# Copyright 2025 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
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# limitations under the License.

"""Tests for the attention layer."""

from absl.testing import parameterized

import numpy as np
import tensorflow as tf, tf_keras

from official.nlp.modeling.layers import reuse_attention as attention


class ReuseMultiHeadAttentionTest(tf.test.TestCase, 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 = attention.ReuseMultiHeadAttention(
        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 = attention.ReuseMultiHeadAttention(
        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)
    self.assertEqual(output.shape.as_list(), [None, 40, 80])

  def test_attention_scores(self):
    """Test attention outputs with coefficients."""
    test_layer = attention.ReuseMultiHeadAttention(
        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, return_attention_scores=True)
    self.assertEqual(output.shape.as_list(), [None, 40, 80])
    self.assertEqual(coef.shape.as_list(), [None, 12, 40, 40])

  def test_attention_scores_with_values(self):
    """Test attention outputs with coefficients."""
    test_layer = attention.ReuseMultiHeadAttention(
        num_heads=12, key_dim=64)
    # Create a 3-dimensional input (the first dimension is implicit).
    query = tf_keras.Input(shape=(40, 80))
    value = tf_keras.Input(shape=(60, 80))
    output, coef = test_layer(query, value, return_attention_scores=True)
    self.assertEqual(output.shape.as_list(), [None, 40, 80])
    self.assertEqual(coef.shape.as_list(), [None, 12, 40, 60])

  @parameterized.named_parameters(
      ("with_bias", True, 0), ("no_bias", False, 0),
      ("reuse_all_with_bias", True, -1), ("reuse_all_no_bias", False, -1),
      ("reuse_partial_with_bias", True, 1),
      ("reuse_partial_no_bias", False, 1))
  def test_masked_attention(self, use_bias, reuse_attention):
    """Test with a mask tensor."""
    test_layer = attention.ReuseMultiHeadAttention(
        num_heads=2, key_dim=2, use_bias=use_bias,
        reuse_attention=reuse_attention)
    # 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))
    reuse_attention_scores = tf_keras.Input(shape=(2, 4, 2))
    output = test_layer(query=query, value=value, attention_mask=mask_tensor,
                        reuse_attention_scores=reuse_attention_scores)
    # Create a model containing the test layer.
    model = tf_keras.Model(
        [query, value, mask_tensor, reuse_attention_scores], 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))
    reuse_scores = np.random.random_sample((batch_size, 2, 4, 2))
    # 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, reuse_scores])

    # 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, reuse_scores])

    # Because one data is masked and one is not, the outputs should not be the
    # same.
    if reuse_attention == -1:
      self.assertAllEqual(masked_output_data, unmasked_output_data)
    else:
      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, value=value, key=key, attention_mask=mask_tensor,
                        reuse_attention_scores=reuse_attention_scores)
    model = tf_keras.Model(
        [query, value, key, mask_tensor, reuse_attention_scores], output)

    masked_output_data = model.predict(
        [from_data, to_data, to_data, mask_data, reuse_scores])
    unmasked_output_data = model.predict(
        [from_data, to_data, to_data, null_mask_data, reuse_scores])
    # Because one data is masked and one is not, the outputs should not be the
    # same.
    if reuse_attention == -1:
      self.assertAllEqual(masked_output_data, unmasked_output_data)
    else:
      self.assertNotAllClose(masked_output_data, unmasked_output_data)
    if reuse_attention > 0:
      self.assertLen(test_layer._output_dense, 2)
    if use_bias:
      if reuse_attention == 0:
        self.assertLen(test_layer._query_dense.trainable_variables, 2)
      self.assertLen(test_layer._output_dense[0].trainable_variables, 2)
      if len(test_layer._output_dense) == 2:
        self.assertLen(test_layer._output_dense[1].trainable_variables, 1)
    else:
      if reuse_attention == 0:
        self.assertLen(test_layer._query_dense.trainable_variables, 1)
      self.assertLen(test_layer._output_dense[0].trainable_variables, 1)
      if len(test_layer._output_dense) == 2:
        self.assertLen(test_layer._output_dense[1].trainable_variables, 1)

  def test_initializer(self):
    """Test with a specified initializer."""
    test_layer = attention.ReuseMultiHeadAttention(
        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)
    self.assertEqual(output.shape.as_list(), [None, 40, 80])

  def test_masked_attention_with_scores(self):
    """Test with a mask tensor."""
    test_layer = attention.ReuseMultiHeadAttention(
        num_heads=2, key_dim=2)
    # 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)

    # Create a model containing attention scores.
    output, scores = test_layer(
        query=query, value=value, attention_mask=mask_tensor,
        return_attention_scores=True)
    model = tf_keras.Model([query, value, mask_tensor], [output, scores])
    masked_output_data_score, masked_score = model.predict(
        [from_data, to_data, mask_data])
    unmasked_output_data_score, unmasked_score = model.predict(
        [from_data, to_data, null_mask_data])
    self.assertNotAllClose(masked_output_data_score, unmasked_output_data_score)
    self.assertAllClose(masked_output_data, masked_output_data_score)
    self.assertAllClose(unmasked_output_data, unmasked_output_data_score)
    self.assertNotAllClose(masked_score, unmasked_score)

  @parameterized.named_parameters(
      ("4d_inputs_1freebatch_mask2", [3, 4], [3, 2], [4, 2],
       (2,)), ("4d_inputs_1freebatch_mask3", [3, 4], [3, 2], [3, 4, 2], (2,)),
      ("4d_inputs_1freebatch_mask4", [3, 4], [3, 2], [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)),
      ("5D_inputs_2D_attention_fullmask", [5, 3, 4], [5, 3, 2], [5, 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 = attention.ReuseMultiHeadAttention(
        num_heads=2, 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")
    # Invoke the same data, but with a null mask (where no elements are masked).
    null_mask_data = np.ones(mask_shape)
    # Because one data is masked and one is not, the outputs should not be the
    # same.
    query_tensor = tf_keras.Input(query_shape[1:], name="query")
    value_tensor = tf_keras.Input(value_shape[1:], name="value")
    mask_tensor = tf_keras.Input(mask_shape[1:], name="mask")
    output = test_layer(query=query_tensor, value=value_tensor,
                        attention_mask=mask_tensor)
    model = tf_keras.Model([query_tensor, value_tensor, mask_tensor], output)

    self.assertNotAllClose(
        model.predict([query, value, mask_data]),
        model.predict([query, value, null_mask_data]))

  def test_dropout(self):
    test_layer = attention.ReuseMultiHeadAttention(
        num_heads=2, key_dim=2, dropout=0.5)

    # Generate data for the input (non-mask) tensors.
    from_data = tf_keras.backend.ones(shape=(32, 4, 8))
    to_data = tf_keras.backend.ones(shape=(32, 2, 8))
    train_out = test_layer(from_data, to_data, None, None, None, True)
    test_out = test_layer(from_data, to_data, None, None, None, False)

    # Output should be close when not in training mode,
    # and should not be close when enabling dropout in training mode.
    self.assertNotAllClose(
        tf_keras.backend.eval(train_out),
        tf_keras.backend.eval(test_out))

  def test_non_masked_self_attention_with_reuse(self):
    """Test with one input (self-attenntion) and no mask tensor."""
    test_layer = attention.ReuseMultiHeadAttention(
        num_heads=12, key_dim=64, reuse_attention=True)
    # Create a 3-dimensional input (the first dimension is implicit).
    query = tf_keras.Input(shape=(40, 80))
    reuse_scores = tf_keras.Input(shape=(12, 40, 40))
    output = test_layer(query, query, reuse_attention_scores=reuse_scores)
    self.assertEqual(output.shape.as_list(), [None, 40, 80])

  @parameterized.named_parameters(
      ("no_reuse_with_pe_max_seq_length_20", False, 20),
      ("reuse_all_with_pe_max_seq_length_20", True, 20),
      ("reuse_partial_with_pe_max_seq_length_20", 5, 20),
      ("no_reuse_with_pe_max_seq_length_40", False, 40),
      ("reuse_all_with_pe_max_seq_length_40", True, 40),
      ("reuse_partial_with_pe_max_seq_length_40", 5, 40))
  def test_non_masked_self_attention_with_relative_pe(self, reuse_attention,
                                                      pe_max_seq_length):
    """Test with one input (self-attenntion) and no mask tensor."""
    test_layer = attention.ReuseMultiHeadAttention(
        num_heads=12, key_dim=64, reuse_attention=reuse_attention,
        use_relative_pe=True, pe_max_seq_length=pe_max_seq_length)
    # Create a 3-dimensional input (the first dimension is implicit).
    query = tf_keras.Input(shape=(40, 80))
    reuse_scores = tf_keras.Input(shape=(12, 40, 40))
    output = test_layer(query, query, reuse_attention_scores=reuse_scores)
    self.assertEqual(output.shape.as_list(), [None, 40, 80])
    query = tf_keras.Input(shape=(30, 80))
    reuse_scores = tf_keras.Input(shape=(12, 30, 30))
    output = test_layer(query, query, reuse_attention_scores=reuse_scores)
    self.assertEqual(output.shape.as_list(), [None, 30, 80])
    query = tf_keras.Input(shape=(30, 80))
    key = tf_keras.Input(shape=(20, 80))
    reuse_scores = tf_keras.Input(shape=(12, 30, 20))
    output = test_layer(query, key, reuse_attention_scores=reuse_scores)
    self.assertEqual(output.shape.as_list(), [None, 30, 80])
    query = tf_keras.Input(shape=(50, 80))
    key = tf_keras.Input(shape=(60, 80))
    reuse_scores = tf_keras.Input(shape=(12, 50, 60))
    output = test_layer(query, key, reuse_attention_scores=reuse_scores)
    self.assertEqual(output.shape.as_list(), [None, 50, 80])

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