import gradio as gr
import spaces
import torch
from diffusers import AutoencoderKL, TCDScheduler
from diffusers.models.model_loading_utils import load_state_dict
from huggingface_hub import hf_hub_download

from controlnet_union import ControlNetModel_Union
from pipeline_fill_sd_xl import StableDiffusionXLFillPipeline

from PIL import Image, ImageDraw
import numpy as np

# Load VAE and ControlNet (shared components)
vae = AutoencoderKL.from_pretrained(
    "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
).to("cuda")

config_file = hf_hub_download(
    "xinsir/controlnet-union-sdxl-1.0",
    filename="config_promax.json",
)
config = ControlNetModel_Union.load_config(config_file)
controlnet_model = ControlNetModel_Union.from_config(config)
model_file = hf_hub_download(
    "xinsir/controlnet-union-sdxl-1.0",
    filename="diffusion_pytorch_model_promax.safetensors",
)
sstate_dict = load_state_dict(model_file)
controlnet, _, _, _, _ = ControlNetModel_Union._load_pretrained_model(
    controlnet_model, sstate_dict, model_file, "xinsir/controlnet-union-sdxl-1.0"
)
controlnet.to(device="cuda", dtype=torch.float16)

# Define available models
models = {
    "RealVisXL V5.0 Lightning": "SG161222/RealVisXL_V5.0_Lightning",
    "RealVisXL V4.0 Lightning": "SG161222/RealVisXL_V4.0_Lightning",
}

# Load default pipeline
default_model = "RealVisXL V5.0 Lightning"
pipe = StableDiffusionXLFillPipeline.from_pretrained(
    models[default_model],
    torch_dtype=torch.float16,
    vae=vae,
    controlnet=controlnet,
    variant="fp16",
).to("cuda")
pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)

# Function to load pipeline based on selected model
def load_pipeline(model_name):
    repo_id = models[model_name]
    new_pipe = StableDiffusionXLFillPipeline.from_pretrained(
        repo_id,
        torch_dtype=torch.float16,
        vae=vae,
        controlnet=controlnet,
        variant="fp16",
    ).to("cuda")
    new_pipe.scheduler = TCDScheduler.from_config(new_pipe.scheduler.config)
    return new_pipe

# Prepare image and mask function (unchanged)
def prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
    target_size = (width, height)

    scale_factor = min(target_size[0] / image.width, target_size[1] / image.height)
    new_width = int(image.width * scale_factor)
    new_height = int(image.height * scale_factor)
    
    source = image.resize((new_width, new_height), Image.LANCZOS)

    if resize_option == "Full":
        resize_percentage = 100
    elif resize_option == "50%":
        resize_percentage = 50
    elif resize_option == "33%":
        resize_percentage = 33
    elif resize_option == "25%":
        resize_percentage = 25
    else:  # Custom
        resize_percentage = custom_resize_percentage

    resize_factor = resize_percentage / 100
    new_width = int(source.width * resize_factor)
    new_height = int(source.height * resize_factor)

    new_width = max(new_width, 64)
    new_height = max(new_height, 64)

    source = source.resize((new_width, new_height), Image.LANCZOS)

    overlap_x = int(new_width * (overlap_percentage / 100))
    overlap_y = int(new_height * (overlap_percentage / 100))

    overlap_x = max(overlap_x, 1)
    overlap_y = max(overlap_y, 1)

    if alignment == "Middle":
        margin_x = (target_size[0] - new_width) // 2
        margin_y = (target_size[1] - new_height) // 2
    elif alignment == "Left":
        margin_x = 0
        margin_y = (target_size[1] - new_height) // 2
    elif alignment == "Right":
        margin_x = target_size[0] - new_width
        margin_y = (target_size[1] - new_height) // 2
    elif alignment == "Top":
        margin_x = (target_size[0] - new_width) // 2
        margin_y = 0
    elif alignment == "Bottom":
        margin_x = (target_size[0] - new_width) // 2
        margin_y = target_size[1] - new_height

    margin_x = max(0, min(margin_x, target_size[0] - new_width))
    margin_y = max(0, min(margin_y, target_size[1] - new_height))

    background = Image.new('RGB', target_size, (255, 255, 255))
    background.paste(source, (margin_x, margin_y))

    mask = Image.new('L', target_size, 255)
    mask_draw = ImageDraw.Draw(mask)

    white_gaps_patch = 2

    left_overlap = margin_x + overlap_x if overlap_left else margin_x + white_gaps_patch
    right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width - white_gaps_patch
    top_overlap = margin_y + overlap_y if overlap_top else margin_y + white_gaps_patch
    bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height - white_gaps_patch
    
    if alignment == "Left":
        left_overlap = margin_x + overlap_x if overlap_left else margin_x
    elif alignment == "Right":
        right_overlap = margin_x + new_width - overlap_x if overlap_right else margin_x + new_width
    elif alignment == "Top":
        top_overlap = margin_y + overlap_y if overlap_top else margin_y
    elif alignment == "Bottom":
        bottom_overlap = margin_y + new_height - overlap_y if overlap_bottom else margin_y + new_height

    mask_draw.rectangle([
        (left_overlap, top_overlap),
        (right_overlap, bottom_overlap)
    ], fill=0)

    return background, mask

# Updated inference function to use selected pipeline
@spaces.GPU(duration=24)
def infer(pipeline, image, width, height, overlap_percentage, num_inference_steps, resize_option, custom_resize_percentage, prompt_input, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
    background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom)
    
    cnet_image = background.copy()
    cnet_image.paste(0, (0, 0), mask)

    final_prompt = f"{prompt_input} , high quality, 4k"

    (
        prompt_embeds,
        negative_prompt_embeds,
        pooled_prompt_embeds,
        negative_pooled_prompt_embeds,
    ) = pipeline.encode_prompt(final_prompt, "cuda", True)

    for image in pipeline(
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=negative_prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
        image=cnet_image,
        num_inference_steps=num_inference_steps
    ):
        pass
    generated_image = image

    generated_image = generated_image.convert("RGBA")
    cnet_image.paste(generated_image, (0, 0), mask)

    return cnet_image

# Utility functions (unchanged)
def clear_result():
    return gr.update(value=None)

def preload_presets(target_ratio, ui_width, ui_height):
    if target_ratio == "9:16":
        return 720, 1280, gr.update()
    elif target_ratio == "16:9":
        return 1280, 720, gr.update()
    elif target_ratio == "1:1":
        return 1024, 1024, gr.update()
    elif target_ratio == "Custom":
        return ui_width, ui_height, gr.update(open=True)

def select_the_right_preset(user_width, user_height):
    if user_width == 720 and user_height == 1280:
        return "9:16"
    elif user_width == 1280 and user_height == 720:
        return "16:9"
    elif user_width == 1024 and user_height == 1024:
        return "1:1"
    else:
        return "Custom"

def toggle_custom_resize_slider(resize_option):
    return gr.update(visible=(resize_option == "Custom"))

def update_history(new_image, history):
    if history is None:
        history = []
    history.insert(0, new_image)
    return history

# CSS and title (unchanged)
css = """
h1 {
  text-align: center;
  display: block;
}
"""

title = """<h1 align="center">Diffusers Image Outpaint Lightning</h1>
"""

# Gradio interface with model selection
with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
    with gr.Column():
        gr.HTML(title)

        with gr.Row():
            with gr.Column():
                input_image = gr.Image(
                    type="pil",
                    label="Input Image"
                )

                with gr.Row():
                    with gr.Column(scale=2):
                        prompt_input = gr.Textbox(label="Prompt (Optional)")
                    with gr.Column(scale=1):
                        run_button = gr.Button("Generate")

                with gr.Row():
                    model_selector = gr.Dropdown(
                        label="Select Model",
                        choices=list(models.keys()),
                        value="RealVisXL V5.0 Lightning",
                    )

                with gr.Row():
                    target_ratio = gr.Radio(
                        label="Expected Ratio",
                        choices=["9:16", "16:9", "1:1", "Custom"],
                        value="9:16",
                        scale=2
                    )
                    alignment_dropdown = gr.Dropdown(
                        choices=["Middle", "Left", "Right", "Top", "Bottom"],
                        value="Middle",
                        label="Alignment"
                    )

                with gr.Accordion(label="Advanced settings", open=False) as settings_panel:
                    with gr.Column():
                        with gr.Row():
                            width_slider = gr.Slider(
                                label="Target Width",
                                minimum=720,
                                maximum=1536,
                                step=8,
                                value=720,
                            )
                            height_slider = gr.Slider(
                                label="Target Height",
                                minimum=720,
                                maximum=1536,
                                step=8,
                                value=1280,
                            )
                        
                        num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=12, step=1, value=8)
                        with gr.Group():
                            overlap_percentage = gr.Slider(
                                label="Mask overlap (%)",
                                minimum=1,
                                maximum=50,
                                value=10,
                                step=1
                            )
                            with gr.Row():
                                overlap_top = gr.Checkbox(label="Overlap Top", value=True)
                                overlap_right = gr.Checkbox(label="Overlap Right", value=True)
                            with gr.Row():
                                overlap_left = gr.Checkbox(label="Overlap Left", value=True)
                                overlap_bottom = gr.Checkbox(label="Overlap Bottom", value=True)
                        with gr.Row():
                            resize_option = gr.Radio(
                                label="Resize input image",
                                choices=["Full", "50%", "33%", "25%", "Custom"],
                                value="Full"
                            )
                            custom_resize_percentage = gr.Slider(
                                label="Custom resize (%)",
                                minimum=1,
                                maximum=100,
                                step=1,
                                value=50,
                                visible=False
                            )
                            
                gr.Examples(
                    examples=[
                        ["./examples/example_1.webp", 1280, 720, "Middle"],
                        ["./examples/example_2.jpg", 1440, 810, "Left"],
                        ["./examples/example_3.jpg", 1024, 1024, "Top"],
                        ["./examples/example_3.jpg", 1024, 1024, "Bottom"],
                    ],
                    inputs=[input_image, width_slider, height_slider, alignment_dropdown],
                )

            with gr.Column():
                result = gr.Image(
                    interactive=False,
                    label="Generated Image",
                    format="png",
                )
                history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)

        # State to hold the current pipeline
        pipeline_state = gr.State(value=pipe)

        # Update pipeline when model is selected
        model_selector.change(
            fn=load_pipeline,
            inputs=model_selector,
            outputs=pipeline_state,
        )

        target_ratio.change(
            fn=preload_presets,
            inputs=[target_ratio, width_slider, height_slider],
            outputs=[width_slider, height_slider, settings_panel],
            queue=False
        )

        width_slider.change(
            fn=select_the_right_preset,
            inputs=[width_slider, height_slider],
            outputs=[target_ratio],
            queue=False
        )

        height_slider.change(
            fn=select_the_right_preset,
            inputs=[width_slider, height_slider],
            outputs=[target_ratio],
            queue=False
        )

        resize_option.change(
            fn=toggle_custom_resize_slider,
            inputs=[resize_option],
            outputs=[custom_resize_percentage],
            queue=False
        )
        
        run_button.click(
            fn=clear_result,
            inputs=None,
            outputs=result,
        ).then(
            fn=infer,
            inputs=[pipeline_state, input_image, width_slider, height_slider, overlap_percentage, num_inference_steps,
                    resize_option, custom_resize_percentage, prompt_input, alignment_dropdown,
                    overlap_left, overlap_right, overlap_top, overlap_bottom],
            outputs=result,
        ).then(
            fn=lambda x, history: update_history(x, history),
            inputs=[result, history_gallery],
            outputs=history_gallery,
        )

        prompt_input.submit(
            fn=clear_result,
            inputs=None,
            outputs=result,
        ).then(
            fn=infer,
            inputs=[pipeline_state, input_image, width_slider, height_slider, overlap_percentage, num_inference_steps,
                    resize_option, custom_resize_percentage, prompt_input, alignment_dropdown,
                    overlap_left, overlap_right, overlap_top, overlap_bottom],
            outputs=result,
        ).then(
            fn=lambda x, history: update_history(x, history),
            inputs=[result, history_gallery],
            outputs=history_gallery,
        )

demo.queue(max_size=20).launch(share=False, ssr_mode=False, show_error=True)