# Copyright (c) 2020 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,
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from __future__ import annotations

import warnings
from typing import TYPE_CHECKING

from typing_extensions import NotRequired

from paddle import _C_ops, pir

from ..base import framework
from ..base.framework import in_dynamic_or_pir_mode
from .optimizer import Optimizer

if TYPE_CHECKING:
    from collections.abc import Sequence

    from typing_extensions import NotRequired

    from paddle import Tensor
    from paddle.nn.clip import GradientClipBase
    from paddle.optimizer.lr import LRScheduler
    from paddle.regularizer import WeightDecayRegularizer

    from .optimizer import _ParameterConfig

    class _RMSPropParameterConfig(_ParameterConfig):
        epsilon: NotRequired[float]
        momentum: NotRequired[float]
        rho: NotRequired[float]
        centered: NotRequired[bool]


__all__ = []


class RMSProp(Optimizer):
    r"""
    Root Mean Squared Propagation (RMSProp) is an unpublished, adaptive learning
    rate method. The original slides proposed RMSProp: Slide 29 of
    http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf .

    The original equation is as follows:

    ..  math::

        r(w, t) & = \rho r(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2

        w & = w - \frac{\eta} {\sqrt{r(w,t) + \epsilon}} \nabla Q_{i}(w)

    The first equation calculates moving average of the squared gradient for
    each weight. Then dividing the gradient by :math:`sqrt{v(w,t)}`.

    In some cases, adding a momentum term :math: `\\beta` is beneficial.
    In our implementation, Nesterov momentum is used:

    ..  math::

        r(w, t) & = \rho r(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2

        v(w, t) & = \beta v(w, t-1) + \frac{\eta} {\sqrt{r(w,t) +
            \epsilon}} \nabla Q_{i}(w)

        w & = w - v(w, t)

    if centered is True:

    ..  math::

        r(w, t) & = \rho r(w, t-1) + (1 - \rho)(\nabla Q_{i}(w))^2

        g(w, t) & = \rho g(w, t-1) + (1 - \rho)\nabla Q_{i}(w)

        v(w, t) & = \beta v(w, t-1) + \frac{\eta} {\sqrt{r(w,t) - (g(w, t))^2 +
            \epsilon}} \nabla Q_{i}(w)

        w & = w - v(w, t)

    where, :math:`\rho` is a hyperparameter and typical values are 0.9, 0.95
    and so on. :math:`\beta` is the momentum term. :math:`\epsilon` is a
    smoothing term to avoid division by zero, usually set somewhere in range
    from 1e-4 to 1e-8.


    Parameters:
        learning_rate (float|LRScheduler): The learning rate used to update ``Parameter``.
          It can be a float value or a LRScheduler.
        rho(float, optional): rho is :math:`\rho` in equation, default is 0.95.
        epsilon(float, optional): :math:`\epsilon` in equation is smoothing term to
          avoid division by zero, default is 1e-6.
        momentum(float, optional): :math:`\beta` in equation is the momentum term,
          default is 0.0.
        centered(bool, optional): If True, gradients are normalized by the estimated variance of
          the gradient; if False, by the uncentered second moment. Setting this to
          True may help with training, but is slightly more expensive in terms of
          computation and memory. Defaults to False.
        parameters (list|tuple|None, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``.
          This parameter is required in dygraph mode. And you can specify different options for
          different parameter groups such as the learning rate, weight decay, etc,
          then the parameters are list of dict. Note that the learning_rate in parameter groups
          represents the scale of base learning_rate.
          The default value is None in static graph mode, at this time all parameters will be updated.
        weight_decay (int|float|WeightDecayRegularizer|None, optional): The strategy of regularization.
          It can be a int or float value as coeff of L2 regularization or \
          :ref:`api_paddle_regularizer_L1Decay`, :ref:`api_paddle_regularizer_L2Decay`.
          If a parameter has set regularizer using :ref:`api_paddle_ParamAttr` already,
          the regularization setting here in optimizer will be ignored for this parameter.
          Otherwise, the regularization setting here in optimizer will take effect.
          Default None, meaning there is no regularization.
        grad_clip (GradientClipBase|None, optional): Gradient clipping strategy, it's an instance of
          some derived class of ``GradientClipBase`` . There are three clipping strategies
          ( :ref:`api_paddle_nn_ClipGradByGlobalNorm` , :ref:`api_paddle_nn_ClipGradByNorm` ,
          :ref:`api_paddle_nn_ClipGradByValue` ). Default None, meaning there is no gradient clipping.
        name (str|None, optional): Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name`.
            The default value is None.

    Examples:
            .. code-block:: python

                >>> import paddle

                >>> inp = paddle.rand([10,10], dtype="float32")
                >>> linear = paddle.nn.Linear(10, 10)
                >>> out = linear(inp)
                >>> loss = paddle.mean(out)

                >>> rmsprop = paddle.optimizer.RMSProp(
                ...     learning_rate=0.1,
                ...     parameters=linear.parameters(),
                ...     weight_decay=0.01
                ... )
                >>> out.backward()
                >>> rmsprop.step()
                >>> rmsprop.clear_grad()

                >>> # Note that the learning_rate of linear_2 is 0.01.
                >>> linear_1 = paddle.nn.Linear(10, 10)
                >>> linear_2 = paddle.nn.Linear(10, 10)
                >>> inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)
                >>> out = linear_1(inp)
                >>> out = linear_2(out)
                >>> loss = paddle.mean(out)
                >>> rmsprop = paddle.optimizer.RMSProp(
                ...     learning_rate=0.1,
                ...     parameters=[{  # type: ignore
                ...         'params': linear_1.parameters()
                ...     }, {
                ...         'params': linear_2.parameters(),
                ...         'weight_decay': 0.001,
                ...         'learning_rate': 0.1
                ...     }],
                ...     weight_decay=0.01
                ... )
                >>> out.backward()
                >>> rmsprop.step()
                >>> rmsprop.clear_grad()
    """

    _momentum_acc_str = "momentum"
    _mean_square_acc_str = "mean_square"
    _mean_grad_acc_str = "mean_grad"

    def __init__(
        self,
        learning_rate: float | LRScheduler,
        rho: float = 0.95,
        epsilon: float = 1.0e-6,
        momentum: float = 0.0,
        centered: bool = False,
        parameters: (
            Sequence[Tensor] | Sequence[_RMSPropParameterConfig] | None
        ) = None,
        weight_decay: float | WeightDecayRegularizer | None = None,
        grad_clip: GradientClipBase | None = None,
        name: str | None = None,
    ) -> None:
        if learning_rate is None:
            raise ValueError("learning_rate is not set.")
        if rho is None:
            raise ValueError("rho is not set.")
        if epsilon is None:
            raise ValueError("epsilon is not set.")
        if momentum is None:
            raise ValueError("momentum is not set.")
        if not 0.0 <= epsilon:
            raise ValueError("Invalid value of epsilon, expect epsilon >= 0.")
        if not 0.0 <= momentum:
            raise ValueError("Invalid value of momentum, expect momentum >= 0.")
        if not 0.0 <= rho:
            raise ValueError("Invalid value of rho, expect rho >= 0.")

        super().__init__(
            learning_rate=learning_rate,
            parameters=parameters,
            weight_decay=weight_decay,
            grad_clip=grad_clip,
            name=name,
        )

        self.type = "rmsprop"
        self._rho = rho
        self._epsilon = epsilon
        self._momentum = momentum
        self._centered = centered
        self._multi_precision = False
        self._master_weights = {}
        self._default_dict = {
            'rho': rho,
            'epsilon': epsilon,
            'momentum': momentum,
            'centered': centered,
        }

    def _create_accumulators(self, block, parameters):
        if not isinstance(block, (framework.Block, pir.Block)):
            raise TypeError("block is not instance of Block.")

        if isinstance(parameters, dict):
            parameters = parameters.get('params')

        for p in parameters:
            if p.name in self._already_create_accumulator:
                continue

            if self._multi_precision and self._is_dtype_fp16_or_bf16(p.dtype):
                master_p = self._create_master_weight(p)
                self._add_accumulator(self._momentum_acc_str, master_p)
                self._add_accumulator(self._mean_square_acc_str, master_p)
                self._add_accumulator(self._mean_grad_acc_str, master_p)
                self._already_create_accumulator.add(p.name)
                continue
            if (
                self._is_dtype_fp16_or_bf16(p.dtype)
                and not self._multi_precision
            ):
                warnings.warn(
                    "Accumulating with FP16 in optimizer can lead to poor accuracy or slow convergence."
                    "Consider using multi_precision=True option of the Lars optimizer."
                )
            self._add_accumulator(self._momentum_acc_str, p)
            self._add_accumulator(self._mean_square_acc_str, p)
            self._add_accumulator(self._mean_grad_acc_str, p)
            self._already_create_accumulator.add(p.name)

    def _append_optimize_op(self, block, param_and_grad):
        if not isinstance(block, (framework.Block, pir.Block)):
            raise TypeError("block is not instance of Block.")

        if isinstance(param_and_grad, dict):
            param_and_grad = self._update_param_group(param_and_grad)

        momentum_acc = self._get_accumulator_master(
            self._momentum_acc_str, param_and_grad[0]
        )
        mean_square_acc = self._get_accumulator_master(
            self._mean_square_acc_str, param_and_grad[0]
        )
        mean_grad_acc = self._get_accumulator_master(
            self._mean_grad_acc_str, param_and_grad[0]
        )
        find_master = self._multi_precision and self._is_dtype_fp16_or_bf16(
            param_and_grad[0].dtype
        )
        master_weight = (
            self._master_weights[param_and_grad[0].name]
            if find_master
            else None
        )

        if in_dynamic_or_pir_mode():
            _C_ops.rmsprop_(
                param_and_grad[0],
                mean_square_acc,
                param_and_grad[1],
                momentum_acc,
                self._create_param_lr(param_and_grad),
                mean_grad_acc,
                master_weight,
                self._epsilon,
                self._rho,
                self._momentum,
                self._centered,
                find_master,
            )
            return None
        else:
            inputs = {
                "Param": param_and_grad[0],
                "Grad": param_and_grad[1],
                "Moment": momentum_acc,
                "MeanSquare": mean_square_acc,
                "MeanGrad": mean_grad_acc,
                "LearningRate": self._create_param_lr(param_and_grad),
            }

            outputs = {
                "ParamOut": param_and_grad[0],
                "MomentOut": momentum_acc,
                "MeanSquareOut": mean_square_acc,
                "MeanGradOut": mean_grad_acc,
            }

            if find_master:
                inputs["MasterParam"] = master_weight
                outputs["MasterParamOut"] = master_weight
            rmsprop_op = block.append_op(
                type=self.type,
                inputs=inputs,
                outputs=outputs,
                attrs={
                    "epsilon": self._epsilon,
                    "decay": self._rho,
                    "momentum": self._momentum,
                    "centered": self._centered,
                },
                stop_gradient=True,
            )

            return rmsprop_op

    def _update_param_group(self, parameters):
        self._epsilon = parameters.get('epsilon', self._default_dict['epsilon'])
        self._rho = parameters.get('rho', self._default_dict['rho'])
        self._momentum = parameters.get(
            'momentum', self._default_dict['momentum']
        )
        self._centered = parameters.get(
            'centered', self._default_dict['centered']
        )
        parameters = parameters.get('params')
        return parameters