// Copyright 2012-2016 The Rust Project Developers. See the COPYRIGHT
// file at the top-level directory of this distribution and at
// http://rust-lang.org/COPYRIGHT.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.

// Benchmarking code is taken from the bencher crate in the Android tree at
// https://android.googlesource.com/platform/external/rust/crates/bencher.
// Some modifications have been made to adapt it for use in Rust test TAs
// generated by the unittest-rust library.

use core::cmp;
use core::time::Duration;

use crate::stats;

/// Manager of the benchmarking runs.
///
/// This is fed into functions marked with `#[bench]` to allow for
/// set-up & tear-down before running a piece of code repeatedly via a
/// call to `iter`.
#[derive(Copy, Clone)]
pub struct Bencher {
    iterations: u64,
    dur: Duration,
    pub bytes: u64,
}

// TODO(b/357115387): Replace with std::time::Instant::now().
fn get_time_ns() -> u64 {
    let mut secure_time_ns = 0i64;
    // Safety: external syscall gets valid raw pointer to a `i64` as defined
    // in the Trusty syscall table at
    // https://android.googlesource.com/trusty/lk/trusty/+/refs/heads/main/lib/trusty/include/syscall_table.h
    unsafe { trusty_sys::gettime(0, 0, &mut secure_time_ns) };
    secure_time_ns as u64
}

#[derive(Clone, PartialEq)]
pub struct BenchSamples {
    pub ns_iter_summ: stats::Summary,
    mb_s: usize,
}

impl Bencher {
    /// Callback for benchmark functions to run in their body.
    pub fn iter<T, F>(&mut self, mut inner: F)
    where
        F: FnMut() -> T,
    {
        let start = get_time_ns();
        let k = self.iterations;
        for _ in 0..k {
            core::hint::black_box(inner());
        }
        self.dur = Duration::from_nanos(get_time_ns() - start);
    }

    pub fn ns_elapsed(&mut self) -> u64 {
        self.dur.as_secs() * 1_000_000_000 + (self.dur.subsec_nanos() as u64)
    }

    pub fn ns_per_iter(&mut self) -> u64 {
        if self.iterations == 0 {
            0
        } else {
            self.ns_elapsed() / cmp::max(self.iterations, 1)
        }
    }

    pub fn bench_n<F>(&mut self, n: u64, f: F)
    where
        F: FnOnce(&mut Bencher),
    {
        self.iterations = n;
        f(self);
    }

    // This is a more statistics-driven benchmark algorithm
    pub fn auto_bench<F>(&mut self, mut f: F) -> stats::Summary
    where
        F: FnMut(&mut Bencher),
    {
        // Initial bench run to get ballpark figure.
        let mut n = 1;
        self.bench_n(n, |x| f(x));
        let cold = self.ns_per_iter();

        // Try to estimate iter count for 1ms falling back to 1m
        // iterations if first run took < 1ns.
        if cold == 0 {
            n = 1_000_000;
        } else {
            n = 1_000_000 / cmp::max(cold, 1);
        }
        // if the first run took more than 1ms we don't want to just
        // be left doing 0 iterations on every loop. The unfortunate
        // side effect of not being able to do as many runs is
        // automatically handled by the statistical analysis below
        // (i.e. larger error bars).
        if n == 0 {
            n = 1;
        }

        let mut total_run = Duration::new(0, 0);
        let samples: &mut [f64] = &mut [0.0_f64; 50];
        loop {
            let loop_start = get_time_ns();

            for p in &mut *samples {
                self.bench_n(n, |x| f(x));
                *p = self.ns_per_iter() as f64;
            }

            stats::winsorize(samples, 5.0);
            let summ = stats::Summary::new(cold as f64, samples);

            for p in &mut *samples {
                self.bench_n(5 * n, |x| f(x));
                *p = self.ns_per_iter() as f64;
            }

            stats::winsorize(samples, 5.0);
            let summ5 = stats::Summary::new(cold as f64, samples);
            let loop_run = Duration::from_nanos(get_time_ns() - loop_start);

            // If we've run for 100ms and seem to have converged to a
            // stable median.
            if loop_run > Duration::from_millis(100)
                && summ.median_abs_dev_pct < 1.0
                && summ.median - summ5.median < summ5.median_abs_dev
            {
                return summ5;
            }

            total_run += loop_run;
            // Longest we ever run for is 3s.
            if total_run > Duration::from_secs(3) {
                return summ5;
            }

            // If we overflow here just return the results so far. We check a
            // multiplier of 10 because we're about to multiply by 2 and the
            // next iteration of the loop will also multiply by 5 (to calculate
            // the summ5 result)
            n = match n.checked_mul(10) {
                Some(_) => n * 2,
                None => return summ5,
            };
        }
    }
}

pub fn benchmark<F>(f: F) -> BenchSamples
where
    F: FnMut(&mut Bencher),
{
    let mut bs = Bencher { iterations: 0, dur: Duration::new(0, 0), bytes: 0 };
    let ns_iter_summ = bs.auto_bench(f);
    let ns_iter = cmp::max(ns_iter_summ.median as u64, 1);
    let mb_s = bs.bytes * 1000 / ns_iter;
    BenchSamples { ns_iter_summ: ns_iter_summ, mb_s: mb_s as usize }
}