import gradio as gr
import jax
from PIL import Image
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import load_image
import jax.numpy as jnp
import numpy as np


controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
    "mfidabel/controlnet-segment-anything", dtype=jnp.float32
)

pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.float32
)

# Add ControlNet params and Replicate
params["controlnet"] = controlnet_params
p_params = replicate(params)

# Description
title = "# 🧨 ControlNet on Segment Anything 🤗"
description = """This is a demo on 🧨 ControlNet based on Meta's [Segment Anything Model](https://segment-anything.com/).

                Upload a Segment Anything Segmentation Map, write a prompt, and generate images 🤗 This demo is still Work in Progress, so don't expect it to work well for now !! 

                
                Test some of the examples below to give it a try ⬇️
              """

examples = [["a modern main room of a house", "low quality", "examples/condition_image_1.png"],
            ["new york buildings,  Vincent Van Gogh starry night ", "low quality, monochrome", "examples/condition_image_2.png"],
            ["contemporary living room,  high quality, 4k, realistic", "low quality, monochrome, low res", "examples/condition_image_3.png"]]


# Inference Function
def infer(prompts, negative_prompts, image, num_inference_steps = 50, seed = 4, num_samples = 4):
    rng = jax.random.PRNGKey(int(seed))
    num_inference_steps = int(num_inference_steps)
    image = Image.fromarray(image, mode="RGB")
    num_samples = max(jax.device_count(), int(num_samples))
    p_rng = jax.random.split(rng, jax.device_count())
    
    prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
    negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples)
    processed_image = pipe.prepare_image_inputs([image] * num_samples)
    
    prompt_ids = shard(prompt_ids)
    negative_prompt_ids = shard(negative_prompt_ids)
    processed_image = shard(processed_image)
    
    output = pipe(
        prompt_ids=prompt_ids,
        image=processed_image,
        params=p_params,
        prng_seed=p_rng,
        num_inference_steps=num_inference_steps,
        neg_prompt_ids=negative_prompt_ids,
        jit=True,
    ).images

    output = output.reshape((num_samples,) + output.shape[-3:])

    print(output.shape)
    
    final_image = [np.array(x*255, dtype=np.uint8) for x in output]

    del output
    
    return final_image

with gr.Blocks(css="h1 { text-align: center }") as demo:
    # Title
    gr.Markdown(title)
    # Description
    gr.Markdown(description)

    # Images
    with gr.Row(variant="panel"):
        with gr.Column(scale=2):
            cond_img = gr.Image(label="Input")\
                    .style(height=200)
        with gr.Column(scale=1):
            output = gr.Gallery(label="Generated images")\
                    .style(height=200, rows=[2], columns=[1, 2], object_fit="contain")
        
    # Submit & Clear
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(lines=1, label="Prompt")
            negative_prompt = gr.Textbox(lines=1, label="Negative Prompt")

        with gr.Column():
            with gr.Accordion("Advanced options", open=False):
                num_steps = gr.Slider(10, 60, 50, step=1, label="Steps")
                seed = gr.Slider(0, 1024, 4, step=1, label="Seed")
                num_samples = gr.Slider(1, 4, 4, step=1, label="Nº Samples")
                
            submit = gr.Button("Generate")
            # TODO: Download Button

    # Examples
    gr.Examples(examples=examples,
                inputs=[prompt, negative_prompt, cond_img],
                outputs=output,
                fn=infer,
                cache_examples=True)

    
    submit.click(infer, 
                 inputs=[prompt, negative_prompt, cond_img, num_steps, seed, num_samples],
                 outputs = output)
    
demo.queue()
demo.launch()