Revert D30279364: [codemod][lint][fbcode/c*] Enable BLACK by default

Test Plan: revert-hammer

Differential Revision:
D30279364 (pytorch@b004307)

Original commit changeset: c1ed77dfe43a

fbshipit-source-id: eab50857675c51e0088391af06ec0ecb14e2347e

android/pytorch_android/generate_test_torchscripts.py

@@ -2,16 +2,14 @@
 
 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):
@@ -75,9 +73,7 @@ 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
@@ -118,7 +114,7 @@ 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()
@@ -136,5 +132,4 @@ 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

@@ -5,20 +5,12 @@
 
 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)
@@ -39,7 +39,9 @@ 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:")
@@ -49,55 +51,40 @@ 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

@@ -1,9 +1,13 @@
 #!/usr/bin/env python3
 
 
+
+
+
+
 import argparse
-from subprocess import call
 from textwrap import dedent
+from subprocess import call
 
 
 def parse_lines(lines):
@@ -103,11 +107,9 @@ 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

@@ -1,7 +1,12 @@
+
+
+
+
+
 import errno
 import os
-import shutil
 import tempfile
+import shutil
 
 from caffe2.distributed.python import StoreHandlerTimeoutError
 from caffe2.distributed.store_ops_test_util import StoreOpsTests
@@ -42,9 +47,10 @@ 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

@@ -1,3 +1,8 @@
+
+
+
+
+
 import os
 import uuid
 
@@ -27,9 +32,7 @@ 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

@@ -2,9 +2,13 @@
 # Module caffe2.distributed.store_ops_test_util
 
 
+
+
+
 from multiprocessing import Process, Queue
 
 import numpy as np
+
 from caffe2.python import core, workspace
 
 
@@ -21,16 +25,18 @@ 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)
@@ -50,13 +56,7 @@ 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)