from __future__ import annotations

import statistics
import sys
import time
from pathlib import Path

ROOT = Path(__file__).resolve().parents[2]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

import torch

from kernels.triton.tiled_matmul import triton_tiled_matmul
from reference.torch_matmul import torch_matmul


def benchmark(fn, *args, warmup: int = 5, reps: int = 20) -> float:
    for _ in range(warmup):
        fn(*args)
    if args[0].is_cuda:
        torch.cuda.synchronize()
    times_ms = []
    for _ in range(reps):
        if args[0].is_cuda:
            torch.cuda.synchronize()
        start = time.perf_counter()
        fn(*args)
        if args[0].is_cuda:
            torch.cuda.synchronize()
        times_ms.append((time.perf_counter() - start) * 1e3)
    return statistics.median(times_ms)


def main() -> None:
    device = "cuda" if torch.cuda.is_available() else "cpu"
    for m, k, n in [(128, 128, 128), (512, 512, 512)]:
        a = torch.randn(m, k, device=device)
        b = torch.randn(k, n, device=device)
        ref_ms = benchmark(torch_matmul, a, b)
        print(f"torch_matmul {m}x{k}x{n}: {ref_ms:.3f} ms")
        if device == "cuda":
            try:
                triton_ms = benchmark(triton_tiled_matmul, a, b)
                print(f"triton_tiled_matmul {m}x{k}x{n}: {triton_ms:.3f} ms")
            except (NotImplementedError, RuntimeError) as exc:
                print(f"triton_tiled_matmul {m}x{k}x{n}: skipped ({exc})")


if __name__ == "__main__":
    main()