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
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(output_queue: SimpleQueue, pipe: Any, step_index: int, timestep: float, callback_kwargs: dict[str, Any]) -> dict[str, Any]:
    rr.set_time_sequence("iteration", step_index)
    rr.set_time_seconds("timestep", timestep)
    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]

    # output_queue.put(("log", "mvs/image", rr.Image(image)))
    # output_queue.put(("log", "mvs/latents", rr.Tensor(latents.squeeze())))

    return callback_kwargs

@spaces.GPU
def generate_mvs(output_queue: SimpleQueue, input_image, sample_steps, sample_seed):

    seed_everything(sample_seed)

    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]

    output_queue.put(("z123_image", z123_image))

    # sampling
    # 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)     # (960, 640, 3)
    # 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

@spaces.GPU
def make3d(output_queue: SimpleQueue, images: Image.Image):
    print(f'type(images)={type(images)}')
    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)
    render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device)
    print(f'type(input_cameras)={type(input_cameras)}')

    images = images.unsqueeze(0).to(device)
    images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)
    print(f'type(images)={type(images)}')


    mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
    print(mesh_fpath)
    mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
    mesh_dirname = os.path.dirname(mesh_fpath)
    video_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.mp4")
    mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")

    with torch.no_grad():
        # get triplane
        planes = model.forward_planes(images, input_cameras)
        print(f'type(planes)={type(planes)}')

        # # get video
        chunk_size = 20 if IS_FLEXICUBES else 1
        render_size = 384
        
        # frames = []
        for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
            if IS_FLEXICUBES:
                frame = model.forward_geometry(
                    planes,
                    render_cameras[:, i:i+chunk_size],
                    render_size=render_size,
                )['img']
            else:
                frame = model.synthesizer(
                    planes,
                    cameras=render_cameras[:, i:i+chunk_size],
                    render_size=render_size,
                )['images_rgb']

            print(f'type(frame)={type(frame)}')
            output_queue.put(("log", "3dvideo", rr.Image(frame)))
            # frames.append(frame)

        # frames = torch.cat(frames, dim=1)

        # images_to_video(
        #     frames[0],
        #     video_fpath,
        #     fps=30,
        # )

        # print(f"Video saved to {video_fpath}")

        # get mesh
        mesh_out = model.extract_mesh(
            planes,
            use_texture_map=False,
            **infer_config,
        )
        print(f'type(mesh_out)={type(mesh_out)}')


        vertices, faces, vertex_colors = mesh_out
        vertices = vertices[:, [1, 2, 0]]
        print(f'type(vertices)={type(vertices)}')
        print(f'type(faces)={type(faces)}')
        print(f'type(vertex_colors)={type(vertex_colors)}')

        save_glb(vertices, faces, vertex_colors, mesh_glb_fpath)
        save_obj(vertices, faces, vertex_colors, mesh_fpath)
        
        print(f"Mesh saved to {mesh_fpath}")

    return mesh_out

@rr.thread_local_stream("InstantMesh")
def log_to_rr(input_image, do_remove_background, sample_steps, sample_seed):
    preprocessed_image = preprocess(input_image, do_remove_background)

    stream = rr.binary_stream()

    rr.log("preprocessed_image", rr.Image(preprocessed_image))

    yield stream.read()

    output_queue = SimpleQueue()

    # mvs_thread = threading.Thread(target=generate_mvs, args=[output_queue, input_image, sample_steps, sample_seed])
    # mvs_thread.start()

    # while True:
    #     msg = output_queue.get()
    #     if msg[0] == "z123_image":
    #         z123_image = msg[1]
    #         break
    #     elif msg[0] == "log":
    #         entity_path = msg[1]
    #         entity = msg[2]
    #         rr.log(entity_path, entity)
    #         yield stream.read()
    
    # mvs_thread.join()

    # rr.log("z123image", rr.Image(z123_image))
    # yield stream.read()

    # mesh_fpath, mesh_glb_fpath = make3d(output_queue, z123_image)

    # while not output_queue.empty():
    #     msg = output_queue.get()
    #     if msg[0] == "log":
    #         entity_path = msg[1]
    #         entity = msg[2]
    #         rr.log(entity_path, entity)
    #         yield stream.read()
        

_HEADER_ = '''
<h2><b>Official 🤗 Gradio Demo</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.

Code: <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>GitHub</a>. Techenical report: <a href='https://arxiv.org/abs/2404.07191' target='_blank'>ArXiv</a>.

❗️❗️❗️**Important Notes:**
- Our demo can export a .obj mesh with vertex colors or a .glb mesh now. If you prefer to export a .obj mesh with a **texture map**, please refer to our <a href='https://github.com/TencentARC/InstantMesh?tab=readme-ov-file#running-with-command-line' target='_blank'>Github Repo</a>.
- The 3D mesh generation results highly depend on the quality of generated multi-view images. Please try a different **seed value** if the result is unsatisfying (Default: 42).
'''

_CITE_ = r"""
If InstantMesh is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>Github Repo</a>. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/InstantMesh?style=social)](https://github.com/TencentARC/InstantMesh)
---
📝 **Citation**

If you find our work useful for your research or applications, please cite using this bibtex:
```bibtex
@article{xu2024instantmesh,
  title={InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models},
  author={Xu, Jiale and Cheng, Weihao and Gao, Yiming and Wang, Xintao and Gao, Shenghua and Shan, Ying},
  journal={arXiv preprint arXiv:2404.07191},
  year={2024}
}
```

📋 **License**

Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/spaces/TencentARC/InstantMesh/blob/main/LICENSE) for details.

📧 **Contact**

If you have any questions, feel free to open a discussion or contact us at <b>bluestyle928@gmail.com</b>.
"""


with gr.Blocks() as demo:
    gr.Markdown(_HEADER_)
    with gr.Row(variant="panel"):
        with gr.Column():
            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():
            
            viewer = Rerun(streaming=True, height=800)

            # 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).''')

    gr.Markdown(_CITE_)

    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]
    )
    # 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],
    #     outputs=[mv_images, mv_show_images]
        
    # ).success(
    #     fn=make3d,
    #     inputs=[mv_images],
    #     outputs=[output_model_obj, output_model_glb]
    # )

demo.launch()