fix: Update inference tests for WatsonX model deprecations and API changes

src/unitxt/inference.py

@@ -2770,7 +2770,10 @@ def _send_requests(
             if tool_call:
                 if "tool_calls" in output:
                     func = output["tool_calls"][0]["function"]
-                    prediction = f'{{"name": "{func["name"]}", "arguments": {func["arguments"]}}}'
+                    arguments = func["arguments"]
+                    while isinstance(arguments, str):
+                        arguments = json.loads(arguments)
+                    prediction = f'{{"name": "{func["name"]}", "arguments": {json.dumps(arguments)}}}'
                 else:
                     prediction = output["content"]
             else:

tests/inference/test_inference_engine.py

@@ -3,7 +3,7 @@
 import shutil
 import time
 from functools import lru_cache
-from typing import Any, Dict, List, cast
+from typing import Any, Dict, List
 
 import unitxt
 from unitxt import create_dataset
@@ -16,7 +16,6 @@
     HFPipelineBasedInferenceEngine,
     LiteLLMInferenceEngine,
     OllamaInferenceEngine,
-    OptionSelectingByLogProbsInferenceEngine,
     RITSInferenceEngine,
     TextGenerationInferenceOutput,
     WMLInferenceEngineChat,
@@ -159,7 +158,7 @@ def test_llava_inference_engine(self):
 
     def test_watsonx_inference(self):
         model = WMLInferenceEngineGeneration(
-            model_name="ibm/granite-3-8b-instruct",
+            model_name="ibm/granite-4-h-small",
             data_classification_policy=["public"],
             random_seed=111,
             min_new_tokens=1,
@@ -178,7 +177,7 @@ def test_watsonx_inference(self):
 
     def test_watsonx_chat_inference(self):
         model = WMLInferenceEngineChat(
-            model_name="ibm/granite-3-8b-instruct",
+            model_name="ibm/granite-4-h-small",
             data_classification_policy=["public"],
             temperature=0,
         )
@@ -193,7 +192,7 @@ def test_watsonx_inference_with_external_client(self):
         from ibm_watsonx_ai.client import APIClient, Credentials
 
         model = WMLInferenceEngineGeneration(
-            model_name="ibm/granite-3-8b-instruct",
+            model_name="ibm/granite-4-h-small",
             data_classification_policy=["public"],
             random_seed=111,
             min_new_tokens=1,
@@ -278,17 +277,13 @@ def test_option_selecting_by_log_prob_inference_engines(self):
             },
         ]
 
-        watsonx_engine = WMLInferenceEngineGeneration(
-            model_name="ibm/granite-3-8b-instruct"
+        engine = HFOptionSelectingInferenceEngine(
+            model_name=local_decoder_model, batch_size=1
         )
-
-        for engine in [watsonx_engine]:
-            dataset = cast(OptionSelectingByLogProbsInferenceEngine, engine).select(
-                dataset
-            )
-            self.assertEqual(dataset[0]["prediction"], "world")
-            self.assertEqual(dataset[1]["prediction"], "the")
-            self.assertEqual(dataset[2]["prediction"], "telephone number")
+        predictions = engine.infer(dataset)
+        self.assertEqual(predictions[0], "world")
+        self.assertEqual(predictions[1], "the")
+        self.assertEqual(predictions[2], "telephone number")
 
     def test_hf_auto_model_inference_engine_batching(self):
         model = HFAutoModelInferenceEngine(
@@ -339,23 +334,6 @@ def test_hf_auto_model_inference_engine(self):
         self.assertEqual(results[0], "365")
 
     def test_watsonx_inference_with_images(self):
-        dataset = get_image_dataset()
-
-        inference_engine = WMLInferenceEngineChat(
-            model_name="meta-llama/llama-3-2-11b-vision-instruct",
-            max_tokens=128,
-            top_logprobs=3,
-            temperature=0.0,
-        )
-
-        results = inference_engine.infer_log_probs(
-            dataset.select([0]), return_meta_data=True
-        )
-        self.assertEqual(results[0].generated_text, "The capital of Texas is Austin.")
-        self.assertTrue(isoftype(results, List[TextGenerationInferenceOutput]))
-        self.assertEqual(results[0].stop_reason, "stop")
-        self.assertTrue(isoftype(results[0].prediction, List[Dict[str, Any]]))
-
         dataset = get_image_dataset(format="formats.chat_api")
 
         inference_engine = WMLInferenceEngineChat(
@@ -398,8 +376,8 @@ def test_log_prob_scoring_inference_engine(self):
 
         log_probs = engine.get_log_probs(["hello world", "by universe"])
 
-        self.assertAlmostEqual(log_probs[0], -9.77, places=2)
-        self.assertAlmostEqual(log_probs[1], -11.92, places=2)
+        self.assertAlmostEqual(log_probs[0], -9.81, places=2)
+        self.assertAlmostEqual(log_probs[1], -12.0, places=2)
 
     def test_option_selecting_inference_engine(self):
         dataset = [
@@ -644,7 +622,7 @@ def test_wml_chat_tool_calling(self):
             seed=123,
             max_tokens=256,
             temperature=0.0,
-            model_name="ibm/granite-3-8b-instruct",
+            model_name="ibm/granite-4-h-small",
         )
 
         results = chat.infer(dataset, return_meta_data=False)

tests/inference/test_inference_metrics.py

@@ -3,7 +3,7 @@
     BertScore,
 )
 from unitxt.settings_utils import get_settings
-from unitxt.test_utils.metrics import test_metric
+from unitxt.test_utils.metrics import test_metric as apply_metric_test
 
 from tests.utils import UnitxtInferenceTestCase
 
@@ -51,7 +51,7 @@ def test_bert_score_deberta_base_mnli(self):
             "score_name": "f1",
             "num_of_instances": 2,
         }
-        test_metric(
+        apply_metric_test(
             metric=metric,
             predictions=predictions,
             references=references,

tests/library/test_metric_service.py

@@ -9,7 +9,7 @@
     get_remote_metrics_names,
 )
 from unitxt.metrics import RemoteMetric
-from unitxt.test_utils.metrics import test_metric
+from unitxt.test_utils.metrics import test_metric as apply_metric_test
 
 from tests.utils import UnitxtTestCase
 
@@ -130,7 +130,7 @@ def request_callback(request, uri, response_headers):
 
         metric = RemoteMetric(endpoint=endpoint, metric_name=metric_name)
 
-        test_metric(
+        apply_metric_test(
             metric=metric,
             predictions=predictions,
             references=references,