import gradio as gr
import torch

from diffuserslocal.src.diffusers import UNet2DConditionModel
import diffuserslocal.src.diffusers as diffusers
from share_btn import community_icon_html, loading_icon_html, share_js
from diffuserslocal.src.diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_ldm3d_inpaint import StableDiffusionLDM3DInpaintPipeline
from PIL import Image
import numpy as np
import cv2

device = "cuda" if torch.cuda.is_available() else "cpu"

# Inpainting pipeline


unet = UNet2DConditionModel.from_pretrained("pablodawson/ldm3d-inpainting", cache_dir="cache", subfolder="unet", in_channels=9, low_cpu_mem_usage=False, ignore_mismatched_sizes=True)
pipe = StableDiffusionLDM3DInpaintPipeline.from_pretrained("Intel/ldm3d-4c", cache_dir="cache" ).to(device)

# Depth estimation
model_type = "DPT_Large"     # MiDaS v3 - Large     (highest accuracy, slowest inference speed)
#model_type = "DPT_Hybrid"   # MiDaS v3 - Hybrid    (medium accuracy, medium inference speed)
#model_type = "MiDaS_small"  # MiDaS v2.1 - Small   (lowest accuracy, highest inference speed)

midas = torch.hub.load("intel-isl/MiDaS", model_type)

midas.to(device)
midas.eval()

midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")

if model_type == "DPT_Large" or model_type == "DPT_Hybrid":
    transform = midas_transforms.dpt_transform
else:
    transform = midas_transforms.small_transform


def estimate_depth(image):

    input_batch = transform(image).to(device)

    with torch.no_grad():
        prediction = midas(input_batch)

        prediction = torch.nn.functional.interpolate(
            prediction.unsqueeze(1),
            size=image.shape[:2],
            mode="bicubic",
            align_corners=False,
        ).squeeze()

    output = prediction.cpu().numpy()

    output= 255 * (output - np.min(output))/(np.max(output) - np.min(output))
    
    return Image.fromarray(output.astype("uint8"))


def read_content(file_path: str) -> str:
    """read the content of target file
    """
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()

    return content

def predict(dict, depth, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
    if negative_prompt == "":
        negative_prompt = None
    scheduler_class_name = scheduler.split("-")[0]

    init_image = cv2.resize(dict["image"], (512, 512))

    mask = Image.fromarray(cv2.resize(dict["mask"], (512, 512))[:,:,0])
    mask.save("temp_mask.jpg")

    if (depth is None):
        depth_image = estimate_depth(init_image)
        
    else:
        depth_image = depth
        depth_image = Image.fromarray(depth_image[:,:,0].astype("uint8"))

    init_image = Image.fromarray(init_image.astype("uint8"))
    init_image.save("temp_image.jpg")
    
    depth_image.save("temp_depth.jpg")
    #scheduler = getattr(diffusers, scheduler_class_name)
    #pipe.scheduler = scheduler.from_pretrained("Intel/ldm3d-4c", subfolder="scheduler")
    
    
    depth_image = depth_image.resize((512, 512))
    
    output = pipe(prompt = prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask, depth_image=depth_image, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength)
    
    depth_out = np.array(output.depth[0])
    
    output_depth_vis = (depth_out - np.min(output.depth[0])) / (np.max(depth_out) - np.min(depth_out)) * 255
    output_depth_vis = output_depth_vis.astype("uint8")
    output_depth = Image.fromarray(output_depth_vis)

    return output.rgb[0], output_depth, gr.update(visible=True)


css = '''
.gradio-container{max-width: 1100px !important}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
    from {
        transform: rotate(0deg);
    }
    to {
        transform: rotate(360deg);
    }
}
#share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;}
div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
#share-btn-container:hover {background-color: #060606}
#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;}
#share-btn * {all: unset}
#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
#share-btn-container .wrap {display: none !important}
#share-btn-container.hidden {display: none!important}
#prompt input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;}
#run_button{position:absolute;margin-top: 11px;right: 0;margin-right: 0.8em;border-bottom-left-radius: 0px;
    border-top-left-radius: 0px;}
#prompt-container{margin-top:-18px;}
#prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0}
#image_upload{border-bottom-left-radius: 0px;border-bottom-right-radius: 0px}
'''

image_blocks = gr.Blocks(css=css, elem_id="total-container")
with image_blocks as demo:
    gr.HTML(read_content("header.html"))
    with gr.Row():
                with gr.Column():
                    image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="numpy", label="Upload",height=400)
                    depth = gr.Image(source='upload', elem_id="depth_upload", type="numpy", label="Upload",height=400)
                    
                    with gr.Row(elem_id="prompt-container", mobile_collapse=False, equal_height=True):
                        with gr.Row():
                            prompt = gr.Textbox(placeholder="Your prompt (what you want in place of what is erased)", show_label=False, elem_id="prompt")
                            btn = gr.Button("Inpaint!", elem_id="run_button")
                    
                    with gr.Accordion(label="Advanced Settings", open=False):
                        with gr.Row(mobile_collapse=False, equal_height=True):
                            guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
                            steps = gr.Number(value=20, minimum=10, maximum=30, step=1, label="steps")
                            strength = gr.Number(value=0.99, minimum=0.01, maximum=0.99, step=0.01, label="strength")
                            negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt", info="what you don't want to see in the image")
                        with gr.Row(mobile_collapse=False, equal_height=True):
                            schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler", "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras", "DPMSolverMultistepScheduler-Karras-SDE"]
                            scheduler = gr.Dropdown(label="Schedulers", choices=schedulers, value="EulerDiscreteScheduler")
                        
                with gr.Column():
                    image_out = gr.Image(label="Output", elem_id="output-img", height=400)
                    depth_out = gr.Image(label="Depth", elem_id="depth-img", height=400)

                    with gr.Group(elem_id="share-btn-container", visible=False) as share_btn_container:
                        community_icon = gr.HTML(community_icon_html)
                        loading_icon = gr.HTML(loading_icon_html)
                        share_button = gr.Button("Share to community", elem_id="share-btn",visible=True)

    btn.click(fn=predict, inputs=[image, depth, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, depth_out, share_btn_container], api_name='run')
    prompt.submit(fn=predict, inputs=[image, depth, prompt, negative_prompt, guidance_scale, steps, strength, scheduler], outputs=[image_out, depth_out, share_btn_container])
    share_button.click(None, [], [], _js=share_js)

    gr.Examples(
                examples=[
                    ["./imgs/aaa (8).png"],
                    ["./imgs/download (1).jpeg"],
                    ["./imgs/0_oE0mLhfhtS_3Nfm2.png"],
                    ["./imgs/02_HubertyBlog-1-1024x1024.jpg"],
                    ["./imgs/jdn_jacques_de_nuce-1024x1024.jpg"],
                    ["./imgs/c4ca473acde04280d44128ad8ee09e8a.jpg"],
                    ["./imgs/canam-electric-motorcycles-scaled.jpg"],
                    ["./imgs/e8717ce80b394d1b9a610d04a1decd3a.jpeg"],
                    ["./imgs/Nature___Mountains_Big_Mountain_018453_31.jpg"],
                    ["./imgs/Multible-sharing-room_ccexpress-2-1024x1024.jpeg"],
                ],
                fn=predict,
                inputs=[image],
                cache_examples=False,
    )
    gr.HTML(
        """
            <div class="footer">
                <p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face
                </p>
            </div>
        """
    )

image_blocks.queue(max_size=25).launch()