Fork the tf_contextlib to keras and use tf_decorator which is a __internal__ API.

PiperOrigin-RevId: 331790478
Change-Id: Idb3f4fec529b0a1c012d3fd2f67d5f6f440d6806

tensorflow/python/keras/BUILD

@@ -95,6 +95,7 @@ py_library(
         "//tensorflow/python/distribute:multi_worker_util",
         "//tensorflow/python/keras/engine:keras_tensor",
         "//tensorflow/python/keras/utils:control_flow_util",
+        "//tensorflow/python/keras/utils:tf_contextlib",
         "//tensorflow/python/keras/utils:tf_inspect",
     ],
 )

tensorflow/python/keras/backend.py

@@ -55,6 +55,7 @@ from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras import backend_config
 from tensorflow.python.keras.engine import keras_tensor
 from tensorflow.python.keras.utils import control_flow_util
+from tensorflow.python.keras.utils import tf_contextlib
 from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import clip_ops
@@ -83,7 +84,6 @@ from tensorflow.python.training.tracking import util as tracking_util
 from tensorflow.python.util import dispatch
 from tensorflow.python.util import nest
 from tensorflow.python.util import object_identity
-from tensorflow.python.util import tf_contextlib
 from tensorflow.python.util.tf_export import keras_export
 from tensorflow.tools.docs import doc_controls
 

tensorflow/python/keras/distribute/distributed_training_utils.py

@@ -40,6 +40,7 @@ from tensorflow.python.keras import metrics as metrics_module
 from tensorflow.python.keras import optimizers
 from tensorflow.python.keras.engine import training_utils
 from tensorflow.python.keras.optimizer_v2 import optimizer_v2
+from tensorflow.python.keras.utils import tf_contextlib
 from tensorflow.python.keras.utils.mode_keys import ModeKeys
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
@@ -49,7 +50,6 @@ from tensorflow.python.ops.ragged import ragged_concat_ops
 from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_contextlib
 
 
 def set_weights(distribution_strategy, dist_model, weights):

tensorflow/python/keras/legacy_tf_layers/base.py

@@ -27,12 +27,12 @@ from tensorflow.python.keras import backend
 from tensorflow.python.keras.engine import base_layer
 from tensorflow.python.keras.engine import base_layer_utils
 from tensorflow.python.keras.mixed_precision.experimental import policy
+from tensorflow.python.keras.utils import tf_contextlib
 from tensorflow.python.ops import variable_scope as vs
 from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.util import function_utils
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_contextlib
 from tensorflow.python.util.tf_export import tf_export
 
 # Avoid breaking users who directly import this symbol from this file.

tensorflow/python/keras/saving/saved_model/saved_model_test.py

@@ -53,6 +53,7 @@ from tensorflow.python.keras.saving.saved_model import load as keras_load
 from tensorflow.python.keras.saving.saved_model import save_impl as keras_save
 from tensorflow.python.keras.utils import control_flow_util
 from tensorflow.python.keras.utils import generic_utils
+from tensorflow.python.keras.utils import tf_contextlib
 from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
@@ -63,7 +64,6 @@ from tensorflow.python.ops.ragged import ragged_factory_ops
 from tensorflow.python.platform import test
 from tensorflow.python.saved_model import load as tf_load
 from tensorflow.python.saved_model import save as tf_save
-from tensorflow.python.util import tf_contextlib
 
 
 class LayerWithLearningPhase(keras.engine.base_layer.Layer):

tensorflow/python/keras/testing_utils.py

@@ -46,8 +46,8 @@ from tensorflow.python.keras.optimizer_v2 import adamax as adamax_v2
 from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_descent_v2
 from tensorflow.python.keras.optimizer_v2 import nadam as nadam_v2
 from tensorflow.python.keras.optimizer_v2 import rmsprop as rmsprop_v2
+from tensorflow.python.keras.utils import tf_contextlib
 from tensorflow.python.keras.utils import tf_inspect
-from tensorflow.python.util import tf_contextlib
 from tensorflow.python.util import tf_decorator
 
 

tensorflow/python/keras/utils/BUILD

@@ -197,6 +197,15 @@ py_library(
     ],
 )
 
+py_library(
+    name = "tf_contextlib",
+    srcs = ["tf_contextlib.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:util",
+    ],
+)
+
 py_library(
     name = "tf_inspect",
     srcs = ["tf_inspect.py"],

tensorflow/python/keras/utils/generic_utils.py

@@ -29,9 +29,9 @@ import types as python_types
 
 import numpy as np
 import six
+from tensorflow.python.keras.utils import tf_contextlib
 from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_contextlib
 from tensorflow.python.util import tf_decorator
 from tensorflow.python.util.tf_export import keras_export
 

tensorflow/python/keras/utils/tf_contextlib.py

@@ -0,0 +1,36 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""TFDecorator-aware replacements for the contextlib module."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import contextlib as _contextlib
+
+from tensorflow.python.util import tf_decorator
+
+
+def contextmanager(target):
+  """A tf_decorator-aware wrapper for `contextlib.contextmanager`.
+
+  Usage is identical to `contextlib.contextmanager`.
+
+  Args:
+    target: A callable to be wrapped in a contextmanager.
+  Returns:
+    A callable that can be used inside of a `with` statement.
+  """
+  context_manager = _contextlib.contextmanager(target)
+  return tf_decorator.make_decorator(target, context_manager, 'contextmanager')

tensorflow/python/keras/utils/tf_utils.py

@@ -30,13 +30,13 @@ from tensorflow.python.framework import tensor_spec
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.framework import type_spec
 from tensorflow.python.keras import backend as K
+from tensorflow.python.keras.utils import tf_contextlib
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.ops.ragged import ragged_tensor_value
 from tensorflow.python.util import nest
 from tensorflow.python.util import object_identity
-from tensorflow.python.util import tf_contextlib
 
 
 def is_tensor_or_tensor_list(v):