import numpy as np

import torch
import torch.nn as nn

from openselfsup.utils import print_log

from . import builder
from .registry import MODELS
from .utils import Sobel


@MODELS.register_module
class DeepCluster(nn.Module):
    """DeepCluster.

    Implementation of "Deep Clustering for Unsupervised Learning
    of Visual Features (https://arxiv.org/abs/1807.05520)".

    Args:
        backbone (nn.Module): Module of backbone ConvNet.
        with_sobel (bool): Whether to apply a Sobel filter on images. Default: False.
        neck (nn.Module): Module of deep features to compact feature vectors.
        head (nn.Module): Module of loss functions.
        pretrained (str, optional): Path to pre-trained weights. Default: None.
    """

    def __init__(self,
                 backbone,
                 with_sobel=False,
                 neck=None,
                 head=None,
                 pretrained=None):
        super(DeepCluster, self).__init__()
        self.with_sobel = with_sobel
        if with_sobel:
            self.sobel_layer = Sobel()
        self.backbone = builder.build_backbone(backbone)
        self.neck = builder.build_neck(neck)
        if head is not None:
            self.head = builder.build_head(head)
        self.init_weights(pretrained=pretrained)

        # reweight
        self.num_classes = head.num_classes
        self.loss_weight = torch.ones((self.num_classes, ),
                                      dtype=torch.float32).cuda()
        self.loss_weight /= self.loss_weight.sum()

    def init_weights(self, pretrained=None):
        """Initialize the weights of model.

        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Default: None.
        """
        if pretrained is not None:
            print_log('load model from: {}'.format(pretrained), logger='root')
        self.backbone.init_weights(pretrained=pretrained)
        self.neck.init_weights(init_linear='kaiming')
        self.head.init_weights(init_linear='normal')

    def forward_backbone(self, img):
        """Forward backbone.

        Args:
            img (Tensor): Input images of shape (N, C, H, W).
                Typically these should be mean centered and std scaled.

        Returns:
            tuple[Tensor]: backbone outputs.
        """
        if self.with_sobel:
            img = self.sobel_layer(img)
        x = self.backbone(img)
        return x

    def forward_train(self, img, pseudo_label, **kwargs):
        """Forward computation during training.

        Args:
            img (Tensor): Input images of shape (N, C, H, W).
                Typically these should be mean centered and std scaled.
            pseudo_label (Tensor): Label assignments.
            kwargs: Any keyword arguments to be used to forward.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        x = self.forward_backbone(img)
        assert len(x) == 1
        feature = self.neck(x)
        outs = self.head(feature)
        loss_inputs = (outs, pseudo_label)
        losses = self.head.loss(*loss_inputs)
        return losses

    def forward_test(self, img, **kwargs):
        x = self.forward_backbone(img)  # tuple
        outs = self.head(x)
        keys = ['head{}'.format(i) for i in range(len(outs))]
        out_tensors = [out.cpu() for out in outs]  # NxC
        return dict(zip(keys, out_tensors))

    def forward(self, img, mode='train', **kwargs):
        if mode == 'train':
            return self.forward_train(img, **kwargs)
        elif mode == 'test':
            return self.forward_test(img, **kwargs)
        elif mode == 'extract':
            return self.forward_backbone(img)
        else:
            raise Exception("No such mode: {}".format(mode))

    def set_reweight(self, labels, reweight_pow=0.5):
        """Loss re-weighting.

        Re-weighting the loss according to the number of samples in each class.

        Args:
            labels (numpy.ndarray): Label assignments.
            reweight_pow (float): The power of re-weighting. Default: 0.5.
        """
        hist = np.bincount(
            labels, minlength=self.num_classes).astype(np.float32)
        inv_hist = (1. / (hist + 1e-10))**reweight_pow
        weight = inv_hist / inv_hist.sum()
        self.loss_weight.copy_(torch.from_numpy(weight))
        self.head.criterion = nn.CrossEntropyLoss(weight=self.loss_weight)