"""Validate fused H3 modulation and native NVFP4 production."""

from __future__ import annotations

import argparse
import json
import time
from pathlib import Path

import torch

from h3_blackwell_runtime.adaln import H3CurveAdaLN
from h3_blackwell_runtime.attention import rms_norm
from h3_blackwell_runtime.block import H3DiTBlock, modulate_segments
from h3_blackwell_runtime.checkpoint import H3Checkpoint
from h3_blackwell_runtime.h3_fusion import segment_index
from h3_blackwell_runtime.nvfp4_quant import (
    nvfp4_activation_scale,
    vortex_native_quantize_modulated_nvfp4,
)
from h3_blackwell_runtime.packing import H3PromptPacker
from h3_blackwell_runtime.sampler import _audio_sigma, _model_sigma, beta_sigmas
from h3_blackwell_runtime.t2v import random_av_latents


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--model-path", default="/models/minimax_h3_fl2va_pruned_nvfp4.safetensors")
    parser.add_argument("--output", type=Path, required=True)
    parser.add_argument("--block-index", type=int, default=24)
    parser.add_argument("--rows", type=int, default=2048)
    parser.add_argument("--width", type=int, default=1344)
    parser.add_argument("--height", type=int, default=768)
    parser.add_argument("--frames", type=int, default=124)
    parser.add_argument("--steps", type=int, default=12)
    parser.add_argument("--sampler-step", type=int, default=1)
    parser.add_argument("--seed", type=int, default=440420)
    parser.add_argument("--text-tokens", type=int, default=100)
    parser.add_argument("--warmup", type=int, default=3)
    parser.add_argument("--iterations", type=int, default=10)
    parser.add_argument("--device", default="cuda")
    return parser.parse_args()


def main() -> None:
    args = parse_args()
    torch.manual_seed(args.seed)
    checkpoint = H3Checkpoint(args.model_path, device=args.device)
    block = H3DiTBlock.from_checkpoint(checkpoint, args.block_index, attention_backend="sage2").eval()
    adaln = H3CurveAdaLN.from_checkpoint(
        checkpoint, f"blocks.{args.block_index}.adaln_proj",
    ).eval()
    packer = H3PromptPacker(checkpoint)
    video, audio, _ = random_av_latents(
        args.width, args.height, args.frames, args.seed, device=args.device,
    )
    sigma = beta_sigmas(args.steps, device=args.device)[args.sampler_step - 1]
    native_audio = audio.to(torch.bfloat16) * (_audio_sigma(sigma) / sigma)
    text = torch.randn(
        1, args.text_tokens, 5376, device=args.device, dtype=torch.bfloat16,
    )
    hidden, timesteps, segments, _, _, _ = packer(
        text, video, native_audio, _model_sigma(sigma),
    )
    rows = min(args.rows, hidden.shape[0])
    hidden = hidden[:rows].contiguous()
    clipped_segments = []
    for start, stop, table_row in segments:
        if start >= rows:
            break
        clipped_segments.append((start, min(stop, rows), table_row))
    shift_msa, scale_msa, *_ = (value.detach() for value in adaln(timesteps))

    with torch.inference_mode():
        normalized = rms_norm(hidden, block.norm1_weight, block.norm_eps)
        materialized = modulate_segments(
            normalized, shift_msa, scale_msa, clipped_segments,
        )
        reference_scale = nvfp4_activation_scale(materialized).float()
        import comfy_kitchen as ck
        from comfy_kitchen.tensor import TensorCoreNVFP4Layout

        reference_qdata, reference_sfa = ck.quantize_nvfp4(
            materialized,
            reference_scale,
            pad_16x=TensorCoreNVFP4Layout.get_padded_shape(tuple(materialized.shape))
            != tuple(materialized.shape),
        )
        fused_scale, fused_qdata, fused_sfa = vortex_native_quantize_modulated_nvfp4(
            normalized,
            shift_msa.contiguous(),
            scale_msa.contiguous(),
            segment_index(rows, clipped_segments, hidden.device),
        )
        torch.cuda.synchronize()

        def run_fused():
            return vortex_native_quantize_modulated_nvfp4(
                normalized,
                shift_msa.contiguous(),
                scale_msa.contiguous(),
                segment_index(rows, clipped_segments, hidden.device),
            )

        for _ in range(args.warmup):
            run_fused()
        fused_times = []
        for _ in range(args.iterations):
            torch.cuda.synchronize()
            started = time.perf_counter()
            run_fused()
            torch.cuda.synchronize()
            fused_times.append(time.perf_counter() - started)

    report = {
        "device": torch.cuda.get_device_name(),
        "block_index": args.block_index,
        "rows": rows,
        "width": hidden.shape[1],
        "scale_equal": torch.equal(fused_scale, reference_scale),
        "scale_reference": reference_scale.item(),
        "scale_fused": fused_scale.item(),
        "qdata_equal": torch.equal(fused_qdata, reference_qdata),
        "qdata_differences": torch.count_nonzero(fused_qdata != reference_qdata).item(),
        "sfa_equal": torch.equal(fused_sfa.view(torch.uint8), reference_sfa.view(torch.uint8)),
        "sfa_differences": torch.count_nonzero(
            fused_sfa.view(torch.uint8) != reference_sfa.view(torch.uint8)
        ).item(),
        "fused_producer_p50_ms": sorted(fused_times)[len(fused_times) // 2] * 1000.0,
    }
    report["equal"] = report["scale_equal"] and report["qdata_equal"] and report["sfa_equal"]
    args.output.parent.mkdir(parents=True, exist_ok=True)
    args.output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
    print(json.dumps(report, indent=2), flush=True)
    if not report["equal"]:
        raise RuntimeError("fused modulation producer is not byte-exact")


if __name__ == "__main__":
    main()