from transformers import AutoFeatureExtractor
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from PIL import Image
import numpy as np

def check_safety(x_image):
    safety_model_id = "CompVis/stable-diffusion-safety-checker"
    safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
    safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)

    safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
    x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
    assert x_checked_image.shape[0] == len(has_nsfw_concept)
    for i in range(len(has_nsfw_concept)):
        if has_nsfw_concept[i]:
            x_checked_image[i] = load_replacement(x_checked_image[i])
    return x_checked_image, has_nsfw_concept

def numpy_to_pil(images):
    """
    Convert a numpy image or a batch of images to a PIL image.
    """
    if images.ndim == 3:
        images = images[None, ...]
    images = (images * 255).round().astype("uint8")
    pil_images = [Image.fromarray(image) for image in images]

    return pil_images

def load_replacement(x):
    try:
        hwc = x.shape
        y = Image.open("html/NSFW_replace.jpg").convert("RGB").resize((hwc[1], hwc[0]))
        y = (np.array(y)/255.0).astype(x.dtype)
        assert y.shape == x.shape
        return y
    except Exception as e:
        return x