"""Compare direct and upstream full VAE decode on a saved latent."""

import argparse
import gc
import sys
from pathlib import Path

import torch
from safetensors.torch import load_file

from h3_blackwell_runtime.vae_decoder import MiniMaxH3VideoVAE as DirectVAE


parser = argparse.ArgumentParser()
parser.add_argument("--latent", type=Path, required=True)
parser.add_argument("--frames", type=int)
parser.add_argument("--comfy-path", default="/opt/ComfyUI")
args = parser.parse_args()

sys.path.insert(0, args.comfy_path)
from h3_blackwell_runtime.upstream_vae import MiniMaxH3VideoVAE as UpstreamVAE  # noqa: E402


def pixelize(decoded: torch.Tensor, frames: int) -> torch.Tensor:
    return ((decoded[:, :, :frames].clamp(-1, 1) + 1) * 127.5).to(torch.uint8).cpu()


def summarize(a: torch.Tensor, b: torch.Tensor) -> None:
    diff = (a.float() - b.float()).abs()
    frame_rows = []
    for index in range(diff.shape[2]):
        frame = diff[:, :, index]
        frame_rows.append((index, float(frame.max()), float(frame.mean()), float(torch.quantile(frame.flatten(), 0.99))))
    print({
        "shape": tuple(a.shape),
        "max": float(diff.max()),
        "mean": float(diff.mean()),
        "top_max": sorted(frame_rows, key=lambda row: row[1], reverse=True)[:12],
        "top_mean": sorted(frame_rows, key=lambda row: row[2], reverse=True)[:12],
    }, flush=True)


state = torch.load(args.latent, map_location="cuda", weights_only=False)
latent = state["latent"].to("cuda") if isinstance(state, dict) else state.to("cuda")
frames = args.frames or int(state.get("frames", latent.shape[2] * 4))

with torch.inference_mode():
    direct = DirectVAE.from_safetensors("/vae/minimax_h3_video_vae_fp16.safetensors", device="cuda").eval()
    direct_pixels = pixelize(direct.decode(latent.to(next(direct.parameters()).dtype)), frames)
    del direct
    torch.cuda.empty_cache()
    gc.collect()

    upstream = UpstreamVAE().to("cuda", dtype=torch.float16).eval()
    upstream.load_state_dict(load_file("/vae/minimax_h3_video_vae_fp16.safetensors", device="cuda"), strict=True)
    upstream_pixels = pixelize(upstream.decode(latent.to(next(upstream.parameters()).dtype)), frames)

summarize(direct_pixels, upstream_pixels)