import torch
from functools import reduce
from operator import mul

from torch import nn

from datasets.trajectory_dataset import TrajData
from ml_lib.evaluation.classification import ROCEvaluation
from models.generators.cnn import CNNRouteGeneratorModel

import matplotlib.pyplot as plt


class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):

    name = 'CNNRouteGeneratorDiscriminated'

    def training_step(self, batch_xy, batch_nb, *args, **kwargs):
        batch_x, label = batch_xy

        generated_alternative, z, mu, logvar = self(batch_x)
        map_array, trajectory = batch_x

        map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
        pred_label = self.discriminator(map_stack)
        discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))

        # see Appendix B from VAE paper:
        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
        # https://arxiv.org/abs/1312.6114
        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
        kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
        # Dimensional Resizing
        kld_loss /= reduce(mul, self.in_shape)

        loss = (kld_loss + discriminated_bce_loss) / 2
        return dict(loss=loss, log=dict(loss=loss,
                                        discriminated_bce_loss=discriminated_bce_loss,
                                        kld_loss=kld_loss)
                    )

    def _test_val_step(self, batch_xy, batch_nb, *args):
        batch_x, label = batch_xy

        generated_alternative, z, mu, logvar = self(batch_x)
        map_array, trajectory = batch_x

        map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
        pred_label = self.discriminator(map_stack)

        discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
        return dict(discriminated_bce_loss=discriminated_bce_loss, batch_nb=batch_nb,
                    pred_label=pred_label, label=label, generated_alternative=generated_alternative)

    def validation_step(self, *args):
        return self._test_val_step(*args)

    def validation_epoch_end(self, outputs: list):
        return self._test_val_epoch_end(outputs)

    def _test_val_epoch_end(self, outputs, test=False):
        evaluation = ROCEvaluation(plot_roc=True)
        pred_label = torch.cat([x['pred_label'] for x in outputs])
        labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
        mean_losses = torch.stack([x['discriminated_bce_loss'] for x in outputs]).mean()

        # Sci-py call ROC eval call is eval(true_label, prediction)
        roc_auc, tpr, fpr = evaluation(labels.cpu().numpy(), pred_label.cpu().numpy(), )
        if test:
            # self.logger.log_metrics(score_dict)
            self.logger.log_image(f'{self.name}_ROC-Curve', plt.gcf(), step=self.global_step)
        plt.clf()

        maps, trajectories, labels, val_restul_dict = self.generate_random()

        from generator_eval import GeneratorVisualizer
        g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
        fig = g.draw()
        self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
        plt.clf()

        return dict(mean_losses=mean_losses, roc_auc=roc_auc, epoch=self.current_epoch)

    def test_step(self, *args):
        return self._test_val_step(*args)

    def test_epoch_end(self, outputs):
        return self._test_val_epoch_end(outputs, test=True)

    @property
    def discriminator(self):
        if self._disc is None:
            raise RuntimeError('Set the Discriminator first; "set_discriminator(disc_model)')
        return self._disc

    def set_discriminator(self, disc_model):
        if self._disc is not None:
            raise RuntimeError('Discriminator has already been set... What are trying to do?')
        self._disc = disc_model

    def __init__(self, *params):
        raise NotImplementedError
        super(CNNRouteGeneratorDiscriminated, self).__init__(*params, issubclassed=True)

        self._disc = None

        self.criterion = nn.BCELoss()

        self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route', preprocessed=True,
                                length=self.hparams.data_param.dataset_length, normalized=True)