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    // Copyright 2017 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.
    //
    // Based on jitterentropy-library, http://www.chronox.de/jent.html.
    // Copyright Stephan Mueller <smueller@chronox.de>, 2014 - 2017.
    //
    // With permission from Stephan Mueller to relicense the Rust translation under
    // the MIT license.

    //! Non-physical true random number generator based on timing jitter.

    use Rng;

    use core::{fmt, mem, ptr};
    #[cfg(feature="std")]
    use std::sync::atomic::{AtomicUsize, ATOMIC_USIZE_INIT, Ordering};

    const MEMORY_BLOCKS: usize = 64;
    const MEMORY_BLOCKSIZE: usize = 32;
    const MEMORY_SIZE: usize = MEMORY_BLOCKS * MEMORY_BLOCKSIZE;

    /// A true random number generator based on jitter in the CPU execution time,
    /// and jitter in memory access time.
    ///
    /// This is a true random number generator, as opposed to pseudo-random
    /// generators. Random numbers generated by `JitterRng` can be seen as fresh
    /// entropy. A consequence is that is orders of magnitude slower than `OsRng`
    /// and PRNGs (about 10^3 .. 10^6 slower).
    ///
    /// There are very few situations where using this RNG is appropriate. Only very
    /// few applications require true entropy. A normal PRNG can be statistically
    /// indistinguishable, and a cryptographic PRNG should also be as impossible to
    /// predict.
    ///
    /// Use of `JitterRng` is recommended for initializing cryptographic PRNGs when
    /// `OsRng` is not available.
    ///
    /// This implementation is based on
    /// [Jitterentropy](http://www.chronox.de/jent.html) version 2.1.0.
    //
    // Note: the C implementation relies on being compiled without optimizations.
    // This implementation goes through lengths to make the compiler not optimise
    // out what is technically dead code, but that does influence timing jitter.
    pub struct JitterRng {
        data: u64, // Actual random number
        // Number of rounds to run the entropy collector per 64 bits
        rounds: u32,
        // Timer and previous time stamp, used by `measure_jitter`
        timer: fn() -> u64,
        prev_time: u64,
        // Deltas used for the stuck test
        last_delta: i64,
        last_delta2: i64,
        // Memory for the Memory Access noise source
        mem_prev_index: usize,
        mem: [u8; MEMORY_SIZE],
        // Make `next_u32` not waste 32 bits
        data_remaining: Option<u32>,
    }

    // Custom Debug implementation that does not expose the internal state
    impl fmt::Debug for JitterRng {
        fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
            write!(f, "JitterRng {{}}")
        }
    }

    /// An error that can occur when `test_timer` fails.
    #[derive(Debug, Clone, PartialEq, Eq)]
    pub enum TimerError {
        /// No timer available.
        NoTimer,
        /// Timer too coarse to use as an entropy source.
        CoarseTimer,
        /// Timer is not monotonically increasing.
        NotMonotonic,
        /// Variations of deltas of time too small.
        TinyVariantions,
        /// Too many stuck results (indicating no added entropy).
        TooManyStuck,
        #[doc(hidden)]
        __Nonexhaustive,
    }

    impl TimerError {
        fn description(&self) -> &'static str {
            match *self {
                TimerError::NoTimer => "no timer available",
                TimerError::CoarseTimer => "coarse timer",
                TimerError::NotMonotonic => "timer not monotonic",
                TimerError::TinyVariantions => "time delta variations too small",
                TimerError::TooManyStuck => "too many stuck results",
                TimerError::__Nonexhaustive => unreachable!(),
            }
        }
    }

    impl fmt::Display for TimerError {
        fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
            write!(f, "{}", self.description())
        }
    }

    #[cfg(feature="std")]
    impl ::std::error::Error for TimerError {
        fn description(&self) -> &str {
            self.description()
        }
    }

    // Initialise to zero; must be positive
    #[cfg(feature="std")]
    static JITTER_ROUNDS: AtomicUsize = ATOMIC_USIZE_INIT;

    impl JitterRng {
        /// Create a new `JitterRng`.
        /// Makes use of `std::time` for a timer.
        ///
        /// During initialization CPU execution timing jitter is measured a few
        /// hundred times. If this does not pass basic quality tests, an error is
        /// returned. The test result is cached to make subsequent calls faster.
        #[cfg(feature="std")]
        pub fn new() -> Result<JitterRng, TimerError> {
            let mut ec = JitterRng::new_with_timer(platform::get_nstime);
            let mut rounds = JITTER_ROUNDS.load(Ordering::Relaxed) as u32;
            if rounds == 0 {
                // No result yet: run test.
                // This allows the timer test to run multiple times; we don't care.
                rounds = ec.test_timer()?;
                JITTER_ROUNDS.store(rounds as usize, Ordering::Relaxed);
            }
            ec.set_rounds(rounds);
            Ok(ec)
        }

        /// Create a new `JitterRng`.
        /// A custom timer can be supplied, making it possible to use `JitterRng` in
        /// `no_std` environments.
        ///
        /// The timer must have nanosecond precision.
        ///
        /// This method is more low-level than `new()`. It is the responsibility of
        /// the caller to run `test_timer` before using any numbers generated with
        /// `JitterRng`, and optionally call `set_rounds()`.
        pub fn new_with_timer(timer: fn() -> u64) -> JitterRng {
            let mut ec = JitterRng {
                data: 0,
                rounds: 64,
                timer: timer,
                prev_time: 0,
                last_delta: 0,
                last_delta2: 0,
                mem_prev_index: 0,
                mem: [0; MEMORY_SIZE],
                data_remaining: None,
            };

            // Fill `data`, `prev_time`, `last_delta` and `last_delta2` with
            // non-zero values.
            ec.prev_time = timer();
            ec.gen_entropy();

            // Do a single read from `self.mem` to make sure the Memory Access noise
            // source is not optimised out.
            // Note: this read is important, it effects optimisations for the entire
            // module!
            black_box(ec.mem[0]);

            ec
        }

        /// Configures how many rounds are used to generate each 64-bit value.
        /// This must be greater than zero, and has a big impact on performance
        /// and output quality.
        ///
        /// `new_with_timer` conservatively uses 64 rounds, but often less rounds
        /// can be used. The `test_timer()` function returns the minimum number of
        /// rounds required for full strength (platform dependent), so one may use
        /// `rng.set_rounds(rng.test_timer()?);` or cache the value.
        pub fn set_rounds(&mut self, rounds: u32) {
            assert!(rounds > 0);
            self.rounds = rounds;
        }

        // Calculate a random loop count used for the next round of an entropy
        // collection, based on bits from a fresh value from the timer.
        //
        // The timer is folded to produce a number that contains at most `n_bits`
        // bits.
        //
        // Note: A constant should be added to the resulting random loop count to
        // prevent loops that run 0 times.
        #[inline(never)]
        fn random_loop_cnt(&mut self, n_bits: u32) -> u32 {
            let mut rounds = 0;

            let mut time = (self.timer)();
            // Mix with the current state of the random number balance the random
            // loop counter a bit more.
            time ^= self.data;

            // We fold the time value as much as possible to ensure that as many
            // bits of the time stamp are included as possible.
            let folds = (64 + n_bits - 1) / n_bits;
            let mask = (1 << n_bits) - 1;
            for _ in 0..folds {
                rounds ^= time & mask;
                time = time >> n_bits;
            }

            rounds as u32
        }

        // CPU jitter noise source
        // Noise source based on the CPU execution time jitter
        //
        // This function injects the individual bits of the time value into the
        // entropy pool using an LFSR.
        //
        // The code is deliberately inefficient with respect to the bit shifting.
        // This function not only acts as folding operation, but this function's
        // execution is used to measure the CPU execution time jitter. Any change to
        // the loop in this function implies that careful retesting must be done.
        #[inline(never)]
        fn lfsr_time(&mut self, time: u64, var_rounds: bool) {
            fn lfsr(mut data: u64, time: u64) -> u64{
                for i in 1..65 {
                    let mut tmp = time << (64 - i);
                    tmp = tmp >> (64 - 1);

                    // Fibonacci LSFR with polynomial of
                    // x^64 + x^61 + x^56 + x^31 + x^28 + x^23 + 1 which is
                    // primitive according to
                    // http://poincare.matf.bg.ac.rs/~ezivkovm/publications/primpol1.pdf
                    // (the shift values are the polynomial values minus one
                    // due to counting bits from 0 to 63). As the current
                    // position is always the LSB, the polynomial only needs
                    // to shift data in from the left without wrap.
                    data ^= tmp;
                    data ^= (data >> 63) & 1;
                    data ^= (data >> 60) & 1;
                    data ^= (data >> 55) & 1;
                    data ^= (data >> 30) & 1;
                    data ^= (data >> 27) & 1;
                    data ^= (data >> 22) & 1;
                    data = data.rotate_left(1);
                }
                data
            }

            // Note: in the reference implementation only the last round effects
            // `self.data`, all the other results are ignored. To make sure the
            // other rounds are not optimised out, we first run all but the last
            // round on a throw-away value instead of the real `self.data`.
            let mut lfsr_loop_cnt = 0;
            if var_rounds { lfsr_loop_cnt = self.random_loop_cnt(4) };

            let mut throw_away: u64 = 0;
            for _ in 0..lfsr_loop_cnt {
                throw_away = lfsr(throw_away, time);
            }
            black_box(throw_away);

            self.data = lfsr(self.data, time);
        }

        // Memory Access noise source
        // This is a noise source based on variations in memory access times
        //
        // This function performs memory accesses which will add to the timing
        // variations due to an unknown amount of CPU wait states that need to be
        // added when accessing memory. The memory size should be larger than the L1
        // caches as outlined in the documentation and the associated testing.
        //
        // The L1 cache has a very high bandwidth, albeit its access rate is usually
        // slower than accessing CPU registers. Therefore, L1 accesses only add
        // minimal variations as the CPU has hardly to wait. Starting with L2,
        // significant variations are added because L2 typically does not belong to
        // the CPU any more and therefore a wider range of CPU wait states is
        // necessary for accesses. L3 and real memory accesses have even a wider
        // range of wait states. However, to reliably access either L3 or memory,
        // the `self.mem` memory must be quite large which is usually not desirable.
        #[inline(never)]
        fn memaccess(&mut self, var_rounds: bool) {
            let mut acc_loop_cnt = 128;
            if var_rounds { acc_loop_cnt += self.random_loop_cnt(4) };

            let mut index = self.mem_prev_index;
            for _ in 0..acc_loop_cnt {
                // Addition of memblocksize - 1 to index with wrap around logic to
                // ensure that every memory location is hit evenly.
                // The modulus also allows the compiler to remove the indexing
                // bounds check.
                index = (index + MEMORY_BLOCKSIZE - 1) % MEMORY_SIZE;

                // memory access: just add 1 to one byte
                // memory access implies read from and write to memory location
                let tmp = self.mem[index];
                self.mem[index] = tmp.wrapping_add(1);
            }
            self.mem_prev_index = index;
        }

        // Stuck test by checking the:
        // - 1st derivation of the jitter measurement (time delta)
        // - 2nd derivation of the jitter measurement (delta of time deltas)
        // - 3rd derivation of the jitter measurement (delta of delta of time
        //   deltas)
        //
        // All values must always be non-zero.
        // This test is a heuristic to see whether the last measurement holds
        // entropy.
        fn stuck(&mut self, current_delta: i64) -> bool {
            let delta2 = self.last_delta - current_delta;
            let delta3 = delta2 - self.last_delta2;

            self.last_delta = current_delta;
            self.last_delta2 = delta2;

            current_delta == 0 || delta2 == 0 || delta3 == 0
        }

        // This is the heart of the entropy generation: calculate time deltas and
        // use the CPU jitter in the time deltas. The jitter is injected into the
        // entropy pool.
        //
        // Ensure that `self.prev_time` is primed before using the output of this
        // function. This can be done by calling this function and not using its
        // result.
        fn measure_jitter(&mut self) -> Option<()> {
            // Invoke one noise source before time measurement to add variations
            self.memaccess(true);

            // Get time stamp and calculate time delta to previous
            // invocation to measure the timing variations
            let time = (self.timer)();
            // Note: wrapping_sub combined with a cast to `i64` generates a correct
            // delta, even in the unlikely case this is a timer that is not strictly
            // monotonic.
            let current_delta = time.wrapping_sub(self.prev_time) as i64;
            self.prev_time = time;

            // Call the next noise source which also injects the data
            self.lfsr_time(current_delta as u64, true);

            // Check whether we have a stuck measurement (i.e. does the last
            // measurement holds entropy?).
            if self.stuck(current_delta) { return None };

            // Rotate the data buffer by a prime number (any odd number would
            // do) to ensure that every bit position of the input time stamp
            // has an even chance of being merged with a bit position in the
            // entropy pool. We do not use one here as the adjacent bits in
            // successive time deltas may have some form of dependency. The
            // chosen value of 7 implies that the low 7 bits of the next
            // time delta value is concatenated with the current time delta.
            self.data = self.data.rotate_left(7);

            Some(())
        }

        // Shuffle the pool a bit by mixing some value with a bijective function
        // (XOR) into the pool.
        //
        // The function generates a mixer value that depends on the bits set and
        // the location of the set bits in the random number generated by the
        // entropy source. Therefore, based on the generated random number, this
        // mixer value can have 2^64 different values. That mixer value is
        // initialized with the first two SHA-1 constants. After obtaining the
        // mixer value, it is XORed into the random number.
        //
        // The mixer value is not assumed to contain any entropy. But due to the
        // XOR operation, it can also not destroy any entropy present in the
        // entropy pool.
        #[inline(never)]
        fn stir_pool(&mut self) {
            // This constant is derived from the first two 32 bit initialization
            // vectors of SHA-1 as defined in FIPS 180-4 section 5.3.1
            // The order does not really matter as we do not rely on the specific
            // numbers. We just pick the SHA-1 constants as they have a good mix of
            // bit set and unset.
            const CONSTANT: u64 = 0x67452301efcdab89;

            // The start value of the mixer variable is derived from the third
            // and fourth 32 bit initialization vector of SHA-1 as defined in
            // FIPS 180-4 section 5.3.1
            let mut mixer = 0x98badcfe10325476;

            // This is a constant time function to prevent leaking timing
            // information about the random number.
            // The normal code is:
            // ```
            // for i in 0..64 {
            //     if ((self.data >> i) & 1) == 1 { mixer ^= CONSTANT; }
            // }
            // ```
            // This is a bit fragile, as LLVM really wants to use branches here, and
            // we rely on it to not recognise the opportunity.
            for i in 0..64 {
                let apply = (self.data >> i) & 1;
                let mask = !apply.wrapping_sub(1);
                mixer ^= CONSTANT & mask;
                mixer = mixer.rotate_left(1);
            }

            self.data ^= mixer;
        }

        fn gen_entropy(&mut self) -> u64 {
            // Prime `self.prev_time`, and run the noice sources to make sure the
            // first loop round collects the expected entropy.
            let _ = self.measure_jitter();

            for _ in 0..self.rounds {
                // If a stuck measurement is received, repeat measurement
                // Note: we do not guard against an infinite loop, that would mean
                // the timer suddenly became broken.
                while self.measure_jitter().is_none() {}
            }

            self.stir_pool();
            self.data
        }

        /// Basic quality tests on the timer, by measuring CPU timing jitter a few
        /// hundred times.
        ///
        /// If succesful, this will return the estimated number of rounds necessary
        /// to collect 64 bits of entropy. Otherwise a `TimerError` with the cause
        /// of the failure will be returned.
        pub fn test_timer(&mut self) -> Result<u32, TimerError> {
            // We could add a check for system capabilities such as `clock_getres`
            // or check for `CONFIG_X86_TSC`, but it does not make much sense as the
            // following sanity checks verify that we have a high-resolution timer.

            #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))]
            return Err(TimerError::NoTimer);

            let mut delta_sum = 0;
            let mut old_delta = 0;

            let mut time_backwards = 0;
            let mut count_mod = 0;
            let mut count_stuck = 0;

            // TESTLOOPCOUNT needs some loops to identify edge systems.
            // 100 is definitely too little.
            const TESTLOOPCOUNT: u64 = 300;
            const CLEARCACHE: u64 = 100;

            for i in 0..(CLEARCACHE + TESTLOOPCOUNT) {
                // Measure time delta of core entropy collection logic
                let time = (self.timer)();
                self.memaccess(true);
                self.lfsr_time(time, true);
                let time2 = (self.timer)();

                // Test whether timer works
                if time == 0 || time2 == 0 {
                    return Err(TimerError::NoTimer);
                }
                let delta = time2.wrapping_sub(time) as i64;

                // Test whether timer is fine grained enough to provide delta even
                // when called shortly after each other -- this implies that we also
                // have a high resolution timer
                if delta == 0 {
                    return Err(TimerError::CoarseTimer);
                }

                // Up to here we did not modify any variable that will be
                // evaluated later, but we already performed some work. Thus we
                // already have had an impact on the caches, branch prediction,
                // etc. with the goal to clear it to get the worst case
                // measurements.
                if i < CLEARCACHE { continue; }

                if self.stuck(delta) { count_stuck += 1; }

                // Test whether we have an increasing timer.
                if !(time2 > time) { time_backwards += 1; }

                // Count the number of times the counter increases in steps of 100ns
                // or greater.
                if (delta % 100) == 0 { count_mod += 1; }

                // Ensure that we have a varying delta timer which is necessary for
                // the calculation of entropy -- perform this check only after the
                // first loop is executed as we need to prime the old_delta value
                delta_sum += (delta - old_delta).abs() as u64;
                old_delta = delta;
            }

            // We allow the time to run backwards for up to three times.
            // This can happen if the clock is being adjusted by NTP operations.
            // If such an operation just happens to interfere with our test, it
            // should not fail. The value of 3 should cover the NTP case being
            // performed during our test run.
            if time_backwards > 3 {
                return Err(TimerError::NotMonotonic);
            }

            // Test that the available amount of entropy per round does not get to
            // low. We expect 1 bit of entropy per round as a reasonable minimum
            // (although less is possible, it means the collector loop has to run
            // much more often).
            // `assert!(delta_average >= log2(1))`
            // `assert!(delta_sum / TESTLOOPCOUNT >= 1)`
            // `assert!(delta_sum >= TESTLOOPCOUNT)`
            if delta_sum < TESTLOOPCOUNT {
                return Err(TimerError::TinyVariantions);
            }

            // Ensure that we have variations in the time stamp below 100 for at
            // least 10% of all checks -- on some platforms, the counter increments
            // in multiples of 100, but not always
            if count_mod > (TESTLOOPCOUNT * 9 / 10) {
                return Err(TimerError::CoarseTimer);
            }

            // If we have more than 90% stuck results, then this Jitter RNG is
            // likely to not work well.
            if count_stuck > (TESTLOOPCOUNT * 9 / 10) {
                return Err(TimerError::TooManyStuck);
            }

            // Estimate the number of `measure_jitter` rounds necessary for 64 bits
            // of entropy.
            //
            // We don't try very hard to come up with a good estimate of the
            // available bits of entropy per round here for two reasons:
            // 1. Simple estimates of the available bits (like Shannon entropy) are
            //    too optimistic.
            // 2)  Unless we want to waste a lot of time during intialization, there
            //     only a small number of samples are available.
            //
            // Therefore we use a very simple and conservative estimate:
            // `let bits_of_entropy = log2(delta_average) / 2`.
            //
            // The number of rounds `measure_jitter` should run to collect 64 bits
            // of entropy is `64 / bits_of_entropy`.
            //
            // To have smaller rounding errors, intermediate values are multiplied
            // by `FACTOR`. To compensate for `log2` and division rounding down,
            // add 1.
            let delta_average = delta_sum / TESTLOOPCOUNT;
            // println!("delta_average: {}", delta_average);

            const FACTOR: u32  = 3;
            fn log2(x: u64) -> u32 { 64 - x.leading_zeros() }

            // pow(δ, FACTOR) must be representable; if you have overflow reduce FACTOR
            Ok(64 * 2 * FACTOR / (log2(delta_average.pow(FACTOR)) + 1))
        }

        /// Statistical test: return the timer delta of one normal run of the
        /// `JitterEntropy` entropy collector.
        ///
        /// Setting `var_rounds` to `true` will execute the memory access and the
        /// CPU jitter noice sources a variable amount of times (just like a real
        /// `JitterEntropy` round).
        ///
        /// Setting `var_rounds` to `false` will execute the noice sources the
        /// minimal number of times. This can be used to measure the minimum amount
        /// of entropy one round of entropy collector can collect in the worst case.
        ///
        /// # Example
        ///
        /// Use `timer_stats` to run the [NIST SP 800-90B Entropy Estimation Suite]
        /// (https://github.com/usnistgov/SP800-90B_EntropyAssessment).
        ///
        /// This is the recommended way to test the quality of `JitterRng`. It
        /// should be run before using the RNG on untested hardware, after changes
        /// that could effect how the code is optimised, and after major compiler
        /// compiler changes, like a new LLVM version.
        ///
        /// First generate two files `jitter_rng_var.bin` and `jitter_rng_var.min`.
        ///
        /// Execute `python noniid_main.py -v jitter_rng_var.bin 8`, and validate it
        /// with `restart.py -v jitter_rng_var.bin 8 <min-entropy>`.
        /// This number is the expected amount of entropy that is at least available
        /// for each round of the entropy collector. This number should be greater
        /// than the amount estimated with `64 / test_timer()`.
        ///
        /// Execute `python noniid_main.py -v -u 4 jitter_rng_var.bin 4`, and
        /// validate it with `restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy>`.
        /// This number is the expected amount of entropy that is available in the
        /// last 4 bits of the timer delta after running noice sources. Note that
        /// a value of 3.70 is the minimum estimated entropy for true randomness.
        ///
        /// Execute `python noniid_main.py -v -u 4 jitter_rng_var.bin 4`, and
        /// validate it with `restart.py -v -u 4 jitter_rng_var.bin 4 <min-entropy>`.
        /// This number is the expected amount of entropy that is available to the
        /// entropy collecter if both noice sources only run their minimal number of
        /// times. This measures the absolute worst-case, and gives a lower bound
        /// for the available entropy.
        ///
        /// ```rust,no_run
        /// use rand::JitterRng;
        ///
        /// # use std::error::Error;
        /// # use std::fs::File;
        /// # use std::io::Write;
        /// #
        /// # fn try_main() -> Result<(), Box<Error>> {
        /// fn get_nstime() -> u64 {
        ///     use std::time::{SystemTime, UNIX_EPOCH};
        ///
        ///     let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap();
        ///     // The correct way to calculate the current time is
        ///     // `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64`
        ///     // But this is faster, and the difference in terms of entropy is
        ///     // negligible (log2(10^9) == 29.9).
        ///     dur.as_secs() << 30 | dur.subsec_nanos() as u64
        /// }
        ///
        /// // Do not initialize with `JitterRng::new`, but with `new_with_timer`.
        /// // 'new' always runst `test_timer`, and can therefore fail to
        /// // initialize. We want to be able to get the statistics even when the
        /// // timer test fails.
        /// let mut rng = JitterRng::new_with_timer(get_nstime);
        ///
        /// // 1_000_000 results are required for the NIST SP 800-90B Entropy
        /// // Estimation Suite
        /// // FIXME: this number is smaller here, otherwise the Doc-test is too slow
        /// const ROUNDS: usize = 10_000;
        /// let mut deltas_variable: Vec<u8> = Vec::with_capacity(ROUNDS);
        /// let mut deltas_minimal: Vec<u8> = Vec::with_capacity(ROUNDS);
        ///
        /// for _ in 0..ROUNDS {
        ///     deltas_variable.push(rng.timer_stats(true) as u8);
        ///     deltas_minimal.push(rng.timer_stats(false) as u8);
        /// }
        ///
        /// // Write out after the statistics collection loop, to not disturb the
        /// // test results.
        /// File::create("jitter_rng_var.bin")?.write(&deltas_variable)?;
        /// File::create("jitter_rng_min.bin")?.write(&deltas_minimal)?;
        /// #
        /// # Ok(())
        /// # }
        /// #
        /// # fn main() {
        /// #     try_main().unwrap();
        /// # }
        /// ```
        #[cfg(feature="std")]
        pub fn timer_stats(&mut self, var_rounds: bool) -> i64 {
            let time = platform::get_nstime();
            self.memaccess(var_rounds);
            self.lfsr_time(time, var_rounds);
            let time2 = platform::get_nstime();
            time2.wrapping_sub(time) as i64
        }
    }

    #[cfg(feature="std")]
    mod platform {
        #[cfg(not(any(target_os = "macos", target_os = "ios", target_os = "windows", all(target_arch = "wasm32", not(target_os = "emscripten")))))]
        pub fn get_nstime() -> u64 {
            use std::time::{SystemTime, UNIX_EPOCH};

            let dur = SystemTime::now().duration_since(UNIX_EPOCH).unwrap();
            // The correct way to calculate the current time is
            // `dur.as_secs() * 1_000_000_000 + dur.subsec_nanos() as u64`
            // But this is faster, and the difference in terms of entropy is negligible
            // (log2(10^9) == 29.9).
            dur.as_secs() << 30 | dur.subsec_nanos() as u64
        }

        #[cfg(any(target_os = "macos", target_os = "ios"))]
        pub fn get_nstime() -> u64 {
            extern crate libc;
            // On Mac OS and iOS std::time::SystemTime only has 1000ns resolution.
            // We use `mach_absolute_time` instead. This provides a CPU dependent unit,
            // to get real nanoseconds the result should by multiplied by numer/denom
            // from `mach_timebase_info`.
            // But we are not interested in the exact nanoseconds, just entropy. So we
            // use the raw result.
            unsafe { libc::mach_absolute_time() }
        }

        #[cfg(target_os = "windows")]
        pub fn get_nstime() -> u64 {
            extern crate winapi;
            unsafe {
                let mut t = super::mem::zeroed();
                winapi::um::profileapi::QueryPerformanceCounter(&mut t);
                *t.QuadPart() as u64
            }
        }

        #[cfg(all(target_arch = "wasm32", not(target_os = "emscripten")))]
        pub fn get_nstime() -> u64 {
            unreachable!()
        }
    }

    // A function that is opaque to the optimizer to assist in avoiding dead-code
    // elimination. Taken from `bencher`.
    fn black_box<T>(dummy: T) -> T {
        unsafe {
            let ret = ptr::read_volatile(&dummy);
            mem::forget(dummy);
            ret
        }
    }

    impl Rng for JitterRng {
        fn next_u32(&mut self) -> u32 {
            // We want to use both parts of the generated entropy
            if let Some(high) = self.data_remaining.take() {
                high
            } else {
                let data = self.next_u64();
                self.data_remaining = Some((data >> 32) as u32);
                data as u32
            }
        }

        fn next_u64(&mut self) -> u64 {
           self.gen_entropy()
        }

        fn fill_bytes(&mut self, dest: &mut [u8]) {
            let mut left = dest;
            while left.len() >= 8 {
                let (l, r) = {left}.split_at_mut(8);
                left = r;
                let chunk: [u8; 8] = unsafe {
                    mem::transmute(self.next_u64().to_le())
                };
                l.copy_from_slice(&chunk);
            }
            let n = left.len();
            if n > 0 {
                let chunk: [u8; 8] = unsafe {
                    mem::transmute(self.next_u64().to_le())
                };
                left.copy_from_slice(&chunk[..n]);
            }
        }
    }

    // There are no tests included because (1) this is an "external" RNG, so output
    // is not reproducible and (2) `test_timer` *will* fail on some platforms.

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