# 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.
# ==============================================================================

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import unittest

import tensorflow as tf  # pylint: disable=g-bad-import-order
from tensorflow.python import eager as tfe  # pylint: disable=g-bad-import-order

from official.r1.mnist import mnist
from official.r1.mnist import mnist_eager
from official.utils.misc import keras_utils


def device():
  return '/device:GPU:0' if tfe.context.num_gpus() else '/device:CPU:0'


def data_format():
  return 'channels_first' if tfe.context.num_gpus() else 'channels_last'


def random_dataset():
  batch_size = 64
  images = tf.random_normal([batch_size, 784])
  labels = tf.random_uniform([batch_size], minval=0, maxval=10, dtype=tf.int32)
  return tf.data.Dataset.from_tensors((images, labels))


def train(defun=False):
  model = mnist.create_model(data_format())
  if defun:
    model.call = tf.function(model.call)
  optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
  dataset = random_dataset()
  with tf.device(device()):
    mnist_eager.train(model, optimizer, dataset,
                      step_counter=tf.train.get_or_create_global_step())


def evaluate(defun=False):
  model = mnist.create_model(data_format())
  dataset = random_dataset()
  if defun:
    model.call = tf.function(model.call)
  with tf.device(device()):
    mnist_eager.test(model, dataset)


class MNISTTest(tf.test.TestCase):
  """Run tests for MNIST eager loop.

  MNIST eager uses contrib and will not work with TF 2.0.  All tests are
  disabled if using TF 2.0.
  """

  def setUp(self):
    if not keras_utils.is_v2_0():
      tf.compat.v1.enable_v2_behavior()
    super(MNISTTest, self).setUp()

  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
  def test_train(self):
    train(defun=False)

  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
  def test_evaluate(self):
    evaluate(defun=False)

  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
  def test_train_with_defun(self):
    train(defun=True)

  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
  def test_evaluate_with_defun(self):
    evaluate(defun=True)


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