[Wav2Vec2] Fix torch srcipt (#24062)

* [Wav2Vec2] Fix torch srcipt

* fix more

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -1178,8 +1178,7 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
         if isinstance(module, (Wav2Vec2Encoder, Wav2Vec2EncoderStableLayerNorm, Wav2Vec2FeatureEncoder)):
             module.gradient_checkpointing = value
 
-    @property
-    def _adapters(self):
+    def _get_adapters(self):
         if self.config.adapter_attn_dim is None:
             raise ValueError(f"{self.__class__} has no adapter layers. Make sure to define `config.adapter_attn_dim`.")
 
@@ -1339,7 +1338,7 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
                     f" directory containing a file named {filepath}."
                 )
 
-        adapter_weights = self._adapters
+        adapter_weights = self._get_adapters()
         unexpected_keys = set(state_dict.keys()) - set(adapter_weights.keys())
         missing_keys = set(adapter_weights.keys()) - set(state_dict.keys())
 

tests/models/wav2vec2/test_modeling_wav2vec2.py

@@ -297,7 +297,7 @@ class Wav2Vec2ModelTester:
         config.adapter_attn_dim = 16
         model = Wav2Vec2ForCTC(config=config)
 
-        self.parent.assertIsNotNone(model._adapters)
+        self.parent.assertIsNotNone(model._get_adapters())
 
         model.to(torch_device)
         model.eval()
@@ -1146,7 +1146,7 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
             model = Wav2Vec2ForCTC.from_pretrained(tempdir)
 
             logits = get_logits(model, input_features)
-            adapter_weights = model._adapters
+            adapter_weights = model._get_adapters()
 
             # save safe weights
             safe_filepath = os.path.join(tempdir, WAV2VEC2_ADAPTER_SAFE_FILE.format("eng"))
@@ -1168,7 +1168,7 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
             model = Wav2Vec2ForCTC.from_pretrained(tempdir)
 
             logits = get_logits(model, input_features)
-            adapter_weights = model._adapters
+            adapter_weights = model._get_adapters()
 
             # save pt weights
             pt_filepath = os.path.join(tempdir, WAV2VEC2_ADAPTER_PT_FILE.format("eng"))