"""Compare ComfyUI's effective AdaLN dispatch tensors with direct FP32 linear."""

import torch
import torch.nn.functional as functional

from h3_blackwell_runtime.checkpoint import H3Checkpoint


capture_dir = "/artifacts/capture"
inputs = torch.load(f"{capture_dir}/input.pt", map_location="cuda", weights_only=False)
expected = torch.load(f"{capture_dir}/block0_norm1_adaln.pt", map_location="cuda", weights_only=False)
weight = expected["effective_weight"]
bias = expected["effective_bias"]
curve = H3Checkpoint("/models/minimax_h3_ref2va_pruned_nvfp4.safetensors").tensor("adaln_t_table", dtype=torch.float32)
position = inputs["timesteps"].float().clamp(0, 1) * (curve.shape[0] - 1)
lower = position.floor().long().clamp(max=curve.shape[0] - 2)
embedding = torch.lerp(curve[lower], curve[lower + 1], (position - lower).unsqueeze(1))
values = functional.linear(embedding.to(weight.dtype), weight, bias).float().view(embedding.shape[0] * 3, 6 * expected["shift"].shape[-1])
shift, scale, *_ = values.chunk(6, dim=-1)

print(f"effective_weight_dtype={weight.dtype} effective_bias_dtype={bias.dtype}")
for name, actual, reference in (("shift", shift, expected["shift"]), ("scale", scale, expected["scale"])):
    delta = (actual.float() - reference.float()).abs()
    print(f"{name} max_abs={delta.max().item():.6g} mean_abs={delta.mean().item():.6g}")