# 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 stateful tf.keras LSTM models using DistributionStrategy."""

import numpy as np
import tensorflow.compat.v2 as tf

import keras
from keras.distribute import keras_correctness_test_base
from keras.optimizers.legacy import gradient_descent as gradient_descent_keras


def strategies_for_stateful_embedding_model():
    """Returns TPUStrategy with single core device assignment."""

    return [
        tf.__internal__.distribute.combinations.tpu_strategy_one_core,
    ]


def test_combinations_for_stateful_embedding_model():
    return tf.__internal__.test.combinations.combine(
        distribution=strategies_for_stateful_embedding_model(),
        mode="graph",
        use_numpy=False,
        use_validation_data=False,
    )


class DistributionStrategyStatefulLstmModelCorrectnessTest(
    keras_correctness_test_base.TestDistributionStrategyEmbeddingModelCorrectnessBase  # noqa: E501
):
    def get_model(
        self,
        max_words=10,
        initial_weights=None,
        distribution=None,
        input_shapes=None,
    ):
        del input_shapes
        batch_size = keras_correctness_test_base._GLOBAL_BATCH_SIZE

        with keras_correctness_test_base.MaybeDistributionScope(distribution):
            word_ids = keras.layers.Input(
                shape=(max_words,),
                batch_size=batch_size,
                dtype=np.int32,
                name="words",
            )
            word_embed = keras.layers.Embedding(input_dim=20, output_dim=10)(
                word_ids
            )
            lstm_embed = keras.layers.LSTM(
                units=4, return_sequences=False, stateful=True
            )(word_embed)

            preds = keras.layers.Dense(2, activation="softmax")(lstm_embed)
            model = keras.Model(inputs=[word_ids], outputs=[preds])

            if initial_weights:
                model.set_weights(initial_weights)

            optimizer_fn = gradient_descent_keras.SGD

            model.compile(
                optimizer=optimizer_fn(learning_rate=0.1),
                loss="sparse_categorical_crossentropy",
                metrics=["sparse_categorical_accuracy"],
            )
        return model

    # TODO(jhseu): Disabled to fix b/130808953. Need to investigate why it
    # doesn't work and enable for DistributionStrategy more generally.
    @tf.__internal__.distribute.combinations.generate(
        test_combinations_for_stateful_embedding_model()
    )
    def disabled_test_stateful_lstm_model_correctness(
        self, distribution, use_numpy, use_validation_data
    ):
        self.run_correctness_test(
            distribution, use_numpy, use_validation_data, is_stateful_model=True
        )

    @tf.__internal__.distribute.combinations.generate(
        tf.__internal__.test.combinations.times(
            keras_correctness_test_base.test_combinations_with_tpu_strategies_graph()  # noqa: E501
        )
    )
    def test_incorrectly_use_multiple_cores_for_stateful_lstm_model(
        self, distribution, use_numpy, use_validation_data
    ):
        with self.assertRaisesRegex(
            ValueError, "not yet supported with tf.distribute.Strategy"
        ):
            self.run_correctness_test(
                distribution,
                use_numpy,
                use_validation_data,
                is_stateful_model=True,
            )


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