Update requirements.

jopfra/flatten.py

@@ -25,16 +25,13 @@ def size(self) -> int:
         return int(np.prod(self._batch_shape))
 
     @overload
-    def flatten(self, y: AnyNDArray) -> AnyNDArray:
-        ...
+    def flatten(self, y: AnyNDArray) -> AnyNDArray: ...
 
     @overload
-    def flatten(self, y: tc.Tensor) -> tc.Tensor:
-        ...
+    def flatten(self, y: tc.Tensor) -> tc.Tensor: ...
 
     @overload
-    def flatten(self, y: Evaluation) -> Evaluation:
-        ...
+    def flatten(self, y: Evaluation) -> Evaluation: ...
 
     @check_shapes(
         "y: [batch_shape..., item_shape...]",
@@ -57,16 +54,13 @@ def flatten(
         return np.reshape(y, (self.size,) + y.shape[self.dim :])
 
     @overload
-    def unflatten(self, y: AnyNDArray) -> AnyNDArray:
-        ...
+    def unflatten(self, y: AnyNDArray) -> AnyNDArray: ...
 
     @overload
-    def unflatten(self, y: tc.Tensor) -> tc.Tensor:
-        ...
+    def unflatten(self, y: tc.Tensor) -> tc.Tensor: ...
 
     @overload
-    def unflatten(self, y: Evaluation) -> Evaluation:
-        ...
+    def unflatten(self, y: Evaluation) -> Evaluation: ...
 
     @check_shapes(
         "y: [prod_batch_shape, item_shape...]",

jopfra/minimisers/api.py

@@ -9,8 +9,7 @@
 
 class SingleMinimiser(ABC):
     @abstractmethod
-    def single_minimise(self, problem: Problem, batch_shape: tuple[int, ...]) -> Evaluation:
-        ...
+    def single_minimise(self, problem: Problem, batch_shape: tuple[int, ...]) -> Evaluation: ...
 
 
 single_minimisers: dict[str, SingleMinimiser] = {}
@@ -21,8 +20,7 @@ def single_minimise(self, problem: Problem, batch_shape: tuple[int, ...]) -> Eva
 
 class StoppingCriteria(ABC):
     @abstractmethod
-    def stop(self) -> Stop:
-        ...
+    def stop(self) -> Stop: ...
 
 
 class IterMinimiserAdapter(SingleMinimiser):
@@ -43,8 +41,9 @@ def single_minimise(self, problem: Problem, batch_shape: tuple[int, ...]) -> Eva
 
 class IterMinimiser(ABC):
     @abstractmethod
-    def iter_minimise(self, problem: Problem, batch_shape: tuple[int, ...]) -> Iterator[Evaluation]:
-        ...
+    def iter_minimise(
+        self, problem: Problem, batch_shape: tuple[int, ...]
+    ) -> Iterator[Evaluation]: ...
 
     def to_single(self, criteria: StoppingCriteria) -> IterMinimiserAdapter:
         return IterMinimiserAdapter(self, criteria)

jopfra/problems/api.py

@@ -39,37 +39,30 @@ def n_inputs(self) -> int:
 
 class Problem(Protocol):
     @property
-    def name(self) -> str:
-        ...
+    def name(self) -> str: ...
 
     @property
-    def n_inputs(self) -> int:
-        ...
+    def n_inputs(self) -> int: ...
 
     @property
-    def domain_lower(self) -> AnyNDArray:
-        ...
+    def domain_lower(self) -> AnyNDArray: ...
 
     @property
-    def domain_upper(self) -> AnyNDArray:
-        ...
+    def domain_upper(self) -> AnyNDArray: ...
 
     @property
-    def known_optima(self) -> Collection[AnyNDArray]:
-        ...
+    def known_optima(self) -> Collection[AnyNDArray]: ...
 
     @check_shapes(
         "x: [batch..., n_inputs]",
         "return: [batch...]",
     )
-    def __call__(self, x: AnyNDArray) -> Evaluation:
-        ...
+    def __call__(self, x: AnyNDArray) -> Evaluation: ...
 
     @check_shapes(
         "x: [n_inputs]",
     )
-    def plot(self, dest: MiscDir, x: AnyNDArray) -> None:
-        ...
+    def plot(self, dest: MiscDir, x: AnyNDArray) -> None: ...
 
 
 problems: dict[str, Problem] = {}
@@ -83,12 +76,10 @@ def plot(self, dest: MiscDir, x: AnyNDArray) -> None:
 
 
 class ProblemFunc(Protocol):
-    def __call__(self, x: AnyNDArray) -> tuple[AnyNDArray, AnyNDArray]:
-        ...
+    def __call__(self, x: AnyNDArray) -> tuple[AnyNDArray, AnyNDArray]: ...
 
     @property
-    def __name__(self) -> str:
-        ...
+    def __name__(self) -> str: ...
 
 
 check_plot_shapes = check_shapes(
@@ -97,8 +88,7 @@ def __name__(self) -> str:
 
 
 class PlotFunc(Protocol):
-    def __call__(self, dest: MiscDir, x: AnyNDArray) -> None:
-        ...
+    def __call__(self, dest: MiscDir, x: AnyNDArray) -> None: ...
 
 
 @dataclass(order=True, frozen=True)
@@ -160,17 +150,14 @@ def _wrap(func: ProblemFunc) -> Problem:
 
 
 class TorchProblemFunc(Protocol):
-    def __call__(self, x: tc.Tensor) -> tc.Tensor:
-        ...
+    def __call__(self, x: tc.Tensor) -> tc.Tensor: ...
 
     @property
-    def __name__(self) -> str:
-        ...
+    def __name__(self) -> str: ...
 
 
 class TorchPlotFunc(Protocol):
-    def __call__(self, dest: MiscDir, x: tc.Tensor) -> None:
-        ...
+    def __call__(self, dest: MiscDir, x: tc.Tensor) -> None: ...
 
 
 def torch_problem(