import torch
import torch.nn as nn
from einops.einops import rearrange

from .backbone import build_backbone
from .utils.position_encoding import PositionEncodingSine
from .submodules import LocalFeatureTransformer, FinePreprocess
import warnings
from .utils.coarse_matching import CoarseMatching
warnings.simplefilter("ignore", UserWarning)
from .utils.fine_matching import FineMatching


class LoFTR(nn.Module):
    def __init__(self, config):
        super().__init__()
        # Misc
        self.config = config

        # Modules
        self.backbone = build_backbone(config)
        self.pos_encoding = PositionEncodingSine(
            config['coarse']['d_model'],
            temp_bug_fix=False)
        self.loftr_coarse = LocalFeatureTransformer(config['coarse'])
        self.coarse_matching = CoarseMatching(config['match_coarse'])
        self.fine_preprocess = FinePreprocess(config)
        self.loftr_fine = LocalFeatureTransformer(config["fine"])
        self.fine_matching = FineMatching()

        """
        outdoor_ds.ckpt: {OrderedDict: 211}
            backbone: {OrderedDict: 107}
            loftr_coarse: {OrderedDict: 80}             
            loftr_fine: {OrderedDict: 20}             
            fine_preprocess: {OrderedDict: 4}             
        """
        if config['weight'] is not None:
            weights = torch.load(config['weight'], map_location='cpu')
            self.load_state_dict(weights)
            # print(config['weight'] + ' load success.')

    def forward(self, data):
        """
        Update:
            data (dict): {
                'image0': (torch.Tensor): (N, 1, H, W)
                'image1': (torch.Tensor): (N, 1, H, W)
                'mask0'(optional) : (torch.Tensor): (N, H, W) '0' indicates a padded position
                'mask1'(optional) : (torch.Tensor): (N, H, W)
            }
        """
        # 1. Local Feature CNN
        data.update({
            'bs': data['image0'].size(0),
            'hw0_i': data['image0'].shape[2:], 'hw1_i': data['image1'].shape[2:]
        })

        if data['hw0_i'] == data['hw1_i']:  # faster & better BN convergence
            feats_c, feats_f = self.backbone(torch.cat([data['color0'], data['color1']], dim=0)) # h == h0 == h1, w == w0 == w1feats_c: (bs*2, 256, h//8, w//8), feats_f: (bs*2, 128, h//2, w//2)
            (feat_c0, feat_c1), (feat_f0, feat_f1) = feats_c.split(data['bs']), feats_f.split(data['bs']) # feat_c0, feat_c1: (bs, 256, h//8, w//8), feat_f0, feat_f1: (bs, 128, h//2, w//2)
        else:  # handle different input shapes
            (feat_c0, feat_f0), (feat_c1, feat_f1) = self.backbone(data['color0']), self.backbone(data['color1'])

        data.update({
            'hw0_c': feat_c0.shape[2:], 'hw1_c': feat_c1.shape[2:],
            'hw0_f': feat_f0.shape[2:], 'hw1_f': feat_f1.shape[2:]
        })

        # 2. coarse-level loftr module
        b, c, h0, w0 = feat_c0.size()
        _, _, h1, w1 = feat_c1.size()
        # add featmap with positional encoding, then flatten it to sequence [N, HW, C]
        feat_c0 = rearrange(self.pos_encoding(feat_c0), 'n c h w -> n (h w) c')
        feat_c1 = rearrange(self.pos_encoding(feat_c1), 'n c h w -> n (h w) c')

        mask_c0 = mask_c1 = None  # mask is useful in training
        if 'mask0' in data:
            mask_c0, mask_c1 = data['mask0'].flatten(-2), data['mask1'].flatten(-2)
        feat_c0, feat_c1 = self.loftr_coarse(feat_c0, feat_c1, mask_c0, mask_c1)

        # 3. match coarse-level
        self.coarse_matching(feat_c0, feat_c1, data, mask_c0=mask_c0, mask_c1=mask_c1)

        # 4. fine-level refinement
        feat_f0_unfold, feat_f1_unfold = self.fine_preprocess(feat_f0, feat_f1, feat_c0, feat_c1, data)
        if feat_f0_unfold.size(0) != 0:  # at least one coarse level predicted
            feat_f0_unfold, feat_f1_unfold = self.loftr_fine(feat_f0_unfold, feat_f1_unfold)

        # 5. match fine-level
        self.fine_matching(feat_f0_unfold, feat_f1_unfold, data)

    def load_state_dict(self, state_dict, *args, **kwargs):
        for k in list(state_dict.keys()):
            if k.startswith('model.'):
                state_dict[k.replace('model.', '', 1)] = state_dict.pop(k)
            if k.startswith('matcher.'):
                state_dict[k.replace('matcher.', '', 1)] = state_dict.pop(k)
        return super().load_state_dict(state_dict, *args, **kwargs)