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91 lines
3.7 KiB
Rust
91 lines
3.7 KiB
Rust
use crate::constructor::{Constructor, SplitConstructorSet};
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use crate::pat::{DeconstructedPat, PatOrWild};
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use crate::{MatchArm, PatCx};
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/// A column of patterns in a match, where a column is the intuitive notion of "subpatterns that
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/// inspect the same subvalue/place".
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/// This is used to traverse patterns column-by-column for lints. Despite similarities with the
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/// algorithm in [`crate::usefulness`], this does a different traversal. Notably this is linear in
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/// the depth of patterns, whereas `compute_exhaustiveness_and_usefulness` is worst-case exponential
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/// (exhaustiveness is NP-complete). The core difference is that we treat sub-columns separately.
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///
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/// This is not used in the usefulness algorithm; only in lints.
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#[derive(Debug)]
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pub struct PatternColumn<'p, Cx: PatCx> {
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/// This must not contain an or-pattern. `expand_and_push` takes care to expand them.
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patterns: Vec<&'p DeconstructedPat<Cx>>,
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}
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impl<'p, Cx: PatCx> PatternColumn<'p, Cx> {
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pub fn new(arms: &[MatchArm<'p, Cx>]) -> Self {
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let patterns = Vec::with_capacity(arms.len());
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let mut column = PatternColumn { patterns };
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for arm in arms {
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column.expand_and_push(PatOrWild::Pat(arm.pat));
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}
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column
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}
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/// Pushes a pattern onto the column, expanding any or-patterns into its subpatterns.
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/// Internal method, prefer [`PatternColumn::new`].
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fn expand_and_push(&mut self, pat: PatOrWild<'p, Cx>) {
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// We flatten or-patterns and skip algorithm-generated wildcards.
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if pat.is_or_pat() {
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self.patterns.extend(
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pat.flatten_or_pat().into_iter().filter_map(|pat_or_wild| pat_or_wild.as_pat()),
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)
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} else if let Some(pat) = pat.as_pat() {
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self.patterns.push(pat)
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}
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}
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pub fn head_ty(&self) -> Option<&Cx::Ty> {
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self.patterns.first().map(|pat| pat.ty())
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}
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pub fn iter(&self) -> impl Iterator<Item = &'p DeconstructedPat<Cx>> {
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self.patterns.iter().copied()
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}
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/// Do constructor splitting on the constructors of the column.
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pub fn analyze_ctors(
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&self,
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cx: &Cx,
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ty: &Cx::Ty,
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) -> Result<SplitConstructorSet<Cx>, Cx::Error> {
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let column_ctors = self.patterns.iter().map(|p| p.ctor());
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let ctors_for_ty = cx.ctors_for_ty(ty)?;
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Ok(ctors_for_ty.split(column_ctors))
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}
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/// Does specialization: given a constructor, this takes the patterns from the column that match
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/// the constructor, and outputs their fields.
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/// This returns one column per field of the constructor. They usually all have the same length
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/// (the number of patterns in `self` that matched `ctor`), except that we expand or-patterns
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/// which may change the lengths.
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pub fn specialize(
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&self,
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cx: &Cx,
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ty: &Cx::Ty,
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ctor: &Constructor<Cx>,
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) -> Vec<PatternColumn<'p, Cx>> {
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let arity = ctor.arity(cx, ty);
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if arity == 0 {
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return Vec::new();
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}
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// We specialize the column by `ctor`. This gives us `arity`-many columns of patterns. These
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// columns may have different lengths in the presence of or-patterns (this is why we can't
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// reuse `Matrix`).
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let mut specialized_columns: Vec<_> =
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(0..arity).map(|_| Self { patterns: Vec::new() }).collect();
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let relevant_patterns =
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self.patterns.iter().filter(|pat| ctor.is_covered_by(cx, pat.ctor()).unwrap_or(false));
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for pat in relevant_patterns {
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let specialized = pat.specialize(ctor, arity);
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for (subpat, column) in specialized.into_iter().zip(&mut specialized_columns) {
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column.expand_and_push(subpat);
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}
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}
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specialized_columns
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}
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}
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