rustc_pattern_analysis/
pat_column.rs

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1use crate::constructor::{Constructor, SplitConstructorSet};
2use crate::pat::{DeconstructedPat, PatOrWild};
3use crate::{MatchArm, PatCx};
4
5/// A column of patterns in a match, where a column is the intuitive notion of "subpatterns that
6/// inspect the same subvalue/place".
7/// This is used to traverse patterns column-by-column for lints. Despite similarities with the
8/// algorithm in [`crate::usefulness`], this does a different traversal. Notably this is linear in
9/// the depth of patterns, whereas `compute_exhaustiveness_and_usefulness` is worst-case exponential
10/// (exhaustiveness is NP-complete). The core difference is that we treat sub-columns separately.
11///
12/// This is not used in the usefulness algorithm; only in lints.
13#[derive(Debug)]
14pub struct PatternColumn<'p, Cx: PatCx> {
15    /// This must not contain an or-pattern. `expand_and_push` takes care to expand them.
16    patterns: Vec<&'p DeconstructedPat<Cx>>,
17}
18
19impl<'p, Cx: PatCx> PatternColumn<'p, Cx> {
20    pub fn new(arms: &[MatchArm<'p, Cx>]) -> Self {
21        let patterns = Vec::with_capacity(arms.len());
22        let mut column = PatternColumn { patterns };
23        for arm in arms {
24            column.expand_and_push(PatOrWild::Pat(arm.pat));
25        }
26        column
27    }
28    /// Pushes a pattern onto the column, expanding any or-patterns into its subpatterns.
29    /// Internal method, prefer [`PatternColumn::new`].
30    fn expand_and_push(&mut self, pat: PatOrWild<'p, Cx>) {
31        // We flatten or-patterns and skip algorithm-generated wildcards.
32        if pat.is_or_pat() {
33            self.patterns.extend(
34                pat.flatten_or_pat().into_iter().filter_map(|pat_or_wild| pat_or_wild.as_pat()),
35            )
36        } else if let Some(pat) = pat.as_pat() {
37            self.patterns.push(pat)
38        }
39    }
40
41    pub fn head_ty(&self) -> Option<&Cx::Ty> {
42        self.patterns.first().map(|pat| pat.ty())
43    }
44    pub fn iter(&self) -> impl Iterator<Item = &'p DeconstructedPat<Cx>> {
45        self.patterns.iter().copied()
46    }
47
48    /// Do constructor splitting on the constructors of the column.
49    pub fn analyze_ctors(
50        &self,
51        cx: &Cx,
52        ty: &Cx::Ty,
53    ) -> Result<SplitConstructorSet<Cx>, Cx::Error> {
54        let column_ctors = self.patterns.iter().map(|p| p.ctor());
55        let ctors_for_ty = cx.ctors_for_ty(ty)?;
56        Ok(ctors_for_ty.split(column_ctors))
57    }
58
59    /// Does specialization: given a constructor, this takes the patterns from the column that match
60    /// the constructor, and outputs their fields.
61    /// This returns one column per field of the constructor. They usually all have the same length
62    /// (the number of patterns in `self` that matched `ctor`), except that we expand or-patterns
63    /// which may change the lengths.
64    pub fn specialize(
65        &self,
66        cx: &Cx,
67        ty: &Cx::Ty,
68        ctor: &Constructor<Cx>,
69    ) -> Vec<PatternColumn<'p, Cx>> {
70        let arity = ctor.arity(cx, ty);
71        if arity == 0 {
72            return Vec::new();
73        }
74
75        // We specialize the column by `ctor`. This gives us `arity`-many columns of patterns. These
76        // columns may have different lengths in the presence of or-patterns (this is why we can't
77        // reuse `Matrix`).
78        let mut specialized_columns: Vec<_> =
79            (0..arity).map(|_| Self { patterns: Vec::new() }).collect();
80        let relevant_patterns =
81            self.patterns.iter().filter(|pat| ctor.is_covered_by(cx, pat.ctor()).unwrap_or(false));
82        for pat in relevant_patterns {
83            let specialized = pat.specialize(ctor, arity);
84            for (subpat, column) in specialized.into_iter().zip(&mut specialized_columns) {
85                column.expand_and_push(subpat);
86            }
87        }
88        specialized_columns
89    }
90}
rustc_pattern_analysis/pat_column.rs

rustc_pattern_analysis/
pat_column.rs
