import sys
import os

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from omegaconf import OmegaConf

sys.path.append("./dinov2")
import hubconf

CONFIG_PATH = './configs/pics.yaml'

def load_config(path=CONFIG_PATH):
    if not os.path.exists(path):
        raise FileNotFoundError(f"Config file not found at {path}")
    return OmegaConf.load(path)

config = load_config()

DINOv2_weight_path = config.model.params.cond_stage_config.weight

class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm (with cast back to input dtype)."""

    def forward(self, x: torch.Tensor):
        orig_type = x.dtype
        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        return x.to(orig_type)

class AbstractEncoder(nn.Module):
    def __init__(self):
        super().__init__()

    def encode(self, *args, **kwargs):
        raise NotImplementedError


def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


class FrozenDinoV2Encoder(AbstractEncoder):
    """
    Uses the DINOv2 encoder for image
    """
    def __init__(self, device="cuda", freeze=True):
        super().__init__()
        dinov2 = hubconf.dinov2_vitg14() 
        state_dict = torch.load(DINOv2_weight_path)
        dinov2.load_state_dict(state_dict, strict=False)
        self.model = dinov2.to(device)
        self.device = device
        if freeze:
            self.freeze()
        self.image_mean = torch.tensor([0.485, 0.456, 0.406]).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
        self.image_std =  torch.tensor([0.229, 0.224, 0.225]).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)        
        self.projector = nn.Linear(1536,1024)

    def freeze(self):
        self.model.eval()
        for param in self.model.parameters():
            param.requires_grad = False

    def forward(self, image):
        if isinstance(image,list):
            image = torch.cat(image,0)

        image = (image.to(self.device)  - self.image_mean.to(self.device)) / self.image_std.to(self.device)
        features = self.model.forward_features(image)
        tokens = features["x_norm_patchtokens"]
        image_features  = features["x_norm_clstoken"]
        image_features = image_features.unsqueeze(1)
        hint = torch.cat([image_features,tokens],1) # 8,257,1024
        hint = self.projector(hint)
        return hint

    def encode(self, image):
        return self(image)