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from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any
ALLOWED_STATUS = {
"scheduled",
"waiting_checkpoint",
"completed",
"needs_replan",
"in_progress",
"failed",
}
def _as_mapping(value: Any) -> dict[str, Any]:
if isinstance(value, dict):
return {str(key): item for key, item in value.items()}
return {}
def _as_non_negative_int(value: Any) -> int | None:
try:
parsed = int(value)
except (TypeError, ValueError):
return None
if parsed < 0:
return None
return parsed
def _validate_cycle_payload(cycle: dict[str, Any]) -> list[str]:
errors: list[str] = []
for key in (
"due_count",
"executed_count",
"blocked_count",
"progressed_step_count",
"retry_scheduled_count",
"verification_failure_count",
"replan_count",
):
if key not in cycle:
continue
if _as_non_negative_int(cycle.get(key)) is None:
errors.append(f"invalid_cycle_{key}")
return errors
def _validate_status_payload(status: dict[str, Any]) -> list[str]:
errors: list[str] = []
for key in (
"autonomy_task_count",
"needs_replan_count",
"retry_pending_count",
"backlog_step_count",
):
if key not in status:
continue
if _as_non_negative_int(status.get(key)) is None:
errors.append(f"invalid_status_{key}")
failure_taxonomy = status.get("failure_taxonomy")
if failure_taxonomy is not None:
if not isinstance(failure_taxonomy, dict):
errors.append("invalid_status_failure_taxonomy")
else:
for key, value in failure_taxonomy.items():
reason_code = str(key).strip().lower()
if not reason_code:
errors.append("invalid_status_failure_taxonomy_reason")
continue
if _as_non_negative_int(value) is None:
errors.append("invalid_status_failure_taxonomy_value")
return errors
def _compare_expected(
*,
label: str,
actual: dict[str, Any],
expected: dict[str, Any],
) -> list[str]:
mismatches: list[str] = []
for key, expected_value in expected.items():
actual_value = actual.get(key)
if actual_value != expected_value:
mismatches.append(
f"{label}.{key}: expected={expected_value!r} actual={actual_value!r}"
)
return mismatches
def _evaluate_case(case: dict[str, Any]) -> dict[str, Any]:
case_id = str(case.get("id", "case"))
actual_cycle = _as_mapping(case.get("actual_cycle"))
actual_status = _as_mapping(case.get("actual_status"))
expected_cycle = _as_mapping(case.get("expected_cycle"))
expected_status = _as_mapping(case.get("expected_status"))
validation_errors = _validate_cycle_payload(actual_cycle)
validation_errors.extend(_validate_status_payload(actual_status))
mismatches = _compare_expected(
label="cycle",
actual=actual_cycle,
expected=expected_cycle,
)
mismatches.extend(
_compare_expected(
label="status",
actual=actual_status,
expected=expected_status,
)
)
for status_key in ("min_replan_count", "max_replan_count"):
if status_key not in case:
continue
threshold = _as_non_negative_int(case.get(status_key))
actual_value = _as_non_negative_int(actual_cycle.get("replan_count"))
if threshold is None:
mismatches.append(f"{status_key}: invalid threshold")
continue
if actual_value is None:
mismatches.append("cycle.replan_count missing/invalid")
continue
if status_key.startswith("min") and actual_value < threshold:
mismatches.append(
f"cycle.replan_count below min ({actual_value} < {threshold})"
)
if status_key.startswith("max") and actual_value > threshold:
mismatches.append(
f"cycle.replan_count above max ({actual_value} > {threshold})"
)
for status_key, actual_key in (
("min_retry_pending_count", "retry_pending_count"),
("min_needs_replan_count", "needs_replan_count"),
("min_backlog_step_count", "backlog_step_count"),
):
if status_key not in case:
continue
threshold = _as_non_negative_int(case.get(status_key))
actual_value = _as_non_negative_int(actual_status.get(actual_key))
if threshold is None:
mismatches.append(f"{status_key}: invalid threshold")
continue
if actual_value is None:
mismatches.append(f"status.{actual_key} missing/invalid")
continue
if actual_value < threshold:
mismatches.append(
f"status.{actual_key} below min ({actual_value} < {threshold})"
)
if "required_statuses" in case:
required_statuses = case.get("required_statuses")
status_counts = _as_mapping(actual_status.get("status_counts"))
if not isinstance(required_statuses, list):
mismatches.append("required_statuses must be a list")
else:
for item in required_statuses:
status_name = str(item).strip().lower()
if status_name not in ALLOWED_STATUS:
mismatches.append(f"invalid required status: {status_name!r}")
continue
count = _as_non_negative_int(status_counts.get(status_name))
if count is None or count <= 0:
mismatches.append(f"required status missing: {status_name}")
if "min_failure_taxonomy_total" in case:
threshold = _as_non_negative_int(case.get("min_failure_taxonomy_total"))
failure_taxonomy = _as_mapping(actual_status.get("failure_taxonomy"))
total = 0
for value in failure_taxonomy.values():
parsed = _as_non_negative_int(value)
if parsed is not None:
total += parsed
if threshold is None:
mismatches.append("min_failure_taxonomy_total: invalid threshold")
elif total < threshold:
mismatches.append(
f"status.failure_taxonomy total below min ({total} < {threshold})"
)
passed = not validation_errors and not mismatches
return {
"id": case_id,
"passed": passed,
"validation_errors": validation_errors,
"mismatches": mismatches,
}
def _evaluate_results(
*,
dataset_path: Path,
results: list[dict[str, Any]],
strict: bool,
min_pass_rate: float | None,
max_failed: int | None,
min_cases: int | None,
duplicate_ids: list[str],
) -> dict[str, Any]:
passed = sum(1 for row in results if bool(row.get("passed")))
failed = len(results) - passed
pass_rate = (passed / len(results)) if results else 0.0
accepted = (failed == 0) if strict else (passed >= failed)
failure_reasons: list[str] = []
if strict and failed > 0:
failure_reasons.append("strict_failed_cases")
if not strict and passed < failed:
failure_reasons.append("non_strict_majority_failed")
if min_pass_rate is not None and pass_rate < min_pass_rate:
accepted = False
failure_reasons.append("pass_rate_below_threshold")
if max_failed is not None and failed > max_failed:
accepted = False
failure_reasons.append("failed_count_above_threshold")
if min_cases is not None and len(results) < min_cases:
accepted = False
failure_reasons.append("insufficient_case_count")
if duplicate_ids:
accepted = False
failure_reasons.append("duplicate_case_ids")
return {
"dataset": str(dataset_path),
"strict": strict,
"thresholds": {
"min_pass_rate": min_pass_rate,
"max_failed": max_failed,
"min_cases": min_cases,
},
"case_count": len(results),
"passed": passed,
"failed": failed,
"pass_rate": pass_rate,
"accepted": accepted,
"failure_reasons": failure_reasons,
"duplicate_ids": duplicate_ids,
"results": results,
}
def main() -> int:
parser = argparse.ArgumentParser(
description="Run deterministic planner autonomy-cycle contract checks."
)
parser.add_argument("dataset", help="Path to autonomy cycle contract dataset JSON")
parser.add_argument("--output", default="")
parser.add_argument("--strict", action="store_true")
parser.add_argument(
"--min-pass-rate",
type=float,
default=None,
help="Optional minimum pass-rate acceptance threshold in [0.0, 1.0].",
)
parser.add_argument(
"--max-failed",
type=int,
default=None,
help="Optional maximum failed-case acceptance threshold (>= 0).",
)
parser.add_argument(
"--min-cases",
type=int,
default=None,
help="Optional minimum number of evaluation cases required.",
)
parser.add_argument(
"--require-unique-ids",
action="store_true",
help="Fail if case IDs are duplicated.",
)
args = parser.parse_args()
dataset_path = Path(args.dataset)
if args.min_pass_rate is not None and (args.min_pass_rate < 0.0 or args.min_pass_rate > 1.0):
raise SystemExit("--min-pass-rate must be between 0.0 and 1.0.")
if args.max_failed is not None and args.max_failed < 0:
raise SystemExit("--max-failed must be >= 0.")
if args.min_cases is not None and args.min_cases < 0:
raise SystemExit("--min-cases must be >= 0.")
payload = json.loads(dataset_path.read_text(encoding="utf-8"))
cases = payload.get("cases", []) if isinstance(payload, dict) else []
if not isinstance(cases, list):
raise SystemExit("Dataset format error: expected top-level object with 'cases' list.")
case_rows = [case for case in cases if isinstance(case, dict)]
results = [_evaluate_case(case) for case in case_rows]
case_ids = [str(case.get("id", "")).strip() for case in case_rows]
id_counts: dict[str, int] = {}
for case_id in case_ids:
if not case_id:
continue
id_counts[case_id] = id_counts.get(case_id, 0) + 1
duplicate_ids = sorted(case_id for case_id, count in id_counts.items() if count > 1)
if not args.require_unique_ids:
duplicate_ids = []
summary = _evaluate_results(
dataset_path=dataset_path,
results=results,
strict=bool(args.strict),
min_pass_rate=args.min_pass_rate,
max_failed=args.max_failed,
min_cases=args.min_cases,
duplicate_ids=duplicate_ids,
)
text = json.dumps(summary, indent=2)
print(text)
if args.output:
out_path = Path(args.output)
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text(text, encoding="utf-8")
return 0 if summary["accepted"] else 1
if __name__ == "__main__":
raise SystemExit(main())
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