Deleted vendor folder

1 files changed, 0 insertions, 1307 deletions

diff --git a/vendor/strsim/src/lib.rs b/vendor/strsim/src/lib.rs
deleted file mode 100644
index 8118277..0000000
--- a/vendor/strsim/src/lib.rs
+++ /dev/null
@@ -1,1307 +0,0 @@
-//! 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,
-            jaro_winkler("Friedrich Nietzsche", "Fran-Paul Sartre"),
-            0.001
-        );
-    }
-
-    #[test]
-    fn jaro_winkler_long_prefix() {
-        assert_delta!(0.866, jaro_winkler("cheeseburger", "cheese fries"), 0.001);
-    }
-
-    #[test]
-    fn jaro_winkler_more_names() {
-        assert_delta!(0.868, jaro_winkler("Thorkel", "Thorgier"), 0.001);
-    }
-
-    #[test]
-    fn jaro_winkler_length_of_one() {
-        assert_delta!(0.738, jaro_winkler("Dinsdale", "D"), 0.001);
-    }
-
-    #[test]
-    fn jaro_winkler_very_long_prefix() {
-        assert_delta!(
-            0.98519,
-            jaro_winkler("thequickbrownfoxjumpedoverx", "thequickbrownfoxjumpedovery")
-        );
-    }
-
-    #[test]
-    fn levenshtein_empty() {
-        assert_eq!(0, levenshtein("", ""));
-    }
-
-    #[test]
-    fn levenshtein_same() {
-        assert_eq!(0, levenshtein("levenshtein", "levenshtein"));
-    }
-
-    #[test]
-    fn levenshtein_diff_short() {
-        assert_eq!(3, levenshtein("kitten", "sitting"));
-    }
-
-    #[test]
-    fn levenshtein_diff_with_space() {
-        assert_eq!(5, levenshtein("hello, world", "bye, world"));
-    }
-
-    #[test]
-    fn levenshtein_diff_multibyte() {
-        assert_eq!(3, levenshtein("öঙ香", "abc"));
-        assert_eq!(3, levenshtein("abc", "öঙ香"));
-    }
-
-    #[test]
-    fn levenshtein_diff_longer() {
-        let a = "The quick brown fox jumped over the angry dog.";
-        let b = "Lorem ipsum dolor sit amet, dicta latine an eam.";
-        assert_eq!(37, levenshtein(a, b));
-    }
-
-    #[test]
-    fn levenshtein_first_empty() {
-        assert_eq!(7, levenshtein("", "sitting"));
-    }
-
-    #[test]
-    fn levenshtein_second_empty() {
-        assert_eq!(6, levenshtein("kitten", ""));
-    }
-
-    #[test]
-    fn normalized_levenshtein_diff_short() {
-        assert_delta!(0.57142, normalized_levenshtein("kitten", "sitting"));
-    }
-
-    #[test]
-    fn normalized_levenshtein_for_empty_strings() {
-        assert_delta!(1.0, normalized_levenshtein("", ""));
-    }
-
-    #[test]
-    fn normalized_levenshtein_first_empty() {
-        assert_delta!(0.0, normalized_levenshtein("", "second"));
-    }
-
-    #[test]
-    fn normalized_levenshtein_second_empty() {
-        assert_delta!(0.0, normalized_levenshtein("first", ""));
-    }
-
-    #[test]
-    fn normalized_levenshtein_identical_strings() {
-        assert_delta!(1.0, normalized_levenshtein("identical", "identical"));
-    }
-
-    #[test]
-    fn osa_distance_empty() {
-        assert_eq!(0, osa_distance("", ""));
-    }
-
-    #[test]
-    fn osa_distance_same() {
-        assert_eq!(0, osa_distance("damerau", "damerau"));
-    }
-
-    #[test]
-    fn osa_distance_first_empty() {
-        assert_eq!(7, osa_distance("", "damerau"));
-    }
-
-    #[test]
-    fn osa_distance_second_empty() {
-        assert_eq!(7, osa_distance("damerau", ""));
-    }
-
-    #[test]
-    fn osa_distance_diff() {
-        assert_eq!(3, osa_distance("ca", "abc"));
-    }
-
-    #[test]
-    fn osa_distance_diff_short() {
-        assert_eq!(3, osa_distance("damerau", "aderua"));
-    }
-
-    #[test]
-    fn osa_distance_diff_reversed() {
-        assert_eq!(3, osa_distance("aderua", "damerau"));
-    }
-
-    #[test]
-    fn osa_distance_diff_multibyte() {
-        assert_eq!(3, osa_distance("öঙ香", "abc"));
-        assert_eq!(3, osa_distance("abc", "öঙ香"));
-    }
-
-    #[test]
-    fn osa_distance_diff_unequal_length() {
-        assert_eq!(6, osa_distance("damerau", "aderuaxyz"));
-    }
-
-    #[test]
-    fn osa_distance_diff_unequal_length_reversed() {
-        assert_eq!(6, osa_distance("aderuaxyz", "damerau"));
-    }
-
-    #[test]
-    fn osa_distance_diff_comedians() {
-        assert_eq!(5, osa_distance("Stewart", "Colbert"));
-    }
-
-    #[test]
-    fn osa_distance_many_transpositions() {
-        assert_eq!(4, osa_distance("abcdefghijkl", "bacedfgihjlk"));
-    }
-
-    #[test]
-    fn osa_distance_diff_longer() {
-        let a = "The quick brown fox jumped over the angry dog.";
-        let b = "Lehem ipsum dolor sit amet, dicta latine an eam.";
-        assert_eq!(36, osa_distance(a, b));
-    }
-
-    #[test]
-    fn osa_distance_beginning_transposition() {
-        assert_eq!(1, osa_distance("foobar", "ofobar"));
-    }
-
-    #[test]
-    fn osa_distance_end_transposition() {
-        assert_eq!(1, osa_distance("specter", "spectre"));
-    }
-
-    #[test]
-    fn osa_distance_restricted_edit() {
-        assert_eq!(4, osa_distance("a cat", "an abct"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_empty() {
-        assert_eq!(0, damerau_levenshtein("", ""));
-    }
-
-    #[test]
-    fn damerau_levenshtein_same() {
-        assert_eq!(0, damerau_levenshtein("damerau", "damerau"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_first_empty() {
-        assert_eq!(7, damerau_levenshtein("", "damerau"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_second_empty() {
-        assert_eq!(7, damerau_levenshtein("damerau", ""));
-    }
-
-    #[test]
-    fn damerau_levenshtein_diff() {
-        assert_eq!(2, damerau_levenshtein("ca", "abc"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_diff_short() {
-        assert_eq!(3, damerau_levenshtein("damerau", "aderua"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_diff_reversed() {
-        assert_eq!(3, damerau_levenshtein("aderua", "damerau"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_diff_multibyte() {
-        assert_eq!(3, damerau_levenshtein("öঙ香", "abc"));
-        assert_eq!(3, damerau_levenshtein("abc", "öঙ香"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_diff_unequal_length() {
-        assert_eq!(6, damerau_levenshtein("damerau", "aderuaxyz"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_diff_unequal_length_reversed() {
-        assert_eq!(6, damerau_levenshtein("aderuaxyz", "damerau"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_diff_comedians() {
-        assert_eq!(5, damerau_levenshtein("Stewart", "Colbert"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_many_transpositions() {
-        assert_eq!(4, damerau_levenshtein("abcdefghijkl", "bacedfgihjlk"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_diff_longer() {
-        let a = "The quick brown fox jumped over the angry dog.";
-        let b = "Lehem ipsum dolor sit amet, dicta latine an eam.";
-        assert_eq!(36, damerau_levenshtein(a, b));
-    }
-
-    #[test]
-    fn damerau_levenshtein_beginning_transposition() {
-        assert_eq!(1, damerau_levenshtein("foobar", "ofobar"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_end_transposition() {
-        assert_eq!(1, damerau_levenshtein("specter", "spectre"));
-    }
-
-    #[test]
-    fn damerau_levenshtein_unrestricted_edit() {
-        assert_eq!(3, damerau_levenshtein("a cat", "an abct"));
-    }
-
-    #[test]
-    fn normalized_damerau_levenshtein_diff_short() {
-        assert_delta!(
-            0.27272,
-            normalized_damerau_levenshtein("levenshtein", "löwenbräu")
-        );
-    }
-
-    #[test]
-    fn normalized_damerau_levenshtein_for_empty_strings() {
-        assert_delta!(1.0, normalized_damerau_levenshtein("", ""));
-    }
-
-    #[test]
-    fn normalized_damerau_levenshtein_first_empty() {
-        assert_delta!(0.0, normalized_damerau_levenshtein("", "flower"));
-    }
-
-    #[test]
-    fn normalized_damerau_levenshtein_second_empty() {
-        assert_delta!(0.0, normalized_damerau_levenshtein("tree", ""));
-    }
-
-    #[test]
-    fn normalized_damerau_levenshtein_identical_strings() {
-        assert_delta!(
-            1.0,
-            normalized_damerau_levenshtein("sunglasses", "sunglasses")
-        );
-    }
-
-    #[test]
-    fn sorensen_dice_all() {
-        // test cases taken from
-        // https://github.com/aceakash/string-similarity/blob/f83ba3cd7bae874c20c429774e911ae8cff8bced/src/spec/index.spec.js#L11
-
-        assert_delta!(1.0, sorensen_dice("a", "a"));
-        assert_delta!(0.0, sorensen_dice("a", "b"));
-        assert_delta!(1.0, sorensen_dice("", ""));
-        assert_delta!(0.0, sorensen_dice("a", ""));
-        assert_delta!(0.0, sorensen_dice("", "a"));
-        assert_delta!(1.0, sorensen_dice("apple event", "apple    event"));
-        assert_delta!(0.90909, sorensen_dice("iphone", "iphone x"));
-        assert_delta!(0.0, sorensen_dice("french", "quebec"));
-        assert_delta!(1.0, sorensen_dice("france", "france"));
-        assert_delta!(0.2, sorensen_dice("fRaNce", "france"));
-        assert_delta!(0.8, sorensen_dice("healed", "sealed"));
-        assert_delta!(
-            0.78788,
-            sorensen_dice("web applications", "applications of the web")
-        );
-        assert_delta!(
-            0.92,
-            sorensen_dice(
-                "this will have a typo somewhere",
-                "this will huve a typo somewhere"
-            )
-        );
-        assert_delta!(
-            0.60606,
-            sorensen_dice(
-                "Olive-green table for sale, in extremely good condition.",
-                "For sale: table in very good  condition, olive green in colour."
-            )
-        );
-        assert_delta!(
-            0.25581,
-            sorensen_dice(
-                "Olive-green table for sale, in extremely good condition.",
-                "For sale: green Subaru Impreza, 210,000 miles"
-            )
-        );
-        assert_delta!(
-            0.14118,
-            sorensen_dice(
-                "Olive-green table for sale, in extremely good condition.",
-                "Wanted: mountain bike with at least 21 gears."
-            )
-        );
-        assert_delta!(
-            0.77419,
-            sorensen_dice("this has one extra word", "this has one word")
-        );
-    }
-}