1 files changed, 177 insertions, 0 deletions
diff --git a/compiler/rustc_span/src/lev_distance.rs b/compiler/rustc_span/src/lev_distance.rs
new file mode 100644
index 000000000..61e4b98a8
--- /dev/null
+++ b/compiler/rustc_span/src/lev_distance.rs
@@ -0,0 +1,177 @@
+//! Levenshtein distances.
+//!
+//! The [Levenshtein distance] is a metric for measuring the difference between two strings.
+//!
+//! [Levenshtein distance]: https://en.wikipedia.org/wiki/Levenshtein_distance
+
+use crate::symbol::Symbol;
+use std::cmp;
+
+#[cfg(test)]
+mod tests;
+
+/// Finds the Levenshtein distance between two strings.
+///
+/// Returns None if the distance exceeds the limit.
+pub fn lev_distance(a: &str, b: &str, limit: usize) -> Option<usize> {
+    let n = a.chars().count();
+    let m = b.chars().count();
+    let min_dist = if n < m { m - n } else { n - m };
+
+    if min_dist > limit {
+        return None;
+    }
+    if n == 0 || m == 0 {
+        return (min_dist <= limit).then_some(min_dist);
+    }
+
+    let mut dcol: Vec<_> = (0..=m).collect();
+
+    for (i, sc) in a.chars().enumerate() {
+        let mut current = i;
+        dcol[0] = current + 1;
+
+        for (j, tc) in b.chars().enumerate() {
+            let next = dcol[j + 1];
+            if sc == tc {
+                dcol[j + 1] = current;
+            } else {
+                dcol[j + 1] = cmp::min(current, next);
+                dcol[j + 1] = cmp::min(dcol[j + 1], dcol[j]) + 1;
+            }
+            current = next;
+        }
+    }
+
+    (dcol[m] <= limit).then_some(dcol[m])
+}
+
+/// Provides a word similarity score between two words that accounts for substrings being more
+/// meaningful than a typical Levenshtein distance. The lower the score, the closer the match.
+/// 0 is an identical match.
+///
+/// Uses the Levenshtein distance between the two strings and removes the cost of the length
+/// difference. If this is 0 then it is either a substring match or a full word match, in the
+/// substring match case we detect this and return `1`. To prevent finding meaningless substrings,
+/// eg. "in" in "shrink", we only perform this subtraction of length difference if one of the words
+/// is not greater than twice the length of the other. For cases where the words are close in size
+/// but not an exact substring then the cost of the length difference is discounted by half.
+///
+/// Returns `None` if the distance exceeds the limit.
+pub fn lev_distance_with_substrings(a: &str, b: &str, limit: usize) -> Option<usize> {
+    let n = a.chars().count();
+    let m = b.chars().count();
+
+    // Check one isn't less than half the length of the other. If this is true then there is a
+    // big difference in length.
+    let big_len_diff = (n * 2) < m || (m * 2) < n;
+    let len_diff = if n < m { m - n } else { n - m };
+    let lev = lev_distance(a, b, limit + len_diff)?;
+
+    // This is the crux, subtracting length difference means exact substring matches will now be 0
+    let score = lev - len_diff;
+
+    // If the score is 0 but the words have different lengths then it's a substring match not a full
+    // word match
+    let score = if score == 0 && len_diff > 0 && !big_len_diff {
+        1 // Exact substring match, but not a total word match so return non-zero
+    } else if !big_len_diff {
+        // Not a big difference in length, discount cost of length difference
+        score + (len_diff + 1) / 2
+    } else {
+        // A big difference in length, add back the difference in length to the score
+        score + len_diff
+    };
+
+    (score <= limit).then_some(score)
+}
+
+/// Finds the best match for given word in the given iterator where substrings are meaningful.
+///
+/// A version of [`find_best_match_for_name`] that uses [`lev_distance_with_substrings`] as the score
+/// for word similarity. This takes an optional distance limit which defaults to one-third of the
+/// given word.
+///
+/// Besides the modified Levenshtein, we use case insensitive comparison to improve accuracy
+/// on an edge case with a lower(upper)case letters mismatch.
+pub fn find_best_match_for_name_with_substrings(
+    candidates: &[Symbol],
+    lookup: Symbol,
+    dist: Option<usize>,
+) -> Option<Symbol> {
+    find_best_match_for_name_impl(true, candidates, lookup, dist)
+}
+
+/// Finds the best match for a given word in the given iterator.
+///
+/// As a loose rule to avoid the obviously incorrect suggestions, it takes
+/// an optional limit for the maximum allowable edit distance, which defaults
+/// to one-third of the given word.
+///
+/// Besides Levenshtein, we use case insensitive comparison to improve accuracy
+/// on an edge case with a lower(upper)case letters mismatch.
+pub fn find_best_match_for_name(
+    candidates: &[Symbol],
+    lookup: Symbol,
+    dist: Option<usize>,
+) -> Option<Symbol> {
+    find_best_match_for_name_impl(false, candidates, lookup, dist)
+}
+
+#[cold]
+fn find_best_match_for_name_impl(
+    use_substring_score: bool,
+    candidates: &[Symbol],
+    lookup: Symbol,
+    dist: Option<usize>,
+) -> Option<Symbol> {
+    let lookup = lookup.as_str();
+    let lookup_uppercase = lookup.to_uppercase();
+
+    // Priority of matches:
+    // 1. Exact case insensitive match
+    // 2. Levenshtein distance match
+    // 3. Sorted word match
+    if let Some(c) = candidates.iter().find(|c| c.as_str().to_uppercase() == lookup_uppercase) {
+        return Some(*c);
+    }
+
+    let mut dist = dist.unwrap_or_else(|| cmp::max(lookup.len(), 3) / 3);
+    let mut best = None;
+    for c in candidates {
+        match if use_substring_score {
+            lev_distance_with_substrings(lookup, c.as_str(), dist)
+        } else {
+            lev_distance(lookup, c.as_str(), dist)
+        } {
+            Some(0) => return Some(*c),
+            Some(d) => {
+                dist = d - 1;
+                best = Some(*c);
+            }
+            None => {}
+        }
+    }
+    if best.is_some() {
+        return best;
+    }
+
+    find_match_by_sorted_words(candidates, lookup)
+}
+
+fn find_match_by_sorted_words(iter_names: &[Symbol], lookup: &str) -> Option<Symbol> {
+    iter_names.iter().fold(None, |result, candidate| {
+        if sort_by_words(candidate.as_str()) == sort_by_words(lookup) {
+            Some(*candidate)
+        } else {
+            result
+        }
+    })
+}
+
+fn sort_by_words(name: &str) -> String {
+    let mut split_words: Vec<&str> = name.split('_').collect();
+    // We are sorting primitive &strs and can use unstable sort here.
+    split_words.sort_unstable();
+    split_words.join("_")
+}