text: """Tests for eager mode Saver."""
  # 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.
  # ==============================================================================
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  
  from tensorflow.contrib.eager.python import saver as _saver
  from tensorflow.python.eager import context
  from tensorflow.python.eager import graph_callable
  from tensorflow.python.eager import test
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.training import adam
  from tensorflow.python.training import gradient_descent
  from tensorflow.python.training import momentum
  from tensorflow.python.training import rmsprop
  
  
  class SaverTest(test.TestCase):
  
    def _dev(self):
      return '/device:GPU:0' if context.num_gpus() else '/device:CPU:0'
  
    def testBasics(self):
      with ops.device(self._dev()):
        v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
        def model():
          return array_ops.constant(2.0) * v1
  
        ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
  
        _ = model()
        saver = _saver.Saver([v1])
        saver.save(ckpt_prefix)
        v1.assign(2.0)
        self.assertEqual(v1.read_value().numpy(), 2.0)
  
        saver.restore(ckpt_prefix)
        self.assertEqual(v1.read_value().numpy(), 1.0)
  
    def testSameNameNoClobbering(self):
      with ops.device(self._dev()):
        # Note that this test purposefully uses Graphs rather than
        # IsolateTest. Users are more likely to accidentally create the same
        # variable name this way.
        first_graph = ops.Graph()
        with first_graph.as_default():
          v1_first_graph = resource_variable_ops.ResourceVariable(1.0, name='v1')
        with ops.Graph().as_default():
          v1_second_graph = resource_variable_ops.ResourceVariable(2.0, name='v1')
          saver = _saver.Saver([v1_first_graph, v1_second_graph])
        ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
        with self.assertRaisesRegexp(ValueError, 'v1'):
          saver.save(ckpt_prefix)
  
    def testSameObjectOK(self):
      with ops.device(self._dev()):
        v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
        # While different objects with the same shared_name are not good, passing
        # in the same object multiple times is fine.
        saver = _saver.Saver([v1, v1])
        ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
        saver.save(ckpt_prefix)
  
    def testSaveByDict(self):
      with ops.device(self._dev()):
        v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
        v2 = resource_variable_ops.ResourceVariable(1.0, name='v2')
        def model():
          return array_ops.constant(2.0) * v1 * v2
  
        ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
  
        # Save the variables under different names.
        _ = model()
        saver = _saver.Saver({'ckpt/v1': v1, 'ckpt/v2': v2})
        saver.save(ckpt_prefix)
        v1.assign(2.0)
        v2.assign(2.0)
        self.assertEqual(v1.read_value().numpy(), 2.0)
        self.assertEqual(v2.read_value().numpy(), 2.0)
        # Can still restore it.
        saver.restore(ckpt_prefix)
        self.assertEqual(v1.read_value().numpy(), 1.0)
        # However, cannot restore it with default name.
        with self.assertRaisesOpError('not found in checkpoint'):
          saver = _saver.Saver([v1, v2]).restore(ckpt_prefix)
  
        # Can specify which variable in ckpt to restore to which variable.
        def map_func(x):
          return {'v3': 'ckpt/v1', 'v4': 'ckpt/v2'}.get(x, x)
        with _saver.restore_variables_on_create(ckpt_prefix, map_func):
          v3 = resource_variable_ops.ResourceVariable(2.0, name='v3')
          v4 = resource_variable_ops.ResourceVariable(2.0, name='v4')
        self.assertEqual(v3.read_value().numpy(), 1.0)
        self.assertEqual(v4.read_value().numpy(), 1.0)
  
    def testRestoreOnCreate(self):
      with ops.device(self._dev()):
        def model(init_val):
          v1 = resource_variable_ops.ResourceVariable(init_val, name='v1')
          return array_ops.constant(1.0) * v1, v1
  
        ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
        _, v1 = model(1.0)
        saver = _saver.Saver([v1])
        saver.save(ckpt_prefix)
  
        with ops.Graph().as_default():
          saver = _saver.Saver([v1])
          with _saver.restore_variables_on_create(ckpt_prefix):
            # Value is from checkpoint, but not from argument.
            ret, _ = model(2.0)
            self.assertEqual(ret.numpy(), 1.0)
  
    def testRestoreNotFound(self):
      with ops.device(self._dev()):
        def model(v):
          return array_ops.constant(1.0) * v
  
        ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
        v = resource_variable_ops.ResourceVariable(1.0, name='v1')
        _ = model(v)
        saver = _saver.Saver([v])
        saver.save(ckpt_prefix)
  
        with self.assertRaisesRegexp(errors.NotFoundError,
                                     'v2 not found in checkpoint'):
          with _saver.restore_variables_on_create(ckpt_prefix):
            _ = model(resource_variable_ops.ResourceVariable(1.0, name='v2'))
  
    def testSaveRestoreGraphCallable(self):
      with ops.device(self._dev()):
        @graph_callable.graph_callable(
            [graph_callable.ShapeAndDtype(shape=(), dtype=dtypes.float32)])
        def model(x):
          v = variable_scope.get_variable(
              'v', initializer=init_ops.zeros_initializer(), shape=())
          return v + x
  
        # Default 2 + 0 = 2
        self.assertEqual(
            2, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
  
        # Save the variable value 0.
        ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
        _saver.Saver(model.variables).save(ckpt_prefix)
  
        # update variable to 1, so that 2 + 1 = 3
        model.variables[0].assign(1.)
        self.assertEqual(
            3, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
  
        # load the variable value 0, so that 2 + 0 = 2
        _saver.Saver(model.variables).restore(ckpt_prefix)
        self.assertEqual(
            2, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
  
        # update checkpoint variable to 1 and memory value to 2.
        model.variables[0].assign(1.)
        _saver.Saver(model.variables).save(ckpt_prefix)
        model.variables[0].assign(2.)
        self.assertEqual(
            4, model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
  
        # reset the graph and reload on create, so that 1 + 2 = 3
        with ops.Graph().as_default():
          with _saver.restore_variables_on_create(ckpt_prefix):
            @graph_callable.graph_callable(
                [graph_callable.ShapeAndDtype(shape=(), dtype=dtypes.float32)])
            def model2(x):
              v = variable_scope.get_variable(
                  'v', initializer=init_ops.zeros_initializer(), shape=())
              return v + x
  
            self.assertEqual(
                3, model2(array_ops.constant(2, dtype=dtypes.float32)).numpy())
  
  
  class GetOptimizerTests(test.TestCase):
  
    def _optimizer_test_template(self, optimizer):
      """Checks save and restore. Returns the optimizer variables."""
      v = resource_variable_ops.ResourceVariable([[2., 3.]], name='v')
      loss_fn = lambda: v[0, 0] ** 2 + v[0, 1] ** 2
      optimizer.minimize(loss_fn)
      optimizer_variables = _saver.get_optimizer_variables(optimizer)
      saver = _saver.Saver(optimizer_variables + [v])
      checkpoint_path = saver.save(self.get_temp_dir())
      optimizer.minimize(loss_fn)
      after_first_minimize = v.numpy()
      # After we restore, the next step should be exactly the same as the one we
      # just did.
      saver.restore(checkpoint_path)
      optimizer.minimize(loss_fn)
      self.assertAllEqual(after_first_minimize, v.numpy())
      return optimizer_variables
  
    def testAdam(self):
      optimizer = adam.AdamOptimizer(0.1)
      self._optimizer_test_template(optimizer)
  
    def testGradientDescent(self):
      optimizer = gradient_descent.GradientDescentOptimizer(0.02)
      self.assertEqual(0, len(self._optimizer_test_template(optimizer)))
  
    def testMomentum(self):
      optimizer = momentum.MomentumOptimizer(
          learning_rate=0.03,
          momentum=0.5)
      self._optimizer_test_template(optimizer)
  
    def testRMSProp(self):
      optimizer = rmsprop.RMSPropOptimizer(0.01)
      self._optimizer_test_template(optimizer)
  
  if __name__ == '__main__':
    test.main()
  