# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import copy
import paddle

__all__ = ["build_optimizer"]


def build_lr_scheduler(lr_config, epochs, step_each_epoch):
    from . import learning_rate

    lr_config.update({"epochs": epochs, "step_each_epoch": step_each_epoch})
    lr_name = lr_config.pop("name", "Const")
    lr = getattr(learning_rate, lr_name)(**lr_config)()
    return lr


def build_optimizer(config, epochs, step_each_epoch, model):
    from . import regularizer, optimizer

    config = copy.deepcopy(config)
    # step1 build lr
    lr = build_lr_scheduler(config.pop("lr"), epochs, step_each_epoch)

    # step2 build regularization
    if "regularizer" in config and config["regularizer"] is not None:
        reg_config = config.pop("regularizer")
        reg_name = reg_config.pop("name")
        if not hasattr(regularizer, reg_name):
            reg_name += "Decay"
        reg = getattr(regularizer, reg_name)(**reg_config)()
    elif "weight_decay" in config:
        reg = config.pop("weight_decay")
    else:
        reg = None

    # step3 build optimizer
    optim_name = config.pop("name")
    if "clip_norm" in config:
        clip_norm = config.pop("clip_norm")
        grad_clip = paddle.nn.ClipGradByNorm(clip_norm=clip_norm)
    elif "clip_norm_global" in config:
        clip_norm = config.pop("clip_norm_global")
        grad_clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=clip_norm)
    else:
        grad_clip = None
    optim = getattr(optimizer, optim_name)(
        learning_rate=lr, weight_decay=reg, grad_clip=grad_clip, **config
    )
    return optim(model), lr