from modules_forge.supported_preprocessor import Preprocessor, PreprocessorParameter
from modules_forge.shared import preprocessor_dir, add_supported_preprocessor
from modules_forge.utils import resize_image_with_pad
from modules.modelloader import load_file_from_url

import types
import torch
import numpy as np

from einops import rearrange
from annotator.normalbae.models.NNET import NNET
from annotator.normalbae import load_checkpoint
from torchvision import transforms


class PreprocessorNormalBae(Preprocessor):
    def __init__(self):
        super().__init__()
        self.name = 'normalbae'
        self.tags = ['NormalMap']
        self.model_filename_filters = ['normal']
        self.slider_resolution = PreprocessorParameter(
            label='Resolution', minimum=128, maximum=2048, value=512, step=8, visible=True)
        self.slider_1 = PreprocessorParameter(visible=False)
        self.slider_2 = PreprocessorParameter(visible=False)
        self.slider_3 = PreprocessorParameter(visible=False)
        self.show_control_mode = True
        self.do_not_need_model = False
        self.sorting_priority = 100  # higher goes to top in the list

    def load_model(self):
        if self.model_patcher is not None:
            return

        model_path = load_file_from_url(
            "https://huggingface.co/lllyasviel/Annotators/resolve/main/scannet.pt",
            model_dir=preprocessor_dir)

        args = types.SimpleNamespace()
        args.mode = 'client'
        args.architecture = 'BN'
        args.pretrained = 'scannet'
        args.sampling_ratio = 0.4
        args.importance_ratio = 0.7
        model = NNET(args)
        model = load_checkpoint(model_path, model)
        self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

        self.model_patcher = self.setup_model_patcher(model)

    def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, **kwargs):
        input_image, remove_pad = resize_image_with_pad(input_image, resolution)

        self.load_model()

        self.move_all_model_patchers_to_gpu()

        assert input_image.ndim == 3
        image_normal = input_image

        with torch.no_grad():
            image_normal = self.send_tensor_to_model_device(torch.from_numpy(image_normal))
            image_normal = image_normal / 255.0
            image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
            image_normal = self.norm(image_normal)

            normal = self.model_patcher.model(image_normal)
            normal = normal[0][-1][:, :3]
            normal = ((normal + 1) * 0.5).clip(0, 1)

            normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
            normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8)

        return remove_pad(normal_image)


add_supported_preprocessor(PreprocessorNormalBae())