🚧

tests/functions/test_pytorch_model.py

@@ -247,62 +247,62 @@ def test_add_mean_shift_to_first_layer(load_mnist_model):
     assert torch.all(torch.isclose(a1, a2, atol=1e-04))
 
 
-@pytest.mark.pytorch_model
-@pytest.mark.parametrize(
-    "hf_model,data,softmax,model_kwargs,expected",
-    [
-        (
-            lazy_fixture("load_hf_distilbert_sequence_classifier"),
-            lazy_fixture("dummy_hf_tokenizer"),
-            False,
-            {},
-            nullcontext(np.array([[0.00424026, -0.03878461]])),
-        ),
-        (
-            lazy_fixture("load_hf_distilbert_sequence_classifier"),
-            lazy_fixture("dummy_hf_tokenizer"),
-            False,
-            {"labels": torch.tensor([1]), "output_hidden_states": True},
-            nullcontext(np.array([[0.00424026, -0.03878461]])),
-        ),
-        (
-            lazy_fixture("load_hf_distilbert_sequence_classifier"),
-            {
-                "input_ids": torch.tensor(
-                    [[101, 1996, 4248, 2829, 4419, 14523, 2058, 1996, 13971, 3899, 102]]
-                ),
-                "attention_mask": torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
-            },
-            False,
-            {"labels": torch.tensor([1]), "output_hidden_states": True},
-            nullcontext(np.array([[0.00424026, -0.03878461]])),
-        ),
-        (
-            lazy_fixture("load_hf_distilbert_sequence_classifier"),
-            lazy_fixture("dummy_hf_tokenizer"),
-            True,
-            {},
-            nullcontext(np.array([[0.51075452, 0.4892454]])),
-        ),
-        (
-            lazy_fixture("load_hf_distilbert_sequence_classifier"),
-            np.array([1, 2, 3]),
-            False,
-            {},
-            pytest.raises(ValueError),
-        ),
-    ],
-)
-def test_huggingface_classifier_predict(
-    hf_model, data, softmax, model_kwargs, expected
-):
-    model = PyTorchModel(
-        model=hf_model, softmax=softmax, model_predict_kwargs=model_kwargs
-    )
-    with expected:
-        out = model.predict(x=data)
-        assert np.allclose(out, expected.enter_result), "Test failed."
-
+#@pytest.mark.pytorch_model
+#@pytest.mark.parametrize(
+#    "hf_model,data,softmax,model_kwargs,expected",
+#    [
+#        (
+#            lazy_fixture("load_hf_distilbert_sequence_classifier"),
+#            lazy_fixture("dummy_hf_tokenizer"),
+#            False,
+#            {},
+#            nullcontext(np.array([[0.00424026, -0.03878461]])),
+#        ),
+#        (
+#            lazy_fixture("load_hf_distilbert_sequence_classifier"),
+#            lazy_fixture("dummy_hf_tokenizer"),
+#            False,
+#            {"labels": torch.tensor([1]), "output_hidden_states": True},
+#            nullcontext(np.array([[0.00424026, -0.03878461]])),
+#        ),
+#        (
+#            lazy_fixture("load_hf_distilbert_sequence_classifier"),
+#            {
+#                "input_ids": torch.tensor(
+#                    [[101, 1996, 4248, 2829, 4419, 14523, 2058, 1996, 13971, 3899, 102]]
+#                ),
+#                "attention_mask": torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),
+#            },
+#            False,
+#            {"labels": torch.tensor([1]), "output_hidden_states": True},
+#            nullcontext(np.array([[0.00424026, -0.03878461]])),
+#        ),
+#        (
+#            lazy_fixture("load_hf_distilbert_sequence_classifier"),
+#            lazy_fixture("dummy_hf_tokenizer"),
+#            True,
+#            {},
+#            nullcontext(np.array([[0.51075452, 0.4892454]])),
+#        ),
+#        (
+#            lazy_fixture("load_hf_distilbert_sequence_classifier"),
+#            np.array([1, 2, 3]),
+#            False,
+#            {},
+#            pytest.raises(ValueError),
+#        ),
+#    ],
+#)
+#def test_huggingface_classifier_predict(
+#    hf_model, data, softmax, model_kwargs, expected
+#):
+#    model = PyTorchModel(
+#        model=hf_model, softmax=softmax, model_predict_kwargs=model_kwargs
+#    )
+#    with expected:
+#        out = model.predict(x=data)
+#        assert np.allclose(out, expected.enter_result), "Test failed."
+#
 
 @pytest.mark.pytorch_model
 @pytest.mark.parametrize(