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

# ====== 以下为加载模型等初始化逻辑，与原始代码保持一致 ======
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")

pipe = StableDiffusionXLFillPipeline.from_pretrained(
    "SG161222/RealVisXL_V5.0_Lightning",
    torch_dtype=torch.float16,
    vae=vae,
    controlnet=model,
    variant="fp16",
).to("cuda")

pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)

# ====== 工具函数，与原始代码相同 ======
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(images, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
    """
    预览逻辑：只取上传的第一张图片进行预览。
    若没有上传任何图片，则返回 None。
    """
    if not images or len(images) == 0:
        return None
    
    # 只取第一张做预览
    image = Image.open(images[0].name).convert("RGB")
    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))  # alpha=64
    
    # 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

# ====== 核心推理函数：改为一次处理多张图片 ======
@spaces.GPU(duration=24)
def infer(images, width, height, overlap_percentage, num_inference_steps,
          resize_option, custom_resize_percentage, prompt_input, alignment,
          overlap_left, overlap_right, overlap_top, overlap_bottom):
    """
    一次处理多张图片，返回列表供 ImageSlider 展示。
    """
    if not images or len(images) == 0:
        return []  # 没有上传任何图片时返回空列表

    final_results = []

    for file_obj in images:
        # 读取单张图片
        image = Image.open(file_obj.name).convert("RGB")
        
        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)

        # 调用 pipeline 进行推理
        # （原先用 yield 逐帧返回，这里改成收集最后结果）
        pipeline_output = 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
        )

        # pipeline_output 本身就是一张或多张图
        # 如果是 diffusers 常规用法，一般返回的是一张图列表
        if isinstance(pipeline_output, list):
            outpainted_image = pipeline_output[-1]
        else:
            outpainted_image = pipeline_output.images[0]

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

        # 把最终合成后的图加到结果
        final_results.append(cnet_image)

    # 返回所有图片的列表，给 ImageSlider 展示
    return final_results

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

# 让 "Use as Input Image" 在多张图场景下，只取第一张或直接无效化
def use_output_as_input(output_images):
    """
    假设我们只取生成列表的第一张作为新的输入。
    如果 output_images 为空或没有图，则返回 None。
    """
    if not output_images:
        return gr.update(value=None)
    return gr.update(value=output_images[0])  # 只取第一张

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

title = """<h1 align="center">Diffusers Image Outpaint (Multi-image)</h1>
<div align="center">Drop multiple images you would like to extend, pick your expected ratio and hit Generate.</div>
"""

# ====== 构建 Gradio 界面 ======
with gr.Blocks(css=css) as demo:
    with gr.Column():
        gr.HTML(title)

        with gr.Row():
            with gr.Column():
                # 改为多图上传
                input_images = gr.Files(
                    type="file",
                    label="Input Images",
                    file_count="multiple"
                )

                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():
                    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
                            )
                        
                        with gr.Column():
                            preview_button = gr.Button("Preview alignment and mask")
                            
                # 这里的Examples示例，第一个参数需要是文件列表
                # 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_images, width_slider, height_slider, alignment_dropdown],
                # )

            with gr.Column():
                result = ImageSlider(
                    interactive=False,
                    label="Generated Images",
                )
                use_as_input_button = gr.Button("Use as Input Image", visible=False)

                history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
                preview_image = gr.Image(label="Preview")

    # ====== 绑定各种交互 ======

    use_as_input_button.click(
        fn=use_output_as_input,
        inputs=[result],
        outputs=[input_images]
    )
    
    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
    )
    
    # 点击 "Generate" 按钮执行推理
    run_button.click(  # 先清空
        fn=clear_result,
        inputs=None,
        outputs=result,
    ).then(  # 再推理
        fn=infer,
        inputs=[
            input_images, 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(  # 更新历史
        fn=lambda x, history: update_history(x[-1] if x else None, history),  # 取最后一张放历史
        inputs=[result, history_gallery],
        outputs=history_gallery,
    ).then(  # 显示 "Use as Input Image" 按钮
        fn=lambda: gr.update(visible=True),
        inputs=None,
        outputs=use_as_input_button,
    )

    # 回车提交Prompt也可触发推理
    prompt_input.submit(
        fn=clear_result,
        inputs=None,
        outputs=result,
    ).then(
        fn=infer,
        inputs=[
            input_images, 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[-1] if x else None, history),
        inputs=[result, history_gallery],
        outputs=history_gallery,
    ).then(
        fn=lambda: gr.update(visible=True),
        inputs=None,
        outputs=use_as_input_button,
    )

    # 预览按钮：只对第一张图预览
    preview_button.click(
        fn=preview_image_and_mask,
        inputs=[
            input_images, width_slider, height_slider, overlap_percentage,
            resize_option, custom_resize_percentage, alignment_dropdown,
            overlap_left, overlap_right, overlap_top, overlap_bottom
        ],
        outputs=preview_image,
        queue=False
    )

demo.queue(max_size=12).launch(share=False)