Added test case to improve the coverage

Author: amitsrivastava78

keras/src/quantizers/gptq_core_test.py

@@ -0,0 +1,70 @@
+import pytest
+
+from keras.src import layers
+from keras.src import models
+from keras.src.quantizers import gptq_core
+from keras.src.quantizers.gptq_config import GPTQConfig
+
+
+def _get_model_no_embedding():
+    """Returns a simple model that lacks an Embedding layer."""
+    return models.Sequential([layers.Dense(10, input_shape=(5,))])
+
+
+def _get_model_no_blocks():
+    """Returns a model with an embedding layer but no subsequent container
+    layers."""
+    return models.Sequential([layers.Embedding(100, 10, input_shape=(5,))])
+
+
+class MockTokenizer:
+    """A mock tokenizer that mimics the real API for testing."""
+
+    def tokenize(self, text):
+        return [ord(c) for c in "".join(text)]
+
+    def __call__(self, text):
+        return self.tokenize(text)
+
+
+architecture_test_cases = [
+    (
+        _get_model_no_embedding(),
+        "Could not automatically find an embedding layer",
+        "no_embedding_layer",
+    ),
+    (
+        _get_model_no_blocks(),
+        "Could not automatically find any transformer-like blocks",
+        "no_transformer_blocks",
+    ),
+]
+
+
+@pytest.mark.requires_trainable_backend
+class TestGPTQCore:
+    def test_get_dataloader_with_empty_dataset(self):
+        """
+        Tests that get_dataloader raises a ValueError for an empty dataset.
+        """
+        with pytest.raises(ValueError, match="Provided dataset is empty"):
+            gptq_core.get_dataloader(
+                tokenizer=MockTokenizer(), seqlen=10, dataset=[], nsamples=10
+            )
+
+    @pytest.mark.parametrize(
+        "model, match_message, test_id",
+        architecture_test_cases,
+        ids=[case[-1] for case in architecture_test_cases],
+    )
+    def test_apply_gptq_with_unsupported_architectures(
+        self, model, match_message, test_id
+    ):
+        """
+        Tests that quantize fails correctly for various unsupported model
+        architectures.
+        """
+        config = GPTQConfig(dataset=["test"], tokenizer=MockTokenizer())
+
+        with pytest.raises(ValueError, match=match_message):
+            model.quantize("gptq", config=config)