Update to mypy 0.990 (#801)

requirements.txt

@@ -24,7 +24,7 @@ pytest-cov>=2.7.0,<2.8.0
 coverage>=5.0.0,<6.0.0
 mock>=2.0.0,<3.0.0
 flake8>=3.5.0,<3.6.0
-mypy>=0.980,<0.990; python_version >= "3.7"
+mypy>=0.990,<0.1000; python_version >= "3.7"
 types-mock>=0.1.1
 types-contextvars>=0.1.2; python_version < "3.7"
 types-dataclasses>=0.1.3; python_version < "3.7"

thinc/loss.py

@@ -1,5 +1,6 @@
 from typing import Tuple, Sequence, cast, TypeVar, Generic, Any, Union, Optional, List
 from typing import Dict
+from abc import abstractmethod
 
 from .types import Floats2d, Ints1d
 from .util import get_array_module, to_categorical
@@ -27,9 +28,11 @@ def __init__(self, **kwargs: Any) -> None:
     def __call__(self, guesses: GuessT, truths: TruthT) -> Tuple[GradT, LossT]:
         return self.get_grad(guesses, truths), self.get_loss(guesses, truths)
 
+    @abstractmethod
     def get_grad(self, guesses: GuessT, truths: TruthT) -> GradT:
         ...
 
+    @abstractmethod
     def get_loss(self, guesses: GuessT, truths: TruthT) -> LossT:
         ...
 

thinc/tests/backends/test_ops.py

@@ -727,7 +727,9 @@ def torch_softmax_with_temperature(
     Yt = torch.nn.functional.softmax(Xt_temp, dim=-1)
     Yt.backward(dYt)
 
-    return cast(Floats2d, torch2xp(Yt)), cast(Floats2d, torch2xp(Xt.grad))
+    return cast(Floats2d, torch2xp(Yt)), cast(
+        Floats2d, torch2xp(cast(torch.Tensor, Xt.grad))
+    )
 
 
 @pytest.mark.skipif(not has_torch, reason="needs PyTorch")

thinc/tests/layers/test_softmax.py

@@ -55,7 +55,7 @@ def torch_softmax_with_temperature(
 
     return cast(
         Floats2d, torch2xp(torch.nn.functional.softmax(XWbt_temp, dim=-1))
-    ), cast(Floats2d, torch2xp(Xt.grad))
+    ), cast(Floats2d, torch2xp(cast(torch.Tensor, Xt.grad)))
 
 
 @pytest.mark.skipif(not has_torch, reason="needs PyTorch")