import torch
from torch import nn, optim
from torch.nn import functional as F


class TemperatureScaler(nn.Module):
    def __init__(self):
        super(TemperatureScaler, self).__init__()
        self.temperature = nn.Parameter(torch.ones(1))

    def forward(self, logits):
        return self.temperature_scale(logits)

    def calibrate(self, logits):
        return self.temperature_scale(logits)

    def temperature_scale(self, logits):
        """
        Perform temperature scaling on logits
        """
        temperature = self.temperature.unsqueeze(1).expand(logits.size(0), logits.size(1))
        return logits / temperature

    # This function probably should live outside of this class, but whatever
    def fit(self, logits, labels):
        """
        Tune the tempearature of the model (using the validation set).
        We're going to set it to optimize NLL.
        valid_loader (DataLoader): validation set loader
        """
        self.cuda()
        nll_criterion = nn.CrossEntropyLoss().cuda()
        ece_criterion = _ECELoss().cuda()
        # Next: optimize the temperature w.r.t. NLL
        optimizer = optim.LBFGS([self.temperature], lr=0.01, max_iter=1000)

        def eval():
            optimizer.zero_grad()
            loss = nll_criterion(self.temperature_scale(logits), labels)
            loss.backward()
            return loss
        optimizer.step(eval)

        return self.temperature.item()


class _ECELoss(nn.Module):
    """
    Calculates the Expected Calibration Error of a model.
    (This isn't necessary for temperature scaling, just a cool metric).

    The input to this loss is the logits of a model, NOT the softmax scores.

    This divides the confidence outputs into equally-sized interval bins.
    In each bin, we compute the confidence gap:

    bin_gap = | avg_confidence_in_bin - accuracy_in_bin |

    We then return a weighted average of the gaps, based on the number
    of samples in each bin

    See: Naeini, Mahdi Pakdaman, Gregory F. Cooper, and Milos Hauskrecht.
    "Obtaining Well Calibrated Probabilities Using Bayesian Binning." AAAI.
    2015.
    """
    def __init__(self, n_bins=15):
        """
        n_bins (int): number of confidence interval bins
        """
        super(_ECELoss, self).__init__()
        bin_boundaries = torch.linspace(0, 1, n_bins + 1)
        self.bin_lowers = bin_boundaries[:-1]
        self.bin_uppers = bin_boundaries[1:]

    def forward(self, logits, labels):
        softmaxes = F.softmax(logits, dim=1)
        confidences, predictions = torch.max(softmaxes, 1)
        accuracies = predictions.eq(labels)

        ece = torch.zeros(1, device=logits.device)
        for bin_lower, bin_upper in zip(self.bin_lowers, self.bin_uppers):
            # Calculated |confidence - accuracy| in each bin
            in_bin = confidences.gt(bin_lower.item()) * confidences.le(bin_upper.item())
            prop_in_bin = in_bin.float().mean()
            if prop_in_bin.item() > 0:
                accuracy_in_bin = accuracies[in_bin].float().mean()
                avg_confidence_in_bin = confidences[in_bin].mean()
                ece += torch.abs(avg_confidence_in_bin - accuracy_in_bin) * prop_in_bin

        return ece
