import os
import cv2
import numpy as np
import torch
import gradio as gr
# import spaces

from glob import glob
from typing import Tuple

from PIL import Image
# from gradio_imageslider import ImageSlider
import transformers
import torch
from torchvision import transforms

import requests
from io import BytesIO
import zipfile

from rankseg import RankSEG


torch.set_float32_matmul_precision('high')
# torch.jit.script = lambda f: f

device = "cuda" if torch.cuda.is_available() else "cpu"
RANKSEG_METRICS = ["dice", "iou"]


def rgba2rgb(img):
    """
    Convert RGBA image to RGB with white background.
    Supports both PIL.Image and numpy.ndarray.
    """

    # 1. Handle PIL Image
    if isinstance(img, Image.Image):
        img = img.convert("RGBA")
        bg = Image.new("RGBA", img.size, (255, 255, 255))
        return Image.alpha_composite(bg, img).convert("RGB")

    # 2. Handle Numpy Array (OpenCV)
    elif isinstance(img, np.ndarray):
        # Grayscale to RGB
        if img.ndim == 2:
            return np.stack([img] * 3, axis=-1)

        # Already 3 channels
        if img.shape[2] == 3:
            return img

        # RGBA to RGB (blending with white)
        elif img.shape[2] == 4:
            # Normalize alpha to 0-1 and keep shape (H, W, 1)
            alpha = img[..., 3:4].astype(float) / 255.0
            foreground = img[..., :3].astype(float)
            background = 255.0

            # Blend formula: source * alpha + bg * (1 - alpha)
            out = foreground * alpha + background * (1.0 - alpha)

            return out.clip(0, 255).astype(np.uint8)

    else:
        raise TypeError(f"Unsupported type: {type(img)}")


## CPU version refinement
def FB_blur_fusion_foreground_estimator_cpu(image, FG, B, alpha, r=90):
    if isinstance(image, Image.Image):
        image = np.array(image) / 255.0
    blurred_alpha = cv2.blur(alpha, (r, r))[:, :, None]

    blurred_FGA = cv2.blur(FG * alpha, (r, r))
    blurred_FG = blurred_FGA / (blurred_alpha + 1e-5)

    blurred_B1A = cv2.blur(B * (1 - alpha), (r, r))
    blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5)
    FG = blurred_FG + alpha * (image - alpha * blurred_FG - (1 - alpha) * blurred_B)
    FG = np.clip(FG, 0, 1)
    return FG, blurred_B


def FB_blur_fusion_foreground_estimator_cpu_2(image, alpha, r=90):
    # Thanks to the source: https://github.com/Photoroom/fast-foreground-estimation
    alpha = alpha[:, :, None]
    FG, blur_B = FB_blur_fusion_foreground_estimator_cpu(image, image, image, alpha, r)
    return FB_blur_fusion_foreground_estimator_cpu(image, FG, blur_B, alpha, r=6)[0]


## GPU version refinement
def mean_blur(x, kernel_size):
    """
    equivalent to cv.blur
    x:  [B, C, H, W]
    """
    if kernel_size % 2 == 0:
        pad_l = kernel_size // 2 - 1
        pad_r = kernel_size // 2
        pad_t = kernel_size // 2 - 1
        pad_b = kernel_size // 2
    else:
        pad_l = pad_r = pad_t = pad_b = kernel_size // 2

    x_padded = torch.nn.functional.pad(x, (pad_l, pad_r, pad_t, pad_b), mode='replicate')

    return torch.nn.functional.avg_pool2d(x_padded, kernel_size=(kernel_size, kernel_size), stride=1, count_include_pad=False)

def FB_blur_fusion_foreground_estimator_gpu(image, FG, B, alpha, r=90):
    as_dtype = lambda x, dtype: x.to(dtype) if x.dtype != dtype else x

    input_dtype = image.dtype
    # convert image to float to avoid overflow
    image = as_dtype(image, torch.float32)
    FG = as_dtype(FG, torch.float32)
    B = as_dtype(B, torch.float32)
    alpha = as_dtype(alpha, torch.float32)

    blurred_alpha = mean_blur(alpha, kernel_size=r)

    blurred_FGA = mean_blur(FG * alpha, kernel_size=r)
    blurred_FG = blurred_FGA / (blurred_alpha + 1e-5)

    blurred_B1A = mean_blur(B * (1 - alpha), kernel_size=r)
    blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5)

    FG_output = blurred_FG + alpha * (image - alpha * blurred_FG - (1 - alpha) * blurred_B)
    FG_output = torch.clamp(FG_output, 0, 1)

    return as_dtype(FG_output, input_dtype), as_dtype(blurred_B, input_dtype)


def FB_blur_fusion_foreground_estimator_gpu_2(image, alpha, r=90):
    # Thanks to the source: https://github.com/ZhengPeng7/BiRefNet/issues/226#issuecomment-3016433728
    FG, blur_B = FB_blur_fusion_foreground_estimator_gpu(image, image, image, alpha, r)
    return FB_blur_fusion_foreground_estimator_gpu(image, FG, blur_B, alpha, r=6)[0]


def refine_foreground(image, mask, r=90, device='cuda'):
    """both image and mask are in range of [0, 1]"""
    if mask.size != image.size:
        mask = mask.resize(image.size)

    if device == 'cuda':
        image = transforms.functional.to_tensor(image).float().cuda()
        mask = transforms.functional.to_tensor(mask).float().cuda()
        image = image.unsqueeze(0)
        mask = mask.unsqueeze(0)

        estimated_foreground = FB_blur_fusion_foreground_estimator_gpu_2(image, mask, r=r)

        estimated_foreground = estimated_foreground.squeeze()
        estimated_foreground = (estimated_foreground.mul(255.0)).to(torch.uint8)
        estimated_foreground = estimated_foreground.permute(1, 2, 0).contiguous().cpu().numpy().astype(np.uint8)
    else:
        image = np.array(image, dtype=np.float32) / 255.0
        mask = np.array(mask, dtype=np.float32) / 255.0
        estimated_foreground = FB_blur_fusion_foreground_estimator_cpu_2(image, mask, r=r)
        estimated_foreground = (estimated_foreground * 255.0).astype(np.uint8)

    estimated_foreground = Image.fromarray(np.ascontiguousarray(estimated_foreground))

    return estimated_foreground


def get_rankseg_pred(pred: torch.Tensor, metric: str) -> torch.Tensor:
    # BiRefNet produces a single foreground probability map, so RankSEG should
    # return a binary mask for that one channel instead of a multiclass map.
    rankseg = RankSEG(metric=metric, output_mode='multilabel', solver='RMA')
    probs = pred.unsqueeze(0).unsqueeze(0).to(torch.float32)
    return rankseg.predict(probs).squeeze(0).squeeze(0).to(torch.float32)


def get_soft_gate(rankseg_mask: torch.Tensor, dilate_kernel: int = 9, blur_kernel: int = 15) -> torch.Tensor:
    support = rankseg_mask.unsqueeze(0).unsqueeze(0).to(torch.float32)
    dilated = torch.nn.functional.max_pool2d(
        support,
        kernel_size=dilate_kernel,
        stride=1,
        padding=dilate_kernel // 2,
    )
    soft_gate = torch.nn.functional.avg_pool2d(
        dilated,
        kernel_size=blur_kernel,
        stride=1,
        padding=blur_kernel // 2,
    )
    return soft_gate.squeeze(0).squeeze(0).clamp(0, 1)


def build_hybrid_alpha(pred: torch.Tensor, rankseg_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    soft_gate = get_soft_gate(rankseg_mask)
    hard_alpha = (pred * rankseg_mask.to(torch.float32)).clamp(0, 1)
    soft_alpha = torch.where(rankseg_mask > 0, pred, pred * soft_gate).clamp(0, 1)
    return hard_alpha, soft_alpha


def build_alpha_cutout(image: Image.Image, mask: Image.Image) -> Image.Image:
    output = image.copy()
    output.putalpha(mask.resize(image.size))
    return output


def build_masked_image(image: Image.Image, mask: Image.Image) -> Image.Image:
    refined = refine_foreground(image, mask, device=device)
    refined.putalpha(mask.resize(image.size))
    return refined


def load_image(image_src):
    if isinstance(image_src, str):
        if os.path.isfile(image_src):
            image_ori = Image.open(image_src)
        else:
            response = requests.get(image_src)
            response.raise_for_status()
            image_data = BytesIO(response.content)
            image_ori = Image.open(image_data)
    else:
        image_ori = Image.fromarray(image_src)

    if image_ori.mode == 'RGBA':
        image_ori = rgba2rgb(image_ori)

    return image_ori.convert('RGB')


class ImagePreprocessor():
    def __init__(self, resolution: Tuple[int, int] = (1024, 1024)) -> None:
        # Input resolution is on WxH.
        self.transform_image = transforms.Compose([
            transforms.Resize(resolution[::-1]),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ])

    def proc(self, image: Image.Image) -> torch.Tensor:
        image = self.transform_image(image)
        return image


usage_to_weights_file = {
    'General': 'BiRefNet',
    'General-HR': 'BiRefNet_HR',
    'Matting-HR': 'BiRefNet_HR-matting',
    'Matting': 'BiRefNet-matting',
    'Portrait': 'BiRefNet-portrait',
    'General-reso_512': 'BiRefNet_512x512',
    'General-Lite': 'BiRefNet_lite',
    'General-Lite-2K': 'BiRefNet_lite-2K',
    'Anime-Lite': 'BiRefNet_lite-Anime',
    'DIS': 'BiRefNet-DIS5K',
    'HRSOD': 'BiRefNet-HRSOD',
    'COD': 'BiRefNet-COD',
    'DIS-TR_TEs': 'BiRefNet-DIS5K-TR_TEs',
    'General-legacy': 'BiRefNet-legacy',
    'General-dynamic': 'BiRefNet_dynamic',
}

birefnet = transformers.AutoModelForImageSegmentation.from_pretrained('/'.join(('zhengpeng7', usage_to_weights_file['General'])), trust_remote_code=True)
birefnet.to(device)
birefnet.eval(); birefnet.half()


# @spaces.GPU
def predict(images, resolution, weights_file, enable_rankseg, rankseg_metric):
    assert (images is not None), 'AssertionError: images cannot be None.'

    global birefnet
    # Load BiRefNet with chosen weights
    _weights_file = '/'.join(('zhengpeng7', usage_to_weights_file[weights_file] if weights_file is not None else usage_to_weights_file['General']))
    print('Using weights: {}.'.format(_weights_file))
    birefnet = transformers.AutoModelForImageSegmentation.from_pretrained(_weights_file, trust_remote_code=True)
    birefnet.to(device)
    birefnet.eval(); birefnet.half()

    try:
        resolution = [int(int(reso)//32*32) for reso in resolution.strip().split('x')]
    except:
        if weights_file in ['General-HR', 'Matting-HR']:
            resolution = (2048, 2048)
        elif weights_file in ['General-Lite-2K']:
            resolution = (2560, 1440)
        elif weights_file in ['General-reso_512']:
            resolution = (512, 512)
        else:
            if weights_file in ['General-dynamic']:
                resolution = None
                print('Using the original size (div by 32) for inference.')
            else:
                resolution = (1024, 1024)
        print('Invalid resolution input. Automatically changed to 1024x1024 / 2048x2048 / 2560x1440.')

    if isinstance(images, list):
        raw_save_paths = []
        rankseg_hard_save_paths = []
        rankseg_soft_save_paths = []
        save_dir = 'preds-BiRefNet'
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        tab_is_batch = True
    else:
        images = [images]
        tab_is_batch = False

    rankseg_metric = (rankseg_metric or 'dice').lower()
    if rankseg_metric not in RANKSEG_METRICS:
        rankseg_metric = 'dice'

    for image_src in images:
        image = load_image(image_src)
        # Preprocess the image
        if resolution is None:
            resolution_div_by_32 = [int(int(reso)//32*32) for reso in image.size]
            if resolution_div_by_32 != resolution:
                resolution = resolution_div_by_32
        image_preprocessor = ImagePreprocessor(resolution=tuple(resolution))
        image_proc = image_preprocessor.proc(image)
        image_proc = image_proc.unsqueeze(0)

        # Prediction
        with torch.no_grad():
            preds = birefnet(image_proc.to(device).half())[-1].sigmoid().float().cpu()
        pred = preds[0].squeeze()

        pred_pil = transforms.ToPILImage()(pred)
        raw_image_masked = build_alpha_cutout(image, pred_pil)
        rankseg_hard_image_masked = None
        rankseg_soft_image_masked = None
        if enable_rankseg:
            rankseg_pred = get_rankseg_pred(pred, rankseg_metric)
            hard_alpha, soft_alpha = build_hybrid_alpha(pred, rankseg_pred)
            rankseg_hard_image_masked = build_alpha_cutout(image, transforms.ToPILImage()(hard_alpha))
            rankseg_soft_image_masked = build_alpha_cutout(image, transforms.ToPILImage()(soft_alpha))

        if device == 'cuda':
            torch.cuda.empty_cache()

        if tab_is_batch:
            image_name = os.path.splitext(os.path.basename(image_src))[0]
            raw_save_file_path = os.path.join(save_dir, f"{image_name}_pred.png")
            raw_image_masked.save(raw_save_file_path)
            raw_save_paths.append(raw_save_file_path)
            if enable_rankseg and rankseg_hard_image_masked is not None:
                rankseg_hard_save_file_path = os.path.join(save_dir, f"{image_name}_pred_rankseg.png")
                rankseg_hard_image_masked.save(rankseg_hard_save_file_path)
                rankseg_hard_save_paths.append(rankseg_hard_save_file_path)
            if enable_rankseg and rankseg_soft_image_masked is not None:
                rankseg_soft_save_file_path = os.path.join(save_dir, f"{image_name}_pred_softgate.png")
                rankseg_soft_image_masked.save(rankseg_soft_save_file_path)
                rankseg_soft_save_paths.append(rankseg_soft_save_file_path)

    if tab_is_batch:
        zip_file_path = os.path.join(save_dir, "{}.zip".format(save_dir))
        with zipfile.ZipFile(zip_file_path, 'w') as zipf:
            for file in raw_save_paths + rankseg_hard_save_paths + rankseg_soft_save_paths:
                zipf.write(file, os.path.basename(file))
        return raw_save_paths, rankseg_hard_save_paths, rankseg_soft_save_paths, zip_file_path
    else:
        return image, raw_image_masked, rankseg_hard_image_masked, rankseg_soft_image_masked


examples = [[_] for _ in glob('examples/*')][:]
# Add the option of resolution in a text box.
for idx_example, example in enumerate(examples):
    if 'My_' in example[0]:
        example_resolution = '2048x2048'
        model_choice = 'Matting-HR'
    else:
        example_resolution = '1024x1024'
        model_choice = 'General'
    examples[idx_example] = examples[idx_example] + [example_resolution, model_choice, True, 'dice']

examples_url = [
    ['https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg'],
]
for idx_example_url, example_url in enumerate(examples_url):
    examples_url[idx_example_url] = examples_url[idx_example_url] + ['1024x1024', 'General', True, 'dice']

descriptions = ('Upload a picture, our model will extract a highly accurate segmentation of the subject in it.\n)'
                 ' The resolution used in our training was `1024x1024`, which is the suggested resolution to obtain good results! `2048x2048` is suggested for BiRefNet_HR.\n'
                 ' Our codes can be found at https://github.com/ZhengPeng7/BiRefNet.\n'
                 ' We also maintain the HF model of BiRefNet at https://huggingface.co/ZhengPeng7/BiRefNet for easier access.')

tab_image = gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(label='Upload an image', image_mode='RGBA'),  # Keep alpha channel
        gr.Textbox(lines=1, placeholder="Type the resolution (`WxH`) you want, e.g., `1024x1024`.", label="Resolution"),
        gr.Radio(list(usage_to_weights_file.keys()), value='General', label="Weights", info="Choose the weights you want."),
        gr.Checkbox(value=True, label="Enable RankSEG"),
        gr.Radio(RANKSEG_METRICS, value='dice', label="RankSEG metric", info="Choose the target metric for RankSEG post-processing.")
    ],
    outputs=[
        gr.Image(label="Original image", type="pil", format='png'),
        gr.Image(label="BiRefNet pred alpha", type="pil", format='png'),
        gr.Image(label="BiRefNet pred x RankSEG", type="pil", format='png'),
        gr.Image(label="BiRefNet soft-gated hybrid", type="pil", format='png'),
    ],
    examples=examples,
    api_name="image",
    description=descriptions,
)

tab_text = gr.Interface(
    fn=predict,
    inputs=[
        gr.Textbox(label="Paste an image URL"),
        gr.Textbox(lines=1, placeholder="Type the resolution (`WxH`) you want, e.g., `1024x1024`.", label="Resolution"),
        gr.Radio(list(usage_to_weights_file.keys()), value='General', label="Weights", info="Choose the weights you want."),
        gr.Checkbox(value=True, label="Enable RankSEG"),
        gr.Radio(RANKSEG_METRICS, value='dice', label="RankSEG metric", info="Choose the target metric for RankSEG post-processing.")
    ],
    outputs=[
        gr.Image(label="Original image", type="pil", format='png'),
        gr.Image(label="BiRefNet pred alpha", type="pil", format='png'),
        gr.Image(label="BiRefNet pred x RankSEG", type="pil", format='png'),
        gr.Image(label="BiRefNet soft-gated hybrid", type="pil", format='png'),
    ],
    examples=examples_url,
    api_name="URL",
    description=descriptions+'\nTab-URL is partially modified from https://huggingface.co/spaces/not-lain/background-removal, thanks to this great work!',
)

tab_batch = gr.Interface(
    fn=predict,
    inputs=[
        gr.File(label="Upload multiple images", type="filepath", file_count="multiple"),
        gr.Textbox(lines=1, placeholder="Type the resolution (`WxH`) you want, e.g., `1024x1024`.", label="Resolution"),
        gr.Radio(list(usage_to_weights_file.keys()), value='General', label="Weights", info="Choose the weights you want."),
        gr.Checkbox(value=True, label="Enable RankSEG"),
        gr.Radio(RANKSEG_METRICS, value='dice', label="RankSEG metric", info="Choose the target metric for RankSEG post-processing.")
    ],
    outputs=[
        gr.Gallery(label="BiRefNet pred alpha results"),
        gr.Gallery(label="BiRefNet pred x RankSEG results"),
        gr.Gallery(label="BiRefNet soft-gated hybrid results"),
        gr.File(label="Download masked images."),
    ],
    api_name="batch",
    description=descriptions+'\nTab-batch is partially modified from https://huggingface.co/spaces/NegiTurkey/Multi_Birefnetfor_Background_Removal, thanks to this great work!',
)

demo = gr.TabbedInterface(
    [tab_image, tab_text, tab_batch],
    ['image', 'URL', 'batch'],
    title="Official Online Demo of BiRefNet",
)

if __name__ == "__main__":
    demo.launch(debug=True)