feat: complete test for dataset

hyperbench/tests/data/dataset_test.py

@@ -1,3 +1,5 @@
+import zstandard as zstd
+import json
 import requests
 import torch
 import pytest
@@ -852,3 +854,42 @@ class TestDataset(Dataset):
         assert torch.allclose(
             result, torch.tensor([1.5, 0.8])
         )  # weight, score (insertion order)
+
+
+def test_load_from_hif_file_exists():
+    """Test loading dataset when file already exists locally (skip download)."""
+    dataset_name = "ALGEBRA"
+
+    sample_hif = {
+        "network-type": "undirected",
+        "nodes": [{"node": "0"}, {"node": "1"}],
+        "edges": [{"edge": "0"}],
+        "incidences": [{"node": "0", "edge": "0"}],
+    }
+
+    mock_hypergraph = HIFHypergraph(
+        network_type="undirected",
+        nodes=[{"node": "0"}, {"node": "1"}],
+        edges=[{"edge": "0"}],
+        incidences=[{"node": "0", "edge": "0"}],
+    )
+
+    with (
+        patch("hyperbench.data.dataset.requests.get") as mock_get,
+        patch("hyperbench.data.dataset.os.path.exists", return_value=True),
+        patch("builtins.open", mock_open()) as mock_file,
+        patch("hyperbench.data.dataset.zstd.ZstdDecompressor") as mock_decomp,
+        patch("hyperbench.data.dataset.tempfile.NamedTemporaryFile") as mock_temp,
+        patch("hyperbench.data.dataset.json.load", return_value=sample_hif),
+        patch("hyperbench.data.dataset.validate_hif_json", return_value=True),
+        patch.object(HIFHypergraph, "from_hif", return_value=mock_hypergraph),
+    ):
+        mock_dctx = mock_decomp.return_value
+        mock_dctx.copy_stream = lambda input_f, tmp_file: None
+
+        mock_temp_instance = mock_temp.return_value.__enter__.return_value
+        mock_temp_instance.name = "/tmp/decompressed.json"
+
+        result = HIFConverter.load_from_hif(dataset_name, save_on_disk=True)
+        mock_get.assert_not_called()
+        assert result == mock_hypergraph