# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

"""
The code below is 
directly from https://raw.githubusercontent.com/IssamLaradji/sls/master/others/cocob.py
Two coin betting optimization algorithms are implemented here :
Cocob Backprop: https://arxiv.org/pdf/1705.07795.pdf
Cocob through Ons: https://arxiv.org/pdf/1705.07795.pdf
both of which do not require any learning rates and yet
have optimal convergence gauarantees for non-smooth
convex functions.

Cocob-Ons is an experimental variation from paper.
Please don't use it yet.

Please check http://francesco.orabona.com/papers/slides_cocob.pdf for
simple explanation for going from coin betting game to convex optimization.
Both algorithms are similar except the coin betting strategy used.
"""

import torch
from torch import optim
import math

class CocobBackprop(optim.Optimizer):
    """Implements Cocob-Backprop .

    It has been proposed in `Training Deep Networks without Learning Rates
    Through Coin Betting`__.

    Arguments:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        alpha (float, optional): positive number to adjust betting fraction.
            Theoretical convergence gauarantee does not depend on choice of
            alpha (default: 100.0)

    __ https://arxiv.org/pdf/1705.07795.pdf
    """
    def __init__(self, params, alpha=100.0, eps=1e-8):
        self.alpha = alpha
        self.eps = eps
        defaults = dict(alpha=alpha, eps=eps)
        super(CocobBackprop, self).__init__(params, defaults)

    def step(self, closure=None):

        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for param in group['params']:
                if param.grad is None:
                    continue

                grad = param.grad.data
                state = self.state[param]
                param_shape = param.shape

                # Better bets for -ve gradient
                neg_grad = - grad

                if len(state) == 0:
                    # Happens only once at the begining of optimization start
                    # Set initial parameter weights and zero reward
                    state['initial_weight'] = param.data
                    state['reward'] = param.new_zeros(param_shape)

                    # Don't bet anything for first round
                    state['bet'] = param.new_zeros(param_shape)

                    # Initialize internal states useful for computing betting fraction
                    state['neg_grads_sum'] = param.new_zeros(param_shape)
                    state['grads_abs_sum'] = param.new_zeros(param_shape)
                    state['max_observed_scale'] = self.eps * param.new_ones(param_shape)

                # load states in variables
                initial_weight = state['initial_weight']
                reward = state['reward']
                bet = state['bet']
                neg_grads_sum = state['neg_grads_sum']
                grads_abs_sum = state['grads_abs_sum']
                max_observed_scale = state['max_observed_scale']

                # Update internal states useful for computing betting fraction
                max_observed_scale = torch.max(max_observed_scale, torch.abs(grad))
                grads_abs_sum += torch.abs(grad)
                neg_grads_sum += neg_grad

                # Based on how much the Better bets on -ve gradient prediction,
                # check how much the Better won (-ve if lost)
                win_amount = bet * neg_grad

                # Update better's reward. Negative reward is not allowed.
                reward = torch.max(reward + win_amount, torch.zeros_like(reward))

                # Better decides the bet fraction based on so-far observations
                bet_fraction = neg_grads_sum / (max_observed_scale * (torch.max(grads_abs_sum + max_observed_scale, self.alpha * max_observed_scale)))

                # Better makes the bet according to decided betting fraction.
                bet = bet_fraction * (max_observed_scale + reward)

                # Set parameter weights
                param.data = initial_weight + bet

                # save state back in memory
                state['neg_grads_sum'] = neg_grads_sum
                state['grads_abs_sum'] = grads_abs_sum
                state['max_observed_scale'] = max_observed_scale
                state['reward'] = reward
                state['bet'] = bet
                # For Cocob-Backprop bet_fraction need not be maintained in state. Only kept for visualization.
                state['bet_fraction'] = bet_fraction

        return loss


class CocobOns(optim.Optimizer):
    """Implements Coin-Betting through ONS .

    It has been proposed in `Black-Box Reductions for Parameter-free
    Online Learning in Banach Spaces`__.

    Cocob-Ons is an experimental variation from the paper.
    Do not use it yet.

    Arguments:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        eps (float, optional): positive initial wealth for betting algorithm.
            Theoretical convergence gauarantee does not depend on choice of
            eps (default: 1e-8)

    __ https://arxiv.org/pdf/1705.07795.pdf
    """
    def __init__(self, params, eps=1e-8):

        self.eps = eps
        defaults = dict(eps=eps)
        super(CocobOns, self).__init__(params, defaults)

    def step(self, closure=None):

        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for param in group['params']:
                if param.grad is None:
                    continue

                grad = param.grad.data
                state = self.state[param]
                param_shape = param.data.shape

                # Clip gradients to be in (-1, 1)
                grad.clamp_(-1.0, 1.0)

                # Better bets for -ve gradient
                neg_grad = - grad

                if len(state) == 0:
                    # Happens only once at the begining of optimization start
                    # Set initial parameter weights and zero reward
                    state['initial_weight'] = param.data
                    state['wealth'] = self.eps * param.new_ones(param_shape)

                    # Don't bet anything for first round
                    state['bet_fraction'] = param.new_zeros(param_shape)
                    state['bet'] = param.new_zeros(param_shape)

                    # Initialize internal states useful for computing betting fraction
                    state['z_square_sum'] = param.new_zeros(param_shape)

                # load states in memory
                wealth = state['wealth']
                bet_fraction = state['bet_fraction']
                z_square_sum = state['z_square_sum']
                initial_weight = state['initial_weight']
                bet = state['bet']

                # Based on how much the Better bets on -ve gradient prediction,
                # check how much the Better won (-ve if lost)
                win_amount = bet * neg_grad

                # Update better's wealth based on what he won / lost.
                wealth = wealth + win_amount

                # Better decides the bet fraction based on so-far observations
                # z, A variable notations from Algo 1 in paper)
                z = grad / (1-(bet_fraction*grad))
                z_square_sum = z_square_sum + (z*z)
                A = 1 + z_square_sum

                bet_fraction = (bet_fraction - (2/(2 - math.log(3)))*(z / A))
                bet_fraction.clamp_(-0.5, 0.5)

                # Better makes the bet according to decided betting fraction.
                bet = bet_fraction * wealth

                # Set parameter weights
                param.data = initial_weight + bet

                # save state back in memory
                state['bet_fraction'] = bet_fraction
                state['wealth'] = wealth
                state['z_square_sum'] = z_square_sum
                state['bet'] = bet

        return loss