Merge branch '29-lint-fixes' into 29-lint-workflow

Author: smokestacklightnin

setup.py

@@ -25,68 +25,67 @@
 # built by setuptools, it will be incorrectly treated as a purelib. The
 # following works around that bug.
 class _InstallPlatlib(install):
-
-  def finalize_options(self):
-    install.finalize_options(self)
-    self.install_lib = self.install_platlib
+    def finalize_options(self):
+        install.finalize_options(self)
+        self.install_lib = self.install_platlib
 
 
 class BinaryDistribution(Distribution):
-  """This class is needed in order to create OS specific wheels."""
+    """This class is needed in order to create OS specific wheels."""
 
-  def is_pure(self):
-    return False
+    def is_pure(self):
+        return False
 
-  def has_ext_modules(self):
-    return True
+    def has_ext_modules(self):
+        return True
 
 
 def select_constraint(default, nightly=None, git_master=None):
-  """Select dependency constraint based on TFX_DEPENDENCY_SELECTOR env var."""
-  selector = os.environ.get('TFX_DEPENDENCY_SELECTOR')
-  if selector == 'UNCONSTRAINED':
-    return ''
-  if selector == 'NIGHTLY' and nightly is not None:
-    return nightly
-  if selector == 'GIT_MASTER' and git_master is not None:
-    return git_master
-  return default
+    """Select dependency constraint based on TFX_DEPENDENCY_SELECTOR env var."""
+    selector = os.environ.get("TFX_DEPENDENCY_SELECTOR")
+    if selector == "UNCONSTRAINED":
+        return ""
+    if selector == "NIGHTLY" and nightly is not None:
+        return nightly
+    if selector == "GIT_MASTER" and git_master is not None:
+        return git_master
+    return default
 
 
 # Get the long description from the README file.
-with open('README.md') as fp:
-  _LONG_DESCRIPTION = fp.read()
+with open("README.md") as fp:
+    _LONG_DESCRIPTION = fp.read()
 
-with open('struct2tensor/version.py') as fp:
-  globals_dict = {}
-  exec(fp.read(), globals_dict)  # pylint: disable=exec-used
-__version__ = globals_dict['__version__']
+with open("struct2tensor/version.py") as fp:
+    globals_dict = {}
+    exec(fp.read(), globals_dict)  # pylint: disable=exec-used
+__version__ = globals_dict["__version__"]
 
 setup(
-    name='struct2tensor',
+    name="struct2tensor",
     version=__version__,
     description=(
-        'Struct2Tensor is a package for parsing and manipulating'
-        ' structured data for TensorFlow'
+        "Struct2Tensor is a package for parsing and manipulating"
+        " structured data for TensorFlow"
     ),
-    author='Google LLC',
-    author_email='[email protected]',
+    author="Google LLC",
+    author_email="[email protected]",
     # Contained modules and scripts.
     packages=find_packages(),
     install_requires=[
         # TODO(b/263060885): Remove the explicit numpy dependency once TF works
         # with numpy>=1.24.
-        'numpy>=1.22',
+        "numpy>=1.22",
         'protobuf>=4.25.2,<6.0.0;python_version>="3.11"',
         'protobuf>=4.21.6,<6.0.0;python_version<"3.11"',
-        'tensorflow>=2.17,<2.18',
-        'tensorflow-metadata'
+        "tensorflow>=2.17,<2.18",
+        "tensorflow-metadata"
         + select_constraint(
-            default='>=1.17.0,<1.18.0',
-            nightly='>=1.18.0.dev',
-            git_master='@git+https://github.com/tensorflow/metadata@master',
+            default=">=1.17.0,<1.18.0",
+            nightly=">=1.18.0.dev",
+            git_master="@git+https://github.com/tensorflow/metadata@master",
         ),
-        'pyarrow>=10,<11',
+        "pyarrow>=10,<11",
     ],
     extras_require={
         'lint': [
@@ -95,33 +94,33 @@ def select_constraint(default, nightly=None, git_master=None):
     },
     # Add in any packaged data.
     include_package_data=True,
-    package_data={'': ['*.lib', '*.so']},
+    package_data={"": ["*.lib", "*.so"]},
     zip_safe=False,
     distclass=BinaryDistribution,
     # PyPI package information.
     classifiers=[
-        'Development Status :: 4 - Beta',
-        'Intended Audience :: Developers',
-        'Intended Audience :: Education',
-        'Intended Audience :: Science/Research',
-        'License :: OSI Approved :: Apache Software License',
-        'Operating System :: MacOS :: MacOS X',
-        'Operating System :: POSIX :: Linux',
-        'Programming Language :: Python',
-        'Programming Language :: Python :: 3.9',
-        'Programming Language :: Python :: 3.10',
-        'Programming Language :: Python :: 3.11',
-        'Programming Language :: Python :: 3 :: Only',
-        'Topic :: Scientific/Engineering',
-        'Topic :: Scientific/Engineering :: Artificial Intelligence',
-        'Topic :: Scientific/Engineering :: Mathematics',
-        'Topic :: Software Development',
-        'Topic :: Software Development :: Libraries',
-        'Topic :: Software Development :: Libraries :: Python Modules',
+        "Development Status :: 4 - Beta",
+        "Intended Audience :: Developers",
+        "Intended Audience :: Education",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: Apache Software License",
+        "Operating System :: MacOS :: MacOS X",
+        "Operating System :: POSIX :: Linux",
+        "Programming Language :: Python",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3 :: Only",
+        "Topic :: Scientific/Engineering",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Topic :: Scientific/Engineering :: Mathematics",
+        "Topic :: Software Development",
+        "Topic :: Software Development :: Libraries",
+        "Topic :: Software Development :: Libraries :: Python Modules",
     ],
-    license='Apache 2.0',
-    keywords='tensorflow protobuf machine learning',
+    license="Apache 2.0",
+    keywords="tensorflow protobuf machine learning",
     long_description=_LONG_DESCRIPTION,
-    long_description_content_type='text/markdown',
-    cmdclass={'install': _InstallPlatlib},
+    long_description_content_type="text/markdown",
+    cmdclass={"install": _InstallPlatlib},
 )

struct2tensor/__pycache__/__init__.cpython-311.pyc

@@ -38,64 +38,66 @@
 
 
 class EquiJoinIndicesBenchmarks(struct2tensor_benchmark_util.OpsBenchmarks):
-  """Benchmarks for EquiJoinIndices."""
-
-  @parameterized.named_parameters(*[
-      dict(
-          testcase_name="equi_join_indices_monotonic_increasing",
-          fn_name="equi_join_indices_monotonic_increasing",
-          fn_args=[],
-          data_key="monotonic_increasing",
-      ),
-      dict(
-          testcase_name="equi_join_indices_random",
-          fn_name="equi_join_indices_random",
-          fn_args=[],
-          data_key="random",
-      ),
-  ])
-  def test_equi_join_indices(self, fn_name, fn_args, data_key):
-
-    def benchmark_fn(session):
-      a = tf.compat.v1.placeholder(dtype=tf.int64, shape=(None,))
-      b = tf.compat.v1.placeholder(dtype=tf.int64, shape=(None,))
-      result = struct2tensor_ops.equi_join_indices(a, b)
-      with tf.control_dependencies(result):
-        x = tf.constant(1)
-      return session.make_callable(x, feed_list=[a, b])
-
-    self.run_benchmarks(fn_name, benchmark_fn, fn_args, data_key)
+    """Benchmarks for EquiJoinIndices."""
+
+    @parameterized.named_parameters(
+        *[
+            dict(
+                testcase_name="equi_join_indices_monotonic_increasing",
+                fn_name="equi_join_indices_monotonic_increasing",
+                fn_args=[],
+                data_key="monotonic_increasing",
+            ),
+            dict(
+                testcase_name="equi_join_indices_random",
+                fn_name="equi_join_indices_random",
+                fn_args=[],
+                data_key="random",
+            ),
+        ]
+    )
+    def test_equi_join_indices(self, fn_name, fn_args, data_key):
+        def benchmark_fn(session):
+            a = tf.compat.v1.placeholder(dtype=tf.int64, shape=(None,))
+            b = tf.compat.v1.placeholder(dtype=tf.int64, shape=(None,))
+            result = struct2tensor_ops.equi_join_indices(a, b)
+            with tf.control_dependencies(result):
+                x = tf.constant(1)
+            return session.make_callable(x, feed_list=[a, b])
+
+        self.run_benchmarks(fn_name, benchmark_fn, fn_args, data_key)
 
 
 class EquiJoinAnyIndicesBenchmarks(struct2tensor_benchmark_util.OpsBenchmarks):
-  """Benchmarks for EquiJoinAnyIndices."""
-
-  @parameterized.named_parameters(*[
-      dict(
-          testcase_name="equi_join_any_indices_monotonic_increasing",
-          fn_name="equi_join_any_indices_monotonic_increasing",
-          fn_args=[],
-          data_key="monotonic_increasing",
-      ),
-      dict(
-          testcase_name="equi_join_any_indices_random",
-          fn_name="equi_join_any_indices_random",
-          fn_args=[],
-          data_key="random",
-      ),
-  ])
-  def test_equi_join_indices(self, fn_name, fn_args, data_key):
-
-    def benchmark_fn(session):
-      a = tf.compat.v1.placeholder(dtype=tf.int64, shape=(None,))
-      b = tf.compat.v1.placeholder(dtype=tf.int64, shape=(None,))
-      result = struct2tensor_ops.equi_join_any_indices(a, b)
-      with tf.control_dependencies(result):
-        x = tf.constant(1)
-      return session.make_callable(x, feed_list=[a, b])
-
-    self.run_benchmarks(fn_name, benchmark_fn, fn_args, data_key)
+    """Benchmarks for EquiJoinAnyIndices."""
+
+    @parameterized.named_parameters(
+        *[
+            dict(
+                testcase_name="equi_join_any_indices_monotonic_increasing",
+                fn_name="equi_join_any_indices_monotonic_increasing",
+                fn_args=[],
+                data_key="monotonic_increasing",
+            ),
+            dict(
+                testcase_name="equi_join_any_indices_random",
+                fn_name="equi_join_any_indices_random",
+                fn_args=[],
+                data_key="random",
+            ),
+        ]
+    )
+    def test_equi_join_indices(self, fn_name, fn_args, data_key):
+        def benchmark_fn(session):
+            a = tf.compat.v1.placeholder(dtype=tf.int64, shape=(None,))
+            b = tf.compat.v1.placeholder(dtype=tf.int64, shape=(None,))
+            result = struct2tensor_ops.equi_join_any_indices(a, b)
+            with tf.control_dependencies(result):
+                x = tf.constant(1)
+            return session.make_callable(x, feed_list=[a, b])
+
+        self.run_benchmarks(fn_name, benchmark_fn, fn_args, data_key)
 
 
 if __name__ == "__main__":
-  tf.test.main()
+    tf.test.main()