import os

import cv2
import numpy as np
import torch
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image
import safetensors
from modules import devices
from modules.shared import opts
from modules.control.util import HWC3, resize_image
from .zoedepth.models.zoedepth.zoedepth_v1 import ZoeDepth
from .zoedepth.models.zoedepth_nk.zoedepth_nk_v1 import ZoeDepthNK
from .zoedepth.utils.config import get_config


class ZoeDetector:
    def __init__(self, model):
        self.model = model

    @classmethod
    def from_pretrained(cls, pretrained_model_or_path, model_type="zoedepth", filename=None, cache_dir=None):
        filename = filename or "ZoeD_M12_N.pt"
        if os.path.isdir(pretrained_model_or_path):
            model_path = os.path.join(pretrained_model_or_path, filename)
        else:
            model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir)
        if model_type == "zoedepth":
            model_cls = ZoeDepth
        elif model_type == "zoedepth_nk":
            model_cls = ZoeDepthNK
        else:
            raise ValueError(f"ZoeDepth unknown model type {model_type}")
        conf = get_config(model_type, "infer")
        model = model_cls.build_from_config(conf)
        # model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))['model'])
        if model_path.lower().endswith('.safetensors'):
            model_dict = safetensors.torch.load_file(model_path, device='cpu')
        else:
            model_dict = torch.load(model_path, map_location=torch.device('cpu'))
        if hasattr(model_dict, 'model'):
            model_dict = model_dict['model']
        model.load_state_dict(model_dict, strict=False)
        # timm compatibility issue <https://github.com/isl-org/ZoeDepth/issues/82>
        for b in model.core.core.pretrained.model.blocks:
            b.drop_path = torch.nn.Identity()
        model.eval()
        return cls(model)

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

    def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type=None, gamma_corrected=False):
        self.model.to(devices.device)
        device = next(iter(self.model.parameters())).device
        if not isinstance(input_image, np.ndarray):
            input_image = np.array(input_image, dtype=np.uint8)
            output_type = output_type or "pil"
        else:
            output_type = output_type or "np"
        input_image = HWC3(input_image)
        input_image = resize_image(input_image, detect_resolution)
        assert input_image.ndim == 3
        image_depth = input_image
        image_depth = torch.from_numpy(image_depth).float().to(device)
        image_depth = image_depth / 255.0
        image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
        depth = self.model.infer(image_depth)
        if opts.control_move_processor:
            self.model.to('cpu')
        depth = depth[0, 0].cpu().numpy()
        vmin = np.percentile(depth, 2)
        vmax = np.percentile(depth, 85)
        depth -= vmin
        depth /= vmax - vmin
        depth = 1.0 - depth
        if gamma_corrected:
            depth = np.power(depth, 2.2)
        depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8)
        detected_map = depth_image
        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 output_type == "pil":
            detected_map = Image.fromarray(detected_map)
        return detected_map