fix pyupgrade

Author: juanitorduz

.pre-commit-config.yaml

@@ -20,7 +20,7 @@ repos:
           )$
       - id: check-merge-conflict
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.11
+    rev: v0.1.13
     hooks:
       - id: ruff
         args: ["--fix", "--show-source"]

pyproject.toml

@@ -130,8 +130,8 @@ disable = ["C0330", "C0326"]
 
 
 [tool.ruff]
-select = ["C", "E", "F", "I", "W", "UP"]
-ignore = ["E501", "E741", "C408", "C901"]
+select = ["C", "E", "F", "I", "UP", "W"]
+ignore = ["C408", "C901", "E501", "E741", "UP031"]
 exclude = ["doc/", "pytensor/_version.py", "bin/pytensor_cache.py"]
 
 

pytensor/graph/basic.py

@@ -17,7 +17,6 @@
     TYPE_CHECKING,
     Any,
     Callable,
-    Deque,
     Generic,
     Optional,
     TypeVar,
@@ -1376,7 +1375,7 @@ def _compute_deps_cache(io):
     )
 
     _clients: dict[T, list[T]] = {}
-    sources: Deque[T] = deque()
+    sources: deque[T] = deque()
     search_res_len: int = 0
     for snode, children in search_res:
         search_res_len += 1

pytensor/link/vm.py

@@ -13,7 +13,7 @@
 from collections import defaultdict
 from collections.abc import Sequence
 from itertools import zip_longest
-from typing import TYPE_CHECKING, Any, DefaultDict, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 from pytensor.configdefaults import config
 from pytensor.graph.basic import Apply, Constant, Variable
@@ -977,7 +977,7 @@ def reduce_storage_allocations(
 
         """
         # Collect Reallocation Info
-        compute_map_re: DefaultDict[Variable, list[bool]] = defaultdict(lambda: [False])
+        compute_map_re: defaultdict[Variable, list[bool]] = defaultdict(lambda: [False])
         for var in self.fgraph.inputs:
             compute_map_re[var][0] = True
 

pytensor/tensor/blas.py

@@ -1811,9 +1811,7 @@ def contiguous(var, ndim):
             return " && ".join(
                 [
                     " && ".join(
-                        "{strides}[{i}] > 0 && {strides}[{i}] % type_size == 0".format(
-                            strides=strides, i=i
-                        )
+                        f"{strides}[{i}] > 0 && {strides}[{i}] % type_size == 0"
                         for i in range(1, ndim)
                     ),
                     "(%s)"

pytensor/tensor/conv/abstract_conv.py

@@ -639,8 +639,8 @@ def border_mode_to_pad(mode, convdim, kshp):
                 border += ((m[0], m[1]),)
             else:
                 raise ValueError(
-                    "invalid border mode {}. The tuple can only contain "
-                    "integers or tuples of length 2".format(mode)
+                    f"invalid border mode {mode}. The tuple can only contain "
+                    "integers or tuples of length 2"
                 )
         pad = border
     elif mode == "full":
@@ -651,9 +651,9 @@ def border_mode_to_pad(mode, convdim, kshp):
         pad = ((0, 0),) * convdim
     else:
         raise ValueError(
-            "invalid border_mode {}, which must be either "
+            f"invalid border_mode {mode}, which must be either "
             '"valid", "full", "half", an integer or a tuple '
-            "of length {}".format(mode, convdim)
+            f"of length {convdim}"
         )
     return pad
 
@@ -2153,15 +2153,15 @@ def __init__(
         if isinstance(border_mode, int):
             if border_mode < 0:
                 raise ValueError(
-                    "invalid border_mode {}, which must be a "
-                    "non-negative integer".format(border_mode)
+                    f"invalid border_mode {border_mode}, which must be a "
+                    "non-negative integer"
                 )
             border_mode = (border_mode,) * convdim
         elif isinstance(border_mode, tuple):
             if len(border_mode) != convdim:
                 raise ValueError(
-                    "invalid border_mode {}, which must be a "
-                    "tuple of length {}".format(border_mode, convdim)
+                    f"invalid border_mode {border_mode}, which must be a "
+                    f"tuple of length {convdim}"
                 )
             new_border_mode = ()
             for mode in border_mode:
@@ -2189,9 +2189,9 @@ def __init__(
             border_mode = new_border_mode
         elif border_mode not in ("valid", "full", "half"):
             raise ValueError(
-                "invalid border_mode {}, which must be either "
+                f"invalid border_mode {border_mode}, which must be either "
                 '"valid", "full", "half", an integer or a tuple '
-                "of length {}".format(border_mode, convdim)
+                f"of length {convdim}"
             )
         if isinstance(border_mode, tuple) and all(
             mode == (0, 0) or mode == 0 for mode in border_mode
@@ -2284,9 +2284,7 @@ def conv(
         """
         if mode not in ("valid", "full"):
             raise ValueError(
-                "invalid mode {}, which must be either " '"valid" or "full"'.format(
-                    mode
-                )
+                f"invalid mode {mode}, which must be either " '"valid" or "full"'
             )
         if isinstance(dilation, int):
             dilation = (dilation,) * self.convdim

pytensor/tensor/random/var.py

@@ -13,9 +13,7 @@ def __str__(self):
 
 class RandomGeneratorSharedVariable(SharedVariable):
     def __str__(self):
-        return self.name or "RandomGeneratorSharedVariable({})".format(
-            repr(self.container)
-        )
+        return self.name or f"RandomGeneratorSharedVariable({repr(self.container)})"
 
 
 @shared_constructor.register(np.random.RandomState)

pytensor/tensor/rewriting/elemwise.py

@@ -1,8 +1,8 @@
 import sys
 from collections import defaultdict, deque
 from collections.abc import Generator
-from functools import lru_cache
-from typing import DefaultDict, TypeVar
+from functools import cache
+from typing import TypeVar
 from warnings import warn
 
 import pytensor
@@ -686,13 +686,13 @@ def find_next_fuseable_subgraph(
             Elemwise Composite before being accessed again in the next iteration.
             """
 
-            FUSEABLE_MAPPING = DefaultDict[Variable, list[Apply]]
-            UNFUSEABLE_MAPPING = DefaultDict[Variable, set[ApplyOrOutput]]
+            FUSEABLE_MAPPING = defaultdict[Variable, list[Apply]]
+            UNFUSEABLE_MAPPING = defaultdict[Variable, set[ApplyOrOutput]]
 
             def initialize_fuseable_mappings(
                 *, fg: FunctionGraph
             ) -> tuple[FUSEABLE_MAPPING, UNFUSEABLE_MAPPING]:
-                @lru_cache(maxsize=None)
+                @cache
                 def elemwise_scalar_op_has_c_code(node: Apply) -> bool:
                     # TODO: This should not play a role in non-c backends!
                     if node.op.scalar_op.supports_c_code(node.inputs, node.outputs):
@@ -754,9 +754,9 @@ def find_fuseable_subgraph(
                 VT = TypeVar("VT", list, set)
 
                 def shallow_clone_defaultdict(
-                    d: DefaultDict[KT, VT],
-                ) -> DefaultDict[KT, VT]:
-                    new_dict: DefaultDict[KT, VT] = defaultdict(d.default_factory)
+                    d: defaultdict[KT, VT],
+                ) -> defaultdict[KT, VT]:
+                    new_dict: defaultdict[KT, VT] = defaultdict(d.default_factory)
                     new_dict.update({k: v.copy() for k, v in d.items()})
                     return new_dict
 

pytensor/tensor/shape.py

@@ -917,7 +917,7 @@ def shape_padaxis(t, axis):
 
     ndim = _t.ndim + 1
     if not -ndim <= axis < ndim:
-        msg = "axis {0} is out of bounds [-{1}, {1})".format(axis, ndim)
+        msg = f"axis {axis} is out of bounds [-{ndim}, {ndim})"
         raise IndexError(msg)
     if axis < 0:
         axis += ndim

pytensor/tensor/utils.py

@@ -167,10 +167,10 @@ def broadcast_static_dim_lengths(
 # Copied verbatim from numpy.lib.function_base
 # https://github.com/numpy/numpy/blob/f2db090eb95b87d48a3318c9a3f9d38b67b0543c/numpy/lib/function_base.py#L1999-L2029
 _DIMENSION_NAME = r"\w+"
-_CORE_DIMENSION_LIST = "(?:{0:}(?:,{0:})*)?".format(_DIMENSION_NAME)
+_CORE_DIMENSION_LIST = f"(?:{_DIMENSION_NAME}(?:,{_DIMENSION_NAME})*)?"
 _ARGUMENT = rf"\({_CORE_DIMENSION_LIST}\)"
-_ARGUMENT_LIST = "{0:}(?:,{0:})*".format(_ARGUMENT)
-_SIGNATURE = "^{0:}->{0:}$".format(_ARGUMENT_LIST)
+_ARGUMENT_LIST = f"{_ARGUMENT}(?:,{_ARGUMENT})*"
+_SIGNATURE = f"^{_ARGUMENT_LIST}->{_ARGUMENT_LIST}$"
 
 
 def _parse_gufunc_signature(signature):

tests/compile/test_shared.py

@@ -162,7 +162,7 @@ def f(var, val):
         with pytest.raises(TypeError):
             f(b, 8)
 
-        b = shared(float(7.234), strict=True)
+        b = shared(7.234, strict=True)
         assert b.type == dscalar
         with pytest.raises(TypeError):
             f(b, 8)
@@ -268,7 +268,7 @@ def f(var, val):
         f(b, 8)
         assert b.get_value() == 8
 
-        b = shared(float(7.234), allow_downcast=True)
+        b = shared(7.234, allow_downcast=True)
         assert b.type == dscalar
         f(b, 8)
         assert b.get_value() == 8

tests/link/c/test_params_type.py

@@ -42,7 +42,7 @@ def c_code_cache_version(self):
 
     def c_support_code_apply(self, node, name):
         float_type = node.inputs[0].type.dtype_specs()[1]
-        return """
+        return f"""
         /* Computes: x = a*x*x + b*x + c for x in tensor. */
         int quadratic_{name}(PyArrayObject* tensor, {float_type} a, {float_type} b, {float_type} c) {{
             NpyIter* iterator = NpyIter_New(tensor,
@@ -70,10 +70,7 @@ def c_support_code_apply(self, node, name):
             NpyIter_Deallocate(iterator);
             return 0;
         }}
-        """.format(
-            name=name,
-            float_type=float_type,
-        )
+        """
 
     def c_code(self, node, name, inputs, outputs, sub):
         return """

tests/tensor/conv/c_conv3d_corr3d_ref.py

@@ -82,15 +82,15 @@ def __init__(
         if isinstance(border_mode, int):
             if border_mode < 0:
                 raise ValueError(
-                    "invalid border_mode {}, which must be a "
-                    "non-negative integer".format(border_mode)
+                    f"invalid border_mode {border_mode}, which must be a "
+                    "non-negative integer"
                 )
             border_mode = (border_mode, border_mode, border_mode)
         if isinstance(border_mode, tuple):
             if len(border_mode) != 3 or min(border_mode) < 0:
                 raise ValueError(
-                    "invalid border_mode {}, which must be a tuple of "
-                    "three non-negative integers".format(border_mode)
+                    f"invalid border_mode {border_mode}, which must be a tuple of "
+                    "three non-negative integers"
                 )
             pad_h, pad_w, pad_d = map(int, border_mode)
             border_mode = (pad_h, pad_w, pad_d)
@@ -99,9 +99,9 @@ def __init__(
             or border_mode in ("valid", "full", "half")
         ):
             raise ValueError(
-                "invalid border_mode {}, which must be either "
+                f"invalid border_mode {border_mode}, which must be either "
                 '"valid", "full", "half", an integer or a tuple of three'
-                " integers".format(border_mode)
+                " integers"
             )
         self.border_mode = border_mode
         if len(subsample) != 3:

tests/tensor/conv/c_conv_corr_ref.py

@@ -91,15 +91,15 @@ def __init__(
         if isinstance(border_mode, int):
             if border_mode < 0:
                 raise ValueError(
-                    "invalid border_mode {}, which must be a "
-                    "non-negative integer".format(border_mode)
+                    f"invalid border_mode {border_mode}, which must be a "
+                    "non-negative integer"
                 )
             border_mode = ((border_mode, border_mode),) * 2
         elif isinstance(border_mode, tuple):
             if len(border_mode) != 2:
                 raise ValueError(
-                    "invalid border_mode {} which must be a "
-                    "tuple of length 2".format(border_mode)
+                    f"invalid border_mode {border_mode} which must be a "
+                    "tuple of length 2"
                 )
             border = ()
             for mode in border_mode:
@@ -115,9 +115,9 @@ def __init__(
             border_mode = border
         elif border_mode not in ("valid", "full", "half"):
             raise ValueError(
-                "invalid border_mode {}, which must be either "
+                f"invalid border_mode {border_mode}, which must be either "
                 '"valid", "full", "half", an integer or a tuple '
-                "of two integers or a pair of integers".format(border_mode)
+                "of two integers or a pair of integers"
             )
         self.border_mode = border_mode
         if len(subsample) != 2:

tests/tensor/test_basic.py

@@ -3247,10 +3247,10 @@ def test_autocast_custom():
         assert (fvector() + 1.0).dtype == "float32"
         assert (dvector() + np.float32(1.1)).dtype == "float64"
         assert (dvector() + np.float64(1.1)).dtype == "float64"
-        assert (dvector() + float(1.1)).dtype == "float64"
+        assert (dvector() + 1.1).dtype == "float64"
         assert (fvector() + np.float32(1.1)).dtype == "float32"
         assert (fvector() + np.float64(1.1)).dtype == "float64"
-        assert (fvector() + float(1.1)).dtype == config.floatX
+        assert (fvector() + 1.1).dtype == config.floatX
         assert (lvector() + np.int64(1)).dtype == "int64"
         assert (lvector() + np.int32(1)).dtype == "int64"
         assert (lvector() + np.int16(1)).dtype == "int64"

versioneer.py

@@ -316,7 +316,7 @@
 import subprocess
 import sys
 from pathlib import Path
-from typing import Callable, Dict
+from typing import Callable
 import functools
 
 have_tomllib = True
@@ -415,8 +415,8 @@ class NotThisMethod(Exception):
 
 
 # these dictionaries contain VCS-specific tools
-LONG_VERSION_PY: Dict[str, str] = {}
-HANDLERS: Dict[str, Dict[str, Callable]] = {}
+LONG_VERSION_PY: dict[str, str] = {}
+HANDLERS: dict[str, dict[str, Callable]] = {}
 
 
 def register_vcs_handler(vcs, method):  # decorator
@@ -1141,7 +1141,7 @@ def git_get_keywords(versionfile_abs):
     # _version.py.
     keywords = {}
     try:
-        with open(versionfile_abs, "r") as fobj:
+        with open(versionfile_abs) as fobj:
             for line in fobj:
                 if line.strip().startswith("git_refnames ="):
                     mo = re.search(r'=\s*"(.*)"', line)
@@ -1380,7 +1380,7 @@ def do_vcs_install(versionfile_source, ipy):
         files.append(versioneer_file)
     present = False
     try:
-        with open(".gitattributes", "r") as fobj:
+        with open(".gitattributes") as fobj:
             for line in fobj:
                 if line.strip().startswith(versionfile_source):
                     if "export-subst" in line.strip().split()[1:]:
@@ -2128,7 +2128,7 @@ def do_setup():
                        "__init__.py")
     if os.path.exists(ipy):
         try:
-            with open(ipy, "r") as f:
+            with open(ipy) as f:
                 old = f.read()
         except OSError:
             old = ""
@@ -2160,7 +2160,7 @@ def scan_setup_py():
     found = set()
     setters = False
     errors = 0
-    with open("setup.py", "r") as f:
+    with open("setup.py") as f:
         for line in f.readlines():
             if "import versioneer" in line:
                 found.add("import")