import spaces

import os
import imageio
import numpy as np
import torch
import rembg
from PIL import Image
from torchvision.transforms import v2
from pytorch_lightning import seed_everything
from omegaconf import OmegaConf
from einops import rearrange, repeat
from tqdm import tqdm
import threading
from queue import SimpleQueue
from typing import Any
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
import rerun as rr
import rerun.blueprint as rrb
from gradio_rerun import Rerun

import src
from src.utils.train_util import instantiate_from_config
from src.utils.camera_util import (
    FOV_to_intrinsics, 
    get_zero123plus_input_cameras,
    get_circular_camera_poses,
)
from src.utils.mesh_util import save_obj, save_glb
from src.utils.infer_util import remove_background, resize_foreground, images_to_video
from src.models.lrm_mesh import InstantMesh

import tempfile
from functools import partial

from huggingface_hub import hf_hub_download

import gradio as gr


def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
    """
    Get the rendering camera parameters.
    """
    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 images_to_video(images, output_path, fps=30):
    # images: (N, C, H, W)
    os.makedirs(os.path.dirname(output_path), exist_ok=True)
    frames = []
    for i in range(images.shape[0]):
        frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8).clip(0, 255)
        assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \
            f"Frame shape mismatch: {frame.shape} vs {images.shape}"
        assert frame.min() >= 0 and frame.max() <= 255, \
            f"Frame value out of range: {frame.min()} ~ {frame.max()}"
        frames.append(frame)
    imageio.mimwrite(output_path, np.stack(frames), fps=fps, codec='h264')


###############################################################################
# Configuration.
###############################################################################

import shutil

def find_cuda():
    # Check if CUDA_HOME or CUDA_PATH environment variables are set
    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

    # Search for the nvcc executable in the system's PATH
    nvcc_path = shutil.which('nvcc')

    if nvcc_path:
        # Remove the 'bin/nvcc' part to get the CUDA installation path
        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
        return cuda_path

    return None

cuda_path = find_cuda()

if cuda_path:
    print(f"CUDA installation found at: {cuda_path}")
else:
    print("CUDA installation not found")

config_path = 'configs/instant-mesh-large.yaml'
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 = True if config_name.startswith('instant-mesh') else False

device = torch.device('cuda')

# load diffusion model
print('Loading diffusion model ...')
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'
)

# load custom white-background UNet
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)
print(f'type(pipeline)={type(pipeline)}')

# load reconstruction model
print('Loading reconstruction model ...')
model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
model: InstantMesh = 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)

print('Loading Finished!')

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 pipeline_callback(log_queue: SimpleQueue, pipe: Any, step_index: int, timestep: float, callback_kwargs: dict[str, Any]) -> dict[str, Any]:
    latents = callback_kwargs["latents"]
    image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]  # type: ignore[attr-defined]
    image = pipe.image_processor.postprocess(image, output_type="np").squeeze()  # type: ignore[attr-defined]

    log_queue.put(("mvs", rr.Image(image)))
    log_queue.put(("latents", rr.Tensor(latents.squeeze())))

    return callback_kwargs

def generate_mvs(log_queue, input_image, sample_steps, sample_seed):

    seed_everything(sample_seed)

    return pipeline(
        input_image,
        num_inference_steps=sample_steps,
        callback_on_step_end=lambda *args, **kwargs: pipeline_callback(log_queue, *args, **kwargs),
    ).images[0]

    # def thread_target(output_queue, input_image, sample_steps):
    #     z123_image = pipeline(
    #         input_image,
    #         num_inference_steps=sample_steps,
    #         callback_on_step_end=lambda *args, **kwargs: pipeline_callback(output_queue, *args, **kwargs),
    #     ).images[0]
    #     log_queue.put(("z123_image", z123_image))


    # output_queue = SimpleQueue()
    # z123_thread = threading.Thread(
    #     target=thread_target,
    #     args=
    #     [
    #         output_queue,
    #         input_image,
    #         sample_steps,
    #     ]
    # )
    # z123_thread.start()

    # while True:
    #     msg = output_queue.get()
    #     yield msg
    #     if msg[0] == "z123_image":            
    #         break
    # z123_thread.join()

# def make3d(images: Image.Image):
#     output_queue = SimpleQueue()
#     handle = threading.Thread(target=_make3d, args=[output_queue, images])
#     handle.start()
#     while True:
#         msg = output_queue.get()
#         yield msg
#         if msg[0] == "mesh":
#             break
#     handle.join()

def make3d(log_queue, images: Image.Image):
    global model
    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()     # (3, 960, 640)
    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)        # (6, 3, 320, 320)

    input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).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

    with torch.no_grad():
        # get triplane
        planes = model.forward_planes(images, input_cameras)

        # get mesh
        mesh_out = model.extract_mesh(
            planes,
            use_texture_map=False,
            **infer_config,
        )

        vertices, faces, vertex_colors = mesh_out

        log_queue.put(
            (
                "mesh", 
                rr.Mesh3D(
                    vertex_positions=vertices, 
                    vertex_colors=vertex_colors, 
                    triangle_indices=faces
                ),
            )
        )
    
    return mesh_out

def generate_blueprint() -> rrb.Blueprint:
    return rrb.Blueprint(
        rrb.Horizontal(
            rrb.Spatial3DView(origin="mesh"),
            rrb.Grid(
                rrb.Spatial2DView(origin="z123image"),
                rrb.Spatial2DView(origin="preprocessed_image"),
                rrb.Spatial2DView(origin="mvs"),
                rrb.TensorView(origin="latents", ),
            ),
            column_shares=[1, 1],
        ),
        
        collapse_panels=True,
    )

def compute(log_queue, input_image, do_remove_background, sample_steps, sample_seed):

    preprocessed_image = preprocess(input_image, do_remove_background)

    log_queue.put(("preprocessed_image", rr.Image(preprocessed_image)))
    # rr.log("preprocessed_image", rr.Image(preprocessed_image))

    z123_image = generate_mvs(log_queue, preprocessed_image, sample_steps, sample_seed)
    
    log_queue.put(("z123image", rr.Image(z123_image)))
    # rr.log("z123image", rr.Image(z123_image))

    mesh_out = make3d(log_queue, z123_image)

    log_queue.put("done")

    
@spaces.GPU
@rr.thread_local_stream("InstantMesh")
def log_to_rr(input_image, do_remove_background, sample_steps, sample_seed):

    log_queue = SimpleQueue()
    
    stream = rr.binary_stream()

    blueprint = generate_blueprint()
    rr.send_blueprint(blueprint)
    yield stream.read()

    handle = threading.Thread(target=compute, args=[log_queue, input_image, do_remove_background, sample_steps, sample_seed])
    handle.start()
    while True:
        msg = log_queue.get()
        if msg == "done":
            break
        else:
            entity_path, entity = msg
            rr.log(entity_path, entity)
            yield stream.read()
    handle.join()
    
    # return mesh

_HEADER_ = '''
<h2><b>Duplicate of the <a href=https://huggingface.co/spaces/TencentARC/InstantMesh>InstantMesh space</a> that uses <a href=https://rerun.io/>Rerun</a> for visualization.</b></h2>
<h2><a href='https://github.com/TencentARC/InstantMesh' target='_blank'><b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b></a></h2>

**InstantMesh** is a feed-forward framework for efficient 3D mesh generation from a single image based on the LRM/Instant3D architecture.

Technical report: <a href='https://arxiv.org/abs/2404.07191' target='_blank'>ArXiv</a>.

'''



with gr.Blocks() as demo:
    gr.Markdown(_HEADER_)
    with gr.Row(variant="panel"):
        with gr.Column(scale=1):
            with gr.Row():
                input_image = gr.Image(
                    label="Input Image",
                    image_mode="RGBA",
                    sources="upload",
                    #width=256,
                    #height=256,
                    type="pil",
                    elem_id="content_image",
                )
            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(scale=2):
            
            viewer = Rerun(streaming=True, height=800)

            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=log_to_rr,
        inputs=[input_image, do_remove_background, sample_steps, sample_seed],
        outputs=[viewer]
    )

demo.launch()