Auto-format code with black (#723)

Co-authored-by: explosion-bot <explosion-bot@users.noreply.github.com>

thinc/tests/conftest.py

@@ -3,9 +3,10 @@
 
 # Functionally disable deadline settings for tests
 # to prevent spurious test failures in CI builds.
-settings.register_profile("no_deadlines", deadline=2*60*1000) # in ms
+settings.register_profile("no_deadlines", deadline=2 * 60 * 1000)  # in ms
 settings.load_profile("no_deadlines")
 
+
 def pytest_sessionstart(session):
     # If Tensorflow is installed, attempt to enable memory growth
     # to prevent it from allocating all of the GPU's free memory

thinc/tests/test_util.py

@@ -12,6 +12,7 @@
 ALL_XP = [numpy]
 try:
     import cupy
+
     ALL_XP.append(cupy)
 except ImportError:
     pass
@@ -49,10 +50,12 @@ def test_get_width_fail(obj):
 
 @pytest.mark.parametrize("xp", ALL_XP)
 def test_array_module_cpu_gpu_helpers(xp):
-    error = ("Only numpy and cupy arrays are supported"
-             ", but found <class 'int'> instead. If "
-             "get_array_module module wasn't called "
-             "directly, this might indicate a bug in Thinc.")
+    error = (
+        "Only numpy and cupy arrays are supported"
+        ", but found <class 'int'> instead. If "
+        "get_array_module module wasn't called "
+        "directly, this might indicate a bug in Thinc."
+    )
     with pytest.raises(ValueError, match=error):
         get_array_module(0)
     zeros = xp.zeros((1, 2))
@@ -135,14 +138,10 @@ def test_to_categorical(label_smoothing):
     ):
         to_categorical(numpy.asarray([0, 0, 0]), label_smoothing=0.01),
 
-    with pytest.raises(
-        ValueError, match=r"label_smoothing parameter"
-    ):
+    with pytest.raises(ValueError, match=r"label_smoothing parameter"):
         to_categorical(numpy.asarray([0, 1, 2, 3, 4]), label_smoothing=0.8)
 
-    with pytest.raises(
-        ValueError, match=r"label_smoothing parameter"
-    ):
+    with pytest.raises(ValueError, match=r"label_smoothing parameter"):
         to_categorical(numpy.asarray([0, 1, 2, 3, 4]), label_smoothing=0.88)