"""Validate the opt-in Nvfp4Linear CuTe QKV runtime dispatch."""

from __future__ import annotations

import argparse
import json
import os
from pathlib import Path

import torch

from profile_nvfp4_linear import representative_inputs


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--output", type=Path, required=True)
    parser.add_argument("--rows", type=int, default=2048)
    parser.add_argument("--warmup", type=int, default=3)
    parser.add_argument("--iterations", type=int, default=10)
    parser.add_argument("--model-path", default="/models/minimax_h3_fl2va_pruned_nvfp4.safetensors")
    parser.add_argument("--block-index", type=int, default=24)
    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("--device", default="cuda")
    return parser.parse_args()


def measure(fn, warmup: int, iterations: int) -> float:
    for _ in range(warmup):
        fn()
    torch.cuda.synchronize()
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    start.record()
    for _ in range(iterations):
        fn()
    end.record()
    end.synchronize()
    return start.elapsed_time(end) / iterations


def main() -> None:
    args = parse_args()
    block, inputs, metadata = representative_inputs(args)
    linear = block.attention.qkv_proj
    x = inputs["attn_qkv_proj"][: args.rows].contiguous()
    if linear.role != "h3_attn_qkv":
        raise RuntimeError(f"Expected h3_attn_qkv role, got {linear.role!r}")

    os.environ.pop("H3_CUTE_QKV_RING", None)
    with torch.inference_mode():
        reference = linear(x)
    os.environ["H3_CUTE_QKV_RING"] = "1"
    with torch.inference_mode():
        candidate = linear(x)
    torch.cuda.synchronize()
    delta = candidate.float() - reference.float()

    with torch.inference_mode():
        ring_ms = measure(lambda: linear(x), args.warmup, args.iterations)
        os.environ.pop("H3_CUTE_QKV_RING", None)
        reference_ms = measure(lambda: linear(x), args.warmup, args.iterations)

    report = {
        "device": torch.cuda.get_device_name(),
        "metadata": metadata,
        "block_index": args.block_index,
        "rows": args.rows,
        "role": linear.role,
        "equal": torch.equal(candidate, reference),
        "max_abs": delta.abs().max().item(),
        "mean_abs": delta.abs().mean().item(),
        "ring_ms": ring_ms,
        "reference_ms": reference_ms,
        "improvement_percent": (1.0 - ring_ms / reference_ms) * 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()