import os
import random

import autocuda
from pyabsa.utils.pyabsa_utils import fprint

from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, \
    DPMSolverMultistepScheduler
import gradio as gr
import torch
from PIL import Image
import utils
import datetime
import time
import psutil
from Waifu2x.magnify import ImageMagnifier

magnifier = ImageMagnifier()

start_time = time.time()
is_colab = utils.is_google_colab()

CUDA_VISIBLE_DEVICES = ''
device = autocuda.auto_cuda()

dtype = torch.float16 if device != 'cpu' else torch.float32


class Model:
    def __init__(self, name, path="", prefix=""):
        self.name = name
        self.path = path
        self.prefix = prefix
        self.pipe_t2i = None
        self.pipe_i2i = None


models = [
     Model("DnD Cover Art", "sd-dreambooth-library/dndcoverart-v1", "Use the token 'dndcoverart'"), 
     Model("test2", "Jackflack09/mrsrm", "testing2"),
]
#  Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
#  Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
#  Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
#  Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ")
# Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
# Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
# Model("Robo Diffusion", "nousr/robo-diffusion", ""),

scheduler = DPMSolverMultistepScheduler(
    beta_start=0.00085,
    beta_end=0.012,
    beta_schedule="scaled_linear",
    num_train_timesteps=1000,
    trained_betas=None,
    predict_epsilon=True,
    thresholding=False,
    algorithm_type="dpmsolver++",
    solver_type="midpoint",
    lower_order_final=True,
)

custom_model = None
if is_colab:
    models.insert(0, Model("Custom model"))
    custom_model = models[0]

last_mode = "txt2img"
current_model = models[1] if is_colab else models[0]
current_model_path = current_model.path

if is_colab:
    pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=dtype, scheduler=scheduler,
                                                   safety_checker=lambda images, clip_input: (images, False))

else:  # download all models
    print(f"{datetime.datetime.now()} Downloading vae...")
    vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=dtype)
    for model in models:
        try:
            print(f"{datetime.datetime.now()} Downloading {model.name} model...")
            unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=dtype)
            model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae,
                                                                     torch_dtype=dtype, scheduler=scheduler,
                                                                     safety_checker=None)
            model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae,
                                                                            torch_dtype=dtype,
                                                                            scheduler=scheduler, safety_checker=None)
        except Exception as e:
            print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e))
            models.remove(model)
    pipe = models[0].pipe_t2i

# model.pipe_i2i = torch.compile(model.pipe_i2i)
# model.pipe_t2i = torch.compile(model.pipe_t2i)
if torch.cuda.is_available():
    pipe = pipe.to(device)


# device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"


def error_str(error, title="Error"):
    return f"""#### {title}
            {error}""" if error else ""


def custom_model_changed(path):
    models[0].path = path
    global current_model
    current_model = models[0]


def on_model_change(model_name):
    prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name),
                                       None) + "\" is prefixed automatically" if model_name != models[
        0].name else "Don't forget to use the custom model prefix in the prompt!"

    return gr.update(visible=model_name == models[0].name), gr.update(placeholder=prefix)


def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5,
              neg_prompt="", scale_factor=2):
    fprint(psutil.virtual_memory())  # print memory usage
    prompt = 'detailed fingers, beautiful hands,' + prompt
    fprint(f"Prompt: {prompt}")
    global current_model
    for model in models:
        if model.name == model_name:
            current_model = model
            model_path = current_model.path

    generator = torch.Generator(device).manual_seed(seed) if seed != 0 else None

    try:
        if img is not None:
            return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height,
                              generator, scale_factor), None
        else:
            return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator,
                              scale_factor), None
    except Exception as e:
        return None, error_str(e)
    # if img is not None:
    #     return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height,
    #                       generator, scale_factor), None
    # else:
    #     return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator, scale_factor), None


def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator, scale_factor):
    print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")

    global last_mode
    global pipe
    global current_model_path
    if model_path != current_model_path or last_mode != "txt2img":
        current_model_path = model_path

        if is_colab or current_model == custom_model:
            pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=dtype,
                                                           scheduler=scheduler,
                                                           safety_checker=lambda images, clip_input: (images, False))
        else:
            # pipe = pipe.to("cpu")
            pipe = current_model.pipe_t2i

        if torch.cuda.is_available():
            pipe = pipe.to(device)
        last_mode = "txt2img"

    prompt = current_model.prefix + prompt
    result = pipe(
        prompt,
        negative_prompt=neg_prompt,
        # num_images_per_prompt=n_images,
        num_inference_steps=int(steps),
        guidance_scale=guidance,
        width=width,
        height=height,
        generator=generator)
    result.images[0] = magnifier.magnify(result.images[0], scale_factor=scale_factor)

    # save image
    result.images[0].save("imgs/result-{}.png".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))
    return replace_nsfw_images(result)


def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator, scale_factor):
    fprint(f"{datetime.datetime.now()} img_to_img, model: {model_path}")

    global last_mode
    global pipe
    global current_model_path
    if model_path != current_model_path or last_mode != "img2img":
        current_model_path = model_path

        if is_colab or current_model == custom_model:
            pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=dtype,
                                                                  scheduler=scheduler,
                                                                  safety_checker=lambda images, clip_input: (
                                                                      images, False))
        else:
            # pipe = pipe.to("cpu")
            pipe = current_model.pipe_i2i

        if torch.cuda.is_available():
            pipe = pipe.to(device)
        last_mode = "img2img"

    prompt = current_model.prefix + prompt
    ratio = min(height / img.height, width / img.width)
    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
    result = pipe(
        prompt,
        negative_prompt=neg_prompt,
        # num_images_per_prompt=n_images,
        image=img,
        num_inference_steps=int(steps),
        strength=strength,
        guidance_scale=guidance,
        # width=width,
        # height=height,
        generator=generator)
    result.images[0] = magnifier.magnify(result.images[0], scale_factor=scale_factor)

    # save image
    result.images[0].save("imgs/result-{}.png".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))
    return replace_nsfw_images(result)


def replace_nsfw_images(results):
    if is_colab:
        return results.images[0]
    if hasattr(results, "nsfw_content_detected") and results.nsfw_content_detected:
        for i in range(len(results.images)):
            if results.nsfw_content_detected[i]:
                results.images[i] = Image.open("nsfw.png")
    return results.images[0]


css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
"""
with gr.Blocks(css=css) as demo:
    if not os.path.exists('imgs'):
        os.mkdir('imgs')

    gr.Markdown('# Super Resolution Anime Diffusion')
    gr.Markdown(
        "## Author: [yangheng95](https://github.com/yangheng95)  Github:[Github](https://github.com/yangheng95/SuperResolutionAnimeDiffusion)")
    gr.Markdown("### This demo is running on a CPU, so it will take at least 20 minutes. "
                "If you have a GPU, you can clone from [Github](https://github.com/yangheng95/SuperResolutionAnimeDiffusion) and run it locally.")
    gr.Markdown("### FYI: to generate a 512*512 image and magnify 4x, it only takes 5~8 seconds on a RTX 2080 GPU")
    gr.Markdown(
        "### You can duplicate this demo on HuggingFace Spaces, click [here](https://huggingface.co/spaces/yangheng/Super-Resolution-Anime-Diffusion?duplicate=true)")

    with gr.Row():
        with gr.Column(scale=55):
            with gr.Group():
                gr.Markdown("Text to image")

                model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)

                with gr.Box(visible=False) as custom_model_group:
                    custom_model_path = gr.Textbox(label="Custom model path",
                                                   placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion",
                                                   interactive=True)
                    gr.HTML(
                        "<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")

                with gr.Row():
                    prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,
                                        placeholder="Enter prompt. Style applied automatically").style(container=False)
                with gr.Row():
                    generate = gr.Button(value="Generate")

                with gr.Row():
                    with gr.Group():
                        neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")

                image_out = gr.Image(height=512)
                # gallery = gr.Gallery(
                #     label="Generated images", show_label=False, elem_id="gallery"
                # ).style(grid=[1], height="auto")
            error_output = gr.Markdown()

        with gr.Column(scale=45):
            with gr.Group():
                gr.Markdown("Image to Image")

                with gr.Row():
                    with gr.Group():
                        image = gr.Image(label="Image", height=256, tool="editor", type="pil")
                        strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01,
                                             value=0.5)

                with gr.Row():
                    with gr.Group():
                        # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)

                        with gr.Row():
                            guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
                            steps = gr.Slider(label="Steps", value=15, minimum=2, maximum=75, step=1)

                        with gr.Row():
                            width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
                            height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
                        with gr.Row():
                            scale_factor = gr.Slider(1, 8, label='Scale factor (to magnify image) (1, 2, 4, 8)',
                                                     value=2,
                                                     step=1)

                        seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)

    if is_colab:
        model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
        custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
    # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)

    gr.Markdown("### based on [Anything V3](https://huggingface.co/Linaqruf/anything-v3.0)")

    inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, scale_factor]
    outputs = [image_out, error_output]
    prompt.submit(inference, inputs=inputs, outputs=outputs)
    generate.click(inference, inputs=inputs, outputs=outputs, api_name="generate")

    prompt_keys = [
        'girl', 'lovely', 'cute', 'beautiful eyes', 'cumulonimbus clouds', 'detailed fingers',
        random.choice(['dress']),
        random.choice(['white hair']),
        random.choice(['blue eyes']),
        random.choice(['flower meadow']),
        random.choice(['Elif', 'Angel'])
    ]
    prompt.value = ','.join(prompt_keys)
    ex = gr.Examples([
        [models[0].name, prompt.value, 7.5, 15],

    ], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False)

print(f"Space built in {time.time() - start_time:.2f} seconds")

if not is_colab:
    demo.queue(concurrency_count=2)
demo.launch(debug=is_colab, enable_queue=True, share=is_colab)