import spaces
import huggingface_hub

huggingface_hub.snapshot_download(
    repo_id='h94/IP-Adapter',
    allow_patterns=[
        'models/**',
        'sdxl_models/**',
    ],
    local_dir='./',
    local_dir_use_symlinks=False,
)

import gradio as gr
from diffusers import StableDiffusionXLControlNetInpaintPipeline, ControlNetModel
from rembg import remove
from PIL import Image
import torch
from ip_adapter import IPAdapterXL
from ip_adapter.utils import register_cross_attention_hook, get_net_attn_map, attnmaps2images
from PIL import Image, ImageChops, ImageEnhance
import numpy as np

import os
import glob
import torch
import cv2
import argparse
from diffusers.models.attention_processor import AttnProcessor2_0
import DPT.util.io

from torchvision.transforms import Compose

from DPT.dpt.models import DPTDepthModel
from DPT.dpt.midas_net import MidasNet_large
from DPT.dpt.transforms import Resize, NormalizeImage, PrepareForNet

"""
Get ZeST Ready
"""
base_model_path = "Lykon/dreamshaper-xl-lightning"
image_encoder_path = "models/image_encoder"
ip_ckpt = "sdxl_models/ip-adapter_sdxl_vit-h.bin"
controlnet_path = "diffusers/controlnet-depth-sdxl-1.0"
device = "cuda"
torch.cuda.empty_cache()

# load SDXL pipeline
controlnet = ControlNetModel.from_pretrained(controlnet_path, variant="fp16", use_safetensors=True, torch_dtype=torch.float16).to(device)
pipe = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(
    base_model_path,
    controlnet=controlnet,
    use_safetensors=True,
    torch_dtype=torch.float16,
    add_watermarker=False,
).to(device)
pipe.unet = register_cross_attention_hook(pipe.unet)
pipe.unet.set_attn_processor(AttnProcessor2_0())
ip_model = IPAdapterXL(pipe, image_encoder_path, ip_ckpt, device)


"""
Get Depth Model Ready
"""
model_path = "DPT/weights/dpt_hybrid-midas-501f0c75.pt"
net_w = net_h = 384
model = DPTDepthModel(
    path=model_path,
    backbone="vitb_rn50_384",
    non_negative=True,
    enable_attention_hooks=False,
)
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])

transform = Compose(
        [
            Resize(
                net_w,
                net_h,
                resize_target=None,
                keep_aspect_ratio=True,
                ensure_multiple_of=32,
                resize_method="minimal",
                image_interpolation_method=cv2.INTER_CUBIC,
            ),
            normalization,
            PrepareForNet(),
        ]
    )

model.eval()

@spaces.GPU()
def greet(input_image, material_exemplar):
    
    """
    Compute depth map from input_image
    """
    
    img = np.array(input_image)
    
    img_input = transform({"image": img})["image"]

    # compute
    with torch.no_grad():
        sample = torch.from_numpy(img_input).unsqueeze(0)

        # if optimize == True and device == torch.device("cuda"):
        #     sample = sample.to(memory_format=torch.channels_last)
        #     sample = sample.half()

        prediction = model.forward(sample)
        prediction = (
            torch.nn.functional.interpolate(
                prediction.unsqueeze(1),
                size=img.shape[:2],
                mode="bicubic",
                align_corners=False,
            )
            .squeeze()
            .cpu()
            .numpy()
        )
        
    depth_min = prediction.min()
    depth_max = prediction.max()
    bits = 2
    max_val = (2 ** (8 * bits)) - 1

    if depth_max - depth_min > np.finfo("float").eps:
        out = max_val * (prediction - depth_min) / (depth_max - depth_min)
    else:
        out = np.zeros(prediction.shape, dtype=depth.dtype)
    
    out = (out / 256).astype('uint8')
    depth_map = Image.fromarray(out).resize((1024, 1024))
    
    
    """
    Process foreground decolored image
    """
    rm_bg = remove(input_image)
    target_mask = rm_bg.convert("RGB").point(lambda x: 0 if x < 1 else 255).convert('L').convert('RGB')
    mask_target_img = ImageChops.lighter(input_image, target_mask)
    invert_target_mask = ImageChops.invert(target_mask)
    gray_target_image = input_image.convert('L').convert('RGB')
    gray_target_image = ImageEnhance.Brightness(gray_target_image)
    factor = 1.0  # Try adjusting this to get the desired brightness
    gray_target_image = gray_target_image.enhance(factor)
    grayscale_img = ImageChops.darker(gray_target_image, target_mask)
    img_black_mask = ImageChops.darker(input_image, invert_target_mask)
    grayscale_init_img = ImageChops.lighter(img_black_mask, grayscale_img)
    init_img = grayscale_init_img
    
    """
    Process material exemplar and resize all images
    """
    ip_image = material_exemplar.resize((1024, 1024))
    init_img = init_img.resize((1024,1024))
    mask = target_mask.resize((1024, 1024))
    
    
    num_samples = 1
    images = ip_model.generate(guidance_scale=2, pil_image=ip_image, image=init_img, control_image=depth_map, mask_image=mask, controlnet_conditioning_scale=0.9, num_samples=num_samples, num_inference_steps=4, seed=42)
    
    return images[0]

css = """
#col-container{
    margin: 0 auto;
    max-width: 960px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("""
        # ZeST: Zero-Shot Material Transfer from a Single Image
        <p>Upload two images -- input image and material exemplar. (both 1024*1024 for better results) <br />
        ZeST extracts the material from the exemplar and cast it onto the input image following the original lighting cues.</p>
        """)
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    input_image = gr.Image(type="pil", label="input image")
                    input_image2 = gr.Image(type="pil", label = "material examplar")
                submit_btn = gr.Button("Submit")
                gr.Examples(
                    examples = [["demo_assets/input_imgs/pumpkin.png", "demo_assets/material_exemplars/cup_glaze.png"]],
                    inputs = [input_image, input_image2]
                )
            with gr.Column():
                output_image = gr.Image(label="transfer result")
    submit_btn.click(fn=greet, inputs=[input_image, input_image2], outputs=[output_image])

demo.queue().launch()