# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
warm up cosine annealing learning rate.
"""
import math
import numpy as np

from .linear_warmup import linear_warmup_lr


def warmup_cosine_annealing_lr(lr, steps_per_epoch, warmup_epochs, max_epoch, T_max, eta_min=0):
    """warm up cosine annealing learning rate."""
    base_lr = lr
    warmup_init_lr = 0
    total_steps = int(max_epoch * steps_per_epoch)
    warmup_steps = int(warmup_epochs * steps_per_epoch)

    lr_each_step = []
    for i in range(total_steps):
        last_epoch = i // steps_per_epoch
        if i < warmup_steps:
            lr = linear_warmup_lr(i + 1, warmup_steps, base_lr, warmup_init_lr)
        else:
            lr = eta_min + (base_lr - eta_min) * (1. + math.cos(math.pi*last_epoch / T_max)) / 2
        lr_each_step.append(lr)

    return np.array(lr_each_step).astype(np.float32)