# Copyright 2022 The KerasNLP Authors
#
# 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
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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"""Tests for DistilBERT masked language model preprocessor layer."""

import os

import pytest
import tensorflow as tf
from absl.testing import parameterized
from tensorflow import keras

from keras_nlp.src.models.distil_bert.distil_bert_masked_lm_preprocessor import (
    DistilBertMaskedLMPreprocessor,
)
from keras_nlp.src.models.distil_bert.distil_bert_tokenizer import (
    DistilBertTokenizer,
)


class DistilBertMaskedLMPreprocessorTest(
    tf.test.TestCase, parameterized.TestCase
):
    def setUp(self):
        self.vocab = ["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]"]
        self.vocab += ["THE", "QUICK", "BROWN", "FOX"]
        self.vocab += ["the", "quick", "brown", "fox"]

        self.preprocessor = DistilBertMaskedLMPreprocessor(
            tokenizer=DistilBertTokenizer(
                vocabulary=self.vocab,
            ),
            # Simplify out testing by masking every available token.
            mask_selection_rate=1.0,
            mask_token_rate=1.0,
            random_token_rate=0.0,
            mask_selection_length=5,
            sequence_length=8,
        )

    def test_preprocess_strings(self):
        input_data = " THE QUICK BROWN FOX."

        x, y, sw = self.preprocessor(input_data)
        self.assertAllEqual(x["token_ids"], [2, 4, 4, 4, 4, 4, 3, 0])
        self.assertAllEqual(x["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0])
        self.assertAllEqual(x["mask_positions"], [1, 2, 3, 4, 5])
        self.assertAllEqual(y, [5, 6, 7, 8, 1])
        self.assertAllEqual(sw, [1.0, 1.0, 1.0, 1.0, 1.0])

    def test_preprocess_list_of_strings(self):
        input_data = [" THE QUICK BROWN FOX."] * 4

        x, y, sw = self.preprocessor(input_data)
        self.assertAllEqual(x["token_ids"], [[2, 4, 4, 4, 4, 4, 3, 0]] * 4)
        self.assertAllEqual(x["padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4)
        self.assertAllEqual(x["mask_positions"], [[1, 2, 3, 4, 5]] * 4)
        self.assertAllEqual(y, [[5, 6, 7, 8, 1]] * 4)
        self.assertAllEqual(sw, [[1.0, 1.0, 1.0, 1.0, 1.0]] * 4)

    def test_preprocess_dataset(self):
        sentences = tf.constant([" THE QUICK BROWN FOX."] * 4)
        ds = tf.data.Dataset.from_tensor_slices(sentences)
        ds = ds.map(self.preprocessor)
        x, y, sw = ds.batch(4).take(1).get_single_element()
        self.assertAllEqual(x["token_ids"], [[2, 4, 4, 4, 4, 4, 3, 0]] * 4)
        self.assertAllEqual(x["padding_mask"], [[1, 1, 1, 1, 1, 1, 1, 0]] * 4)
        self.assertAllEqual(x["mask_positions"], [[1, 2, 3, 4, 5]] * 4)
        self.assertAllEqual(y, [[5, 6, 7, 8, 1]] * 4)
        self.assertAllEqual(sw, [[1.0, 1.0, 1.0, 1.0, 1.0]] * 4)

    def test_mask_multiple_sentences(self):
        sentence_one = tf.constant(" THE QUICK")
        sentence_two = tf.constant(" BROWN FOX.")

        x, y, sw = self.preprocessor((sentence_one, sentence_two))
        self.assertAllEqual(x["token_ids"], [2, 4, 4, 3, 4, 4, 4, 3])
        self.assertAllEqual(x["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 1])
        self.assertAllEqual(x["mask_positions"], [1, 2, 4, 5, 6])
        self.assertAllEqual(y, [5, 6, 7, 8, 1])
        self.assertAllEqual(sw, [1.0, 1.0, 1.0, 1.0, 1.0])

    def test_no_masking_zero_rate(self):
        no_mask_preprocessor = DistilBertMaskedLMPreprocessor(
            self.preprocessor.tokenizer,
            mask_selection_rate=0.0,
            mask_selection_length=5,
            sequence_length=8,
        )
        input_data = " THE QUICK BROWN FOX."

        x, y, sw = no_mask_preprocessor(input_data)
        self.assertAllEqual(x["token_ids"], [2, 5, 6, 7, 8, 1, 3, 0])
        self.assertAllEqual(x["padding_mask"], [1, 1, 1, 1, 1, 1, 1, 0])
        self.assertAllEqual(x["mask_positions"], [0, 0, 0, 0, 0])
        self.assertAllEqual(y, [0, 0, 0, 0, 0])
        self.assertAllEqual(sw, [0.0, 0.0, 0.0, 0.0, 0.0])

    def test_serialization(self):
        config = keras.utils.serialize_keras_object(self.preprocessor)
        new_preprocessor = keras.utils.deserialize_keras_object(config)
        self.assertEqual(
            new_preprocessor.get_config(),
            self.preprocessor.get_config(),
        )

    @parameterized.named_parameters(
        ("tf_format", "tf", "model"),
        ("keras_format", "keras_v3", "model.keras"),
    )
    @pytest.mark.large
    def test_saved_model(self, save_format, filename):
        input_data = tf.constant([" THE QUICK BROWN FOX."])

        inputs = keras.Input(dtype="string", shape=())
        outputs = self.preprocessor(inputs)
        model = keras.Model(inputs, outputs)

        path = os.path.join(self.get_temp_dir(), filename)
        # Don't save traces in the tf format, we check compilation elsewhere.
        kwargs = {"save_traces": False} if save_format == "tf" else {}
        model.save(path, save_format=save_format, **kwargs)

        restored_model = keras.models.load_model(path)
        outputs = model(input_data)[0]["token_ids"]
        restored_outputs = restored_model(input_data)[0]["token_ids"]
        self.assertAllEqual(outputs, restored_outputs)

