def test_early_stop_processor_multi_eos(self):
        input_ids = None
        eos_token_id = [2, 3]
        min_eos_p = 0.1  ## some small float

        scores = self._get_uniform_logits(2, 4)
        scores[0][eos_token_id] = -6  ## less than log(min_eos_p)

        esp = BarkEosPrioritizerLogitsProcessor(eos_token_id=eos_token_id, min_eos_p=min_eos_p)
        actual_scores = esp(input_ids, scores)
        expected_scores_list = [
            scores[0].tolist(),
            [float("-inf"), float("-inf"), scores[0][0], scores[0][0]],
        ]
        self.assertListEqual(actual_scores.tolist(), expected_scores_list)

self = <transformers.generation.logits_process.BarkEosPrioritizerLogitsProcessor object at 0x12f1e0220>
input_ids = None
scores = tensor([[ 0.2500,  0.2500, -6.0000, -6.0000],
        [ 0.2500,  0.2500,  0.2500,  0.2500]])

    @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
        if self.min_eos_p:
            probs = torch.nn.functional.softmax(scores.float(), dim=-1)
            # create scores full of -inf except for the eos_token_id
            early_stop_scores = torch.ones_like(scores) * -float("inf")
            early_stop_scores[:, self.eos_token_id] = scores[:, self.eos_token_id]
    
            do_early_stop = probs[:, self.eos_token_id] > self.min_eos_p
            # do_early_stop = torch.any(do_early_stop, dim=1, keepdim=True)
>           scores = torch.where(do_early_stop, early_stop_scores, scores)
E           RuntimeError: The size of tensor a (2) must match the size of tensor b (4) at non-singleton dimension 1

src/transformers/generation/logits_process.py:2142: RuntimeError