# 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 GRU layer."""

import copy

from absl.testing import parameterized
import numpy as np

from tensorflow.python import keras
from tensorflow.python.eager import context
from tensorflow.python.framework import dtypes
from tensorflow.python.keras import combinations
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras import testing_utils
from tensorflow.python.keras.utils import np_utils
from tensorflow.python.platform import test


@keras_parameterized.run_all_keras_modes
class GRULayerTest(keras_parameterized.TestCase):

  def test_return_sequences_GRU(self):
    num_samples = 2
    timesteps = 3
    embedding_dim = 4
    units = 2
    testing_utils.layer_test(
        keras.layers.GRU,
        kwargs={'units': units,
                'return_sequences': True},
        input_shape=(num_samples, timesteps, embedding_dim))

  @test.disable_with_predicate(
      pred=test.is_built_with_rocm,
      skip_message='Double type is not yet supported in ROCm')
  @testing_utils.run_v2_only
  def test_float64_GRU(self):
    num_samples = 2
    timesteps = 3
    embedding_dim = 4
    units = 2
    testing_utils.layer_test(
        keras.layers.GRU,
        kwargs={'units': units,
                'return_sequences': True,
                'dtype': 'float64'},
        input_shape=(num_samples, timesteps, embedding_dim),
        input_dtype='float64')

  def test_dynamic_behavior_GRU(self):
    num_samples = 2
    timesteps = 3
    embedding_dim = 4
    units = 2
    layer = keras.layers.GRU(units, input_shape=(None, embedding_dim))
    model = keras.models.Sequential()
    model.add(layer)
    model.compile(
        'rmsprop',
        'mse',
        run_eagerly=testing_utils.should_run_eagerly())
    x = np.random.random((num_samples, timesteps, embedding_dim))
    y = np.random.random((num_samples, units))
    model.train_on_batch(x, y)

  def test_dropout_GRU(self):
    num_samples = 2
    timesteps = 3
    embedding_dim = 4
    units = 2
    testing_utils.layer_test(
        keras.layers.GRU,
        kwargs={'units': units,
                'dropout': 0.1,
                'recurrent_dropout': 0.1},
        input_shape=(num_samples, timesteps, embedding_dim))

  def test_recurrent_dropout_with_implementation_restriction(self):
    layer = keras.layers.GRU(2, recurrent_dropout=0.1, implementation=2)
    # The implementation is force to 1 due to the limit of recurrent_dropout.
    self.assertEqual(layer.implementation, 1)

  @parameterized.parameters([0, 1, 2])
  def test_implementation_mode_GRU(self, implementation_mode):
    num_samples = 2
    timesteps = 3
    embedding_dim = 4
    units = 2
    testing_utils.layer_test(
        keras.layers.GRU,
        kwargs={'units': units,
                'implementation': implementation_mode},
        input_shape=(num_samples, timesteps, embedding_dim))

  def test_reset_after_GRU(self):
    num_samples = 2
    timesteps = 3
    embedding_dim = 4
    units = 2

    (x_train, y_train), _ = testing_utils.get_test_data(
        train_samples=num_samples,
        test_samples=0,
        input_shape=(timesteps, embedding_dim),
        num_classes=units)
    y_train = np_utils.to_categorical(y_train, units)

    inputs = keras.layers.Input(shape=[timesteps, embedding_dim])
    gru_layer = keras.layers.GRU(units,
                                 reset_after=True)
    output = gru_layer(inputs)
    gru_model = keras.models.Model(inputs, output)
    gru_model.compile(
        'rmsprop',
        'mse',
        run_eagerly=testing_utils.should_run_eagerly())
    gru_model.fit(x_train, y_train)
    gru_model.predict(x_train)

  @test.disable_with_predicate(
      pred=test.is_built_with_rocm,
      skip_message='MIOpen only supports packed input output')
  def test_with_masking_layer_GRU(self):
    layer_class = keras.layers.GRU
    inputs = np.random.random((2, 3, 4))
    targets = np.abs(np.random.random((2, 3, 5)))
    targets /= targets.sum(axis=-1, keepdims=True)
    model = keras.models.Sequential()
    model.add(keras.layers.Masking(input_shape=(3, 4)))
    model.add(layer_class(units=5, return_sequences=True, unroll=False))
    model.compile(
        loss='categorical_crossentropy',
        optimizer='rmsprop',
        run_eagerly=testing_utils.should_run_eagerly())
    model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1)

  @test.disable_with_predicate(
      pred=test.is_built_with_rocm,
      skip_message='MIOpen only supports packed input output')
  def test_statefulness_GRU(self):
    num_samples = 2
    timesteps = 3
    embedding_dim = 4
    units = 2
    layer_class = keras.layers.GRU

    model = keras.models.Sequential()
    model.add(
        keras.layers.Embedding(
            4,
            embedding_dim,
            mask_zero=True,
            input_length=timesteps,
            batch_input_shape=(num_samples, timesteps)))
    layer = layer_class(
        units, return_sequences=False, stateful=True, weights=None)
    model.add(layer)
    model.compile(
        optimizer='sgd',
        loss='mse',
        run_eagerly=testing_utils.should_run_eagerly())
    out1 = model.predict(np.ones((num_samples, timesteps)))
    self.assertEqual(out1.shape, (num_samples, units))

    # train once so that the states change
    model.train_on_batch(
        np.ones((num_samples, timesteps)), np.ones((num_samples, units)))
    out2 = model.predict(np.ones((num_samples, timesteps)))

    # if the state is not reset, output should be different
    self.assertNotEqual(out1.max(), out2.max())

    # check that output changes after states are reset
    # (even though the model itself didn't change)
    layer.reset_states()
    out3 = model.predict(np.ones((num_samples, timesteps)))
    self.assertNotEqual(out2.max(), out3.max())

    # check that container-level reset_states() works
    model.reset_states()
    out4 = model.predict(np.ones((num_samples, timesteps)))
    np.testing.assert_allclose(out3, out4, atol=1e-5)

    # check that the call to `predict` updated the states
    out5 = model.predict(np.ones((num_samples, timesteps)))
    self.assertNotEqual(out4.max(), out5.max())

    # Check masking
    layer.reset_states()

    left_padded_input = np.ones((num_samples, timesteps))
    left_padded_input[0, :1] = 0
    left_padded_input[1, :2] = 0
    out6 = model.predict(left_padded_input)

    layer.reset_states()

    right_padded_input = np.ones((num_samples, timesteps))
    right_padded_input[0, -1:] = 0
    right_padded_input[1, -2:] = 0
    out7 = model.predict(right_padded_input)

    np.testing.assert_allclose(out7, out6, atol=1e-5)

  def test_get_initial_states(self):
    batch_size = 4
    cell = keras.layers.GRUCell(20)
    initial_state = cell.get_initial_state(
        batch_size=batch_size, dtype=dtypes.float32)
    _, state = cell(np.ones((batch_size, 20), dtype=np.float32), initial_state)
    self.assertEqual(state.shape, initial_state.shape)


@combinations.generate(combinations.combine(mode=['graph', 'eager']))
class GRULayerGenericTest(test.TestCase):

  def test_constraints_GRU(self):
    embedding_dim = 4
    layer_class = keras.layers.GRU
    k_constraint = keras.constraints.max_norm(0.01)
    r_constraint = keras.constraints.max_norm(0.01)
    b_constraint = keras.constraints.max_norm(0.01)
    layer = layer_class(
        5,
        return_sequences=False,
        weights=None,
        input_shape=(None, embedding_dim),
        kernel_constraint=k_constraint,
        recurrent_constraint=r_constraint,
        bias_constraint=b_constraint)
    layer.build((None, None, embedding_dim))
    self.assertEqual(layer.cell.kernel.constraint, k_constraint)
    self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint)
    self.assertEqual(layer.cell.bias.constraint, b_constraint)

  def test_from_config_GRU(self):
    layer_class = keras.layers.GRU
    for stateful in (False, True):
      l1 = layer_class(units=1, stateful=stateful)
      l2 = layer_class.from_config(l1.get_config())
      assert l1.get_config() == l2.get_config()

  def test_deep_copy_GRU(self):
    cell = keras.layers.GRUCell(5)
    copied_cell = copy.deepcopy(cell)
    self.assertEqual(copied_cell.units, 5)
    self.assertEqual(cell.get_config(), copied_cell.get_config())

  def test_regularizers_GRU(self):
    embedding_dim = 4
    layer_class = keras.layers.GRU
    layer = layer_class(
        5,
        return_sequences=False,
        weights=None,
        input_shape=(None, embedding_dim),
        kernel_regularizer=keras.regularizers.l1(0.01),
        recurrent_regularizer=keras.regularizers.l1(0.01),
        bias_regularizer='l2',
        activity_regularizer='l1')
    layer.build((None, None, 2))
    self.assertEqual(len(layer.losses), 3)

    x = keras.backend.variable(np.ones((2, 3, 2)))
    layer(x)
    if context.executing_eagerly():
      self.assertEqual(len(layer.losses), 4)
    else:
      self.assertEqual(len(layer.get_losses_for(x)), 1)


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