import argparse
import os

def get_token() -> str:
    tok = os.environ.get("HF_TOKEN", "")
    if tok:
        return tok
    for p in ("/content/king_hf_token.txt", "/content/hf_token.txt"):
        if os.path.isfile(p):
            return open(p).read().strip()
    try:
        from google.colab import userdata

        return userdata.get("HF_TOKEN")
    except Exception:
        pass
    raise RuntimeError("HF_TOKEN not found (env, /content/king_hf_token.txt or Colab secret 'HF_TOKEN')")


PROMPTS = [
    "a futuristic royal palace at sunset, highly detailed, 8k, golden hour, epic composition",
    "majestic arabian knight on horseback, desert landscape, cinematic lighting, photorealistic, 4k",
    "a cozy modern indoor living room with warm lighting, ultra detailed interior photography",
    "a serene outdoor garden path with blooming flowers and golden sunlight, highly detailed",
    "cosmic king on a throne of stars, nebula background, majestic, epic fantasy, concept art",
    "grand mosque at night, illuminated, reflective pool, stars, ultra detailed, architectural photography",
]


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--new_lora", default="RASHID778/king2-image")
    ap.add_argument("--old_repo", default="RASHID778/king2-image-dataset")
    ap.add_argument("--old_file", default="king2-original/pytorch_lora_weights.safetensors")
    ap.add_argument("--output_repo", default="RASHID778/king2-image")
    ap.add_argument("--steps", type=int, default=30)
    ap.add_argument("--guidance", type=float, default=7.5)
    ap.add_argument("--size", type=int, default=768)
    ap.add_argument("--prompts", default="")
    args = ap.parse_args()

    import torch
    from diffusers import AutoencoderKL, DiffusionPipeline
    from huggingface_hub import HfApi, hf_hub_download, login
    from PIL import Image, ImageDraw
    from safetensors.torch import load_file

    login(token=get_token())
    api = HfApi()
    device = "cuda"

    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
    pipe = DiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-xl-base-1.0",
        vae=vae,
        torch_dtype=torch.float16,
        variant="fp16",
        use_safetensors=True,
    ).to(device)

    prompts = [p for p in (args.prompts.splitlines() + PROMPTS) if p.strip()][:8]

    pipe.load_lora_weights(args.new_lora, adapter_name="new")
    old_path = hf_hub_download(
        args.old_repo,
        args.old_file,
        repo_type="dataset",
        local_dir="/content/king2-old",
    )
    old_sd = load_file(old_path, device="cpu")
    pipe.load_lora_weights(lora_state_dict=old_sd, adapter_name="old")
    print(f"[lora] loaded new + old adapters ({len(prompts)} prompts)", flush=True)

    neg = "blurry, low quality, distorted, ugly, bad anatomy, watermark, text"
    os.makedirs("/content/verify", exist_ok=True)
    for i, prompt in enumerate(prompts):
        grid = None
        for adapter in ("new", "old"):
            pipe.set_adapters([adapter])
            img = pipe(
                prompt,
                num_inference_steps=args.steps,
                guidance_scale=args.guidance,
                height=args.size,
                width=args.size,
                negative_prompt=neg,
            ).images[0]
            canvas = Image.new("RGB", (img.width, img.height + 40), (255, 255, 255))
            canvas.paste(img, (0, 0))
            draw = ImageDraw.Draw(canvas)
            draw.text((10, img.height + 10), f"{adapter.upper()}", fill=(0, 0, 0))
            grid = canvas if grid is None else Image.fromarray(
                __import__("numpy").concatenate(
                    [__import__("numpy").asarray(grid), __import__("numpy").asarray(canvas)],
                    axis=1,
                )
            )
        out = f"/content/verify/verify_{i:02d}.png"
        grid.save(out)
        print(f"[gen] {i} -> {out} :: {prompt[:80]}", flush=True)

    api.upload_folder(
        repo_id=args.output_repo,
        folder_path="/content/verify",
        path_in_repo="examples_v2",
        commit_message="king2-image V2 old-vs-new verification images",
    )
    print("[ok] uploaded examples_v2/ (new on top, old below)", flush=True)


if __name__ == "__main__":
    main()