import math
import torch
from torch.optim.optimizer import Optimizer


class DenseSparseAdam(Optimizer):
	"""

	"""
	def __init__(self, params, lr=3e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.0):
		if not 0.0 <= lr:
			raise ValueError("Invalid learning rate: {}".format(lr))
		if not 0.0 <= eps:
			raise ValueError("Invalid epsilon value: {}".format(eps))
		if not 0.0 <= betas[0] < 1.0:
			raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
		if not 0.0 <= betas[1] < 1.0:
			raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
		defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
		super(DenseSparseAdam, self).__init__(params, defaults)

	def step(self, closure=None):
		"""
		Performs a single optimization step.

		Parameters
		----------
		closure : ``callable``, optional.
			A closure that reevaluates the model and returns the loss.
		"""
		loss = None
		if closure is not None:
			loss = closure()

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

				state = self.state[p]

				# State initialization
				if 'step' not in state:
					state['step'] = 0
				if 'exp_avg' not in state:
					# Exponential moving average of gradient values
					state['exp_avg'] = torch.zeros_like(p.data)
				if 'exp_avg_sq' not in state:
					# Exponential moving average of squared gradient values
					state['exp_avg_sq'] = torch.zeros_like(p.data)

				state['step'] += 1

				exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
				beta1, beta2 = group['betas']

				weight_decay = group['weight_decay']

				if grad.is_sparse:
					grad = grad.coalesce()  # the update is non-linear so indices must be unique
					grad_indices = grad._indices()
					grad_values = grad._values()
					size = grad.size()

					def make_sparse(values):
						constructor = grad.new
						if grad_indices.dim() == 0 or values.dim() == 0:
							return constructor().resize_as_(grad)
						return constructor(grad_indices, values, size)

					# Decay the first and second moment running average coefficient
					#      old <- b * old + (1 - b) * new
					# <==> old += (1 - b) * (new - old)
					old_exp_avg_values = exp_avg.sparse_mask(grad)._values()
					exp_avg_update_values = grad_values.sub(old_exp_avg_values).mul_(1 - beta1)
					exp_avg.add_(make_sparse(exp_avg_update_values))
					old_exp_avg_sq_values = exp_avg_sq.sparse_mask(grad)._values()
					exp_avg_sq_update_values = grad_values.pow(2).sub_(old_exp_avg_sq_values).mul_(1 - beta2)
					exp_avg_sq.add_(make_sparse(exp_avg_sq_update_values))

					# Dense addition again is intended, avoiding another sparse_mask
					numer = exp_avg_update_values.add_(old_exp_avg_values)
					exp_avg_sq_update_values.add_(old_exp_avg_sq_values)
					denom = exp_avg_sq_update_values.sqrt_().add_(group['eps'])
					del exp_avg_update_values, exp_avg_sq_update_values

					bias_correction1 = 1 - beta1 ** state['step']
					bias_correction2 = 1 - beta2 ** state['step']
					step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

					p.data.add_(make_sparse(-step_size * numer.div_(denom)))
					if weight_decay > 0.0:
						p.data.add_(-group['lr'] * weight_decay, p.data.sparse_mask(grad))
				else:
					# Decay the first and second moment running average coefficient
					# exp_avg.mul_(beta1).add_(1 - beta1, grad)
					exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
					exp_avg_sq.mul_(beta2).addcmul_(grad, grad,value=1 - beta2)
					denom = exp_avg_sq.sqrt().add_(group['eps'])

					bias_correction1 = 1 - beta1 ** state['step']
					bias_correction2 = 1 - beta2 ** state['step']
					step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

					p.data.addcdiv_(exp_avg, denom, value=-step_size)
					if weight_decay > 0.0:
						p.data.add_(-group['lr'] * weight_decay, p.data)

		return loss