import os
import numpy as np
from typing import Literal, cast
import torch
from PIL import Image, ImageOps
from diffusers import DiffusionPipeline
import gradio as gr
from gradio.components.image_editor import EditorValue
import spaces

DEVICE = "cuda"

MAIN_MODEL_REPO_ID = os.getenv("MAIN_MODEL_REPO_ID", None)
SUB_MODEL_REPO_ID = os.getenv("SUB_MODEL_REPO_ID", None)
SUB_MODEL_SUBFOLDER = os.getenv("SUB_MODEL_SUBFOLDER", None)

if MAIN_MODEL_REPO_ID is None:
    raise ValueError("MAIN_MODEL_REPO_ID is not set")
if SUB_MODEL_REPO_ID is None:
    raise ValueError("SUB_MODEL_REPO_ID is not set")
if SUB_MODEL_SUBFOLDER is None:
    raise ValueError("SUB_MODEL_SUBFOLDER is not set")

pipeline = DiffusionPipeline.from_pretrained(
    MAIN_MODEL_REPO_ID,
    torch_dtype=torch.bfloat16,
    custom_pipeline=SUB_MODEL_REPO_ID,
).to(DEVICE)


def crop_divisible_by_16(image: Image.Image) -> Image.Image:
    w, h = image.size
    w = w - w % 16
    h = h - h % 16
    return image.crop((0, 0, w, h))


@spaces.GPU(duration=150)
def predict(
    image_and_mask: EditorValue | None,
    prompt: str = "",
    seed: int = 0,
    num_inference_steps: int = 28,
    max_dimension: int = 704,
    condition_scale: float = 1.0,
    checkpoint_step: int = 10_000,
    checkpoint_version: Literal["v1", "v2"] = "v1",
    progress: gr.Progress = gr.Progress(track_tqdm=True),  # noqa: ARG001, B008
) -> Image.Image | None:
    # ) -> tuple[Image.Image, Image.Image] | None:
    if not image_and_mask:
        gr.Info("Please upload an image and draw a mask")
        return None
    image_np = image_and_mask["background"]
    image_np = cast(np.ndarray, image_np)

    # If the image is empty, return None
    if np.sum(image_np) == 0:
        gr.Info("Please upload an image")
        return None

    alpha_channel = image_and_mask["layers"][0]
    alpha_channel = cast(np.ndarray, alpha_channel)
    mask_np = np.where(alpha_channel[:, :, 3] == 0, 0, 255).astype(np.uint8)

    # if mask_np is empty, return None
    if np.sum(mask_np) == 0:
        gr.Info("Please mark the areas you want to remove")
        return None

    try:
        pipeline.load(
            SUB_MODEL_REPO_ID,
            subfolder=f"model/{checkpoint_version}/ckpt/{checkpoint_step}",
        )
    except Exception as e:
        gr.Info(
            f"Error loading checkpoint (model/{checkpoint_version}/ckpt/{checkpoint_step}): {e}"
        )
        return None

    image = Image.fromarray(image_np)
    # Resize to max dimension
    image.thumbnail((max_dimension, max_dimension))
    # Ensure dimensions are multiple of 16 (for VAE)
    image = crop_divisible_by_16(image)

    mask = Image.fromarray(mask_np)
    mask.thumbnail((max_dimension, max_dimension))
    mask = crop_divisible_by_16(mask)
    # Invert the mask
    mask = ImageOps.invert(mask)

    # Image masked is the image with the mask applied (black background)
    image_masked = Image.new("RGB", image.size, (0, 0, 0))
    image_masked.paste(image, (0, 0), mask)

    generator = torch.Generator(device="cpu").manual_seed(seed)

    final_image = pipeline(
        condition_image=image_masked,
        condition_scale=condition_scale,
        prompt=prompt,
        num_inference_steps=num_inference_steps,
        generator=generator,
        max_sequence_length=512,
        latent_lora=True if checkpoint_version == "v2" else False,
    ).images[0]

    return final_image


intro_markdown = r"""
# Inpainting Demo
"""

css = r"""
#col-left {
    margin: 0 auto;
    max-width: 650px;
}
#col-right {
    margin: 0 auto;
    max-width: 650px;
}
#col-showcase {
    margin: 0 auto;
    max-width: 1100px;
}
"""


with gr.Blocks(css=css) as demo:
    gr.Markdown(intro_markdown)

    with gr.Row() as content:
        with gr.Column(elem_id="col-left"):
            gr.HTML(
                """
            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
                <div>
                Step 1. Upload a room image ⬇️
                </div>
            </div>
            """,
                max_height=50,
            )
            image_and_mask = gr.ImageMask(
                label="Image and Mask",
                layers=False,
                height="full",
                width="full",
                show_fullscreen_button=False,
                sources=["upload"],
                show_download_button=False,
                interactive=True,
                brush=gr.Brush(default_size=75, colors=["#000000"], color_mode="fixed"),
                transforms=[],
            )
            prompt = gr.Textbox(label="Prompt", value="An empty room")
            checkpoint_step = gr.Slider(
                label="Checkpoint Step",
                minimum=1_000,
                maximum=20_000,
                step=1_000,
                value=10_000,
            )
            checkpoint_version = gr.Radio(
                ["v1", "v2"],
                label="Checkpoint Version",
                interactive=True,
            )
        with gr.Column(elem_id="col-right"):
            gr.HTML(
                """
            <div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
                <div>
                Step 2. Press Run to launch
                </div>
            </div>
            """,
                max_height=50,
            )
            # image_slider = ImageSlider(
            #     label="Result",
            #     interactive=False,
            # )
            result = gr.Image(label="Result")
            run_button = gr.Button("Run")

            with gr.Accordion("Advanced Settings", open=False):
                seed = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=100_000,
                    step=1,
                    value=0,
                )
                condition_scale = gr.Slider(
                    label="Condition Scale",
                    minimum=-10.0,
                    maximum=10.0,
                    step=0.10,
                    value=1.0,
                )
                with gr.Column():
                    max_dimension = gr.Slider(
                        label="Max Dimension",
                        minimum=512,
                        maximum=2048,
                        step=128,
                        value=704,
                    )

                    num_inference_steps = gr.Slider(
                        label="Number of inference steps",
                        minimum=1,
                        maximum=50,
                        step=1,
                        value=28,
                    )

    run_button.click(
        fn=predict,
        inputs=[
            image_and_mask,
            prompt,
            seed,
            num_inference_steps,
            max_dimension,
            condition_scale,
            checkpoint_step,
            checkpoint_version,
        ],
        # outputs=[image_slider],
        outputs=[result],
    )


demo.launch()