waffles-solana-runtime 1.15.0

use {
    crate::{
        accounts_db::{SnapshotStorages, PUBKEY_BINS_FOR_CALCULATING_HASHES},
        ancestors::Ancestors,
        rent_collector::RentCollector,
    },
    core::ops::Range,
    log::*,
    memmap2::MmapMut,
    rayon::prelude::*,
    solana_measure::measure::Measure,
    solana_sdk::{
        hash::{Hash, Hasher},
        pubkey::Pubkey,
        slot_history::Slot,
        sysvar::epoch_schedule::EpochSchedule,
    },
    std::{
        borrow::Borrow,
        convert::TryInto,
        fs::File,
        io::{BufWriter, Write},
        sync::{
            atomic::{AtomicU64, AtomicUsize, Ordering},
            Mutex,
        },
    },
    tempfile::tempfile,
};
pub const MERKLE_FANOUT: usize = 16;

/// the data passed through the processing functions
pub type SortedDataByPubkey<'a> = Vec<&'a [CalculateHashIntermediate]>;

/// 1 file containing account hashes sorted by pubkey, mapped into memory
struct MmapAccountHashesFile {
    mmap: MmapMut,
}

impl MmapAccountHashesFile {
    /// return a slice of account hashes starting at 'index'
    fn read(&self, index: usize) -> &[Hash] {
        let start = std::mem::size_of::<Hash>() * index;
        let item_slice: &[u8] = &self.mmap[start..];
        let remaining_elements = item_slice.len() / std::mem::size_of::<Hash>();
        unsafe {
            let item = item_slice.as_ptr() as *const Hash;
            std::slice::from_raw_parts(item, remaining_elements)
        }
    }
}

/// 1 file containing account hashes sorted by pubkey
#[derive(Default)]
pub struct AccountHashesFile {
    /// # hashes and an open file that will be deleted on drop. None if there are zero hashes to represent, and thus, no file.
    count_and_writer: Option<(usize, BufWriter<File>)>,
}

impl AccountHashesFile {
    /// map the file into memory and return a reader that can access it by slice
    fn get_reader(&mut self) -> Option<(usize, MmapAccountHashesFile)> {
        std::mem::take(&mut self.count_and_writer).map(|(count, writer)| {
            let file = Some(writer.into_inner().unwrap());
            (
                count,
                MmapAccountHashesFile {
                    mmap: unsafe { MmapMut::map_mut(file.as_ref().unwrap()).unwrap() },
                },
            )
        })
    }

    /// # hashes stored in this file
    pub fn count(&self) -> usize {
        self.count_and_writer
            .as_ref()
            .map(|(count, _)| *count)
            .unwrap_or_default()
    }

    /// write 'hash' to the file
    /// If the file isn't open, create it first.
    pub fn write(&mut self, hash: &Hash) {
        if self.count_and_writer.is_none() {
            // we have hashes to write but no file yet, so create a file that will auto-delete on drop
            self.count_and_writer = Some((0, BufWriter::new(tempfile().unwrap())));
        }
        let mut count_and_writer = self.count_and_writer.as_mut().unwrap();
        assert_eq!(
            std::mem::size_of::<Hash>(),
            count_and_writer.1.write(hash.as_ref()).unwrap()
        );
        count_and_writer.0 += 1;
    }
}

#[derive(Debug)]
#[allow(dead_code)]
pub struct FullSnapshotAccountsHashInfo {
    /// accounts hash over all accounts when the full snapshot was taken
    hash: AccountsHash,
    /// slot where full snapshot was taken
    slot: Slot,
}

/// parameters to calculate accounts hash
#[derive(Debug)]
pub struct CalcAccountsHashConfig<'a> {
    /// true to use a thread pool dedicated to bg operations
    pub use_bg_thread_pool: bool,
    /// verify every hash in append vec/write cache with a recalculated hash
    /// this option will be removed
    pub check_hash: bool,
    /// 'ancestors' is used to get storages
    pub ancestors: Option<&'a Ancestors>,
    /// does hash calc need to consider account data that exists in the write cache?
    /// if so, 'ancestors' will be used for this purpose as well as storages.
    pub epoch_schedule: &'a EpochSchedule,
    pub rent_collector: &'a RentCollector,
    /// used for tracking down hash mismatches after the fact
    pub store_detailed_debug_info_on_failure: bool,
    /// `Some` if this is an incremental snapshot which only hashes slots since the base full snapshot
    pub full_snapshot: Option<FullSnapshotAccountsHashInfo>,
}

impl<'a> CalcAccountsHashConfig<'a> {
    /// return true if we should cache accounts hash intermediate data between calls
    pub fn get_should_cache_hash_data() -> bool {
        // when we are skipping rewrites, we cannot rely on the cached data from old append vecs, so we have to disable caching for now
        // skipping rewrites is not enabled in this branch. It requires a cli argument.
        true
    }
}

// smallest, 3 quartiles, largest, average
pub type StorageSizeQuartileStats = [usize; 6];

#[derive(Debug, Default)]
pub struct HashStats {
    pub mark_time_us: u64,
    pub scan_time_total_us: u64,
    pub zeros_time_total_us: u64,
    pub hash_time_total_us: u64,
    pub hash_time_pre_us: u64,
    pub sort_time_total_us: u64,
    pub hash_total: usize,
    pub unreduced_entries: usize,
    pub num_snapshot_storage: usize,
    pub num_slots: usize,
    pub num_dirty_slots: usize,
    pub collect_snapshots_us: u64,
    pub storage_sort_us: u64,
    pub min_bin_size: usize,
    pub max_bin_size: usize,
    pub storage_size_quartiles: StorageSizeQuartileStats,
    /// time spent hashing during rehash calls
    pub rehash_hash_us: AtomicU64,
    /// time spent determining whether to rehash during rehash calls
    pub rehash_calc_us: AtomicU64,
    /// # rehashes that took place and were necessary
    pub rehash_required: AtomicUsize,
    /// # rehashes that took place and were UNnecessary
    pub rehash_unnecessary: AtomicUsize,
    pub oldest_root: Slot,
    pub roots_older_than_epoch: AtomicUsize,
    pub accounts_in_roots_older_than_epoch: AtomicUsize,
    pub append_vec_sizes_older_than_epoch: AtomicUsize,
    /// # ancient append vecs encountered
    pub ancient_append_vecs: AtomicUsize,
    pub longest_ancient_scan_us: AtomicU64,
    pub sum_ancient_scans_us: AtomicU64,
    pub count_ancient_scans: AtomicU64,
}
impl HashStats {
    pub fn calc_storage_size_quartiles(&mut self, storages: &SnapshotStorages) {
        let mut sum = 0;
        let mut sizes = storages
            .iter()
            .flat_map(|storages| {
                let result = storages
                    .iter()
                    .map(|storage| {
                        let cap = storage.accounts.capacity() as usize;
                        sum += cap;
                        cap
                    })
                    .collect::<Vec<_>>();
                result
            })
            .collect::<Vec<_>>();
        sizes.sort_unstable();
        let len = sizes.len();
        self.storage_size_quartiles = if len == 0 {
            StorageSizeQuartileStats::default()
        } else {
            [
                *sizes.first().unwrap(),
                sizes[len / 4],
                sizes[len * 2 / 4],
                sizes[len * 3 / 4],
                *sizes.last().unwrap(),
                sum / len,
            ]
        };
    }

    pub fn log(&mut self) {
        let total_time_us = self.scan_time_total_us
            + self.zeros_time_total_us
            + self.hash_time_total_us
            + self.collect_snapshots_us
            + self.storage_sort_us;
        datapoint_info!(
            "calculate_accounts_hash_from_storages",
            ("mark_time_us", self.mark_time_us, i64),
            ("accounts_scan_us", self.scan_time_total_us, i64),
            ("eliminate_zeros_us", self.zeros_time_total_us, i64),
            ("hash_us", self.hash_time_total_us, i64),
            ("hash_time_pre_us", self.hash_time_pre_us, i64),
            ("sort", self.sort_time_total_us, i64),
            ("hash_total", self.hash_total, i64),
            ("storage_sort_us", self.storage_sort_us, i64),
            ("unreduced_entries", self.unreduced_entries as i64, i64),
            (
                "collect_snapshots_us",
                self.collect_snapshots_us as i64,
                i64
            ),
            (
                "num_snapshot_storage",
                self.num_snapshot_storage as i64,
                i64
            ),
            ("num_slots", self.num_slots as i64, i64),
            ("num_dirty_slots", self.num_dirty_slots as i64, i64),
            ("min_bin_size", self.min_bin_size as i64, i64),
            ("max_bin_size", self.max_bin_size as i64, i64),
            (
                "storage_size_min",
                self.storage_size_quartiles[0] as i64,
                i64
            ),
            (
                "storage_size_quartile_1",
                self.storage_size_quartiles[1] as i64,
                i64
            ),
            (
                "storage_size_quartile_2",
                self.storage_size_quartiles[2] as i64,
                i64
            ),
            (
                "storage_size_quartile_3",
                self.storage_size_quartiles[3] as i64,
                i64
            ),
            (
                "storage_size_max",
                self.storage_size_quartiles[4] as i64,
                i64
            ),
            (
                "storage_size_avg",
                self.storage_size_quartiles[5] as i64,
                i64
            ),
            ("total_us", total_time_us as i64, i64),
            (
                "rehashed_rewrites",
                self.rehash_required.load(Ordering::Relaxed) as i64,
                i64
            ),
            (
                "rehash_hash_us",
                self.rehash_hash_us.load(Ordering::Relaxed) as i64,
                i64
            ),
            (
                "rehash_calc_us",
                self.rehash_calc_us.load(Ordering::Relaxed) as i64,
                i64
            ),
            (
                "rehashed_rewrites_unnecessary",
                self.rehash_unnecessary.load(Ordering::Relaxed) as i64,
                i64
            ),
            (
                "roots_older_than_epoch",
                self.roots_older_than_epoch.load(Ordering::Relaxed) as i64,
                i64
            ),
            ("oldest_root", self.oldest_root as i64, i64),
            (
                "ancient_append_vecs",
                self.ancient_append_vecs.load(Ordering::Relaxed) as i64,
                i64
            ),
            (
                "longest_ancient_scan_us",
                self.longest_ancient_scan_us.load(Ordering::Relaxed) as i64,
                i64
            ),
            (
                "sum_ancient_scans_us",
                self.sum_ancient_scans_us.load(Ordering::Relaxed) as i64,
                i64
            ),
            (
                "count_ancient_scans",
                self.count_ancient_scans.load(Ordering::Relaxed) as i64,
                i64
            ),
            (
                "append_vec_sizes_older_than_epoch",
                self.append_vec_sizes_older_than_epoch
                    .load(Ordering::Relaxed) as i64,
                i64
            ),
            (
                "accounts_in_roots_older_than_epoch",
                self.accounts_in_roots_older_than_epoch
                    .load(Ordering::Relaxed) as i64,
                i64
            ),
        );
    }
}

/// While scanning appendvecs, this is the info that needs to be extracted, de-duped, and sorted from what is stored in an append vec.
/// Note this can be saved/loaded during hash calculation to a memory mapped file whose contents are
/// [CalculateHashIntermediate]
#[repr(C)]
#[derive(Default, Debug, PartialEq, Eq, Clone)]
pub struct CalculateHashIntermediate {
    pub hash: Hash,
    pub lamports: u64,
    pub pubkey: Pubkey,
}

impl CalculateHashIntermediate {
    pub fn new(hash: Hash, lamports: u64, pubkey: Pubkey) -> Self {
        Self {
            hash,
            lamports,
            pubkey,
        }
    }
}

#[derive(Default, Debug, PartialEq, Eq)]
pub struct CumulativeOffset {
    pub index: Vec<usize>,
    pub start_offset: usize,
}

impl CumulativeOffset {
    pub fn new(index: Vec<usize>, start_offset: usize) -> CumulativeOffset {
        Self {
            index,
            start_offset,
        }
    }
}

pub trait ExtractSliceFromRawData<'b, T: 'b> {
    fn extract<'a>(&'b self, offset: &'a CumulativeOffset, start: usize) -> &'b [T];
}

impl<'b, T: 'b> ExtractSliceFromRawData<'b, T> for Vec<Vec<T>> {
    fn extract<'a>(&'b self, offset: &'a CumulativeOffset, start: usize) -> &'b [T] {
        &self[offset.index[0]][start..]
    }
}

impl<'b, T: 'b> ExtractSliceFromRawData<'b, T> for Vec<Vec<Vec<T>>> {
    fn extract<'a>(&'b self, offset: &'a CumulativeOffset, start: usize) -> &'b [T] {
        &self[offset.index[0]][offset.index[1]][start..]
    }
}

// Allow retrieving &[start..end] from a logical src: Vec<T>, where src is really Vec<Vec<T>> (or later Vec<Vec<Vec<T>>>)
// This model prevents callers from having to flatten which saves both working memory and time.
#[derive(Default, Debug)]
pub struct CumulativeOffsets {
    cumulative_offsets: Vec<CumulativeOffset>,
    total_count: usize,
}

/// used by merkle tree calculation to lookup account hashes by overall index
#[derive(Default)]
pub struct CumulativeHashesFromFiles {
    /// source of hashes in order
    readers: Vec<MmapAccountHashesFile>,
    /// look up reader index and offset by overall index
    cumulative: CumulativeOffsets,
}

impl CumulativeHashesFromFiles {
    /// Calculate offset from overall index to which file and offset within that file based on the length of each hash file.
    /// Also collect readers to access the data.
    pub fn from_files(hashes: Vec<AccountHashesFile>) -> Self {
        let mut readers = Vec::with_capacity(hashes.len());
        let cumulative = CumulativeOffsets::new(hashes.into_iter().filter_map(|mut hash_file| {
            // ignores all hashfiles that have zero entries
            hash_file.get_reader().map(|(count, reader)| {
                readers.push(reader);
                count
            })
        }));
        Self {
            cumulative,
            readers,
        }
    }

    /// total # of items referenced
    pub fn total_count(&self) -> usize {
        self.cumulative.total_count
    }

    // return the biggest slice possible that starts at the overall index 'start'
    pub fn get_slice(&self, start: usize) -> &[Hash] {
        let (start, offset) = self.cumulative.find(start);
        let data_source_index = offset.index[0];
        let data = &self.readers[data_source_index];
        // unwrap here because we should never ask for data that doesn't exist. If we do, then cumulative calculated incorrectly.
        data.read(start)
    }
}

impl CumulativeOffsets {
    pub fn new<I>(iter: I) -> Self
    where
        I: Iterator<Item = usize>,
    {
        let mut total_count: usize = 0;
        let cumulative_offsets: Vec<_> = iter
            .enumerate()
            .filter_map(|(i, len)| {
                if len > 0 {
                    let result = CumulativeOffset::new(vec![i], total_count);
                    total_count += len;
                    Some(result)
                } else {
                    None
                }
            })
            .collect();

        Self {
            cumulative_offsets,
            total_count,
        }
    }

    pub fn from_raw<T>(raw: &[Vec<T>]) -> Self {
        Self::new(raw.iter().map(|v| v.len()))
    }

    pub fn from_raw_2d<T>(raw: &[Vec<Vec<T>>]) -> Self {
        let mut total_count: usize = 0;
        let mut cumulative_offsets = Vec::with_capacity(0);
        for (i, v_outer) in raw.iter().enumerate() {
            for (j, v) in v_outer.iter().enumerate() {
                let len = v.len();
                if len > 0 {
                    if cumulative_offsets.is_empty() {
                        // the first inner, non-empty vector we find gives us an approximate rectangular shape
                        cumulative_offsets = Vec::with_capacity(raw.len() * v_outer.len());
                    }
                    cumulative_offsets.push(CumulativeOffset::new(vec![i, j], total_count));
                    total_count += len;
                }
            }
        }

        Self {
            cumulative_offsets,
            total_count,
        }
    }

    /// find the index of the data source that contains 'start'
    fn find_index(&self, start: usize) -> usize {
        assert!(!self.cumulative_offsets.is_empty());
        match self.cumulative_offsets[..].binary_search_by(|index| index.start_offset.cmp(&start)) {
            Ok(index) => index,
            Err(index) => index - 1, // we would insert at index so we are before the item at index
        }
    }

    /// given overall start index 'start'
    /// return ('start', which is the offset into the data source at 'index',
    ///     and 'index', which is the data source to use)
    fn find(&self, start: usize) -> (usize, &CumulativeOffset) {
        let index = self.find_index(start);
        let index = &self.cumulative_offsets[index];
        let start = start - index.start_offset;
        (start, index)
    }

    // return the biggest slice possible that starts at 'start'
    pub fn get_slice<'a, 'b, T, U>(&'a self, raw: &'b U, start: usize) -> &'b [T]
    where
        U: ExtractSliceFromRawData<'b, T> + 'b,
    {
        let (start, index) = self.find(start);
        raw.extract(index, start)
    }
}

#[derive(Debug, Default)]
pub struct AccountsHasher {
    pub filler_account_suffix: Option<Pubkey>,
}

impl AccountsHasher {
    /// true if it is possible that there are filler accounts present
    pub fn filler_accounts_enabled(&self) -> bool {
        self.filler_account_suffix.is_some()
    }

    pub fn calculate_hash(hashes: Vec<Vec<Hash>>) -> (Hash, usize) {
        let cumulative_offsets = CumulativeOffsets::from_raw(&hashes);

        let hash_total = cumulative_offsets.total_count;
        let result = AccountsHasher::compute_merkle_root_from_slices(
            hash_total,
            MERKLE_FANOUT,
            None,
            |start: usize| cumulative_offsets.get_slice(&hashes, start),
            None,
        );
        (result.0, hash_total)
    }

    pub fn compute_merkle_root(hashes: Vec<(Pubkey, Hash)>, fanout: usize) -> Hash {
        Self::compute_merkle_root_loop(hashes, fanout, |t| &t.1)
    }

    // this function avoids an infinite recursion compiler error
    pub fn compute_merkle_root_recurse(hashes: Vec<Hash>, fanout: usize) -> Hash {
        Self::compute_merkle_root_loop(hashes, fanout, |t| t)
    }

    pub fn div_ceil(x: usize, y: usize) -> usize {
        let mut result = x / y;
        if x % y != 0 {
            result += 1;
        }
        result
    }

    // For the first iteration, there could be more items in the tuple than just hash and lamports.
    // Using extractor allows us to avoid an unnecessary array copy on the first iteration.
    pub fn compute_merkle_root_loop<T, F>(hashes: Vec<T>, fanout: usize, extractor: F) -> Hash
    where
        F: Fn(&T) -> &Hash + std::marker::Sync,
        T: std::marker::Sync,
    {
        if hashes.is_empty() {
            return Hasher::default().result();
        }

        let mut time = Measure::start("time");

        let total_hashes = hashes.len();
        let chunks = Self::div_ceil(total_hashes, fanout);

        let result: Vec<_> = (0..chunks)
            .into_par_iter()
            .map(|i| {
                let start_index = i * fanout;
                let end_index = std::cmp::min(start_index + fanout, total_hashes);

                let mut hasher = Hasher::default();
                for item in hashes.iter().take(end_index).skip(start_index) {
                    let h = extractor(item);
                    hasher.hash(h.as_ref());
                }

                hasher.result()
            })
            .collect();
        time.stop();
        debug!("hashing {} {}", total_hashes, time);

        if result.len() == 1 {
            result[0]
        } else {
            Self::compute_merkle_root_recurse(result, fanout)
        }
    }

    fn calculate_three_level_chunks(
        total_hashes: usize,
        fanout: usize,
        max_levels_per_pass: Option<usize>,
        specific_level_count: Option<usize>,
    ) -> (usize, usize, bool) {
        const THREE_LEVEL_OPTIMIZATION: usize = 3; // this '3' is dependent on the code structure below where we manually unroll
        let target = fanout.pow(THREE_LEVEL_OPTIMIZATION as u32);

        // Only use the 3 level optimization if we have at least 4 levels of data.
        // Otherwise, we'll be serializing a parallel operation.
        let threshold = target * fanout;
        let mut three_level = max_levels_per_pass.unwrap_or(usize::MAX) >= THREE_LEVEL_OPTIMIZATION
            && total_hashes >= threshold;
        if three_level {
            if let Some(specific_level_count_value) = specific_level_count {
                three_level = specific_level_count_value >= THREE_LEVEL_OPTIMIZATION;
            }
        }
        let (num_hashes_per_chunk, levels_hashed) = if three_level {
            (target, THREE_LEVEL_OPTIMIZATION)
        } else {
            (fanout, 1)
        };
        (num_hashes_per_chunk, levels_hashed, three_level)
    }

    // This function is designed to allow hashes to be located in multiple, perhaps multiply deep vecs.
    // The caller provides a function to return a slice from the source data.
    pub fn compute_merkle_root_from_slices<'a, F, T>(
        total_hashes: usize,
        fanout: usize,
        max_levels_per_pass: Option<usize>,
        get_hash_slice_starting_at_index: F,
        specific_level_count: Option<usize>,
    ) -> (Hash, Vec<Hash>)
    where
        // returns a slice of hashes starting at the given overall index
        F: Fn(usize) -> &'a [T] + std::marker::Sync,
        T: Borrow<Hash> + std::marker::Sync + 'a,
    {
        if total_hashes == 0 {
            return (Hasher::default().result(), vec![]);
        }

        let mut time = Measure::start("time");

        let (num_hashes_per_chunk, levels_hashed, three_level) = Self::calculate_three_level_chunks(
            total_hashes,
            fanout,
            max_levels_per_pass,
            specific_level_count,
        );

        let chunks = Self::div_ceil(total_hashes, num_hashes_per_chunk);

        // initial fetch - could return entire slice
        let data = get_hash_slice_starting_at_index(0);
        let data_len = data.len();

        let result: Vec<_> = (0..chunks)
            .into_par_iter()
            .map(|i| {
                // summary:
                // this closure computes 1 or 3 levels of merkle tree (all chunks will be 1 or all will be 3)
                // for a subset (our chunk) of the input data [start_index..end_index]

                // index into get_hash_slice_starting_at_index where this chunk's range begins
                let start_index = i * num_hashes_per_chunk;
                // index into get_hash_slice_starting_at_index where this chunk's range ends
                let end_index = std::cmp::min(start_index + num_hashes_per_chunk, total_hashes);

                // will compute the final result for this closure
                let mut hasher = Hasher::default();

                // index into 'data' where we are currently pulling data
                // if we exhaust our data, then we will request a new slice, and data_index resets to 0, the beginning of the new slice
                let mut data_index = start_index;
                // source data, which we may refresh when we exhaust
                let mut data = data;
                // len of the source data
                let mut data_len = data_len;

                if !three_level {
                    // 1 group of fanout
                    // The result of this loop is a single hash value from fanout input hashes.
                    for i in start_index..end_index {
                        if data_index >= data_len {
                            // we exhausted our data, fetch next slice starting at i
                            data = get_hash_slice_starting_at_index(i);
                            data_len = data.len();
                            data_index = 0;
                        }
                        hasher.hash(data[data_index].borrow().as_ref());
                        data_index += 1;
                    }
                } else {
                    // hash 3 levels of fanout simultaneously.
                    // This codepath produces 1 hash value for between 1..=fanout^3 input hashes.
                    // It is equivalent to running the normal merkle tree calculation 3 iterations on the input.
                    //
                    // big idea:
                    //  merkle trees usually reduce the input vector by a factor of fanout with each iteration
                    //  example with fanout 2:
                    //   start:     [0,1,2,3,4,5,6,7]      in our case: [...16M...] or really, 1B
                    //   iteration0 [.5, 2.5, 4.5, 6.5]                 [... 1M...]
                    //   iteration1 [1.5, 5.5]                          [...65k...]
                    //   iteration2 3.5                                 [...4k... ]
                    //  So iteration 0 consumes N elements, hashes them in groups of 'fanout' and produces a vector of N/fanout elements
                    //   and the process repeats until there is only 1 hash left.
                    //
                    //  With the three_level code path, we make each chunk we iterate of size fanout^3 (4096)
                    //  So, the input could be 16M hashes and the output will be 4k hashes, or N/fanout^3
                    //  The goal is to reduce the amount of data that has to be constructed and held in memory.
                    //  When we know we have enough hashes, then, in 1 pass, we hash 3 levels simultaneously, storing far fewer intermediate hashes.
                    //
                    // Now, some details:
                    // The result of this loop is a single hash value from fanout^3 input hashes.
                    // concepts:
                    //  what we're conceptually hashing: "raw_hashes"[start_index..end_index]
                    //   example: [a,b,c,d,e,f]
                    //   but... hashes[] may really be multiple vectors that are pieced together.
                    //   example: [[a,b],[c],[d,e,f]]
                    //   get_hash_slice_starting_at_index(any_index) abstracts that and returns a slice starting at raw_hashes[any_index..]
                    //   such that the end of get_hash_slice_starting_at_index may be <, >, or = end_index
                    //   example: get_hash_slice_starting_at_index(1) returns [b]
                    //            get_hash_slice_starting_at_index(3) returns [d,e,f]
                    // This code is basically 3 iterations of merkle tree hashing occurring simultaneously.
                    // The first fanout raw hashes are hashed in hasher_k. This is iteration0
                    // Once hasher_k has hashed fanout hashes, hasher_k's result hash is hashed in hasher_j and then discarded
                    // hasher_k then starts over fresh and hashes the next fanout raw hashes. This is iteration0 again for a new set of data.
                    // Once hasher_j has hashed fanout hashes (from k), hasher_j's result hash is hashed in hasher and then discarded
                    // Once hasher has hashed fanout hashes (from j), then the result of hasher is the hash for fanout^3 raw hashes.
                    // If there are < fanout^3 hashes, then this code stops when it runs out of raw hashes and returns whatever it hashed.
                    // This is always how the very last elements work in a merkle tree.
                    let mut i = start_index;
                    while i < end_index {
                        let mut hasher_j = Hasher::default();
                        for _j in 0..fanout {
                            let mut hasher_k = Hasher::default();
                            let end = std::cmp::min(end_index - i, fanout);
                            for _k in 0..end {
                                if data_index >= data_len {
                                    // we exhausted our data, fetch next slice starting at i
                                    data = get_hash_slice_starting_at_index(i);
                                    data_len = data.len();
                                    data_index = 0;
                                }
                                hasher_k.hash(data[data_index].borrow().as_ref());
                                data_index += 1;
                                i += 1;
                            }
                            hasher_j.hash(hasher_k.result().as_ref());
                            if i >= end_index {
                                break;
                            }
                        }
                        hasher.hash(hasher_j.result().as_ref());
                    }
                }

                hasher.result()
            })
            .collect();
        time.stop();
        debug!("hashing {} {}", total_hashes, time);

        if let Some(mut specific_level_count_value) = specific_level_count {
            specific_level_count_value -= levels_hashed;
            if specific_level_count_value == 0 {
                (Hash::default(), result)
            } else {
                assert!(specific_level_count_value > 0);
                // We did not hash the number of levels required by 'specific_level_count', so repeat
                Self::compute_merkle_root_from_slices_recurse(
                    result,
                    fanout,
                    max_levels_per_pass,
                    Some(specific_level_count_value),
                )
            }
        } else {
            (
                if result.len() == 1 {
                    result[0]
                } else {
                    Self::compute_merkle_root_recurse(result, fanout)
                },
                vec![], // no intermediate results needed by caller
            )
        }
    }

    pub fn compute_merkle_root_from_slices_recurse(
        hashes: Vec<Hash>,
        fanout: usize,
        max_levels_per_pass: Option<usize>,
        specific_level_count: Option<usize>,
    ) -> (Hash, Vec<Hash>) {
        Self::compute_merkle_root_from_slices(
            hashes.len(),
            fanout,
            max_levels_per_pass,
            |start| &hashes[start..],
            specific_level_count,
        )
    }

    pub fn accumulate_account_hashes(mut hashes: Vec<(Pubkey, Hash)>) -> Hash {
        Self::sort_hashes_by_pubkey(&mut hashes);

        Self::compute_merkle_root_loop(hashes, MERKLE_FANOUT, |i| &i.1)
    }

    pub fn sort_hashes_by_pubkey(hashes: &mut Vec<(Pubkey, Hash)>) {
        hashes.par_sort_unstable_by(|a, b| a.0.cmp(&b.0));
    }

    pub fn compare_two_hash_entries(
        a: &CalculateHashIntermediate,
        b: &CalculateHashIntermediate,
    ) -> std::cmp::Ordering {
        // note partial_cmp only returns None with floating point comparisons
        a.pubkey.partial_cmp(&b.pubkey).unwrap()
    }

    pub fn checked_cast_for_capitalization(balance: u128) -> u64 {
        balance.try_into().unwrap_or_else(|_| {
            panic!("overflow is detected while summing capitalization: {balance}")
        })
    }

    /// return references to cache hash data, grouped by bin, sourced from 'sorted_data_by_pubkey',
    /// which is probably a mmapped file.
    pub(crate) fn get_binned_data<'a>(
        sorted_data_by_pubkey: &'a Vec<&'a [CalculateHashIntermediate]>,
        bins: usize,
        bin_range: &Range<usize>,
    ) -> Vec<Vec<&'a [CalculateHashIntermediate]>> {
        // get slices per bin from each slice
        use crate::pubkey_bins::PubkeyBinCalculator24;
        let binner = PubkeyBinCalculator24::new(bins);
        sorted_data_by_pubkey
            .par_iter()
            .map(|all_bins| {
                let mut last_start_index = 0;
                let mut result = Vec::with_capacity(bin_range.len());
                let mut current_bin = bin_range.start;
                let max_inclusive = all_bins.len();
                for i in 0..=max_inclusive {
                    let this_bin = if i != max_inclusive {
                        let entry = &all_bins[i];
                        let this_bin = binner.bin_from_pubkey(&entry.pubkey);
                        if this_bin == current_bin {
                            // this pk is in the same bin as we're currently investigating, so keep iterating
                            continue;
                        }
                        this_bin
                    } else {
                        // we exhausted the source data, so 'this bin' is now the end (exclusive) bin
                        // this case exists to handle the +1 case
                        bin_range.end
                    };
                    // we found the first pubkey in the bin after the bin we were investigating
                    // or we passed the end of the input list.
                    // So, the bin we were investigating is now complete.
                    result.push(&all_bins[last_start_index..i]);
                    last_start_index = i;
                    ((current_bin + 1)..this_bin).for_each(|_| {
                        // the source data could contain a pubey from bin 1, then bin 5, skipping the bins in between.
                        // In that case, fill in 2..5 with empty
                        result.push(&all_bins[0..0]); // empty slice
                    });
                    current_bin = this_bin;
                }
                result
            })
            .collect::<Vec<_>>()
    }

    /// returns:
    /// Vec, with one entry per bin
    ///  for each entry, Vec<Hash> in pubkey order
    /// If return Vec<AccountHashesFile> was flattened, it would be all hashes, in pubkey order.
    fn de_dup_and_eliminate_zeros<'a>(
        &self,
        sorted_data_by_pubkey: &'a [SortedDataByPubkey<'a>],
        stats: &mut HashStats,
        max_bin: usize,
    ) -> (Vec<AccountHashesFile>, u64) {
        // 1. eliminate zero lamport accounts
        // 2. pick the highest slot or (slot = and highest version) of each pubkey
        // 3. produce this output:
        // a. vec: PUBKEY_BINS_FOR_CALCULATING_HASHES in pubkey order
        //      vec: individual hashes in pubkey order, 1 hash per
        // b. lamports
        let mut zeros = Measure::start("eliminate zeros");
        let min_max_sum_entries_hashes = Mutex::new((usize::MAX, usize::MIN, 0u64, 0usize, 0usize));
        let hashes: Vec<_> = (0..max_bin)
            .into_par_iter()
            .map(|bin| {
                let (hashes_file, lamports_bin, unreduced_entries_count) =
                    self.de_dup_accounts_in_parallel(sorted_data_by_pubkey, bin);
                {
                    let mut lock = min_max_sum_entries_hashes.lock().unwrap();
                    let (mut min, mut max, mut lamports_sum, mut entries, mut hash_total) = *lock;
                    min = std::cmp::min(min, unreduced_entries_count);
                    max = std::cmp::max(max, unreduced_entries_count);
                    lamports_sum = Self::checked_cast_for_capitalization(
                        lamports_sum as u128 + lamports_bin as u128,
                    );
                    entries += unreduced_entries_count;
                    hash_total += hashes_file.count();
                    *lock = (min, max, lamports_sum, entries, hash_total);
                }
                hashes_file
            })
            .collect();
        zeros.stop();
        stats.zeros_time_total_us += zeros.as_us();
        let (min, max, lamports_sum, entries, hash_total) =
            *min_max_sum_entries_hashes.lock().unwrap();
        stats.min_bin_size = min;
        stats.max_bin_size = max;
        stats.unreduced_entries += entries;
        stats.hash_total += hash_total;
        (hashes, lamports_sum)
    }

    // returns true if this vector was exhausted
    fn get_item<'a, 'b>(
        min_index: usize,
        bin: usize,
        first_items: &'a mut Vec<Pubkey>,
        pubkey_division: &'b [SortedDataByPubkey<'b>],
        indexes: &'a mut [usize],
        first_item_to_pubkey_division: &'a mut Vec<usize>,
    ) -> &'b CalculateHashIntermediate {
        let first_item = first_items[min_index];
        let key = &first_item;
        let division_index = first_item_to_pubkey_division[min_index];
        let bin = &pubkey_division[division_index][bin];
        let mut index = indexes[division_index];
        index += 1;
        while index < bin.len() {
            // still more items where we found the previous key, so just increment the index for that slot group, skipping all pubkeys that are equal
            if &bin[index].pubkey == key {
                index += 1;
                continue; // duplicate entries of same pubkey, so keep skipping
            }

            // point to the next pubkey > key
            first_items[min_index] = bin[index].pubkey;
            indexes[division_index] = index;
            break;
        }

        if index >= bin.len() {
            // stop looking in this vector - we exhausted it
            first_items.remove(min_index);
            first_item_to_pubkey_division.remove(min_index);
        }

        // this is the previous first item that was requested
        &bin[index - 1]
    }

    // go through: [..][pubkey_bin][..] and return hashes and lamport sum
    //   slot groups^                ^accounts found in a slot group, sorted by pubkey, higher slot, write_version
    // 1. eliminate zero lamport accounts
    // 2. pick the highest slot or (slot = and highest version) of each pubkey
    // 3. produce this output:
    //   a. AccountHashesFile: individual account hashes in pubkey order
    //   b. lamport sum
    //   c. unreduced count (ie. including duplicates and zero lamport)
    fn de_dup_accounts_in_parallel<'a>(
        &self,
        pubkey_division: &'a [SortedDataByPubkey<'a>],
        pubkey_bin: usize,
    ) -> (AccountHashesFile, u64, usize) {
        let len = pubkey_division.len();
        let mut unreduced_count = 0;
        let mut indexes = vec![0; len];
        let mut first_items = Vec::with_capacity(len);
        // map from index of an item in first_items[] to index of the corresponding item in pubkey_division[]
        // this will change as items in pubkey_division[] are exhausted
        let mut first_item_to_pubkey_division = Vec::with_capacity(len);
        let mut hashes = AccountHashesFile::default();
        // initialize 'first_items', which holds the current lowest item in each slot group
        pubkey_division.iter().enumerate().for_each(|(i, bins)| {
            // check to make sure we can do bins[pubkey_bin]
            if bins.len() > pubkey_bin {
                let sub = bins[pubkey_bin];
                if !sub.is_empty() {
                    unreduced_count += bins[pubkey_bin].len(); // sum for metrics
                    first_items.push(bins[pubkey_bin][0].pubkey);
                    first_item_to_pubkey_division.push(i);
                }
            }
        });
        let mut overall_sum = 0;
        let mut duplicate_pubkey_indexes = Vec::with_capacity(len);
        let filler_accounts_enabled = self.filler_accounts_enabled();

        // this loop runs once per unique pubkey contained in any slot group
        while !first_items.is_empty() {
            let loop_stop = { first_items.len() - 1 }; // we increment at the beginning of the loop
            let mut min_index = 0;
            let mut min_pubkey = first_items[min_index];
            let mut first_item_index = 0; // we will start iterating at item 1. +=1 is first instruction in loop

            // this loop iterates over each slot group to find the minimum pubkey at the maximum slot
            // it also identifies duplicate pubkey entries at lower slots and remembers those to skip them after
            while first_item_index < loop_stop {
                first_item_index += 1;
                let key = &first_items[first_item_index];
                let cmp = min_pubkey.cmp(key);
                match cmp {
                    std::cmp::Ordering::Less => {
                        continue; // we still have the min item
                    }
                    std::cmp::Ordering::Equal => {
                        // we found the same pubkey in a later slot, so remember the lower slot as a duplicate
                        duplicate_pubkey_indexes.push(min_index);
                    }
                    std::cmp::Ordering::Greater => {
                        // this is the new min pubkey
                        min_pubkey = *key;
                    }
                }
                // this is the new index of the min entry
                min_index = first_item_index;
            }
            // get the min item, add lamports, get hash
            let item = Self::get_item(
                min_index,
                pubkey_bin,
                &mut first_items,
                pubkey_division,
                &mut indexes,
                &mut first_item_to_pubkey_division,
            );

            // add lamports, get hash as long as the lamports are > 0
            if item.lamports != 0
                && (!filler_accounts_enabled || !self.is_filler_account(&item.pubkey))
            {
                overall_sum = Self::checked_cast_for_capitalization(
                    item.lamports as u128 + overall_sum as u128,
                );
                hashes.write(&item.hash);
            }
            if !duplicate_pubkey_indexes.is_empty() {
                // skip past duplicate keys in earlier slots
                // reverse this list because get_item can remove first_items[*i] when *i is exhausted
                //  and that would mess up subsequent *i values
                duplicate_pubkey_indexes.iter().rev().for_each(|i| {
                    Self::get_item(
                        *i,
                        pubkey_bin,
                        &mut first_items,
                        pubkey_division,
                        &mut indexes,
                        &mut first_item_to_pubkey_division,
                    );
                });
                duplicate_pubkey_indexes.clear();
            }
        }
        (hashes, overall_sum, unreduced_count)
    }

    fn is_filler_account(&self, pubkey: &Pubkey) -> bool {
        crate::accounts_db::AccountsDb::is_filler_account_helper(
            pubkey,
            self.filler_account_suffix.as_ref(),
        )
    }

    // input:
    // vec: group of slot data, ordered by Slot (low to high)
    //   vec: [0..bins] - where bins are pubkey ranges (these are ordered by Pubkey range)
    //     vec: [..] - items which fit in the containing bin. Sorted by: Pubkey, higher Slot, higher Write version (if pubkey =)
    pub fn rest_of_hash_calculation(
        &self,
        data_sections_by_pubkey: Vec<SortedDataByPubkey<'_>>,
        mut stats: &mut HashStats,
    ) -> (Hash, u64) {
        let (hashes, total_lamports) = self.de_dup_and_eliminate_zeros(
            &data_sections_by_pubkey,
            stats,
            PUBKEY_BINS_FOR_CALCULATING_HASHES,
        );

        let cumulative = CumulativeHashesFromFiles::from_files(hashes);

        let mut hash_time = Measure::start("hash");
        let (hash, _) = Self::compute_merkle_root_from_slices(
            cumulative.total_count(),
            MERKLE_FANOUT,
            None,
            |start| cumulative.get_slice(start),
            None,
        );
        hash_time.stop();
        stats.hash_time_total_us += hash_time.as_us();
        (hash, total_lamports)
    }
}

/// Hash of all the accounts
#[derive(Debug, Default, Copy, Clone, Eq, PartialEq, Serialize, Deserialize, AbiExample)]
pub struct AccountsHash(pub Hash);

#[cfg(test)]
pub mod tests {
    use {super::*, std::str::FromStr};

    #[test]
    fn test_account_hashes_file() {
        // 0 hashes
        let mut file = AccountHashesFile::default();
        assert!(file.get_reader().is_none());
        let hashes = (0..2).map(|i| Hash::new(&[i; 32])).collect::<Vec<_>>();

        // 1 hash
        file.write(&hashes[0]);
        let reader = file.get_reader().unwrap();
        assert_eq!(&[hashes[0]][..], reader.1.read(0));
        assert!(reader.1.read(1).is_empty());

        // multiple hashes
        let mut file = AccountHashesFile::default();
        assert!(file.get_reader().is_none());
        hashes.iter().for_each(|hash| file.write(hash));
        let reader = file.get_reader().unwrap();
        (0..2).for_each(|i| assert_eq!(&hashes[i..], reader.1.read(i)));
        assert!(reader.1.read(2).is_empty());
    }

    #[test]
    fn test_cumulative_hashes_from_files() {
        (0..4).for_each(|permutation| {
            let hashes = (0..2).map(|i| Hash::new(&[i + 1; 32])).collect::<Vec<_>>();

            let mut combined = Vec::default();

            // 0 hashes
            let file0 = AccountHashesFile::default();

            // 1 hash
            let mut file1 = AccountHashesFile::default();
            file1.write(&hashes[0]);
            combined.push(hashes[0]);

            // multiple hashes
            let mut file2 = AccountHashesFile::default();
            hashes.iter().for_each(|hash| {
                file2.write(hash);
                combined.push(*hash);
            });

            let hashes = if permutation == 0 {
                vec![file0, file1, file2]
            } else if permutation == 1 {
                // include more empty files
                vec![
                    file0,
                    file1,
                    AccountHashesFile::default(),
                    file2,
                    AccountHashesFile::default(),
                ]
            } else if permutation == 2 {
                vec![file1, file2]
            } else {
                // swap file2 and 1
                let one = combined.remove(0);
                combined.push(one);
                vec![
                    file2,
                    AccountHashesFile::default(),
                    AccountHashesFile::default(),
                    file1,
                ]
            };

            let cumulative = CumulativeHashesFromFiles::from_files(hashes);
            let len = combined.len();
            assert_eq!(cumulative.total_count(), len);
            (0..combined.len()).for_each(|start| {
                let mut retreived = Vec::default();
                let mut cumulative_start = start;
                // read all data
                while retreived.len() < (len - start) {
                    let this_one = cumulative.get_slice(cumulative_start);
                    retreived.extend(this_one.iter());
                    cumulative_start += this_one.len();
                    assert_ne!(0, this_one.len());
                }
                assert_eq!(
                    &combined[start..],
                    &retreived[..],
                    "permutation: {permutation}"
                );
            });
        });
    }

    #[test]
    fn test_accountsdb_div_ceil() {
        assert_eq!(AccountsHasher::div_ceil(10, 3), 4);
        assert_eq!(AccountsHasher::div_ceil(0, 1), 0);
        assert_eq!(AccountsHasher::div_ceil(0, 5), 0);
        assert_eq!(AccountsHasher::div_ceil(9, 3), 3);
        assert_eq!(AccountsHasher::div_ceil(9, 9), 1);
    }

    #[test]
    #[should_panic(expected = "attempt to divide by zero")]
    fn test_accountsdb_div_ceil_fail() {
        assert_eq!(AccountsHasher::div_ceil(10, 0), 0);
    }

    fn for_rest(original: &[CalculateHashIntermediate]) -> Vec<SortedDataByPubkey<'_>> {
        vec![vec![original]]
    }

    #[test]
    fn test_accountsdb_rest_of_hash_calculation() {
        solana_logger::setup();

        let mut account_maps = Vec::new();

        let key = Pubkey::new(&[11u8; 32]);
        let hash = Hash::new(&[1u8; 32]);
        let val = CalculateHashIntermediate::new(hash, 88, key);
        account_maps.push(val);

        // 2nd key - zero lamports, so will be removed
        let key = Pubkey::new(&[12u8; 32]);
        let hash = Hash::new(&[2u8; 32]);
        let val = CalculateHashIntermediate::new(hash, 0, key);
        account_maps.push(val);

        let accounts_hash = AccountsHasher::default();
        let result = accounts_hash
            .rest_of_hash_calculation(for_rest(&account_maps), &mut HashStats::default());
        let expected_hash = Hash::from_str("8j9ARGFv4W2GfML7d3sVJK2MePwrikqYnu6yqer28cCa").unwrap();
        assert_eq!((result.0, result.1), (expected_hash, 88));

        // 3rd key - with pubkey value before 1st key so it will be sorted first
        let key = Pubkey::new(&[10u8; 32]);
        let hash = Hash::new(&[2u8; 32]);
        let val = CalculateHashIntermediate::new(hash, 20, key);
        account_maps.insert(0, val);

        let result = accounts_hash
            .rest_of_hash_calculation(for_rest(&account_maps), &mut HashStats::default());
        let expected_hash = Hash::from_str("EHv9C5vX7xQjjMpsJMzudnDTzoTSRwYkqLzY8tVMihGj").unwrap();
        assert_eq!((result.0, result.1), (expected_hash, 108));

        // 3rd key - with later slot
        let key = Pubkey::new(&[10u8; 32]);
        let hash = Hash::new(&[99u8; 32]);
        let val = CalculateHashIntermediate::new(hash, 30, key);
        account_maps.insert(1, val);

        let result = accounts_hash
            .rest_of_hash_calculation(for_rest(&account_maps), &mut HashStats::default());
        let expected_hash = Hash::from_str("7NNPg5A8Xsg1uv4UFm6KZNwsipyyUnmgCrznP6MBWoBZ").unwrap();
        assert_eq!((result.0, result.1), (expected_hash, 118));
    }

    fn one_range() -> usize {
        1
    }

    fn zero_range() -> usize {
        0
    }

    #[test]
    fn test_accountsdb_de_dup_accounts_zero_chunks() {
        let vec = [vec![vec![CalculateHashIntermediate {
            lamports: 1,
            ..CalculateHashIntermediate::default()
        }]]];
        let temp_vec = vec.to_vec();
        let slice = convert_to_slice2(&temp_vec);
        let (mut hashes, lamports, _) =
            AccountsHasher::default().de_dup_accounts_in_parallel(&slice, 0);
        assert_eq!(&[Hash::default()], hashes.get_reader().unwrap().1.read(0));
        assert_eq!(lamports, 1);
    }

    fn get_vec_vec(hashes: Vec<AccountHashesFile>) -> Vec<Vec<Hash>> {
        hashes.into_iter().map(get_vec).collect()
    }
    fn get_vec(mut hashes: AccountHashesFile) -> Vec<Hash> {
        hashes
            .get_reader()
            .map(|r| r.1.read(0).to_vec())
            .unwrap_or_default()
    }

    #[test]
    fn test_accountsdb_de_dup_accounts_empty() {
        solana_logger::setup();
        let accounts_hash = AccountsHasher::default();

        let vec = vec![vec![], vec![]];
        let (hashes, lamports) =
            accounts_hash.de_dup_and_eliminate_zeros(&vec, &mut HashStats::default(), one_range());
        assert_eq!(
            vec![Hash::default(); 0],
            get_vec_vec(hashes)
                .into_iter()
                .flatten()
                .collect::<Vec<_>>(),
        );
        assert_eq!(lamports, 0);
        let vec = vec![];
        let (hashes, lamports) =
            accounts_hash.de_dup_and_eliminate_zeros(&vec, &mut HashStats::default(), zero_range());
        let empty: Vec<Vec<Hash>> = Vec::default();
        assert_eq!(empty, get_vec_vec(hashes));
        assert_eq!(lamports, 0);

        let (hashes, lamports, _) = accounts_hash.de_dup_accounts_in_parallel(&[], 1);
        assert_eq!(vec![Hash::default(); 0], get_vec(hashes));
        assert_eq!(lamports, 0);

        let (hashes, lamports, _) = accounts_hash.de_dup_accounts_in_parallel(&[], 2);
        assert_eq!(vec![Hash::default(); 0], get_vec(hashes));
        assert_eq!(lamports, 0);
    }

    #[test]
    fn test_accountsdb_de_dup_accounts_from_stores() {
        solana_logger::setup();

        let key_a = Pubkey::new(&[1u8; 32]);
        let key_b = Pubkey::new(&[2u8; 32]);
        let key_c = Pubkey::new(&[3u8; 32]);
        const COUNT: usize = 6;
        let hashes = (0..COUNT).map(|i| Hash::new(&[i as u8; 32]));
        // create this vector
        // abbbcc
        let keys = [key_a, key_b, key_b, key_b, key_c, key_c];

        let accounts: Vec<_> = hashes
            .zip(keys.iter())
            .enumerate()
            .map(|(i, (hash, key))| CalculateHashIntermediate::new(hash, (i + 1) as u64, *key))
            .collect();

        type ExpectedType = (String, bool, u64, String);
        let expected:Vec<ExpectedType> = vec![
            // ("key/lamports key2/lamports ...",
            // is_last_slice
            // result lamports
            // result hashes)
            // "a5" = key_a, 5 lamports
            ("a1", false, 1, "[11111111111111111111111111111111]"),
            ("a1b2", false, 3, "[11111111111111111111111111111111, 4vJ9JU1bJJE96FWSJKvHsmmFADCg4gpZQff4P3bkLKi]"),
            ("a1b2b3", false, 4, "[11111111111111111111111111111111, 8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"),
            ("a1b2b3b4", false, 5, "[11111111111111111111111111111111, CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"),
            ("a1b2b3b4c5", false, 10, "[11111111111111111111111111111111, CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"),
            ("b2", false, 2, "[4vJ9JU1bJJE96FWSJKvHsmmFADCg4gpZQff4P3bkLKi]"),
            ("b2b3", false, 3, "[8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"),
            ("b2b3b4", false, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"),
            ("b2b3b4c5", false, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"),
            ("b3", false, 3, "[8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"),
            ("b3b4", false, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"),
            ("b3b4c5", false, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"),
            ("b4", false, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"),
            ("b4c5", false, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"),
            ("c5", false, 5, "[GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"),
            ("a1", true, 1, "[11111111111111111111111111111111]"),
            ("a1b2", true, 3, "[11111111111111111111111111111111, 4vJ9JU1bJJE96FWSJKvHsmmFADCg4gpZQff4P3bkLKi]"),
            ("a1b2b3", true, 4, "[11111111111111111111111111111111, 8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"),
            ("a1b2b3b4", true, 5, "[11111111111111111111111111111111, CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"),
            ("a1b2b3b4c5", true, 10, "[11111111111111111111111111111111, CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"),
            ("b2", true, 2, "[4vJ9JU1bJJE96FWSJKvHsmmFADCg4gpZQff4P3bkLKi]"),
            ("b2b3", true, 3, "[8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"),
            ("b2b3b4", true, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"),
            ("b2b3b4c5", true, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"),
            ("b3", true, 3, "[8qbHbw2BbbTHBW1sbeqakYXVKRQM8Ne7pLK7m6CVfeR]"),
            ("b3b4", true, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"),
            ("b3b4c5", true, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"),
            ("b4", true, 4, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8]"),
            ("b4c5", true, 9, "[CktRuQ2mttgRGkXJtyksdKHjUdc2C4TgDzyB98oEzy8, GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"),
            ("c5", true, 5, "[GgBaCs3NCBuZN12kCJgAW63ydqohFkHEdfdEXBPzLHq]"),
            ].into_iter().map(|item| {
                let result: ExpectedType = (
                    item.0.to_string(),
                    item.1,
                    item.2,
                    item.3.to_string(),
                );
                result
            }).collect();

        let hash = AccountsHasher::default();
        let mut expected_index = 0;
        for last_slice in 0..2 {
            for start in 0..COUNT {
                for end in start + 1..COUNT {
                    let is_last_slice = last_slice == 1;
                    let accounts = accounts.clone();
                    let slice = &accounts[start..end];

                    let slice2 = vec![vec![slice.to_vec()]];
                    let slice = &slice2[..];
                    let slice_temp = convert_to_slice2(&slice2);
                    let (hashes2, lamports2, _) = hash.de_dup_accounts_in_parallel(&slice_temp, 0);
                    let slice3 = convert_to_slice2(&slice2);
                    let (hashes3, lamports3, _) = hash.de_dup_accounts_in_parallel(&slice3, 0);
                    let vec = slice.to_vec();
                    let slice4 = convert_to_slice2(&vec);
                    let (hashes4, lamports4) = hash.de_dup_and_eliminate_zeros(
                        &slice4,
                        &mut HashStats::default(),
                        end - start,
                    );
                    let vec = slice.to_vec();
                    let slice5 = convert_to_slice2(&vec);
                    let (hashes5, lamports5) = hash.de_dup_and_eliminate_zeros(
                        &slice5,
                        &mut HashStats::default(),
                        end - start,
                    );
                    let vec = slice.to_vec();
                    let slice5 = convert_to_slice2(&vec);
                    let (hashes6, lamports6) = hash.de_dup_and_eliminate_zeros(
                        &slice5,
                        &mut HashStats::default(),
                        end - start,
                    );

                    let hashes2 = get_vec(hashes2);
                    let hashes3 = get_vec(hashes3);
                    let hashes4 = get_vec_vec(hashes4);
                    let hashes5 = get_vec_vec(hashes5);
                    let hashes6 = get_vec_vec(hashes6);

                    assert_eq!(hashes2, hashes3);
                    let expected2 = hashes2.clone();
                    assert_eq!(
                        expected2,
                        hashes4.into_iter().flatten().collect::<Vec<_>>(),
                        "last_slice: {last_slice}, start: {start}, end: {end}, slice: {slice:?}"
                    );
                    assert_eq!(
                        expected2.clone(),
                        hashes5.iter().flatten().copied().collect::<Vec<_>>(),
                        "last_slice: {last_slice}, start: {start}, end: {end}, slice: {slice:?}"
                    );
                    assert_eq!(
                        expected2.clone(),
                        hashes6.iter().flatten().copied().collect::<Vec<_>>()
                    );
                    assert_eq!(lamports2, lamports3);
                    assert_eq!(lamports2, lamports4);
                    assert_eq!(lamports2, lamports5);
                    assert_eq!(lamports2, lamports6);

                    let human_readable = slice[0][0]
                        .iter()
                        .map(|v| {
                            let mut s = (if v.pubkey == key_a {
                                "a"
                            } else if v.pubkey == key_b {
                                "b"
                            } else {
                                "c"
                            })
                            .to_string();

                            s.push_str(&v.lamports.to_string());
                            s
                        })
                        .collect::<String>();

                    let hash_result_as_string = format!("{hashes2:?}");

                    let packaged_result: ExpectedType = (
                        human_readable,
                        is_last_slice,
                        lamports2,
                        hash_result_as_string,
                    );
                    assert_eq!(expected[expected_index], packaged_result);

                    // for generating expected results
                    // error!("{:?},", packaged_result);
                    expected_index += 1;
                }
            }
        }
    }

    #[test]
    fn test_accountsdb_compare_two_hash_entries() {
        solana_logger::setup();
        let key = Pubkey::new_unique();
        let hash = Hash::new_unique();
        let val = CalculateHashIntermediate::new(hash, 1, key);

        // slot same, version <
        let hash2 = Hash::new_unique();
        let val2 = CalculateHashIntermediate::new(hash2, 4, key);
        assert_eq!(
            std::cmp::Ordering::Equal, // no longer comparing slots or versions
            AccountsHasher::compare_two_hash_entries(&val, &val2)
        );

        // slot same, vers =
        let hash3 = Hash::new_unique();
        let val3 = CalculateHashIntermediate::new(hash3, 2, key);
        assert_eq!(
            std::cmp::Ordering::Equal,
            AccountsHasher::compare_two_hash_entries(&val, &val3)
        );

        // slot same, vers >
        let hash4 = Hash::new_unique();
        let val4 = CalculateHashIntermediate::new(hash4, 6, key);
        assert_eq!(
            std::cmp::Ordering::Equal, // no longer comparing slots or versions
            AccountsHasher::compare_two_hash_entries(&val, &val4)
        );

        // slot >, version <
        let hash5 = Hash::new_unique();
        let val5 = CalculateHashIntermediate::new(hash5, 8, key);
        assert_eq!(
            std::cmp::Ordering::Equal, // no longer comparing slots or versions
            AccountsHasher::compare_two_hash_entries(&val, &val5)
        );
    }

    fn test_de_dup_accounts_in_parallel<'a>(
        account_maps: &'a [SortedDataByPubkey<'a>],
    ) -> (AccountHashesFile, u64, usize) {
        AccountsHasher::default().de_dup_accounts_in_parallel(account_maps, 0)
    }

    #[test]
    fn test_accountsdb_remove_zero_balance_accounts() {
        solana_logger::setup();

        let key = Pubkey::new_unique();
        let hash = Hash::new_unique();
        let mut account_maps = Vec::new();
        let val = CalculateHashIntermediate::new(hash, 1, key);
        account_maps.push(val.clone());

        let vecs = vec![vec![account_maps.to_vec()]];
        let slice = convert_to_slice2(&vecs);
        let (hashfile, lamports, count) = test_de_dup_accounts_in_parallel(&slice);
        assert_eq!(
            (get_vec(hashfile), lamports, count),
            (vec![val.hash], val.lamports, 1)
        );

        // zero original lamports, higher version
        let val = CalculateHashIntermediate::new(hash, 0, key);
        account_maps.push(val); // has to be after previous entry since account_maps are in slot order

        let vecs = vec![vec![account_maps.to_vec()]];
        let slice = convert_to_slice2(&vecs);
        let (hashfile, lamports, count) = test_de_dup_accounts_in_parallel(&slice);
        assert_eq!((get_vec(hashfile), lamports, count), (vec![], 0, 2));
    }

    #[test]
    fn test_accountsdb_cumulative_offsets1_d() {
        let input = vec![vec![0, 1], vec![], vec![2, 3, 4], vec![]];
        let cumulative = CumulativeOffsets::from_raw(&input);

        let src: Vec<_> = input.clone().into_iter().flatten().collect();
        let len = src.len();
        assert_eq!(cumulative.total_count, len);
        assert_eq!(cumulative.cumulative_offsets.len(), 2); // 2 non-empty vectors

        const DIMENSION: usize = 0;
        assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION], 0);
        assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION], 2);

        assert_eq!(cumulative.cumulative_offsets[0].start_offset, 0);
        assert_eq!(cumulative.cumulative_offsets[1].start_offset, 2);

        for start in 0..len {
            let slice = cumulative.get_slice(&input, start);
            let len = slice.len();
            assert!(len > 0);
            assert_eq!(&src[start..(start + len)], slice);
        }

        let input = vec![vec![], vec![0, 1], vec![], vec![2, 3, 4], vec![]];
        let cumulative = CumulativeOffsets::from_raw(&input);

        let src: Vec<_> = input.clone().into_iter().flatten().collect();
        let len = src.len();
        assert_eq!(cumulative.total_count, len);
        assert_eq!(cumulative.cumulative_offsets.len(), 2); // 2 non-empty vectors

        assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION], 1);
        assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION], 3);

        assert_eq!(cumulative.cumulative_offsets[0].start_offset, 0);
        assert_eq!(cumulative.cumulative_offsets[1].start_offset, 2);

        for start in 0..len {
            let slice = cumulative.get_slice(&input, start);
            let len = slice.len();
            assert!(len > 0);
            assert_eq!(&src[start..(start + len)], slice);
        }

        let input: Vec<Vec<u32>> = vec![vec![]];
        let cumulative = CumulativeOffsets::from_raw(&input);

        let len = input.into_iter().flatten().count();
        assert_eq!(cumulative.total_count, len);
        assert_eq!(cumulative.cumulative_offsets.len(), 0); // 2 non-empty vectors
    }

    #[should_panic(expected = "is_empty")]
    #[test]
    fn test_accountsdb_cumulative_find_empty() {
        let input = CumulativeOffsets {
            cumulative_offsets: vec![],
            total_count: 0,
        };
        input.find(0);
    }

    #[test]
    fn test_accountsdb_cumulative_find() {
        let input = CumulativeOffsets {
            cumulative_offsets: vec![CumulativeOffset {
                index: vec![0],
                start_offset: 0,
            }],
            total_count: 0,
        };
        assert_eq!(input.find(0), (0, &input.cumulative_offsets[0]));

        let input = CumulativeOffsets {
            cumulative_offsets: vec![
                CumulativeOffset {
                    index: vec![0],
                    start_offset: 0,
                },
                CumulativeOffset {
                    index: vec![1],
                    start_offset: 2,
                },
            ],
            total_count: 0,
        };
        assert_eq!(input.find(0), (0, &input.cumulative_offsets[0])); // = first start_offset
        assert_eq!(input.find(1), (1, &input.cumulative_offsets[0])); // > first start_offset
        assert_eq!(input.find(2), (0, &input.cumulative_offsets[1])); // = last start_offset
        assert_eq!(input.find(3), (1, &input.cumulative_offsets[1])); // > last start_offset
    }

    #[test]
    fn test_accountsdb_cumulative_offsets2_d() {
        let input: Vec<Vec<Vec<u64>>> = vec![vec![vec![0, 1], vec![], vec![2, 3, 4], vec![]]];
        let cumulative = CumulativeOffsets::from_raw_2d(&input);

        let src: Vec<_> = input.clone().into_iter().flatten().flatten().collect();
        let len = src.len();
        assert_eq!(cumulative.total_count, len);
        assert_eq!(cumulative.cumulative_offsets.len(), 2); // 2 non-empty vectors

        const DIMENSION_0: usize = 0;
        const DIMENSION_1: usize = 1;
        assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_0], 0);
        assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_1], 0);
        assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_0], 0);
        assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_1], 2);

        assert_eq!(cumulative.cumulative_offsets[0].start_offset, 0);
        assert_eq!(cumulative.cumulative_offsets[1].start_offset, 2);

        for start in 0..len {
            let slice: &[u64] = cumulative.get_slice(&input, start);
            let len = slice.len();
            assert!(len > 0);
            assert_eq!(&src[start..(start + len)], slice);
        }

        let input = vec![vec![vec![], vec![0, 1], vec![], vec![2, 3, 4], vec![]]];
        let cumulative = CumulativeOffsets::from_raw_2d(&input);

        let src: Vec<_> = input.clone().into_iter().flatten().flatten().collect();
        let len = src.len();
        assert_eq!(cumulative.total_count, len);
        assert_eq!(cumulative.cumulative_offsets.len(), 2); // 2 non-empty vectors

        assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_0], 0);
        assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_1], 1);
        assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_0], 0);
        assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_1], 3);

        assert_eq!(cumulative.cumulative_offsets[0].start_offset, 0);
        assert_eq!(cumulative.cumulative_offsets[1].start_offset, 2);

        for start in 0..len {
            let slice: &[u64] = cumulative.get_slice(&input, start);
            let len = slice.len();
            assert!(len > 0);
            assert_eq!(&src[start..(start + len)], slice);
        }

        let input: Vec<Vec<Vec<u32>>> = vec![vec![]];
        let cumulative = CumulativeOffsets::from_raw_2d(&input);

        let len = input.into_iter().flatten().count();
        assert_eq!(cumulative.total_count, len);
        assert_eq!(cumulative.cumulative_offsets.len(), 0); // 2 non-empty vectors

        let input = vec![
            vec![vec![0, 1]],
            vec![vec![]],
            vec![vec![], vec![2, 3, 4], vec![]],
        ];
        let cumulative = CumulativeOffsets::from_raw_2d(&input);

        let src: Vec<_> = input.clone().into_iter().flatten().flatten().collect();
        let len = src.len();
        assert_eq!(cumulative.total_count, len);
        assert_eq!(cumulative.cumulative_offsets.len(), 2); // 2 non-empty vectors

        assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_0], 0);
        assert_eq!(cumulative.cumulative_offsets[0].index[DIMENSION_1], 0);
        assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_0], 2);
        assert_eq!(cumulative.cumulative_offsets[1].index[DIMENSION_1], 1);

        assert_eq!(cumulative.cumulative_offsets[0].start_offset, 0);
        assert_eq!(cumulative.cumulative_offsets[1].start_offset, 2);

        for start in 0..len {
            let slice: &[u64] = cumulative.get_slice(&input, start);
            let len = slice.len();
            assert!(len > 0);
            assert_eq!(&src[start..(start + len)], slice);
        }
    }

    fn test_hashing_larger(hashes: Vec<(Pubkey, Hash)>, fanout: usize) -> Hash {
        let result = AccountsHasher::compute_merkle_root(hashes.clone(), fanout);
        let reduced: Vec<_> = hashes.iter().map(|x| x.1).collect();
        let result2 = test_hashing(reduced, fanout);
        assert_eq!(result, result2, "len: {}", hashes.len());
        result
    }

    fn test_hashing(hashes: Vec<Hash>, fanout: usize) -> Hash {
        let temp: Vec<_> = hashes.iter().map(|h| (Pubkey::default(), *h)).collect();
        let result = AccountsHasher::compute_merkle_root(temp, fanout);
        let reduced: Vec<_> = hashes.clone();
        let result2 = AccountsHasher::compute_merkle_root_from_slices(
            hashes.len(),
            fanout,
            None,
            |start| &reduced[start..],
            None,
        );
        assert_eq!(result, result2.0, "len: {}", hashes.len());

        let result2 = AccountsHasher::compute_merkle_root_from_slices(
            hashes.len(),
            fanout,
            Some(1),
            |start| &reduced[start..],
            None,
        );
        assert_eq!(result, result2.0, "len: {}", hashes.len());

        let max = std::cmp::min(reduced.len(), fanout * 2);
        for left in 0..max {
            for right in left + 1..max {
                let src = vec![
                    vec![reduced[0..left].to_vec(), reduced[left..right].to_vec()],
                    vec![reduced[right..].to_vec()],
                ];
                let offsets = CumulativeOffsets::from_raw_2d(&src);

                let get_slice = |start: usize| -> &[Hash] { offsets.get_slice(&src, start) };
                let result2 = AccountsHasher::compute_merkle_root_from_slices(
                    offsets.total_count,
                    fanout,
                    None,
                    get_slice,
                    None,
                );
                assert_eq!(result, result2.0);
            }
        }
        result
    }

    #[test]
    fn test_accountsdb_compute_merkle_root_large() {
        solana_logger::setup();

        // handle fanout^x -1, +0, +1 for a few 'x's
        const FANOUT: usize = 3;
        let mut hash_counts: Vec<_> = (1..6)
            .flat_map(|x| {
                let mark = FANOUT.pow(x);
                vec![mark - 1, mark, mark + 1]
            })
            .collect();

        // saturate the test space for threshold to threshold + target
        // this hits right before we use the 3 deep optimization and all the way through all possible partial last chunks
        let target = FANOUT.pow(3);
        let threshold = target * FANOUT;
        hash_counts.extend(threshold - 1..=threshold + target);

        for hash_count in hash_counts {
            let hashes: Vec<_> = (0..hash_count).map(|_| Hash::new_unique()).collect();

            test_hashing(hashes, FANOUT);
        }
    }

    #[test]
    fn test_accountsdb_compute_merkle_root() {
        solana_logger::setup();

        let expected_results = vec![
            (0, 0, "GKot5hBsd81kMupNCXHaqbhv3huEbxAFMLnpcX2hniwn", 0),
            (0, 1, "8unXKJYTxrR423HgQxbDmx29mFri1QNrzVKKDxEfc6bj", 0),
            (0, 2, "6QfkevXLLqbfAaR1kVjvMLFtEXvNUVrpmkwXqgsYtCFW", 1),
            (0, 3, "G3FrJd9JrXcMiqChTSfvEdBL2sCPny3ebiUy9Xxbn7a2", 3),
            (0, 4, "G3sZXHhwoCFuNyWy7Efffr47RBW33ibEp7b2hqNDmXdu", 6),
            (0, 5, "78atJJYpokAPKMJwHxUW8SBDvPkkSpTBV7GiB27HwosJ", 10),
            (0, 6, "7c9SM2BmCRVVXdrEdKcMK91MviPqXqQMd8QAb77tgLEy", 15),
            (0, 7, "3hsmnZPhf22UvBLiZ4dVa21Qsdh65CCrtYXsb8MxoVAa", 21),
            (0, 8, "5bwXUiC6RCRhb8fqvjvUXT6waU25str3UXA3a6Aq1jux", 28),
            (0, 9, "3NNtQKH6PaYpCnFBtyi2icK9eYX3YM5pqA3SKaXtUNzu", 36),
            (1, 0, "GKot5hBsd81kMupNCXHaqbhv3huEbxAFMLnpcX2hniwn", 0),
            (1, 1, "4GWVCsnEu1iRyxjAB3F7J7C4MMvcoxFWtP9ihvwvDgxY", 0),
            (1, 2, "8ML8Te6Uw2mipFr2v9sMZDcziXzhVqJo2qeMJohg1CJx", 1),
            (1, 3, "AMEuC3AgqAeRBGBhSfTmuMdfbAiXJnGmKv99kHmcAE1H", 3),
            (1, 4, "HEnDuJLHpsQfrApimGrovTqPEF6Vkrx2dKFr3BDtYzWx", 6),
            (1, 5, "6rH69iP2yM1o565noZN1EqjySW4PhYUskz3c5tXePUfV", 10),
            (1, 6, "7qEQMEXdfSPjbZ3q4cuuZwebDMvTvuaQ3dBiHoDUKo9a", 15),
            (1, 7, "GDJz7LSKYjqqz6ujCaaQRJRmQ7TLNCwYJhdT84qT4qwk", 21),
            (1, 8, "HT9krPLVTo3rr5WZQBQFrbqWs8SbYScXfnt8EVuobboM", 28),
            (1, 9, "8y2pMgqMdRsvqw6BQXm6wtz3qxGPss72i6H6gVpPyeda", 36),
        ];

        let mut expected_index = 0;
        let start = 0;
        let default_fanout = 2;
        // test 0..3 recursions (at fanout = 2) and 1 item remainder. The internals have 1 special case first loop and subsequent loops are the same types.
        let iterations = default_fanout * default_fanout * default_fanout + 2;
        for pass in 0..2 {
            let fanout = if pass == 0 {
                default_fanout
            } else {
                MERKLE_FANOUT
            };
            for count in start..iterations {
                let mut input: Vec<_> = (0..count)
                    .map(|i| {
                        let key = Pubkey::new(&[(pass * iterations + count) as u8; 32]);
                        let hash = Hash::new(&[(pass * iterations + count + i + 1) as u8; 32]);
                        (key, hash)
                    })
                    .collect();

                let result = if pass == 0 {
                    test_hashing_larger(input.clone(), fanout)
                } else {
                    // this sorts inside
                    let early_result = AccountsHasher::accumulate_account_hashes(
                        input.iter().map(|i| (i.0, i.1)).collect::<Vec<_>>(),
                    );
                    AccountsHasher::sort_hashes_by_pubkey(&mut input);
                    let result = AccountsHasher::compute_merkle_root(input.clone(), fanout);
                    assert_eq!(early_result, result);
                    result
                };
                // compare against captured, expected results for hash (and lamports)
                assert_eq!(
                    (
                        pass,
                        count,
                        &*(result.to_string()),
                        expected_results[expected_index].3
                    ), // we no longer calculate lamports
                    expected_results[expected_index]
                );
                expected_index += 1;
            }
        }
    }

    #[test]
    #[should_panic(expected = "overflow is detected while summing capitalization")]
    fn test_accountsdb_lamport_overflow() {
        solana_logger::setup();

        let offset = 2;
        let input = vec![
            CalculateHashIntermediate::new(
                Hash::new(&[1u8; 32]),
                u64::MAX - offset,
                Pubkey::new_unique(),
            ),
            CalculateHashIntermediate::new(Hash::new(&[2u8; 32]), offset + 1, Pubkey::new_unique()),
        ];
        AccountsHasher::default().de_dup_accounts_in_parallel(&[convert_to_slice(&[input])], 0);
    }

    fn convert_to_slice(
        input: &[Vec<CalculateHashIntermediate>],
    ) -> Vec<&[CalculateHashIntermediate]> {
        input.iter().map(|v| &v[..]).collect::<Vec<_>>()
    }

    fn convert_to_slice2(
        input: &[Vec<Vec<CalculateHashIntermediate>>],
    ) -> Vec<Vec<&[CalculateHashIntermediate]>> {
        input
            .iter()
            .map(|v| v.iter().map(|v| &v[..]).collect::<Vec<_>>())
            .collect::<Vec<_>>()
    }

    #[test]
    #[should_panic(expected = "overflow is detected while summing capitalization")]
    fn test_accountsdb_lamport_overflow2() {
        solana_logger::setup();

        let offset = 2;
        let input = vec![
            vec![CalculateHashIntermediate::new(
                Hash::new(&[1u8; 32]),
                u64::MAX - offset,
                Pubkey::new_unique(),
            )],
            vec![CalculateHashIntermediate::new(
                Hash::new(&[2u8; 32]),
                offset + 1,
                Pubkey::new_unique(),
            )],
        ];
        AccountsHasher::default().de_dup_and_eliminate_zeros(
            &[convert_to_slice(&input)],
            &mut HashStats::default(),
            2, // accounts above are in 2 groups
        );
    }

    #[test]
    fn test_get_binned_data() {
        let data = [CalculateHashIntermediate::new(
            Hash::default(),
            1,
            Pubkey::new(&[1u8; 32]),
        )];
        let data2 = vec![&data[..]];
        let bins = 1;
        let result = AccountsHasher::get_binned_data(&data2, bins, &(0..bins));
        assert_eq!(result, vec![vec![&data[..]]]);
        let bins = 2;
        let result = AccountsHasher::get_binned_data(&data2, bins, &(0..bins));
        assert_eq!(result, vec![vec![&data[..], &data[0..0]]]);
        let data = [CalculateHashIntermediate::new(
            Hash::default(),
            1,
            Pubkey::new(&[255u8; 32]),
        )];
        let data2 = vec![&data[..]];
        let result = AccountsHasher::get_binned_data(&data2, bins, &(0..bins));
        assert_eq!(result, vec![vec![&data[0..0], &data[..]]]);
        let data = [
            CalculateHashIntermediate::new(Hash::default(), 1, Pubkey::new(&[254u8; 32])),
            CalculateHashIntermediate::new(Hash::default(), 1, Pubkey::new(&[255u8; 32])),
        ];
        let data2 = vec![&data[..]];
        let result = AccountsHasher::get_binned_data(&data2, bins, &(0..bins));
        assert_eq!(result, vec![vec![&data[0..0], &data[..]]]);
        let data = [
            CalculateHashIntermediate::new(Hash::default(), 1, Pubkey::new(&[1u8; 32])),
            CalculateHashIntermediate::new(Hash::default(), 1, Pubkey::new(&[255u8; 32])),
        ];
        let data2 = vec![&data[..]];
        let result = AccountsHasher::get_binned_data(&data2, bins, &(0..bins));
        assert_eq!(result, vec![vec![&data[0..1], &data[1..2]]]);
    }
}