# Copyright 2018 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 that show that DistributionStrategy works with optimizer v2."""

import tensorflow.compat.v2 as tf

from absl.testing import parameterized
import numpy as np

import keras
from keras.optimizer_v2 import adam
from keras.optimizer_v2 import gradient_descent


def get_model():
  x = keras.layers.Input(shape=(3,), name='input')
  y = keras.layers.Dense(4, name='dense')(x)
  model = keras.Model(x, y)
  return model


class MirroredStrategyOptimizerV2Test(tf.test.TestCase, parameterized.TestCase):

  @tf.__internal__.distribute.combinations.generate(
      tf.__internal__.test.combinations.combine(
          distribution=[
              tf.__internal__.distribute.combinations.central_storage_strategy_with_two_gpus,
          ],
          mode=['graph', 'eager']))
  def testKerasOptimizerWithUnequalInput(self, distribution):
    with distribution.scope():
      var = tf.Variable(
          2.0, name='var', aggregation=tf.VariableAggregation.SUM)
      optimizer = adam.Adam(learning_rate=0.01, beta_1=0.2, beta_2=0.2)
      all_vars = []

      def model_fn():

        def loss_fn():
          replica_id = _replica_id()
          return tf.cast(replica_id + 1, dtype=tf.float32) * 0.5 * var

        train_op = optimizer.minimize(loss_fn, var_list=[var])

        return train_op, optimizer

      def train_fn():
        train_op, optimizer = distribution.extended.call_for_each_replica(
            model_fn)
        if not all_vars:
          all_vars.append(var)
          all_vars.append(optimizer.get_slot(var, 'm'))
          all_vars.append(optimizer.get_slot(var, 'v'))
        return distribution.group(train_op)

      if not tf.executing_eagerly():
        with self.cached_session() as sess:
          train_fn = sess.make_callable(train_fn())
      self.evaluate(tf.compat.v1.global_variables_initializer())

      # first step.
      train_fn()
      # var(1) = var(0) - lr * m(1) * sqrt(1 - beta2) / sqrt(v(1)) / (1 - beta1)
      #        = 2.0 - 0.01 * 1.2 * sqrt(0.8) / sqrt(1.8) / 0.8
      self.assertAllClose(1.99, self.evaluate(all_vars[0]))
      # m(1) = beta1 * m(0) + (1-beta1) * grad = 0.2 * 0 + 0.8 * (1 + 2) / 2
      self.assertAllClose(1.2, self.evaluate(all_vars[1]))
      # v(1) = beta2 * v(0) + (1-beta2) * grad^2 = 0.2 * 0 + 0.8 * 2.25
      self.assertAllClose(1.8, self.evaluate(all_vars[2]))

      # second step.
      train_fn()
      # var(1) = var(0) - lr * 2 = 1.98
      self.assertAllClose(1.98, self.evaluate(all_vars[0]))
      # m(2) = beta1 * m(1) + (1-beta1) * grad = 0.2 * 1.2 + 0.8 * 1.5
      self.assertAllClose(1.44, self.evaluate(all_vars[1]))
      # v(2) = beta2 * v(1) + (1-beta2) * grad^2 = 0.2 * 1.8 + 0.8 * 2.25
      self.assertAllClose(2.16, self.evaluate(all_vars[2]))

  @tf.__internal__.distribute.combinations.generate(
      tf.__internal__.test.combinations.combine(
          distribution=[
              tf.__internal__.distribute.combinations.central_storage_strategy_with_two_gpus,
          ],
          mode=['graph', 'eager']))
  def testOptimizerWithKerasModelAndNumpyArrays(self, distribution):
    with self.cached_session():
      with distribution.scope():
        model = get_model()
        optimizer = gradient_descent.SGD(0.001)
        loss = 'mse'
        metrics = ['mae']
        model.compile(
            optimizer,
            loss,
            metrics=metrics)

      inputs = np.zeros((64, 3), dtype=np.float32)
      targets = np.zeros((64, 4), dtype=np.float32)

      model.fit(
          inputs,
          targets,
          epochs=1,
          batch_size=2,
          verbose=0,
          validation_data=(inputs, targets))
      model.evaluate(inputs, targets)
      model.predict(inputs)


def _replica_id():
  replica_id = tf.distribute.get_replica_context().replica_id_in_sync_group
  if not isinstance(replica_id, tf.Tensor):
    replica_id = tf.constant(replica_id)
  return replica_id


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