import sharp from "sharp";
import { Canvas, Image, ImageData } from "canvas";
// Use the ESM no-bundle build to avoid the default Node entrypoint
// which hardcodes require('@tensorflow/tfjs-node') and crashes in Docker.
// This build uses pure @tensorflow/tfjs (JS-only, no native C++ bindings).
// @ts-ignore - direct path import has no type declarations
import * as faceapi from "@vladmandic/face-api/dist/face-api.esm-nobundle.js";
import * as tf from "@tensorflow/tfjs";
import path from "path";
import { fileURLToPath } from "url";

// Polyfill required by face-api for Node.js
faceapi.env.monkeyPatch({ Canvas, Image, ImageData } as any);

const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);
const MODEL_URL = path.join(__dirname, "../models");

// State flag to ensure we only load weights once
let modelsLoaded = false;

async function loadModelsOnce() {
  if (modelsLoaded) return;
  // Initialize pure JS CPU backend (no native bindings needed)
  await tf.setBackend("cpu");
  await tf.ready();

  // Load the microscopic TinyFaceDetector (~190KB)
  await faceapi.nets.tinyFaceDetector.loadFromDisk(MODEL_URL);
  modelsLoaded = true;
}

export interface ProcessImageOptions {
  width: number;
  height: number;
  format?: "webp" | "jpeg" | "png" | "avif";
  quality?: number;
}

/**
 * Maps a URL based on the IMGPROXY_URL_MAPPING environment variable.
 * Format: "match1:replace1,match2:replace2"
 */
export function mapUrl(url: string, mappingString?: string): string {
  if (!mappingString) return url;

  const mappings = mappingString.split(",").map((m) => {
    if (m.includes("|")) {
      return m.split("|");
    }

    // Legacy support for simple "host:target" or cases where one side might have a protocol
    // We try to find the split point that isn't part of a protocol "://"
    const colonIndices = [];
    for (let i = 0; i < m.length; i++) {
      if (m[i] === ":") {
        // Check if this colon is part of "://"
        if (!(m[i + 1] === "/" && m[i + 2] === "/")) {
          colonIndices.push(i);
        }
      }
    }

    if (colonIndices.length === 0) return [m];

    // In legacy mode with colons, we take the LAST non-protocol colon as the separator
    // This handles "http://host:port" or "host:http://target" better
    const lastColon = colonIndices[colonIndices.length - 1];
    return [m.substring(0, lastColon), m.substring(lastColon + 1)];
  });

  let mappedUrl = url;

  for (const [match, replace] of mappings) {
    if (match && replace && url.includes(match)) {
      mappedUrl = url.replace(match, replace);
    }
  }

  return mappedUrl;
}

/**
 * Parses legacy imgproxy options string.
 * Example: rs:fill:300:400/q:80
 */
export function parseImgproxyOptions(
  optionsStr: string,
): Partial<ProcessImageOptions> {
  const parts = optionsStr.split("/");
  const options: Partial<ProcessImageOptions> = {};

  for (const part of parts) {
    if (part.startsWith("rs:")) {
      const [, , w, h] = part.split(":");
      if (w) options.width = parseInt(w, 10);
      if (h) options.height = parseInt(h, 10);
    } else if (part.startsWith("q:")) {
      const q = part.split(":")[1];
      if (q) options.quality = parseInt(q, 10);
    } else if (part.startsWith("ext:")) {
      const ext = part.split(":")[1] as any;
      if (["webp", "jpeg", "png", "avif"].includes(ext)) {
        options.format = ext;
      }
    }
  }

  return options;
}

export async function processImageWithSmartCrop(
  inputBuffer: Buffer,
  options: ProcessImageOptions,
): Promise<Buffer> {
  const sharpImage = sharp(inputBuffer);
  const metadata = await sharpImage.metadata();

  if (!metadata.width || !metadata.height) {
    throw new Error("Could not read image metadata");
  }

  // Load ML models (noop if already loaded)
  await loadModelsOnce();

  // Convert sharp image to a Node-compatible canvas Image for face-api
  const jpegBuffer = await sharpImage.jpeg().toBuffer();
  const img = new Image();
  img.src = jpegBuffer;
  const canvas = new Canvas(img.width, img.height);
  const ctx = canvas.getContext("2d");
  ctx.drawImage(img, 0, 0, img.width, img.height);

  // Detect faces locally using the tiny model
  // Requires explicit any cast since the types expect HTML elements in browser contexts
  const detections = await faceapi.detectAllFaces(
    canvas as any,
    new faceapi.TinyFaceDetectorOptions(),
  );

  let cropPosition: "center" | "attention" | number = "attention"; // Fallback to sharp's attention if no faces

  if (detections.length > 0) {
    // We have faces! Calculate the bounding box that contains all of them
    let minX = metadata.width;
    let minY = metadata.height;
    let maxX = 0;
    let maxY = 0;

    for (const det of detections) {
      const box = det.box;
      if (box.x < minX) minX = Math.max(0, box.x);
      if (box.y < minY) minY = Math.max(0, box.y);
      if (box.x + box.width > maxX)
        maxX = Math.min(metadata.width, box.x + box.width);
      if (box.y + box.height > maxY)
        maxY = Math.min(metadata.height, box.y + box.height);
    }

    const centerX = Math.floor(minX + (maxX - minX) / 2);
    const centerY = Math.floor(minY + (maxY - minY) / 2);

    const targetRatio = options.width / options.height;
    const currentRatio = metadata.width / metadata.height;

    let cropWidth = metadata.width;
    let cropHeight = metadata.height;

    // Determine the maximal crop window that maintains aspect ratio
    if (currentRatio > targetRatio) {
      cropWidth = Math.floor(metadata.height * targetRatio);
    } else {
      cropHeight = Math.floor(metadata.width / targetRatio);
    }

    // Center the crop window over the center of the faces
    let cropX = Math.floor(centerX - cropWidth / 2);
    let cropY = Math.floor(centerY - cropHeight / 2);

    // Keep crop window inside image bounds
    if (cropX < 0) cropX = 0;
    if (cropY < 0) cropY = 0;
    if (cropX + cropWidth > metadata.width) cropX = metadata.width - cropWidth;
    if (cropY + cropHeight > metadata.height)
      cropY = metadata.height - cropHeight;

    // Pre-crop the image to isolate the faces before resizing
    sharpImage.extract({
      left: cropX,
      top: cropY,
      width: cropWidth,
      height: cropHeight,
    });

    // As we manually calculated the exact focal box, we can now just center it
    cropPosition = "center";
  }

  let finalImage = sharpImage.resize(options.width, options.height, {
    fit: "cover",
    position: cropPosition,
  });

  const format = options.format || "webp";
  const quality = options.quality || 80;

  if (format === "webp") {
    finalImage = finalImage.webp({ quality });
  } else if (format === "jpeg") {
    finalImage = finalImage.jpeg({ quality });
  } else if (format === "png") {
    finalImage = finalImage.png({ quality });
  } else if (format === "avif") {
    finalImage = finalImage.avif({ quality });
  }

  return finalImage.toBuffer();
}