import inspect
from argparse import Namespace

from torch import nn
from torch.nn import ModuleList

from ml_lib.modules.blocks import ConvModule, LinearModule
from ml_lib.modules.util import (LightningBaseModule, Splitter, Merger)
from util.module_mixins import CombinedModelMixins


class BandwiseConvClassifier(CombinedModelMixins,
                             LightningBaseModule
                             ):
    def __init__(self, in_shape, n_classes, weight_init, activation,
                 use_bias, use_norm, dropout, lat_dim, filters,
                 lr, weight_decay, sto_weight_avg, lr_warm_restart_epochs, opt_reset_interval,
                 loss, scheduler, lr_scheduler_parameter
                 ):
        # TODO: Move this to parent class, or make it much easieer to access....
        a = dict(locals())
        params = {arg: a[arg] for arg in inspect.signature(self.__init__).parameters.keys() if arg != 'self'}
        super(BandwiseConvClassifier, self).__init__(params)

        # Model Paramters
        # =============================================================================
        # Additional parameters
        self.n_band_sections = 8

        # Modules
        # =============================================================================
        self.split = Splitter(in_shape, self.n_band_sections)

        k = 3
        self.band_list = ModuleList()
        for band in range(self.n_band_sections):
            last_shape = self.split.shape[band]
            conv_list = ModuleList()
            for conv_filters in self.params.filters:
                conv_list.append(ConvModule(last_shape, conv_filters, (k, k), conv_stride=(2, 2), conv_padding=2,
                                            **self.params.module_kwargs))
                last_shape = conv_list[-1].shape
                # self.conv_list.append(ConvModule(last_shape, 1, 1, conv_stride=1, **self.params.module_kwargs))
                # last_shape = self.conv_list[-1].shape
            self.band_list.append(conv_list)

        self.merge = Merger(self.band_list[-1][-1].shape, self.n_band_sections)

        self.full_1 = LinearModule(self.merge.shape, self.params.lat_dim, **self.params.module_kwargs)
        self.full_2 = LinearModule(self.full_1.shape, self.params.lat_dim, **self.params.module_kwargs)

        # Make Decision between binary and Multiclass Classification
        logits = n_classes if n_classes > 2 else 1
        module_kwargs = self.params.module_kwargs
        module_kwargs.update(activation=(nn.Softmax if logits > 1 else nn.Sigmoid))
        self.full_out = LinearModule(self.full_2.shape, logits, **module_kwargs)

    def forward(self, batch, **kwargs):
        tensors = self.split(batch)
        for idx, (tensor, convs) in enumerate(zip(tensors, self.band_list)):
            for conv in convs:
                tensor = conv(tensor)
            tensors[idx] = tensor

        tensor = self.merge(tensors)
        tensor = self.full_1(tensor)
        tensor = self.full_2(tensor)
        tensor = self.full_out(tensor)
        return Namespace(main_out=tensor)