[codemod][lint][fbcode/c*] Enable BLACK by default

Test Plan: manual inspection & sandcastle

Reviewed By: zertosh

Differential Revision: D30279364

fbshipit-source-id: c1ed77dfe43a3bde358f92737cd5535ae5d13c9a

android/pytorch_android/generate_test_torchscripts.py

@@ -2,14 +2,16 @@
 
 OUTPUT_DIR = "src/androidTest/assets/"
 
+
 def scriptAndSave(module, fileName):
-    print('-' * 80)
+    print("-" * 80)
     script_module = torch.jit.script(module)
     print(script_module.graph)
     outputFileName = OUTPUT_DIR + fileName
     script_module.save(outputFileName)
     print("Saved to " + outputFileName)
-    print('=' * 80)
+    print("=" * 80)
+
 
 class Test(torch.jit.ScriptModule):
     def __init__(self):
@@ -73,7 +75,9 @@ def listBoolDisjunction(self, input: List[bool]) -> bool:
         return res
 
     @torch.jit.script_method
-    def tupleIntSumReturnTuple(self, input: Tuple[int, int, int]) -> Tuple[Tuple[int, int, int], int]:
+    def tupleIntSumReturnTuple(
+        self, input: Tuple[int, int, int]
+    ) -> Tuple[Tuple[int, int, int], int]:
         sum = 0
         for x in input:
             sum += x
@@ -114,7 +118,7 @@ def testNonContiguous(self):
     @torch.jit.script_method
     def conv2d(self, x: Tensor, w: Tensor, toChannelsLast: bool) -> Tensor:
         r = torch.nn.functional.conv2d(x, w)
-        if (toChannelsLast):
+        if toChannelsLast:
             r = r.contiguous(memory_format=torch.channels_last)
         else:
             r = r.contiguous()
@@ -132,4 +136,5 @@ def contiguousChannelsLast(self, x: Tensor) -> Tensor:
     def contiguousChannelsLast3d(self, x: Tensor) -> Tensor:
         return x.contiguous(memory_format=torch.channels_last_3d)
 
+
 scriptAndSave(Test(), "test.pt")

android/test_app/make_assets.py

@@ -5,12 +5,20 @@
 
 resnet18 = torchvision.models.resnet18(pretrained=True)
 resnet18.eval()
-resnet18_traced = torch.jit.trace(resnet18, torch.rand(1, 3, 224, 224)).save("app/src/main/assets/resnet18.pt")
+resnet18_traced = torch.jit.trace(resnet18, torch.rand(1, 3, 224, 224)).save(
+    "app/src/main/assets/resnet18.pt"
+)
 
 resnet50 = torchvision.models.resnet50(pretrained=True)
 resnet50.eval()
-torch.jit.trace(resnet50, torch.rand(1, 3, 224, 224)).save("app/src/main/assets/resnet50.pt")
+torch.jit.trace(resnet50, torch.rand(1, 3, 224, 224)).save(
+    "app/src/main/assets/resnet50.pt"
+)
 
-mobilenet2q = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+mobilenet2q = torchvision.models.quantization.mobilenet_v2(
+    pretrained=True, quantize=True
+)
 mobilenet2q.eval()
-torch.jit.trace(mobilenet2q, torch.rand(1, 3, 224, 224)).save("app/src/main/assets/mobilenet2q.pt")
+torch.jit.trace(mobilenet2q, torch.rand(1, 3, 224, 224)).save(
+    "app/src/main/assets/mobilenet2q.pt"
+)

android/test_app/make_assets_custom.py

@@ -21,5 +21,5 @@
 # Dump root ops used by the model (for custom build optimization).
 ops = torch.jit.export_opnames(traced_script_module)
 
-with open('MobileNetV2.yaml', 'w') as output:
+with open("MobileNetV2.yaml", "w") as output:
     yaml.dump(ops, output)

binaries/bench_gen/bench_gen.py

@@ -3,13 +3,13 @@
 import argparse
 import ast
 
+from caffe2.python import workspace, brew
 from caffe2.python.model_helper import ModelHelper
 from caffe2.python.predictor import mobile_exporter
-from caffe2.python import workspace, brew
 
 
 def parse_kwarg(kwarg_str):
-    key, value = kwarg_str.split('=')
+    key, value = kwarg_str.split("=")
     try:
         value = ast.literal_eval(value)
     except ValueError:
@@ -30,7 +30,7 @@ def main(args):
 
     iters = int(args.instances)
     for i in range(iters):
-        input_blob_name = input_name + (str(i) if i > 0 and args.chain else '')
+        input_blob_name = input_name + (str(i) if i > 0 and args.chain else "")
         output_blob_name = output_name + str(i + 1)
         add_op = getattr(brew, op_type)
         add_op(model, input_blob_name, output_blob_name, **kwargs)
@@ -39,9 +39,7 @@ def main(args):
 
     workspace.RunNetOnce(model.param_init_net)
 
-    init_net, predict_net = mobile_exporter.Export(
-        workspace, model.net, model.params
-    )
+    init_net, predict_net = mobile_exporter.Export(workspace, model.net, model.params)
 
     if args.debug:
         print("init_net:")
@@ -51,40 +49,55 @@ def main(args):
         for op in predict_net.op:
             print(" ", op.type, op.input, "-->", op.output)
 
-    with open(args.predict_net, 'wb') as f:
+    with open(args.predict_net, "wb") as f:
         f.write(predict_net.SerializeToString())
-    with open(args.init_net, 'wb') as f:
+    with open(args.init_net, "wb") as f:
         f.write(init_net.SerializeToString())
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="Utilitity to generate Caffe2 benchmark models.")
+        description="Utilitity to generate Caffe2 benchmark models."
+    )
     parser.add_argument("operator", help="Caffe2 operator to benchmark.")
-    parser.add_argument("-b", "--blob",
-                        help="Instantiate a blob --blob name=dim1,dim2,dim3",
-                        action='append')
+    parser.add_argument(
+        "-b",
+        "--blob",
+        help="Instantiate a blob --blob name=dim1,dim2,dim3",
+        action="append",
+    )
     parser.add_argument("--context", help="Context to run on.", default="CPU")
-    parser.add_argument("--kwargs", help="kwargs to pass to operator.",
-                        nargs="*", type=parse_kwarg, default=[])
-    parser.add_argument("--init_net", help="Output initialization net.",
-                        default="init_net.pb")
-    parser.add_argument("--predict_net", help="Output prediction net.",
-                        default="predict_net.pb")
-    parser.add_argument("--benchmark_name",
-                        help="Name of the benchmark network",
-                        default="benchmark")
-    parser.add_argument("--input_name", help="Name of the input blob.",
-                        default="data")
-    parser.add_argument("--output_name", help="Name of the output blob.",
-                        default="output")
-    parser.add_argument("--instances",
-                        help="Number of instances to run the operator.",
-                        default="1")
-    parser.add_argument("-d", "--debug", help="Print debug information.",
-                        action='store_true')
-    parser.add_argument("-c", "--chain",
-                        help="Chain ops together (create data dependencies)",
-                        action='store_true')
+    parser.add_argument(
+        "--kwargs",
+        help="kwargs to pass to operator.",
+        nargs="*",
+        type=parse_kwarg,
+        default=[],
+    )
+    parser.add_argument(
+        "--init_net", help="Output initialization net.", default="init_net.pb"
+    )
+    parser.add_argument(
+        "--predict_net", help="Output prediction net.", default="predict_net.pb"
+    )
+    parser.add_argument(
+        "--benchmark_name", help="Name of the benchmark network", default="benchmark"
+    )
+    parser.add_argument("--input_name", help="Name of the input blob.", default="data")
+    parser.add_argument(
+        "--output_name", help="Name of the output blob.", default="output"
+    )
+    parser.add_argument(
+        "--instances", help="Number of instances to run the operator.", default="1"
+    )
+    parser.add_argument(
+        "-d", "--debug", help="Print debug information.", action="store_true"
+    )
+    parser.add_argument(
+        "-c",
+        "--chain",
+        help="Chain ops together (create data dependencies)",
+        action="store_true",
+    )
     args = parser.parse_args()
     main(args)

caffe2/core/nomnigraph/op_gen.py

@@ -1,13 +1,9 @@
 #!/usr/bin/env python3
 
 
-
-
-
-
 import argparse
-from textwrap import dedent
 from subprocess import call
+from textwrap import dedent
 
 
 def parse_lines(lines):
@@ -107,9 +103,11 @@ def gen_class(op, op_def):
             type=t, lower_name=lower_name + default_arg
         )
         attr_init = "{private_name}({lower_name})".format(
-            private_name=private_name, lower_name=lower_name)
+            private_name=private_name, lower_name=lower_name
+        )
         attr_declare = "{type} {private_name};".format(
-            type=t, private_name=private_name)
+            type=t, private_name=private_name
+        )
         attr_get = dedent(
             """
               {type} get{name}() const {{

caffe2/distributed/file_store_handler_op_test.py

@@ -1,12 +1,7 @@
-
-
-
-
-
 import errno
 import os
-import tempfile
 import shutil
+import tempfile
 
 from caffe2.distributed.python import StoreHandlerTimeoutError
 from caffe2.distributed.store_ops_test_util import StoreOpsTests
@@ -47,10 +42,9 @@ def create_store_handler(self):
         store_handler = "store_handler"
         workspace.RunOperatorOnce(
             core.CreateOperator(
-                "FileStoreHandlerCreate",
-                [],
-                [store_handler],
-                path=path))
+                "FileStoreHandlerCreate", [], [store_handler], path=path
+            )
+        )
 
         return store_handler
 

caffe2/distributed/redis_store_handler_op_test.py

@@ -1,8 +1,3 @@
-
-
-
-
-
 import os
 import uuid
 
@@ -32,7 +27,9 @@ def create_store_handler(self):
                 [store_handler],
                 prefix=self.uuid,
                 host=os.getenv("REDIS_HOST", "localhost"),
-                port=int(os.getenv("REDIS_PORT", 6379))))
+                port=int(os.getenv("REDIS_PORT", 6379)),
+            )
+        )
         return store_handler
 
     def test_set_get(self):

caffe2/distributed/store_ops_test_util.py

@@ -2,13 +2,9 @@
 # Module caffe2.distributed.store_ops_test_util
 
 
-
-
-
 from multiprocessing import Process, Queue
 
 import numpy as np
-
 from caffe2.python import core, workspace
 
 
@@ -25,18 +21,16 @@ def _test_set_get(cls, queue, create_store_handler_fn, index, num_procs):
             workspace.FeedBlob(blob, value)
             workspace.RunOperatorOnce(
                 core.CreateOperator(
-                    "StoreSet",
-                    [store_handler, blob],
-                    [],
-                    blob_name=blob))
+                    "StoreSet", [store_handler, blob], [], blob_name=blob
+                )
+            )
 
         output_blob = "output_blob"
         workspace.RunOperatorOnce(
             core.CreateOperator(
-                "StoreGet",
-                [store_handler],
-                [output_blob],
-                blob_name=blob))
+                "StoreGet", [store_handler], [output_blob], blob_name=blob
+            )
+        )
 
         try:
             np.testing.assert_array_equal(workspace.FetchBlob(output_blob), 1)
@@ -56,7 +50,13 @@ def test_set_get(cls, create_store_handler_fn):
         for index in range(num_procs):
             proc = Process(
                 target=cls._test_set_get,
-                args=(queue, create_store_handler_fn, index, num_procs, ))
+                args=(
+                    queue,
+                    create_store_handler_fn,
+                    index,
+                    num_procs,
+                ),
+            )
             proc.start()
             procs.append(proc)
 
@@ -71,6 +71,6 @@ def test_set_get(cls, create_store_handler_fn):
     @classmethod
     def test_get_timeout(cls, create_store_handler_fn):
         store_handler = create_store_handler_fn()
-        net = core.Net('get_missing_blob')
-        net.StoreGet([store_handler], 1, blob_name='blob')
+        net = core.Net("get_missing_blob")
+        net.StoreGet([store_handler], 1, blob_name="blob")
         workspace.RunNetOnce(net)