Fix mypy in tensor/slinalg.py

pytensor/tensor/slinalg.py

@@ -2,7 +2,7 @@ import logging
 import typing
 import warnings
 from functools import reduce
-from typing import TYPE_CHECKING, Literal
+from typing import TYPE_CHECKING, Literal, cast
 
 import numpy as np
 import scipy.linalg
@@ -141,7 +141,7 @@ def cholesky(x, lower=True, on_error="raise", check_finite=False):
 class SolveBase(Op):
     """Base class for `scipy.linalg` matrix equation solvers."""
 
-    __props__ = (
+    __props__: tuple[str, ...] = (
         "lower",
         "check_finite",
         "b_ndim",
@@ -352,7 +352,7 @@ def solve_triangular(
         This will influence how batched dimensions are interpreted.
     """
     b_ndim = _default_b_ndim(b, b_ndim)
-    return Blockwise(
+    ret = Blockwise(
         SolveTriangular(
             lower=lower,
             trans=trans,
@@ -361,6 +361,7 @@ def solve_triangular(
             b_ndim=b_ndim,
         )
     )(a, b)
+    return cast(TensorVariable, ret)
 
 
 class Solve(SolveBase):
@@ -714,9 +715,7 @@ class BilinearSolveDiscreteLyapunov(Op):
 
 
 _solve_continuous_lyapunov = SolveContinuousLyapunov()
-_solve_bilinear_direct_lyapunov = typing.cast(
-    typing.Callable, BilinearSolveDiscreteLyapunov()
-)
+_solve_bilinear_direct_lyapunov = cast(typing.Callable, BilinearSolveDiscreteLyapunov())
 
 
 def _direct_solve_discrete_lyapunov(A: "TensorLike", Q: "TensorLike") -> TensorVariable:
@@ -729,7 +728,7 @@ def _direct_solve_discrete_lyapunov(A: "TensorLike", Q: "TensorLike") -> TensorV
         AA = kron(A_, A_)
 
     X = solve(pt.eye(AA.shape[0]) - AA, Q_.ravel())
-    return typing.cast(TensorVariable, reshape(X, Q_.shape))
+    return cast(TensorVariable, reshape(X, Q_.shape))
 
 
 def solve_discrete_lyapunov(
@@ -765,7 +764,7 @@ def solve_discrete_lyapunov(
     if method == "direct":
         return _direct_solve_discrete_lyapunov(A, Q)
     if method == "bilinear":
-        return typing.cast(TensorVariable, _solve_bilinear_direct_lyapunov(A, Q))
+        return cast(TensorVariable, _solve_bilinear_direct_lyapunov(A, Q))
 
 
 def solve_continuous_lyapunov(A: "TensorLike", Q: "TensorLike") -> TensorVariable:
@@ -785,7 +784,7 @@ def solve_continuous_lyapunov(A: "TensorLike", Q: "TensorLike") -> TensorVariabl
 
     """
 
-    return typing.cast(TensorVariable, _solve_continuous_lyapunov(A, Q))
+    return cast(TensorVariable, _solve_continuous_lyapunov(A, Q))
 
 
 class SolveDiscreteARE(pt.Op):
@@ -866,9 +865,7 @@ def solve_discrete_are(A, B, Q, R, enforce_Q_symmetric=False) -> TensorVariable:
         Square matrix of shape M x M, representing the solution to the DARE
     """
 
-    return typing.cast(
-        TensorVariable, SolveDiscreteARE(enforce_Q_symmetric)(A, B, Q, R)
-    )
+    return cast(TensorVariable, SolveDiscreteARE(enforce_Q_symmetric)(A, B, Q, R))
 
 
 def _largest_common_dtype(tensors: typing.Sequence[TensorVariable]) -> np.dtype:

scripts/mypy-failing.txt

@@ -24,7 +24,6 @@ pytensor/tensor/random/basic.py
 pytensor/tensor/random/op.py
 pytensor/tensor/random/utils.py
 pytensor/tensor/rewriting/basic.py
-pytensor/tensor/slinalg.py
 pytensor/tensor/type.py
 pytensor/tensor/type_other.py
 pytensor/tensor/variable.py