| from __future__ import annotations |
|
|
| import ast |
| import difflib |
| from typing import Tuple |
|
|
|
|
| def _normalize(code: str) -> Tuple[str, str]: |
| """ |
| Deterministic normalization for grading. |
| |
| Returns: |
| (ast_unparsed, stripped_source) |
| """ |
| src = (code or "").replace("\r\n", "\n").strip() |
| try: |
| tree = ast.parse(src) |
| normalized = ast.unparse(tree).strip() |
| return normalized, src |
| except Exception: |
| return "", src |
|
|
|
|
| def grade_task(output: str, expected_output: str) -> float: |
| """ |
| Deterministic score in [0.0, 1.0] comparing output vs expected_output. |
| |
| - If both parse as Python, we compare normalized AST-unparse strings. |
| - Otherwise, we fall back to a whitespace-stripped diff similarity. |
| """ |
| out_norm, out_src = _normalize(output) |
| exp_norm, exp_src = _normalize(expected_output) |
|
|
| if out_norm and exp_norm: |
| if out_norm == exp_norm: |
| return 1.0 |
| ratio = difflib.SequenceMatcher(a=exp_norm, b=out_norm).ratio() |
| return float(max(0.0, min(1.0, ratio))) |
|
|
| |
| a = " ".join(exp_src.split()) |
| b = " ".join(out_src.split()) |
| if not a and not b: |
| return 1.0 |
| ratio = difflib.SequenceMatcher(a=a, b=b).ratio() |
| return float(max(0.0, min(1.0, ratio))) |
|
|
|
|
