annotate rust/hg-core/src/dagops.rs @ 46568:0d840b9d200d

copies-rust: track "overwrites" directly within CopySource

Overwrite are "rare enough" that explicitly keeping track of them is going to be
"cheap", or at least much cheaper that issuing many `is_ancestor` calls.

Even a simple implementation using no specific optimisation (eg: using the
generic HashSet type) yield good result in most cases.

They are interesting optimization to can do on top of that. We will implement
them in later changesets.

We tried different approach to speed up the overwrite detection and this one
seems the most promising. Without further optimization, we already see sizable
speedup on various cases.

Repo            Case                                  Source-Rev   Dest-Rev      # of revisions  old time    new time      Difference    Factor    time per rev
---------------------------------------------------------------------------------------------------------------------------------------------------------------
mozilla-try     x00000_revs_x_added_0_copies          6a320851d377 1ebb79acd503 : 363753 revs,   5.138169 s,   4.482399 s,  -0.655770 s, × 0.8724,    12 µs/rev
mozilla-try     x00000_revs_x_added_x_copies          5173c4b6f97c 95d83ee7242d : 362229 revs,   5.127809 s,   4.480366 s,  -0.647443 s, × 0.8737,    12 µs/rev
mozilla-try     x00000_revs_x000_added_x_copies       9126823d0e9c ca82787bb23c : 359344 revs,   4.971136 s,   4.369070 s,  -0.602066 s, × 0.8789,    12 µs/rev
mozilla-try     x00000_revs_x0000_added_x0000_copies  8d3fafa80d4b eb884023b810 : 192665 revs,   1.741678 s,   1.592506 s,  -0.149172 s, × 0.9144,     8 µs/rev

However, some of the case doing a lot of overwrite get significantly slower.
The one with a really problematic slowdown are the special "head reducing" merge
in mozilla-try so I am not too worried about them. In addition, further
changeset are going to improve the performance of all this.

Repo            Case                                  Source-Rev   Dest-Rev      # of revisions  old time    new time      Difference    Factor    time per rev
---------------------------------------------------------------------------------------------------------------------------------------------------------------
mozilla-try     x0000_revs_xx000_added_x000_copies    89294cd501d9 7ccb2fc7ccb5 :  97052 revs,   1.343373 s,   2.119204 s,  +0.775831 s, × 1.5775,    21 µs/rev
mozilla-try     x00000_revs_x00000_added_x0000_copies 1b661134e2ca 1ae03d022d6d : 228985 revs,  40.314822 s,  87.824489 s, +47.509667 s, × 2.1785,   383 µs/rev
mozilla-try     x00000_revs_x00000_added_x000_copies  9b2a99adc05e 8e29777b48e6 : 382065 revs,  20.048029 s,  43.304637 s, +23.256608 s, × 2.1600,   113 µs/rev


Full benchmark below:

Repo            Case                                  Source-Rev   Dest-Rev      # of revisions  old time    new time      Difference    Factor    time per rev
---------------------------------------------------------------------------------------------------------------------------------------------------------------
mercurial       x_revs_x_added_0_copies               ad6b123de1c7 39cfcef4f463 :      1 revs,   0.000042 s,   0.000043 s,  +0.000001 s, × 1.0238,    43 µs/rev
mercurial       x_revs_x_added_x_copies               2b1c78674230 0c1d10351869 :      6 revs,   0.000110 s,   0.000114 s,  +0.000004 s, × 1.0364,    19 µs/rev
mercurial       x000_revs_x000_added_x_copies         81f8ff2a9bf2 dd3267698d84 :   1032 revs,   0.004945 s,   0.004937 s,  -0.000008 s, × 0.9984,     4 µs/rev
pypy            x_revs_x_added_0_copies               aed021ee8ae8 099ed31b181b :      9 revs,   0.000192 s,   0.000339 s,  +0.000147 s, × 1.7656,    37 µs/rev
pypy            x_revs_x000_added_0_copies            4aa4e1f8e19a 359343b9ac0e :      1 revs,   0.000049 s,   0.000049 s,  +0.000000 s, × 1.0000,    49 µs/rev
pypy            x_revs_x_added_x_copies               ac52eb7bbbb0 72e022663155 :      7 revs,   0.000112 s,   0.000202 s,  +0.000090 s, × 1.8036,    28 µs/rev
pypy            x_revs_x00_added_x_copies             c3b14617fbd7 ace7255d9a26 :      1 revs,   0.000323 s,   0.000409 s,  +0.000086 s, × 1.2663,   409 µs/rev
pypy            x_revs_x000_added_x000_copies         df6f7a526b60 a83dc6a2d56f :      6 revs,   0.010042 s,   0.011984 s,  +0.001942 s, × 1.1934,  1997 µs/rev
pypy            x000_revs_xx00_added_0_copies         89a76aede314 2f22446ff07e :   4785 revs,   0.049813 s,   0.050820 s,  +0.001007 s, × 1.0202,    10 µs/rev
pypy            x000_revs_x000_added_x_copies         8a3b5bfd266e 2c68e87c3efe :   6780 revs,   0.079937 s,   0.087953 s,  +0.008016 s, × 1.1003,    12 µs/rev
pypy            x000_revs_x000_added_x000_copies      89a76aede314 7b3dda341c84 :   5441 revs,   0.059412 s,   0.062902 s,  +0.003490 s, × 1.0587,    11 µs/rev
pypy            x0000_revs_x_added_0_copies           d1defd0dc478 c9cb1334cc78 :  43645 revs,   0.533769 s,   0.679234 s,  +0.145465 s, × 1.2725,    15 µs/rev
pypy            x0000_revs_xx000_added_0_copies       bf2c629d0071 4ffed77c095c :      2 revs,   0.013147 s,   0.013095 s,  -0.000052 s, × 0.9960,  6547 µs/rev
pypy            x0000_revs_xx000_added_x000_copies    08ea3258278e d9fa043f30c0 :  11316 revs,   0.110680 s,   0.120910 s,  +0.010230 s, × 1.0924,    10 µs/rev
netbeans        x_revs_x_added_0_copies               fb0955ffcbcd a01e9239f9e7 :      2 revs,   0.000085 s,   0.000087 s,  +0.000002 s, × 1.0235,    43 µs/rev
netbeans        x_revs_x000_added_0_copies            6f360122949f 20eb231cc7d0 :      2 revs,   0.000107 s,   0.000107 s,  +0.000000 s, × 1.0000,    53 µs/rev
netbeans        x_revs_x_added_x_copies               1ada3faf6fb6 5a39d12eecf4 :      3 revs,   0.000175 s,   0.000186 s,  +0.000011 s, × 1.0629,    62 µs/rev
netbeans        x_revs_x00_added_x_copies             35be93ba1e2c 9eec5e90c05f :      9 revs,   0.000720 s,   0.000754 s,  +0.000034 s, × 1.0472,    83 µs/rev
netbeans        x000_revs_xx00_added_0_copies         eac3045b4fdd 51d4ae7f1290 :   1421 revs,   0.010019 s,   0.010443 s,  +0.000424 s, × 1.0423,     7 µs/rev
netbeans        x000_revs_x000_added_x_copies         e2063d266acd 6081d72689dc :   1533 revs,   0.015602 s,   0.015697 s,  +0.000095 s, × 1.0061,    10 µs/rev
netbeans        x000_revs_x000_added_x000_copies      ff453e9fee32 411350406ec2 :   5750 revs,   0.058759 s,   0.063528 s,  +0.004769 s, × 1.0812,    11 µs/rev
netbeans        x0000_revs_xx000_added_x000_copies    588c2d1ced70 1aad62e59ddd :  66949 revs,   0.491550 s,   0.545515 s,  +0.053965 s, × 1.1098,     8 µs/rev
mozilla-central x_revs_x_added_0_copies               3697f962bb7b 7015fcdd43a2 :      2 revs,   0.000087 s,   0.000089 s,  +0.000002 s, × 1.0230,    44 µs/rev
mozilla-central x_revs_x000_added_0_copies            dd390860c6c9 40d0c5bed75d :      8 revs,   0.000268 s,   0.000265 s,  -0.000003 s, × 0.9888,    33 µs/rev
mozilla-central x_revs_x_added_x_copies               8d198483ae3b 14207ffc2b2f :      9 revs,   0.000181 s,   0.000381 s,  +0.000200 s, × 2.1050,    42 µs/rev
mozilla-central x_revs_x00_added_x_copies             98cbc58cc6bc 446a150332c3 :      7 revs,   0.000661 s,   0.000672 s,  +0.000011 s, × 1.0166,    96 µs/rev
mozilla-central x_revs_x000_added_x000_copies         3c684b4b8f68 0a5e72d1b479 :      3 revs,   0.003256 s,   0.003497 s,  +0.000241 s, × 1.0740,  1165 µs/rev
mozilla-central x_revs_x0000_added_x0000_copies       effb563bb7e5 c07a39dc4e80 :      6 revs,   0.066749 s,   0.073204 s,  +0.006455 s, × 1.0967, 12200 µs/rev
mozilla-central x000_revs_xx00_added_0_copies         6100d773079a 04a55431795e :   1593 revs,   0.006462 s,   0.006482 s,  +0.000020 s, × 1.0031,     4 µs/rev
mozilla-central x000_revs_x000_added_x_copies         9f17a6fc04f9 2d37b966abed :     41 revs,   0.004919 s,   0.005066 s,  +0.000147 s, × 1.0299,   123 µs/rev
mozilla-central x000_revs_x000_added_x000_copies      7c97034feb78 4407bd0c6330 :   7839 revs,   0.062421 s,   0.065707 s,  +0.003286 s, × 1.0526,     8 µs/rev
mozilla-central x0000_revs_xx000_added_0_copies       9eec5917337d 67118cc6dcad :    615 revs,   0.026633 s,   0.026800 s,  +0.000167 s, × 1.0063,    43 µs/rev
mozilla-central x0000_revs_xx000_added_x000_copies    f78c615a656c 96a38b690156 :  30263 revs,   0.197792 s,   0.203856 s,  +0.006064 s, × 1.0307,     6 µs/rev
mozilla-central x00000_revs_x0000_added_x0000_copies  6832ae71433c 4c222a1d9a00 : 153721 revs,   1.259970 s,   1.293394 s,  +0.033424 s, × 1.0265,     8 µs/rev
mozilla-central x00000_revs_x00000_added_x000_copies  76caed42cf7c 1daa622bbe42 : 204976 revs,   1.689184 s,   1.698239 s,  +0.009055 s, × 1.0054,     8 µs/rev
mozilla-try     x_revs_x_added_0_copies               aaf6dde0deb8 9790f499805a :      2 revs,   0.000865 s,   0.000875 s,  +0.000010 s, × 1.0116,   437 µs/rev
mozilla-try     x_revs_x000_added_0_copies            d8d0222927b4 5bb8ce8c7450 :      2 revs,   0.000893 s,   0.000891 s,  -0.000002 s, × 0.9978,   445 µs/rev
mozilla-try     x_revs_x_added_x_copies               092fcca11bdb 936255a0384a :      4 revs,   0.000172 s,   0.000292 s,  +0.000120 s, × 1.6977,    73 µs/rev
mozilla-try     x_revs_x00_added_x_copies             b53d2fadbdb5 017afae788ec :      2 revs,   0.001159 s,   0.003939 s,  +0.002780 s, × 3.3986,  1969 µs/rev
mozilla-try     x_revs_x000_added_x000_copies         20408ad61ce5 6f0ee96e21ad :      1 revs,   0.031621 s,   0.033027 s,  +0.001406 s, × 1.0445, 33027 µs/rev
mozilla-try     x_revs_x0000_added_x0000_copies       effb563bb7e5 c07a39dc4e80 :      6 revs,   0.068571 s,   0.073703 s,  +0.005132 s, × 1.0748, 12283 µs/rev
mozilla-try     x000_revs_xx00_added_0_copies         6100d773079a 04a55431795e :   1593 revs,   0.006452 s,   0.006469 s,  +0.000017 s, × 1.0026,     4 µs/rev
mozilla-try     x000_revs_x000_added_x_copies         9f17a6fc04f9 2d37b966abed :     41 revs,   0.005443 s,   0.005278 s,  -0.000165 s, × 0.9697,   128 µs/rev
mozilla-try     x000_revs_x000_added_x000_copies      1346fd0130e4 4c65cbdabc1f :   6657 revs,   0.063180 s,   0.064995 s,  +0.001815 s, × 1.0287,     9 µs/rev
mozilla-try     x0000_revs_x_added_0_copies           63519bfd42ee a36a2a865d92 :  40314 revs,   0.293564 s,   0.301041 s,  +0.007477 s, × 1.0255,     7 µs/rev
mozilla-try     x0000_revs_x_added_x_copies           9fe69ff0762d bcabf2a78927 :  38690 revs,   0.286595 s,   0.285575 s,  -0.001020 s, × 0.9964,     7 µs/rev
mozilla-try     x0000_revs_xx000_added_x_copies       156f6e2674f2 4d0f2c178e66 :   8598 revs,   0.083256 s,   0.085597 s,  +0.002341 s, × 1.0281,     9 µs/rev
mozilla-try     x0000_revs_xx000_added_0_copies       9eec5917337d 67118cc6dcad :    615 revs,   0.027282 s,   0.027118 s,  -0.000164 s, × 0.9940,    44 µs/rev
mozilla-try     x0000_revs_xx000_added_x000_copies    89294cd501d9 7ccb2fc7ccb5 :  97052 revs,   1.343373 s,   2.119204 s,  +0.775831 s, × 1.5775,    21 µs/rev
mozilla-try     x0000_revs_x0000_added_x0000_copies   e928c65095ed e951f4ad123a :  52031 revs,   0.665737 s,   0.701479 s,  +0.035742 s, × 1.0537,    13 µs/rev
mozilla-try     x00000_revs_x_added_0_copies          6a320851d377 1ebb79acd503 : 363753 revs,   5.138169 s,   4.482399 s,  -0.655770 s, × 0.8724,    12 µs/rev
mozilla-try     x00000_revs_x00000_added_0_copies     dc8a3ca7010e d16fde900c9c :  34414 revs,   0.573276 s,   0.574082 s,  +0.000806 s, × 1.0014,    16 µs/rev
mozilla-try     x00000_revs_x_added_x_copies          5173c4b6f97c 95d83ee7242d : 362229 revs,   5.127809 s,   4.480366 s,  -0.647443 s, × 0.8737,    12 µs/rev
mozilla-try     x00000_revs_x000_added_x_copies       9126823d0e9c ca82787bb23c : 359344 revs,   4.971136 s,   4.369070 s,  -0.602066 s, × 0.8789,    12 µs/rev
mozilla-try     x00000_revs_x0000_added_x0000_copies  8d3fafa80d4b eb884023b810 : 192665 revs,   1.741678 s,   1.592506 s,  -0.149172 s, × 0.9144,     8 µs/rev
mozilla-try     x00000_revs_x00000_added_x0000_copies 1b661134e2ca 1ae03d022d6d : 228985 revs,  40.314822 s,  87.824489 s, +47.509667 s, × 2.1785,   383 µs/rev
mozilla-try     x00000_revs_x00000_added_x000_copies  9b2a99adc05e 8e29777b48e6 : 382065 revs,  20.048029 s,  43.304637 s, +23.256608 s, × 2.1600,   113 µs/rev
private                                                                         : 459513 revs,  37.179470 s,  33.853687 s,  -3.325783 s, × 0.9105,    73 µs/rev

Differential Revision: https://phab.mercurial-scm.org/D9644

// dagops.rs
//
// Copyright 2019 Georges Racinet <[e-mail]>
//
// This software may be used and distributed according to the terms of the
// GNU General Public License version 2 or any later version.

//! Miscellaneous DAG operations
//!
//! # Terminology
//! - By *relative heads* of a collection of revision numbers (`Revision`), we
//!   mean those revisions that have no children among the collection.
//! - Similarly *relative roots* of a collection of `Revision`, we mean those
//!   whose parents, if any, don't belong to the collection.
use super::{Graph, GraphError, Revision, NULL_REVISION};
use crate::ancestors::AncestorsIterator;
use std::collections::{BTreeSet, HashSet};

fn remove_parents<S: std::hash::BuildHasher>(
    graph: &impl Graph,
    rev: Revision,
    set: &mut HashSet<Revision, S>,
) -> Result<(), GraphError> {
    for parent in graph.parents(rev)?.iter() {
        if *parent != NULL_REVISION {
            set.remove(parent);
        }
    }
    Ok(())
}

/// Relative heads out of some revisions, passed as an iterator.
///
/// These heads are defined as those revisions that have no children
/// among those emitted by the iterator.
///
/// # Performance notes
/// Internally, this clones the iterator, and builds a `HashSet` out of it.
///
/// This function takes an `Iterator` instead of `impl IntoIterator` to
/// guarantee that cloning the iterator doesn't result in cloning the full
/// construct it comes from.
pub fn heads<'a>(
    graph: &impl Graph,
    iter_revs: impl Clone + Iterator<Item = &'a Revision>,
) -> Result<HashSet<Revision>, GraphError> {
    let mut heads: HashSet<Revision> = iter_revs.clone().cloned().collect();
    heads.remove(&NULL_REVISION);
    for rev in iter_revs {
        if *rev != NULL_REVISION {
            remove_parents(graph, *rev, &mut heads)?;
        }
    }
    Ok(heads)
}

/// Retain in `revs` only its relative heads.
///
/// This is an in-place operation, so that control of the incoming
/// set is left to the caller.
/// - a direct Python binding would probably need to build its own `HashSet`
///   from an incoming iterable, even if its sole purpose is to extract the
///   heads.
/// - a Rust caller can decide whether cloning beforehand is appropriate
///
/// # Performance notes
/// Internally, this function will store a full copy of `revs` in a `Vec`.
pub fn retain_heads<S: std::hash::BuildHasher>(
    graph: &impl Graph,
    revs: &mut HashSet<Revision, S>,
) -> Result<(), GraphError> {
    revs.remove(&NULL_REVISION);
    // we need to construct an iterable copy of revs to avoid itering while
    // mutating
    let as_vec: Vec<Revision> = revs.iter().cloned().collect();
    for rev in as_vec {
        if rev != NULL_REVISION {
            remove_parents(graph, rev, revs)?;
        }
    }
    Ok(())
}

/// Roots of `revs`, passed as a `HashSet`
///
/// They are returned in arbitrary order
pub fn roots<G: Graph, S: std::hash::BuildHasher>(
    graph: &G,
    revs: &HashSet<Revision, S>,
) -> Result<Vec<Revision>, GraphError> {
    let mut roots: Vec<Revision> = Vec::new();
    for rev in revs {
        if graph
            .parents(*rev)?
            .iter()
            .filter(|p| **p != NULL_REVISION)
            .all(|p| !revs.contains(p))
        {
            roots.push(*rev);
        }
    }
    Ok(roots)
}

/// Compute the topological range between two collections of revisions
///
/// This is equivalent to the revset `<roots>::<heads>`.
///
/// Currently, the given `Graph` has to implement `Clone`, which means
/// actually cloning just a reference-counted Python pointer if
/// it's passed over through `rust-cpython`. This is due to the internal
/// use of `AncestorsIterator`
///
/// # Algorithmic details
///
/// This is a two-pass swipe inspired from what `reachableroots2` from
/// `mercurial.cext.parsers` does to obtain the same results.
///
/// - first, we climb up the DAG from `heads` in topological order, keeping
///   them in the vector `heads_ancestors` vector, and adding any element of
///   `roots` we find among them to the resulting range.
/// - Then, we iterate on that recorded vector so that a revision is always
///   emitted after its parents and add all revisions whose parents are already
///   in the range to the results.
///
/// # Performance notes
///
/// The main difference with the C implementation is that
/// the latter uses a flat array with bit flags, instead of complex structures
/// like `HashSet`, making it faster in most scenarios. In theory, it's
/// possible that the present implementation could be more memory efficient
/// for very large repositories with many branches.
pub fn range(
    graph: &(impl Graph + Clone),
    roots: impl IntoIterator<Item = Revision>,
    heads: impl IntoIterator<Item = Revision>,
) -> Result<BTreeSet<Revision>, GraphError> {
    let mut range = BTreeSet::new();
    let roots: HashSet<Revision> = roots.into_iter().collect();
    let min_root: Revision = match roots.iter().cloned().min() {
        None => {
            return Ok(range);
        }
        Some(r) => r,
    };

    // Internally, AncestorsIterator currently maintains a `HashSet`
    // of all seen revision, which is also what we record, albeit in an ordered
    // way. There's room for improvement on this duplication.
    let ait = AncestorsIterator::new(graph.clone(), heads, min_root, true)?;
    let mut heads_ancestors: Vec<Revision> = Vec::new();
    for revres in ait {
        let rev = revres?;
        if roots.contains(&rev) {
            range.insert(rev);
        }
        heads_ancestors.push(rev);
    }

    for rev in heads_ancestors.into_iter().rev() {
        for parent in graph.parents(rev)?.iter() {
            if *parent != NULL_REVISION && range.contains(parent) {
                range.insert(rev);
            }
        }
    }
    Ok(range)
}

#[cfg(test)]
mod tests {

    use super::*;
    use crate::testing::SampleGraph;

    /// Apply `retain_heads()` to the given slice and return as a sorted `Vec`
    fn retain_heads_sorted(
        graph: &impl Graph,
        revs: &[Revision],
    ) -> Result<Vec<Revision>, GraphError> {
        let mut revs: HashSet<Revision> = revs.iter().cloned().collect();
        retain_heads(graph, &mut revs)?;
        let mut as_vec: Vec<Revision> = revs.iter().cloned().collect();
        as_vec.sort();
        Ok(as_vec)
    }

    #[test]
    fn test_retain_heads() -> Result<(), GraphError> {
        assert_eq!(retain_heads_sorted(&SampleGraph, &[4, 5, 6])?, vec![5, 6]);
        assert_eq!(
            retain_heads_sorted(&SampleGraph, &[4, 1, 6, 12, 0])?,
            vec![1, 6, 12]
        );
        assert_eq!(
            retain_heads_sorted(&SampleGraph, &[1, 2, 3, 4, 5, 6, 7, 8, 9])?,
            vec![3, 5, 8, 9]
        );
        Ok(())
    }

    /// Apply `heads()` to the given slice and return as a sorted `Vec`
    fn heads_sorted(
        graph: &impl Graph,
        revs: &[Revision],
    ) -> Result<Vec<Revision>, GraphError> {
        let heads = heads(graph, revs.iter())?;
        let mut as_vec: Vec<Revision> = heads.iter().cloned().collect();
        as_vec.sort();
        Ok(as_vec)
    }

    #[test]
    fn test_heads() -> Result<(), GraphError> {
        assert_eq!(heads_sorted(&SampleGraph, &[4, 5, 6])?, vec![5, 6]);
        assert_eq!(
            heads_sorted(&SampleGraph, &[4, 1, 6, 12, 0])?,
            vec![1, 6, 12]
        );
        assert_eq!(
            heads_sorted(&SampleGraph, &[1, 2, 3, 4, 5, 6, 7, 8, 9])?,
            vec![3, 5, 8, 9]
        );
        Ok(())
    }

    /// Apply `roots()` and sort the result for easier comparison
    fn roots_sorted(
        graph: &impl Graph,
        revs: &[Revision],
    ) -> Result<Vec<Revision>, GraphError> {
        let set: HashSet<_> = revs.iter().cloned().collect();
        let mut as_vec = roots(graph, &set)?;
        as_vec.sort();
        Ok(as_vec)
    }

    #[test]
    fn test_roots() -> Result<(), GraphError> {
        assert_eq!(roots_sorted(&SampleGraph, &[4, 5, 6])?, vec![4]);
        assert_eq!(
            roots_sorted(&SampleGraph, &[4, 1, 6, 12, 0])?,
            vec![0, 4, 12]
        );
        assert_eq!(
            roots_sorted(&SampleGraph, &[1, 2, 3, 4, 5, 6, 7, 8, 9])?,
            vec![1, 8]
        );
        Ok(())
    }

    /// Apply `range()` and convert the result into a Vec for easier comparison
    fn range_vec(
        graph: impl Graph + Clone,
        roots: &[Revision],
        heads: &[Revision],
    ) -> Result<Vec<Revision>, GraphError> {
        range(&graph, roots.iter().cloned(), heads.iter().cloned())
            .map(|bs| bs.into_iter().collect())
    }

    #[test]
    fn test_range() -> Result<(), GraphError> {
        assert_eq!(range_vec(SampleGraph, &[0], &[4])?, vec![0, 1, 2, 4]);
        assert_eq!(range_vec(SampleGraph, &[0], &[8])?, vec![]);
        assert_eq!(
            range_vec(SampleGraph, &[5, 6], &[10, 11, 13])?,
            vec![5, 10]
        );
        assert_eq!(
            range_vec(SampleGraph, &[5, 6], &[10, 12])?,
            vec![5, 6, 9, 10, 12]
        );
        Ok(())
    }
}