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e93bbca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | 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)))
# Fallback: compare raw text (still deterministic).
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)))
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