# Copyright 2016 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 embedding layers."""

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

import numpy as np

from tensorflow.python import keras
from tensorflow.python.eager import backprop
from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras import testing_utils
from tensorflow.python.platform import test
from tensorflow.python.training import adagrad


class EmbeddingTest(keras_parameterized.TestCase):

  @keras_parameterized.run_all_keras_modes
  def test_embedding(self):
    if tf_test_util.is_gpu_available():
      self.skipTest('Only test embedding on CPU.')

    testing_utils.layer_test(
        keras.layers.Embedding,
        kwargs={'output_dim': 4,
                'input_dim': 10,
                'input_length': 2},
        input_shape=(3, 2),
        input_dtype='int32',
        expected_output_dtype='float32')

    testing_utils.layer_test(
        keras.layers.Embedding,
        kwargs={'output_dim': 4,
                'input_dim': 10,
                'mask_zero': True},
        input_shape=(3, 2),
        input_dtype='int32',
        expected_output_dtype='float32')

    testing_utils.layer_test(
        keras.layers.Embedding,
        kwargs={'output_dim': 4,
                'input_dim': 10,
                'mask_zero': True},
        input_shape=(3, 4, 2),
        input_dtype='int32',
        expected_output_dtype='float32')

    testing_utils.layer_test(
        keras.layers.Embedding,
        kwargs={'output_dim': 4,
                'input_dim': 10,
                'mask_zero': True,
                'input_length': (None, 2)},
        input_shape=(3, 4, 2),
        input_dtype='int32',
        expected_output_dtype='float32')

  @keras_parameterized.run_all_keras_modes
  def test_embedding_correctness(self):
    layer = keras.layers.Embedding(output_dim=2, input_dim=2)
    model = keras.models.Sequential([layer])

    layer.set_weights([np.array([[1, 1], [2, 2]])])
    model.run_eagerly = testing_utils.should_run_eagerly()
    model._experimental_run_tf_function = testing_utils.should_run_tf_function()
    outputs = model.predict(np.array([[0, 1, 0]], dtype='int32'))
    self.assertAllClose(outputs, [[[1, 1], [2, 2], [1, 1]]])

  @tf_test_util.run_in_graph_and_eager_modes
  def test_eager_gpu_cpu(self):
    l = keras.layers.Embedding(output_dim=2, input_dim=2)
    l.build((None, 2))
    inputs = keras.backend.constant([[0, 1, 0]], dtype='int32')
    with backprop.GradientTape() as tape:
      output = l(inputs)
    gs = tape.gradient(output, l.weights)
    opt = adagrad.AdagradOptimizer(0.1)
    opt.apply_gradients(zip(gs, l.weights))
    self.assertAllEqual(len(gs), 1)

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