# Copyright 2018 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.
# ==============================================================================
"""Strategy and optimizer combinations for combinations.combine()."""

import tensorflow.compat.v2 as tf

from keras.optimizers import adam as adam_experimental
from keras.optimizers.legacy import adadelta as adadelta_keras_v2
from keras.optimizers.legacy import adagrad as adagrad_keras_v2
from keras.optimizers.legacy import adam as adam_keras_v2
from keras.optimizers.legacy import adamax as adamax_keras_v2
from keras.optimizers.legacy import ftrl as ftrl_keras_v2
from keras.optimizers.legacy import (
    gradient_descent as gradient_descent_keras_v2,
)
from keras.optimizers.legacy import nadam as nadam_keras_v2
from keras.optimizers.legacy import rmsprop as rmsprop_keras_v2

gradient_descent_optimizer_v1_fn = (
    tf.__internal__.test.combinations.NamedObject(
        "GradientDescentV1",
        lambda: tf.compat.v1.train.GradientDescentOptimizer(0.001),
    )
)
adagrad_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
    "AdagradV1", lambda: tf.compat.v1.train.AdagradOptimizer(0.001)
)
adam_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
    "AdamV1", lambda: tf.compat.v1.train.AdamOptimizer(0.001, epsilon=1)
)
ftrl_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
    "FtrlV1", lambda: tf.compat.v1.train.FtrlOptimizer(0.001)
)
rmsprop_optimizer_v1_fn = tf.__internal__.test.combinations.NamedObject(
    "RmsPropV1", lambda: tf.compat.v1.train.RMSPropOptimizer(0.001)
)

# TODO(shiningsun): consider adding the other v1 optimizers
optimizers_v1 = [
    gradient_descent_optimizer_v1_fn,
    adagrad_optimizer_v1_fn,
    ftrl_optimizer_v1_fn,
    rmsprop_optimizer_v1_fn,
]

adadelta_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
    "AdadeltaKerasV2", lambda: adadelta_keras_v2.Adadelta(0.001)
)
adagrad_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
    "AdagradKerasV2", lambda: adagrad_keras_v2.Adagrad(0.001)
)
adam_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
    "AdamKerasV2", lambda: adam_keras_v2.Adam(0.001, epsilon=1.0)
)
adam_experimental_fn = tf.__internal__.test.combinations.NamedObject(
    "AdamExperimental", lambda: adam_experimental.Adam(0.001)
)
adamax_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
    "AdamaxKerasV2", lambda: adamax_keras_v2.Adamax(0.001, epsilon=1.0)
)
nadam_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
    "NadamKerasV2", lambda: nadam_keras_v2.Nadam(0.001, epsilon=1.0)
)
ftrl_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
    "FtrlKerasV2", lambda: ftrl_keras_v2.Ftrl(0.001)
)
gradient_descent_optimizer_keras_v2_fn = (
    tf.__internal__.test.combinations.NamedObject(
        "GradientDescentKerasV2", lambda: gradient_descent_keras_v2.SGD(0.001)
    )
)
rmsprop_optimizer_keras_v2_fn = tf.__internal__.test.combinations.NamedObject(
    "RmsPropKerasV2", lambda: rmsprop_keras_v2.RMSprop(0.001)
)

# TODO(shiningsun): consider adding the other v2 optimizers
optimizers_v2 = [
    gradient_descent_optimizer_keras_v2_fn,
    adagrad_optimizer_keras_v2_fn,
]

optimizers_v1_and_v2 = optimizers_v1 + optimizers_v2


def distributions_and_v1_optimizers():
    """A common set of combination with DistributionStrategies and
    Optimizers."""
    return tf.__internal__.test.combinations.combine(
        distribution=[
            tf.__internal__.distribute.combinations.one_device_strategy,
            tf.__internal__.distribute.combinations.mirrored_strategy_with_gpu_and_cpu,  # noqa: E501
            tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus,  # noqa: E501
            tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus_no_merge_call,  # noqa: E501
        ],
        optimizer_fn=optimizers_v1,
    )


def distributions_and_v2_optimizers():
    """A common set of combination with DistributionStrategies and
    Optimizers."""
    return tf.__internal__.test.combinations.combine(
        distribution=[
            tf.__internal__.distribute.combinations.one_device_strategy,
            tf.__internal__.distribute.combinations.mirrored_strategy_with_gpu_and_cpu,  # noqa: E501
            tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus,  # noqa: E501
            tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus_no_merge_call,  # noqa: E501
        ],
        optimizer_fn=optimizers_v2,
    )


def distributions_and_v1_and_v2_optimizers():
    """A common set of combination with DistributionStrategies and
    Optimizers."""
    return tf.__internal__.test.combinations.combine(
        distribution=[
            tf.__internal__.distribute.combinations.one_device_strategy,
            tf.__internal__.distribute.combinations.mirrored_strategy_with_gpu_and_cpu,  # noqa: E501
            tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus,  # noqa: E501
            tf.__internal__.distribute.combinations.mirrored_strategy_with_two_gpus_no_merge_call,  # noqa: E501
        ],
        optimizer_fn=optimizers_v1_and_v2,
    )