[VITS] Fix nightly tests
diff --git a/tests/models/vits/test_modeling_vits.py b/tests/models/vits/test_modeling_vits.py
index f28c4686aec27..e767b036bea78 100644
--- a/tests/models/vits/test_modeling_vits.py
+++ b/tests/models/vits/test_modeling_vits.py
@@ -27,6 +27,7 @@
     is_flaky,
     is_torch_available,
     require_torch,
+    require_torch_multi_gpu,
     slow,
     torch_device,
 )
@@ -177,6 +178,30 @@ def test_model_forward(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_forward(*config_and_inputs)
 
+    @require_torch_multi_gpu
+    # override to force all elements of the batch to have the same sequence length across GPUs
+    def test_multi_gpu_data_parallel_forward(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.use_stochastic_duration_prediction = False
+
+        # move input tensors to cuda:O
+        for key, value in inputs_dict.items():
+            if torch.is_tensor(value):
+                # make all elements of the batch the same -> ensures the output seq lengths are the same for DP
+                value[1:] = value[0]
+                inputs_dict[key] = value.to(0)
+
+        for model_class in self.all_model_classes:
+            model = model_class(config=config)
+            model.to(0)
+            model.eval()
+
+            # Wrap model in nn.DataParallel
+            model = torch.nn.DataParallel(model)
+            set_seed(555)
+            with torch.no_grad():
+                _ = model(**self._prepare_for_class(inputs_dict, model_class)).waveform
+
     @unittest.skip("VITS is not deterministic")
     def test_determinism(self):
         pass

# What does this PR do?

Fixes the tokenizer integration test and multi-GPU test that failed on the nightly run: https://github.com/huggingface/transformers/actions/runs/6068405445/job/16461410319

The tokenizer fix is trivial (needed to update the commit ID)!

The multi-GPU test was failing because the output sequence length for VITS is a function of the model **inputs**, rather than being a function of the input **sequence lengths** only.

Let's say we have 2 GPUs over which we want to run DP:
* GPU 1 outputs a sequence length of `N`, which is computed based on the input in the first element of the batch `x`
* GPU 2 outputs a sequence length of `M`, which is computed based on the input in the second element of the batch `y`

=> there is nothing to enforce that `N = M`, since the VITS output sequence length is a function of the inputs. Thus, we cannot concatenate the inputs after running the forward pass, since they have different dims. 

```python
# pseudo code for data parallelism
input_1, input_2 = torch.split(input, 2, dim=0)

output_1 = model(input_1) 
output_2 = model(input_2)

output = torch.concatenate([output_1, output_2], dim=0)  # breaks because input_1 and input_2 have different sequence lengths
```

The fix for the test is to pass the same inputs to both GPUs, and disable the stochastic duration predictor. This way, we get consistent outputs across our GPUs.