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

from .backbone import build_backbone
from .utils.position_encoding import PositionEncodingSine
from .loftr_module import LocalFeatureTransformer, FinePreprocess
from .utils.coarse_matching import CoarseMatching
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=config["coarse"]["temp_bug_fix"]
        )
        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()

    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["image0"], data["image1"]], dim=0))
            (feat_c0, feat_c1), (feat_f0, feat_f1) = feats_c.split(data["bs"]), feats_f.split(data["bs"])
        else:  # handle different input shapes
            (feat_c0, feat_f0), (feat_c1, feat_f1) = self.backbone(data["image0"]), self.backbone(data["image1"])

        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
        # 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("matcher."):
                state_dict[k.replace("matcher.", "", 1)] = state_dict.pop(k)
        return super().load_state_dict(state_dict, *args, **kwargs)
