chore: import upstream snapshot with attribution

examples/flows/evaluation/eval-accuracy-maths-to-code-maf/run_eval.py

@@ -0,0 +1,49 @@
+import argparse
+import asyncio
+import json
+from pathlib import Path
+from aggregation import aggregate
+from eval_runner import EvalRunner
+from workflow import EvalInput, create_workflow
+
+DEFAULT_DATA = Path(__file__).parent / "test_data.jsonl"
+
+
+def load_dataset(path: Path) -> list[EvalInput]:
+    rows = []
+    with open(path, encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                obj = json.loads(line)
+                rows.append(EvalInput(groundtruth=str(obj["groundtruth"]), prediction=str(obj["prediction"])))
+    return rows
+
+
+async def main(data_path: Path, concurrency: int):
+    dataset = load_dataset(data_path)
+    print(f"Loaded {len(dataset)} rows from {data_path}")
+
+    runner = EvalRunner(
+        workflow_factory=create_workflow,
+        aggregate_fn=aggregate,
+        concurrency=concurrency,
+        input_mapping={"values": "processed_results"},
+    )
+    result = await runner.run(dataset)
+
+    print("\n--- Metrics ---")
+    for key, value in result.metrics.items():
+        print(f"  {key}: {value}")
+    if result.errors:
+        print(f"\n--- Errors ({len(result.errors)}) ---")
+        for idx, err in result.errors:
+            print(f"  Row {idx}: {err}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data", type=Path, default=DEFAULT_DATA)
+    parser.add_argument("--concurrency", type=int, default=5)
+    args = parser.parse_args()
+    asyncio.run(main(args.data, args.concurrency))