import os
import shutil
import tempfile
import time
from os import path


import gradio as gr
import numpy as np
import rembg
import spaces
import torch
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler, StableDiffusionXLPipeline, LCMScheduler
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 safetensors.torch import load_file
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 (remove_background, resize_foreground)
from src.utils.mesh_util import save_glb, save_obj
from src.utils.train_util import instantiate_from_config

torch.backends.cuda.matmul.allow_tf32 = True

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


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


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


@spaces.GPU
def make3d(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()
    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
    print(mesh_fpath)
    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)

        print(f"Mesh saved to {mesh_fpath}")

    return mesh_fpath, mesh_glb_fpath


@spaces.GPU
def process_image(num_images, prompt):
    global pipe
    with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
        return pipe(
            prompt=[prompt]*num_images,
            generator=torch.Generator().manual_seed(123),
            num_inference_steps=1,
            guidance_scale=0.,
            height=int(512),
            width=int(512),
            timesteps=[800]
        ).images


# Configuration
cuda_path = find_cuda()
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 = config_name.startswith('instant-mesh')
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'
)

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)

# 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 = 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)

# # Load text-to-image model
# print('Loading text-to-image model ...')

# pipe = StableDiffusionXLPipeline.from_pretrained(
#     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16)
# pipe.to(device="cuda", dtype=torch.bfloat16)

# unet_state = load_file(hf_hub_download(
#     "ByteDance/Hyper-SD", "Hyper-SDXL-1step-Unet.safetensors"), device="cuda")
# pipe.unet.load_state_dict(unet_state)
# pipe.scheduler = LCMScheduler.from_config(
#     pipe.scheduler.config, timestep_spacing="trailing")

print('Loading Finished!')

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

demo.launch()