Switch code formatter to ruff (#2582)

Summary:
Pull Request resolved: #2582

Use the ruff code formatter. This diff

- Changes the Meta-internal config to use ruff.
- Updates `pyproject.toml` to use ruff as the code formatter.
- Applies the new formatting across the codebase.

This does NOT change the code sorting engine (that is still usort). We can update that in a next step.

Reviewed By: saitcakmak

Differential Revision: D64517061

fbshipit-source-id: bf9a4ed258b9489fc757c810fe8bbff0a5d018c4

Author: Balandat

botorch/acquisition/cached_cholesky.py

@@ -8,6 +8,7 @@
 Abstract class for acquisition functions leveraging a cached Cholesky
 decomposition of the posterior covariance over f(X_baseline).
 """
+
 from __future__ import annotations
 
 import warnings

botorch/acquisition/fixed_feature.py

@@ -93,7 +93,6 @@ def __init__(
         if isinstance(values, Tensor):
             new_values = values.detach().clone()
         else:
-
             dtype = get_dtype_of_sequence(values)
             device = get_device_of_sequence(values)
 

botorch/acquisition/input_constructors.py

@@ -1258,7 +1258,6 @@ def construct_inputs_qKG(
     }
 
     if with_current_value:
-
         X = _get_dataset_field(training_data, "X", first_only=True)
         _bounds = torch.as_tensor(bounds, dtype=X.dtype, device=X.device)
 
@@ -1868,7 +1867,6 @@ def _get_ref_point(
     objective_thresholds: Tensor,
     objective: MCMultiOutputObjective | None = None,
 ) -> Tensor:
-
     if objective is None:
         ref_point = objective_thresholds
     elif isinstance(objective, RiskMeasureMCObjective):

botorch/acquisition/multi_objective/analytic.py

@@ -16,7 +16,6 @@
 
 """
 
-
 from __future__ import annotations
 
 from itertools import product

botorch/acquisition/multi_objective/base.py

@@ -8,7 +8,6 @@
 Base classes for multi-objective acquisition functions.
 """
 
-
 from __future__ import annotations
 
 from abc import ABC, abstractmethod

botorch/acquisition/multi_objective/joint_entropy_search.py

@@ -15,6 +15,7 @@
     2022.
 
 """
+
 from __future__ import annotations
 
 from abc import abstractmethod

botorch/acquisition/multi_objective/logei.py

@@ -48,7 +48,6 @@
 class qLogExpectedHypervolumeImprovement(
     MultiObjectiveMCAcquisitionFunction, SubsetIndexCachingMixin
 ):
-
     _log: bool = True
 
     def __init__(
@@ -321,7 +320,6 @@ class qLogNoisyExpectedHypervolumeImprovement(
     NoisyExpectedHypervolumeMixin,
     qLogExpectedHypervolumeImprovement,
 ):
-
     _log: bool = True
 
     def __init__(

botorch/acquisition/prior_guided.py

@@ -15,6 +15,7 @@
     Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization.
     ICLR 2022.
 """
+
 from __future__ import annotations
 
 from botorch.acquisition.acquisition import AcquisitionFunction

botorch/exceptions/warnings.py

@@ -7,6 +7,7 @@
 r"""
 Botorch Warnings.
 """
+
 import warnings
 
 

botorch/generation/utils.py

@@ -21,7 +21,7 @@
 
 
 def _convert_nonlinear_inequality_constraints(
-    nonlinear_inequality_constraints: list[Callable | tuple[Callable, bool]]
+    nonlinear_inequality_constraints: list[Callable | tuple[Callable, bool]],
 ) -> list[tuple[Callable, bool]]:
     """Convert legacy defintions of nonlinear inequality constraints into the new
     format. Assumes intra-point constraints.