import os
import shutil
import tempfile
from functools import partial

import gradio as gr
import numpy as np
import rembg
import torch
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
from einops import rearrange
from huggingface_hub import hf_hub_download
from omegaconf import OmegaConf
from PIL import Image
from pytorch_lightning import seed_everything
from torchvision.transforms import v2
from tqdm import tqdm

from src.utils.camera_util import FOV_to_intrinsics, get_circular_camera_poses, get_zero123plus_input_cameras
from src.utils.infer_util import images_to_video, remove_background, resize_foreground
from src.utils.mesh_util import save_glb, save_obj
from src.utils.train_util import instantiate_from_config


def find_cuda():
    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
    if cuda_home and os.path.exists(cuda_home):
        return cuda_home

    nvcc_path = shutil.which('nvcc')
    if nvcc_path:
        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
        return cuda_path

    return None


def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
    c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
    if is_flexicubes:
        cameras = torch.linalg.inv(c2ws)
        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
    else:
        extrinsics = c2ws.flatten(-2)
        intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
        cameras = torch.cat([extrinsics, intrinsics], dim=-1)
        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
    return cameras


def load_models(config_path):
    config = OmegaConf.load(config_path)
    config_name = os.path.basename(config_path).replace('.yaml', '')
    model_config = config.model_config
    infer_config = config.infer_config

    is_flexicubes = config_name.startswith('instant-mesh')

    device = torch.device('cuda')

    pipeline = DiffusionPipeline.from_pretrained(
        "sudo-ai/zero123plus-v1.2",
        custom_pipeline="zero123plus",
        torch_dtype=torch.float16,
    )
    pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
        pipeline.scheduler.config, timestep_spacing='trailing'
    )

    unet_ckpt_path = hf_hub_download(
        repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
    state_dict = torch.load(unet_ckpt_path, map_location='cpu')
    pipeline.unet.load_state_dict(state_dict, strict=True)

    pipeline = pipeline.to(device)

    model_ckpt_path = hf_hub_download(
        repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
    model = instantiate_from_config(model_config)
    state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
    state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
    model.load_state_dict(state_dict, strict=True)

    model = model.to(device)

    return pipeline, model, is_flexicubes, infer_config


def check_input_image(input_image):
    if input_image is None:
        raise gr.Error("No image uploaded!")


def preprocess(input_image, do_remove_background):
    rembg_session = rembg.new_session() if do_remove_background else None

    if do_remove_background:
        input_image = remove_background(input_image, rembg_session)
        input_image = resize_foreground(input_image, 0.85)

    return input_image


def generate_mvs(input_image, sample_steps, sample_seed, pipeline):
    seed_everything(sample_seed)

    z123_image = pipeline(
        input_image,
        num_inference_steps=sample_steps
    ).images[0]

    show_image = np.asarray(z123_image, dtype=np.uint8)
    show_image = torch.from_numpy(show_image)
    show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2)
    show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
    show_image = Image.fromarray(show_image.numpy())

    return z123_image, show_image


def make3d(images, model, is_flexicubes, infer_config):
    device = torch.device('cuda')

    if is_flexicubes:
        model.init_flexicubes_geometry(device, use_renderer=False)
    model = model.eval()

    images = np.asarray(images, dtype=np.float32) / 255.0
    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()
    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)

    input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device)
    render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=is_flexicubes).to(device)

    images = images.unsqueeze(0).to(device)
    images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)

    mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
    mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
    mesh_dirname = os.path.dirname(mesh_fpath)
    mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")

    with torch.no_grad():
        planes = model.forward_planes(images, input_cameras)
        mesh_out = model.extract_mesh(planes, use_texture_map=False, **infer_config)

        vertices, faces, vertex_colors = mesh_out
        vertices = vertices[:, [1, 2, 0]]

        save_glb(vertices, faces, vertex_colors, mesh_glb_fpath)
        save_obj(vertices, faces, vertex_colors, mesh_fpath)

    return mesh_fpath, mesh_glb_fpath


def launch_demo(config_path):
    cuda_path = find_cuda()
    if cuda_path:
        print(f"CUDA installation found at: {cuda_path}")
    else:
        print("CUDA installation not found")

    pipeline, model, is_flexicubes, infer_config = load_models(config_path)

    with gr.Blocks() as demo:
        with gr.Row(variant="panel"):
            with gr.Column():
                with gr.Row():
                    input_image = gr.Image(
                        label="Input Image",
                        image_mode="RGBA",
                        sources="upload",
                        type="pil",
                        elem_id="content_image",
                    )
                    processed_image = gr.Image(
                        label="Processed Image",
                        image_mode="RGBA",
                        type="pil",
                        interactive=False
                    )
                with gr.Row():
                    with gr.Group():
                        do_remove_background = gr.Checkbox(
                            label="Remove Background", value=True
                        )
                        sample_seed = gr.Number(
                            value=42, label="Seed Value", precision=0)

                        sample_steps = gr.Slider(
                            label="Sample Steps",
                            minimum=30,
                            maximum=75,
                            value=75,
                            step=5
                        )

                with gr.Row():
                    submit = gr.Button(
                        "Generate", elem_id="generate", variant="primary")

                with gr.Row(variant="panel"):
                    gr.Examples(
                        examples=[
                            os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
                        ],
                        inputs=[input_image],
                        label="Examples",
                        cache_examples=False,
                        examples_per_page=16
                    )

            with gr.Column():
                with gr.Row():
                    with gr.Column():
                        mv_show_images = gr.Image(
                            label="Generated Multi-views",
                            type="pil",
                            width=379,
                            interactive=False
                        )

                with gr.Row():
                    with gr.Tab("OBJ"):
                        output_model_obj = gr.Model3D(
                            label="Output Model (OBJ Format)",
                            interactive=False,
                        )
                        gr.Markdown(
                            "Note: Downloaded .obj model will be flipped. Export .glb instead or manually flip it before usage.")
                    with gr.Tab("GLB"):
                        output_model_glb = gr.Model3D(
                            label="Output Model (GLB Format)",
                            interactive=False,
                        )
                        gr.Markdown(
                            "Note: The model shown here has a darker appearance. Download to get correct results.")

                with gr.Row():
                    gr.Markdown(
                        '''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')

        mv_images = gr.State()

        submit.click(fn=check_input_image, inputs=[input_image]).success(
            fn=preprocess,
            inputs=[input_image, do_remove_background],
            outputs=[processed_image],
        ).success(
            fn=generate_mvs,
            inputs=[processed_image, sample_steps, sample_seed, pipeline],
            outputs=[mv_images, mv_show_images]
        ).success(
            fn=make3d,
            inputs=[mv_images, model, is_flexicubes, infer_config],
            outputs=[output_model_obj, output_model_glb]
        )

    demo.launch()


if __name__ == "__main__":
    config_path = 'configs/instant-mesh-large.yaml'
    launch_demo(config_path)