Merge pull request #24688 from BoboTiG:fix-invalid-seq-warnings

PiperOrigin-RevId: 228216549

tensorflow/contrib/kernel_methods/python/losses.py

@@ -34,7 +34,7 @@ def sparse_multiclass_hinge_loss(
     scope=None,
     loss_collection=ops.GraphKeys.LOSSES,
     reduction=losses.Reduction.SUM_BY_NONZERO_WEIGHTS):
-  """Adds Ops for computing the multiclass hinge loss.
+  r"""Adds Ops for computing the multiclass hinge loss.
 
   The implementation is based on the following paper:
   On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines

tensorflow/contrib/learn/python/learn/learn_io/generator_io_test.py

@@ -174,7 +174,7 @@ def generator():
       return np.arange(32, 36)
 
     with self.cached_session():
-      with self.assertRaisesRegexp(TypeError, 'x\(\) must be generator'):
+      with self.assertRaisesRegexp(TypeError, r'x\(\) must be generator'):
         failing_input_fn = generator_io.generator_input_fn(
             generator, batch_size=2, shuffle=False, num_epochs=1)
         failing_input_fn()
@@ -185,7 +185,7 @@ def generator():
       yield np.arange(32, 36)
 
     with self.cached_session():
-      with self.assertRaisesRegexp(TypeError, 'x\(\) must yield dict'):
+      with self.assertRaisesRegexp(TypeError, r'x\(\) must yield dict'):
         failing_input_fn = generator_io.generator_input_fn(
             generator, batch_size=2, shuffle=False, num_epochs=1)
         failing_input_fn()

tensorflow/contrib/opt/python/training/adam_gs_optimizer.py

@@ -41,7 +41,7 @@ class AdamGSOptimizer(optimizer.Optimizer):
   def __init__(self, global_step=0, learning_rate=0.001,
                beta1=0.9, beta2=0.999, epsilon=1e-8,
                use_locking=False, name="Adam"):
-    """Construct a new Adam optimizer.
+    r"""Construct a new Adam optimizer.
 
     Branched from tf.train.AdamOptimizer. The only difference is to pass
     global step for computing beta1 and beta2 accumulators, instead of having
@@ -83,23 +83,20 @@ def __init__(self, global_step=0, learning_rate=0.001,
     Args:
       global_step: tensorflow variable indicating the step.
       learning_rate: A Tensor or a floating point value.  The learning rate.
-      beta1: A float value or a constant float tensor.
-        The exponential decay rate for the 1st moment estimates.
-      beta2: A float value or a constant float tensor.
-        The exponential decay rate for the 2nd moment estimates.
+      beta1: A float value or a constant float tensor. The exponential decay
+        rate for the 1st moment estimates.
+      beta2: A float value or a constant float tensor. The exponential decay
+        rate for the 2nd moment estimates.
       epsilon: A small constant for numerical stability. This epsilon is
         "epsilon hat" in the Kingma and Ba paper (in the formula just before
         Section 2.1), not the epsilon in Algorithm 1 of the paper.
       use_locking: If True use locks for update operations.
       name: Optional name for the operations created when applying gradients.
-        Defaults to "Adam".
-
-    @compatibility(eager)
-    When eager execution is enabled, `learning_rate`, `beta1`, `beta2`, and
-    `epsilon` can each be a callable that takes no arguments and returns the
-    actual value to use. This can be useful for changing these values across
-    different invocations of optimizer functions.
-    @end_compatibility
+        Defaults to "Adam".  @compatibility(eager) When eager execution is
+        enabled, `learning_rate`, `beta1`, `beta2`, and `epsilon` can each be a
+        callable that takes no arguments and returns the actual value to use.
+        This can be useful for changing these values across different
+        invocations of optimizer functions. @end_compatibility
     """
     super(AdamGSOptimizer, self).__init__(use_locking, name)
     self._lr = learning_rate

tensorflow/contrib/optimizer_v2/adam.py

@@ -36,7 +36,7 @@ class AdamOptimizer(optimizer_v2.OptimizerV2):
 
   def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8,
                use_locking=False, name="Adam"):
-    """Construct a new Adam optimizer.
+    r"""Construct a new Adam optimizer.
 
     Initialization:
 

tensorflow/python/client/session_test.py

@@ -2036,7 +2036,7 @@ def testAutoConvertAndCheckData(self):
     with self.cached_session() as sess:
       a = array_ops.placeholder(dtype=dtypes.string)
       with self.assertRaisesRegexp(
-          TypeError, 'Type of feed value 1 with type <(\w+) \'int\'> is not'):
+          TypeError, r'Type of feed value 1 with type <(\w+) \'int\'> is not'):
         sess.run(a, feed_dict={a: 1})
 
 

tensorflow/python/feature_column/feature_column_test.py

@@ -1832,7 +1832,7 @@ def test_static_batch_size_mismatch(self):
       }
     with self.assertRaisesRegexp(
         ValueError,
-        'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+        r'Batch size \(first dimension\) of each feature must be same.'):
       fc.linear_model(features, [price1, price2])
 
   def test_subset_of_static_batch_size_mismatch(self):
@@ -1847,7 +1847,7 @@ def test_subset_of_static_batch_size_mismatch(self):
       }
       with self.assertRaisesRegexp(
           ValueError,
-          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+          r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
         fc.linear_model(features, [price1, price2, price3])
 
   def test_runtime_batch_size_mismatch(self):
@@ -2467,7 +2467,7 @@ def test_static_batch_size_mismatch(self):
       }
     with self.assertRaisesRegexp(
         ValueError,
-        'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+        r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
       get_keras_linear_model_predictions(features, [price1, price2])
 
   def test_subset_of_static_batch_size_mismatch(self):
@@ -2482,7 +2482,7 @@ def test_subset_of_static_batch_size_mismatch(self):
       }
       with self.assertRaisesRegexp(
           ValueError,
-          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+          r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
         get_keras_linear_model_predictions(features, [price1, price2, price3])
 
   def test_runtime_batch_size_mismatch(self):
@@ -2974,7 +2974,7 @@ def test_static_batch_size_mismatch(self):
       }
       with self.assertRaisesRegexp(
           ValueError,
-          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+          r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
         fc.input_layer(features, [price1, price2])
 
   def test_subset_of_static_batch_size_mismatch(self):
@@ -2989,7 +2989,7 @@ def test_subset_of_static_batch_size_mismatch(self):
       }
       with self.assertRaisesRegexp(
           ValueError,
-          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+          r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
         fc.input_layer(features, [price1, price2, price3])
 
   def test_runtime_batch_size_mismatch(self):

tensorflow/python/feature_column/feature_column_v2_test.py

@@ -2052,7 +2052,7 @@ def test_static_batch_size_mismatch(self):
       }
     with self.assertRaisesRegexp(
         ValueError,
-        'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+        r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
       model = fc.LinearModel([price1, price2])
       model(features)
 
@@ -2068,7 +2068,7 @@ def test_subset_of_static_batch_size_mismatch(self):
       }
       with self.assertRaisesRegexp(
           ValueError,
-          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+          r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
         model = fc.LinearModel([price1, price2, price3])
         model(features)
 
@@ -2818,7 +2818,7 @@ def test_static_batch_size_mismatch(self):
       }
     with self.assertRaisesRegexp(
         ValueError,
-        'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+        r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
       fc_old.linear_model(features, [price1, price2])
 
   def test_subset_of_static_batch_size_mismatch(self):
@@ -2833,7 +2833,7 @@ def test_subset_of_static_batch_size_mismatch(self):
       }
       with self.assertRaisesRegexp(
           ValueError,
-          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+          r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
         fc_old.linear_model(features, [price1, price2, price3])
 
   def test_runtime_batch_size_mismatch(self):
@@ -3435,7 +3435,7 @@ def test_static_batch_size_mismatch(self):
       }
       with self.assertRaisesRegexp(
           ValueError,
-          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+          r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
         fc.DenseFeatures([price1, price2])(features)
 
   def test_subset_of_static_batch_size_mismatch(self):
@@ -3450,7 +3450,7 @@ def test_subset_of_static_batch_size_mismatch(self):
       }
       with self.assertRaisesRegexp(
           ValueError,
-          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+          r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
         fc.DenseFeatures([price1, price2, price3])(features)
 
   def test_runtime_batch_size_mismatch(self):
@@ -4141,7 +4141,7 @@ def test_static_batch_size_mismatch(self):
       }
       with self.assertRaisesRegexp(
           ValueError,
-          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+          r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
         fc_old.input_layer(features, [price1, price2])
 
   def test_subset_of_static_batch_size_mismatch(self):
@@ -4156,7 +4156,7 @@ def test_subset_of_static_batch_size_mismatch(self):
       }
       with self.assertRaisesRegexp(
           ValueError,
-          'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
+          r'Batch size \(first dimension\) of each feature must be same.'):  # pylint: disable=anomalous-backslash-in-string
         fc_old.input_layer(features, [price1, price2, price3])
 
   def test_runtime_batch_size_mismatch(self):

tensorflow/python/kernel_tests/confusion_matrix_test.py

@@ -470,9 +470,8 @@ def testUnsqueezableLabels(self):
           labels_placeholder: label_values,
           predictions_placeholder: prediction_values
       }
-      with self.assertRaisesRegexp(
-          errors_impl.InvalidArgumentError,
-          "Can not squeeze dim\[2\]"):
+      with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
+                                   r"Can not squeeze dim\[2\]"):
         dynamic_labels.eval(feed_dict=feed_dict)
       self.assertAllEqual(
           prediction_values, dynamic_predictions.eval(feed_dict=feed_dict))
@@ -498,9 +497,8 @@ def testUnsqueezablePredictions(self):
       }
       self.assertAllEqual(
           label_values, dynamic_labels.eval(feed_dict=feed_dict))
-      with self.assertRaisesRegexp(
-          errors_impl.InvalidArgumentError,
-          "Can not squeeze dim\[2\]"):
+      with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
+                                   r"Can not squeeze dim\[2\]"):
         dynamic_predictions.eval(feed_dict=feed_dict)
 
 

tensorflow/python/training/adam.py

@@ -39,7 +39,7 @@ class AdamOptimizer(optimizer.Optimizer):
 
   def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8,
                use_locking=False, name="Adam"):
-    """Construct a new Adam optimizer.
+    r"""Construct a new Adam optimizer.
 
     Initialization:
 
@@ -75,23 +75,20 @@ def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8,
 
     Args:
       learning_rate: A Tensor or a floating point value.  The learning rate.
-      beta1: A float value or a constant float tensor.
-        The exponential decay rate for the 1st moment estimates.
-      beta2: A float value or a constant float tensor.
-        The exponential decay rate for the 2nd moment estimates.
+      beta1: A float value or a constant float tensor. The exponential decay
+        rate for the 1st moment estimates.
+      beta2: A float value or a constant float tensor. The exponential decay
+        rate for the 2nd moment estimates.
       epsilon: A small constant for numerical stability. This epsilon is
         "epsilon hat" in the Kingma and Ba paper (in the formula just before
         Section 2.1), not the epsilon in Algorithm 1 of the paper.
       use_locking: If True use locks for update operations.
       name: Optional name for the operations created when applying gradients.
-        Defaults to "Adam".
-
-    @compatibility(eager)
-    When eager execution is enabled, `learning_rate`, `beta1`, `beta2`, and
-    `epsilon` can each be a callable that takes no arguments and returns the
-    actual value to use. This can be useful for changing these values across
-    different invocations of optimizer functions.
-    @end_compatibility
+        Defaults to "Adam".  @compatibility(eager) When eager execution is
+        enabled, `learning_rate`, `beta1`, `beta2`, and `epsilon` can each be a
+        callable that takes no arguments and returns the actual value to use.
+        This can be useful for changing these values across different
+        invocations of optimizer functions. @end_compatibility
     """
     super(AdamOptimizer, self).__init__(use_locking, name)
     self._lr = learning_rate

tensorflow/tools/ci_build/copy_binary.py

@@ -33,7 +33,7 @@
 import zipfile
 
 TF_NIGHTLY_REGEX = (r"(.+)tf_nightly(|_gpu)-(\d\.[\d]{1,2}"
-                    "\.\d.dev[\d]{0,8})-(.+)\.whl")
+                    r"\.\d.dev[\d]{0,8})-(.+)\.whl")
 BINARY_STRING_TEMPLATE = "%s-%s-%s.whl"
 
 

tensorflow/tools/dist_test/scripts_allreduce/k8s_generate_yaml_lib.py

@@ -195,7 +195,7 @@ def generate_RSA(bits=2048, exponent=65537):
 
 def get_change_ssh_port(use_hostnet, port):
   if use_hostnet == 1:
-    return "sed -i '/Port 22/c\Port {}' /etc/ssh/sshd_config".format(port)
+    return r"sed -i '/Port 22/c\Port {}' /etc/ssh/sshd_config".format(port)
 
   return ''
 

third_party/toolchains/preconfig/ubuntu14.04/gcc-nvcc-cuda10.0/windows/msvc_wrapper_for_nvcc.py

@@ -103,8 +103,9 @@ def InvokeNvcc(argv, log=False):
     The return value of calling os.system('nvcc ' + args)
   """
 
-  src_files = [f for f in argv if
-               re.search('\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)]
+  src_files = [
+      f for f in argv if re.search(r'\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)
+  ]
   if len(src_files) == 0:
     raise Error('No source files found for cuda compilation.')
 

third_party/toolchains/preconfig/ubuntu14.04/gcc-nvcc-cuda9.0/windows/msvc_wrapper_for_nvcc.py

@@ -103,8 +103,9 @@ def InvokeNvcc(argv, log=False):
     The return value of calling os.system('nvcc ' + args)
   """
 
-  src_files = [f for f in argv if
-               re.search('\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)]
+  src_files = [
+      f for f in argv if re.search(r'\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)
+  ]
   if len(src_files) == 0:
     raise Error('No source files found for cuda compilation.')
 

third_party/toolchains/preconfig/ubuntu14.04/gcc-nvcc/windows/msvc_wrapper_for_nvcc.py

@@ -103,8 +103,9 @@ def InvokeNvcc(argv, log=False):
     The return value of calling os.system('nvcc ' + args)
   """
 
-  src_files = [f for f in argv if
-               re.search('\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)]
+  src_files = [
+      f for f in argv if re.search(r'\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)
+  ]
   if len(src_files) == 0:
     raise Error('No source files found for cuda compilation.')