import gradio as gr
import cv2
import torch
import numpy as np
from torchvision import transforms

# title = "Remove Bg"
# description = "Automatically remove the image background from a profile photo."
# article = "<p style='text-align: center'><a href='https://news.machinelearning.sg/posts/beautiful_profile_pics_remove_background_image_with_deeplabv3/'>Blog</a> | <a href='https://github.com/eugenesiow/practical-ml'>Github Repo</a></p>"

import argparse, os
import cv2
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import nullcontext

from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.modules.diffusionmodules.openaimodel import clear_feature_dic,get_feature_dic
from ldm.models.seg_module import Segmodule

import numpy as np

os.environ["CUDA_VISIBLE_DEVICES"] = "1"

def chunk(it, size):
    it = iter(it)
    return iter(lambda: tuple(islice(it, size)), ())


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_model_from_config(config, ckpt, verbose=False):
    # print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    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.cuda()
    model.eval()
    return model


def put_watermark(img, wm_encoder=None):
    if wm_encoder is not None:
        img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
        img = wm_encoder.encode(img, 'dwtDct')
        img = Image.fromarray(img[:, :, ::-1])
    return img


def load_replacement(x):
    try:
        hwc = x.shape
        y = Image.open("assets/rick.jpeg").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:
        return x

def plot_mask(img, masks, colors=None, alpha=0.8,indexlist=[0,1]) -> np.ndarray:
    H,W= masks.shape[0],masks.shape[1]
    color_list=[[255,97,0],[128,42,42],[220,220,220],[255,153,18],[56,94,15],[127,255,212],[210,180,140],[221,160,221],[255,0,0],[255,128,0],[255,255,0],[128,255,0],[0,255,0],[0,255,128],[0,255,255],[0,128,255],[0,0,255],[128,0,255],[255,0,255],[255,0,128]]*6
    final_color_list=[np.array([[i]*512]*512) for i in color_list]
    
    background=np.ones(img.shape)*255
    count=0
    colors=final_color_list[indexlist[count]]
    for mask, color in zip(masks, colors):
        color=final_color_list[indexlist[count]]
        mask = np.stack([mask, mask, mask], -1)
        img = np.where(mask, img * (1 - alpha) + color * alpha,background*0.4+img*0.6 )
        count+=1
    return img.astype(np.uint8)

def create_parser():

    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--prompt",
        type=str,
        nargs="?",
        default="a photo of a lion on a mountain top at sunset",
        help="the prompt to render"
    )
    parser.add_argument(
        "--category",
        type=str,
        nargs="?",
        default="lion",
        help="the category to ground"
    )
    parser.add_argument(
        "--outdir",
        type=str,
        nargs="?",
        help="dir to write results to",
        default="outputs/txt2img-samples"
    )
    parser.add_argument(
        "--skip_grid",
        action='store_true',
        help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
    )
    parser.add_argument(
        "--skip_save",
        action='store_true',
        help="do not save individual samples. For speed measurements.",
    )
    parser.add_argument(
        "--ddim_steps",
        type=int,
        default=50,
        help="number of ddim sampling steps",
    )
    parser.add_argument(
        "--plms",
        action='store_true',
        help="use plms sampling",
    )
    parser.add_argument(
        "--laion400m",
        action='store_true',
        help="uses the LAION400M model",
    )
    parser.add_argument(
        "--fixed_code",
        action='store_true',
        help="if enabled, uses the same starting code across samples ",
    )
    parser.add_argument(
        "--ddim_eta",
        type=float,
        default=0.0,
        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
    )
    parser.add_argument(
        "--n_iter",
        type=int,
        default=1,
        help="sample this often",
    )
    parser.add_argument(
        "--H",
        type=int,
        default=512,
        help="image height, in pixel space",
    )
    parser.add_argument(
        "--W",
        type=int,
        default=512,
        help="image width, in pixel space",
    )
    parser.add_argument(
        "--C",
        type=int,
        default=4,
        help="latent channels",
    )
    parser.add_argument(
        "--f",
        type=int,
        default=8,
        help="downsampling factor",
    )
    parser.add_argument(
        "--n_samples",
        type=int,
        default=1,
        help="how many samples to produce for each given prompt. A.k.a. batch size",
    )
    parser.add_argument(
        "--n_rows",
        type=int,
        default=0,
        help="rows in the grid (default: n_samples)",
    )
    parser.add_argument(
        "--scale",
        type=float,
        default=7.5,
        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
    )
    parser.add_argument(
        "--from-file",
        type=str,
        help="if specified, load prompts from this file",
    )
    parser.add_argument(
        "--config",
        type=str,
        default="configs/stable-diffusion/v1-inference.yaml",
        help="path to config which constructs model",
    )
    parser.add_argument(
        "--sd_ckpt",
        type=str,
        default="stable_diffusion.ckpt",
        help="path to checkpoint of stable diffusion model",
    )
    parser.add_argument(
        "--grounding_ckpt",
        type=str,
        default="grounding_module.pth",
        help="path to checkpoint of grounding module",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=42,
        help="the seed (for reproducible sampling)",
    )
    parser.add_argument(
        "--precision",
        type=str,
        help="evaluate at this precision",
        choices=["full", "autocast"],
        default="autocast"
    )
    opt = parser.parse_args()
    
    return opt


def inference(input_prompt, input_category):

    opt = create_parser()

    seed_everything(opt.seed)
    
    tic = time.time()
    config = OmegaConf.load(f"{opt.config}")
    model = load_model_from_config(config, f"{opt.sd_ckpt}")
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = model.to(device)
    toc = time.time()
    seg_module=Segmodule().to(device)
        
    seg_module.load_state_dict(torch.load(opt.grounding_ckpt, map_location="cpu"), strict=True)
    # print('load time:',toc-tic)
    sampler = DDIMSampler(model)

    os.makedirs(opt.outdir, exist_ok=True)
    outpath = opt.outdir
    batch_size = opt.n_samples
    precision_scope = autocast if opt.precision=="autocast" else nullcontext
    with torch.no_grad():
        with precision_scope("cuda"):
            with model.ema_scope():
                prompt = input_prompt
                text = input_category
                trainclass = text
                if not opt.from_file:
                    assert prompt is not None
                    data = [batch_size * [prompt]]

                else:
                    # print(f"reading prompts from {opt.from_file}")
                    with open(opt.from_file, "r") as f:
                        data = f.read().splitlines()
                        data = list(chunk(data, batch_size))

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

                start_code = None
                if opt.fixed_code:
                    # print('start_code')
                    start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
                for n in trange(opt.n_iter, desc="Sampling"):
                    for prompts in tqdm(data, desc="data"):
                        clear_feature_dic()
                        uc = None
                        if opt.scale != 1.0:
                            uc = model.get_learned_conditioning(batch_size * [""])
                        if isinstance(prompts, tuple):
                            prompts = list(prompts)
                        
                        c = model.get_learned_conditioning(prompts)
                        shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
                        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_T=start_code)

                        x_samples_ddim = model.decode_first_stage(samples_ddim)
                        diffusion_features = get_feature_dic()
                
                        
                        x_sample = torch.clamp((x_samples_ddim[0] + 1.0) / 2.0, min=0.0, max=1.0)
                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                        
                        Image.fromarray(x_sample.astype(np.uint8)).save("demo/demo.png")    
                        img = x_sample.astype(np.uint8)
                        
                        class_name = trainclass
                        
                        query_text ="a photograph of a " + class_name
                        c_split = model.cond_stage_model.tokenizer.tokenize(query_text)
                        
                        sen_text_embedding = model.get_learned_conditioning(query_text)
                        class_embedding = sen_text_embedding[:, 5:len(c_split)+1, :]
                        
                        if class_embedding.size()[1] > 1:
                            class_embedding = torch.unsqueeze(class_embedding.mean(1), 1)
                        text_embedding = class_embedding
                    
                        text_embedding = text_embedding.repeat(batch_size, 1, 1)
                        
                                    
                        pred_seg_total = seg_module(diffusion_features, text_embedding)
                    
                        
                        pred_seg = torch.unsqueeze(pred_seg_total[0,0,:,:], 0).unsqueeze(0)
                            
                        label_pred_prob = torch.sigmoid(pred_seg)
                        label_pred_mask = torch.zeros_like(label_pred_prob, dtype=torch.float32)
                        label_pred_mask[label_pred_prob > 0.5] = 1
                        annotation_pred = label_pred_mask[0][0].cpu()
                        
                        mask = annotation_pred.numpy()
                        mask = np.expand_dims(mask, 0)
                        done_image_mask = plot_mask(img, mask, alpha=0.9, indexlist=[0])
                        # cv2.imwrite(os.path.join("demo/demo_mask.png"), done_image_mask)
                        
                        # torchvision.utils.save_image(annotation_pred, os.path.join("demo/demo_segresult.png"), normalize=True, scale_each=True)
                        generated_image = x_sample
                        generated_mask = done_image_mask
                        return [generated_image, generated_mask]


# def make_transparent_foreground(pic, mask):
#     # split the image into channels
#     b, g, r = cv2.split(np.array(pic).astype('uint8'))
#     # add an alpha channel with and fill all with transparent pixels (max 255)
#     a = np.ones(mask.shape, dtype='uint8') * 255
#     # merge the alpha channel back
#     alpha_im = cv2.merge([b, g, r, a], 4)
#     # create a transparent background
#     bg = np.zeros(alpha_im.shape)
#     # setup the new mask
#     new_mask = np.stack([mask, mask, mask, mask], axis=2)
#     # copy only the foreground color pixels from the original image where mask is set
#     foreground = np.where(new_mask, alpha_im, bg).astype(np.uint8)

#     return foreground


# def remove_background(input_image):
#     preprocess = transforms.Compose([
#         transforms.ToTensor(),
#         transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
#     ])

#     input_tensor = preprocess(input_image)
#     input_batch = input_tensor.unsqueeze(0)  # create a mini-batch as expected by the model

#     # move the input and model to GPU for speed if available
#     if torch.cuda.is_available():
#         input_batch = input_batch.to('cuda')
#         model.to('cuda')

#     with torch.no_grad():
#         output = model(input_batch)['out'][0]
#     output_predictions = output.argmax(0)

#     # create a binary (black and white) mask of the profile foreground
#     mask = output_predictions.byte().cpu().numpy()
#     background = np.zeros(mask.shape)
#     bin_mask = np.where(mask, 255, background).astype(np.uint8)

#     foreground = make_transparent_foreground(input_image, bin_mask)

#     return foreground, bin_mask


# def inference(img):
#     foreground, _ = remove_background(img)
#     return foreground


# torch.hub.download_url_to_file('https://pbs.twimg.com/profile_images/691700243809718272/z7XZUARB_400x400.jpg',
#                                'demis.jpg')
# torch.hub.download_url_to_file('https://hai.stanford.edu/sites/default/files/styles/person_medium/public/2020-03/hai_1512feifei.png?itok=INFuLABp',
#                                'lifeifei.png')
# model = torch.hub.load('pytorch/vision:v0.6.0', 'deeplabv3_resnet101', pretrained=True)
# model.eval()

# gr.Interface(
#     inference,
#     gr.inputs.Textbox(label='Prompt', default='a photo of a lion on a mountain top at sunset'),
#     gr.inputs.Textbox(label='category', default='lion'),
#     gr.outputs.Image(type="pil", label="Output"),
#     # title=title,
#     # description=description,
#     # article=article,
#     # examples=[['demis.jpg'], ['lifeifei.png']],
#     # enable_queue=True
# ).launch(debug=False)

def main():

    # def load_example(
    #     steps: int,
    #     randomize_seed: bool,
    #     seed: int,
    #     randomize_cfg: bool,
    #     text_cfg_scale: float,
    #     image_cfg_scale: float,
    # ):
    #     example_instruction = random.choice(example_instructions)
    #     return [example_image, example_instruction] + generate(
    #         example_image,
    #         example_instruction,
    #         steps,
    #         randomize_seed,
    #         seed,
    #         randomize_cfg,
    #         text_cfg_scale,
    #         image_cfg_scale,
    #     )

    # def generate(
    #     input_image: Image.Image,
    #     instruction: str,
    #     steps: int,
    #     randomize_seed: bool,
    #     seed: int,
    #     randomize_cfg: bool,
    #     text_cfg_scale: float,
    #     image_cfg_scale: float,
    # ):
    #     seed = random.randint(0, 100000) if randomize_seed else seed
    #     text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
    #     image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale

    #     width, height = input_image.size
    #     factor = 512 / max(width, height)
    #     factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
    #     width = int((width * factor) // 64) * 64
    #     height = int((height * factor) // 64) * 64
    #     input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)

    #     if instruction == "":
    #         return [input_image, seed]

    #     generator = torch.manual_seed(seed)
    #     edited_image = pipe(
    #         instruction, image=input_image,
    #         guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale,
    #         num_inference_steps=steps, generator=generator,
    #     ).images[0]
    #     return [seed, text_cfg_scale, image_cfg_scale, edited_image]

    # def reset():
    #     return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None]

    with gr.Blocks() as demo:
        gr.HTML("""<h1 style="font-weight: 900; margin-bottom: 7px;">
   InstructPix2Pix: Learning to Follow Image Editing Instructions
</h1>
<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
<br/>
<a href="https://huggingface.co/spaces/timbrooks/instruct-pix2pix?duplicate=true">
<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
<p/>""")
        with gr.Row():
            # with gr.Column(scale=1, min_width=100):
            #     load_button = gr.Button("Load Example")
            # with gr.Column(scale=1, min_width=100):
            #     reset_button = gr.Button("Reset")
            with gr.Column(scale=3):
                Prompt = gr.Textbox(lines=1, label="Prompt", interactive=True)
            with gr.Column(scale=2):
                Category = gr.Textbox(lines=1, label="Category", interactive=True)
            with gr.Column(scale=1, min_width=100):
                generate_button = gr.Button("Generate")

        with gr.Row():
            generated_image = gr.Image(label="Generated Image", type="pil", interactive=False)
            generated_mask = gr.Image(label=f"Generated Mask", type="pil", interactive=False)
            generated_image.style(height=512, width=512)
            generated_mask.style(height=512, width=512)

        # with gr.Row():
        #     steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
        #     randomize_seed = gr.Radio(
        #         ["Fix Seed", "Randomize Seed"],
        #         value="Randomize Seed",
        #         type="index",
        #         show_label=False,
        #         interactive=True,
        #     )
        #     seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
        #     randomize_cfg = gr.Radio(
        #         ["Fix CFG", "Randomize CFG"],
        #         value="Fix CFG",
        #         type="index",
        #         show_label=False,
        #         interactive=True,
        #     )
        #     text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
        #     image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)

        # gr.Markdown(help_text)

        # load_button.click(
        #     fn=load_example,
        #     inputs=[
        #         steps,
        #         randomize_seed,
        #         seed,
        #         randomize_cfg,
        #         text_cfg_scale,
        #         image_cfg_scale,
        #     ],
        #     outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
        # )
        generate_button.click(
            fn=inference,
            inputs=[
                Prompt,
                Category,
            ],
            outputs=[generated_image, generated_mask],
        )
        # reset_button.click(
        #     fn=reset,
        #     inputs=[],
        #     outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image],
        # )

    demo.queue(concurrency_count=1)
    demo.launch(share=False)



if __name__ == "__main__":
    main()