from .network import UNet
from .util import seg2img
import torch
import os
import cv2
from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download, BDS_MODEL_NAME
from huggingface_hub import hf_hub_download
from PIL import Image
from einops import rearrange
from .anime_segmentation import AnimeSegmentation
import numpy as np

class AnimeFaceSegmentor:
    def __init__(self, model, seg_model):
        self.model = model
        self.seg_model = seg_model
        self.device = "cpu"

    @classmethod
    def from_pretrained(cls, pretrained_model_or_path=BDS_MODEL_NAME, filename="UNet.pth", seg_filename="isnetis.ckpt"):
        model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder="Annotators")
        seg_model_path = custom_hf_download("skytnt/anime-seg", seg_filename)

        model = UNet()
        ckpt = torch.load(model_path, map_location="cpu")
        model.load_state_dict(ckpt)
        model.eval()
        
        seg_model = AnimeSegmentation(seg_model_path)
        seg_model.net.eval()
        return cls(model, seg_model)

    def to(self, device):
        self.model.to(device)
        self.seg_model.net.to(device)
        self.device = device
        return self

    def __call__(self, input_image, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", remove_background=True, **kwargs):
        input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
        input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method)

        with torch.no_grad():
            if remove_background:
                print(input_image.shape)
                mask, input_image = self.seg_model(input_image, 0) #Don't resize image as it is resized
            image_feed = torch.from_numpy(input_image).float().to(self.device)
            image_feed = rearrange(image_feed, 'h w c -> 1 c h w')
            image_feed = image_feed / 255
            seg = self.model(image_feed).squeeze(dim=0)
            result = seg2img(seg.cpu().detach().numpy())
        
        detected_map = HWC3(result)
        detected_map = remove_pad(detected_map)
        if remove_background:
            mask = remove_pad(mask)
            H, W, C = detected_map.shape
            tmp = np.zeros([H, W, C + 1])
            tmp[:,:,:C] = detected_map
            tmp[:,:,3:] = mask
            detected_map = tmp
        
        if output_type == "pil":
            detected_map = Image.fromarray(detected_map[..., :3])
        
        return detected_map