apply Black 2024 style in fbcode (8/16)

Reviewed By: aleivag

Differential Revision: D54447737

fbshipit-source-id: 6c05d7941c6b4f1787b8da6cf810693f48a96c4e

torch_glow/tests/functionality/to_glow_num_devices_to_use_test.py

@@ -42,7 +42,7 @@ def devices_to_use_test_helper(self, input, num_replications):
 
         self.assertEqual(type(g), type(t))
         self.assertEqual(len(g), len(t))
-        for (gi, ti) in zip(g, t):
+        for gi, ti in zip(g, t):
             self.assertTrue(torch.allclose(gi, ti))
 
     def devices_to_use_test(self):

torch_glow/tests/functionality/to_glow_tuple_output_test.py

@@ -55,7 +55,7 @@ def tuple_test_helper(self, ModType):
         self.assertEqual(type(g), type(t))
         self.assertEqual(len(g), len(t))
 
-        for (gi, ti) in zip(g, t):
+        for gi, ti in zip(g, t):
             self.assertTrue(torch.allclose(gi, ti))
 
         # test module ser/de with tuple output

torch_glow/tests/functionality/to_glow_write_to_onnx_test.py

@@ -85,7 +85,7 @@ def lower_and_write_to_onnx_helper(self, ModType, onnx_prefix):
         self.assertEqual(type(g), type(t))
         self.assertEqual(len(g), len(t))
 
-        for (gi, ti) in zip(g, t):
+        for gi, ti in zip(g, t):
             self.assertTrue(torch.allclose(gi, ti))
 
         assert os.path.exists(onnx_prefix + ".onnxtxt")

torch_glow/tests/nodes/embedding_bag_test.py

@@ -63,9 +63,11 @@ class TestQuantizedEmbeddingBag(utils.TorchGlowTestCase):
                     bits="_4bit" if is4bit else "_byte",
                     weighted="_weighted" if is_weighted else "",
                     fp16="_fp16" if use_fp16 else "",
-                    sample_weights="_sample_weights_fp16"
-                    if per_sample_weights_fp16 and is_weighted
-                    else "",
+                    sample_weights=(
+                        "_sample_weights_fp16"
+                        if per_sample_weights_fp16 and is_weighted
+                        else ""
+                    ),
                     backend="_" + DEFAULT_BACKEND,
                 ),
                 num_lengths,
@@ -157,9 +159,11 @@ def forward(self, indices, offsets):
             indices,
             offsets,
             fusible_ops={
-                "quantized::embedding_bag_4bit_rowwise_offsets"
-                if is4bit
-                else "quantized::embedding_bag_byte_rowwise_offsets"
+                (
+                    "quantized::embedding_bag_4bit_rowwise_offsets"
+                    if is4bit
+                    else "quantized::embedding_bag_byte_rowwise_offsets"
+                )
             },
             fp16=use_fp16,
             # FP16 version is known to yeild different results, so our

torch_glow/tests/utils.py

@@ -62,10 +62,22 @@ def ephemeral_torchglow_settings(
         torch_glow.setGlowBackend(backend)
         yield
     finally:
-        torch_glow.enable_convert_to_fp16() if old_fp16 else torch_glow.disable_convert_to_fp16()
+        (
+            torch_glow.enable_convert_to_fp16()
+            if old_fp16
+            else torch_glow.disable_convert_to_fp16()
+        )
         torch_glow.enable_clip_fp16() if old_clip else torch_glow.disable_clip_fp16()
-        torch_glow.enable_convert_fused_to_fp16() if old_convert_fused else torch_glow.disable_convert_fused_to_fp16()
-        torch_glow.enableFusionPass_DO_NOT_USE_THIS() if old_fusion else torch_glow.disableFusionPass()
+        (
+            torch_glow.enable_convert_fused_to_fp16()
+            if old_convert_fused
+            else torch_glow.disable_convert_fused_to_fp16()
+        )
+        (
+            torch_glow.enableFusionPass_DO_NOT_USE_THIS()
+            if old_fusion
+            else torch_glow.disableFusionPass()
+        )
         torch_glow.setGlowBackend(old_backend)
         torch_glow.setFusionBlocklist(old_blocklist)
 

utils/caffe2_pb_runner.py

@@ -132,9 +132,9 @@ def mode_128to127(x):
     for h in range(0, model_props[MODEL].image_size):
         for c in range(0, model_props[MODEL].num_color_channels):
             # WHC -> CWH, RGB -> BGR
-            transposed_image[0][model_props[MODEL].num_color_channels - c - 1][w][
-                h
-            ] = model_props[MODEL].image_mode_op(img[w][h][c])
+            transposed_image[0][model_props[MODEL].num_color_channels - c - 1][w][h] = (
+                model_props[MODEL].image_mode_op(img[w][h][c])
+            )
 
 final_image = transposed_image
 

utils/trace_parser.py

@@ -166,13 +166,13 @@ def dumpAccumulate(events, keyfunc, traceTime):
         nameMap[name].append(ev.selfTime())
 
     layers = []
-    for (name, times) in nameMap.items():
+    for name, times in nameMap.items():
         layers.append(
             (name, len(times), numpy.mean(times), numpy.std(times), numpy.sum(times))
         )
 
     # Iterate sorted by total time.
-    for (name, num, mean, stddev, total) in sorted(
+    for name, num, mean, stddev, total in sorted(
         layers, key=itemgetter(4), reverse=True
     ):
         mean = formatUs(mean)