# Copyright 2019 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 the attention layer."""

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

from absl.testing import parameterized
import numpy as np
import tensorflow as tf

from tensorflow.python.keras import keras_parameterized  # pylint: disable=g-direct-tensorflow-import
from official.nlp.modeling.layers import talking_heads_attention


# This decorator runs the test in V1, V2-Eager, and V2-Functional mode. It
# guarantees forward compatibility of this code for the V2 switchover.
# This test is revised base on attention.MultiHeadAttentionTest.
@keras_parameterized.run_all_keras_modes
class TalkingHeadsAttentionTest(keras_parameterized.TestCase):

  @parameterized.named_parameters(
      ("key_value_same_proj", None, None, [40, 80]),
      ("key_value_different_proj", 32, 60, [40, 60]),
  )
  def test_non_masked_attention(self, value_dim, output_shape, output_dims):
    """Test that the attention layer can be created without a mask tensor."""
    test_layer = talking_heads_attention.TalkingHeadsAttention(
        num_heads=12,
        key_dim=64,
        value_dim=value_dim,
        output_shape=output_shape)
    # Create a 3-dimensional input (the first dimension is implicit).
    query = tf.keras.Input(shape=(40, 80))
    value = tf.keras.Input(shape=(20, 80))
    output = test_layer(query=query, value=value)
    self.assertEqual(output.shape.as_list(), [None] + output_dims)

  def test_non_masked_self_attention(self):
    """Test with one input (self-attenntion) and no mask tensor."""
    test_layer = talking_heads_attention.TalkingHeadsAttention(
        num_heads=12, key_dim=64)
    # Create a 3-dimensional input (the first dimension is implicit).
    query = tf.keras.Input(shape=(40, 80))
    output = test_layer(query=query, value=query)
    self.assertEqual(output.shape.as_list(), [None, 40, 80])

  def test_attention_scores(self):
    """Test attention outputs with coefficients."""
    test_layer = talking_heads_attention.TalkingHeadsAttention(
        num_heads=12, key_dim=64)
    # Create a 3-dimensional input (the first dimension is implicit).
    query = tf.keras.Input(shape=(40, 80))
    output, coef = test_layer(query=query, value=query,
                              return_attention_scores=True)
    self.assertEqual(output.shape.as_list(), [None, 40, 80])
    self.assertEqual(coef.shape.as_list(), [None, 12, 40, 40])

  @parameterized.named_parameters(("with_bias", True), ("no_bias", False))
  def test_masked_attention(self, use_bias):
    """Test with a mask tensor."""
    test_layer = talking_heads_attention.TalkingHeadsAttention(
        num_heads=12, key_dim=2, use_bias=use_bias)
    # Create a 3-dimensional input (the first dimension is implicit).
    batch_size = 3
    query = tf.keras.Input(shape=(4, 8))
    value = tf.keras.Input(shape=(2, 8))
    mask_tensor = tf.keras.Input(shape=(4, 2))
    output = test_layer(query=query, value=value, attention_mask=mask_tensor)

    # Create a model containing the test layer.
    model = tf.keras.Model([query, value, mask_tensor], output)

    # Generate data for the input (non-mask) tensors.
    from_data = 10 * np.random.random_sample((batch_size, 4, 8))
    to_data = 10 * np.random.random_sample((batch_size, 2, 8))

    # Invoke the data with a random set of mask data. This should mask at least
    # one element.
    mask_data = np.random.randint(2, size=(batch_size, 4, 2))
    masked_output_data = model.predict([from_data, to_data, mask_data])

    # Invoke the same data, but with a null mask (where no elements are masked).
    null_mask_data = np.ones((batch_size, 4, 2))
    unmasked_output_data = model.predict([from_data, to_data, null_mask_data])

    # Because one data is masked and one is not, the outputs should not be the
    # same.
    self.assertNotAllClose(masked_output_data, unmasked_output_data)

    # Tests the layer with three inputs: Q, K, V.
    key = tf.keras.Input(shape=(2, 8))
    output = test_layer(
        query=query, value=value, key=key, attention_mask=mask_tensor)
    model = tf.keras.Model([query, value, key, mask_tensor], output)

    masked_output_data = model.predict([from_data, to_data, to_data, mask_data])
    unmasked_output_data = model.predict(
        [from_data, to_data, to_data, null_mask_data])
    # Because one data is masked and one is not, the outputs should not be the
    # same.
    self.assertNotAllClose(masked_output_data, unmasked_output_data)

    if use_bias:
      self.assertLen(test_layer._query_dense.trainable_variables, 2)
      self.assertLen(test_layer._output_dense.trainable_variables, 2)
    else:
      self.assertLen(test_layer._query_dense.trainable_variables, 1)
      self.assertLen(test_layer._output_dense.trainable_variables, 1)

  def test_initializer(self):
    """Test with a specified initializer."""
    test_layer = talking_heads_attention.TalkingHeadsAttention(
        num_heads=12,
        key_dim=64,
        kernel_initializer=tf.keras.initializers.TruncatedNormal(stddev=0.02))
    # Create a 3-dimensional input (the first dimension is implicit).
    query = tf.keras.Input(shape=(40, 80))
    output = test_layer(query=query, value=query)
    self.assertEqual(output.shape.as_list(), [None, 40, 80])

  @parameterized.named_parameters(
      ("4d_inputs_one_free_batch", [3, 4], [3, 2], [4, 2], (2,)),
      ("4D_inputs_2D_attention", [3, 4], [3, 2], [3, 4, 3, 2], (1, 2)),
      ("5D_inputs_2D_attention", [5, 3, 4], [5, 3, 2], [3, 4, 3, 2], (2, 3)))
  def test_high_dim_attention(self, q_dims, v_dims, mask_dims, attention_axes):
    """Test with a mask tensor."""
    test_layer = talking_heads_attention.TalkingHeadsAttention(
        num_heads=12, key_dim=2, attention_axes=attention_axes)
    batch_size, hidden_size = 3, 8
    # Generate data for the input (non-mask) tensors.
    query_shape = [batch_size] + q_dims + [hidden_size]
    value_shape = [batch_size] + v_dims + [hidden_size]
    mask_shape = [batch_size] + mask_dims
    query = 10 * np.random.random_sample(query_shape)
    value = 10 * np.random.random_sample(value_shape)

    # Invoke the data with a random set of mask data. This should mask at least
    # one element.
    mask_data = np.random.randint(2, size=mask_shape).astype("bool")
    output = test_layer(query=query, value=value, attention_mask=mask_data)

    # Invoke the same data, but with a null mask (where no elements are masked).
    null_mask_data = np.ones(mask_shape)
    unmasked_output = test_layer(
        query=query, value=value, attention_mask=null_mask_data)
    # Because one data is masked and one is not, the outputs should not be the
    # same.
    self.assertNotAllClose(output, unmasked_output)


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