use crate::adjustments::{CellularDistanceFunction, CellularReturnType, DomainWarpType, FractalType, NoiseType};
use core_types::ATTR_TRANSFORM;
use core_types::color::Color;
use core_types::color::{Alpha, AlphaMut, Channel, LinearChannel, Luminance, RGBMut};
use core_types::context::{Ctx, ExtractFootprint};
use core_types::list::Item;
use core_types::math::bbox::Bbox;
use core_types::transform::Transform;
use dyn_any::DynAny;
use fastnoise_lite;
use glam::{DAffine2, DVec2, Vec2};
use graphene_resource::Resource;
use rand::prelude::*;
use rand_chacha::ChaCha8Rng;
use raster_types::Image;
use raster_types::{Bitmap, BitmapMut};
use raster_types::{CPU, Raster};
use std::fmt::Debug;
use std::hash::Hash;

#[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"))]
pub fn sample_image(ctx: impl ExtractFootprint + Clone + Send, image_frame: Item<Raster<CPU>>) -> Item<Raster<CPU>> {
	let image_frame_transform: DAffine2 = image_frame.attribute_cloned_or_default(ATTR_TRANSFORM);

	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 size = intersection.size();

	// If the image would not be visible, return it unchanged
	if size.x <= 0. || size.y <= 0. {
		return image_frame;
	}

	let (image, mut attributes) = image_frame.into_parts();
	let (width, height) = (image.width, image.height);

	// Resize the image using the image crate
	let data = bytemuck::cast_vec(image.into_data().data);
	let image_size = DAffine2::from_scale(DVec2::new(width as f64, height as f64));
	let size_px = image_size.transform_vector2(size).as_uvec2();

	let image_buffer = ::image::Rgba32FImage::from_raw(width, 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 < width || new_height < 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);
	attributes.insert(ATTR_TRANSFORM, new_transform);

	Item::from_parts(Raster::new_cpu(image), attributes)
}

#[node_macro::node(category("Raster: Channels"))]
pub fn combine_channels(
	_: impl Ctx,
	_primary: (),
	#[expose] red: Item<Raster<CPU>>,
	#[expose] green: Item<Raster<CPU>>,
	#[expose] blue: Item<Raster<CPU>>,
	#[expose] alpha: Item<Raster<CPU>>,
) -> Item<Raster<CPU>> {
	// An unconnected channel arrives as the default zero-sized raster, which counts as absent
	let present = |channel: Item<Raster<CPU>>| (channel.element().width > 0 && channel.element().height > 0).then_some(channel);
	let (red, green, blue, alpha) = (present(red), present(green), present(blue), present(alpha));

	// Take this item's transform and blending attributes from the first present channel
	let Some(attributes) = [&red, &green, &blue, &alpha].iter().find_map(|channel| channel.as_ref()).map(|channel| channel.attributes().clone()) else {
		return Item::default();
	};

	// All present channels must share the same dimensions; a mismatch yields the default zero-sized raster
	let channel_dimensions = [&red, &green, &blue, &alpha].map(|channel| channel.as_ref().map(|channel| (channel.element().width, channel.element().height)));
	let Some(&(width, height)) = channel_dimensions.iter().flatten().next() else {
		return Item::default();
	};
	if channel_dimensions.iter().flatten().any(|&(other_width, other_height)| other_width != width || other_height != height) {
		return Item::default();
	}

	// Set each output pixel's channels from the present inputs, defaulting absent color channels to 0 and absent alpha to 1
	let mut image = Image::new(width, height, Color::TRANSPARENT);
	for y in 0..image.height() {
		for x in 0..image.width() {
			let image_pixel = image.get_pixel_mut(x, y).unwrap();

			match red.as_ref().and_then(|r| r.element().get_pixel(x, y)) {
				Some(r) => image_pixel.set_red(r.l().cast_linear_channel()),
				None => image_pixel.set_red(Channel::from_linear(0.)),
			}
			match green.as_ref().and_then(|g| g.element().get_pixel(x, y)) {
				Some(g) => image_pixel.set_green(g.l().cast_linear_channel()),
				None => image_pixel.set_green(Channel::from_linear(0.)),
			}
			match blue.as_ref().and_then(|b| b.element().get_pixel(x, y)) {
				Some(b) => image_pixel.set_blue(b.l().cast_linear_channel()),
				None => image_pixel.set_blue(Channel::from_linear(0.)),
			}
			match alpha.as_ref().and_then(|a| a.element().get_pixel(x, y)) {
				Some(a) => image_pixel.set_alpha(a.l().cast_linear_channel()),
				None => image_pixel.set_alpha(Channel::from_linear(1.)),
			}
		}
	}

	Item::from_parts(Raster::new_cpu(image), attributes)
}

#[node_macro::node(category("Raster"))]
pub fn mask(
	_: impl Ctx,
	/// The image to be masked.
	image: Item<Raster<CPU>>,
	/// The stencil to be used for masking.
	#[expose]
	stencil: Item<Raster<CPU>>,
) -> Item<Raster<CPU>> {
	// An absent stencil arrives as the default empty raster, leaving the image unmasked
	if stencil.element().width == 0 || stencil.element().height == 0 {
		return image;
	}
	let stencil_size = DVec2::new(stencil.element().width as f64, stencil.element().height as f64);

	let mut row = image;
	let image_size = DVec2::new(row.element().width as f64, row.element().height as f64);
	let stencil_transform: DAffine2 = stencil.attribute_cloned_or_default(ATTR_TRANSFORM);
	let mask_size = stencil_transform.scale_magnitudes();

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

	// Transforms a point from the background image to the foreground image
	let transform_attribute: DAffine2 = row.attribute_cloned_or_default(ATTR_TRANSFORM);
	let bg_to_fg = transform_attribute * DAffine2::from_scale(1. / image_size);
	let stencil_transform_inverse = stencil_transform.inverse();

	for y in 0..row.element().height {
		for x in 0..row.element().width {
			let image_point = DVec2::new(x as f64, y as f64);
			let mask_point = bg_to_fg.transform_point2(image_point);
			let local_mask_point = stencil_transform_inverse.transform_point2(mask_point);
			let mask_point = stencil_transform.transform_point2(local_mask_point.clamp(DVec2::ZERO, DVec2::ONE));
			let mask_point = (DAffine2::from_scale(stencil_size) * stencil_transform.inverse()).transform_point2(mask_point);

			let image_pixel = row.element_mut().data_mut().get_pixel_mut(x, y).unwrap();
			let mask_pixel = stencil.element().sample(mask_point);
			*image_pixel = image_pixel.multiplied_alpha(mask_pixel.l().cast_linear_channel());
		}
	}

	row
}

#[node_macro::node(category(""))]
pub fn extend_image_to_bounds(_: impl Ctx, image: Item<Raster<CPU>>, bounds: Item<DAffine2>) -> Item<Raster<CPU>> {
	let bounds = *bounds.element();

	let image_transform: DAffine2 = image.attribute_cloned_or_default(ATTR_TRANSFORM);
	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, mut attributes) = image.into_parts();
	let (image_width, image_height) = (image.width, image.height);
	if image_width == 0 || image_height == 0 {
		return empty_image((), Item::new_from_element(bounds), Item::new_from_element(Color::TRANSPARENT));
	}

	let orig_image_scale = DVec2::new(image_width as f64, image_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_image = 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_height {
		let old_start = y * image_width;
		let new_start = (y + offset_in_new_image.y) * new_image.width + offset_in_new_image.x;
		let old_row = &image.data[old_start as usize..(old_start + image_width) as usize];
		let new_row = &mut new_image.data[new_start as usize..(new_start + image_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);

	attributes.insert(ATTR_TRANSFORM, new_texture_to_layer_space);
	Item::from_parts(Raster::new_cpu(new_image), attributes)
}

#[node_macro::node(category("Debug"))]
pub fn empty_image(_: impl Ctx, transform: Item<DAffine2>, color: Item<Color>) -> Item<Raster<CPU>> {
	let transform = transform.into_element();
	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.into_element());

	Item::new_from_element(Raster::new_cpu(image)).with_attribute(ATTR_TRANSFORM, transform)
}

#[node_macro::node(category(""))]
pub fn image<'a: 'n>(_: impl Ctx, resource: Item<Resource>) -> Item<Raster<CPU>> {
	let resource = resource.into_element();
	let image_data = resource.as_ref();

	let Some(image) = ::image::load_from_memory(image_data).ok() else {
		return Item::default();
	};
	let image = image.to_rgba32f();
	let image = Image {
		data: image
			.chunks(4)
			.map(|pixel| {
				let alpha = pixel[3];
				Color::from_gamma_srgb_channels(pixel[0] * alpha, pixel[1] * alpha, pixel[2] * alpha, alpha)
			})
			.collect(),
		width: image.width(),
		height: image.height(),
		..Default::default()
	};
	Item::new_from_element(Raster::new_cpu(image))
}

/// Generates customizable procedural noise patterns.
#[node_macro::node(category("Raster: Pattern"))]
#[allow(clippy::too_many_arguments)]
pub fn noise_pattern(
	ctx: impl ExtractFootprint + Ctx,
	_primary: (),
	#[default(true)] clip: Item<bool>,
	seed: Item<u32>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_scale")]
	#[default(10.)]
	scale: Item<f64>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_noise_type")] noise_type: Item<NoiseType>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_domain_warp_type")] domain_warp_type: Item<DomainWarpType>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_domain_warp_amplitude")]
	#[default(100.)]
	domain_warp_amplitude: Item<f64>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_fractal_type")] fractal_type: Item<FractalType>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_fractal_octaves")]
	#[default(3)]
	fractal_octaves: Item<u32>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_fractal_lacunarity")]
	#[default(2.)]
	fractal_lacunarity: Item<f64>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_fractal_gain")]
	#[default(0.5)]
	fractal_gain: Item<f64>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_fractal_weighted_strength")] fractal_weighted_strength: Item<f64>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_ping_pong_strength")]
	#[default(2.)]
	fractal_ping_pong_strength: Item<f64>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_cellular_distance_function")] cellular_distance_function: Item<CellularDistanceFunction>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_cellular_return_type")] cellular_return_type: Item<CellularReturnType>,
	#[widget(ParsedWidgetOverride::Custom = "noise_properties_cellular_jitter")]
	#[default(1.)]
	cellular_jitter: Item<f64>,
) -> Item<Raster<CPU>> {
	let (clip, seed, scale, domain_warp_amplitude) = (*clip.element(), *seed.element(), *scale.element(), *domain_warp_amplitude.element());
	let (fractal_octaves, fractal_lacunarity, fractal_gain) = (*fractal_octaves.element(), *fractal_lacunarity.element(), *fractal_gain.element());
	let (fractal_weighted_strength, fractal_ping_pong_strength, cellular_jitter) = (*fractal_weighted_strength.element(), *fractal_ping_pong_strength.element(), *cellular_jitter.element());
	let (noise_type, domain_warp_type, fractal_type) = (noise_type.into_element(), domain_warp_type.into_element(), fractal_type.into_element());
	let (cellular_distance_function, cellular_return_type) = (cellular_distance_function.into_element(), cellular_return_type.into_element());

	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 Item::default();
	}

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

	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);
				}
			}

			return Item::new_from_element(Raster::new_cpu(image)).with_attribute(ATTR_TRANSFORM, transform);
		}
	};
	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);
		}
	}

	Item::new_from_element(Raster::new_cpu(image)).with_attribute(ATTR_TRANSFORM, transform)
}

#[node_macro::node(category("Raster: Pattern"))]
pub fn mandelbrot(ctx: impl ExtractFootprint + Send) -> Item<Raster<CPU>> {
	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 Item::default();
	}

	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));
		}
	}

	Item::new_from_element(Raster::new_cpu(Image {
		width,
		height,
		data,
		..Default::default()
	}))
	.with_attribute(ATTR_TRANSFORM, DAffine2::from_translation(offset) * DAffine2::from_scale(size))
}

#[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.)
}