import gradio as gr
import numpy as np
import random

import spaces
from models import TVARPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "michellemoorre/var-test"


pipe = TVARPipeline.from_pretrained(model_repo_id, device=device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


@spaces.GPU(duration=65)
def infer(
    prompt,
    negative_prompt="",
    seed=42,
    randomize_seed=False,
    guidance_scale=4.0,
    top_k=450,
    top_p=0.95,
    re=False,
    re_max_depth=10,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    image = pipe(
        prompt=prompt,
        null_prompt=negative_prompt,
        cfg=guidance_scale,
        top_p=top_p,
        top_k=top_k,
        re=re,
        g_seed=seed,
    )[0]

    return image, seed

# TODO: add examples from preview
examples = [
        "A capybara wearing a suit holding a sign that reads Hello World",
]

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(" # [OpenTVAR](https://huggingface.co/stabilityai/stable-diffusion-3.5-large)")
        gr.Markdown("[Learn more](https://stability.ai/news/introducing-stable-diffusion-3-5) about the OpenTVAR.")
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

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

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )

            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():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=7.5,
                    step=0.1,
                    value=4.5,
                )
            with gr.Row():
                top_k = gr.Slider(
                    label="Sampling top k",
                    minimum=1,
                    maximum=1000,
                    step=10,
                    value=450,
                )
                top_p = gr.Slider(
                    label="Sampling top p",
                    minimum=0.0,
                    maximum=1.,
                    step=0.05,
                    value=0.95,
                )
            with gr.Row():
                re = gr.Checkbox(label="Rejection Sampling", value=False)
                re_max_depth = gr.Slider(
                    label="Rejection Sampling Depth",
                    minimum=0,
                    maximum=20,
                    step=1,
                    value=10,
                )

        gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=True)# cache_mode="lazy")
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            guidance_scale,
            top_k,
            top_p,
            re,
            re_max_depth,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()