//! This library implements string similarity metrics.

#![forbid(unsafe_code)]

#![allow(

    // these casts are sometimes needed. They restrict the length of input iterators

    // but there isn't really any way around this except for always working with

    // 128 bit types

    clippy::cast_possible_wrap,

    clippy::cast_sign_loss,

    clippy::cast_precision_loss,

    // not practical

    clippy::needless_pass_by_value,

    clippy::similar_names,

    // noisy

    clippy::missing_errors_doc,

    clippy::missing_panics_doc,

    clippy::must_use_candidate,

    // todo https://github.com/rapidfuzz/strsim-rs/issues/59

    clippy::range_plus_one

)]

use std::char;

use std::cmp::{max, min};

use std::collections::HashMap;

use std::convert::TryFrom;

use std::error::Error;

use std::fmt::{self, Display, Formatter};

use std::hash::Hash;

use std::mem;

use std::str::Chars;

#[derive(Debug, PartialEq)]

pub enum StrSimError {

    DifferentLengthArgs,

}

impl Display for StrSimError {

    fn fmt(&self, fmt: &mut Formatter) -> Result<(), fmt::Error> {

        let text = match self {

            StrSimError::DifferentLengthArgs => "Differing length arguments provided",

        };

        write!(fmt, "{}", text)

    }

}

impl Error for StrSimError {}

pub type HammingResult = Result<usize, StrSimError>;

/// Calculates the number of positions in the two sequences where the elements

/// differ. Returns an error if the sequences have different lengths.

pub fn generic_hamming<Iter1, Iter2, Elem1, Elem2>(a: Iter1, b: Iter2) -> HammingResult

where

    Iter1: IntoIterator<Item = Elem1>,

    Iter2: IntoIterator<Item = Elem2>,

    Elem1: PartialEq<Elem2>,

{

    let (mut ita, mut itb) = (a.into_iter(), b.into_iter());

    let mut count = 0;

    loop {

        match (ita.next(), itb.next()) {

            (Some(x), Some(y)) => {

                if x != y {

                    count += 1;

                }

            }

            (None, None) => return Ok(count),

            _ => return Err(StrSimError::DifferentLengthArgs),

        }

    }

}

/// Calculates the number of positions in the two strings where the characters

/// differ. Returns an error if the strings have different lengths.

///

/// ```

/// use strsim::{hamming, StrSimError::DifferentLengthArgs};

///

/// assert_eq!(Ok(3), hamming("hamming", "hammers"));

///

/// assert_eq!(Err(DifferentLengthArgs), hamming("hamming", "ham"));

/// ```

pub fn hamming(a: &str, b: &str) -> HammingResult {

    generic_hamming(a.chars(), b.chars())

}

/// Calculates the Jaro similarity between two sequences. The returned value

/// is between 0.0 and 1.0 (higher value means more similar).

pub fn generic_jaro<'a, 'b, Iter1, Iter2, Elem1, Elem2>(a: &'a Iter1, b: &'b Iter2) -> f64

where

    &'a Iter1: IntoIterator<Item = Elem1>,

    &'b Iter2: IntoIterator<Item = Elem2>,

    Elem1: PartialEq<Elem2>,

{

    let a_len = a.into_iter().count();

    let b_len = b.into_iter().count();

    if a_len == 0 && b_len == 0 {

        return 1.0;

    } else if a_len == 0 || b_len == 0 {

        return 0.0;

    }

    let mut search_range = max(a_len, b_len) / 2;

    search_range = search_range.saturating_sub(1);

    // combine memory allocations to reduce runtime

    let mut flags_memory = vec![false; a_len + b_len];

    let (a_flags, b_flags) = flags_memory.split_at_mut(a_len);

    let mut matches = 0_usize;

    for (i, a_elem) in a.into_iter().enumerate() {

        // prevent integer wrapping

        let min_bound = if i > search_range {

            i - search_range

        } else {

            0

        };

        let max_bound = min(b_len, i + search_range + 1);

        for (j, b_elem) in b.into_iter().enumerate().take(max_bound) {

            if min_bound <= j && a_elem == b_elem && !b_flags[j] {

                a_flags[i] = true;

                b_flags[j] = true;

                matches += 1;

                break;

            }

        }

    }

    let mut transpositions = 0_usize;

    if matches != 0 {

        let mut b_iter = b_flags.iter().zip(b);

        for (a_flag, ch1) in a_flags.iter().zip(a) {

            if *a_flag {

                loop {

                    if let Some((b_flag, ch2)) = b_iter.next() {

                        if !*b_flag {

                            continue;

                        }

                        if ch1 != ch2 {

                            transpositions += 1;

                        }

                        break;

                    }

                }

            }

        }

    }

    transpositions /= 2;

    if matches == 0 {

        0.0

    } else {

        ((matches as f64 / a_len as f64)

            + (matches as f64 / b_len as f64)

            + ((matches - transpositions) as f64 / matches as f64))

            / 3.0

    }

}

struct StringWrapper<'a>(&'a str);

impl<'a, 'b> IntoIterator for &'a StringWrapper<'b> {

    type Item = char;

    type IntoIter = Chars<'b>;

    fn into_iter(self) -> Self::IntoIter {

        self.0.chars()

    }

}

/// Calculates the Jaro similarity between two strings. The returned value

/// is between 0.0 and 1.0 (higher value means more similar).

///

/// ```

/// use strsim::jaro;

///

/// assert!((0.392 - jaro("Friedrich Nietzsche", "Jean-Paul Sartre")).abs() <

///         0.001);

/// ```

pub fn jaro(a: &str, b: &str) -> f64 {

    generic_jaro(&StringWrapper(a), &StringWrapper(b))

}

/// Like Jaro but gives a boost to sequences that have a common prefix.

pub fn generic_jaro_winkler<'a, 'b, Iter1, Iter2, Elem1, Elem2>(a: &'a Iter1, b: &'b Iter2) -> f64

where

    &'a Iter1: IntoIterator<Item = Elem1>,

    &'b Iter2: IntoIterator<Item = Elem2>,

    Elem1: PartialEq<Elem2>,

{

    let sim = generic_jaro(a, b);

    if sim > 0.7 {

        let prefix_length = a

            .into_iter()

            .take(4)

            .zip(b)

            .take_while(|(a_elem, b_elem)| a_elem == b_elem)

            .count();

        sim + 0.1 * prefix_length as f64 * (1.0 - sim)

    } else {

        sim

    }

}

/// Like Jaro but gives a boost to strings that have a common prefix.

///

/// ```

/// use strsim::jaro_winkler;

///

/// assert!((0.866 - jaro_winkler("cheeseburger", "cheese fries")).abs() <

///         0.001);

/// ```

pub fn jaro_winkler(a: &str, b: &str) -> f64 {

    generic_jaro_winkler(&StringWrapper(a), &StringWrapper(b))

}

/// Calculates the minimum number of insertions, deletions, and substitutions

/// required to change one sequence into the other.

///

/// ```

/// use strsim::generic_levenshtein;

///

/// assert_eq!(3, generic_levenshtein(&[1,2,3], &[1,2,3,4,5,6]));

/// ```

pub fn generic_levenshtein<'a, 'b, Iter1, Iter2, Elem1, Elem2>(a: &'a Iter1, b: &'b Iter2) -> usize

where

    &'a Iter1: IntoIterator<Item = Elem1>,

    &'b Iter2: IntoIterator<Item = Elem2>,

    Elem1: PartialEq<Elem2>,

{

    let b_len = b.into_iter().count();

    let mut cache: Vec<usize> = (1..b_len + 1).collect();

    let mut result = b_len;

    for (i, a_elem) in a.into_iter().enumerate() {

        result = i + 1;

        let mut distance_b = i;

        for (j, b_elem) in b.into_iter().enumerate() {

            let cost = usize::from(a_elem != b_elem);

            let distance_a = distance_b + cost;

            distance_b = cache[j];

            result = min(result + 1, min(distance_a, distance_b + 1));

            cache[j] = result;

        }

    }

    result

}

/// Calculates the minimum number of insertions, deletions, and substitutions

/// required to change one string into the other.

///

/// ```

/// use strsim::levenshtein;

///

/// assert_eq!(3, levenshtein("kitten", "sitting"));

/// ```

pub fn levenshtein(a: &str, b: &str) -> usize {

    generic_levenshtein(&StringWrapper(a), &StringWrapper(b))

}

/// Calculates a normalized score of the Levenshtein algorithm between 0.0 and

/// 1.0 (inclusive), where 1.0 means the strings are the same.

///

/// ```

/// use strsim::normalized_levenshtein;

///

/// assert!((normalized_levenshtein("kitten", "sitting") - 0.57142).abs() < 0.00001);

/// assert!((normalized_levenshtein("", "") - 1.0).abs() < 0.00001);

/// assert!(normalized_levenshtein("", "second").abs() < 0.00001);

/// assert!(normalized_levenshtein("first", "").abs() < 0.00001);

/// assert!((normalized_levenshtein("string", "string") - 1.0).abs() < 0.00001);

/// ```

pub fn normalized_levenshtein(a: &str, b: &str) -> f64 {

    if a.is_empty() && b.is_empty() {

        return 1.0;

    }

    1.0 - (levenshtein(a, b) as f64) / (a.chars().count().max(b.chars().count()) as f64)

}

/// Like Levenshtein but allows for adjacent transpositions. Each substring can

/// only be edited once.

///

/// ```

/// use strsim::osa_distance;

///

/// assert_eq!(3, osa_distance("ab", "bca"));

/// ```

pub fn osa_distance(a: &str, b: &str) -> usize {

    let b_len = b.chars().count();

    // 0..=b_len behaves like 0..b_len.saturating_add(1) which could be a different size

    // this leads to significantly worse code gen when swapping the vectors below

    let mut prev_two_distances: Vec<usize> = (0..b_len + 1).collect();

    let mut prev_distances: Vec<usize> = (0..b_len + 1).collect();

    let mut curr_distances: Vec<usize> = vec![0; b_len + 1];

    let mut prev_a_char = char::MAX;

    let mut prev_b_char = char::MAX;

    for (i, a_char) in a.chars().enumerate() {

        curr_distances[0] = i + 1;

        for (j, b_char) in b.chars().enumerate() {

            let cost = usize::from(a_char != b_char);

            curr_distances[j + 1] = min(

                curr_distances[j] + 1,

                min(prev_distances[j + 1] + 1, prev_distances[j] + cost),

            );

            if i > 0 && j > 0 && a_char != b_char && a_char == prev_b_char && b_char == prev_a_char

            {

                curr_distances[j + 1] = min(curr_distances[j + 1], prev_two_distances[j - 1] + 1);

            }

            prev_b_char = b_char;

        }

        mem::swap(&mut prev_two_distances, &mut prev_distances);

        mem::swap(&mut prev_distances, &mut curr_distances);

        prev_a_char = a_char;

    }

    // access prev_distances instead of curr_distances since we swapped

    // them above. In case a is empty this would still contain the correct value

    // from initializing the last element to b_len

    prev_distances[b_len]

}

/* Returns the final index for a value in a single vector that represents a fixed

2d grid */

fn flat_index(i: usize, j: usize, width: usize) -> usize {

    j * width + i

}

/// Like optimal string alignment, but substrings can be edited an unlimited

/// number of times, and the triangle inequality holds.

///

/// ```

/// use strsim::generic_damerau_levenshtein;

///

/// assert_eq!(2, generic_damerau_levenshtein(&[1,2], &[2,3,1]));

/// ```

pub fn generic_damerau_levenshtein<Elem>(a_elems: &[Elem], b_elems: &[Elem]) -> usize

where

    Elem: Eq + Hash + Clone,

{

    let a_len = a_elems.len();

    let b_len = b_elems.len();

    if a_len == 0 {

        return b_len;

    }

    if b_len == 0 {

        return a_len;

    }

    let width = a_len + 2;

    let mut distances = vec![0; (a_len + 2) * (b_len + 2)];

    let max_distance = a_len + b_len;

    distances[0] = max_distance;

    for i in 0..(a_len + 1) {

        distances[flat_index(i + 1, 0, width)] = max_distance;

        distances[flat_index(i + 1, 1, width)] = i;

    }

    for j in 0..(b_len + 1) {

        distances[flat_index(0, j + 1, width)] = max_distance;

        distances[flat_index(1, j + 1, width)] = j;

    }

    let mut elems: HashMap<Elem, usize> = HashMap::with_capacity(64);

    for i in 1..(a_len + 1) {

        let mut db = 0;

        for j in 1..(b_len + 1) {

            let k = match elems.get(&b_elems[j - 1]) {

                Some(&value) => value,

                None => 0,

            };

            let insertion_cost = distances[flat_index(i, j + 1, width)] + 1;

            let deletion_cost = distances[flat_index(i + 1, j, width)] + 1;

            let transposition_cost =

                distances[flat_index(k, db, width)] + (i - k - 1) + 1 + (j - db - 1);

            let mut substitution_cost = distances[flat_index(i, j, width)] + 1;

            if a_elems[i - 1] == b_elems[j - 1] {

                db = j;

                substitution_cost -= 1;

            }

            distances[flat_index(i + 1, j + 1, width)] = min(

                substitution_cost,

                min(insertion_cost, min(deletion_cost, transposition_cost)),

            );

        }

        elems.insert(a_elems[i - 1].clone(), i);

    }

    distances[flat_index(a_len + 1, b_len + 1, width)]

}

#[derive(Clone, Copy, PartialEq, Eq)]

struct RowId {

    val: isize,

}

impl Default for RowId {

    fn default() -> Self {

        Self { val: -1 }

    }

}

#[derive(Default, Clone)]

struct GrowingHashmapMapElemChar<ValueType> {

    key: u32,

    value: ValueType,

}

/// specialized hashmap to store user provided types

/// this implementation relies on a couple of base assumptions in order to simplify the implementation

/// - the hashmap does not have an upper limit of included items

/// - the default value for the `ValueType` can be used as a dummy value to indicate an empty cell

/// - elements can't be removed

/// - only allocates memory on first write access.

///   This improves performance for hashmaps that are never written to

struct GrowingHashmapChar<ValueType> {

    used: i32,

    fill: i32,

    mask: i32,

    map: Option<Vec<GrowingHashmapMapElemChar<ValueType>>>,

}

impl<ValueType> Default for GrowingHashmapChar<ValueType>

where

    ValueType: Default + Clone + Eq,

{

    fn default() -> Self {

        Self {

            used: 0,

            fill: 0,

            mask: -1,

            map: None,

        }

    }

}

impl<ValueType> GrowingHashmapChar<ValueType>

where

    ValueType: Default + Clone + Eq + Copy,

{

    fn get(&self, key: u32) -> ValueType {

        self.map

            .as_ref()

            .map_or_else(|| Default::default(), |map| map[self.lookup(key)].value)

    }

    fn get_mut(&mut self, key: u32) -> &mut ValueType {

        if self.map.is_none() {

            self.allocate();

        }

        let mut i = self.lookup(key);

        if self

            .map

            .as_ref()

            .expect("map should have been created above")[i]

            .value

            == Default::default()

        {

            self.fill += 1;

            // resize when 2/3 full

            if self.fill * 3 >= (self.mask + 1) * 2 {

                self.grow((self.used + 1) * 2);

                i = self.lookup(key);

            }

            self.used += 1;

        }

        let elem = &mut self

            .map

            .as_mut()

            .expect("map should have been created above")[i];

        elem.key = key;

        &mut elem.value

    }

    fn allocate(&mut self) {

        self.mask = 8 - 1;

        self.map = Some(vec![GrowingHashmapMapElemChar::default(); 8]);

    }

    /// lookup key inside the hashmap using a similar collision resolution

    /// strategy to `CPython` and `Ruby`

    fn lookup(&self, key: u32) -> usize {

        let hash = key;

        let mut i = hash as usize & self.mask as usize;

        let map = self

            .map

            .as_ref()

            .expect("callers have to ensure map is allocated");

        if map[i].value == Default::default() || map[i].key == key {

            return i;

        }

        let mut perturb = key;

        loop {

            i = (i * 5 + perturb as usize + 1) & self.mask as usize;

            if map[i].value == Default::default() || map[i].key == key {

                return i;

            }

            perturb >>= 5;

        }

    }

    fn grow(&mut self, min_used: i32) {

        let mut new_size = self.mask + 1;

        while new_size <= min_used {

            new_size <<= 1;

        }

        self.fill = self.used;

        self.mask = new_size - 1;

        let old_map = std::mem::replace(

            self.map

                .as_mut()

                .expect("callers have to ensure map is allocated"),

            vec![GrowingHashmapMapElemChar::<ValueType>::default(); new_size as usize],

        );

        for elem in old_map {

            if elem.value != Default::default() {

                let j = self.lookup(elem.key);

                let new_elem = &mut self.map.as_mut().expect("map created above")[j];

                new_elem.key = elem.key;

                new_elem.value = elem.value;

                self.used -= 1;

                if self.used == 0 {

                    break;

                }

            }

        }

        self.used = self.fill;

    }

}

struct HybridGrowingHashmapChar<ValueType> {

    map: GrowingHashmapChar<ValueType>,

    extended_ascii: [ValueType; 256],

}

impl<ValueType> HybridGrowingHashmapChar<ValueType>

where

    ValueType: Default + Clone + Copy + Eq,

{

    fn get(&self, key: char) -> ValueType {

        let value = key as u32;

        if value <= 255 {

            let val_u8 = u8::try_from(value).expect("we check the bounds above");

            self.extended_ascii[usize::from(val_u8)]

        } else {

            self.map.get(value)

        }

    }

    fn get_mut(&mut self, key: char) -> &mut ValueType {

        let value = key as u32;

        if value <= 255 {

            let val_u8 = u8::try_from(value).expect("we check the bounds above");

            &mut self.extended_ascii[usize::from(val_u8)]

        } else {

            self.map.get_mut(value)

        }

    }

}

impl<ValueType> Default for HybridGrowingHashmapChar<ValueType>

where

    ValueType: Default + Clone + Copy + Eq,

{

    fn default() -> Self {

        HybridGrowingHashmapChar {

            map: GrowingHashmapChar::default(),

            extended_ascii: [Default::default(); 256],

        }

    }

}

fn damerau_levenshtein_impl<Iter1, Iter2>(s1: Iter1, len1: usize, s2: Iter2, len2: usize) -> usize

where

    Iter1: Iterator<Item = char> + Clone,

    Iter2: Iterator<Item = char> + Clone,

{

    // The implementations is based on the paper

    // `Linear space string correction algorithm using the Damerau-Levenshtein distance`

    // from Chunchun Zhao and Sartaj Sahni

    //

    // It has a runtime complexity of `O(N*M)` and a memory usage of `O(N+M)`.

    let max_val = max(len1, len2) as isize + 1;

    let mut last_row_id = HybridGrowingHashmapChar::<RowId>::default();

    let size = len2 + 2;

    let mut fr = vec![max_val; size];

    let mut r1 = vec![max_val; size];

    let mut r: Vec<isize> = (max_val..max_val + 1)

        .chain(0..(size - 1) as isize)

        .collect();

    for (i, ch1) in s1.enumerate().map(|(i, ch1)| (i + 1, ch1)) {

        mem::swap(&mut r, &mut r1);

        let mut last_col_id: isize = -1;

        let mut last_i2l1 = r[1];

        r[1] = i as isize;

        let mut t = max_val;

        for (j, ch2) in s2.clone().enumerate().map(|(j, ch2)| (j + 1, ch2)) {

            let diag = r1[j] + isize::from(ch1 != ch2);

            let left = r[j] + 1;

            let up = r1[j + 1] + 1;

            let mut temp = min(diag, min(left, up));

            if ch1 == ch2 {

                last_col_id = j as isize; // last occurence of s1_i

                fr[j + 1] = r1[j - 1]; // save H_k-1,j-2

                t = last_i2l1; // save H_i-2,l-1

            } else {

                let k = last_row_id.get(ch2).val;

                let l = last_col_id;

                if j as isize - l == 1 {

                    let transpose = fr[j + 1] + (i as isize - k);

                    temp = min(temp, transpose);

                } else if i as isize - k == 1 {

                    let transpose = t + (j as isize - l);

                    temp = min(temp, transpose);

                }

            }

            last_i2l1 = r[j + 1];

            r[j + 1] = temp;

        }

        last_row_id.get_mut(ch1).val = i as isize;

    }

    r[len2 + 1] as usize

}

/// Like optimal string alignment, but substrings can be edited an unlimited

/// number of times, and the triangle inequality holds.

///

/// ```

/// use strsim::damerau_levenshtein;

///

/// assert_eq!(2, damerau_levenshtein("ab", "bca"));

/// ```

pub fn damerau_levenshtein(a: &str, b: &str) -> usize {

    damerau_levenshtein_impl(a.chars(), a.chars().count(), b.chars(), b.chars().count())

}

/// Calculates a normalized score of the Damerau–Levenshtein algorithm between

/// 0.0 and 1.0 (inclusive), where 1.0 means the strings are the same.

///

/// ```

/// use strsim::normalized_damerau_levenshtein;

///

/// assert!((normalized_damerau_levenshtein("levenshtein", "löwenbräu") - 0.27272).abs() < 0.00001);

/// assert!((normalized_damerau_levenshtein("", "") - 1.0).abs() < 0.00001);

/// assert!(normalized_damerau_levenshtein("", "flower").abs() < 0.00001);

/// assert!(normalized_damerau_levenshtein("tree", "").abs() < 0.00001);

/// assert!((normalized_damerau_levenshtein("sunglasses", "sunglasses") - 1.0).abs() < 0.00001);

/// ```

pub fn normalized_damerau_levenshtein(a: &str, b: &str) -> f64 {

    if a.is_empty() && b.is_empty() {

        return 1.0;

    }

    let len1 = a.chars().count();

    let len2 = b.chars().count();

    let dist = damerau_levenshtein_impl(a.chars(), len1, b.chars(), len2);

    1.0 - (dist as f64) / (max(len1, len2) as f64)

}

/// Returns an Iterator of char tuples.

fn bigrams(s: &str) -> impl Iterator<Item = (char, char)> + '_ {

    s.chars().zip(s.chars().skip(1))

}

/// Calculates a Sørensen-Dice similarity distance using bigrams.

/// See <https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient>.

///

/// ```

/// use strsim::sorensen_dice;

///

/// assert_eq!(1.0, sorensen_dice("", ""));

/// assert_eq!(0.0, sorensen_dice("", "a"));

/// assert_eq!(0.0, sorensen_dice("french", "quebec"));

/// assert_eq!(1.0, sorensen_dice("ferris", "ferris"));

/// assert_eq!(0.8888888888888888, sorensen_dice("feris", "ferris"));

/// ```

pub fn sorensen_dice(a: &str, b: &str) -> f64 {

    // implementation guided by

    // https://github.com/aceakash/string-similarity/blob/f83ba3cd7bae874c20c429774e911ae8cff8bced/src/index.js#L6

    let a: String = a.chars().filter(|&x| !char::is_whitespace(x)).collect();

    let b: String = b.chars().filter(|&x| !char::is_whitespace(x)).collect();

    if a == b {

        return 1.0;

    }

    if a.len() < 2 || b.len() < 2 {

        return 0.0;

    }

    let mut a_bigrams: HashMap<(char, char), usize> = HashMap::new();

    for bigram in bigrams(&a) {

        *a_bigrams.entry(bigram).or_insert(0) += 1;

    }

    let mut intersection_size = 0_usize;

    for bigram in bigrams(&b) {

        a_bigrams.entry(bigram).and_modify(|bi| {

            if *bi > 0 {

                *bi -= 1;

                intersection_size += 1;

            }

        });

    }

    (2 * intersection_size) as f64 / (a.len() + b.len() - 2) as f64

}

#[cfg(test)]

mod tests {

    use super::*;

    macro_rules! assert_delta {

        ($x:expr, $y:expr) => {

            assert_delta!($x, $y, 1e-5);

        };

        ($x:expr, $y:expr, $d:expr) => {

            if ($x - $y).abs() > $d {

                panic!(

                    "assertion failed: actual: `{}`, expected: `{}`: \

                    actual not within < {} of expected",

                    $x, $y, $d

                );

            }

        };

    }

    #[test]

    fn bigrams_iterator() {

        let mut bi = bigrams("abcde");

        assert_eq!(Some(('a', 'b')), bi.next());

        assert_eq!(Some(('b', 'c')), bi.next());

        assert_eq!(Some(('c', 'd')), bi.next());

        assert_eq!(Some(('d', 'e')), bi.next());

        assert_eq!(None, bi.next());

    }

    fn assert_hamming_dist(dist: usize, str1: &str, str2: &str) {

        assert_eq!(Ok(dist), hamming(str1, str2));

    }

    #[test]

    fn hamming_empty() {

        assert_hamming_dist(0, "", "")

    }

    #[test]

    fn hamming_same() {

        assert_hamming_dist(0, "hamming", "hamming")

    }

    #[test]

    fn hamming_numbers() {

        assert_eq!(Ok(1), generic_hamming(&[1, 2, 4], &[1, 2, 3]));

    }

    #[test]

    fn hamming_diff() {

        assert_hamming_dist(3, "hamming", "hammers")

    }

    #[test]

    fn hamming_diff_multibyte() {

        assert_hamming_dist(2, "hamming", "h香mmüng");

    }

    #[test]

    fn hamming_unequal_length() {

        assert_eq!(

            Err(StrSimError::DifferentLengthArgs),

            generic_hamming("ham".chars(), "hamming".chars())

        );

    }

    #[test]

    fn hamming_names() {

        assert_hamming_dist(14, "Friedrich Nietzs", "Jean-Paul Sartre")

    }

    #[test]

    fn jaro_both_empty() {

        assert_eq!(1.0, jaro("", ""));

    }

    #[test]

    fn jaro_first_empty() {

        assert_eq!(0.0, jaro("", "jaro"));

    }

    #[test]

    fn jaro_second_empty() {

        assert_eq!(0.0, jaro("distance", ""));

    }

    #[test]

    fn jaro_same() {

        assert_eq!(1.0, jaro("jaro", "jaro"));

    }

    #[test]

    fn jaro_multibyte() {

        assert_delta!(0.818, jaro("testabctest", "testöঙ香test"), 0.001);

        assert_delta!(0.818, jaro("testöঙ香test", "testabctest"), 0.001);

    }

    #[test]

    fn jaro_diff_short() {

        assert_delta!(0.767, jaro("dixon", "dicksonx"), 0.001);

    }

    #[test]

    fn jaro_diff_one_character() {

        assert_eq!(0.0, jaro("a", "b"));

    }

    #[test]

    fn jaro_same_one_character() {

        assert_eq!(1.0, jaro("a", "a"));

    }

    #[test]

    fn generic_jaro_diff() {

        assert_eq!(0.0, generic_jaro(&[1, 2], &[3, 4]));

    }

    #[test]

    fn jaro_diff_one_and_two() {

        assert_delta!(0.83, jaro("a", "ab"), 0.01);

    }

    #[test]

    fn jaro_diff_two_and_one() {

        assert_delta!(0.83, jaro("ab", "a"), 0.01);

    }

    #[test]

    fn jaro_diff_no_transposition() {

        assert_delta!(0.822, jaro("dwayne", "duane"), 0.001);

    }

    #[test]

    fn jaro_diff_with_transposition() {

        assert_delta!(0.944, jaro("martha", "marhta"), 0.001);

        assert_delta!(0.6, jaro("a jke", "jane a k"), 0.001);

    }

    #[test]

    fn jaro_names() {

        assert_delta!(

            0.392,

            jaro("Friedrich Nietzsche", "Jean-Paul Sartre"),

            0.001

        );

    }

    #[test]

    fn jaro_winkler_both_empty() {

        assert_eq!(1.0, jaro_winkler("", ""));

    }

    #[test]

    fn jaro_winkler_first_empty() {

        assert_eq!(0.0, jaro_winkler("", "jaro-winkler"));

    }

    #[test]

    fn jaro_winkler_second_empty() {

        assert_eq!(0.0, jaro_winkler("distance", ""));

    }

    #[test]

    fn jaro_winkler_same() {

        assert_eq!(1.0, jaro_winkler("Jaro-Winkler", "Jaro-Winkler"));

    }

    #[test]

    fn jaro_winkler_multibyte() {

        assert_delta!(0.89, jaro_winkler("testabctest", "testöঙ香test"), 0.001);

        assert_delta!(0.89, jaro_winkler("testöঙ香test", "testabctest"), 0.001);

    }

    #[test]

    fn jaro_winkler_diff_short() {

        assert_delta!(0.813, jaro_winkler("dixon", "dicksonx"), 0.001);

        assert_delta!(0.813, jaro_winkler("dicksonx", "dixon"), 0.001);

    }

    #[test]

    fn jaro_winkler_diff_one_character() {

        assert_eq!(0.0, jaro_winkler("a", "b"));

    }

    #[test]

    fn jaro_winkler_same_one_character() {

        assert_eq!(1.0, jaro_winkler("a", "a"));

    }

    #[test]

    fn jaro_winkler_diff_no_transposition() {

        assert_delta!(0.84, jaro_winkler("dwayne", "duane"), 0.001);

    }

    #[test]

    fn jaro_winkler_diff_with_transposition() {

        assert_delta!(0.961, jaro_winkler("martha", "marhta"), 0.001);

        assert_delta!(0.6, jaro_winkler("a jke", "jane a k"), 0.001);

    }

    #[test]

    fn jaro_winkler_names() {

        assert_delta!(

            0.452,