Enable UFMT format on test/test_throughput_benchmark.py test/test_typ…

…e_hints.py test/test_type_info.py (pytorch#125906)

Fixes some files in pytorch#123062

Run lintrunner on files:
test/test_throughput_benchmark.py
test/test_type_hints.py
test/test_type_info.py

```bash
$ lintrunner -a --take UFMT --all-files
ok No lint issues.
Successfully applied all patches.
```
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: pytorch#125906
Approved by: https://github.com/shink, https://github.com/soulitzer, https://github.com/malfet

.lintrunner.toml

@@ -1139,11 +1139,8 @@ exclude_patterns = [
     'test/test_tensorexpr.py',
     'test/test_tensorexpr_pybind.py',
     'test/test_testing.py',
-    'test/test_throughput_benchmark.py',
     'test/test_torch.py',
     'test/test_transformers.py',
-    'test/test_type_hints.py',
-    'test/test_type_info.py',
     'test/test_type_promotion.py',
     'test/test_unary_ufuncs.py',
     'test/test_utils.py',

test/test_throughput_benchmark.py

@@ -1,9 +1,10 @@
 # Owner(s): ["module: unknown"]
 
 import torch
+
+from torch.testing._internal.common_utils import run_tests, TemporaryFileName, TestCase
 from torch.utils import ThroughputBenchmark
 
-from torch.testing._internal.common_utils import run_tests, TestCase, TemporaryFileName
 
 class TwoLayerNet(torch.jit.ScriptModule):
     def __init__(self, D_in, H, D_out):
@@ -19,6 +20,7 @@ def forward(self, x1, x2):
         y_pred = self.linear2(cat)
         return y_pred
 
+
 class TwoLayerNetModule(torch.nn.Module):
     def __init__(self, D_in, H, D_out):
         super().__init__()
@@ -32,6 +34,7 @@ def forward(self, x1, x2):
         y_pred = self.linear2(cat)
         return y_pred
 
+
 class TestThroughputBenchmark(TestCase):
     def linear_test(self, Module, profiler_output_path=""):
         D_in = 10
@@ -67,7 +70,6 @@ def linear_test(self, Module, profiler_output_path=""):
 
         print(stats)
 
-
     def test_script_module(self):
         self.linear_test(TwoLayerNet)
 
@@ -79,5 +81,5 @@ def test_profiling(self):
             self.linear_test(TwoLayerNetModule, profiler_output_path=fname)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     run_tests()

test/test_type_hints.py

@@ -1,16 +1,18 @@
 # Owner(s): ["module: typing"]
 
-import unittest
-from torch.testing._internal.common_utils import TestCase, run_tests, set_cwd
-import tempfile
-import torch
 import doctest
-import os
 import inspect
+import os
+import tempfile
+import unittest
 from pathlib import Path
 
+import torch
+from torch.testing._internal.common_utils import run_tests, set_cwd, TestCase
+
 try:
     import mypy.api
+
     HAVE_MYPY = True
 except ImportError:
     HAVE_MYPY = False
@@ -22,7 +24,7 @@ def get_examples_from_docstring(docstr):
     in docstrings; returns a list of lines.
     """
     examples = doctest.DocTestParser().get_examples(docstr)
-    return [f'    {l}' for e in examples for l in e.source.splitlines()]
+    return [f"    {l}" for e in examples for l in e.source.splitlines()]
 
 
 def get_all_examples():
@@ -79,7 +81,7 @@ def test_doc_examples(self):
         """
         Run documentation examples through mypy.
         """
-        fn = Path(__file__).resolve().parent / 'generated_type_hints_smoketest.py'
+        fn = Path(__file__).resolve().parent / "generated_type_hints_smoketest.py"
         with open(fn, "w") as f:
             print(get_all_examples(), file=f)
 
@@ -116,23 +118,25 @@ def test_doc_examples(self):
             try:
                 os.symlink(
                     os.path.dirname(torch.__file__),
-                    os.path.join(tmp_dir, 'torch'),
-                    target_is_directory=True
+                    os.path.join(tmp_dir, "torch"),
+                    target_is_directory=True,
                 )
             except OSError:
-                raise unittest.SkipTest('cannot symlink') from None
+                raise unittest.SkipTest("cannot symlink") from None
             repo_rootdir = Path(__file__).resolve().parent.parent
             # TODO: Would be better not to chdir here, this affects the
             # entire process!
             with set_cwd(str(repo_rootdir)):
-                (stdout, stderr, result) = mypy.api.run([
-                    '--cache-dir=.mypy_cache/doc',
-                    '--no-strict-optional',  # needed because of torch.lu_unpack, see gh-36584
-                    str(fn),
-                ])
+                (stdout, stderr, result) = mypy.api.run(
+                    [
+                        "--cache-dir=.mypy_cache/doc",
+                        "--no-strict-optional",  # needed because of torch.lu_unpack, see gh-36584
+                        str(fn),
+                    ]
+                )
             if result != 0:
                 self.fail(f"mypy failed:\n{stderr}\n{stdout}")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     run_tests()

test/test_type_info.py

@@ -1,27 +1,48 @@
 # Owner(s): ["module: typing"]
 
-from torch.testing._internal.common_utils import TestCase, run_tests, TEST_NUMPY, load_tests, set_default_dtype
+from torch.testing._internal.common_utils import (
+    load_tests,
+    run_tests,
+    set_default_dtype,
+    TEST_NUMPY,
+    TestCase,
+)
 
 # load_tests from common_utils is used to automatically filter tests for
 # sharding on sandcastle. This line silences flake warnings
 load_tests = load_tests
 
 import sys
-import torch
 import unittest
 
+import torch
+
 if TEST_NUMPY:
     import numpy as np
 
 
 class TestDTypeInfo(TestCase):
-
     def test_invalid_input(self):
-        for dtype in [torch.float16, torch.float32, torch.float64, torch.bfloat16, torch.complex64, torch.complex128, torch.bool]:
+        for dtype in [
+            torch.float16,
+            torch.float32,
+            torch.float64,
+            torch.bfloat16,
+            torch.complex64,
+            torch.complex128,
+            torch.bool,
+        ]:
             with self.assertRaises(TypeError):
                 _ = torch.iinfo(dtype)
 
-        for dtype in [torch.int64, torch.int32, torch.int16, torch.int8, torch.uint8, torch.bool]:
+        for dtype in [
+            torch.int64,
+            torch.int32,
+            torch.int16,
+            torch.int8,
+            torch.uint8,
+            torch.bool,
+        ]:
             with self.assertRaises(TypeError):
                 _ = torch.finfo(dtype)
             with self.assertRaises(RuntimeError):
@@ -41,7 +62,13 @@ def test_iinfo(self):
 
     @unittest.skipIf(not TEST_NUMPY, "Numpy not found")
     def test_finfo(self):
-        for dtype in [torch.float16, torch.float32, torch.float64, torch.complex64, torch.complex128]:
+        for dtype in [
+            torch.float16,
+            torch.float32,
+            torch.float64,
+            torch.complex64,
+            torch.complex128,
+        ]:
             x = torch.zeros((2, 2), dtype=dtype)
             xinfo = torch.finfo(x.dtype)
             xn = x.cpu().numpy()
@@ -61,8 +88,8 @@ def test_finfo(self):
         x = torch.zeros((2, 2), dtype=torch.bfloat16)
         xinfo = torch.finfo(x.dtype)
         self.assertEqual(xinfo.bits, 16)
-        self.assertEqual(xinfo.max, 3.38953e+38)
-        self.assertEqual(xinfo.min, -3.38953e+38)
+        self.assertEqual(xinfo.max, 3.38953e38)
+        self.assertEqual(xinfo.min, -3.38953e38)
         self.assertEqual(xinfo.eps, 0.0078125)
         self.assertEqual(xinfo.tiny, 1.17549e-38)
         self.assertEqual(xinfo.tiny, xinfo.smallest_normal)
@@ -76,7 +103,7 @@ def test_finfo(self):
         self.assertEqual(xinfo.bits, 8)
         self.assertEqual(xinfo.max, 57344.0)
         self.assertEqual(xinfo.min, -57344.0)
-        self.assertEqual(xinfo.eps, .25)
+        self.assertEqual(xinfo.eps, 0.25)
         self.assertEqual(xinfo.tiny, 6.10352e-05)
         self.assertEqual(xinfo.resolution, 1.0)
         self.assertEqual(xinfo.dtype, "float8_e5m2")
@@ -86,7 +113,7 @@ def test_finfo(self):
         self.assertEqual(xinfo.bits, 8)
         self.assertEqual(xinfo.max, 448.0)
         self.assertEqual(xinfo.min, -448.0)
-        self.assertEqual(xinfo.eps, .125)
+        self.assertEqual(xinfo.eps, 0.125)
         self.assertEqual(xinfo.tiny, 0.015625)
         self.assertEqual(xinfo.resolution, 1.0)
         self.assertEqual(xinfo.dtype, "float8_e4m3fn")
@@ -111,6 +138,7 @@ def test_to_real(self):
         self.assertEqual(torch.complex64.to_real(), torch.float32)
         self.assertEqual(torch.complex32.to_real(), torch.float16)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     TestCase._default_dtype_check_enabled = True
     run_tests()