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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Tests for Sparse Mixer encoder network."""

from typing import Sequence

from absl.testing import parameterized
import tensorflow as tf, tf_keras

from official.nlp.modeling import layers
from official.nlp.modeling.networks import sparse_mixer


class SparseMixerTest(parameterized.TestCase, tf.test.TestCase):

  def tearDown(self):
    super().tearDown()
    tf_keras.mixed_precision.set_global_policy("float32")

  @parameterized.named_parameters(
      dict(
          testcase_name="sparse_mixer",
          mixing_mechanism=layers.MixingMechanism.LINEAR,
          moe_layers=(1,),
          attention_layers=(2,)),
      dict(
          testcase_name="fnet",
          mixing_mechanism=layers.MixingMechanism.FOURIER,
          moe_layers=(),
          attention_layers=()),
      dict(
          testcase_name="sparse_hnet",
          mixing_mechanism=layers.MixingMechanism.HARTLEY,
          moe_layers=(0, 1, 2),
          attention_layers=(1, 2)),
      dict(
          testcase_name="sparse_bert",
          mixing_mechanism=layers.MixingMechanism.LINEAR,
          moe_layers=(0, 1, 2),  # All layers use MoE
          attention_layers=(0, 1, 2)),  # All layers use attention
  )
  def test_network(self, mixing_mechanism: layers.MixingMechanism,
                   attention_layers: Sequence[int], moe_layers: Sequence[int]):
    num_layers = 3
    hidden_size = 16
    sequence_length = 32
    test_network = sparse_mixer.SparseMixer(
        vocab_size=100,
        hidden_size=hidden_size,
        num_attention_heads=2,
        max_sequence_length=sequence_length,
        num_layers=num_layers,
        moe_layers=moe_layers,
        num_experts=8,
        mixing_mechanism=mixing_mechanism,
        attention_layers=attention_layers)

    batch_size = 4
    word_ids = tf_keras.Input(
        shape=(sequence_length,), batch_size=batch_size, dtype=tf.int32)
    mask = tf_keras.Input(
        shape=(sequence_length,), batch_size=batch_size, dtype=tf.int32)
    type_ids = tf_keras.Input(
        shape=(sequence_length,), batch_size=batch_size, dtype=tf.int32)

    dict_outputs = test_network(
        dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
    data = dict_outputs["sequence_output"]
    pooled = dict_outputs["pooled_output"]

    self.assertIsInstance(test_network.transformer_layers, list)
    self.assertLen(test_network.transformer_layers, 3)
    self.assertIsInstance(test_network.pooler_layer, tf_keras.layers.Dense)

    expected_data_shape = [batch_size, sequence_length, hidden_size]
    expected_pooled_shape = [batch_size, hidden_size]
    self.assertAllEqual(expected_data_shape, data.shape.as_list())
    self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())

    # The default output dtype is float32.
    self.assertAllEqual(tf.float32, data.dtype)
    self.assertAllEqual(tf.float32, pooled.dtype)

  def test_embeddings_as_inputs(self):
    hidden_size = 32
    sequence_length = 8
    test_network = sparse_mixer.SparseMixer(
        vocab_size=100,
        hidden_size=hidden_size,
        num_attention_heads=2,
        max_sequence_length=sequence_length,
        num_layers=3,
        moe_layers=(1,),
        num_experts=4,
        attention_layers=(2,))

    batch_size = 2
    word_ids = tf_keras.Input(
        shape=(sequence_length), batch_size=batch_size, dtype=tf.int32)
    mask = tf_keras.Input(
        shape=(sequence_length,), batch_size=batch_size, dtype=tf.int32)
    type_ids = tf_keras.Input(
        shape=(sequence_length,), batch_size=batch_size, dtype=tf.int32)

    test_network.build(
        dict(input_word_ids=word_ids, input_mask=mask, input_type_ids=type_ids))
    embeddings = test_network.get_embedding_layer()(word_ids)

    # Calls with the embeddings.
    dict_outputs = test_network(
        dict(
            input_word_embeddings=embeddings,
            input_mask=mask,
            input_type_ids=type_ids))
    all_encoder_outputs = dict_outputs["encoder_outputs"]
    pooled = dict_outputs["pooled_output"]

    expected_data_shape = [batch_size, sequence_length, hidden_size]
    expected_pooled_shape = [batch_size, hidden_size]
    self.assertLen(all_encoder_outputs, 3)
    for data in all_encoder_outputs:
      self.assertAllEqual(expected_data_shape, data.shape.as_list())
    self.assertAllEqual(expected_pooled_shape, pooled.shape.as_list())

    # The default output dtype is float32.
    self.assertAllEqual(tf.float32, all_encoder_outputs[-1].dtype)
    self.assertAllEqual(tf.float32, pooled.dtype)


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