import { api } from "./api.js";

export function getPngMetadata(file) {
	return new Promise((r) => {
		const reader = new FileReader();
		reader.onload = (event) => {
			// Get the PNG data as a Uint8Array
			const pngData = new Uint8Array(event.target.result);
			const dataView = new DataView(pngData.buffer);

			// Check that the PNG signature is present
			if (dataView.getUint32(0) !== 0x89504e47) {
				console.error("Not a valid PNG file");
				r();
				return;
			}

			// Start searching for chunks after the PNG signature
			let offset = 8;
			let txt_chunks = {};
			// Loop through the chunks in the PNG file
			while (offset < pngData.length) {
				// Get the length of the chunk
				const length = dataView.getUint32(offset);
				// Get the chunk type
				const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8));
				if (type === "tEXt") {
					// Get the keyword
					let keyword_end = offset + 8;
					while (pngData[keyword_end] !== 0) {
						keyword_end++;
					}
					const keyword = String.fromCharCode(...pngData.slice(offset + 8, keyword_end));
					// Get the text
					const contentArraySegment = pngData.slice(keyword_end + 1, offset + 8 + length);
					const contentJson = Array.from(contentArraySegment).map(s=>String.fromCharCode(s)).join('')
					txt_chunks[keyword] = contentJson;
				}

				offset += 12 + length;
			}

			r(txt_chunks);
		};

		reader.readAsArrayBuffer(file);
	});
}

export function getLatentMetadata(file) {
	return new Promise((r) => {
		const reader = new FileReader();
		reader.onload = (event) => {
			const safetensorsData = new Uint8Array(event.target.result);
			const dataView = new DataView(safetensorsData.buffer);
			let header_size = dataView.getUint32(0, true);
			let offset = 8;
			let header = JSON.parse(String.fromCharCode(...safetensorsData.slice(offset, offset + header_size)));
			r(header.__metadata__);
		};

		reader.readAsArrayBuffer(file);
	});
}

export async function importA1111(graph, parameters) {
	const p = parameters.lastIndexOf("\nSteps:");
	if (p > -1) {
		const embeddings = await api.getEmbeddings();
		const opts = parameters
			.substr(p)
			.split("\n")[1]
			.split(",")
			.reduce((p, n) => {
				const s = n.split(":");
				p[s[0].trim().toLowerCase()] = s[1].trim();
				return p;
			}, {});
		const p2 = parameters.lastIndexOf("\nNegative prompt:", p);
		if (p2 > -1) {
			let positive = parameters.substr(0, p2).trim();
			let negative = parameters.substring(p2 + 18, p).trim();

			const ckptNode = LiteGraph.createNode("CheckpointLoaderSimple");
			const clipSkipNode = LiteGraph.createNode("CLIPSetLastLayer");
			const positiveNode = LiteGraph.createNode("CLIPTextEncode");
			const negativeNode = LiteGraph.createNode("CLIPTextEncode");
			const samplerNode = LiteGraph.createNode("KSampler");
			const imageNode = LiteGraph.createNode("EmptyLatentImage");
			const vaeNode = LiteGraph.createNode("VAEDecode");
			const vaeLoaderNode = LiteGraph.createNode("VAELoader");
			const saveNode = LiteGraph.createNode("SaveImage");
			let hrSamplerNode = null;

			const ceil64 = (v) => Math.ceil(v / 64) * 64;

			function getWidget(node, name) {
				return node.widgets.find((w) => w.name === name);
			}

			function setWidgetValue(node, name, value, isOptionPrefix) {
				const w = getWidget(node, name);
				if (isOptionPrefix) {
					const o = w.options.values.find((w) => w.startsWith(value));
					if (o) {
						w.value = o;
					} else {
						console.warn(`Unknown value '${value}' for widget '${name}'`, node);
						w.value = value;
					}
				} else {
					w.value = value;
				}
			}

			function createLoraNodes(clipNode, text, prevClip, prevModel) {
				const loras = [];
				text = text.replace(/<lora:([^:]+:[^>]+)>/g, function (m, c) {
					const s = c.split(":");
					const weight = parseFloat(s[1]);
					if (isNaN(weight)) {
						console.warn("Invalid LORA", m);
					} else {
						loras.push({ name: s[0], weight });
					}
					return "";
				});

				for (const l of loras) {
					const loraNode = LiteGraph.createNode("LoraLoader");
					graph.add(loraNode);
					setWidgetValue(loraNode, "lora_name", l.name, true);
					setWidgetValue(loraNode, "strength_model", l.weight);
					setWidgetValue(loraNode, "strength_clip", l.weight);
					prevModel.node.connect(prevModel.index, loraNode, 0);
					prevClip.node.connect(prevClip.index, loraNode, 1);
					prevModel = { node: loraNode, index: 0 };
					prevClip = { node: loraNode, index: 1 };
				}

				prevClip.node.connect(1, clipNode, 0);
				prevModel.node.connect(0, samplerNode, 0);
				if (hrSamplerNode) {
					prevModel.node.connect(0, hrSamplerNode, 0);
				}

				return { text, prevModel, prevClip };
			}

			function replaceEmbeddings(text) {
				if(!embeddings.length) return text;
				return text.replaceAll(
					new RegExp(
						"\\b(" + embeddings.map((e) => e.replace(/[.*+?^${}()|[\]\\]/g, "\\$&")).join("\\b|\\b") + ")\\b",
						"ig"
					),
					"embedding:$1"
				);
			}

			function popOpt(name) {
				const v = opts[name];
				delete opts[name];
				return v;
			}

			graph.clear();
			graph.add(ckptNode);
			graph.add(clipSkipNode);
			graph.add(positiveNode);
			graph.add(negativeNode);
			graph.add(samplerNode);
			graph.add(imageNode);
			graph.add(vaeNode);
			graph.add(vaeLoaderNode);
			graph.add(saveNode);

			ckptNode.connect(1, clipSkipNode, 0);
			clipSkipNode.connect(0, positiveNode, 0);
			clipSkipNode.connect(0, negativeNode, 0);
			ckptNode.connect(0, samplerNode, 0);
			positiveNode.connect(0, samplerNode, 1);
			negativeNode.connect(0, samplerNode, 2);
			imageNode.connect(0, samplerNode, 3);
			vaeNode.connect(0, saveNode, 0);
			samplerNode.connect(0, vaeNode, 0);
			vaeLoaderNode.connect(0, vaeNode, 1);

			const handlers = {
				model(v) {
					setWidgetValue(ckptNode, "ckpt_name", v, true);
				},
				"cfg scale"(v) {
					setWidgetValue(samplerNode, "cfg", +v);
				},
				"clip skip"(v) {
					setWidgetValue(clipSkipNode, "stop_at_clip_layer", -v);
				},
				sampler(v) {
					let name = v.toLowerCase().replace("++", "pp").replaceAll(" ", "_");
					if (name.includes("karras")) {
						name = name.replace("karras", "").replace(/_+$/, "");
						setWidgetValue(samplerNode, "scheduler", "karras");
					} else {
						setWidgetValue(samplerNode, "scheduler", "normal");
					}
					const w = getWidget(samplerNode, "sampler_name");
					const o = w.options.values.find((w) => w === name || w === "sample_" + name);
					if (o) {
						setWidgetValue(samplerNode, "sampler_name", o);
					}
				},
				size(v) {
					const wxh = v.split("x");
					const w = ceil64(+wxh[0]);
					const h = ceil64(+wxh[1]);
					const hrUp = popOpt("hires upscale");
					const hrSz = popOpt("hires resize");
					let hrMethod = popOpt("hires upscaler");

					setWidgetValue(imageNode, "width", w);
					setWidgetValue(imageNode, "height", h);

					if (hrUp || hrSz) {
						let uw, uh;
						if (hrUp) {
							uw = w * hrUp;
							uh = h * hrUp;
						} else {
							const s = hrSz.split("x");
							uw = +s[0];
							uh = +s[1];
						}

						let upscaleNode;
						let latentNode;

						if (hrMethod.startsWith("Latent")) {
							latentNode = upscaleNode = LiteGraph.createNode("LatentUpscale");
							graph.add(upscaleNode);
							samplerNode.connect(0, upscaleNode, 0);

							switch (hrMethod) {
								case "Latent (nearest-exact)":
									hrMethod = "nearest-exact";
									break;
							}
							setWidgetValue(upscaleNode, "upscale_method", hrMethod, true);
						} else {
							const decode = LiteGraph.createNode("VAEDecodeTiled");
							graph.add(decode);
							samplerNode.connect(0, decode, 0);
							vaeLoaderNode.connect(0, decode, 1);

							const upscaleLoaderNode = LiteGraph.createNode("UpscaleModelLoader");
							graph.add(upscaleLoaderNode);
							setWidgetValue(upscaleLoaderNode, "model_name", hrMethod, true);

							const modelUpscaleNode = LiteGraph.createNode("ImageUpscaleWithModel");
							graph.add(modelUpscaleNode);
							decode.connect(0, modelUpscaleNode, 1);
							upscaleLoaderNode.connect(0, modelUpscaleNode, 0);

							upscaleNode = LiteGraph.createNode("ImageScale");
							graph.add(upscaleNode);
							modelUpscaleNode.connect(0, upscaleNode, 0);

							const vaeEncodeNode = (latentNode = LiteGraph.createNode("VAEEncodeTiled"));
							graph.add(vaeEncodeNode);
							upscaleNode.connect(0, vaeEncodeNode, 0);
							vaeLoaderNode.connect(0, vaeEncodeNode, 1);
						}

						setWidgetValue(upscaleNode, "width", ceil64(uw));
						setWidgetValue(upscaleNode, "height", ceil64(uh));

						hrSamplerNode = LiteGraph.createNode("KSampler");
						graph.add(hrSamplerNode);
						ckptNode.connect(0, hrSamplerNode, 0);
						positiveNode.connect(0, hrSamplerNode, 1);
						negativeNode.connect(0, hrSamplerNode, 2);
						latentNode.connect(0, hrSamplerNode, 3);
						hrSamplerNode.connect(0, vaeNode, 0);
					}
				},
				steps(v) {
					setWidgetValue(samplerNode, "steps", +v);
				},
				seed(v) {
					setWidgetValue(samplerNode, "seed", +v);
				},
			};

			for (const opt in opts) {
				if (opt in handlers) {
					handlers[opt](popOpt(opt));
				}
			}

			if (hrSamplerNode) {
				setWidgetValue(hrSamplerNode, "steps", getWidget(samplerNode, "steps").value);
				setWidgetValue(hrSamplerNode, "cfg", getWidget(samplerNode, "cfg").value);
				setWidgetValue(hrSamplerNode, "scheduler", getWidget(samplerNode, "scheduler").value);
				setWidgetValue(hrSamplerNode, "sampler_name", getWidget(samplerNode, "sampler_name").value);
				setWidgetValue(hrSamplerNode, "denoise", +(popOpt("denoising strength") || "1"));
			}

			let n = createLoraNodes(positiveNode, positive, { node: clipSkipNode, index: 0 }, { node: ckptNode, index: 0 });
			positive = n.text;
			n = createLoraNodes(negativeNode, negative, n.prevClip, n.prevModel);
			negative = n.text;

			setWidgetValue(positiveNode, "text", replaceEmbeddings(positive));
			setWidgetValue(negativeNode, "text", replaceEmbeddings(negative));

			graph.arrange();

			for (const opt of ["model hash", "ensd"]) {
				delete opts[opt];
			}

			console.warn("Unhandled parameters:", opts);
		}
	}
}
