>,
// }

#[node_macro::node(skip_impl)]
async fn blend_image_tuple<_P, MapFn, _Fg>(images: (ImageFrameTable<_P>, _Fg), map_fn: &'n MapFn) -> ImageFrameTable<_P>
where
	_P: Alpha + Pixel + Debug + Send,
	MapFn: for<'any_input> Node<'any_input, (_P, _P), Output = _P> + 'n + Clone,
	_Fg: Sample<Pixel = _P> + Transform + Clone + Send + 'n,
	GraphicElement: From<Image<_P>>,
{
	let (background, foreground) = images;

	blend_image(foreground, background, map_fn)
}

fn blend_image<'input, _P, MapFn, Frame, Background>(foreground: Frame, background: Background, map_fn: &'input MapFn) -> Background
where
	MapFn: Node<'input, (_P, _P), Output = _P>,
	_P: Pixel + Alpha + Debug,
	Frame: Sample<Pixel = _P> + Transform,
	Background: BitmapMut<Pixel = _P> + Sample<Pixel = _P> + Transform,
{
	blend_image_closure(foreground, background, |a, b| map_fn.eval((a, b)))
}

pub fn blend_image_closure<_P, MapFn, Frame, Background>(foreground: Frame, mut background: Background, map_fn: MapFn) -> Background
where
	MapFn: Fn(_P, _P) -> _P,
	_P: Pixel + Alpha + Debug,
	Frame: Sample<Pixel = _P> + Transform,
	Background: BitmapMut<Pixel = _P> + Sample<Pixel = _P> + Transform,
{
	let background_size = DVec2::new(background.width() as f64, background.height() as f64);

	// Transforms a point from the background image to the foreground image
	let bg_to_fg = background.transform() * DAffine2::from_scale(1. / background_size);

	// Footprint of the foreground image (0,0) (1, 1) in the background image space
	let bg_aabb = Bbox::unit().affine_transform(background.transform().inverse() * foreground.transform()).to_axis_aligned_bbox();

	// Clamp the foreground image to the background image
	let start = (bg_aabb.start * background_size).max(DVec2::ZERO).as_uvec2();
	let end = (bg_aabb.end * background_size).min(background_size).as_uvec2();

	let area = bg_to_fg.transform_point2(DVec2::new(1., 1.)) - bg_to_fg.transform_point2(DVec2::ZERO);
	for y in start.y..end.y {
		for x in start.x..end.x {
			let bg_point = DVec2::new(x as f64, y as f64);
			let fg_point = bg_to_fg.transform_point2(bg_point);

			if let Some(src_pixel) = foreground.sample(fg_point, area) {
				if let Some(dst_pixel) = background.get_pixel_mut(x, y) {
					*dst_pixel = map_fn(src_pixel, *dst_pixel);
				}
			}
		}
	}

	background
}

#[derive(Debug, Clone, Copy)]
pub struct ExtendImageToBoundsNode<Bounds> {
	bounds: Bounds,
}

#[node_macro::old_node_fn(ExtendImageToBoundsNode)]
fn extend_image_to_bounds(image: ImageFrameTable<Color>, bounds: DAffine2) -> ImageFrameTable<Color> {
	let image_aabb = Bbox::unit().affine_transform(image.transform()).to_axis_aligned_bbox();
	let bounds_aabb = Bbox::unit().affine_transform(bounds.transform()).to_axis_aligned_bbox();
	if image_aabb.contains(bounds_aabb.start) && image_aabb.contains(bounds_aabb.end) {
		return image;
	}

	let image_instance = image.one_instance().instance;
	if image_instance.width == 0 || image_instance.height == 0 {
		return empty_image((), bounds, Color::TRANSPARENT);
	}

	let orig_image_scale = DVec2::new(image_instance.width as f64, image_instance.height as f64);
	let layer_to_image_space = DAffine2::from_scale(orig_image_scale) * image.transform().inverse();
	let bounds_in_image_space = Bbox::unit().affine_transform(layer_to_image_space * bounds).to_axis_aligned_bbox();

	let new_start = bounds_in_image_space.start.floor().min(DVec2::ZERO);
	let new_end = bounds_in_image_space.end.ceil().max(orig_image_scale);
	let new_scale = new_end - new_start;

	// Copy over original image into enlarged image.
	let mut new_img = Image::new(new_scale.x as u32, new_scale.y as u32, Color::TRANSPARENT);
	let offset_in_new_image = (-new_start).as_uvec2();
	for y in 0..image_instance.height {
		let old_start = y * image_instance.width;
		let new_start = (y + offset_in_new_image.y) * new_img.width + offset_in_new_image.x;
		let old_row = &image_instance.data[old_start as usize..(old_start + image_instance.width) as usize];
		let new_row = &mut new_img.data[new_start as usize..(new_start + image_instance.width) as usize];
		new_row.copy_from_slice(old_row);
	}

	// Compute new transform.
	// let layer_to_new_texture_space = (DAffine2::from_scale(1. / new_scale) * DAffine2::from_translation(new_start) * layer_to_image_space).inverse();
	let new_texture_to_layer_space = image.transform() * DAffine2::from_scale(1. / orig_image_scale) * DAffine2::from_translation(new_start) * DAffine2::from_scale(new_scale);

	let mut result = ImageFrameTable::new(new_img);
	*result.transform_mut() = new_texture_to_layer_space;
	*result.one_instance_mut().alpha_blending = *image.one_instance().alpha_blending;

	result
}

#[node_macro::node(category("Debug: Raster"))]
fn empty_image(_: impl Ctx, transform: DAffine2, color: Color) -> ImageFrameTable<Color> {
	let width = transform.transform_vector2(DVec2::new(1., 0.)).length() as u32;
	let height = transform.transform_vector2(DVec2::new(0., 1.)).length() as u32;

	let image = Image::new(width, height, color);

	let mut result = ImageFrameTable::new(image);
	*result.transform_mut() = transform;
	*result.one_instance_mut().alpha_blending = AlphaBlending::default();

	result
}

// #[cfg(feature = "serde")]
// macro_rules! generate_imaginate_node {
// 	($($val:ident: $t:ident: $o:ty,)*) => {
// 		pub struct ImaginateNode<P: Pixel, E, C, G, $($t,)*> {
// 			editor_api: E,
// 			controller: C,
// 			generation_id: G,
// 			$($val: $t,)*
// 			cache: std::sync::Arc<std::sync::Mutex<HashMap<u64, Image<P>>>>,
// 			last_generation: std::sync::atomic::AtomicU64,
// 		}

// 		impl<'e, P: Pixel, E, C, G, $($t,)*> ImaginateNode<P, E, C, G, $($t,)*>
// 		where $($t: for<'any_input> Node<'any_input, (), Output = DynFuture<'any_input, $o>>,)*
// 			E: for<'any_input> Node<'any_input, (), Output = DynFuture<'any_input, &'e WasmEditorApi>>,
// 			C: for<'any_input> Node<'any_input, (), Output = DynFuture<'any_input, ImaginateController>>,
// 			G: for<'any_input> Node<'any_input, (), Output = DynFuture<'any_input, u64>>,
// 		{
// 			#[allow(clippy::too_many_arguments)]
// 			pub fn new(editor_api: E, controller: C, $($val: $t,)*  generation_id: G ) -> Self {
// 				Self { editor_api, controller, generation_id, $($val,)* cache: Default::default(), last_generation: std::sync::atomic::AtomicU64::new(u64::MAX) }
// 			}
// 		}

// 		impl<'i, 'e: 'i, P: Pixel + 'i + Hash + Default + Send, E: 'i, C: 'i, G: 'i, $($t: 'i,)*> Node<'i, ImageFrame<P>> for ImaginateNode<P, E, C, G, $($t,)*>
// 		where $($t: for<'any_input> Node<'any_input, (), Output = DynFuture<'any_input, $o>>,)*
// 			E: for<'any_input> Node<'any_input, (), Output = DynFuture<'any_input, &'e WasmEditorApi>>,
// 			C: for<'any_input> Node<'any_input, (), Output = DynFuture<'any_input, ImaginateController>>,
// 			G: for<'any_input> Node<'any_input, (), Output = DynFuture<'any_input, u64>>,
// 		{
// 			type Output = DynFuture<'i, ImageFrame<P>>;

// 			fn eval(&'i self, frame: ImageFrame<P>) -> Self::Output {
// 				let controller = self.controller.eval(());
// 				$(let $val = self.$val.eval(());)*

// 				use std::hash::Hasher;
// 				let mut hasher = rustc_hash::FxHasher::default();
// 				frame.image.hash(&mut hasher);
// 				let hash = hasher.finish();
// 				let editor_api = self.editor_api.eval(());
// 				let cache = self.cache.clone();
// 				let generation_future = self.generation_id.eval(());
// 				let last_generation = &self.last_generation;

// 				Box::pin(async move {
// 					let controller: ImaginateController = controller.await;
// 					let generation_id = generation_future.await;
// 					if generation_id !=  last_generation.swap(generation_id, std::sync::atomic::Ordering::SeqCst) {
// 						let image = super::imaginate::imaginate(frame.image, editor_api, controller, $($val,)*).await;

// 						cache.lock().unwrap().insert(hash, image.clone());

// 						return wrap_image_frame(image, frame.transform);
// 					}
// 					let image = cache.lock().unwrap().get(&hash).cloned().unwrap_or_default();

// 					return wrap_image_frame(image, frame.transform);
// 				})
// 			}
// 		}
// 	}
// }

// fn wrap_image_frame<P: Pixel>(image: Image<P>, transform: DAffine2) -> ImageFrame<P> {
// 	if !transform.decompose_scale().abs_diff_eq(DVec2::ZERO, 0.00001) {
// 		ImageFrame {
// 			image,
// 			transform,
// 			alpha_blending: AlphaBlending::default(),
// 		}
// 	} else {
// 		let resolution = DVec2::new(image.height as f64, image.width as f64);
// 		ImageFrame {
// 			image,
// 			transform: DAffine2::from_scale_angle_translation(resolution, 0., transform.translation),
// 			alpha_blending: AlphaBlending::default(),
// 		}
// 	}
// }

// #[cfg(feature = "serde")]
// generate_imaginate_node! {
// 	seed: Seed: f64,
// 	res: Res: Option<DVec2>,
// 	samples: Samples: u32,
// 	sampling_method: SamplingMethod: ImaginateSamplingMethod,
// 	prompt_guidance: PromptGuidance: f64,
// 	prompt: Prompt: String,
// 	negative_prompt: NegativePrompt: String,
// 	adapt_input_image: AdaptInputImage: bool,
// 	image_creativity: ImageCreativity: f64,
// 	inpaint: Inpaint: bool,
// 	mask_blur: MaskBlur: f64,
// 	mask_starting_fill: MaskStartingFill: ImaginateMaskStartingFill,
// 	improve_faces: ImproveFaces: bool,
// 	tiling: Tiling: bool,
// }

#[node_macro::node(category("Raster"))]
#[allow(clippy::too_many_arguments)]
fn noise_pattern(
	ctx: impl ExtractFootprint + Ctx,
	_primary: (),
	clip: bool,
	seed: u32,
	scale: f64,
	noise_type: NoiseType,
	domain_warp_type: DomainWarpType,
	domain_warp_amplitude: f64,
	fractal_type: FractalType,
	fractal_octaves: u32,
	fractal_lacunarity: f64,
	fractal_gain: f64,
	fractal_weighted_strength: f64,
	fractal_ping_pong_strength: f64,
	cellular_distance_function: CellularDistanceFunction,
	cellular_return_type: CellularReturnType,
	cellular_jitter: f64,
) -> ImageFrameTable<Color> {
	let footprint = ctx.footprint();
	let viewport_bounds = footprint.viewport_bounds_in_local_space();

	let mut size = viewport_bounds.size();
	let mut offset = viewport_bounds.start;
	if clip {
		// TODO: Remove "clip" entirely (and its arbitrary 100x100 clipping square) once we have proper resolution-aware layer clipping
		const CLIPPING_SQUARE_SIZE: f64 = 100.;
		let image_bounds = Bbox::from_transform(DAffine2::from_scale(DVec2::splat(CLIPPING_SQUARE_SIZE))).to_axis_aligned_bbox();
		let intersection = viewport_bounds.intersect(&image_bounds);

		offset = (intersection.start - image_bounds.start).max(DVec2::ZERO);
		size = intersection.size();
	}

	// If the image would not be visible, return an empty image
	if size.x <= 0. || size.y <= 0. {
		return ImageFrameTable::one_empty_image();
	}

	let footprint_scale = footprint.scale();
	let width = (size.x * footprint_scale.x) as u32;
	let height = (size.y * footprint_scale.y) as u32;

	// All
	let mut image = Image::new(width, height, Color::from_luminance(0.5));
	let mut noise = fastnoise_lite::FastNoiseLite::with_seed(seed as i32);
	noise.set_frequency(Some(1. / (scale as f32).max(f32::EPSILON)));

	// Domain Warp
	let domain_warp_type = match domain_warp_type {
		DomainWarpType::None => None,
		DomainWarpType::OpenSimplex2 => Some(fastnoise_lite::DomainWarpType::OpenSimplex2),
		DomainWarpType::OpenSimplex2Reduced => Some(fastnoise_lite::DomainWarpType::OpenSimplex2Reduced),
		DomainWarpType::BasicGrid => Some(fastnoise_lite::DomainWarpType::BasicGrid),
	};
	let domain_warp_active = domain_warp_type.is_some();
	noise.set_domain_warp_type(domain_warp_type);
	noise.set_domain_warp_amp(Some(domain_warp_amplitude as f32));

	// Fractal
	let noise_type = match noise_type {
		NoiseType::Perlin => fastnoise_lite::NoiseType::Perlin,
		NoiseType::OpenSimplex2 => fastnoise_lite::NoiseType::OpenSimplex2,
		NoiseType::OpenSimplex2S => fastnoise_lite::NoiseType::OpenSimplex2S,
		NoiseType::Cellular => fastnoise_lite::NoiseType::Cellular,
		NoiseType::ValueCubic => fastnoise_lite::NoiseType::ValueCubic,
		NoiseType::Value => fastnoise_lite::NoiseType::Value,
		NoiseType::WhiteNoise => {
			// TODO: Generate in layer space, not viewport space

			let mut rng = ChaCha8Rng::seed_from_u64(seed as u64);

			for y in 0..height {
				for x in 0..width {
					let pixel = image.get_pixel_mut(x, y).unwrap();
					let luminance = rng.random_range(0.0..1.) as f32;
					*pixel = Color::from_luminance(luminance);
				}
			}

			let mut result = ImageFrameTable::new(image);
			*result.transform_mut() = DAffine2::from_translation(offset) * DAffine2::from_scale(size);
			*result.one_instance_mut().alpha_blending = AlphaBlending::default();

			return result;
		}
	};
	noise.set_noise_type(Some(noise_type));
	let fractal_type = match fractal_type {
		FractalType::None => fastnoise_lite::FractalType::None,
		FractalType::FBm => fastnoise_lite::FractalType::FBm,
		FractalType::Ridged => fastnoise_lite::FractalType::Ridged,
		FractalType::PingPong => fastnoise_lite::FractalType::PingPong,
		FractalType::DomainWarpProgressive => fastnoise_lite::FractalType::DomainWarpProgressive,
		FractalType::DomainWarpIndependent => fastnoise_lite::FractalType::DomainWarpIndependent,
	};
	noise.set_fractal_type(Some(fractal_type));
	noise.set_fractal_octaves(Some(fractal_octaves as i32));
	noise.set_fractal_lacunarity(Some(fractal_lacunarity as f32));
	noise.set_fractal_gain(Some(fractal_gain as f32));
	noise.set_fractal_weighted_strength(Some(fractal_weighted_strength as f32));
	noise.set_fractal_ping_pong_strength(Some(fractal_ping_pong_strength as f32));

	// Cellular
	let cellular_distance_function = match cellular_distance_function {
		CellularDistanceFunction::Euclidean => fastnoise_lite::CellularDistanceFunction::Euclidean,
		CellularDistanceFunction::EuclideanSq => fastnoise_lite::CellularDistanceFunction::EuclideanSq,
		CellularDistanceFunction::Manhattan => fastnoise_lite::CellularDistanceFunction::Manhattan,
		CellularDistanceFunction::Hybrid => fastnoise_lite::CellularDistanceFunction::Hybrid,
	};
	let cellular_return_type = match cellular_return_type {
		CellularReturnType::CellValue => fastnoise_lite::CellularReturnType::CellValue,
		CellularReturnType::Nearest => fastnoise_lite::CellularReturnType::Distance,
		CellularReturnType::NextNearest => fastnoise_lite::CellularReturnType::Distance2,
		CellularReturnType::Average => fastnoise_lite::CellularReturnType::Distance2Add,
		CellularReturnType::Difference => fastnoise_lite::CellularReturnType::Distance2Sub,
		CellularReturnType::Product => fastnoise_lite::CellularReturnType::Distance2Mul,
		CellularReturnType::Division => fastnoise_lite::CellularReturnType::Distance2Div,
	};
	noise.set_cellular_distance_function(Some(cellular_distance_function));
	noise.set_cellular_return_type(Some(cellular_return_type));
	noise.set_cellular_jitter(Some(cellular_jitter as f32));

	let coordinate_offset = offset.as_vec2();
	let scale = size.as_vec2() / Vec2::new(width as f32, height as f32);
	// Calculate the noise for every pixel
	for y in 0..height {
		for x in 0..width {
			let pixel = image.get_pixel_mut(x, y).unwrap();
			let pos = Vec2::new(x as f32, y as f32);
			let vec = pos * scale + coordinate_offset;

			let (mut x, mut y) = (vec.x, vec.y);
			if domain_warp_active && domain_warp_amplitude > 0. {
				(x, y) = noise.domain_warp_2d(x, y);
			}

			let luminance = (noise.get_noise_2d(x, y) + 1.) * 0.5;
			*pixel = Color::from_luminance(luminance);
		}
	}

	let mut result = ImageFrameTable::new(image);
	*result.transform_mut() = DAffine2::from_translation(offset) * DAffine2::from_scale(size);
	*result.one_instance_mut().alpha_blending = AlphaBlending::default();

	result
}

#[node_macro::node(category("Raster"))]
fn mandelbrot(ctx: impl ExtractFootprint + Send) -> ImageFrameTable<Color> {
	let footprint = ctx.footprint();
	let viewport_bounds = footprint.viewport_bounds_in_local_space();

	let image_bounds = Bbox::from_transform(DAffine2::IDENTITY).to_axis_aligned_bbox();
	let intersection = viewport_bounds.intersect(&image_bounds);
	let size = intersection.size();

	let offset = (intersection.start - image_bounds.start).max(DVec2::ZERO);

	// If the image would not be visible, return an empty image
	if size.x <= 0. || size.y <= 0. {
		return ImageFrameTable::one_empty_image();
	}

	let scale = footprint.scale();
	let width = (size.x * scale.x) as u32;
	let height = (size.y * scale.y) as u32;

	let mut data = Vec::with_capacity(width as usize * height as usize);
	let max_iter = 255;

	let scale = 3. * size.as_vec2() / Vec2::new(width as f32, height as f32);
	let coordinate_offset = offset.as_vec2() * 3. - Vec2::new(2., 1.5);
	for y in 0..height {
		for x in 0..width {
			let pos = Vec2::new(x as f32, y as f32);
			let c = pos * scale + coordinate_offset;

			let iter = mandelbrot_impl(c, max_iter);
			data.push(map_color(iter, max_iter));
		}
	}

	let image = Image {
		width,
		height,
		data,
		..Default::default()
	};
	let mut result = ImageFrameTable::new(image);
	*result.transform_mut() = DAffine2::from_translation(offset) * DAffine2::from_scale(size);
	*result.one_instance_mut().alpha_blending = Default::default();

	result
}

#[inline(always)]
fn mandelbrot_impl(c: Vec2, max_iter: usize) -> usize {
	let mut z = Vec2::new(0., 0.);
	for i in 0..max_iter {
		z = Vec2::new(z.x * z.x - z.y * z.y, 2. * z.x * z.y) + c;
		if z.length_squared() > 4. {
			return i;
		}
	}
	max_iter
}

fn map_color(iter: usize, max_iter: usize) -> Color {
	let v = iter as f32 / max_iter as f32;
	Color::from_rgbaf32_unchecked(v, v, v, 1.)
}