#!/usr/bin/env python3
"""
Train a diffusion model for layout patch generation (skeleton).

Planned: fine-tune Stable Diffusion (or Latent Diffusion) with optional ControlNet edge/skeleton conditions.

Dependencies to consider: diffusers, transformers, accelerate, torch, torchvision, opencv-python.

Current status: CLI skeleton and TODOs only.
"""
from __future__ import annotations

import argparse


def main() -> None:
    parser = argparse.ArgumentParser(description="Train diffusion model for layout patches (skeleton)")
    parser.add_argument("--data_dir", type=str, required=True, help="Prepared dataset root (images/ + conditions/)")
    parser.add_argument("--output_dir", type=str, required=True, help="Checkpoint output directory")
    parser.add_argument("--image_size", type=int, default=256)
    parser.add_argument("--batch_size", type=int, default=8)
    parser.add_argument("--lr", type=float, default=1e-4)
    parser.add_argument("--max_steps", type=int, default=100000)
    parser.add_argument("--use_controlnet", action="store_true", help="Train with ControlNet conditioning")
    parser.add_argument("--condition_types", type=str, nargs="*", default=["edge"], help="e.g., edge skeleton dist")
    args = parser.parse_args()

    # TODO: implement dataset/dataloader (images and optional conditions)
    # TODO: load base pipeline (Stable Diffusion or Latent Diffusion) and optionally ControlNet
    # TODO: set up optimizer, LR schedule, EMA, gradient accumulation, and run training loop
    # TODO: save periodic checkpoints to output_dir

    print("[TODO] Implement diffusion training loop and checkpoints.")


if __name__ == "__main__":
    main()
