# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
from typing import Iterable, Union

import numpy as np

from ..tensor import Parameter, tensor
from .optimizer import Optimizer


class Adagrad(Optimizer):
    r"""Implements Adagrad algorithm.
    
    It has been proposed in `"Adaptive Subgradient Methods for Online Learning
    and Stochastic Optimization" <http://jmlr.org/papers/v12/duchi11a.html>`_.

    Args:
        params: iterable of parameters to optimize or dicts defining
            parameter groups.
        lr: coefficient that scales delta before it is applied
            to the parameters. Default: 1e-2
        lr_decay: learning rate decay. Default: 0
        eps: term added to the denominator to improve
            numerical stability. Default: 1e-10
        weight_decay: weight decay (L2 penalty). Default: 0
    """

    def __init__(
        self,
        params: Union[Iterable[Parameter], dict],
        lr: float = 1e-2,
        lr_decay: float = 0.0,
        eps: float = 1e-10,
        weight_decay: float = 0.0,
    ):
        assert lr >= 0.0, "Invalid learning rate: {}".format(lr)
        assert lr_decay >= 0, "Invalid learning rate decay: {}".format(lr_decay)
        assert eps >= 0.0, "Invalid epsilon value: {}".format(eps)
        assert weight_decay >= 0.0, "Invalid weight_decay value: {}".format(
            weight_decay
        )

        defaults = dict(lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay)
        super().__init__(params, defaults)
        self._disable_type_convert = True

    def _create_state(self, param_group):
        for param in param_group["params"]:
            self._add_state(param, "square_avg")
            self._add_state(param, "step", initializer=0.0)

    def _updates(self, param_group):
        lr = param_group["lr"]
        lr_decay = param_group["lr_decay"]
        weight_decay = param_group["weight_decay"]
        eps = param_group["eps"]

        def make_scalar(val):
            return tensor(val, dtype="float32")

        # since `conver_inputs` is disabled for param updates,
        # scalar should be explicitly tansforred to tensor

        _lr, _lr_decay = map(make_scalar, (lr, lr_decay))
        _weight_decay = make_scalar(weight_decay)
        _eps = make_scalar(eps)

        c1, c2, c05 = map(make_scalar, (1.0, 2.0, 0.5))

        for param in param_group["params"]:

            if param.grad is None:
                continue

            states = self._state[param]
            step = states["step"]
            step += c1
            grad = param.grad
            if weight_decay != 0.0:
                grad = grad + param * _weight_decay

            square_avg = states["square_avg"]
            square_avg += grad ** c2
            delta = grad / (square_avg + _eps) ** c05
            clr = _lr / (c1 + (step - c1) * _lr_decay)

            param -= clr * delta