"""
inference.py - CLI inference script.

Usage:
    python scripts/inference.py \
        --image input.jpg \
        --prompt "make it look like sunset" \
        --checkpoint checkpoints/diffusion_final.pt \
        --vae-checkpoint checkpoints/vae_final.pt \
        --output outputs/edited.png
"""

import argparse
import os
import sys

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import torch
from PIL import Image
from omegaconf import OmegaConf

from model.pipeline import EditPipeline


def main():
    parser = argparse.ArgumentParser(description="Edit an image with a text prompt")
    parser.add_argument("--image", type=str, required=True, help="Input image path")
    parser.add_argument("--prompt", type=str, required=True, help="Edit instruction")
    parser.add_argument("--checkpoint", type=str, required=True, help="Diffusion model checkpoint")
    parser.add_argument("--vae-checkpoint", type=str, default=None, help="VAE checkpoint (if separate)")
    parser.add_argument("--config", type=str, default="config/default.yaml")
    parser.add_argument("--output", type=str, default="outputs/edited.png")
    parser.add_argument("--steps", type=int, default=50, help="DDIM sampling steps")
    parser.add_argument("--text-scale", type=float, default=7.5, help="Text guidance scale")
    parser.add_argument("--image-scale", type=float, default=1.5, help="Image guidance scale")
    parser.add_argument("--seed", type=int, default=None, help="Random seed")
    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
    args = parser.parse_args()

    base_config = OmegaConf.load("config/default.yaml")
    override_config = OmegaConf.load(args.config)
    config = OmegaConf.to_container(OmegaConf.merge(base_config, override_config), resolve=True)

    print(f"Loading model from {args.checkpoint}...")
    pipeline = EditPipeline.from_checkpoint(
        args.checkpoint,
        vae_checkpoint_path=args.vae_checkpoint,
        config=config,
        device=args.device,
    )

    image = Image.open(args.image).convert("RGB")
    print(f"Input: {args.image} ({image.size})")
    print(f"Prompt: {args.prompt}")
    print(f"Steps: {args.steps}, Text scale: {args.text_scale}, Image scale: {args.image_scale}")

    edited = pipeline.edit(
        image=image,
        prompt=args.prompt,
        num_steps=args.steps,
        text_guidance_scale=args.text_scale,
        image_guidance_scale=args.image_scale,
        seed=args.seed,
    )

    os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True)
    edited.save(args.output)
    print(f"Saved edited image: {args.output}")


if __name__ == "__main__":
    main()