from __future__ import annotations

import argparse
import statistics
import sys
import time
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

import torch

from kernels.triton.vector_add import triton_vector_add
from reference.torch_vector_add import torch_vector_add
from tools.lab_extension import build_extension


def benchmark(fn, *args, warmup: int = 5, reps: int = 30) -> 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 report(name: str, elapsed_ms: float, x: torch.Tensor) -> None:
    bytes_moved = 3 * x.numel() * x.element_size()
    gbps = bytes_moved / (elapsed_ms * 1e-3) / 1e9
    print(f"{name}: {elapsed_ms:.3f} ms | effective bandwidth {gbps:.2f} GB/s")


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
    parser.add_argument("--mode", choices=["all", "torch", "triton", "cuda"], default="all")
    parser.add_argument("--numel", type=int, default=1 << 24)
    args = parser.parse_args()

    x = torch.randn(args.numel, device=args.device)
    y = torch.randn(args.numel, device=args.device)

    if args.mode in {"all", "torch"}:
        report("torch", benchmark(torch_vector_add, x, y), x)

    if args.device == "cuda" and args.mode in {"all", "triton"}:
        try:
            report("triton", benchmark(triton_vector_add, x, y), x)
        except (NotImplementedError, RuntimeError) as exc:
            print(f"triton: skipped ({exc})")

    if args.device == "cuda" and args.mode in {"all", "cuda"}:
        ext = build_extension(verbose=False)
        if ext is None or not hasattr(torch.ops, "kernel_lab"):
            print("cuda: skipped (extension unavailable)")
        else:
            try:
                report("cuda", benchmark(torch.ops.kernel_lab.vector_add, x, y), x)
            except Exception as exc:
                print(f"cuda: skipped ({exc})")


if __name__ == "__main__":
    main()