Add Xfund OCR test

tests/test_ocr_google.py

@@ -7,6 +7,7 @@
 from docling_eval.cli.main import evaluate, visualize
 from docling_eval.datamodels.types import BenchMarkNames, EvaluationModality
 from docling_eval.dataset_builders.pixparse_builder import PixparseDatasetBuilder
+from docling_eval.dataset_builders.xfund_builder import XFUNDDatasetBuilder
 from docling_eval.prediction_providers.google_prediction_provider import (
     GoogleDocAIPredictionProvider,
 )
@@ -55,3 +56,45 @@ def test_run_pixparse_builder():
         idir=target_path / "eval_dataset",
         odir=target_path / "evaluations" / EvaluationModality.OCR.value,
     )
+
+
+@pytest.mark.skipif(
+    IS_CI, reason="Skipping test in CI because the dataset is too heavy."
+)
+def test_run_xfund_builder():
+    target_path = Path(f"./scratch/{BenchMarkNames.XFUND.value}_google/")
+    google_provider = GoogleDocAIPredictionProvider(
+        do_visualization=True, ignore_missing_predictions=False
+    )
+
+    dataset = XFUNDDatasetBuilder(
+        dataset_source=target_path / "input_dataset",
+        target=target_path / "gt_dataset",
+        end_index=2,
+    )
+
+    dataset.retrieve_input_dataset()
+    dataset.save_to_disk()  # does all the job of iterating the dataset, making GT+prediction records, and saving them in shards as parquet.
+
+    google_provider.create_prediction_dataset(
+        name=dataset.name,
+        gt_dataset_dir=target_path / "gt_dataset",
+        split="val",  # NOTE: Xfund has val split instead of test
+        target_dataset_dir=target_path / "eval_dataset",
+    )
+
+    evaluate(
+        modality=EvaluationModality.OCR,
+        benchmark=BenchMarkNames.XFUND,
+        idir=target_path / "eval_dataset",
+        odir=target_path / "evaluations" / EvaluationModality.OCR.value,
+        split="val",
+    )
+
+    visualize(
+        modality=EvaluationModality.OCR,
+        benchmark=BenchMarkNames.XFUND,
+        idir=target_path / "eval_dataset",
+        odir=target_path / "evaluations" / EvaluationModality.OCR.value,
+        split="val",
+    )