[Wav2Vec2 - MMS] Correct directly loading adapters weights (#24335)

* Correct direct lang loading

* correct more

* revert black

* Use tie weights instead=

* add tests

* add tests

* make style

tests/models/wav2vec2/test_modeling_wav2vec2.py

@@ -1117,6 +1117,40 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
     def test_feed_forward_chunking(self):
         pass
 
+    def test_load_and_set_attn_adapter(self):
+        processor = Wav2Vec2Processor.from_pretrained(
+            "hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
+        )
+
+        def get_logits(model, input_features):
+            model = model.to(torch_device)
+            batch = processor(
+                input_features,
+                padding=True,
+                sampling_rate=processor.feature_extractor.sampling_rate,
+                return_tensors="pt",
+            )
+
+            with torch.no_grad():
+                logits = model(
+                    input_values=batch["input_values"].to(torch_device),
+                    attention_mask=batch["attention_mask"].to(torch_device),
+                ).logits
+            return logits
+
+        input_features = [np.random.random(16_000 * s) for s in [1, 3, 2, 6]]
+
+        model = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-adapter", target_lang="it")
+
+        logits = get_logits(model, input_features)
+
+        model_2 = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-adapter")
+        model_2.load_adapter("it")
+
+        logits_2 = get_logits(model_2, input_features)
+
+        self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
+
     def test_load_attn_adapter(self):
         processor = Wav2Vec2Processor.from_pretrained(
             "hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True