Fix bug in mixture logprob inference with None indices

pymc/logprob/mixture.py

@@ -62,7 +62,7 @@
     is_basic_idx,
 )
 from pytensor.tensor.type import TensorType
-from pytensor.tensor.type_other import NoneConst, NoneTypeT, SliceConstant, SliceType
+from pytensor.tensor.type_other import NoneConst, NoneTypeT, SliceType
 from pytensor.tensor.variable import TensorVariable
 
 from pymc.logprob.abstract import (
@@ -289,9 +289,10 @@ def find_measurable_index_mixture(fgraph, node):
         # We don't support (non-scalar) integer array indexing as it can pick repeated values,
         # but the Mixture logprob assumes all mixture values are independent
         if any(
-            indices.dtype.startswith("int") and sum(1 - b for b in indices.type.broadcastable) > 0
+            isinstance(indices, TensorVariable)
+            and indices.dtype.startswith("int")
+            and not all(indices.type.broadcastable)
             for indices in mixing_indices
-            if not isinstance(indices, SliceConstant)
         ):
             return None
 

tests/logprob/test_mixture.py

@@ -1156,3 +1156,30 @@ def test_nested_ifelse():
     np.testing.assert_almost_equal(mix_logp_fn(0, test_value), sp.norm.logpdf(test_value, -5, 1))
     np.testing.assert_almost_equal(mix_logp_fn(1, test_value), sp.norm.logpdf(test_value, 0, 1))
     np.testing.assert_almost_equal(mix_logp_fn(2, test_value), sp.norm.logpdf(test_value, 5, 1))
+
+
+def test_advanced_subtensor_none_and_integer():
+    """
+    Test for correct error handling when the logp graph is over-specified.
+
+    Providing values for both a random variable ('a') and its deterministic
+    child ('b') creates a logical conflict. The system should detect this
+    and raise a controlled RuntimeError.
+
+    This test fails if the rewriter instead crashes with the old internal
+    AttributeError bug, which would indicate a regression. Please see: #7762
+    """
+    a = pt.random.normal(0, 1, size=(10,), name="a")
+    inds = np.array([0, 1, 2, 3], dtype="int32")
+    b = a[None, inds]
+
+    b_val = b.type()
+    b_val.name = "b_val"
+    a_val = a.type()
+    a_val.name = "a_val"
+
+    with pytest.raises(
+        RuntimeError,
+        match="logprob terms of the following value variables could not be derived: {b_val}",
+    ):
+        conditional_logp({b: b_val, a: a_val})