#!/usr/bin/env python
# coding: utf-8
import queue
import gradio as gr
import random
import torch
from collections import defaultdict
from diffusers import DiffusionPipeline
from functools import partial
from itertools import zip_longest
from typing import List
from PIL import Image

SELECT_LABEL = "Select as seed"

MODEL_ID = "CompVis/ldm-text2im-large-256"
STEPS = 5   # while running on CPU
ETA = 0.3
GUIDANCE_SCALE = 6

ldm = DiffusionPipeline.from_pretrained(MODEL_ID)

import torch
print(f"cuda: {torch.cuda.is_available()}")

with gr.Blocks(css=".container { max-width: 800px; margin: auto; }") as demo:
    state = gr.Variable({
        'selected': -1,
        'seeds': [random.randint(0, 2 ** 32 - 1) for _ in range(6)]
    })

    def infer_seeded_image(prompt, seed):
        print(f"Prompt: {prompt}, seed: {seed}")
        images, _ = infer_grid(prompt, n=1, seeds=[seed])
        return images[0]

    def infer_grid(prompt, n=6, seeds=[]):
        # Unfortunately we have to iterate instead of requesting all images at once,
        # because we have no way to get the intermediate generation seeds.
        result = defaultdict(list)
        for _, seed in zip_longest(range(n), seeds, fillvalue=None):
            seed = random.randint(0, 2**32 - 1) if seed is None else seed
            _ = torch.manual_seed(seed)
            with torch.autocast("cuda"):
                images = ldm(
                    [prompt],
                    num_inference_steps=STEPS,
                    eta=ETA,
                    guidance_scale=GUIDANCE_SCALE
                )["sample"]
            result["images"].append(images[0])
            result["seeds"].append(seed)
        return result["images"], result["seeds"]

    def infer(prompt, state):
        """
        Outputs:
        - Grid images (list)
        - Seeded Image (Image or None)
        - Grid Box with updated visibility
        - Seeded Box with updated visibility
        """
        grid_images = [None] * 6
        image_with_seed = None
        visible = (False, False)

        if (seed_index := state["selected"]) > -1:
            seed = state["seeds"][seed_index]
            image_with_seed = infer_seeded_image(prompt, seed)
            visible = (False, True)
        else:
            grid_images, seeds = infer_grid(prompt)
            state["seeds"] = seeds
            visible = (True, False)

        boxes = [gr.Box.update(visible=v) for v in visible]
        return grid_images + [image_with_seed] + boxes + [state]

    def update_state(selected_index: int, value, state):
        if value == '':
            others_value = gr.components._Keywords.NO_VALUE
        else:
            others_value = ''
            state["selected"] = selected_index
        return [gr.Radio.update(value=others_value) for _ in range(5)] + [state]

    def clear_seed(state):
        """Update state of Radio buttons, grid, seeded_box"""
        state["selected"] = -1
        return [''] * 6 + [gr.Box.update(visible=True), gr.Box.update(visible=False)] + [state]

    def image_block():
        return gr.Image(
            interactive=False, show_label=False
        ).style(
            # border = (True, True, False, True),
            rounded = (True, True, False, False),
        )

    def radio_block():
        radio = gr.Radio(
            choices=[SELECT_LABEL], interactive=True, show_label=False,
        ).style(
            # border = (False, True, True, True),
            # rounded = (False, False, True, True)
            container=False
        )
        return radio

    gr.Markdown(
        """
        <h1><center>Latent Diffusion Demo</center></h1>
        <p>Type anything to generate a few images that represent your prompt.
        Select one of the results to use as a <b>seed</b> for the next generation:
        you can try variations of your prompt starting from the same state and see how it changes.
        For example, <i>Labrador in the style of Vermeer</i> could be tweaked to
        <i>Labrador in the style of Picasso</i> or <i>Lynx in the style of Van Gogh</i>.
        If your prompts are similar, the tweaked result should also have a similar structure
        but different details or style.</p>
        """
    )
    with gr.Group():
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                text = gr.Textbox(
                    label="Enter your prompt", show_label=False, max_lines=1
                ).style(
                    border=(True, False, True, True),
                    # margin=False,
                    rounded=(True, False, False, True),
                    container=False,
                )
                btn = gr.Button("Run").style(
                    margin=False,
                    rounded=(False, True, True, False),
                )

        ## Can we create a Component with these, so it can participate as an output?
        with (grid := gr.Box()):
            with gr.Row():
                with gr.Box().style(border=None):
                    image1 = image_block()
                    select1 = radio_block()
                with gr.Box().style(border=None):
                    image2 = image_block()
                    select2 = radio_block()
                with gr.Box().style(border=None):
                    image3 = image_block()
                    select3 = radio_block()
            with gr.Row():
                with gr.Box().style(border=None):
                    image4 = image_block()
                    select4 = radio_block()
                with gr.Box().style(border=None):
                    image5 = image_block()
                    select5 = radio_block()
                with gr.Box().style(border=None):
                    image6 = image_block()
                    select6 = radio_block()

        images = [image1, image2, image3, image4, image5, image6]
        selectors = [select1, select2, select3, select4, select5, select6]

        for i, radio in enumerate(selectors):
            others = list(filter(lambda s: s != radio, selectors))
            radio.change(
                partial(update_state, i),
                inputs=[radio, state],
                outputs=others + [state],
                queue=False
            )

    with (seeded_box := gr.Box()):
        seeded_image = image_block()
        clear_seed_button = gr.Button("Return to Grid")
    seeded_box.visible = False
    clear_seed_button.click(
        clear_seed,
        inputs=[state],
        outputs=selectors + [grid, seeded_box] + [state]
    )

    all_images = images + [seeded_image]
    boxes = [grid, seeded_box]
    infer_outputs = all_images + boxes + [state]

    text.submit(
        infer,
        inputs=[text, state],
        outputs=infer_outputs
    )
    btn.click(
        infer,
        inputs=[text, state],
        outputs=infer_outputs
    )

demo.launch(enable_queue=True)