import torch
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm

from .shelf import RAFT
from .interpolation import interpolate
from dot.utils.io import read_config
from dot.utils.torch import get_grid, get_sobel_kernel


class OpticalFlow(nn.Module):
    def __init__(self, height, width, config, load_path):
        super().__init__()
        model_args = read_config(config)
        model_dict = {"raft": RAFT}
        self.model = model_dict[model_args.name](model_args)
        self.name = model_args.name
        if load_path is not None:
            device = next(self.model.parameters()).device
            self.model.load_state_dict(torch.load(load_path, map_location=device))
        coarse_height, coarse_width = height // model_args.patch_size, width // model_args.patch_size
        self.register_buffer("coarse_grid", get_grid(coarse_height, coarse_width))

    def forward(self, data, mode, **kwargs):
        if mode == "flow_with_tracks_init":
            return self.get_flow_with_tracks_init(data, **kwargs)
        elif mode == "motion_boundaries":
            return self.get_motion_boundaries(data, **kwargs)
        elif mode == "feats":
            return self.get_feats(data, **kwargs)
        elif mode == "tracks_for_queries":
            return self.get_tracks_for_queries(data, **kwargs)
        elif mode == "tracks_from_first_to_every_other_frame":
            return self.get_tracks_from_first_to_every_other_frame(data, **kwargs)
        elif mode == "flow_from_last_to_first_frame":
            return self.get_flow_from_last_to_first_frame(data, **kwargs)
        else:
            raise ValueError(f"Unknown mode {mode}")

    def get_motion_boundaries(self, data, boundaries_size=1, boundaries_dilation=4, boundaries_thresh=0.025, **kwargs):
        eps = 1e-12
        src_frame, tgt_frame = data["src_frame"], data["tgt_frame"]
        K = boundaries_size * 2 + 1
        D = boundaries_dilation
        B, _, H, W = src_frame.shape
        reflect = torch.nn.ReflectionPad2d(K // 2)
        sobel_kernel = get_sobel_kernel(K).to(src_frame.device)
        flow, _ = self.model(src_frame, tgt_frame)
        norm_flow = torch.stack([flow[..., 0] / (W - 1), flow[..., 1] / (H - 1)], dim=-1)
        norm_flow = norm_flow.permute(0, 3, 1, 2).reshape(-1, 1, H, W)
        boundaries = F.conv2d(reflect(norm_flow), sobel_kernel)
        boundaries = ((boundaries ** 2).sum(dim=1, keepdim=True) + eps).sqrt()
        boundaries = boundaries.view(-1, 2, H, W).mean(dim=1, keepdim=True)
        if boundaries_dilation > 1:
            boundaries = torch.nn.functional.max_pool2d(boundaries, kernel_size=D * 2, stride=1, padding=D)
            boundaries = boundaries[:, :, -H:, -W:]
        boundaries = boundaries[:, 0]
        boundaries = boundaries - boundaries.reshape(B, -1).min(dim=1)[0].reshape(B, 1, 1)
        boundaries = boundaries / boundaries.reshape(B, -1).max(dim=1)[0].reshape(B, 1, 1)
        boundaries = boundaries > boundaries_thresh
        return {"motion_boundaries": boundaries, "flow": flow}

    def get_feats(self, data, **kwargs):
        video = data["video"]
        feats = []
        for step in tqdm(range(video.size(1)), desc="Extract feats for frame", leave=False):
            feats.append(self.model.encode(video[:, step]))
        feats = torch.stack(feats, dim=1)
        return {"feats": feats}

    def get_flow_with_tracks_init(self, data, is_train=False, interpolation_version="torch3d", alpha_thresh=0.8, **kwargs):
        coarse_flow, coarse_alpha = interpolate(data["src_points"], data["tgt_points"], self.coarse_grid,
                                                version=interpolation_version)
        flow, alpha = self.model(src_frame=data["src_frame"] if "src_feats" not in data else None,
                                 tgt_frame=data["tgt_frame"] if "tgt_feats" not in data else None,
                                 src_feats=data["src_feats"] if "src_feats" in data else None,
                                 tgt_feats=data["tgt_feats"] if "tgt_feats" in data else None,
                                 coarse_flow=coarse_flow,
                                 coarse_alpha=coarse_alpha,
                                 is_train=is_train)
        if not is_train:
            alpha = (alpha > alpha_thresh).float()
        return {"flow": flow, "alpha": alpha, "coarse_flow": coarse_flow, "coarse_alpha": coarse_alpha}

    def get_tracks_for_queries(self, data, **kwargs):
        raise NotImplementedError