import cv2
import torch
import random
import tempfile
import numpy as np
from pathlib import Path
from PIL import Image
from diffusers import (
    ControlNetModel,
    StableDiffusionXLControlNetPipeline,
    UNet2DConditionModel,
    EulerDiscreteScheduler,
)
import spaces
import gradio as gr
from huggingface_hub import hf_hub_download, snapshot_download
from ip_adapter import IPAdapterXL
from safetensors.torch import load_file
from rembg import remove

snapshot_download(
    repo_id="h94/IP-Adapter", allow_patterns="sdxl_models/*", local_dir="."
)

# global variable
MAX_SEED = np.iinfo(np.int32).max
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32

# initialization
base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
image_encoder_path = "sdxl_models/image_encoder"
ip_ckpt = "sdxl_models/ip-adapter_sdxl.bin"

controlnet_path = "diffusers/controlnet-canny-sdxl-1.0"
controlnet = ControlNetModel.from_pretrained(
    controlnet_path, use_safetensors=False, torch_dtype=torch.float16
).to(device)

# load SDXL lightnining

pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    base_model_path,
    controlnet=controlnet,
    torch_dtype=torch.float16,
    variant="fp16",
    add_watermarker=False,
).to(device)
pipe.set_progress_bar_config(disable=True)
pipe.scheduler = EulerDiscreteScheduler.from_config(
    pipe.scheduler.config, timestep_spacing="trailing", prediction_type="epsilon"
)
pipe.unet.load_state_dict(
    load_file(
        hf_hub_download(
            "ByteDance/SDXL-Lightning", "sdxl_lightning_2step_unet.safetensors"
        ),
        device="cuda",
    )
)

# load ip-adapter
# target_blocks=["block"] for original IP-Adapter
# target_blocks=["up_blocks.0.attentions.1"] for style blocks only
# target_blocks = ["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"] # for style+layout blocks
ip_model = IPAdapterXL(
    pipe,
    image_encoder_path,
    ip_ckpt,
    device,
    target_blocks=["up_blocks.0.attentions.1"],
)


def resize_img(
    input_image,
    max_side=1280,
    min_side=1024,
    size=None,
    pad_to_max_side=False,
    mode=Image.BILINEAR,
    base_pixel_number=64,
):
    w, h = input_image.size
    if size is not None:
        w_resize_new, h_resize_new = size
    else:
        ratio = min_side / min(h, w)
        w, h = round(ratio * w), round(ratio * h)
        ratio = max_side / max(h, w)
        input_image = input_image.resize([round(ratio * w), round(ratio * h)], mode)
        w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
        h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
    input_image = input_image.resize([w_resize_new, h_resize_new], mode)

    if pad_to_max_side:
        res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
        offset_x = (max_side - w_resize_new) // 2
        offset_y = (max_side - h_resize_new) // 2
        res[offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new] = (
            np.array(input_image)
        )
        input_image = Image.fromarray(res)
    return input_image

examples = [
    [
        "./asset/0.jpg",
        None,
        "3D model, cute monster, high quality",
        1.0,
        0.0,
    ],
    [
        "./asset/2.jpg",
        "./asset/house.jpg",
        "3d model, house, kawai, cute, sci-fi, solarpunk, high quality",
        1.0,
        0.6,
    ],
]


def run_for_examples(style_image, source_image, prompt, scale, control_scale):
    return create_image(
        image_pil=style_image,
        input_image=source_image,
        prompt=prompt,
        n_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
        scale=scale,
        control_scale=control_scale,
        guidance_scale=0.0,
        num_inference_steps=2,
        seed=42,
        target="Load only style blocks",
        neg_content_prompt="",
        neg_content_scale=0,
    )


@spaces.GPU
def create_image(
    image_pil,
    input_image,
    prompt,
    n_prompt,
    scale,
    control_scale,
    guidance_scale,
    num_inference_steps,
    seed,
    target="Load only style blocks",
    neg_content_prompt=None,
    neg_content_scale=0,
):
    seed = random.randint(0, MAX_SEED) if seed == -1 else seed
    if target == "Load original IP-Adapter":
        # target_blocks=["blocks"] for original IP-Adapter
        ip_model = IPAdapterXL(
            pipe, image_encoder_path, ip_ckpt, device, target_blocks=["blocks"]
        )
    elif target == "Load only style blocks":
        # target_blocks=["up_blocks.0.attentions.1"] for style blocks only
        ip_model = IPAdapterXL(
            pipe,
            image_encoder_path,
            ip_ckpt,
            device,
            target_blocks=["up_blocks.0.attentions.1"],
        )
    elif target == "Load style+layout block":
        # target_blocks = ["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"] # for style+layout blocks
        ip_model = IPAdapterXL(
            pipe,
            image_encoder_path,
            ip_ckpt,
            device,
            target_blocks=["up_blocks.0.attentions.1", "down_blocks.2.attentions.1"],
        )

    if input_image is not None:
        input_image = resize_img(input_image, max_side=1024)
        cv_input_image = pil_to_cv2(input_image)
        detected_map = cv2.Canny(cv_input_image, 50, 200)
        canny_map = Image.fromarray(cv2.cvtColor(detected_map, cv2.COLOR_BGR2RGB))
    else:
        canny_map = Image.new("RGB", (1024, 1024), color=(255, 255, 255))
        control_scale = 0

    if float(control_scale) == 0:
        canny_map = canny_map.resize((1024, 1024))

    if len(neg_content_prompt) > 0 and neg_content_scale != 0:
        images = ip_model.generate(
            pil_image=image_pil,
            prompt=prompt,
            negative_prompt=n_prompt,
            scale=scale,
            guidance_scale=guidance_scale,
            num_samples=1,
            num_inference_steps=num_inference_steps,
            seed=seed,
            image=canny_map,
            controlnet_conditioning_scale=float(control_scale),
            neg_content_prompt=neg_content_prompt,
            neg_content_scale=neg_content_scale,
        )
    else:
        images = ip_model.generate(
            pil_image=image_pil,
            prompt=prompt,
            negative_prompt=n_prompt,
            scale=scale,
            guidance_scale=guidance_scale,
            num_samples=1,
            num_inference_steps=num_inference_steps,
            seed=seed,
            image=canny_map,
            controlnet_conditioning_scale=float(control_scale),
        )
    image = images[0]
    with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmpfile:
        image.save(tmpfile, "JPEG", quality=80, optimize=True, progressive=True)
        return Path(tmpfile.name)


def pil_to_cv2(image_pil):
    image_np = np.array(image_pil)
    image_cv2 = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
    return image_cv2


# Description
title = r"""
<h1 align="center">I2I mit SDXL-Lightning & IP-Adapter</h1>
"""

description = r"""
<b>ARM <3 GoldExtra Testversion<br>
<b>Wir schauen uns gut funktionierende Prompts. Bitte diese notieren und an Hidéo weiterleiten!</b><br>
"""

article = r"""
<br>
Bei Fragen: <a href="mailto:hideo@artificialmuseum.com">Mail an Hidéo</a>
"""

block = gr.Blocks()
with block:
    # description
    gr.Markdown(title)
    gr.Markdown(description)

    with gr.Tabs():
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    with gr.Column():
                        with gr.Row():
#                            with gr.Column():
                            image_pil_pil = gr.Image(label="Style Image", type="pil")
                    with gr.Column():
                        prompt = gr.Textbox(
                            label="Prompt",
                            value="3d render, 3d model, clean 3d style, cute space monster, white backround, cinematic lighting,",
                        )

                        scale = gr.Slider(
                            minimum=0, maximum=2.0, step=0.01, value=1.0, label="Scale"
                        )

                with gr.Accordion(open=False, label="Details (optional)"):
                    target = gr.Radio(
                        [
                            "Load only style blocks",
                            "Load style+layout block",
                            "Load original IP-Adapter",
                        ],
                        value="Load only style blocks",
                        label="Style mode (optional, sb works best!)",
                    )
                    with gr.Column():
                        src_image_pil = gr.Image(
                            label="Source Image (optional)", type="pil"
                        )
                    control_scale = gr.Slider(
                        minimum=0,
                        maximum=1.0,
                        step=0.01,
                        value=0.5,
                        label="ControlNet Scale (test this!)",
                    )

                    n_prompt = gr.Textbox(
                        label="Negative Prompt // n_prompt",
                        value="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
                    )

                    neg_content_prompt = gr.Textbox(
                        label="Negative Content Prompt (Ignore this!)", value=""
                    )
                    neg_content_scale = gr.Slider(
                        minimum=0,
                        maximum=1.0,
                        step=0.01,
                        value=0.5,
                        label="NCS (Ignore this!) // neg_content_scale",
                    )

                    guidance_scale = gr.Slider(
                        minimum=0,
                        maximum=10.0,
                        step=0.01,
                        value=0.0,
                        label="Guidance Scale (test this!)",
                    )
                    num_inference_steps = gr.Slider(
                        minimum=2,
                        maximum=50.0,
                        step=1.0,
                        value=2,
                        label="Inference Steps (optional but test with 2+)",
                    )
                    seed = gr.Slider(
                        minimum=-1,
                        maximum=MAX_SEED,
                        value=-1,
                        step=1,
                        label="Seed Value (Seed-Proof) // -1 == random",
                    )

                generate_button = gr.Button("Simsalabim")

            with gr.Column():
                generated_image = gr.Image(label="Magix uWu")

    inputs = [
        image_pil,
        src_image_pil,
        prompt,
        n_prompt,
        scale,
        control_scale,
        guidance_scale,
        num_inference_steps,
        seed,
        target,
        neg_content_prompt,
        neg_content_scale,
    ]
    outputs = [generated_image]

    gr.on(
        triggers=[
#            prompt.input,
            generate_button.click,
#            guidance_scale.input,
#            scale.input,
#            control_scale.input,
#            seed.input,
        ],
        fn=create_image,
        inputs=inputs,
        outputs=outputs,
        show_progress="minimal",
        show_api=False,
        trigger_mode="always_last",
    )

    gr.Examples(
        examples=examples,
        inputs=[image_pil, src_image_pil, prompt, scale, control_scale],
        fn=run_for_examples,
        outputs=[generated_image],
        cache_examples=True,
    )

    gr.Markdown(article)

block.queue(api_open=False)
block.launch(show_api=False)