# Copyright 2015 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.
# ==============================================================================
"""Tests for Adadelta Optimizer."""

import tensorflow.compat.v2 as tf

from absl.testing import parameterized
import numpy as np
from keras import combinations
from keras.optimizer_v2 import adadelta

_DATA_TYPES = [
    tf.half, tf.float32, tf.float64, tf.complex64,
    tf.complex128
]


class AdadeltaOptimizerTest(tf.test.TestCase, parameterized.TestCase):

  def doTestBasic(self, use_resource=False, use_callable_params=False):
    num_updates = 4  # number of ADADELTA steps to perform
    for dtype in _DATA_TYPES:
      for grad in [0.2, 0.1, 0.01]:
        for lr in [1.0, 0.5, 0.1]:
          var0_init = [1.0, 2.0]
          var1_init = [3.0, 4.0]
          if use_resource:
            var0 = tf.Variable(var0_init, dtype=dtype)
            var1 = tf.Variable(var1_init, dtype=dtype)
          else:
            var0 = tf.Variable(var0_init, dtype=dtype)
            var1 = tf.Variable(var1_init, dtype=dtype)

          grads = tf.constant([grad, grad], dtype=dtype)

          accum = 0.0
          accum_update = 0.0

          # ADADELTA gradient optimizer
          rho = 0.95
          epsilon = 1e-8
          if use_callable_params:
            adadelta_opt = adadelta.Adadelta(
                learning_rate=lambda: lr,  # pylint: disable=cell-var-from-loop
                rho=lambda: rho,  # pylint: disable=cell-var-from-loop
                epsilon=epsilon)  # pylint: disable=cell-var-from-loop
          else:
            adadelta_opt = adadelta.Adadelta(
                learning_rate=lr, rho=rho, epsilon=epsilon)
          if not tf.executing_eagerly():
            adadelta_update = adadelta_opt.apply_gradients(
                zip([grads, grads], [var0, var1]))
            self.evaluate(tf.compat.v1.global_variables_initializer())

            # Assign slots
            slot = [None] * 2
            slot_update = [None] * 2
            slot[0] = adadelta_opt.get_slot(var0, "accum_grad")
            self.assertEqual(slot[0].shape, var0.shape)

            slot_update[0] = adadelta_opt.get_slot(var0, "accum_var")
            self.assertEqual(slot_update[0].shape, var0.shape)

            slot[1] = adadelta_opt.get_slot(var1, "accum_grad")
            self.assertEqual(slot[1].shape, var1.shape)

            slot_update[1] = adadelta_opt.get_slot(var1, "accum_var")
            self.assertEqual(slot_update[1].shape, var1.shape)

          # Fetch params to validate initial values
          self.assertAllClose(var0_init, self.evaluate(var0))
          self.assertAllClose(var1_init, self.evaluate(var1))

          update = [None] * num_updates
          tot_update = 0
          for step in range(num_updates):
            # Run adadelta update for comparison
            if not tf.executing_eagerly():
              self.evaluate(adadelta_update)
            else:
              adadelta_opt.apply_gradients(zip([grads, grads], [var0, var1]))

            # Perform initial update without previous accum values
            accum = accum * rho + (grad**2) * (1 - rho)
            update[step] = (
                np.sqrt(accum_update + epsilon) *
                (1. / np.sqrt(accum + epsilon)) * grad)
            accum_update = (
                accum_update * rho + (update[step]**2) * (1.0 - rho))
            tot_update += update[step] * lr

            if not tf.executing_eagerly():
              # Check that the accumulators have been updated
              # TODO(lxuechen): This is hard to test in eager mode
              for slot_idx in range(2):
                self.assertAllCloseAccordingToType(
                    np.array([accum, accum], dtype=dtype.as_numpy_dtype(0)),
                    self.evaluate(slot[slot_idx]),
                    rtol=1e-5)

                self.assertAllCloseAccordingToType(
                    np.array(
                        [accum_update, accum_update],
                        dtype=dtype.as_numpy_dtype(0)),
                    self.evaluate(slot_update[slot_idx]),
                    rtol=1e-5)

              # Check that the parameters have been updated
              self.assertAllCloseAccordingToType(
                  np.array(
                      [var0_init[0] - tot_update, var0_init[1] - tot_update],
                      dtype=dtype.as_numpy_dtype(0)),
                  self.evaluate(var0),
                  rtol=1e-5)

              self.assertAllCloseAccordingToType(
                  np.array(
                      [var1_init[0] - tot_update, var1_init[1] - tot_update],
                      dtype=dtype.as_numpy_dtype(0)),
                  self.evaluate(var1),
                  rtol=1e-5)

  @combinations.generate(combinations.combine(mode=["graph", "eager"]))
  def testResourceBasic(self):
    self.doTestBasic(use_resource=True)

  @combinations.generate(combinations.combine(mode=["eager"]))
  def testBasicCallableParams(self):
    self.doTestBasic(use_resource=True, use_callable_params=True)

  def testMinimizeSparseResourceVariable(self):
    # TODO(tanzheny, omalleyt): Fix test in eager mode.
    with tf.Graph().as_default():
      for dtype in _DATA_TYPES:
        var0 = tf.Variable([[1.0, 2.0]], dtype=dtype)
        x = tf.constant([[4.0], [5.0]], dtype=dtype)

        def loss():
          pred = tf.matmul(tf.compat.v1.nn.embedding_lookup([var0], [0]), x)  # pylint: disable=cell-var-from-loop
          return pred * pred

        sgd_op = adadelta.Adadelta(1.0, 1.0, 1.0).minimize(
            loss, var_list=[var0])
        self.evaluate(tf.compat.v1.global_variables_initializer())
        # Fetch params to validate initial values
        self.assertAllCloseAccordingToType([[1.0, 2.0]], self.evaluate(var0))
        # Run 1 step of sgd
        self.evaluate(sgd_op)
        # Validate updated params
        self.assertAllCloseAccordingToType([[-111, -138]], self.evaluate(var0))

  def testConstructAdadeltaWithLR(self):
    opt = adadelta.Adadelta(lr=1.0, rho=0.9, epsilon=1.)
    opt_2 = adadelta.Adadelta(learning_rate=0.1, rho=0.9, epsilon=1., lr=1.0)
    opt_3 = adadelta.Adadelta(learning_rate=0.1, rho=0.9, epsilon=1.)
    self.assertIsInstance(opt.lr, tf.Variable)
    self.assertIsInstance(opt_2.lr, tf.Variable)
    self.assertIsInstance(opt_3.lr, tf.Variable)

    self.evaluate(tf.compat.v1.global_variables_initializer())
    self.assertAllClose(self.evaluate(opt.lr), (1.0))
    self.assertAllClose(self.evaluate(opt_2.lr), (1.0))
    self.assertAllClose(self.evaluate(opt_3.lr), (0.1))

  def testConstructAdadeltaWithEpsilonValues(self):
    opt = adadelta.Adadelta(epsilon=None)
    self.assertEqual(opt.epsilon, 1e-7)

    opt = adadelta.Adadelta(epsilon=1e-8)
    self.assertEqual(opt.epsilon, 1e-8)


if __name__ == "__main__":
  tf.test.main()
