import spaces

import os
import random
import math

import torch

torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
torch.backends.cuda.matmul.allow_tf32 = True

import numpy as np

from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import (
    StableDiffusionXLPipeline,
)
from diffusers.schedulers.scheduling_euler_ancestral_discrete import (
    EulerAncestralDiscreteScheduler,
)
from diffusers.models.attention_processor import AttnProcessor2_0
from transformers import AutoModelForCausalLM, AutoTokenizer

import gradio as gr

try:
    from dotenv import load_dotenv

    load_dotenv()
except:
    print("failed to import dotenv (this is not a problem on the production)")

HF_TOKEN = os.environ.get("HF_TOKEN")
assert HF_TOKEN is not None

IMAGE_MODEL_REPO_ID = os.environ.get(
    "IMAGE_MODEL_REPO_ID", "OnomaAIResearch/Illustrious-xl-early-release-v0"
)
DART_V3_REPO_ID = os.environ.get("DART_V3_REPO_ID", None)
assert DART_V3_REPO_ID is not None
CPU_OFFLOAD = os.environ.get("CPU_OFFLOAD", "False").lower() == "true"

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

TEMPLATE = (
    "<|bos|>"
    #
    "<|rating:general|>"
    "{aspect_ratio}"
    "<|length:medium|>"
    #
    "<copyright></copyright>"
    #
    "<character></character>"
    #
    "<general>{subject}"
)
QUALITY_TAGS = ""
NEGATIVE_PROMPT = "bad quality, worst quality, lowres, bad anatomy, sketch, jpeg artifacts, ugly, poorly drawn, signature, watermark, bad anatomy, bad hands, bad feet, retro, old, 2000s, 2010s, 2011s, 2012s, 2013s, multiple views, screencap"
BAN_TAGS = [
    "2005",  # year tags
    "2006",
    "2007",
    "2008",
    "2009",
    "2010",
    "2011",
    "2012",
    "2013",
    "2014",
    "2015",
    "2016",
    "2017",
    "2018",
    "2019",
    "2020",
    "dated",
    "web address",
]

device = "cuda" if torch.cuda.is_available() else "cpu"

dart = AutoModelForCausalLM.from_pretrained(
    DART_V3_REPO_ID,
    torch_dtype=torch.bfloat16,
    token=HF_TOKEN,
    use_cache=True,
    device_map="cpu",
)
dart = dart.eval()
dart = dart.requires_grad_(False)
dart = torch.compile(dart)
tokenizer = AutoTokenizer.from_pretrained(DART_V3_REPO_ID)
BAN_TOKENS = [tokenizer.convert_tokens_to_ids([tag]) for tag in BAN_TAGS]


def load_pipeline():
    vae = AutoencoderKL.from_pretrained(
        "madebyollin/sdxl-vae-fp16-fix",
        torch_dtype=torch.float16,
    )

    pipe = StableDiffusionXLPipeline.from_pretrained(
        IMAGE_MODEL_REPO_ID,
        vae=vae,
        torch_dtype=torch.float16,
        use_safetensors=True,
        add_watermarker=False,
        custom_pipeline="lpw_stable_diffusion_xl",
    )
    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    if CPU_OFFLOAD:  # local
        pipe.enable_sequential_cpu_offload(gpu_id=0, device=device)
    else:
        pipe.to(device)  # for spaces
    return pipe


if torch.cuda.is_available():
    pipe = load_pipeline()
    print("Loaded pipeline")
else:
    pipe = None


def get_aspect_ratio(width: int, height: int) -> str:
    ar = math.log2(width / height)

    if ar <= -1.25:
        return "<|aspect_ratio:too_tall|>"
    elif ar <= -0.75:
        return "<|aspect_ratio:tall_wallpaper|>"
    elif ar <= -0.25:
        return "<|aspect_ratio:tall|>"
    elif ar < 0.25:
        return "<|aspect_ratio:square|>"
    elif ar < 0.75:
        return "<|aspect_ratio:wide|>"
    elif ar < 1.25:
        return "<|aspect_ratio:wide_wallpaper|>"
    else:
        return "<|aspect_ratio:too_wide|>"


@torch.inference_mode
def generate_prompt(subject: str, aspect_ratio: str):
    input_ids = tokenizer.encode_plus(
        TEMPLATE.format(aspect_ratio=aspect_ratio, subject=subject),
        return_tensors="pt",
    ).input_ids
    print("input_ids:", input_ids)

    output_ids = dart.generate(
        input_ids,
        max_new_tokens=256,
        do_sample=True,
        temperature=1.0,
        top_p=1.0,
        top_k=100,
        num_beams=1,
        bad_words_ids=BAN_TOKENS,
    )[0]

    generated = output_ids[len(input_ids) :]
    decoded = ", ".join(
        [
            token
            for token in tokenizer.batch_decode(generated, skip_special_tokens=True)
            if token.strip() != ""
        ]
    )
    print("decoded:", decoded)

    return decoded


def format_prompt(prompt: str, prompt_suffix: str):
    return f"{prompt}, {prompt_suffix}"


@spaces.GPU(duration=20)
@torch.inference_mode
def generate_image(
    prompt: str,
    negative_prompt: str,
    generator,
    width: int,
    height: int,
    guidance_scale: float,
    num_inference_steps: int,
):
    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    return image


def on_generate(
    subject: str,
    suffix: str,
    negative_prompt: str,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)

    ar = get_aspect_ratio(width, height)
    print("ar:", ar)
    prompt = generate_prompt(subject, ar)
    prompt = format_prompt(prompt, suffix)
    print(prompt)

    image = generate_image(
        prompt,
        negative_prompt,
        generator,
        width,
        height,
        guidance_scale,
        num_inference_steps,
    )

    return image, prompt, seed


def on_retry(
    prompt: str,
    negative_prompt: str,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)

    print(prompt)

    image = generate_image(
        prompt,
        negative_prompt,
        generator,
        width,
        height,
        guidance_scale,
        num_inference_steps,
    )

    return image, prompt, seed


css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("""
# IllustriousXL Random Gacha
Image model: [IllustriousXL v0.1](https://huggingface.co/OnomaAIResearch/Illustrious-xl-early-release-v0)
""")

        with gr.Row():
            subject_radio = gr.Dropdown(
                label="Subject",
                choices=["1girl", "2girls", "1boy", "no humans"],
                value="1girl",
            )
            run_button = gr.Button("Pull gacha", variant="primary", scale=0)

        result = gr.Image(label="Gacha result", show_label=False)

        with gr.Accordion("Generation details", open=False):
            with gr.Row():
                prompt_txt = gr.Textbox(label="Generated prompt", interactive=False)
                retry_button = gr.Button("🔄 Retry", scale=0)

        with gr.Accordion("Advanced Settings", open=False):
            prompt_suffix = gr.Text(
                label="Prompt suffix",
                visible=True,
                value=QUALITY_TAGS,
            )
            negative_prompt = gr.Text(
                label="Negative prompt",
                placeholder="Enter a negative prompt",
                visible=True,
                value=NEGATIVE_PROMPT,
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=640,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=960,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=640,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=1344,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=1.0,
                    maximum=10.0,
                    step=0.5,
                    value=6.5,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=20,
                    maximum=40,
                    step=1,
                    value=28,
                )

    gr.on(
        triggers=[run_button.click],
        fn=on_generate,
        inputs=[
            subject_radio,
            prompt_suffix,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, prompt_txt, seed],
    )
    gr.on(
        triggers=[retry_button.click],
        fn=on_retry,
        inputs=[
            prompt_txt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, prompt_txt, seed],
    )

demo.queue().launch()