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.flash_attention_fwd import triton_flash_attention_fwd
from reference.torch_attention import torch_attention
from tools.lab_extension import build_extension


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


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("--batch", type=int, default=2)
    parser.add_argument("--heads", type=int, default=8)
    parser.add_argument("--seq", type=int, default=128)
    parser.add_argument("--dim", type=int, default=64)
    parser.add_argument("--causal", action="store_true")
    args = parser.parse_args()

    q = torch.randn(args.batch, args.heads, args.seq, args.dim, device=args.device)
    k = torch.randn(args.batch, args.heads, args.seq, args.dim, device=args.device)
    v = torch.randn(args.batch, args.heads, args.seq, args.dim, device=args.device)

    if args.mode in {"all", "torch"}:
        elapsed_ms = benchmark(torch_attention, q, k, v, causal=args.causal)
        print(f"torch: {elapsed_ms:.3f} ms")

    if args.device == "cuda" and args.mode in {"all", "triton"}:
        try:
            elapsed_ms = benchmark(triton_flash_attention_fwd, q, k, v, causal=args.causal)
            print(f"triton: {elapsed_ms:.3f} ms")
        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:
                elapsed_ms = benchmark(
                    torch.ops.kernel_lab.flash_attention_fwd, q, k, v, args.causal
                )
                print(f"cuda: {elapsed_ms:.3f} ms")
            except Exception as exc:
                print(f"cuda: skipped ({exc})")


if __name__ == "__main__":
    main()