Initial vendor packages

Signed-off-by: Valentin Popov <valentin@popov.link>

8 files changed, 1864 insertions, 0 deletions

diff --git a/vendor/strsim/.cargo-checksum.json b/vendor/strsim/.cargo-checksum.json
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+{"files":{"CHANGELOG.md":"fef521b697f2b44b73bc64603538a9849cb7d707c5227063546d4bb5ca836f03","Cargo.toml":"7face49fe821809026b3a537695cdcb12a82a5ebbed20b73cfab69b06fa309fb","LICENSE":"1e697ce8d21401fbf1bddd9b5c3fd4c4c79ae1e3bdf51f81761c85e11d5a89cd","README.md":"70806b27bbb20acf1e47aabd68530062aab834b7efeced77fcb3126179b043d0","benches/benches.rs":"2f7fae162a517378b42af04b4b077ffd563171f7341cba55b4efca3b4c30426a","src/lib.rs":"2e340450050784ea8e6e19ea36cbedcf9084ecb7829fc206cacdca4f6f069784","tests/lib.rs":"4c8207a5728b82836795e2f87d7d7834db7276082f5ded640f34822feb750cb4"},"package":"ccbca6f34534eb78dbee83f6b2c9442fea7113f43d9e80ea320f0972ae5dc08d"}
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diff --git a/vendor/strsim/CHANGELOG.md b/vendor/strsim/CHANGELOG.md
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+# Change Log
+
+This project attempts to adhere to [Semantic Versioning](http://semver.org).
+
+## [Unreleased]
+
+## [0.10.1] - (2024-01-07)
+
+### Changed
+
+- improve OSA implementation
+  - reduce runtime
+  - reduce binary size by more than `25%`
+
+- reduce binary size of Levenshtein distance
+
+- improve Damerau-Levenshtein implementation
+  - reduce memory usage from `O(N*M)` to `O(N+M)`
+  - reduce runtime in our own benchmark by more than `70%`
+  - reduce binary size by more than `25%`
+
+- only boost similarity in Jaro-Winkler once the Jaro similarity exceeds 0.7
+
+### Fixed
+
+- Fix transposition counting in Jaro and Jaro-Winkler.
+- Limit common prefix in Jaro-Winkler to 4 characters
+
+## [0.10.0] - (2020-01-31)
+
+### Added
+
+- Sørensen-Dice implementation (thanks [@robjtede](https://github.com/robjtede))
+
+## [0.9.3] - (2019-12-12)
+
+### Fixed
+
+- Fix Jaro and Jaro-Winkler when the arguments have lengths of 1 and are equal.
+  Previously, the functions would erroneously return 0 instead of 1. Thanks to
+  [@vvrably](https://github.com/vvrably) for pointing out the issue.
+
+## [0.9.2] - (2019-05-09)
+
+### Changed
+
+- Revert back to the standard library hashmap because it will use hashbrown very
+  soon
+- Remove ndarray in favor of using a single vector to represent the 2d grid in
+  Damerau-Levenshtein
+
+## [0.9.1] - (2019-04-08)
+
+### Changed
+
+- Faster Damerau-Levenshtein implementation (thanks [@lovasoa](https://github.com/lovasoa))
+
+## [0.9.0] - (2019-04-06)
+
+### Added
+
+- Generic distance functions (thanks [@lovasoa](https://github.com/lovasoa))
+
+## [0.8.0] - (2018-08-19)
+
+### Added
+
+- Normalized versions of Levenshtein and Damerau-Levenshtein (thanks [@gentoid](https://github.com/gentoid))
+
+## [0.7.0] - (2018-01-17)
+
+### Changed
+
+- Faster Levenshtein implementation (thanks [@wdv4758h](https://github.com/wdv4758h))
+
+### Removed
+
+- Remove the "against_vec" functions. They are one-liners now, so they don't
+  seem to add enough value to justify making the API larger. I didn't find
+  anybody using them when I skimmed through a GitHub search. If you do use them,
+  you can change the calls to something like:
+```rust
+let distances = strings.iter().map(|a| jaro(target, a)).collect();
+```
+
+## [0.6.0] - (2016-12-26)
+
+### Added
+
+- Add optimal string alignment distance
+
+### Fixed
+
+- Fix Damerau-Levenshtein implementation (previous implementation was actually
+  optimal string alignment; see this [Damerau-Levenshtein explanation])
+
+## [0.5.2] - (2016-11-21)
+
+### Changed
+
+- Remove Cargo generated documentation in favor of a [docs.rs] link
+
+## [0.5.1] - (2016-08-23)
+
+### Added
+
+- Add Cargo generated documentation
+
+### Fixed
+
+- Fix panic when Jaro or Jaro-Winkler are given strings both with a length of
+  one
+
+## [0.5.0] - (2016-08-11)
+
+### Changed
+
+- Make Hamming faster (thanks @IBUzPE9) when the two strings have the same
+  length but slower when they have different lengths
+
+## [0.4.1] - (2016-04-18)
+
+### Added
+
+- Add Vagrant setup for development
+- Add AppVeyor configuration for Windows CI
+
+### Fixed
+
+- Fix metrics when given strings with multibyte characters (thanks @WanzenBug)
+
+## [0.4.0] - (2015-06-10)
+
+### Added
+
+- For each metric, add a function that takes a vector of strings and returns a
+vector of results (thanks @ovarene)
+
+## [0.3.0] - (2015-04-30)
+
+### Changed
+
+- Remove usage of unstable Rust features
+
+## [0.2.5] - (2015-04-24)
+
+### Fixed
+
+- Remove unnecessary `Float` import from doc tests
+
+## [0.2.4] - (2015-04-15)
+
+### Fixed
+
+- Remove unused `core` feature flag
+
+## [0.2.3] - (2015-04-01)
+
+### Fixed
+
+- Remove now unnecessary `Float` import
+
+## [0.2.2] - (2015-03-29)
+
+### Fixed
+
+- Remove usage of `char_at` (marked as unstable)
+
+## [0.2.1] - (2015-02-20)
+
+### Fixed
+
+- Update bit vector import to match Rust update
+
+## [0.2.0] - (2015-02-19)
+
+### Added
+
+- Implement Damerau-Levenshtein
+- Add tests in docs
+
+## [0.1.1] - (2015-02-10)
+
+### Added
+
+- Configure Travis for CI
+- Add rustdoc comments
+
+### Fixed
+
+- Limit Jaro-Winkler return value to a maximum of 1.0
+- Fix float comparisons in tests
+
+## [0.1.0] - (2015-02-09)
+
+### Added
+
+- Implement Hamming, Jaro, Jaro-Winkler, and Levenshtein
+
+[Unreleased]: https://github.com/rapidfuzz/strsim-rs/compare/0.10.1...HEAD
+[0.10.1]: https://github.com/rapidfuzz/strsim-rs/compare/0.10.0...0.10.1
+[0.10.0]: https://github.com/rapidfuzz/strsim-rs/compare/0.9.3...0.10.0
+[0.9.3]: https://github.com/rapidfuzz/strsim-rs/compare/0.9.2...0.9.3
+[0.9.2]: https://github.com/rapidfuzz/strsim-rs/compare/0.9.1...0.9.2
+[0.9.1]: https://github.com/rapidfuzz/strsim-rs/compare/0.9.0...0.9.1
+[0.9.0]: https://github.com/rapidfuzz/strsim-rs/compare/0.8.0...0.9.0
+[0.8.0]: https://github.com/rapidfuzz/strsim-rs/compare/0.7.0...0.8.0
+[0.7.0]: https://github.com/rapidfuzz/strsim-rs/compare/0.6.0...0.7.0
+[0.6.0]: https://github.com/rapidfuzz/strsim-rs/compare/0.5.2...0.6.0
+[0.5.2]: https://github.com/rapidfuzz/strsim-rs/compare/0.5.1...0.5.2
+[0.5.1]: https://github.com/rapidfuzz/strsim-rs/compare/0.5.0...0.5.1
+[0.5.0]: https://github.com/rapidfuzz/strsim-rs/compare/0.4.1...0.5.0
+[0.4.1]: https://github.com/rapidfuzz/strsim-rs/compare/0.4.0...0.4.1
+[0.4.0]: https://github.com/rapidfuzz/strsim-rs/compare/0.3.0...0.4.0
+[0.3.0]: https://github.com/rapidfuzz/strsim-rs/compare/0.2.5...0.3.0
+[0.2.5]: https://github.com/rapidfuzz/strsim-rs/compare/0.2.4...0.2.5
+[0.2.4]: https://github.com/rapidfuzz/strsim-rs/compare/0.2.3...0.2.4
+[0.2.3]: https://github.com/rapidfuzz/strsim-rs/compare/0.2.2...0.2.3
+[0.2.2]: https://github.com/rapidfuzz/strsim-rs/compare/0.2.1...0.2.2
+[0.2.1]: https://github.com/rapidfuzz/strsim-rs/compare/0.2.0...0.2.1
+[0.2.0]: https://github.com/rapidfuzz/strsim-rs/compare/0.1.1...0.2.0
+[0.1.1]: https://github.com/rapidfuzz/strsim-rs/compare/0.1.0...0.1.1
+[0.1.0]: https://github.com/rapidfuzz/strsim-rs/compare/fabad4...0.1.0
+[docs.rs]: https://docs.rs/strsim/
+[Damerau-Levenshtein explanation]:
+http://scarcitycomputing.blogspot.com/2013/04/damerau-levenshtein-edit-distance.html
diff --git a/vendor/strsim/Cargo.toml b/vendor/strsim/Cargo.toml
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@@ -0,0 +1,39 @@
+# THIS FILE IS AUTOMATICALLY GENERATED BY CARGO
+#
+# When uploading crates to the registry Cargo will automatically
+# "normalize" Cargo.toml files for maximal compatibility
+# with all versions of Cargo and also rewrite `path` dependencies
+# to registry (e.g., crates.io) dependencies.
+#
+# If you are reading this file be aware that the original Cargo.toml
+# will likely look very different (and much more reasonable).
+# See Cargo.toml.orig for the original contents.
+
+[package]
+name = "strsim"
+version = "0.10.1"
+authors = [
+    "Danny Guo <danny@dannyguo.com>",
+    "maxbachmann <oss@maxbachmann.de>",
+]
+exclude = [
+    "/.github",
+    "/dev",
+]
+description = """
+Implementations of string similarity metrics. Includes Hamming, Levenshtein,
+OSA, Damerau-Levenshtein, Jaro, Jaro-Winkler, and Sørensen-Dice.
+"""
+homepage = "https://github.com/rapidfuzz/strsim-rs"
+documentation = "https://docs.rs/strsim/"
+readme = "README.md"
+keywords = [
+    "string",
+    "similarity",
+    "Hamming",
+    "Levenshtein",
+    "Jaro",
+]
+categories = ["text-processing"]
+license = "MIT"
+repository = "https://github.com/rapidfuzz/strsim-rs"
diff --git a/vendor/strsim/LICENSE b/vendor/strsim/LICENSE
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index 0000000..8d1fbe1
--- /dev/null
+++ b/vendor/strsim/LICENSE
@@ -0,0 +1,23 @@
+The MIT License (MIT)
+
+Copyright (c) 2015 Danny Guo
+Copyright (c) 2016 Titus Wormer <tituswormer@gmail.com>
+Copyright (c) 2018 Akash Kurdekar
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/vendor/strsim/README.md b/vendor/strsim/README.md
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index 0000000..f3dcd96
--- /dev/null
+++ b/vendor/strsim/README.md
@@ -0,0 +1,102 @@
+# strsim-rs
+
+[![Crates.io](https://img.shields.io/crates/v/strsim.svg)](https://crates.io/crates/strsim)
+[![Crates.io](https://img.shields.io/crates/l/strsim.svg?maxAge=2592000)](https://github.com/rapidfuzz/strsim-rs/blob/main/LICENSE)
+[![CI status](https://github.com/rapidfuzz/strsim-rs/workflows/CI/badge.svg)](https://github.com/rapidfuzz/strsim-rs/actions?query=branch%3Amain)
+[![unsafe forbidden](https://img.shields.io/badge/unsafe-forbidden-success.svg)](https://github.com/rust-secure-code/safety-dance/)
+
+[Rust](https://www.rust-lang.org) implementations of [string similarity metrics]:
+  - [Hamming]
+  - [Levenshtein] - distance & normalized
+  - [Optimal string alignment]
+  - [Damerau-Levenshtein] - distance & normalized
+  - [Jaro and Jaro-Winkler]
+  - [Sørensen-Dice]
+
+The normalized versions return values between `0.0` and `1.0`, where `1.0` means
+an exact match.
+
+There are also generic versions of the functions for non-string inputs.
+
+## Installation
+
+`strsim` is available on [crates.io](https://crates.io/crates/strsim). Add it to
+your `Cargo.toml`:
+```toml
+[dependencies]
+strsim = "0.10.0"
+```
+
+## Usage
+
+Go to [Docs.rs](https://docs.rs/strsim/) for the full documentation. You can
+also clone the repo, and run `$ cargo doc --open`.
+
+### Examples
+
+```rust
+extern crate strsim;
+
+use strsim::{hamming, levenshtein, normalized_levenshtein, osa_distance,
+             damerau_levenshtein, normalized_damerau_levenshtein, jaro,
+             jaro_winkler, sorensen_dice};
+
+fn main() {
+    match hamming("hamming", "hammers") {
+        Ok(distance) => assert_eq!(3, distance),
+        Err(why) => panic!("{:?}", why)
+    }
+
+    assert_eq!(levenshtein("kitten", "sitting"), 3);
+
+    assert!((normalized_levenshtein("kitten", "sitting") - 0.571).abs() < 0.001);
+
+    assert_eq!(osa_distance("ac", "cba"), 3);
+
+    assert_eq!(damerau_levenshtein("ac", "cba"), 2);
+
+    assert!((normalized_damerau_levenshtein("levenshtein", "löwenbräu") - 0.272).abs() <
+            0.001);
+
+    assert!((jaro("Friedrich Nietzsche", "Jean-Paul Sartre") - 0.392).abs() <
+            0.001);
+
+    assert!((jaro_winkler("cheeseburger", "cheese fries") - 0.911).abs() <
+            0.001);
+
+    assert_eq!(sorensen_dice("web applications", "applications of the web"),
+        0.7878787878787878);
+}
+```
+
+Using the generic versions of the functions:
+
+```rust
+extern crate strsim;
+
+use strsim::generic_levenshtein;
+
+fn main() {
+    assert_eq!(2, generic_levenshtein(&[1, 2, 3], &[0, 2, 5]));
+}
+```
+
+## Contributing
+
+If you don't want to install Rust itself, you can run `$ ./dev` for a
+development CLI if you have [Docker] installed.
+
+Benchmarks require a Nightly toolchain. Run `$ cargo +nightly bench`.
+
+## License
+
+[MIT](https://github.com/rapidfuzz/strsim-rs/blob/main/LICENSE)
+
+[string similarity metrics]:http://en.wikipedia.org/wiki/String_metric
+[Damerau-Levenshtein]:http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance
+[Jaro and Jaro-Winkler]:http://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance
+[Levenshtein]:http://en.wikipedia.org/wiki/Levenshtein_distance
+[Hamming]:http://en.wikipedia.org/wiki/Hamming_distance
+[Optimal string alignment]:https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance#Optimal_string_alignment_distance
+[Sørensen-Dice]:http://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
+[Docker]:https://docs.docker.com/engine/installation/
diff --git a/vendor/strsim/benches/benches.rs b/vendor/strsim/benches/benches.rs
new file mode 100644
index 0000000..15c7041
--- /dev/null
+++ b/vendor/strsim/benches/benches.rs
@@ -0,0 +1,95 @@
+//! Benchmarks for strsim.
+
+#![feature(test)]
+
+extern crate strsim;
+extern crate test;
+use self::test::Bencher;
+
+#[bench]
+fn bench_hamming(bencher: &mut Bencher) {
+    let a = "ACAAGATGCCATTGTCCCCCGGCCTCCTGCTGCTGCTGCTCTCCGGGG";
+    let b = "CCTGGAGGGTGGCCCCACCGGCCGAGACAGCGAGCATATGCAGGAAGC";
+    bencher.iter(|| {
+        strsim::hamming(a, b).unwrap();
+    })
+}
+
+#[bench]
+fn bench_jaro(bencher: &mut Bencher) {
+    let a = "Philosopher Friedrich Nietzsche";
+    let b = "Philosopher Jean-Paul Sartre";
+    bencher.iter(|| {
+        strsim::jaro(a, b);
+    })
+}
+
+#[bench]
+fn bench_jaro_winkler(bencher: &mut Bencher) {
+    let a = "Philosopher Friedrich Nietzsche";
+    let b = "Philosopher Jean-Paul Sartre";
+    bencher.iter(|| {
+        strsim::jaro_winkler(a, b);
+    })
+}
+
+#[bench]
+fn bench_levenshtein(bencher: &mut Bencher) {
+    let a = "Philosopher Friedrich Nietzsche";
+    let b = "Philosopher Jean-Paul Sartre";
+    bencher.iter(|| {
+        strsim::levenshtein(a, b);
+    })
+}
+
+#[bench]
+fn bench_levenshtein_on_u8(bencher: &mut Bencher) {
+    bencher.iter(|| {
+        strsim::generic_levenshtein(&vec![0u8; 30], &vec![7u8; 31]);
+    })
+}
+
+#[bench]
+fn bench_normalized_levenshtein(bencher: &mut Bencher) {
+    let a = "Philosopher Friedrich Nietzsche";
+    let b = "Philosopher Jean-Paul Sartre";
+    bencher.iter(|| {
+        strsim::normalized_levenshtein(a, b);
+    })
+}
+
+#[bench]
+fn bench_osa_distance(bencher: &mut Bencher) {
+    let a = "Philosopher Friedrich Nietzsche";
+    let b = "Philosopher Jean-Paul Sartre";
+    bencher.iter(|| {
+        strsim::osa_distance(a, b);
+    })
+}
+
+#[bench]
+fn bench_damerau_levenshtein(bencher: &mut Bencher) {
+    let a = "Philosopher Friedrich Nietzsche";
+    let b = "Philosopher Jean-Paul Sartre";
+    bencher.iter(|| {
+        strsim::damerau_levenshtein(a, b);
+    })
+}
+
+#[bench]
+fn bench_normalized_damerau_levenshtein(bencher: &mut Bencher) {
+    let a = "Philosopher Friedrich Nietzsche";
+    let b = "Philosopher Jean-Paul Sartre";
+    bencher.iter(|| {
+        strsim::normalized_damerau_levenshtein(a, b);
+    })
+}
+
+#[bench]
+fn bench_sorensen_dice(bencher: &mut Bencher) {
+    let a = "Philosopher Friedrich Nietzsche";
+    let b = "Philosopher Jean-Paul Sartre";
+    bencher.iter(|| {
+        strsim::sorensen_dice(a, b);
+    })
+}
diff --git a/vendor/strsim/src/lib.rs b/vendor/strsim/src/lib.rs
new file mode 100644
index 0000000..8118277
--- /dev/null
+++ b/vendor/strsim/src/lib.rs
@@ -0,0 +1,1307 @@
+//! 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")
+        );
+    }
+}
diff --git a/vendor/strsim/tests/lib.rs b/vendor/strsim/tests/lib.rs
new file mode 100644
index 0000000..991fc6f
--- /dev/null
+++ b/vendor/strsim/tests/lib.rs
@@ -0,0 +1,71 @@
+extern crate strsim;
+
+use strsim::{
+    damerau_levenshtein, hamming, jaro, jaro_winkler, levenshtein, normalized_damerau_levenshtein,
+    normalized_levenshtein, osa_distance,
+};
+
+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 hamming_works() {
+    match hamming("hamming", "hammers") {
+        Ok(distance) => assert_eq!(3, distance),
+        Err(why) => panic!("{:?}", why),
+    }
+}
+
+#[test]
+fn levenshtein_works() {
+    assert_eq!(3, levenshtein("kitten", "sitting"));
+}
+
+#[test]
+fn normalized_levenshtein_works() {
+    assert_delta!(0.57142, normalized_levenshtein("kitten", "sitting"));
+}
+
+#[test]
+fn osa_distance_works() {
+    assert_eq!(3, osa_distance("ac", "cba"));
+}
+
+#[test]
+fn damerau_levenshtein_works() {
+    assert_eq!(2, damerau_levenshtein("ac", "cba"));
+}
+
+#[test]
+fn normalized_damerau_levenshtein_works() {
+    assert_delta!(
+        0.27272,
+        normalized_damerau_levenshtein("levenshtein", "löwenbräu")
+    );
+}
+
+#[test]
+fn jaro_works() {
+    assert_delta!(
+        0.392,
+        jaro("Friedrich Nietzsche", "Jean-Paul Sartre"),
+        0.001
+    );
+}
+
+#[test]
+fn jaro_winkler_works() {
+    assert_delta!(0.866, jaro_winkler("cheeseburger", "cheese fries"), 0.001);
+}