# 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.
# ==============================================================================
"""Correctness tests for tf.keras RNN 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.layers.rnn import gru
from keras.layers.rnn import gru_v1
from keras.layers.rnn import lstm
from keras.layers.rnn import lstm_v1
from keras.mixed_precision import policy
from keras.optimizers.legacy import gradient_descent as gradient_descent_keras
from keras.testing_infra import test_utils


class _DistributionStrategyRnnModelCorrectnessTest(
    keras_correctness_test_base.TestDistributionStrategyEmbeddingModelCorrectnessBase  # noqa: E501
):
    def _get_layer_class(self):
        raise NotImplementedError

    def get_model(
        self,
        max_words=10,
        initial_weights=None,
        distribution=None,
        input_shapes=None,
    ):
        del input_shapes
        rnn_cls = self._get_layer_class()

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

            dense_output = keras.layers.Dense(2)(rnn_embed)
            preds = keras.layers.Softmax(dtype="float32")(dense_output)
            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


@test_utils.run_all_without_tensor_float_32(
    "Uses Dense layers, which call matmul"
)
class DistributionStrategyGruModelCorrectnessTest(
    _DistributionStrategyRnnModelCorrectnessTest
):
    def _get_layer_class(self):
        if tf.__internal__.tf2.enabled():
            if not tf.executing_eagerly():
                self.skipTest(
                    "GRU v2 and legacy graph mode don't work together."
                )
            return gru.GRU
        else:
            return gru_v1.GRU

    @tf.__internal__.distribute.combinations.generate(
        keras_correctness_test_base.test_combinations_for_embedding_model()
        + keras_correctness_test_base.multi_worker_mirrored_eager()
    )
    def test_gru_model_correctness(
        self, distribution, use_numpy, use_validation_data
    ):
        self.run_correctness_test(distribution, use_numpy, use_validation_data)


@test_utils.run_all_without_tensor_float_32(
    "Uses Dense layers, which call matmul"
)
class DistributionStrategyLstmModelCorrectnessTest(
    _DistributionStrategyRnnModelCorrectnessTest
):
    def _get_layer_class(self):
        if tf.__internal__.tf2.enabled():
            if not tf.executing_eagerly():
                self.skipTest(
                    "LSTM v2 and legacy graph mode don't work together."
                )
            return lstm.LSTM
        else:
            return lstm_v1.LSTM

    @tf.__internal__.distribute.combinations.generate(
        keras_correctness_test_base.test_combinations_for_embedding_model()
        + keras_correctness_test_base.multi_worker_mirrored_eager()
    )
    def test_lstm_model_correctness(
        self, distribution, use_numpy, use_validation_data
    ):
        self.run_correctness_test(distribution, use_numpy, use_validation_data)

    @tf.__internal__.distribute.combinations.generate(
        keras_correctness_test_base.test_combinations_for_embedding_model()
        + keras_correctness_test_base.multi_worker_mirrored_eager()
    )
    @test_utils.enable_v2_dtype_behavior
    def test_lstm_model_correctness_mixed_precision(
        self, distribution, use_numpy, use_validation_data
    ):
        if isinstance(
            distribution,
            (
                tf.distribute.experimental.CentralStorageStrategy,
                tf.compat.v1.distribute.experimental.CentralStorageStrategy,
            ),
        ):
            self.skipTest(
                "CentralStorageStrategy is not supported by mixed precision."
            )
        if isinstance(
            distribution,
            (
                tf.distribute.experimental.TPUStrategy,
                tf.compat.v1.distribute.experimental.TPUStrategy,
            ),
        ):
            policy_name = "mixed_bfloat16"
        else:
            policy_name = "mixed_float16"

        with policy.policy_scope(policy_name):
            self.run_correctness_test(
                distribution, use_numpy, use_validation_data
            )


if __name__ == "__main__":
    tf.__internal__.distribute.multi_process_runner.test_main()