Datasets:

ego0op
/

ingot-lean-zig

# lean2zig

Deterministic, rule-based Lean 4 -> Zig 0.17 converter for the integer-arithmetic subset of theorem statements, verified by differential testing.
No model is involved; the same statement always gives the same Zig.

    ZIG_BIN=<zig 0.17.0> python3 tools/lean2zig/run.py --tag full [--limit N] [--batch 150] [--jobs 2]
    ZIG_BIN=<zig> python3 tools/lean2zig/selftest.py        # lean.zig helpers vs a Python bigint reference of Lean semantics
Files: `lean2zig.py` (lexer, Pratt parser, elaborator, emitter; `translate(id, text)`), `lean.zig` (Lean semantics and the vector source,
imported by every emitted file), `run.py` (driver: dedup, translate, batched `zig test`, stats, twin check), `selftest.py`.
Data goes to the gitignored `out/lean2zig/`; the ledger is `results/cloud/lean2zig/stats.json`.

## What it accepts
Binders `(x y : ℕ/ℤ)`, `{..}`, bare auto-bound names, hypotheses; prenex `∀` and `→` in the conclusion are hoisted to parameters/hypotheses;
`+ - * / % ^ ∣ = ≠ ≤ < ≥ > ∧ ∨ ¬ ↔ → ∀ ∃`, `≡ [ZMOD n]`, `[MOD n]`, `∈` of range/Icc/Ico/Ioc/Ioo, `Nat.choose`, `gcd/lcm`, `Int.natAbs`, `|x|`,
`max/min`, `n !`, `fib`, `Nat.sqrt`, `Nat.totient`, `Nat.Prime`, `Even/Odd`, `Coprime`, `if then else`, `∑/∏` over intervals and `Nat.divisors`,
`Finset.filter`, `Finset.card`, casts. Anything else is rejected with a named reason (all reasons of a statement are recorded, the first is its primary).

Typing follows Lean's `binop%` rule: the leaves of an arithmetic tree are unified to one class (ℕ < ℤ, numerals polymorphic, untyped binders unify), so
`a - b` is truncated subtraction exactly when its class resolves to ℕ.

## Semantics (lean.zig)
Everything is `i128`. ℕ `-` truncates at 0; `/` and `%` are Int.ediv/emod (Euclidean) with `x/0 = 0`, `x%0 = x`; `0^0 = 1`. Every helper checks overflow;
an overflow or an inconclusive search (a truncated `∃`/`∀` range, a span over 4096, primality above 1e10) sets a flag and that vector is skipped, never counted as pass or fail.
Finite `∀`/`∃` are walked when bounds are visible in the body (complete) and over a window otherwise (inconclusive unless a witness/counterexample is found).

## Emitted unit
`///` statement, `pub fn <name>Hypotheses`, `<name>Lhs`/`Rhs` (top-level `=`) or `<name>Conclusion`, and one `test`. The test draws vectors from `lean.Source`:
an exhaustive small grid over the free variables (size depends on their number), then edge-weighted random draws. Bounds in hypotheses (`p ≤ n`, `x > 2`, `x ∈ Icc a b`)
become draw ranges, `v = e` hypotheses define `v`, and equations linear in one variable (`4*n+3 = 1351`) are solved for it. `tally.admit` skips overflowed vectors,
counts distinct admitted ones and fails on a false conclusion; `tally.require(K)` needs min(8, size of a literal-bounded domain) distinct admitted vectors.
Failures are named: `TestUnexpectedResult` (semantic mismatch), `TooFewVectors`, `NoVector`, `OnlySkipped`.

## Differential check
The theorems are proved, so the conclusion must hold on every admitted vector; a failure is a translation bug and the row is dropped as "semantic mismatch".
Batches of 150 units go into one file; compile errors are mapped back to units by line, those units are dropped and the batch re-run; a timeout bisects (the zig process group is killed).
Limits of the check: it cannot see bugs the proved statements never exercise (a canary that turned ℕ-subtraction into plain subtraction was caught on only 12 of 2268 units),
which is why the helpers have their own conformance test.

## Twin check
`run.py` reproduces the ID/N/T-normalised 5-gram Jaccard >= 0.90 test of `results/cloud/lean-v2-review/shape_audit.py`, exactly (inverted index, no family split).
It is reported for whole units and for the theorem functions alone, because the test block is shared boilerplate.

	# lean2zig

	Deterministic, rule-based Lean 4 -> Zig 0.17 converter for the integer-arithmetic subset of theorem statements, verified by differential testing.
	No model is involved; the same statement always gives the same Zig.

	ZIG_BIN=<zig 0.17.0> python3 tools/lean2zig/run.py --tag full [--limit N] [--batch 150] [--jobs 2]
	ZIG_BIN=<zig> python3 tools/lean2zig/selftest.py # lean.zig helpers vs a Python bigint reference of Lean semantics

	Files: `lean2zig.py` (lexer, Pratt parser, elaborator, emitter; `translate(id, text)`), `lean.zig` (Lean semantics and the vector source,
	imported by every emitted file), `run.py` (driver: dedup, translate, batched `zig test`, stats, twin check), `selftest.py`.
	Data goes to the gitignored `out/lean2zig/`; the ledger is `results/cloud/lean2zig/stats.json`.

	## What it accepts
	Binders `(x y : ℕ/ℤ)`, `{..}`, bare auto-bound names, hypotheses; prenex `∀` and `→` in the conclusion are hoisted to parameters/hypotheses;
	`+ - * / % ^ ∣ = ≠ ≤ < ≥ > ∧ ∨ ¬ ↔ → ∀ ∃`, `≡ [ZMOD n]`, `[MOD n]`, `∈` of range/Icc/Ico/Ioc/Ioo, `Nat.choose`, `gcd/lcm`, `Int.natAbs`, `\|x\|`,
	`max/min`, `n !`, `fib`, `Nat.sqrt`, `Nat.totient`, `Nat.Prime`, `Even/Odd`, `Coprime`, `if then else`, `∑/∏` over intervals and `Nat.divisors`,
	`Finset.filter`, `Finset.card`, casts. Anything else is rejected with a named reason (all reasons of a statement are recorded, the first is its primary).

	Typing follows Lean's `binop%` rule: the leaves of an arithmetic tree are unified to one class (ℕ < ℤ, numerals polymorphic, untyped binders unify), so
	`a - b` is truncated subtraction exactly when its class resolves to ℕ.

	## Semantics (lean.zig)
	Everything is `i128`. ℕ `-` truncates at 0; `/` and `%` are Int.ediv/emod (Euclidean) with `x/0 = 0`, `x%0 = x`; `0^0 = 1`. Every helper checks overflow;
	an overflow or an inconclusive search (a truncated `∃`/`∀` range, a span over 4096, primality above 1e10) sets a flag and that vector is skipped, never counted as pass or fail.
	Finite `∀`/`∃` are walked when bounds are visible in the body (complete) and over a window otherwise (inconclusive unless a witness/counterexample is found).

	## Emitted unit
	`///` statement, `pub fn <name>Hypotheses`, `<name>Lhs`/`Rhs` (top-level `=`) or `<name>Conclusion`, and one `test`. The test draws vectors from `lean.Source`:
	an exhaustive small grid over the free variables (size depends on their number), then edge-weighted random draws. Bounds in hypotheses (`p ≤ n`, `x > 2`, `x ∈ Icc a b`)
	become draw ranges, `v = e` hypotheses define `v`, and equations linear in one variable (`4*n+3 = 1351`) are solved for it. `tally.admit` skips overflowed vectors,
	counts distinct admitted ones and fails on a false conclusion; `tally.require(K)` needs min(8, size of a literal-bounded domain) distinct admitted vectors.
	Failures are named: `TestUnexpectedResult` (semantic mismatch), `TooFewVectors`, `NoVector`, `OnlySkipped`.

	## Differential check
	The theorems are proved, so the conclusion must hold on every admitted vector; a failure is a translation bug and the row is dropped as "semantic mismatch".
	Batches of 150 units go into one file; compile errors are mapped back to units by line, those units are dropped and the batch re-run; a timeout bisects (the zig process group is killed).
	Limits of the check: it cannot see bugs the proved statements never exercise (a canary that turned ℕ-subtraction into plain subtraction was caught on only 12 of 2268 units),
	which is why the helpers have their own conformance test.

	## Twin check
	`run.py` reproduces the ID/N/T-normalised 5-gram Jaccard >= 0.90 test of `results/cloud/lean-v2-review/shape_audit.py`, exactly (inverted index, no family split).
	It is reported for whole units and for the theorem functions alone, because the test block is shared boilerplate.