import { randomUUID } from "node:crypto";
import { writeFile } from "node:fs/promises";
import { basename, join } from "node:path";
import type { FastifyInstance } from "fastify";
import sharp from "sharp";
import { z } from "zod";
import { validateImageBuffer } from "../../lib/file-validation.js";
import { ensureSharpCompat } from "../../lib/heic-converter.js";
import { createWorkspace } from "../../lib/workspace.js";

const settingsSchema = z.object({
  layout: z.enum(["2x2", "3x3", "1x3", "2x1", "3x1", "1x2"]).default("2x2"),
  gap: z.number().min(0).max(50).default(4),
  backgroundColor: z
    .string()
    .regex(/^#[0-9a-fA-F]{6}$/)
    .default("#FFFFFF"),
});

function parseLayout(layout: string): { cols: number; rows: number } {
  const [cols, rows] = layout.split("x").map(Number);
  return { cols, rows };
}

export function registerCollage(app: FastifyInstance) {
  app.post("/api/v1/tools/collage", async (request, reply) => {
    const files: Array<{ buffer: Buffer; filename: string }> = [];
    let settingsRaw: string | null = null;

    try {
      const parts = request.parts();
      for await (const part of parts) {
        if (part.type === "file") {
          const chunks: Buffer[] = [];
          for await (const chunk of part.file) {
            chunks.push(chunk);
          }
          const buf = Buffer.concat(chunks);
          if (buf.length > 0) {
            files.push({
              buffer: buf,
              filename: basename(part.filename ?? `image-${files.length}`),
            });
          }
        } else if (part.fieldname === "settings") {
          settingsRaw = part.value as string;
        }
      }
    } catch (err) {
      return reply.status(400).send({
        error: "Failed to parse multipart request",
        details: err instanceof Error ? err.message : String(err),
      });
    }

    if (files.length === 0) {
      return reply.status(400).send({ error: "No images provided" });
    }

    // Validate all files and decode HEIC/HEIF
    for (const file of files) {
      const validation = await validateImageBuffer(file.buffer);
      if (!validation.valid) {
        return reply
          .status(400)
          .send({ error: `Invalid file "${file.filename}": ${validation.reason}` });
      }
      file.buffer = await ensureSharpCompat(file.buffer);
    }

    let settings: z.infer<typeof settingsSchema>;
    try {
      const parsed = settingsRaw ? JSON.parse(settingsRaw) : {};
      const result = settingsSchema.safeParse(parsed);
      if (!result.success) {
        return reply.status(400).send({ error: "Invalid settings", details: result.error.issues });
      }
      settings = result.data;
    } catch {
      return reply.status(400).send({ error: "Settings must be valid JSON" });
    }

    try {
      const { cols, rows } = parseLayout(settings.layout);
      const totalSlots = cols * rows;

      // Determine cell size based on first image
      const firstMeta = await sharp(files[0].buffer).metadata();
      const cellW = firstMeta.width ?? 400;
      const cellH = firstMeta.height ?? 400;

      // Canvas dimensions
      const canvasW = cellW * cols + settings.gap * (cols + 1);
      const canvasH = cellH * rows + settings.gap * (rows + 1);

      // Parse background color
      const bgR = parseInt(settings.backgroundColor.slice(1, 3), 16);
      const bgG = parseInt(settings.backgroundColor.slice(3, 5), 16);
      const bgB = parseInt(settings.backgroundColor.slice(5, 7), 16);

      // Create canvas
      const composites: sharp.OverlayOptions[] = [];

      for (let i = 0; i < Math.min(files.length, totalSlots); i++) {
        const row = Math.floor(i / cols);
        const col = i % cols;
        const x = settings.gap + col * (cellW + settings.gap);
        const y = settings.gap + row * (cellH + settings.gap);

        const resized = await sharp(files[i].buffer)
          .resize(cellW, cellH, { fit: "cover" })
          .toBuffer();

        composites.push({ input: resized, top: y, left: x });
      }

      const result = await sharp({
        create: {
          width: canvasW,
          height: canvasH,
          channels: 3,
          background: { r: bgR, g: bgG, b: bgB },
        },
      })
        .composite(composites)
        .png()
        .toBuffer();

      const jobId = randomUUID();
      const workspacePath = await createWorkspace(jobId);
      const filename = "collage.png";
      const outputPath = join(workspacePath, "output", filename);
      await writeFile(outputPath, result);

      return reply.send({
        jobId,
        downloadUrl: `/api/v1/download/${jobId}/${filename}`,
        originalSize: files.reduce((s, f) => s + f.buffer.length, 0),
        processedSize: result.length,
      });
    } catch (err) {
      return reply.status(422).send({
        error: "Collage creation failed",
        details: err instanceof Error ? err.message : "Unknown error",
      });
    }
  });
}