"""Generate a minimal direct, video-only H3 T2V preview without ComfyUI."""

import argparse
from pathlib import Path
import subprocess
import warnings

warnings.filterwarnings("ignore", message="Found GPU0 NVIDIA GB10 which is of cuda capability 12.1.*", category=UserWarning)

import torch

from h3_blackwell_runtime.checkpoint import H3Checkpoint
from h3_blackwell_runtime.denoiser import H3PackedDenoiser
from h3_blackwell_runtime.packing import H3PromptPacker
from h3_blackwell_runtime.qwen3vl_text import Qwen3VLPromptConditioner
from h3_blackwell_runtime.sampler import sample_video_res_multistep
from h3_blackwell_runtime.t2v import random_av_latents
from h3_blackwell_runtime.token_refiner import H3TokenRefiner
from h3_blackwell_runtime.vae_decoder import MiniMaxH3VideoVAE


parser = argparse.ArgumentParser()
parser.add_argument("--prompt", default="A brass-and-paper dragon flies above a rain-washed old city at blue hour.")
parser.add_argument("--output", type=Path, default=Path("/output/direct-h3-preview.mp4"))
parser.add_argument("--width", type=int, default=320)
parser.add_argument("--height", type=int, default=192)
parser.add_argument("--frames", type=int, default=22)
parser.add_argument("--steps", type=int, default=12)
parser.add_argument("--seed", type=int, default=440204)
parser.add_argument("--attention", choices=("sage2", "sdpa", "sage3"), default="sage2")
parser.add_argument("--model-timesteps-capture", type=Path, help="Directory containing captured input_XX.pt H3 timesteps for strict parity checks.")
args = parser.parse_args()

checkpoint = H3Checkpoint("/models/minimax_h3_fl2va_pruned_nvfp4.safetensors")
conditioner = Qwen3VLPromptConditioner(
    "/text-encoders/qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors",
    "/opt/h3-blackwell-runtime/src/h3_blackwell_runtime/qwen25_tokenizer",
)
video, audio, frames = random_av_latents(args.width, args.height, args.frames, args.seed)
model = H3PackedDenoiser.from_checkpoint(checkpoint, attention_backend=args.attention).eval()
text = H3TokenRefiner(checkpoint, attention_backend=args.attention)(conditioner(args.prompt))
model_timesteps = None
if args.model_timesteps_capture is not None:
    model_timesteps = [
        torch.load(args.model_timesteps_capture / f"input_{index:02d}.pt", map_location="cuda", weights_only=False)["timesteps"]
        for index in range(args.steps)
    ]
latent = sample_video_res_multistep(model, H3PromptPacker(checkpoint), text, video, audio, steps=args.steps, model_timesteps=model_timesteps)
vae = MiniMaxH3VideoVAE.from_safetensors("/vae/minimax_h3_video_vae_fp16.safetensors", device="cuda").eval()
pixels = vae.decode(latent.to(next(vae.parameters()).dtype))[:, :, :frames]
pixels = ((pixels[0].permute(1, 2, 3, 0).clamp(-1, 1) + 1) * 127.5).to(torch.uint8).cpu()
args.output.parent.mkdir(parents=True, exist_ok=True)
raw = args.output.with_suffix(".rgb")
pixels.numpy().tofile(raw)
subprocess.run(["ffmpeg", "-y", "-f", "rawvideo", "-pixel_format", "rgb24", "-video_size", f"{pixels.shape[2]}x{pixels.shape[1]}", "-framerate", "24", "-i", str(raw), "-an", "-c:v", "libx264", "-pix_fmt", "yuv420p", str(args.output)], check=True)
raw.unlink()
print({"output": str(args.output), "frames": frames, "shape": tuple(pixels.shape)})