test: cover evaluator skip branches

tests/test_evaluator.py

@@ -36,6 +36,14 @@ class _MapDataset:
         return _MapDataset([self.texts[index] for index in self.selected_indices])
 
 
+class _ChangingDataset(_MapDataset):
+    """Simulate a transformed dataset changing after index selection."""
+
+    def select(self, indices):
+        self.selected_indices = list(indices)
+        return _MapDataset([""])
+
+
 class _Encoding(dict):
     def to(self, _device):
         return self
@@ -154,6 +162,36 @@ def test_causal_lm_rejects_tokenless_and_one_token_sequences():
     assert model.labels == []
 
 
+def test_causal_lm_skips_batch_emptied_by_dataset_transform():
+    dataset = _ChangingDataset(["alpha"])
+    tokenizer = _ScriptedTokenizer([])
+    model = _LossModel([])
+    handle = SimpleNamespace(model=model, tokenizer=tokenizer, task="causal_lm")
+
+    with pytest.raises(RuntimeError, match="zero valid prediction tokens"):
+        Evaluator(handle=handle, dataset=dataset).evaluate()
+
+    assert tokenizer.calls == []
+    assert model.labels == []
+
+
+def test_causal_lm_skips_zero_target_batch_then_reports_valid_perplexity():
+    evaluator, _dataset, _tokenizer, model = _evaluator(
+        ["padded target", "valid target"],
+        [
+            _encoding([[1, 0]], [[1, 0]]),
+            _encoding([[2, 3]], [[1, 1]]),
+        ],
+        [math.log(3.0)],
+        batch_size=1,
+    )
+
+    result = evaluator.evaluate()
+
+    assert result["perplexity"] == pytest.approx(3.0)
+    assert len(model.labels) == 1
+
+
 def test_causal_lm_rejects_missing_loss():
     evaluator, _dataset, _tokenizer, _model = _evaluator(
         ["alpha"],