# Copyright (c) 2019 PaddlePaddle 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
import copy
import paddle
import paddle.nn as nn

import paddle.optimizer as optimizer
from paddle.optimizer.lr import CosineAnnealingDecay
import paddle.regularizer as regularizer
from paddle import cos

from ppdet.core.workspace import register, serializable

__all__ = ['LearningRate', 'OptimizerBuilder']

from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)


@serializable
class CosineDecay(object):
    """
    Cosine learning rate decay

    Args:
        max_epochs (int): max epochs for the training process.
            if you commbine cosine decay with warmup, it is recommended that
            the max_iters is much larger than the warmup iter
    """

    def __init__(self, max_epochs=1000, use_warmup=True):
        self.max_epochs = max_epochs
        self.use_warmup = use_warmup

    def __call__(self,
                 base_lr=None,
                 boundary=None,
                 value=None,
                 step_per_epoch=None):
        assert base_lr is not None, "either base LR or values should be provided"

        max_iters = self.max_epochs * int(step_per_epoch)

        if boundary is not None and value is not None and self.use_warmup:
            for i in range(int(boundary[-1]), max_iters):
                boundary.append(i)

                decayed_lr = base_lr * 0.5 * (
                    math.cos(i * math.pi / max_iters) + 1)
                value.append(decayed_lr)
            return optimizer.lr.PiecewiseDecay(boundary, value)

        return optimizer.lr.CosineAnnealingDecay(base_lr, T_max=max_iters)


@serializable
class PiecewiseDecay(object):
    """
    Multi step learning rate decay

    Args:
        gamma (float | list): decay factor
        milestones (list): steps at which to decay learning rate
    """

    def __init__(self,
                 gamma=[0.1, 0.01],
                 milestones=[8, 11],
                 values=None,
                 use_warmup=True):
        super(PiecewiseDecay, self).__init__()
        if type(gamma) is not list:
            self.gamma = []
            for i in range(len(milestones)):
                self.gamma.append(gamma / 10**i)
        else:
            self.gamma = gamma
        self.milestones = milestones
        self.values = values
        self.use_warmup = use_warmup

    def __call__(self,
                 base_lr=None,
                 boundary=None,
                 value=None,
                 step_per_epoch=None):
        if boundary is not None and self.use_warmup:
            boundary.extend([int(step_per_epoch) * i for i in self.milestones])
        else:
            # do not use LinearWarmup
            boundary = [int(step_per_epoch) * i for i in self.milestones]

        # self.values is setted directly in config 
        if self.values is not None:
            assert len(self.milestones) + 1 == len(self.values)
            return optimizer.lr.PiecewiseDecay(boundary, self.values)

        # value is computed by self.gamma
        if value is not None:
            for i in self.gamma:
                value.append(base_lr * i)

        return optimizer.lr.PiecewiseDecay(boundary, value)


@serializable
class LinearWarmup(object):
    """
    Warm up learning rate linearly

    Args:
        steps (int): warm up steps
        start_factor (float): initial learning rate factor
    """

    def __init__(self, steps=500, start_factor=1. / 3):
        super(LinearWarmup, self).__init__()
        self.steps = steps
        self.start_factor = start_factor

    def __call__(self, base_lr):
        boundary = []
        value = []
        for i in range(self.steps + 1):
            alpha = i / self.steps
            factor = self.start_factor * (1 - alpha) + alpha
            lr = base_lr * factor
            value.append(lr)
            if i > 0:
                boundary.append(i)
        return boundary, value


@register
class LearningRate(object):
    """
    Learning Rate configuration

    Args:
        base_lr (float): base learning rate
        schedulers (list): learning rate schedulers
    """
    __category__ = 'optim'

    def __init__(self,
                 base_lr=0.01,
                 schedulers=[PiecewiseDecay(), LinearWarmup()]):
        super(LearningRate, self).__init__()
        self.base_lr = base_lr
        self.schedulers = schedulers

    def __call__(self, step_per_epoch):
        assert len(self.schedulers) >= 1
        if not self.schedulers[0].use_warmup:
            return self.schedulers[0](base_lr=self.base_lr,
                                      step_per_epoch=step_per_epoch)

        # TODO: split warmup & decay 
        # warmup
        boundary, value = self.schedulers[1](self.base_lr)
        # decay
        decay_lr = self.schedulers[0](self.base_lr, boundary, value,
                                      step_per_epoch)
        return decay_lr


@register
class OptimizerBuilder():
    """
    Build optimizer handles
    Args:
        regularizer (object): an `Regularizer` instance
        optimizer (object): an `Optimizer` instance
    """
    __category__ = 'optim'

    def __init__(self,
                 clip_grad_by_norm=None,
                 regularizer={'type': 'L2',
                              'factor': .0001},
                 optimizer={'type': 'Momentum',
                            'momentum': .9}):
        self.clip_grad_by_norm = clip_grad_by_norm
        self.regularizer = regularizer
        self.optimizer = optimizer

    def __call__(self, learning_rate, params=None):
        if self.clip_grad_by_norm is not None:
            grad_clip = nn.ClipGradByGlobalNorm(
                clip_norm=self.clip_grad_by_norm)
        else:
            grad_clip = None

        if self.regularizer:
            reg_type = self.regularizer['type'] + 'Decay'
            reg_factor = self.regularizer['factor']
            regularization = getattr(regularizer, reg_type)(reg_factor)
        else:
            regularization = None

        optim_args = self.optimizer.copy()
        optim_type = optim_args['type']
        del optim_args['type']
        op = getattr(optimizer, optim_type)
        return op(learning_rate=learning_rate,
                  parameters=params,
                  weight_decay=regularization,
                  grad_clip=grad_clip,
                  **optim_args)


class ModelEMA(object):
    def __init__(self, decay, model, use_thres_step=False):
        self.step = 0
        self.decay = decay
        self.state_dict = dict()
        for k, v in model.state_dict().items():
            self.state_dict[k] = paddle.zeros_like(v)
        self.use_thres_step = use_thres_step

    def update(self, model):
        if self.use_thres_step:
            decay = min(self.decay, (1 + self.step) / (10 + self.step))
        else:
            decay = self.decay
        self._decay = decay
        model_dict = model.state_dict()
        for k, v in self.state_dict.items():
            if '_mean' not in k and '_variance' not in k:
                v = decay * v + (1 - decay) * model_dict[k]
                v.stop_gradient = True
                self.state_dict[k] = v
            else:
                self.state_dict[k] = model_dict[k]
        self.step += 1

    def apply(self):
        state_dict = dict()
        for k, v in self.state_dict.items():
            if '_mean' not in k and '_variance' not in k:
                v = v / (1 - self._decay**self.step)
                v.stop_gradient = True
                state_dict[k] = v
        return state_dict
