Update pyfmt component on FBS:master

.pre-commit-config.yaml

@@ -11,13 +11,13 @@ repos:
             - PyYAML
 
 -   repo: https://github.com/omnilib/ufmt
-    rev: v2.8.0
+    rev: v2.9.0
     hooks:
     -   id: ufmt
         additional_dependencies:
-        - black==24.4.2
-        - usort==1.0.8.post1
-        - ruff-api==0.1.0
+        - black==25.11.0
+        - usort==1.1.0
+        - ruff-api==0.2.0
         - stdlibs==2024.1.28
         args: [format]
 

botorch/acquisition/analytic.py

@@ -12,7 +12,6 @@
 from __future__ import annotations
 
 import math
-
 from abc import ABC, abstractmethod
 from contextlib import nullcontext
 from copy import deepcopy

botorch/acquisition/decoupled.py

@@ -16,7 +16,6 @@
 from botorch.exceptions import BotorchWarning
 from botorch.exceptions.errors import BotorchTensorDimensionError
 from botorch.logging import shape_to_str
-
 from botorch.models.model import ModelList
 from torch import Tensor
 

botorch/acquisition/fixed_feature.py

@@ -12,7 +12,6 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
-
 from numbers import Number
 
 import torch

botorch/acquisition/joint_entropy_search.py

@@ -40,7 +40,6 @@
     t_batch_mode_transform,
 )
 from torch import Tensor
-
 from torch.distributions import Normal
 
 MCMC_DIM = -3  # Only relevant if you do Fully Bayesian GPs.

botorch/acquisition/knowledge_gradient.py

@@ -27,7 +27,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-
 from copy import deepcopy
 from typing import Any
 

botorch/acquisition/max_value_entropy_search.py

@@ -50,7 +50,6 @@
     match_batch_shape,
     t_batch_mode_transform,
 )
-
 from linear_operator.functions import inv_quad
 from linear_operator.utils.cholesky import psd_safe_cholesky
 from scipy.optimize import brentq

botorch/acquisition/multi_objective/hypervolume_knowledge_gradient.py

@@ -27,7 +27,6 @@
     AcquisitionFunction,
     OneShotAcquisitionFunction,
 )
-
 from botorch.acquisition.cost_aware import CostAwareUtility
 from botorch.acquisition.decoupled import DecoupledAcquisitionFunction
 from botorch.acquisition.knowledge_gradient import ProjectedAcquisitionFunction

botorch/acquisition/prior_guided.py

@@ -27,7 +27,6 @@
     t_batch_mode_transform,
 )
 from torch import Tensor
-
 from torch.nn import Module
 
 

botorch/generation/sampling.py

@@ -27,7 +27,6 @@
 )
 from botorch.generation.utils import _flip_sub_unique
 from botorch.models.model import Model
-
 from botorch.models.model_list_gp_regression import ModelListGP
 from botorch.models.multitask import MultiTaskGP
 from botorch.utils.sampling import batched_multinomial

botorch/models/gp_regression_fidelity.py

@@ -26,7 +26,6 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
-
 from typing import Any
 
 import torch

botorch/models/gp_regression_mixed.py

@@ -7,7 +7,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-
 from typing import Any
 
 import torch

botorch/models/higher_order_gp.py

@@ -230,7 +230,7 @@ def __init__(
                 warnings.warn(
                     "HigherOrderGP does not support the outcome_transform "
                     "`Standardize`! Using `FlattenedStandardize` with `output_shape="
-                    f"{train_Y.shape[- num_output_dims:]} and batch_shape="
+                    f"{train_Y.shape[-num_output_dims:]} and batch_shape="
                     f"{batch_shape} instead.",
                     RuntimeWarning,
                     stacklevel=2,

botorch/models/kernels/orthogonal_additive_kernel.py

@@ -12,7 +12,6 @@
 from gpytorch.kernels import Kernel
 from gpytorch.module import Module
 from gpytorch.priors import Prior
-
 from torch import nn, Tensor
 
 _positivity_constraint = Positive()

botorch/models/latent_kronecker_gp.py

@@ -44,7 +44,6 @@
 from gpytorch.likelihoods import GaussianLikelihood
 from gpytorch.likelihoods.likelihood import Likelihood
 from gpytorch.means import Mean, ZeroMean
-
 from gpytorch.models.exact_gp import ExactGP
 from gpytorch.module import Module
 from linear_operator import settings

botorch/models/model_list_gp_regression.py

@@ -13,7 +13,6 @@
 from typing import Any
 
 import torch
-
 from botorch.exceptions.errors import BotorchTensorDimensionError
 from botorch.models.gpytorch import GPyTorchModel, ModelListGPyTorchModel
 from botorch.models.model import FantasizeMixin

botorch/models/relevance_pursuit.py

@@ -18,7 +18,6 @@
 from __future__ import annotations
 
 import math
-
 from abc import ABC, abstractmethod
 from collections.abc import Callable, Sequence
 from copy import copy, deepcopy

botorch/models/robust_relevance_pursuit_model.py

@@ -31,7 +31,6 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-
 from typing import Any, Callable, Mapping, Optional, Sequence
 
 import torch

botorch/models/transforms/outcome.py

@@ -75,8 +75,7 @@ def subset_output(self, idcs: list[int]) -> OutcomeTransform:
             The current outcome transform, subset to the specified output indices.
         """
         raise NotImplementedError(
-            f"{self.__class__.__name__} does not implement the "
-            "`subset_output` method"
+            f"{self.__class__.__name__} does not implement the `subset_output` method"
         )
 
     def untransform(

botorch/models/utils/inducing_point_allocators.py

@@ -25,7 +25,6 @@
 import torch
 from botorch.exceptions.errors import UnsupportedError
 from botorch.models.model import Model
-
 from botorch.utils.probability.utils import ndtr as Phi, phi
 from gpytorch.module import Module
 from linear_operator.operators import LinearOperator

botorch/optim/batched_lbfgs_b.py

@@ -45,7 +45,6 @@
 import typing as tp
 
 import numpy as np
-
 from numpy import array, asarray, zeros
 from scipy.optimize import _lbfgsb
 from scipy.optimize._constraints import old_bound_to_new
@@ -339,9 +338,9 @@ def fmin_l_bfgs_b_batched(
     if ftol is None:
         ftol = factr * np.finfo(float).eps
     else:
-        assert (
-            factr is None
-        ), "ftol and factr cannot be used together, set factr explicitly to None."
+        assert factr is None, (
+            "ftol and factr cannot be used together, set factr explicitly to None."
+        )
 
     # build options
     callback = _wrap_callback(callback)
@@ -672,7 +671,7 @@ def translate_bounds_for_lbfgsb(
             bounds[i] = list(bounds[i])
         if len(bounds[i]) == num_features:
             bounds[i] = sum([bounds[i] for _ in range(q)], [])
-        assert (
-            len(bounds[i]) == num_features * q
-        ), f"Instead got {len(bounds[i])} != {num_features} * {q}."
+        assert len(bounds[i]) == num_features * q, (
+            f"Instead got {len(bounds[i])} != {num_features} * {q}."
+        )
     return list(zip(*bounds))

botorch/optim/closures/core.py

@@ -9,13 +9,11 @@
 from __future__ import annotations
 
 from collections.abc import Callable, Sequence
-
 from typing import Any
 
 import numpy as np
 import numpy.typing as npt
 import torch
-
 from botorch.optim.utils import _handle_numerical_errors
 from botorch.optim.utils.numpy_utils import as_ndarray
 from botorch.utils.context_managers import zero_grad_ctx

botorch/optim/core.py

@@ -18,7 +18,6 @@
 from typing import Any
 
 import numpy.typing as npt
-
 from botorch.optim.closures import NdarrayOptimizationClosure
 from botorch.optim.stopping import StoppingCriterion
 from botorch.optim.utils.numpy_utils import get_bounds_as_ndarray

botorch/optim/fit.py

@@ -9,7 +9,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable, Sequence
-
 from functools import partial
 from typing import Any, Optional
 from warnings import warn

botorch/optim/optimize_homotopy.py

@@ -6,14 +6,11 @@
 from __future__ import annotations
 
 import warnings
-
 from collections.abc import Callable
-
 from typing import Any
 
 import torch
 from botorch.acquisition import AcquisitionFunction
-
 from botorch.generation.gen import TGenCandidates
 from botorch.optim.homotopy import Homotopy
 from botorch.optim.initializers import TGenInitialConditions

botorch/optim/optimize_mixed.py

@@ -929,8 +929,7 @@ def optimize_acqf_mixed_alternating(
 
     if sequential is False:  # pragma: no cover
         raise NotImplementedError(
-            "`optimize_acqf_mixed_alternating` only supports "
-            "sequential optimization."
+            "`optimize_acqf_mixed_alternating` only supports sequential optimization."
         )
 
     cat_dims = cat_dims or {}

botorch/optim/utils/acquisition_utils.py

@@ -59,7 +59,7 @@ def columnwise_clamp(
     out = X.clamp(lower, upper)
     if raise_on_violation and not X.allclose(out):
         raise BotorchError(
-            "Original value(s) are out of bounds: " f"{out=}, {X=}, {lower=}, {upper=}."
+            f"Original value(s) are out of bounds: {out=}, {X=}, {lower=}, {upper=}."
         )
 
     return out

botorch/optim/utils/common.py

@@ -9,7 +9,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-
 from logging import debug as logging_debug
 from warnings import warn_explicit, WarningMessage
 

botorch/optim/utils/model_utils.py

@@ -9,7 +9,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable, Iterator
-
 from re import Pattern
 from typing import Any, NamedTuple
 from warnings import warn

botorch/posteriors/fully_bayesian.py

@@ -6,7 +6,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-
 from warnings import warn
 
 import torch

botorch/sampling/pathwise/features/generators.py

@@ -17,7 +17,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-
 from typing import Any
 
 import torch

botorch/sampling/pathwise/prior_samplers.py

@@ -7,7 +7,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-
 from typing import Any
 
 from botorch.models.approximate_gp import ApproximateGPyTorchModel

botorch/sampling/pathwise/update_strategies.py

@@ -7,13 +7,10 @@
 from __future__ import annotations
 
 from collections.abc import Callable
-
 from types import NoneType
-
 from typing import Any
 
 import torch
-
 from botorch.models.approximate_gp import ApproximateGPyTorchModel
 from botorch.models.transforms.input import InputTransform
 from botorch.sampling.pathwise.features import KernelEvaluationMap

botorch/test_functions/utils.py

@@ -8,7 +8,6 @@
 from __future__ import annotations
 
 import torch
-
 from torch import Tensor
 
 

botorch/test_utils/mock.py

@@ -18,7 +18,6 @@
 from unittest import mock
 
 from botorch.optim.batched_lbfgs_b import fmin_l_bfgs_b_batched
-
 from botorch.optim.initializers import (
     gen_batch_initial_conditions,
     gen_one_shot_kg_initial_conditions,

botorch/utils/constants.py

@@ -7,7 +7,6 @@
 from __future__ import annotations
 
 from collections.abc import Iterator
-
 from functools import lru_cache
 from numbers import Number
 

botorch/utils/constraints.py

@@ -11,15 +11,12 @@
 from __future__ import annotations
 
 import math
-
 from collections.abc import Callable
-
 from functools import partial
 
 import torch
 from gpytorch import settings
 from gpytorch.constraints import Interval
-
 from torch import Tensor
 
 

botorch/utils/containers.py

@@ -9,13 +9,11 @@
 from __future__ import annotations
 
 import dataclasses
-
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, fields
 from typing import Any
 
 import torch
-
 from torch import device as Device, dtype as Dtype, LongTensor, Size, Tensor
 
 

botorch/utils/context_managers.py

@@ -11,7 +11,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable, Generator, Iterable
-
 from contextlib import contextmanager
 from typing import Any, NamedTuple