"""Alternate baseline and CuTe-QKV execution inside one loaded H3 block."""

from __future__ import annotations

import argparse
import json
import os
import time
from pathlib import Path

import torch

from h3_blackwell_runtime.adaln import H3CurveAdaLN
from h3_blackwell_runtime.block import H3DiTBlock
from h3_blackwell_runtime.checkpoint import H3Checkpoint
from h3_blackwell_runtime.packing import H3PromptPacker
from h3_blackwell_runtime.rope import h3_rope_rotation
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("--output", type=Path, required=True)
    parser.add_argument("--model-path", default="/models/minimax_h3_fl2va_pruned_nvfp4.safetensors")
    parser.add_argument("--block-index", type=int, required=True)
    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("--attention", default="sage2")
    parser.add_argument(
        "--feature", choices=("qkv_ring", "modulate_fusion", "swiglu_fusion"), default="qkv_ring",
    )
    parser.add_argument("--warmup", type=int, default=2)
    parser.add_argument("--iterations", type=int, default=10)
    parser.add_argument("--device", default="cuda")
    return parser.parse_args()


def sync() -> None:
    torch.cuda.synchronize()


def summarize(values: list[float]) -> dict[str, float]:
    ordered = sorted(values)
    middle = len(ordered) // 2
    median = (
        ordered[middle]
        if len(ordered) % 2
        else (ordered[middle - 1] + ordered[middle]) / 2
    )
    return {
        "mean_s": sum(values) / len(values),
        "p50_s": median,
        "min_s": ordered[0],
        "max_s": ordered[-1],
    }


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=args.attention,
    ).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,
    )
    sigmas = beta_sigmas(args.steps, device=args.device)
    sigma = sigmas[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, positions, _, _ = packer(
        text, video, native_audio, _model_sigma(sigma),
    )
    rotation = h3_rope_rotation(
        positions.to(args.device),
        checkpoint.tensor("rope.inv_freq", dtype=torch.float32),
        hidden.dtype,
    )
    adaln_values = tuple(value.detach() for value in adaln(timesteps))

    def run(enabled: bool):
        if args.feature == "modulate_fusion":
            block.fused_nvfp4_modulation = enabled
        elif args.feature == "swiglu_fusion":
            block.mlp.fused_nvfp4_swiglu = enabled
        else:
            if enabled:
                os.environ["H3_CUTE_QKV_RING"] = "1"
            else:
                os.environ.pop("H3_CUTE_QKV_RING", None)
        return block(hidden, rotation, *adaln_values, segments)

    with torch.inference_mode():
        reference = run(False)
        candidate = run(True)
        sync()
        delta = candidate.float() - reference.float()
        for _ in range(args.warmup):
            run(False)
            run(True)
        sync()
        baseline_times = []
        candidate_times = []
        last_reference = reference
        last_candidate = candidate
        for _ in range(args.iterations):
            sync()
            started = time.perf_counter()
            last_reference = run(False)
            sync()
            baseline_times.append(time.perf_counter() - started)

            sync()
            started = time.perf_counter()
            last_candidate = run(True)
            sync()
            candidate_times.append(time.perf_counter() - started)

    baseline = summarize(baseline_times)
    candidate_timing = summarize(candidate_times)
    report = {
        "device": torch.cuda.get_device_name(),
        "block_index": args.block_index,
        "feature": args.feature,
        "hidden_shape": list(hidden.shape),
        "iterations": args.iterations,
        "equal": torch.equal(reference, candidate),
        "max_abs": delta.abs().max().item(),
        "mean_abs": delta.abs().mean().item(),
        "reference_checksum": last_reference.float().sum().item(),
        "candidate_checksum": last_candidate.float().sum().item(),
        "baseline": baseline,
        "candidate": candidate_timing,
        "p50_improvement_percent": (
            1.0 - candidate_timing["p50_s"] / baseline["p50_s"]
        ) * 100.0,
    }
    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 __name__ == "__main__":
    main()