Datasets:
Download src/explicit_learning/certificates/smt_program.py from sungguk/visual-answerability: direct link, hf CLI and curl.
- Browser
- Download file 2.47 kB
-
https://huggingface.co/datasets/sungguk/visual-answerability/resolve/main/src/explicit_learning/certificates/smt_program.py
- Command line
-
hf download hf://datasets/sungguk/visual-answerability/src/explicit_learning/certificates/smt_program.py
-
curl -L -o smt_program.py https://huggingface.co/datasets/sungguk/visual-answerability/resolve/main/src/explicit_learning/certificates/smt_program.py
2.47 kB
| """Standard two-copy SMT construction; the certificate checker remains separate. | |
| Z3 API: https://z3prover.github.io/api/html/namespacez3py.html | |
| This implements established relational reasoning, not a new SMT algorithm. | |
| """ | |
| import base64 | |
| import copy | |
| import io | |
| from .finite_program import question, render, validate_observation, validate_program | |
| def construct(program, observed, domain, *, timeout_ms=1000): | |
| import z3 | |
| validate_program(program) | |
| validate_observation(observed, domain) | |
| first = z3.IntVector("first", 4) | |
| second = z3.IntVector("second", 4) | |
| def expression(node, world): | |
| op = node[0] | |
| if op == "cell": | |
| return z3.ToReal(world[node[1]]) | |
| if op == "const": | |
| return z3.RealVal(node[1]) | |
| left, right = (expression(child, world) for child in node[1:]) | |
| operations = { | |
| "add": lambda: left + right, "sub": lambda: left - right, | |
| "mul": lambda: left * right, "div": lambda: left / right, | |
| "min": lambda: z3.If(left <= right, left, right), | |
| "max": lambda: z3.If(left >= right, left, right), | |
| "gt": lambda: z3.If(left > right, z3.RealVal(1), z3.RealVal(0)), | |
| "eq": lambda: z3.If(left == right, z3.RealVal(1), z3.RealVal(0)), | |
| } | |
| return operations[op]() | |
| solver = z3.Solver() | |
| solver.set(timeout=timeout_ms, random_seed=0) | |
| for world in (first, second): | |
| for i, value in enumerate(observed): | |
| solver.add(world[i] >= domain[0], world[i] <= domain[1]) | |
| if value is not None: | |
| solver.add(world[i] == value) | |
| solver.add(expression(program, first) != expression(program, second)) | |
| status = solver.check() | |
| if status == z3.unsat: | |
| return {"status": "constant"} | |
| if status != z3.sat: | |
| return {"status": "unknown", "reason": solver.reason_unknown()} | |
| model = solver.model() | |
| worlds = [[model.eval(value, model_completion=True).as_long() for value in world] | |
| for world in (first, second)] | |
| image = io.BytesIO() | |
| render(observed, domain).save(image, format="PNG") | |
| return {"status": "ambiguous", "proof": { | |
| "schema": "finite_compositional_witness_v1", "domain": list(domain), | |
| "program": copy.deepcopy(program), "question": question(program), | |
| "observation": list(observed), "witnesses": worlds, | |
| "observed_png": base64.b64encode(image.getvalue()).decode("ascii"), | |
| }} | |