"""Compare direct FL2VA H3 and sampler boundaries with captured Comfy state."""

import glob

import torch

from h3_blackwell_runtime.checkpoint import H3Checkpoint
from h3_blackwell_runtime.denoiser import H3PackedDenoiser
from h3_blackwell_runtime.packing import unpatchify_video
from h3_blackwell_runtime.sampler import res_multistep_update


root = "/artifacts/fl2va-sampler-reference"
capture = "/artifacts/capture"
initial = torch.load(f"{root}/initial.pt", map_location="cuda", weights_only=False)
steps = [torch.load(path, map_location="cuda", weights_only=False) for path in sorted(glob.glob(f"{root}/step_*.pt"))]
sigmas = initial["sigmas"].to("cuda")
model = H3PackedDenoiser.from_checkpoint(
    H3Checkpoint("/models/minimax_h3_fl2va_pruned_nvfp4.safetensors"), attention_backend="sage2"
).eval()
video_shape = (1, 24, 7, 12, 20)
video_count = torch.tensor(video_shape).prod().item()
old_video = old_sigma = None

for index, reference in enumerate(steps):
    h3_input = torch.load(f"{capture}/input_{index:02d}.pt", map_location="cuda", weights_only=False)
    h3_output = torch.load(f"{capture}/output_{index:02d}.pt", map_location="cuda", weights_only=False)
    with torch.inference_mode():
        raw_video, _ = model(
            h3_input["hidden"].to("cuda"),
            h3_input["timesteps"].to("cuda"),
            h3_input["position_ids"].to("cuda"),
            h3_input["segments"],
            h3_output["video_segment"],
            h3_output["audio_segment"],
        )
    video_out = unpatchify_video(raw_video, h3_output["video"].shape[2], h3_output["video"].shape[3], h3_output["video"].shape[4])
    h3_delta = (video_out.float() - h3_output["video"].to("cuda").float()).abs()

    state_video = reference["x"].to("cuda").reshape(-1)[:video_count].reshape(video_shape)
    reference_denoised = reference["denoised"].to("cuda").reshape(-1)[:video_count].reshape(video_shape)
    converted_denoised = state_video + sigmas[index] * h3_output["video"].to("cuda")
    denoised_delta = (converted_denoised.float() - reference_denoised.float()).abs()

    if index + 1 < len(steps):
        previous_sigma = sigmas[index - 1] if index else None
        updated = res_multistep_update(state_video, reference_denoised, sigmas[index], sigmas[index + 1], old_video, old_sigma, previous_sigma)
        next_video = steps[index + 1]["x"].to("cuda").reshape(-1)[:video_count].reshape(video_shape)
        update_delta = (updated.float() - next_video.float()).abs()
        update_text = f"update_mean={update_delta.mean().item():.6g} update_max={update_delta.max().item():.6g}"
    else:
        update_text = "update_mean=final-unobserved update_max=final-unobserved"

    print(
        f"step={index:02d} "
        f"h3_mean={h3_delta.mean().item():.6g} h3_max={h3_delta.max().item():.6g} "
        f"denoised_mean={denoised_delta.mean().item():.6g} denoised_max={denoised_delta.max().item():.6g} "
        f"{update_text}"
    )
    old_video, old_sigma = reference_denoised, sigmas[index + 1]