import random
from time import time_ns

import torch

import spaces
import gradio as gr
from transformers import set_seed

from kgen import models
from diff import load_model, encode_prompts
from dtg import process
from meta import (
    DEFAULT_STYLE_LIST,
    MODEL_FORMAT_LIST,
    MODEL_DEFAULT_QUALITY_LIST,
    DEFAULT_NEGATIVE_PROMPT,
)


sdxl_pipe = load_model(model_id="KBlueLeaf/Kohaku-XL-Epsilon", device="cuda")
models.load_model(models.model_list[0])
models.text_model.cuda()

current_dtg_model = models.model_list[0]
current_sdxl_model = "KBlueLeaf/Kohaku-XL-Epsilon"


@spaces.GPU
def gen(
    sdxl_model: str,
    dtg_model: str,
    style: str,
    base_prompt: str,
    addon_prompt: str = "",
):
    global current_dtg_model, current_sdxl_model, sdxl_pipe
    if sdxl_model != current_sdxl_model:
        sdxl_pipe = load_model(model_id=sdxl_model, device="cuda")
        current_sdxl_model = sdxl_model
    if dtg_model != current_dtg_model:
        models.load_model(dtg_model)
        models.text_model.cuda()
        current_dtg_model = dtg_model

    t0 = time_ns()
    seed = random.randint(0, 2**31 - 1)

    prompt = (
        f"{base_prompt}, {addon_prompt}, "
        f"{DEFAULT_STYLE_LIST[style]}, "
        f"{MODEL_DEFAULT_QUALITY_LIST[sdxl_model]}, "
    )
    full_prompt = process(
        prompt,
        aspect_ratio=1.0,
        seed=seed,
        tag_length="short",
        ban_tags=".*alternate.*, character doll, multiple.*, .*cosplay.*, .*name, .*text.*",
        format=MODEL_FORMAT_LIST[sdxl_model],
        temperature=1.2,
    )
    torch.cuda.empty_cache()

    prompt_embeds, negative_prompt_embeds, pooled_embeds2, neg_pooled_embeds2 = (
        encode_prompts(sdxl_pipe, full_prompt, DEFAULT_NEGATIVE_PROMPT)
    )
    set_seed(seed)
    with torch.autocast("cuda"):
        result = sdxl_pipe(
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_embeds2,
            negative_pooled_prompt_embeds=neg_pooled_embeds2,
            num_inference_steps=24,
            width=1024,
            height=1024,
            guidance_scale=6.0,
        ).images[0]
    torch.cuda.empty_cache()
    t1 = time_ns()

    return result.convert("RGB"), full_prompt, f"Cost: {(t1 - t0) / 1e9:.2}sec"


if __name__ == "__main__":
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("""# This Cute Dragon Girl Doesn't Exist""")
        with gr.Accordion("Introduction and Instructions", open=False):
            gr.Markdown(
                """
### What is this:
"This Cute Dragon Girl Doesn't Exist" is a Demo for KGen System(DanTagGen) with SDXL anime models.
It is aimed to show how the DanTagGen can be used to "refine/upsample" simple prompt to help the T2I model.

Since I already have some application and demo on DanTagGen.
This demo is designed to be more "simple" than before.

Just one click, and get the result with high quality and high diversity.

### How to use it:
click "Next" button until you get the dragon girl you like.

### Resources:
- My anime model: [Kohaku XL Epsilon](https://huggingface.co/KBlueLeaf/Kohaku-XL-Epsilon)
- DanTagGen: [DanTagGen](https://huggingface.co/KBlueLeaf/DanTagGen-beta)
- DanTagGen extension: [z-a1111-sd-webui-dtg](https://github.com/KohakuBlueleaf/z-a1111-sd-webui-dtg)
"""
            )
        with gr.Row():
            with gr.Column(scale=3):
                with gr.Row():
                    sdxl_model = gr.Dropdown(
                        MODEL_FORMAT_LIST,
                        label="SDXL Model",
                        value=list(MODEL_FORMAT_LIST)[0],
                    )
                    dtg_model = gr.Dropdown(
                        models.model_list,
                        label="DTG Model",
                        value=models.model_list[0],
                    )
                base_prompt = gr.Textbox(
                    label="Base prompt",
                    lines=1,
                    value="1girl, solo, dragon girl, dragon wings, dragon horns, dragon tail",
                    interactive=False,
                )
                with gr.Row():
                    addon_propmt = gr.Textbox(
                        label="Addon prompt",
                        lines=1,
                        value="cowboy shot, loli",
                    )
                    style = gr.Dropdown(
                        DEFAULT_STYLE_LIST,
                        label="Style",
                        value=list(DEFAULT_STYLE_LIST)[0],
                    )
                submit = gr.Button("Next")
                dtg_output = gr.TextArea(label="DTG output", lines=9, show_copy_button=True)
                cost_time = gr.Markdown()
            with gr.Column(scale=4):
                result = gr.Image(label="Result", type="numpy", interactive=False)

        submit.click(
            fn=gen,
            inputs=[sdxl_model, dtg_model, style, base_prompt, addon_propmt],
            outputs=[result, dtg_output, cost_time],
        )

    demo.launch()