from functools import reduce
from operator import mul

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

from datasets.trajectory_dataset import TrajData
from lib.modules.utils import LightningBaseModule, Flatten
from lib.modules.blocks import ConvModule, ResidualModule


class ConvHomDetector(LightningBaseModule):

    name = 'CNNHomotopyClassifier'

    def configure_optimizers(self):
        return Adam(self.parameters(), lr=self.hparams.lr)

    def training_step(self, batch_xy, batch_nb, *args, **kwargs):
        batch_x, batch_y = batch_xy
        pred_y = self(batch_x)
        loss = F.binary_cross_entropy(pred_y, batch_y.float())
        return {'loss': loss, 'log': dict(loss=loss)}

    def __init__(self, *params):
        super(ConvHomDetector, self).__init__(*params)

        # Dataset
        self.dataset = TrajData(self.hparams.data_param.root)

        # Additional Attributes
        self.map_shape = self.dataset.map_shapes_max

        # Model Paramters
        self.in_shape = self.dataset.map_shapes_max
        assert len(self.in_shape) == 3, f'Image or map shape has to have 3 dims, but had: {len(self.in_shape)}'

        # NN Nodes
        # ============================
        # Convolutional Map Processing
        #
        self.map_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1,
                                     conv_padding=0, conv_filters=self.hparams.model_param.filters[0])
        self.map_res_1 = ResidualModule(self.map_conv_0.shape, ConvModule, 3,
                                        **dict(conv_kernel=3, conv_stride=1,
                                               conv_padding=1, conv_filters=self.hparams.model_param.filters[0]))
        self.map_conv_1 = ConvModule(self.map_res_1.shape, conv_kernel=5, conv_stride=1,
                                     conv_padding=0, conv_filters=self.hparams.model_param.filters[0])
        self.map_res_2 = ResidualModule(self.map_conv_1.shape, ConvModule, 3,
                                        **dict(conv_kernel=3, conv_stride=1,
                                               conv_padding=1, conv_filters=self.hparams.model_param.filters[0]))
        self.map_conv_2 = ConvModule(self.map_res_2.shape, conv_kernel=5, conv_stride=1,
                                     conv_padding=0, conv_filters=self.hparams.model_param.filters[0])
        self.map_res_3 = ResidualModule(self.map_conv_2.shape, ConvModule, 3,
                                        **dict(conv_kernel=3, conv_stride=1,
                                               conv_padding=1, conv_filters=self.hparams.model_param.filters[0]))
        self.map_conv_3 = ConvModule(self.map_res_3.shape, conv_kernel=5, conv_stride=1,
                                     conv_padding=0, conv_filters=self.hparams.model_param.filters[0])

        self.flatten = Flatten(self.map_conv_3.shape)

        # ============================
        # Classifier
        #

        self.linear = nn.Linear(reduce(mul, self.flatten.shape), self.hparams.model_param.classes * 10)
        # Comments on Multi Class labels
        self.classifier = nn.Linear(self.hparams.model_param.classes * 10, 1)  # self.hparams.model_param.classes)
        self.out_activation = nn.Sigmoid()  # nn.Softmax

    def forward(self, x):
        tensor = self.map_conv_0(x)
        tensor = self.map_res_1(tensor)
        tensor = self.map_conv_1(tensor)
        tensor = self.map_res_2(tensor)
        tensor = self.map_conv_2(tensor)
        tensor = self.map_conv_3(tensor)
        tensor = self.flatten(tensor)
        tensor = self.linear(tensor)
        tensor = self.classifier(tensor)
        tensor = self.out_activation(tensor)
        return tensor