"""Evaluate model performance"""

import time
import torch
import numpy as np
from python.model.nnue_linear import LinearEval


def benchmark(model: LinearEval, samples: int = 1000) -> dict:
    """
    Benchmark inference speed.

    Returns:
        dict with speed metrics
    """
    model.eval()
    x = torch.randn(samples, 61072)

    start = time.time()
    with torch.no_grad():
        for _ in range(samples):
            _ = model(x)
    end = time.time()

    return {
        "samples": samples,
        "time_seconds": end - start,
        "ms_per_sample": (end - start) / samples * 1000,
    }