# type: ignore
# Adapted from https://github.com/patrickvonplaten/controlnet_aux/master/src/controlnet_aux/pidi/__init__.py

import os
import warnings

import cv2
import numpy as np
import torch
from einops import rearrange
from PIL import Image

from enfugue.diffusion.support.util import HWC3, nms, resize_image, safe_step
from enfugue.diffusion.support.edge.pidi.model import pidinet


class PidiNetDetector:
    def __init__(self, netNetwork):
        self.netNetwork = netNetwork

    @classmethod
    def from_pretrained(cls, model_path):
        assert os.path.exists(model_path), "pidinet file not accessible"

        netNetwork = pidinet()
        netNetwork.load_state_dict(
            {k.replace("module.", ""): v for k, v in torch.load(model_path)["state_dict"].items()}
        )
        netNetwork.eval()

        return cls(netNetwork)

    def to(self, device):
        self.netNetwork.to(device)
        return self

    def __call__(
        self,
        input_image,
        detect_resolution=512,
        image_resolution=512,
        safe=False,
        output_type="pil",
        scribble=False,
        apply_filter=False,
        **kwargs
    ):
        if "return_pil" in kwargs:
            warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
            output_type = "pil" if kwargs["return_pil"] else "np"
        if type(output_type) is bool:
            warnings.warn(
                "Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions"
            )
            if output_type:
                output_type = "pil"

        device = next(iter(self.netNetwork.parameters())).device
        if not isinstance(input_image, np.ndarray):
            input_image = np.array(input_image, dtype=np.uint8)

        input_image = HWC3(input_image)
        input_image = resize_image(input_image, detect_resolution)
        assert input_image.ndim == 3
        input_image = input_image[:, :, ::-1].copy()
        with torch.no_grad():
            image_pidi = torch.from_numpy(input_image).float().to(device)
            image_pidi = image_pidi / 255.0
            image_pidi = rearrange(image_pidi, "h w c -> 1 c h w")
            edge = self.netNetwork(image_pidi)[-1]
            edge = edge.cpu().numpy()
            if apply_filter:
                edge = edge > 0.5
            if safe:
                edge = safe_step(edge)
            edge = (edge * 255.0).clip(0, 255).astype(np.uint8)

        detected_map = edge[0, 0]
        detected_map = HWC3(detected_map)

        img = resize_image(input_image, image_resolution)
        H, W, C = img.shape

        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)

        if scribble:
            detected_map = nms(detected_map, 127, 3.0)
            detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
            detected_map[detected_map > 4] = 255
            detected_map[detected_map < 255] = 0

        if output_type == "pil":
            detected_map = Image.fromarray(detected_map)

        return detected_map
