Use types.NoneType rather than defining NoneType in BoTorch (#2338)

Summary:
Pull Request resolved: #2338

- Rremove BoTorch `NoneType` and replace its usages with `types.NoneType`, since as of Python 3.10, types.NoneType (once again) exists.
- Use `None` or `Optional` for the purposes of type-hinting (and only for type-hinting) in place of `NoneType` since they are equivalent for type-hinting, although not in general.

Reviewed By: saitcakmak

Differential Revision: D57172472

fbshipit-source-id: b5be445caaf738ac8e227bd6595031c66f22d85e

Author: esantorella

botorch/optim/closures/model_closures.py

@@ -9,11 +9,11 @@
 from __future__ import annotations
 
 from itertools import chain, repeat
+from types import NoneType
 from typing import Any, Callable, Dict, Optional, Sequence, Tuple
 
 from botorch.optim.closures.core import ForwardBackwardClosure
 from botorch.utils.dispatcher import Dispatcher, type_bypassing_encoder
-from botorch.utils.types import NoneType
 from gpytorch.mlls import (
     ExactMarginalLogLikelihood,
     MarginalLogLikelihood,

botorch/optim/utils/numpy_utils.py

@@ -13,7 +13,6 @@
 
 import numpy as np
 import torch
-from botorch.utils.types import NoneType
 from numpy import ndarray
 from torch import Tensor
 
@@ -137,7 +136,7 @@ def set_tensors_from_ndarray_1d(
 def get_bounds_as_ndarray(
     parameters: Dict[str, Tensor],
     bounds: Dict[
-        str, Tuple[Union[float, Tensor, NoneType], Union[float, Tensor, NoneType]]
+        str, Tuple[Optional[Union[float, Tensor]], Optional[Union[float, Tensor]]]
     ],
 ) -> Optional[np.ndarray]:
     r"""Helper method for converting bounds into an ndarray.

botorch/sampling/pathwise/update_strategies.py

@@ -6,6 +6,8 @@
 
 from __future__ import annotations
 
+from types import NoneType
+
 from typing import Any, Callable, Optional, Union
 
 import torch
@@ -20,7 +22,7 @@
     TInputTransform,
 )
 from botorch.utils.dispatcher import Dispatcher
-from botorch.utils.types import DEFAULT, NoneType
+from botorch.utils.types import DEFAULT
 from gpytorch.kernels.kernel import Kernel
 from gpytorch.likelihoods import _GaussianLikelihoodBase, Likelihood
 from gpytorch.models import ApproximateGP, ExactGP, GP

botorch/utils/types.py

@@ -7,9 +7,6 @@
 from __future__ import annotations
 
 
-NoneType = type(None)  # stop gap for the return of NoneType in 3.10
-
-
 class _DefaultType(type):
     r"""
     Private class whose sole instance `DEFAULT` is as a special indicator