# Copyright 2023 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
# 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.

import io
import os

import pytest
import sentencepiece
import tensorflow as tf

from keras_nlp.src.backend import keras
from keras_nlp.src.models.albert.albert_backbone import AlbertBackbone
from keras_nlp.src.models.albert.albert_masked_lm import AlbertMaskedLM
from keras_nlp.src.models.albert.albert_masked_lm_preprocessor import (
    AlbertMaskedLMPreprocessor,
)
from keras_nlp.src.models.albert.albert_tokenizer import AlbertTokenizer
from keras_nlp.src.tests.test_case import TestCase


class AlbertMaskedLMTest(TestCase):
    def setUp(self):
        # Setup model.
        vocab_data = tf.data.Dataset.from_tensor_slices(
            ["the quick brown fox", "the earth is round", "an eagle flew"]
        )

        bytes_io = io.BytesIO()
        sentencepiece.SentencePieceTrainer.train(
            sentence_iterator=vocab_data.as_numpy_iterator(),
            model_writer=bytes_io,
            vocab_size=15,
            model_type="WORD",
            pad_id=0,
            unk_id=1,
            bos_id=2,
            eos_id=3,
            pad_piece="<pad>",
            unk_piece="<unk>",
            bos_piece="[CLS]",
            eos_piece="[SEP]",
            user_defined_symbols="[MASK]",
        )

        proto = bytes_io.getvalue()

        tokenizer = AlbertTokenizer(proto=proto)

        self.preprocessor = AlbertMaskedLMPreprocessor(
            tokenizer=tokenizer,
            # Simplify our 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=5,
        )
        self.backbone = AlbertBackbone(
            vocabulary_size=self.preprocessor.tokenizer.vocabulary_size(),
            num_layers=2,
            num_heads=2,
            embedding_dim=4,
            hidden_dim=4,
            intermediate_dim=4,
            max_sequence_length=self.preprocessor.packer.sequence_length,
        )
        self.masked_lm = AlbertMaskedLM(
            self.backbone,
            preprocessor=self.preprocessor,
        )
        self.masked_lm_no_preprocessing = AlbertMaskedLM(
            self.backbone,
            preprocessor=None,
        )

        self.raw_batch = [
            "quick brown fox",
            "eagle flew over fox",
            "the eagle flew quick",
            "a brown eagle",
        ]
        self.preprocessed_batch = self.preprocessor(self.raw_batch)[0]
        self.raw_dataset = tf.data.Dataset.from_tensor_slices(
            self.raw_batch
        ).batch(2)
        self.preprocessed_dataset = self.raw_dataset.map(self.preprocessor)

    def test_valid_call_classifier(self):
        self.masked_lm(self.preprocessed_batch)

    def test_albert_masked_lm_fit_default_compile(self):
        self.masked_lm.fit(self.raw_dataset)

    def test_classifier_predict(self):
        self.masked_lm.predict(self.raw_batch)
        self.masked_lm.preprocessor = None
        self.masked_lm.predict(self.preprocessed_batch)

    def test_classifier_fit(self):
        self.masked_lm.fit(self.raw_dataset)
        self.masked_lm.preprocessor = None
        self.masked_lm.fit(self.preprocessed_dataset)

    def test_classifier_fit_no_xla(self):
        self.masked_lm.preprocessor = None
        self.masked_lm.compile(
            loss=keras.losses.SparseCategoricalCrossentropy(from_logits=False),
            jit_compile=False,
        )
        self.masked_lm.fit(self.preprocessed_dataset)

    @pytest.mark.large
    def test_saved_model(self):
        model_output = self.masked_lm.predict(self.raw_batch)
        path = os.path.join(self.get_temp_dir(), "model.keras")
        self.masked_lm.save(path, save_format="keras_v3")
        restored_model = keras.models.load_model(path)

        # Check we got the real object back.
        self.assertIsInstance(restored_model, AlbertMaskedLM)
        # Check that output matches.
        restored_output = restored_model.predict(self.raw_batch)
        self.assertAllClose(model_output, restored_output, atol=0.01, rtol=0.01)

