#!/usr/bin/env python

from __future__ import annotations

import os

import gradio as gr
import PIL.Image

from model import Model

DESCRIPTION = """\
# Attend-and-Excite

This is a demo for [Attend-and-Excite](https://arxiv.org/abs/2301.13826).
Attend-and-Excite performs attention-based generative semantic guidance to mitigate subject neglect in Stable Diffusion.
Select a prompt and a set of indices matching the subjects you wish to strengthen (the `Check token indices` cell can help map between a word and its index).
"""

model = Model()


def process_example(
    prompt: str,
    indices_to_alter_str: str,
    seed: int,
    apply_attend_and_excite: bool,
) -> tuple[list[tuple[int, str]], PIL.Image.Image]:
    num_steps = 50
    guidance_scale = 7.5

    token_table = model.get_token_table(prompt)
    result = model.run(prompt, indices_to_alter_str, seed, apply_attend_and_excite, num_steps, guidance_scale)
    return token_table, result


with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )

    with gr.Row():
        with gr.Column():
            prompt = gr.Text(
                label="Prompt",
                max_lines=1,
                placeholder="A pod of dolphins leaping out of the water in an ocean with a ship on the background",
            )
            with gr.Accordion(label="Check token indices", open=False):
                show_token_indices_button = gr.Button("Show token indices")
                token_indices_table = gr.Dataframe(label="Token indices", headers=["Index", "Token"], col_count=2)
            token_indices_str = gr.Text(
                label="Token indices (a comma-separated list indices of the tokens you wish to alter)",
                max_lines=1,
                placeholder="4,16",
            )
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=100000,
                step=1,
                value=0,
            )
            apply_attend_and_excite = gr.Checkbox(label="Apply Attend-and-Excite", value=True)
            num_steps = gr.Slider(
                label="Number of steps",
                minimum=0,
                maximum=100,
                step=1,
                value=50,
            )
            guidance_scale = gr.Slider(
                label="CFG scale",
                minimum=0,
                maximum=50,
                step=0.1,
                value=7.5,
            )
            run_button = gr.Button("Generate")
        with gr.Column():
            result = gr.Image(label="Result")

    with gr.Row():
        examples = [
            [
                "A mouse and a red car",
                "2,6",
                2098,
                True,
            ],
            [
                "A mouse and a red car",
                "2,6",
                2098,
                False,
            ],
            [
                "A horse and a dog",
                "2,5",
                123,
                True,
            ],
            [
                "A horse and a dog",
                "2,5",
                123,
                False,
            ],
            [
                "A painting of an elephant with glasses",
                "5,7",
                123,
                True,
            ],
            [
                "A painting of an elephant with glasses",
                "5,7",
                123,
                False,
            ],
            [
                "A playful kitten chasing a butterfly in a wildflower meadow",
                "3,6,10",
                123,
                True,
            ],
            [
                "A playful kitten chasing a butterfly in a wildflower meadow",
                "3,6,10",
                123,
                False,
            ],
            [
                "A grizzly bear catching a salmon in a crystal clear river surrounded by a forest",
                "2,6,15",
                123,
                True,
            ],
            [
                "A grizzly bear catching a salmon in a crystal clear river surrounded by a forest",
                "2,6,15",
                123,
                False,
            ],
            [
                "A pod of dolphins leaping out of the water in an ocean with a ship on the background",
                "4,16",
                123,
                True,
            ],
            [
                "A pod of dolphins leaping out of the water in an ocean with a ship on the background",
                "4,16",
                123,
                False,
            ],
        ]
        gr.Examples(
            examples=examples,
            inputs=[
                prompt,
                token_indices_str,
                seed,
                apply_attend_and_excite,
            ],
            outputs=[
                token_indices_table,
                result,
            ],
            fn=process_example,
            cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
            examples_per_page=20,
        )

    show_token_indices_button.click(
        fn=model.get_token_table,
        inputs=prompt,
        outputs=token_indices_table,
        queue=False,
        api_name=False,
    )

    inputs = [
        prompt,
        token_indices_str,
        seed,
        apply_attend_and_excite,
        num_steps,
        guidance_scale,
    ]
    prompt.submit(
        fn=model.get_token_table,
        inputs=prompt,
        outputs=token_indices_table,
        queue=False,
        api_name=False,
    ).then(
        fn=model.run,
        inputs=inputs,
        outputs=result,
        api_name=False,
    )
    token_indices_str.submit(
        fn=model.get_token_table,
        inputs=prompt,
        outputs=token_indices_table,
        queue=False,
        api_name=False,
    ).then(
        fn=model.run,
        inputs=inputs,
        outputs=result,
        api_name=False,
    )
    run_button.click(
        fn=model.get_token_table,
        inputs=prompt,
        outputs=token_indices_table,
        queue=False,
        api_name=False,
    ).then(
        fn=model.run,
        inputs=inputs,
        outputs=result,
        api_name="run",
    )

if __name__ == "__main__":
    demo.queue(max_size=10).launch()