Apply PEP585 (use native types for type annotations) (#2461)

Summary:
Pull Request resolved: #2461

PEP585 (implemented with Python 3.9) allows subscripting built-in and standard library types directly, to use them in typing annotations, rather than importing new classes from the typing module. This applies these changes throughout the codebase (excluding tutorials).

Reviewed By: esantorella

Differential Revision: D60967000

fbshipit-source-id: 66909aec9645b50c6c4b5d6050701878be630ed7

botorch/acquisition/analytic.py

@@ -16,7 +16,7 @@
 from abc import ABC
 from contextlib import nullcontext
 from copy import deepcopy
-from typing import Dict, Optional, Tuple, Union
+from typing import Optional, Union
 
 import torch
 from botorch.acquisition.acquisition import AcquisitionFunction
@@ -83,7 +83,7 @@ def set_X_pending(self, X_pending: Optional[Tensor] = None) -> None:
 
     def _mean_and_sigma(
         self, X: Tensor, compute_sigma: bool = True, min_var: float = 1e-12
-    ) -> Tuple[Tensor, Optional[Tensor]]:
+    ) -> tuple[Tensor, Optional[Tensor]]:
         """Computes the first and second moments of the model posterior.
 
         Args:
@@ -449,7 +449,7 @@ def __init__(
         model: Model,
         best_f: Union[float, Tensor],
         objective_index: int,
-        constraints: Dict[int, Tuple[Optional[float], Optional[float]]],
+        constraints: dict[int, tuple[Optional[float], Optional[float]]],
         maximize: bool = True,
     ) -> None:
         r"""Analytic Log Constrained Expected Improvement.
@@ -527,7 +527,7 @@ def __init__(
         model: Model,
         best_f: Union[float, Tensor],
         objective_index: int,
-        constraints: Dict[int, Tuple[Optional[float], Optional[float]]],
+        constraints: dict[int, tuple[Optional[float], Optional[float]]],
         maximize: bool = True,
     ) -> None:
         r"""Analytic Constrained Expected Improvement.
@@ -1134,7 +1134,7 @@ def _get_noiseless_fantasy_model(
 
 def _preprocess_constraint_bounds(
     acqf: Union[LogConstrainedExpectedImprovement, ConstrainedExpectedImprovement],
-    constraints: Dict[int, Tuple[Optional[float], Optional[float]]],
+    constraints: dict[int, tuple[Optional[float], Optional[float]]],
 ) -> None:
     r"""Set up constraint bounds.
 

botorch/acquisition/factory.py

@@ -10,7 +10,7 @@
 
 from __future__ import annotations
 
-from typing import Callable, List, Optional, Union
+from typing import Callable, Optional, Union
 
 import torch
 
@@ -37,7 +37,7 @@ def get_acquisition_function(
     X_observed: Tensor,
     posterior_transform: Optional[PosteriorTransform] = None,
     X_pending: Optional[Tensor] = None,
-    constraints: Optional[List[Callable[[Tensor], Tensor]]] = None,
+    constraints: Optional[list[Callable[[Tensor], Tensor]]] = None,
     eta: Optional[Union[Tensor, float]] = 1e-3,
     mc_samples: int = 512,
     seed: Optional[int] = None,
@@ -48,7 +48,7 @@ def get_acquisition_function(
     marginalize_dim: Optional[int] = None,
     cache_root: bool = True,
     beta: Optional[float] = None,
-    ref_point: Union[None, List[float], Tensor] = None,
+    ref_point: Union[None, list[float], Tensor] = None,
     Y: Optional[Tensor] = None,
     alpha: float = 0.0,
 ) -> monte_carlo.MCAcquisitionFunction:

botorch/acquisition/fixed_feature.py

@@ -11,8 +11,10 @@
 
 from __future__ import annotations
 
+from collections.abc import Sequence
+
 from numbers import Number
-from typing import List, Optional, Sequence, Union
+from typing import Optional, Union
 
 import torch
 from botorch.acquisition.acquisition import AcquisitionFunction
@@ -65,7 +67,7 @@ def __init__(
         self,
         acq_function: AcquisitionFunction,
         d: int,
-        columns: List[int],
+        columns: list[int],
         values: Union[Tensor, Sequence[Union[Tensor, float]]],
     ) -> None:
         r"""Derived Acquisition Function by fixing a subset of input features.