🚨 🚨 Inherited CausalLM Tests (#37590)

* stash commit

* Experiment 1: Try just Gemma

* Experiment 1: Just try Gemma

* make fixup

* Trigger tests

* stash commit

* Try adding Gemma3 as well

* make fixup

* Correct attrib names

* Correct pipeline model mapping

* Add in all_model_classes for Gemma1 again

* Move the pipeline model mapping around again

* make fixup

* Revert Gemma3 changes since it's a VLM

* Let's try Falcon

* Correct attributes

* Correct attributes

* Let's try just overriding get_config() for now

* Do Nemotron too

* And Llama!

* Do llama/persimmon

* Correctly skip tests

* Fix Persimmon

* Include Phimoe

* Fix Gemma2

* Set model_tester_class correctly

* Add GLM

* More models!

* models models models

* make fixup

* Add Qwen3 + Qwen3MoE

* Correct import

* make fixup

* Add the QuestionAnswering classes

* Add the QuestionAnswering classes

* Move pipeline mapping to the right place

* Jetmoe too

* Stop RoPE testing models with no RoPE

* Fix up JetMOE a bit

* Fix up JetMOE a bit

* Can we just force pad_token_id all the time?

* make fixup

* fix starcoder2

* Move pipeline mapping

* Fix RoPE skipping

* Fix RecurrentGemma tests

* Fix Falcon tests

* Add MoE attributes

* Fix values for RoPE testing

* Make sure we set bos_token_id and eos_token_id in an appropriate range

* make fixup

* Fix GLM4

* Add mamba attributes

* Revert bits of JetMOE

* Re-add the JetMOE skips

* Update tests/causal_lm_tester.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Add licence

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

tests/models/gemma2/test_modeling_gemma2.py

@@ -33,7 +33,7 @@ from transformers.testing_utils import (
     torch_device,
 )
 
-from ...models.gemma.test_modeling_gemma import GemmaModelTest, GemmaModelTester
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
 from ...test_configuration_common import ConfigTester
 
 
@@ -48,17 +48,28 @@ if is_torch_available():
     )
 
 
-class Gemma2ModelTester(GemmaModelTester):
+class Gemma2ModelTester(CausalLMModelTester):
     if is_torch_available():
         config_class = Gemma2Config
-        model_class = Gemma2Model
-        for_causal_lm_class = Gemma2ForCausalLM
-        for_sequence_class = Gemma2ForSequenceClassification
-        for_token_class = Gemma2ForTokenClassification
+        base_model_class = Gemma2Model
+        causal_lm_class = Gemma2ForCausalLM
+        sequence_class = Gemma2ForSequenceClassification
+        token_class = Gemma2ForTokenClassification
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Gemma2Model,
+            "text-classification": Gemma2ForSequenceClassification,
+            "token-classification": Gemma2ForTokenClassification,
+            "text-generation": Gemma2ForCausalLM,
+            "zero-shot": Gemma2ForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
 
 
 @require_torch
-class Gemma2ModelTest(GemmaModelTest, unittest.TestCase):
+class Gemma2ModelTest(CausalLMModelTest, unittest.TestCase):
     all_model_classes = (
         (Gemma2Model, Gemma2ForCausalLM, Gemma2ForSequenceClassification, Gemma2ForTokenClassification)
         if is_torch_available()
@@ -75,10 +86,12 @@ class Gemma2ModelTest(GemmaModelTest, unittest.TestCase):
         if is_torch_available()
         else {}
     )
+
     test_headmasking = False
     test_pruning = False
     _is_stateful = True
     model_split_percents = [0.5, 0.6]
+    model_tester_class = Gemma2ModelTester
 
     def setUp(self):
         self.model_tester = Gemma2ModelTester(self)