#!/usr/bin/env python

from __future__ import annotations

import os
import random

import gradio as gr
import numpy as np
import PIL.Image
import torch
from diffusers import UniDiffuserPipeline

DESCRIPTION = "# [UniDiffuser](https://github.com/thu-ml/unidiffuser)"

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶</p>"


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


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    pipe = UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1", torch_dtype=torch.float16)
    pipe.to(device)


def run(
    mode: str,
    prompt: str,
    image: PIL.Image.Image | None,
    seed: int = 0,
    num_steps: int = 20,
    guidance_scale: float = 8.0,
) -> tuple[PIL.Image.Image | None, str]:
    generator = torch.Generator(device=device).manual_seed(seed)
    if mode == "t2i":
        pipe.set_text_to_image_mode()
        sample = pipe(prompt=prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator)
        return sample.images[0], ""
    elif mode == "i2t":
        pipe.set_image_to_text_mode()
        sample = pipe(image=image, num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator)
        return None, sample.text[0]
    elif mode == "joint":
        pipe.set_joint_mode()
        sample = pipe(num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator)
        return sample.images[0], sample.text[0]
    elif mode == "i":
        pipe.set_image_mode()
        sample = pipe(num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator)
        return sample.images[0], ""
    elif mode == "t":
        pipe.set_text_mode()
        sample = pipe(num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator)
        return None, sample.text[0]
    elif mode == "i2t2i":
        pipe.set_image_to_text_mode()
        sample = pipe(image=image, num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator)
        pipe.set_text_to_image_mode()
        sample = pipe(
            prompt=sample.text[0],
            num_inference_steps=num_steps,
            guidance_scale=guidance_scale,
            generator=generator,
        )
        return sample.images[0], ""
    elif mode == "t2i2t":
        pipe.set_text_to_image_mode()
        sample = pipe(prompt=prompt, num_inference_steps=num_steps, guidance_scale=guidance_scale, generator=generator)
        pipe.set_image_to_text_mode()
        sample = pipe(
            image=sample.images[0],
            num_inference_steps=num_steps,
            guidance_scale=guidance_scale,
            generator=generator,
        )
        return None, sample.text[0]
    else:
        raise ValueError


def create_demo(mode_name: str) -> gr.Blocks:
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                mode = gr.Dropdown(
                    label="Mode",
                    choices=[
                        "t2i",
                        "i2t",
                        "joint",
                        "i",
                        "t",
                        "i2t2i",
                        "t2i2t",
                    ],
                    value=mode_name,
                    visible=False,
                )
                prompt = gr.Text(label="Prompt", max_lines=1, visible=mode_name in ["t2i", "t2i2t"])
                image = gr.Image(label="Input image", type="pil", visible=mode_name in ["i2t", "i2t2i"])
                run_button = gr.Button("Run")
                with gr.Accordion("Advanced options", open=False):
                    seed = gr.Slider(
                        label="Seed",
                        minimum=0,
                        maximum=MAX_SEED,
                        step=1,
                        value=0,
                    )
                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                    num_steps = gr.Slider(
                        label="Steps",
                        minimum=1,
                        maximum=100,
                        value=20,
                        step=1,
                    )
                    guidance_scale = gr.Slider(
                        label="Guidance Scale",
                        minimum=0.1,
                        maximum=30.0,
                        value=8.0,
                        step=0.1,
                    )
            with gr.Column():
                result_image = gr.Image(label="Generated image", visible=mode_name in ["t2i", "i", "joint", "i2t2i"])
                result_text = gr.Text(label="Generated text", visible=mode_name in ["i2t", "t", "joint", "t2i2t"])

        gr.on(
            triggers=[prompt.submit, run_button.click],
            fn=randomize_seed_fn,
            inputs=[seed, randomize_seed],
            outputs=seed,
            queue=False,
        ).then(
            fn=run,
            inputs=[
                mode,
                prompt,
                image,
                seed,
                num_steps,
                guidance_scale,
            ],
            outputs=[
                result_image,
                result_text,
            ],
            api_name=f"run_{mode_name}",
        )
    return demo


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.Tabs():
        with gr.TabItem("text2image"):
            create_demo("t2i")
        with gr.TabItem("image2text"):
            create_demo("i2t")
        with gr.TabItem("image variation"):
            create_demo("i2t2i")
        with gr.TabItem("joint generation"):
            create_demo("joint")
        with gr.TabItem("image generation"):
            create_demo("i")
        with gr.TabItem("text generation"):
            create_demo("t")
        with gr.TabItem("text variation"):
            create_demo("t2i2t")

if __name__ == "__main__":
    demo.queue(max_size=20).launch()