# Pidinet
# https://github.com/hellozhuo/pidinet

import os
import torch
import numpy as np
from einops import rearrange
from .model import pidinet
from .util import annotator_ckpts_path, safe_step


class PidiNetDetector:
    def __init__(self, device):
        remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/table5_pidinet.pth"
        modelpath = os.path.join(annotator_ckpts_path, "table5_pidinet.pth")
        if not os.path.exists(modelpath):
            from basicsr.utils.download_util import load_file_from_url
            load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
        self.netNetwork = pidinet()
        self.netNetwork.load_state_dict(
            {k.replace('module.', ''): v for k, v in torch.load(modelpath)['state_dict'].items()})
        self.netNetwork.to(device).eval().requires_grad_(False)

    def __call__(self, input_image):  # , safe=False):
        return self.netNetwork(input_image)[-1]
        # assert input_image.ndim == 3
        # input_image = input_image[:, :, ::-1].copy()
        # with torch.no_grad():
        #     image_pidi = torch.from_numpy(input_image).float().cuda()
        #     image_pidi = image_pidi / 255.0
        #     image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w')
        #     edge = self.netNetwork(image_pidi)[-1]

        #     if safe:
        #         edge = safe_step(edge)
        #     edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
        #     return edge[0][0]