import argparse

import pytorch_lightning as pl
import torch
import torchmetrics
from prototorch.functions.competitions import wtac
from prototorch.functions.distances import euclidean_distance
from prototorch.functions.initializers import get_initializer
from prototorch.functions.losses import glvq_loss
from prototorch.modules.prototypes import Prototypes1D


class GLVQ(pl.LightningModule):
    """Generalized Learning Vector Quantization."""
    def __init__(self, hparams, input_dim, nclasses, **kwargs):
        super().__init__()
        self.lr = hparams.lr
        self.hparams = hparams
        # self.save_hyperparameters(
        #     "lr",
        #     "prototypes_per_class",
        #     "prototype_initializer",
        # )
        self.proto_layer = Prototypes1D(
            input_dim=input_dim,
            nclasses=nclasses,
            prototypes_per_class=hparams.prototypes_per_class,
            prototype_initializer=hparams.prototype_initializer,
            **kwargs)
        self.train_acc = torchmetrics.Accuracy()

    @property
    def prototypes(self):
        return self.proto_layer.prototypes.detach().numpy()

    @property
    def prototype_labels(self):
        return self.proto_layer.prototype_labels.detach().numpy()

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
        return optimizer

    @staticmethod
    def add_model_specific_args(parent_parser):
        parser = argparse.ArgumentParser(parents=[parent_parser],
                                         add_help=False)
        parser.add_argument("--epochs", type=int, default=1)
        parser.add_argument("--lr", type=float, default=1e-2)
        parser.add_argument("--batch_size", type=int, default=32)
        parser.add_argument("--prototypes_per_class", type=int, default=1)
        parser.add_argument("--prototype_initializer",
                            type=str,
                            default="zeros")
        return parser

    def forward(self, x):
        protos = self.proto_layer.prototypes
        dis = euclidean_distance(x, protos)
        return dis

    def training_step(self, train_batch, batch_idx):
        x, y = train_batch
        x = x.view(x.size(0), -1)
        dis = self(x)
        plabels = self.proto_layer.prototype_labels
        mu = glvq_loss(dis, y, prototype_labels=plabels)
        loss = mu.sum(dim=0)
        self.log("train_loss", loss)
        with torch.no_grad():
            preds = wtac(dis, plabels)
        # self.train_acc.update(preds.int(), y.int())
        self.train_acc(
            preds.int(),
            y.int())  # FloatTensors are assumed to be class probabilities
        self.log("Training Accuracy",
                 self.train_acc,
                 on_step=False,
                 on_epoch=True)
        return loss

    # def training_epoch_end(self, outs):
    #     # Calling `self.train_acc.compute()` is
    #     # automatically done by setting `on_epoch=True` when logging in `self.training_step(...)`
    #     self.log("train_acc_epoch", self.train_acc.compute())

    def predict(self, x):
        with torch.no_grad():
            # model.eval()  # ?!
            d = self(x)
            plabels = self.proto_layer.prototype_labels
            y_pred = wtac(d, plabels)
        return y_pred.numpy()


class ImageGLVQ(GLVQ):
    """GLVQ model that constrains the prototypes to the range [0, 1] by
    clamping after updates.
    """
    def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
        self.proto_layer.prototypes.data.clamp_(0., 1.)