Install pytest-sphinx from conda-forge and fix mypy errors

.github/workflows/test.yml

@@ -203,15 +203,14 @@ jobs:
         run: |
 
           if [[ $OS == "macos-15" ]]; then
-            micromamba install --yes -q "python~=${PYTHON_VERSION}" "numpy${NUMPY_VERSION}" scipy pip graphviz cython pytest coverage pytest-cov pytest-benchmark pytest-mock libblas=*=*accelerate;
+            micromamba install --yes -q "python~=${PYTHON_VERSION}" "numpy${NUMPY_VERSION}" scipy pip graphviz cython pytest coverage pytest-cov pytest-benchmark pytest-mock pytest-sphinx libblas=*=*accelerate;
           else
-            micromamba install --yes -q "python~=${PYTHON_VERSION}" mkl "numpy${NUMPY_VERSION}" scipy pip mkl-service graphviz cython pytest coverage pytest-cov pytest-benchmark pytest-mock;
+            micromamba install --yes -q "python~=${PYTHON_VERSION}" mkl "numpy${NUMPY_VERSION}" scipy pip mkl-service graphviz cython pytest coverage pytest-cov pytest-benchmark pytest-mock pytest-sphinx;
           fi
           if [[ $INSTALL_NUMBA == "1" ]]; then micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}" "numba>=0.57"; fi
           if [[ $INSTALL_JAX == "1" ]]; then micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}" jax jaxlib numpyro && pip install tfp-nightly; fi
           if [[ $INSTALL_TORCH == "1" ]]; then micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}" pytorch pytorch-cuda=12.1 "mkl<=2024.0" -c pytorch -c nvidia; fi
           if [[ $INSTALL_XARRAY == "1" ]]; then micromamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}" xarray xarray-einstats; fi
-          pip install pytest-sphinx
 
           pip install -e ./
           micromamba list && pip freeze

environment-osx-arm64.yml

@@ -31,8 +31,7 @@ dependencies:
   - pytest-xdist
   - pytest-benchmark
   - pytest-mock
-  - pip:
-    - pytest-sphinx
+  - pytest-sphinx
   # For building docs
   - sphinx>=5.1.0,<6
   - sphinx_rtd_theme

environment.yml

@@ -33,8 +33,7 @@ dependencies:
   - pytest-xdist
   - pytest-benchmark
   - pytest-mock
-  - pip:
-    - pytest-sphinx
+  - pytest-sphinx
   # For building docs
   - sphinx>=5.1.0,<6
   - sphinx_rtd_theme

pytensor/link/jax/linker.py

@@ -9,8 +9,10 @@
 class JAXLinker(JITLinker):
     """A `Linker` that JIT-compiles NumPy-based operations using JAX."""
 
+    scalar_shape_inputs: tuple[int, ...]
+
     def __init__(self, *args, **kwargs):
-        self.scalar_shape_inputs: tuple[int] = ()  # type: ignore[annotation-unchecked]
+        self.scalar_shape_inputs = ()
         super().__init__(*args, **kwargs)
 
     def fgraph_convert(self, fgraph, input_storage, storage_map, **kwargs):

pytensor/link/numba/dispatch/vectorize_codegen.py

@@ -517,17 +517,17 @@ def make_loop_call(
     output_slices = []
     for output, output_type, bc in zip(outputs, output_types, output_bc, strict=True):
         core_ndim = output_type.ndim - len(bc)
-        size_type = output.shape.type.element  # type: ignore
-        output_shape = cgutils.unpack_tuple(builder, output.shape)  # type: ignore
-        output_strides = cgutils.unpack_tuple(builder, output.strides)  # type: ignore
+        size_type = output.shape.type.element  # pyright: ignore[reportAttributeAccessIssue]
+        output_shape = cgutils.unpack_tuple(builder, output.shape)  # pyright: ignore[reportAttributeAccessIssue]
+        output_strides = cgutils.unpack_tuple(builder, output.strides)  # pyright: ignore[reportAttributeAccessIssue]
 
         idxs_bc = [zero if bc else idx for idx, bc in zip(idxs, bc, strict=True)] + [
             zero
         ] * core_ndim
         ptr = cgutils.get_item_pointer2(
             context,
             builder,
-            output.data,  # type:ignore
+            output.data,
             output_shape,
             output_strides,
             output_type.layout,

pytensor/npy_2_compat.py

@@ -41,7 +41,7 @@
 
 
 if using_numpy_2:
-    ndarray_c_version = np._core._multiarray_umath._get_ndarray_c_version()
+    ndarray_c_version = np._core._multiarray_umath._get_ndarray_c_version()  # type: ignore[attr-defined]
 else:
     ndarray_c_version = np.core._multiarray_umath._get_ndarray_c_version()  # type: ignore[attr-defined]
 

pytensor/scan/utils.py

@@ -109,7 +109,7 @@ def safe_new(
         except TestValueError:
             pass
 
-    return nw_x
+    return type_cast(Variable, nw_x)
 
 
 class until:

pytensor/tensor/einsum.py

@@ -597,10 +597,14 @@ def einsum(subscripts: str, *operands: "TensorLike", optimize=None) -> TensorVar
             # Numpy einsum_path requires arrays even though only the shapes matter
             # It's not trivial to duck-type our way around because of internal call to `asanyarray`
             *[np.empty(shape) for shape in shapes],
-            einsum_call=True,  # Not part of public API
+            # einsum_call is not part of public API
+            einsum_call=True,  # type: ignore[arg-type]
             optimize="optimal",
-        )  # type: ignore
-        np_path = tuple(contraction[0] for contraction in contraction_list)
+        )
+        np_path: PATH | tuple[tuple[int, ...]] = tuple(
+            contraction[0]  # type: ignore[misc]
+            for contraction in contraction_list
+        )
 
         if len(np_path) == 1 and len(np_path[0]) > 2:
             # When there's nothing to optimize, einsum_path reduces all entries simultaneously instead of doing
@@ -610,7 +614,7 @@ def einsum(subscripts: str, *operands: "TensorLike", optimize=None) -> TensorVar
                 subscripts, tensor_operands, path
             )
         else:
-            path = np_path
+            path = cast(PATH, np_path)
 
         optimized = True
 

pytensor/tensor/random/rewriting/numba.py

@@ -53,10 +53,10 @@ def introduce_explicit_core_shape_rv(fgraph, node):
             #  ← dirichlet_rv{"(a)->(a)"}.1 [id F]
             #     └─ ···
     """
-    op: RandomVariable = node.op  # type: ignore[annotation-unchecked]
+    op: RandomVariable = node.op
 
     next_rng, rv = node.outputs
-    shape_feature: ShapeFeature | None = getattr(fgraph, "shape_feature", None)  # type: ignore[annotation-unchecked]
+    shape_feature: ShapeFeature | None = getattr(fgraph, "shape_feature", None)
     if shape_feature:
         core_shape = [
             shape_feature.get_shape(rv, -i - 1) for i in reversed(range(op.ndim_supp))

pytensor/tensor/rewriting/blockwise.py

@@ -102,7 +102,7 @@ def local_blockwise_alloc(fgraph, node):
     This is critical to remove many unnecessary Blockwise, or to reduce the work done by it
     """
 
-    op: Blockwise = node.op  # type: ignore
+    op: Blockwise = node.op
 
     batch_ndim = op.batch_ndim(node)
     if not batch_ndim:

pytensor/tensor/rewriting/numba.py

@@ -65,10 +65,10 @@ def introduce_explicit_core_shape_blockwise(fgraph, node):
         # [Blockwise{SVD{full_matrices=True, compute_uv=True}, (m,n)->(m,m),(k),(n,n)}].2 [id A] 6
         #  └─ ···
     """
-    op: Blockwise = node.op  # type: ignore[annotation-unchecked]
+    op: Blockwise = node.op
     batch_ndim = op.batch_ndim(node)
 
-    shape_feature: ShapeFeature | None = getattr(fgraph, "shape_feature", None)  # type: ignore[annotation-unchecked]
+    shape_feature: ShapeFeature | None = getattr(fgraph, "shape_feature", None)
     if shape_feature:
         core_shapes = [
             [shape_feature.get_shape(out, i) for i in range(batch_ndim, out.type.ndim)]

scripts/mypy-failing.txt

@@ -3,7 +3,6 @@ pytensor/compile/debugmode.py
 pytensor/compile/function/pfunc.py
 pytensor/compile/function/types.py
 pytensor/compile/mode.py
-pytensor/compile/sharedvalue.py
 pytensor/graph/rewriting/basic.py
 pytensor/ifelse.py
 pytensor/link/numba/dispatch/elemwise.py