cleanup of deprecated test methods

PiperOrigin-RevId: 716610454

examples/half_plus_two/half_plus_two_test.py

@@ -34,7 +34,7 @@ def testExportTool(self):
     module_path = os.path.join(self.get_temp_dir(), "half-plus-two-module")
 
     export_tool_path = os.path.join(test_utils.test_srcdir(), EXPORT_TOOL_PATH)
-    self.assertEquals(0, subprocess.call([export_tool_path, module_path]))
+    self.assertEqual(0, subprocess.call([export_tool_path, module_path]))
 
     # Test the Module computes (0.5*input + 2).
     with tf.Graph().as_default():

tensorflow_hub/compressed_module_resolver_test.py

@@ -106,7 +106,7 @@ def testModuleDescriptor(self):
     http_resolver = compressed_module_resolver.HttpCompressedFileResolver()
     path = http_resolver(self.module_handle)
     desc = tf_utils.read_file_to_string(resolver._module_descriptor_file(path))
-    self.assertRegexpMatches(
+    self.assertRegex(
         desc, "Module: %s\n"
         "Download Time: .*\n"
         "Downloader Hostname: %s .PID:%d." % (re.escape(

tensorflow_hub/feature_column_v2_test.py

@@ -175,7 +175,7 @@ def testLoadingDifferentFeatureColumnsFails(self):
 
     # Loading of checkpoints from the first model into the second model should
     # fail.
-    with self.assertRaisesRegexp(AssertionError,
+    with self.assertRaisesRegex(AssertionError,
                                  ".*not bound to checkpointed values.*"):
       model_2.load_weights(checkpoint_path).assert_consumed()
 

tensorflow_hub/keras_layer_test.py

@@ -803,7 +803,7 @@ def test_keras_layer_logs_if_training_zero_variables(self):
         layer([[10.]])
         layer([[10.]])
       self.assertLen(logs.records, 1)  # Duplicate logging is avoided.
-      self.assertRegexpMatches(logs.records[0].msg, "zero trainable weights")
+      self.assertRegex(logs.records[0].msg, "zero trainable weights")
     else:
       # Just test that it runs at all.
       layer([[10.]])

tensorflow_hub/resolver_test.py

@@ -89,7 +89,7 @@ def testCacheDir(self):
     self.assertEqual(cache_dir, None)
     # Use temp dir.
     cache_dir = resolver.tfhub_cache_dir(use_temp=True)
-    self.assertEquals(cache_dir,
+    self.assertEqual(cache_dir,
                       os.path.join(tempfile.gettempdir(), "tfhub_modules"))
     # Use override
     cache_dir = resolver.tfhub_cache_dir(default_cache_dir="/d", use_temp=True)
@@ -138,10 +138,10 @@ def testDirSize(self):
     self.assertEqual(0, resolver._locked_tmp_dir_size(fake_lock_filename))
 
   def testLockFileName(self):
-    self.assertEquals("/a/b/c.lock", resolver._lock_filename("/a/b/c/"))
+    self.assertEqual("/a/b/c.lock", resolver._lock_filename("/a/b/c/"))
 
   def testTempDownloadDir(self):
-    self.assertEquals("/a/b.t.tmp", resolver._temp_download_dir("/a/b/", "t"))
+    self.assertEqual("/a/b.t.tmp", resolver._temp_download_dir("/a/b/", "t"))
 
   def testReadTaskUidFromLockFile(self):
     module_dir = os.path.join(self.get_temp_dir(), "module")
@@ -250,7 +250,7 @@ def fake_download_fn_with_rogue_behavior(handle, tmp_dir):
     self.assertEqual(
         sorted(tf.compat.v1.gfile.ListDirectory(parent_dir)),
         ["module", "module.descriptor.txt"])
-    self.assertRegexpMatches(
+    self.assertRegex(
         tf_utils.read_file_to_string(
             resolver._module_descriptor_file(module_dir)), "Module: module\n"
         "Download Time: .*\n"
@@ -300,7 +300,7 @@ def fake_download_fn(handle, tmp_dir):
     self.assertEqual(
         sorted(tf.compat.v1.gfile.ListDirectory(parent_dir)),
         ["module", "module.descriptor.txt"])
-    self.assertRegexpMatches(
+    self.assertRegex(
         tf_utils.read_file_to_string(
             resolver._module_descriptor_file(module_dir)), "Module: module\n"
         "Download Time: .*\n"

tensorflow_hub/tensor_info_test.py

@@ -182,7 +182,7 @@ def testConvertTensors(self):
 
       # check sparsity
       in1 = tf.compat.v1.sparse_placeholder(tf.int32, [])
-      with self.assertRaisesRegexp(TypeError,
+      with self.assertRaisesRegex(TypeError,
                                    "Got SparseTensor. Expected Tensor."):
         tensor_info.convert_dict_to_compatible_tensor({"a": in1}, targets)