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[ty] Reachability constraints (#18621)

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## Summary



* Completely removes the concept of visibility constraints. Reachability
constraints are now used to model the static visibility of bindings and
declarations. Reachability constraints are *much* easier to reason about
/ work with, since they are applied at the beginning of a branch, and
not applied retroactively. Removing the duplication between visibility
and reachability constraints also leads to major code simplifications
[^1]. For an overview of how the new constraint system works, see the
updated doc comment in `reachability_constraints.rs`.
* Fixes a [control-flow modeling bug
(panic)](https://github.com/astral-sh/ty/issues/365) involving `break`
statements in loops
* Fixes a [bug where](https://github.com/astral-sh/ty/issues/624) where
`elif` branches would have wrong reachability constraints
* Fixes a [bug where](https://github.com/astral-sh/ty/issues/648) code
after infinite loops would not be considered unreachble
* Fixes a panic on the `pywin32` ecosystem project, which we should be
able to move to `good.txt` once this has been merged.
* Removes some false positives in unreachable code because we infer
`Never` more often, due to the fact that reachability constraints now
apply retroactively to *all* active bindings, not just to bindings
inside a branch.
* As one example, this removes the `division-by-zero` diagnostic from
https://github.com/astral-sh/ty/issues/443 because we now infer `Never`
for the divisor.
* Supersedes and includes similar test changes as
https://github.com/astral-sh/ruff/pull/18392


closes https://github.com/astral-sh/ty/issues/365
closes https://github.com/astral-sh/ty/issues/624
closes https://github.com/astral-sh/ty/issues/642
closes https://github.com/astral-sh/ty/issues/648

## Benchmarks

Benchmarks on black, pandas, and sympy showed that this is neither a
performance improvement, nor a regression.

## Test Plan

Regression tests for:
- [x] https://github.com/astral-sh/ty/issues/365
- [x] https://github.com/astral-sh/ty/issues/624
- [x] https://github.com/astral-sh/ty/issues/642
- [x] https://github.com/astral-sh/ty/issues/648

[^1]: I'm afraid this is something that @carljm advocated for since the
beginning, and I'm not sure anymore why we have never seriously tried
this before. So I suggest we do *not* attempt to do a historical deep
dive to find out exactly why this ever became so complicated, and just
enjoy the fact that we eventually arrived here.

---------

Co-authored-by: Carl Meyer <carl@astral.sh>
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//! # Reachability constraints
//!
//! During semantic index building, we record so-called reachability constraints that keep track
//! of a set of conditions that need to apply in order for a certain statement or expression to
//! be reachable from the start of the scope. As an example, consider the following situation where
//! we have just processed two `if`-statements:
//! ```py
//! if test:
//! <is this reachable?>
//! ```
//! In this case, we would record a reachability constraint of `test`, which would later allow us
//! to re-analyze the control flow during type-checking, once we actually know the static truthiness
//! of `test`. When evaluating a constraint, there are three possible outcomes: always true, always
//! false, or ambiguous. For a simple constraint like this, always-true and always-false correspond
//! to the case in which we can infer that the type of `test` is `Literal[True]` or `Literal[False]`.
//! In any other case, like if the type of `test` is `bool` or `Unknown`, we can not statically
//! determine whether `test` is truthy or falsy, so the outcome would be "ambiguous".
//!
//!
//! ## Sequential constraints (ternary AND)
//!
//! Whenever control flow branches, we record reachability constraints. If we already have a
//! constraint, we create a new one using a ternary AND operation. Consider the following example:
//! ```py
//! if test1:
//! if test2:
//! <is this reachable?>
//! ```
//! Here, we would accumulate a reachability constraint of `test1 AND test2`. We can statically
//! determine that this position is *always* reachable only if both `test1` and `test2` are
//! always true. On the other hand, we can statically determine that this position is *never*
//! reachable if *either* `test1` or `test2` is always false. In any other case, we can not
//! determine whether this position is reachable or not, so the outcome is "ambiguous". This
//! corresponds to a ternary *AND* operation in [Kleene] logic:
//!
//! ```text
//! | AND | always-false | ambiguous | always-true |
//! |--------------|--------------|--------------|--------------|
//! | always false | always-false | always-false | always-false |
//! | ambiguous | always-false | ambiguous | ambiguous |
//! | always true | always-false | ambiguous | always-true |
//! ```
//!
//!
//! ## Merged constraints (ternary OR)
//!
//! We also need to consider the case where control flow merges again. Consider a case like this:
//! ```py
//! def _():
//! if test1:
//! pass
//! elif test2:
//! pass
//! else:
//! return
//!
//! <is this reachable?>
//! ```
//! Here, the first branch has a `test1` constraint, and the second branch has a `test2` constraint.
//! The third branch ends in a terminal statement [^1]. When we merge control flow, we need to consider
//! the reachability through either the first or the second branch. The current position is only
//! *definitely* unreachable if both `test1` and `test2` are always false. It is definitely
//! reachable if *either* `test1` or `test2` is always true. In any other case, we can not statically
//! determine whether it is reachable or not. This operation corresponds to a ternary *OR* operation:
//!
//! ```text
//! | OR | always-false | ambiguous | always-true |
//! |--------------|--------------|--------------|--------------|
//! | always false | always-false | ambiguous | always-true |
//! | ambiguous | ambiguous | ambiguous | always-true |
//! | always true | always-true | always-true | always-true |
//! ```
//!
//! [^1]: What's actually happening here is that we merge all three branches using a ternary OR. The
//! third branch has a reachability constraint of `always-false`, and `t OR always-false` is equal
//! to `t` (see first column in that table), so it was okay to omit the third branch in the discussion
//! above.
//!
//!
//! ## Negation
//!
//! Control flow elements like `if-elif-else` or `match` statements can also lead to negated
//! constraints. For example, we record a constraint of `~test` for the `else` branch here:
//! ```py
//! if test:
//! pass
//! else:
//! <is this reachable?>
//! ```
//!
//! ## Explicit ambiguity
//!
//! In some cases, we explicitly record an “ambiguous” constraint. We do this when branching on
//! something that we can not (or intentionally do not want to) analyze statically. `for` loops are
//! one example:
//! ```py
//! def _():
//! for _ in range(2):
//! return
//!
//! <is this reachable?>
//! ```
//! If we would not record any constraints at the branching point, we would have an `always-true`
//! reachability for the no-loop branch, and a `always-false` reachability for the branch which enters
//! the loop. Merging those would lead to a reachability of `always-true OR always-false = always-true`,
//! i.e. we would consider the end of the scope to be unconditionally reachable, which is not correct.
//!
//! Recording an ambiguous constraint at the branching point modifies the constraints in both branches to
//! `always-true AND ambiguous = ambiguous` and `always-false AND ambiguous = always-false`, respectively.
//! Merging these two using OR correctly leads to `ambiguous` for the end-of-scope reachability.
//!
//!
//! ## Reachability constraints and bindings
//!
//! To understand how reachability constraints apply to bindings in particular, consider the following
//! example:
//! ```py
//! x = <unbound> # not a live binding for the use of x below, shadowed by `x = 1`
//! y = <unbound> # reachability constraint: ~test
//!
//! x = 1 # reachability constraint: ~test
//! if test:
//! x = 2 # reachability constraint: test
//!
//! y = 2 # reachability constraint: test
//!
//! use(x)
//! use(y)
//! ```
//! Both the type and the boundness of `x` and `y` are affected by reachability constraints:
//!
//! ```text
//! | `test` truthiness | type of `x` | boundness of `y` |
//! |-------------------|-----------------|------------------|
//! | always false | `Literal[1]` | unbound |
//! | ambiguous | `Literal[1, 2]` | possibly unbound |
//! | always true | `Literal[2]` | bound |
//! ```
//!
//! To achieve this, we apply reachability constraints retroactively to bindings that came before
//! the branching point. In the example above, the `x = 1` binding has a `test` constraint in the
//! `if` branch, and a `~test` constraint in the implicit `else` branch. Since it is shadowed by
//! `x = 2` in the `if` branch, we are only left with the `~test` constraint after control flow
//! has merged again.
//!
//! For live bindings, the reachability constraint therefore refers to the following question:
//! Is the binding reachable from the start of the scope, and is there a control flow path from
//! that binding to a use of that symbol at the current position?
//!
//! In the example above, `x = 1` is always reachable, but that binding can only reach the use of
//! `x` at the current position if `test` is falsy.
//!
//! To handle boundness correctly, we also add implicit `y = <unbound>` bindings at the start of
//! the scope. This allows us to determine whether a symbol is definitely bound (if that implicit
//! `y = <unbound>` binding is not visible), possibly unbound (if the reachability constraint
//! evaluates to `Ambiguous`), or definitely unbound (in case the `y = <unbound>` binding is
//! always visible).
//!
//!
//! ### Representing formulas
//!
//! Given everything above, we can represent a reachability constraint as a _ternary formula_. This
//! is like a boolean formula (which maps several true/false variables to a single true/false
//! result), but which allows the third "ambiguous" value in addition to "true" and "false".
//!
//! [_Binary decision diagrams_][bdd] (BDDs) are a common way to represent boolean formulas when
//! doing program analysis. We extend this to a _ternary decision diagram_ (TDD) to support
//! ambiguous values.
//!
//! A TDD is a graph, and a ternary formula is represented by a node in this graph. There are three
//! possible leaf nodes representing the "true", "false", and "ambiguous" constant functions.
//! Interior nodes consist of a ternary variable to evaluate, and outgoing edges for whether the
//! variable evaluates to true, false, or ambiguous.
//!
//! Our TDDs are _reduced_ and _ordered_ (as is typical for BDDs).
//!
//! An ordered TDD means that variables appear in the same order in all paths within the graph.
//!
//! A reduced TDD means two things: First, we intern the graph nodes, so that we only keep a single
//! copy of interior nodes with the same contents. Second, we eliminate any nodes that are "noops",
//! where the "true" and "false" outgoing edges lead to the same node. (This implies that it
//! doesn't matter what value that variable has when evaluating the formula, and we can leave it
//! out of the evaluation chain completely.)
//!
//! Reduced and ordered decision diagrams are _normal forms_, which means that two equivalent
//! formulas (which have the same outputs for every combination of inputs) are represented by
//! exactly the same graph node. (Because of interning, this is not _equal_ nodes, but _identical_
//! ones.) That means that we can compare formulas for equivalence in constant time, and in
//! particular, can check whether a reachability constraint is statically always true or false,
//! regardless of any Python program state, by seeing if the constraint's formula is the "true" or
//! "false" leaf node.
//!
//! [Kleene]: <https://en.wikipedia.org/wiki/Three-valued_logic#Kleene_and_Priest_logics>
//! [bdd]: https://en.wikipedia.org/wiki/Binary_decision_diagram
use std::cmp::Ordering;
use ruff_index::{Idx, IndexVec};
use rustc_hash::FxHashMap;
use crate::Db;
use crate::dunder_all::dunder_all_names;
use crate::place::{RequiresExplicitReExport, imported_symbol};
use crate::semantic_index::expression::Expression;
use crate::semantic_index::place_table;
use crate::semantic_index::predicate::{
PatternPredicate, PatternPredicateKind, Predicate, PredicateNode, Predicates, ScopedPredicateId,
};
use crate::types::{Truthiness, Type, infer_expression_type};
/// A ternary formula that defines under what conditions a binding is visible. (A ternary formula
/// is just like a boolean formula, but with `Ambiguous` as a third potential result. See the
/// module documentation for more details.)
///
/// The primitive atoms of the formula are [`Predicate`]s, which express some property of the
/// runtime state of the code that we are analyzing.
///
/// We assume that each atom has a stable value each time that the formula is evaluated. An atom
/// that resolves to `Ambiguous` might be true or false, and we can't tell which — but within that
/// evaluation, we assume that the atom has the _same_ unknown value each time it appears. That
/// allows us to perform simplifications like `A ∨ !A → true` and `A ∧ !A → false`.
///
/// That means that when you are constructing a formula, you might need to create distinct atoms
/// for a particular [`Predicate`], if your formula needs to consider how a particular runtime
/// property might be different at different points in the execution of the program.
///
/// reachability constraints are normalized, so equivalent constraints are guaranteed to have equal
/// IDs.
#[derive(Clone, Copy, Eq, Hash, PartialEq)]
pub(crate) struct ScopedReachabilityConstraintId(u32);
impl std::fmt::Debug for ScopedReachabilityConstraintId {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
let mut f = f.debug_tuple("ScopedReachabilityConstraintId");
match *self {
// We use format_args instead of rendering the strings directly so that we don't get
// any quotes in the output: ScopedReachabilityConstraintId(AlwaysTrue) instead of
// ScopedReachabilityConstraintId("AlwaysTrue").
ALWAYS_TRUE => f.field(&format_args!("AlwaysTrue")),
AMBIGUOUS => f.field(&format_args!("Ambiguous")),
ALWAYS_FALSE => f.field(&format_args!("AlwaysFalse")),
_ => f.field(&self.0),
};
f.finish()
}
}
// Internal details:
//
// There are 3 terminals, with hard-coded constraint IDs: true, ambiguous, and false.
//
// _Atoms_ are the underlying Predicates, which are the variables that are evaluated by the
// ternary function.
//
// _Interior nodes_ provide the TDD structure for the formula. Interior nodes are stored in an
// arena Vec, with the constraint ID providing an index into the arena.
#[derive(Clone, Copy, Debug, Eq, Hash, PartialEq)]
struct InteriorNode {
/// A "variable" that is evaluated as part of a TDD ternary function. For reachability
/// constraints, this is a `Predicate` that represents some runtime property of the Python
/// code that we are evaluating.
atom: ScopedPredicateId,
if_true: ScopedReachabilityConstraintId,
if_ambiguous: ScopedReachabilityConstraintId,
if_false: ScopedReachabilityConstraintId,
}
impl ScopedReachabilityConstraintId {
/// A special ID that is used for an "always true" / "always visible" constraint.
pub(crate) const ALWAYS_TRUE: ScopedReachabilityConstraintId =
ScopedReachabilityConstraintId(0xffff_ffff);
/// A special ID that is used for an ambiguous constraint.
pub(crate) const AMBIGUOUS: ScopedReachabilityConstraintId =
ScopedReachabilityConstraintId(0xffff_fffe);
/// A special ID that is used for an "always false" / "never visible" constraint.
pub(crate) const ALWAYS_FALSE: ScopedReachabilityConstraintId =
ScopedReachabilityConstraintId(0xffff_fffd);
fn is_terminal(self) -> bool {
self.0 >= SMALLEST_TERMINAL.0
}
}
impl Idx for ScopedReachabilityConstraintId {
#[inline]
fn new(value: usize) -> Self {
assert!(value <= (SMALLEST_TERMINAL.0 as usize));
#[expect(clippy::cast_possible_truncation)]
Self(value as u32)
}
#[inline]
fn index(self) -> usize {
debug_assert!(!self.is_terminal());
self.0 as usize
}
}
// Rebind some constants locally so that we don't need as many qualifiers below.
const ALWAYS_TRUE: ScopedReachabilityConstraintId = ScopedReachabilityConstraintId::ALWAYS_TRUE;
const AMBIGUOUS: ScopedReachabilityConstraintId = ScopedReachabilityConstraintId::AMBIGUOUS;
const ALWAYS_FALSE: ScopedReachabilityConstraintId = ScopedReachabilityConstraintId::ALWAYS_FALSE;
const SMALLEST_TERMINAL: ScopedReachabilityConstraintId = ALWAYS_FALSE;
/// A collection of reachability constraints for a given scope.
#[derive(Debug, PartialEq, Eq, salsa::Update)]
pub(crate) struct ReachabilityConstraints {
interiors: IndexVec<ScopedReachabilityConstraintId, InteriorNode>,
}
#[derive(Debug, Default, PartialEq, Eq)]
pub(crate) struct ReachabilityConstraintsBuilder {
interiors: IndexVec<ScopedReachabilityConstraintId, InteriorNode>,
interior_cache: FxHashMap<InteriorNode, ScopedReachabilityConstraintId>,
not_cache: FxHashMap<ScopedReachabilityConstraintId, ScopedReachabilityConstraintId>,
and_cache: FxHashMap<
(
ScopedReachabilityConstraintId,
ScopedReachabilityConstraintId,
),
ScopedReachabilityConstraintId,
>,
or_cache: FxHashMap<
(
ScopedReachabilityConstraintId,
ScopedReachabilityConstraintId,
),
ScopedReachabilityConstraintId,
>,
}
impl ReachabilityConstraintsBuilder {
pub(crate) fn build(self) -> ReachabilityConstraints {
ReachabilityConstraints {
interiors: self.interiors,
}
}
/// Returns whether `a` or `b` has a "larger" atom. TDDs are ordered such that interior nodes
/// can only have edges to "larger" nodes. Terminals are considered to have a larger atom than
/// any internal node, since they are leaf nodes.
fn cmp_atoms(
&self,
a: ScopedReachabilityConstraintId,
b: ScopedReachabilityConstraintId,
) -> Ordering {
if a == b || (a.is_terminal() && b.is_terminal()) {
Ordering::Equal
} else if a.is_terminal() {
Ordering::Greater
} else if b.is_terminal() {
Ordering::Less
} else {
self.interiors[a].atom.cmp(&self.interiors[b].atom)
}
}
/// Adds an interior node, ensuring that we always use the same reachability constraint ID for
/// equal nodes.
fn add_interior(&mut self, node: InteriorNode) -> ScopedReachabilityConstraintId {
// If the true and false branches lead to the same node, we can override the ambiguous
// branch to go there too. And this node is then redundant and can be reduced.
if node.if_true == node.if_false {
return node.if_true;
}
*self
.interior_cache
.entry(node)
.or_insert_with(|| self.interiors.push(node))
}
/// Adds a new reachability constraint that checks a single [`Predicate`].
///
/// [`ScopedPredicateId`]s are the “variables” that are evaluated by a TDD. A TDD variable has
/// the same value no matter how many times it appears in the ternary formula that the TDD
/// represents.
///
/// However, we sometimes have to model how a `Predicate` can have a different runtime
/// value at different points in the execution of the program. To handle this, you can take
/// advantage of the fact that the [`Predicates`] arena does not deduplicate `Predicate`s.
/// You can add a `Predicate` multiple times, yielding different `ScopedPredicateId`s, which
/// you can then create separate TDD atoms for.
pub(crate) fn add_atom(
&mut self,
predicate: ScopedPredicateId,
) -> ScopedReachabilityConstraintId {
self.add_interior(InteriorNode {
atom: predicate,
if_true: ALWAYS_TRUE,
if_ambiguous: AMBIGUOUS,
if_false: ALWAYS_FALSE,
})
}
/// Adds a new reachability constraint that is the ternary NOT of an existing one.
pub(crate) fn add_not_constraint(
&mut self,
a: ScopedReachabilityConstraintId,
) -> ScopedReachabilityConstraintId {
if a == ALWAYS_TRUE {
return ALWAYS_FALSE;
} else if a == AMBIGUOUS {
return AMBIGUOUS;
} else if a == ALWAYS_FALSE {
return ALWAYS_TRUE;
}
if let Some(cached) = self.not_cache.get(&a) {
return *cached;
}
let a_node = self.interiors[a];
let if_true = self.add_not_constraint(a_node.if_true);
let if_ambiguous = self.add_not_constraint(a_node.if_ambiguous);
let if_false = self.add_not_constraint(a_node.if_false);
let result = self.add_interior(InteriorNode {
atom: a_node.atom,
if_true,
if_ambiguous,
if_false,
});
self.not_cache.insert(a, result);
result
}
/// Adds a new reachability constraint that is the ternary OR of two existing ones.
pub(crate) fn add_or_constraint(
&mut self,
a: ScopedReachabilityConstraintId,
b: ScopedReachabilityConstraintId,
) -> ScopedReachabilityConstraintId {
match (a, b) {
(ALWAYS_TRUE, _) | (_, ALWAYS_TRUE) => return ALWAYS_TRUE,
(ALWAYS_FALSE, other) | (other, ALWAYS_FALSE) => return other,
(AMBIGUOUS, AMBIGUOUS) => return AMBIGUOUS,
_ => {}
}
// OR is commutative, which lets us halve the cache requirements
let (a, b) = if b.0 < a.0 { (b, a) } else { (a, b) };
if let Some(cached) = self.or_cache.get(&(a, b)) {
return *cached;
}
let (atom, if_true, if_ambiguous, if_false) = match self.cmp_atoms(a, b) {
Ordering::Equal => {
let a_node = self.interiors[a];
let b_node = self.interiors[b];
let if_true = self.add_or_constraint(a_node.if_true, b_node.if_true);
let if_false = self.add_or_constraint(a_node.if_false, b_node.if_false);
let if_ambiguous = if if_true == if_false {
if_true
} else {
self.add_or_constraint(a_node.if_ambiguous, b_node.if_ambiguous)
};
(a_node.atom, if_true, if_ambiguous, if_false)
}
Ordering::Less => {
let a_node = self.interiors[a];
let if_true = self.add_or_constraint(a_node.if_true, b);
let if_false = self.add_or_constraint(a_node.if_false, b);
let if_ambiguous = if if_true == if_false {
if_true
} else {
self.add_or_constraint(a_node.if_ambiguous, b)
};
(a_node.atom, if_true, if_ambiguous, if_false)
}
Ordering::Greater => {
let b_node = self.interiors[b];
let if_true = self.add_or_constraint(a, b_node.if_true);
let if_false = self.add_or_constraint(a, b_node.if_false);
let if_ambiguous = if if_true == if_false {
if_true
} else {
self.add_or_constraint(a, b_node.if_ambiguous)
};
(b_node.atom, if_true, if_ambiguous, if_false)
}
};
let result = self.add_interior(InteriorNode {
atom,
if_true,
if_ambiguous,
if_false,
});
self.or_cache.insert((a, b), result);
result
}
/// Adds a new reachability constraint that is the ternary AND of two existing ones.
pub(crate) fn add_and_constraint(
&mut self,
a: ScopedReachabilityConstraintId,
b: ScopedReachabilityConstraintId,
) -> ScopedReachabilityConstraintId {
match (a, b) {
(ALWAYS_FALSE, _) | (_, ALWAYS_FALSE) => return ALWAYS_FALSE,
(ALWAYS_TRUE, other) | (other, ALWAYS_TRUE) => return other,
(AMBIGUOUS, AMBIGUOUS) => return AMBIGUOUS,
_ => {}
}
// AND is commutative, which lets us halve the cache requirements
let (a, b) = if b.0 < a.0 { (b, a) } else { (a, b) };
if let Some(cached) = self.and_cache.get(&(a, b)) {
return *cached;
}
let (atom, if_true, if_ambiguous, if_false) = match self.cmp_atoms(a, b) {
Ordering::Equal => {
let a_node = self.interiors[a];
let b_node = self.interiors[b];
let if_true = self.add_and_constraint(a_node.if_true, b_node.if_true);
let if_false = self.add_and_constraint(a_node.if_false, b_node.if_false);
let if_ambiguous = if if_true == if_false {
if_true
} else {
self.add_and_constraint(a_node.if_ambiguous, b_node.if_ambiguous)
};
(a_node.atom, if_true, if_ambiguous, if_false)
}
Ordering::Less => {
let a_node = self.interiors[a];
let if_true = self.add_and_constraint(a_node.if_true, b);
let if_false = self.add_and_constraint(a_node.if_false, b);
let if_ambiguous = if if_true == if_false {
if_true
} else {
self.add_and_constraint(a_node.if_ambiguous, b)
};
(a_node.atom, if_true, if_ambiguous, if_false)
}
Ordering::Greater => {
let b_node = self.interiors[b];
let if_true = self.add_and_constraint(a, b_node.if_true);
let if_false = self.add_and_constraint(a, b_node.if_false);
let if_ambiguous = if if_true == if_false {
if_true
} else {
self.add_and_constraint(a, b_node.if_ambiguous)
};
(b_node.atom, if_true, if_ambiguous, if_false)
}
};
let result = self.add_interior(InteriorNode {
atom,
if_true,
if_ambiguous,
if_false,
});
self.and_cache.insert((a, b), result);
result
}
}
impl ReachabilityConstraints {
/// Analyze the statically known reachability for a given constraint.
pub(crate) fn evaluate<'db>(
&self,
db: &'db dyn Db,
predicates: &Predicates<'db>,
mut id: ScopedReachabilityConstraintId,
) -> Truthiness {
loop {
let node = match id {
ALWAYS_TRUE => return Truthiness::AlwaysTrue,
AMBIGUOUS => return Truthiness::Ambiguous,
ALWAYS_FALSE => return Truthiness::AlwaysFalse,
_ => self.interiors[id],
};
let predicate = &predicates[node.atom];
match Self::analyze_single(db, predicate) {
Truthiness::AlwaysTrue => id = node.if_true,
Truthiness::Ambiguous => id = node.if_ambiguous,
Truthiness::AlwaysFalse => id = node.if_false,
}
}
}
fn analyze_single_pattern_predicate_kind<'db>(
db: &'db dyn Db,
predicate_kind: &PatternPredicateKind<'db>,
subject: Expression<'db>,
) -> Truthiness {
match predicate_kind {
PatternPredicateKind::Value(value) => {
let subject_ty = infer_expression_type(db, subject);
let value_ty = infer_expression_type(db, *value);
if subject_ty.is_single_valued(db) {
Truthiness::from(subject_ty.is_equivalent_to(db, value_ty))
} else {
Truthiness::Ambiguous
}
}
PatternPredicateKind::Singleton(singleton) => {
let subject_ty = infer_expression_type(db, subject);
let singleton_ty = match singleton {
ruff_python_ast::Singleton::None => Type::none(db),
ruff_python_ast::Singleton::True => Type::BooleanLiteral(true),
ruff_python_ast::Singleton::False => Type::BooleanLiteral(false),
};
debug_assert!(singleton_ty.is_singleton(db));
if subject_ty.is_equivalent_to(db, singleton_ty) {
Truthiness::AlwaysTrue
} else if subject_ty.is_disjoint_from(db, singleton_ty) {
Truthiness::AlwaysFalse
} else {
Truthiness::Ambiguous
}
}
PatternPredicateKind::Or(predicates) => {
use std::ops::ControlFlow;
let (ControlFlow::Break(truthiness) | ControlFlow::Continue(truthiness)) =
predicates
.iter()
.map(|p| Self::analyze_single_pattern_predicate_kind(db, p, subject))
// this is just a "max", but with a slight optimization: `AlwaysTrue` is the "greatest" possible element, so we short-circuit if we get there
.try_fold(Truthiness::AlwaysFalse, |acc, next| match (acc, next) {
(Truthiness::AlwaysTrue, _) | (_, Truthiness::AlwaysTrue) => {
ControlFlow::Break(Truthiness::AlwaysTrue)
}
(Truthiness::Ambiguous, _) | (_, Truthiness::Ambiguous) => {
ControlFlow::Continue(Truthiness::Ambiguous)
}
(Truthiness::AlwaysFalse, Truthiness::AlwaysFalse) => {
ControlFlow::Continue(Truthiness::AlwaysFalse)
}
});
truthiness
}
PatternPredicateKind::Class(class_expr) => {
let subject_ty = infer_expression_type(db, subject);
let class_ty = infer_expression_type(db, *class_expr).to_instance(db);
class_ty.map_or(Truthiness::Ambiguous, |class_ty| {
if subject_ty.is_subtype_of(db, class_ty) {
Truthiness::AlwaysTrue
} else if subject_ty.is_disjoint_from(db, class_ty) {
Truthiness::AlwaysFalse
} else {
Truthiness::Ambiguous
}
})
}
PatternPredicateKind::Unsupported => Truthiness::Ambiguous,
}
}
fn analyze_single_pattern_predicate(db: &dyn Db, predicate: PatternPredicate) -> Truthiness {
let truthiness = Self::analyze_single_pattern_predicate_kind(
db,
predicate.kind(db),
predicate.subject(db),
);
if truthiness == Truthiness::AlwaysTrue && predicate.guard(db).is_some() {
// Fall back to ambiguous, the guard might change the result.
// TODO: actually analyze guard truthiness
Truthiness::Ambiguous
} else {
truthiness
}
}
fn analyze_single(db: &dyn Db, predicate: &Predicate) -> Truthiness {
match predicate.node {
PredicateNode::Expression(test_expr) => {
let ty = infer_expression_type(db, test_expr);
ty.bool(db).negate_if(!predicate.is_positive)
}
PredicateNode::Pattern(inner) => Self::analyze_single_pattern_predicate(db, inner),
PredicateNode::StarImportPlaceholder(star_import) => {
let place_table = place_table(db, star_import.scope(db));
let symbol_name = place_table
.place_expr(star_import.symbol_id(db))
.expect_name();
let referenced_file = star_import.referenced_file(db);
let requires_explicit_reexport = match dunder_all_names(db, referenced_file) {
Some(all_names) => {
if all_names.contains(symbol_name) {
Some(RequiresExplicitReExport::No)
} else {
tracing::trace!(
"Symbol `{}` (via star import) not found in `__all__` of `{}`",
symbol_name,
referenced_file.path(db)
);
return Truthiness::AlwaysFalse;
}
}
None => None,
};
match imported_symbol(db, referenced_file, symbol_name, requires_explicit_reexport)
.place
{
crate::place::Place::Type(_, crate::place::Boundness::Bound) => {
Truthiness::AlwaysTrue
}
crate::place::Place::Type(_, crate::place::Boundness::PossiblyUnbound) => {
Truthiness::Ambiguous
}
crate::place::Place::Unbound => Truthiness::AlwaysFalse,
}
}
}
}
}