from pydoc import describe
import gradio as gr
import torch
from omegaconf import OmegaConf
import sys 
sys.path.append(".")
sys.path.append('./taming-transformers')
sys.path.append('./latent-diffusion')
from taming.models import vqgan 
from ldm.util import instantiate_from_config

torch.hub.download_url_to_file('http://batbot.ai/models/latent-diffusion/models/ldm/text2img-large/model.ckpt','txt2img-f8-large.ckpt')

#@title Import stuff
import argparse, os, sys, glob
import numpy as np
from PIL import Image
from einops import rearrange
from torchvision.utils import make_grid
import transformers
import gc
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from open_clip import tokenizer
import open_clip

def load_model_from_config(config, ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cuda")
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    if len(m) > 0 and verbose:
        print("missing keys:")
        print(m)
    if len(u) > 0 and verbose:
        print("unexpected keys:")
        print(u)

    model = model.half().cuda()
    model.eval()
    return model

def load_safety_model(clip_model):
    """load the safety model"""
    import autokeras as ak  # pylint: disable=import-outside-toplevel
    from tensorflow.keras.models import load_model  # pylint: disable=import-outside-toplevel
    from os.path import expanduser  # pylint: disable=import-outside-toplevel

    home = expanduser("~")

    cache_folder = home + "/.cache/clip_retrieval/" + clip_model.replace("/", "_")
    if clip_model == "ViT-L/14":
        model_dir = cache_folder + "/clip_autokeras_binary_nsfw"
        dim = 768
    elif clip_model == "ViT-B/32":
        model_dir = cache_folder + "/clip_autokeras_nsfw_b32"
        dim = 512
    else:
        raise ValueError("Unknown clip model")
    if not os.path.exists(model_dir):
        os.makedirs(cache_folder, exist_ok=True)

        from urllib.request import urlretrieve  # pylint: disable=import-outside-toplevel

        path_to_zip_file = cache_folder + "/clip_autokeras_binary_nsfw.zip"
        if clip_model == "ViT-L/14":
            url_model = "https://raw.githubusercontent.com/LAION-AI/CLIP-based-NSFW-Detector/main/clip_autokeras_binary_nsfw.zip"
        elif clip_model == "ViT-B/32":
            url_model = (
                "https://raw.githubusercontent.com/LAION-AI/CLIP-based-NSFW-Detector/main/clip_autokeras_nsfw_b32.zip"
            )
        else:
            raise ValueError("Unknown model {}".format(clip_model))
        urlretrieve(url_model, path_to_zip_file)
        import zipfile  # pylint: disable=import-outside-toplevel

        with zipfile.ZipFile(path_to_zip_file, "r") as zip_ref:
            zip_ref.extractall(cache_folder)

    loaded_model = load_model(model_dir, custom_objects=ak.CUSTOM_OBJECTS)
    loaded_model.predict(np.random.rand(10 ** 3, dim).astype("float32"), batch_size=10 ** 3)

    return loaded_model

def is_unsafe(safety_model, embeddings, threshold=0.5):
    """find unsafe embeddings"""
    nsfw_values = safety_model.predict(embeddings, batch_size=embeddings.shape[0])
    x = np.array([e[0] for e in nsfw_values])
    return True if x > threshold else False

config = OmegaConf.load("latent-diffusion/configs/latent-diffusion/txt2img-1p4B-eval.yaml")
model = load_model_from_config(config, f"txt2img-f8-large.ckpt")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = model.to(device)

#NSFW CLIP Filter
safety_model = load_safety_model("ViT-B/32")
clip_model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='openai')

def run(prompt, steps, width, height, images, scale):
    opt = argparse.Namespace(
        prompt = prompt, 
        outdir='latent-diffusion/outputs',
        ddim_steps = int(steps),
        ddim_eta = 0,
        n_iter = 1,
        W=int(width),
        H=int(height),
        n_samples=int(images),
        scale=scale,
        plms=True
    )

    if opt.plms:
        opt.ddim_eta = 0
        sampler = PLMSSampler(model)
    else:
        sampler = DDIMSampler(model)
    
    os.makedirs(opt.outdir, exist_ok=True)
    outpath = opt.outdir

    prompt = opt.prompt


    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)
    base_count = len(os.listdir(sample_path))

    all_samples=list()
    all_samples_images=list()
    with torch.no_grad():
        with torch.cuda.amp.autocast():
            with model.ema_scope():
                uc = None
                if opt.scale > 0:
                    uc = model.get_learned_conditioning(opt.n_samples * [""])
                for n in range(opt.n_iter):
                    c = model.get_learned_conditioning(opt.n_samples * [prompt])
                    shape = [4, opt.H//8, opt.W//8]
                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
                                                    conditioning=c,
                                                    batch_size=opt.n_samples,
                                                    shape=shape,
                                                    verbose=False,
                                                    unconditional_guidance_scale=opt.scale,
                                                    unconditional_conditioning=uc,
                                                    eta=opt.ddim_eta)

                    x_samples_ddim = model.decode_first_stage(samples_ddim)
                    x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)

                    for x_sample in x_samples_ddim:
                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                        image_vector = Image.fromarray(x_sample.astype(np.uint8))
                        image_preprocess = preprocess(image_vector).unsqueeze(0)
                        with torch.no_grad():
                          image_features = clip_model.encode_image(image_preprocess)
                        image_features /= image_features.norm(dim=-1, keepdim=True)
                        query = image_features.cpu().detach().numpy().astype("float32")
                        unsafe = is_unsafe(safety_model,query,0.5)
                        if(not unsafe):
                            all_samples_images.append(image_vector)
                        else:
                            return(None,None,"Sorry, potential NSFW content was detected on your outputs by our NSFW detection model. Try again with different prompts. If you feel your prompt was not supposed to give NSFW outputs, this may be due to a bias in the model. Read more about biases in the Biases Acknowledgment section below.")
                        #Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:04}.png"))
                        base_count += 1
                    all_samples.append(x_samples_ddim)
                    
    
    # additionally, save as grid
    grid = torch.stack(all_samples, 0)
    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
    grid = make_grid(grid, nrow=2)
    # to image
    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
    
    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'{prompt.replace(" ", "-")}.png'))
    return(Image.fromarray(grid.astype(np.uint8)),all_samples_images,None)

image = gr.outputs.Image(type="pil", label="Your result")
css = ".output-image{height: 528px !important} .output-carousel .output-image{height:272px !important} a{text-decoration: underline}"
iface = gr.Interface(fn=run, inputs=[
    gr.inputs.Textbox(label="Prompt - try adding increments to your prompt such as 'oil on canvas', 'a painting', 'a book cover'",default="chalk pastel drawing of a dog wearing a funny hat"),
    gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=45,maximum=50,minimum=1,step=1),
    gr.inputs.Radio(label="Width", choices=[32,64,128,256],default=256),
    gr.inputs.Radio(label="Height", choices=[32,64,128,256],default=256),
    gr.inputs.Slider(label="Images - How many images you wish to generate", default=2, step=1, minimum=1, maximum=4),
    gr.inputs.Slider(label="Diversity scale - How different from one another you wish the images to be",default=5.0, minimum=1.0, maximum=15.0),
    #gr.inputs.Slider(label="ETA - between 0 and 1. Lower values can provide better quality, higher values can be more diverse",default=0.0,minimum=0.0, maximum=1.0,step=0.1),
    ], 
    outputs=[image,gr.outputs.Carousel(label="Individual images",components=["image"]),gr.outputs.Textbox(label="Error")],
    css=css,
    title="Generate images from text with Latent Diffusion LAION-400M",
    description="<div>By typing a prompt and pressing submit you can generate images based on this prompt. <a href='https://github.com/CompVis/latent-diffusion' target='_blank'>Latent Diffusion</a> is a text-to-image model created by <a href='https://github.com/CompVis' target='_blank'>CompVis</a>, trained on the <a href='https://laion.ai/laion-400-open-dataset/'>LAION-400M dataset.</a><br>This UI to the model was assembled by <a style='color: rgb(245, 158, 11);font-weight:bold' href='https://twitter.com/multimodalart' target='_blank'>@multimodalart</a></div>",
    article="<h4 style='font-size: 110%;margin-top:.5em'>Biases acknowledgment</h4><div>Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exarcbates societal biases. According to the <a href='https://arxiv.org/abs/2112.10752' target='_blank'>Latent Diffusion paper</a>:<i> \"Deep learning modules tend to reproduce or exacerbate biases that are already present in the data\"</i>. The model was trained on an unfiltered version the LAION-400M dataset, which scrapped non-curated image-text-pairs from the internet (the exception being the the removal of illegal content) and is meant to be used for research purposes, such as this one. <a href='https://laion.ai/laion-400-open-dataset/' target='_blank'>You can read more on LAION's website</a></div><h4 style='font-size: 110%;margin-top:1em'>Who owns the images produced by this demo?</h4><div>Definetly not me! Probably you do. I say probably because the Copyright discussion about AI generated art is ongoing. So <a href='https://www.theverge.com/2022/2/21/22944335/us-copyright-office-reject-ai-generated-art-recent-entrance-to-paradise' target='_blank'>it may be the case that everything produced here falls automatically into the public domain</a>. But in any case it is either yours or is in the public domain.</div>")
iface.launch(enable_queue=True)