import gradio as gr
import spaces
import torch
from diffusers import AutoencoderKL, TCDScheduler
from diffusers.models.model_loading_utils import load_state_dict
from gradio_imageslider import ImageSlider
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

MODELS = {
    "RealVisXL V5.0 Lightning": "SG161222/RealVisXL_V5.0_Lightning",
    "Lustify Lightning": "GraydientPlatformAPI/lustify-lightning",
    "Juggernaut XL Lightning": "RunDiffusion/Juggernaut-XL-Lightning",
}

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",
)
state_dict = load_state_dict(model_file)
model, _, _, _, _ = ControlNetModel_Union._load_pretrained_model(
    controlnet_model, state_dict, model_file, "xinsir/controlnet-union-sdxl-1.0"
)
model.to(device="cuda", dtype=torch.float16)

vae = AutoencoderKL.from_pretrained(
    "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
).to("cuda")

# Move pipeline loading into a function to enable lazy loading
def load_pipeline(model_name):
    pipe = StableDiffusionXLFillPipeline.from_pretrained(
        MODELS[model_name],
        torch_dtype=torch.float16,
        vae=vae,
        controlnet=model,
    )
    pipe.to("cuda")
    pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
    return pipe

def can_expand(source_width, source_height, target_width, target_height, alignment):
    """Checks if the image can be expanded based on the alignment."""
    if alignment in ("Left", "Right") and source_width >= target_width:
        return False
    if alignment in ("Top", "Bottom") and source_height >= target_height:
        return False
    return True

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)

    # Calculate the scaling factor to fit the image within the target size
    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)
    
    # Resize the source image to fit within target size
    source = image.resize((new_width, new_height), Image.LANCZOS)

    # Apply resize option using percentages
    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

    # Calculate new dimensions based on percentage
    resize_factor = resize_percentage / 100
    new_width = int(source.width * resize_factor)
    new_height = int(source.height * resize_factor)

    # Ensure minimum size of 64 pixels
    new_width = max(new_width, 64)
    new_height = max(new_height, 64)

    # Resize the image
    source = source.resize((new_width, new_height), Image.LANCZOS)

    # Calculate the overlap in pixels based on the percentage
    overlap_x = int(new_width * (overlap_percentage / 100))
    overlap_y = int(new_height * (overlap_percentage / 100))

    # Ensure minimum overlap of 1 pixel
    overlap_x = max(overlap_x, 1)
    overlap_y = max(overlap_y, 1)

    # Calculate margins based on alignment
    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

    # Adjust margins to eliminate gaps
    margin_x = max(0, min(margin_x, target_size[0] - new_width))
    margin_y = max(0, min(margin_y, target_size[1] - new_height))

    # Create a new background image and paste the resized source image
    background = Image.new('RGB', target_size, (255, 255, 255))
    background.paste(source, (margin_x, margin_y))

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

    # Calculate overlap areas
    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


    # Draw the mask
    mask_draw.rectangle([
        (left_overlap, top_overlap),
        (right_overlap, bottom_overlap)
    ], fill=0)

    return background, mask

def preview_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, 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)
    
    # Create a preview image showing the mask
    preview = background.copy().convert('RGBA')
    
    # Create a semi-transparent red overlay
    red_overlay = Image.new('RGBA', background.size, (255, 0, 0, 64))  # Reduced alpha to 64 (25% opacity)
    
    # Convert black pixels in the mask to semi-transparent red
    red_mask = Image.new('RGBA', background.size, (0, 0, 0, 0))
    red_mask.paste(red_overlay, (0, 0), mask)
    
    # Overlay the red mask on the background
    preview = Image.alpha_composite(preview, red_mask)
    
    return preview

@spaces.GPU(duration=24)
def inpaint(prompt, image, model_name, paste_back):
    global pipe
    if pipe.config.model_name != MODELS[model_name]:
        # Lazily load the pipeline for the selected model
        pipe = load_pipeline(model_name)
    
    mask = Image.fromarray(image["mask"]).convert("L")
    image = Image.fromarray(image["image"])
    
    result = pipe(prompt=prompt, image=image, mask_image=mask).images[0]
    
    if paste_back:
        result.paste(image, (0, 0), Image.fromarray(255 - np.array(mask)))
    
    return result

@spaces.GPU(duration=24)
def outpaint(image, width, height, overlap_percentage, num_inference_steps, resize_option, custom_resize_percentage, prompt_input, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
    # Use the currently loaded pipeline
    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)
    
    if not can_expand(background.width, background.height, width, height, alignment):
        alignment = "Middle"

    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,
    ) = pipe.encode_prompt(final_prompt, "cuda", True)

    for image in pipe(
        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
    ):
        yield cnet_image, image

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

    yield background, cnet_image

def clear_result():
    """Clears the result ImageSlider."""
    return gr.update(value=None)

def preload_presets(target_ratio, ui_width, ui_height):
    """Updates the width and height sliders based on the selected aspect ratio."""
    if target_ratio == "9:16":
        changed_width = 720
        changed_height = 1280
        return changed_width, changed_height, gr.update()
    elif target_ratio == "16:9":
        changed_width = 1280
        changed_height = 720
        return changed_width, changed_height, gr.update()
    elif target_ratio == "1:1":
        changed_width = 1024
        changed_height = 1024
        return changed_width, changed_height, 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):
    """Updates the history gallery with the new image."""
    if history is None:
        history = []
    history.insert(0, new_image)
    return history

css = """
.gradio-container {
    width: 1200px !important;
}
"""

title = """<h1 align="center">Diffusers Image Outpaint</h1>
<div align="center">Drop an image you would like to extend, pick your expected ratio and hit Generate.</div>
<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
    <p style="display: flex;gap: 6px;">
         <a href="https://huggingface.co/spaces/fffiloni/diffusers-image-outpout?duplicate=true">
            <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-md.svg" alt="Duplicate this Space">
        </a> to skip the queue and enjoy faster inference on the GPU of your choice 
    </p>
</div>
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("# Diffusers Inpaint and Outpaint")
    
    with gr.Tabs():
        with gr.TabItem("Inpaint"):
            with gr.Column():
                # inpaint_image = gr.Image(type="pil", label="Input Image", tool="sketch")
                inpaint_image = gr.ImageEditor(type="pil", label="Input Image")

                inpaint_prompt = gr.Textbox(label="Prompt", info="Describe what to inpaint the mask with", lines=3)
                inpaint_model = gr.Dropdown(choices=list(MODELS.keys()), value="RealVisXL V5.0 Lightning", label="Model")
                inpaint_paste_back = gr.Checkbox(True, label="Paste back original")
                inpaint_button = gr.Button("Generate Inpaint")
                inpaint_result = ImageSlider(label="Inpaint Result")

        with gr.TabItem("Outpaint"):
            with gr.Column():
                outpaint_image = gr.Image(type="pil", label="Input Image")
                outpaint_prompt = gr.Textbox(label="Prompt (Optional)")
                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)
                alignment_dropdown = gr.Dropdown(choices=["Middle", "Left", "Right", "Top", "Bottom"], value="Middle", label="Alignment")
                
                with gr.Accordion("Advanced settings", open=False):
                    num_inference_steps = gr.Slider(label="Steps", minimum=4, maximum=12, step=1, value=8)
                    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)
                    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)
                
                outpaint_button = gr.Button("Generate Outpaint")
                preview_button = gr.Button("Preview alignment and mask")
                outpaint_result = ImageSlider(label="Outpaint Result")
                preview_image = gr.Image(label="Preview")

    # Set up event handlers
    inpaint_button.click(
        fn=inpaint,
        inputs=[inpaint_prompt, inpaint_image, inpaint_model, inpaint_paste_back],
        outputs=inpaint_result
    )

    outpaint_button.click(
        fn=outpaint,
        inputs=[outpaint_image, width_slider, height_slider, overlap_percentage, num_inference_steps,
                resize_option, custom_resize_percentage, outpaint_prompt, alignment_dropdown,
                overlap_left, overlap_right, overlap_top, overlap_bottom],
        outputs=outpaint_result
    )

    preview_button.click(
        fn=preview_image_and_mask,
        inputs=[outpaint_image, width_slider, height_slider, overlap_percentage, resize_option, custom_resize_percentage, alignment_dropdown,
                overlap_left, overlap_right, overlap_top, overlap_bottom],
        outputs=preview_image
    )

    resize_option.change(
        fn=lambda x: gr.update(visible=(x == "Custom")),
        inputs=[resize_option],
        outputs=[custom_resize_percentage]
    )

demo.launch(share=False)