rewrite error handling for unresolved inference vars
Pretty much completely rewrites `fn emit_inference_failure_err`.
This new setup should hopefully be easier to extend and is already a lot better when looking for generic arguments.
Because this is a rewrite there are still some parts which are lacking, these are tracked in #94483 and will be fixed in later PRs.
r? `@estebank` `@petrochenkov`
Diagnose anonymous lifetimes errors more uniformly between async and regular fns
Async fns and regular fns are desugared differently. For the former, we create a generic parameter at HIR level. For the latter, we just create an anonymous region for typeck.
I plan to migrate regular fns to the async fn desugaring.
Before that, this PR attempts to merge the diagnostics for both cases.
r? ```@estebank```
Previously whenever a duplicate discriminant was detected for an Enum,
we would print the discriminant bits in the diagnostic without any
casting. This caused us to display incorrect values for negative
discriminants. After this PR we format the discriminant signedness
correctly. Also reworded some of the original error
messages.
Remove mutable_borrow_reservation_conflict lint and allow the code pattern
This was the only breaking issue with the NLL stabilization PR. Lang team decided to go ahead and allow this.
r? `@nikomatsakis`
Closes#59159Closes#56254
Erase type params when suggesting fully qualified path
When suggesting the use of a fully qualified path for a method call that
is ambiguous because it has multiple candidates, erase type params in
the resulting code, as they would result in an error when applied. We
replace them with `_` in the output to rely on inference. There might be
cases where this still produces slighlty incomplete suggestions, but it
otherwise produces many more errors in relatively common cases.
Fix#96292
This attempts to bring better error messages to invalid method calls, by applying some heuristics to identify common mistakes.
The algorithm is inspired by Levenshtein distance and longest common sub-sequence. In essence, we treat the types of the function, and the types of the arguments you provided as two "words" and compute the edits to get from one to the other.
We then modify that algorithm to detect 4 cases:
- A function input is missing
- An extra argument was provided
- The type of an argument is straight up invalid
- Two arguments have been swapped
- A subset of the arguments have been shuffled
(We detect the last two as separate cases so that we can detect two swaps, instead of 4 parameters permuted.)
It helps to understand this argument by paying special attention to terminology: "inputs" refers to the inputs being *expected* by the function, and "arguments" refers to what has been provided at the call site.
The basic sketch of the algorithm is as follows:
- Construct a boolean grid, with a row for each argument, and a column for each input. The cell [i, j] is true if the i'th argument could satisfy the j'th input.
- If we find an argument that could satisfy no inputs, provided for an input that can't be satisfied by any other argument, we consider this an "invalid type".
- Extra arguments are those that can't satisfy any input, provided for an input that *could* be satisfied by another argument.
- Missing inputs are inputs that can't be satisfied by any argument, where the provided argument could satisfy another input
- Swapped / Permuted arguments are identified with a cycle detection algorithm.
As each issue is found, we remove the relevant inputs / arguments and check for more issues. If we find no issues, we match up any "valid" arguments, and start again.
Note that there's a lot of extra complexity:
- We try to stay efficient on the happy path, only computing the diagonal until we find a problem, and then filling in the rest of the matrix.
- Closure arguments are wrapped in a tuple and need to be unwrapped
- We need to resolve closure types after the rest, to allow the most specific type constraints
- We need to handle imported C functions that might be variadic in their inputs.
I tried to document a lot of this in comments in the code and keep the naming clear.
Extends support for generating `DiagnosticMessage::FluentIdentifier`
messages from `SessionDiagnostic` derive to `#[label]`.
Signed-off-by: David Wood <david.wood@huawei.com>
