1 files changed, 0 insertions, 177 deletions
diff --git a/compiler/rustc_span/src/lev_distance.rs b/compiler/rustc_span/src/lev_distance.rs
deleted file mode 100644
index 61e4b98a8..000000000
--- a/compiler/rustc_span/src/lev_distance.rs
+++ /dev/null
@@ -1,177 +0,0 @@
-//! 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("_")
-}