# Open Source Model Licensed under the Apache License Version 2.0 
# and Other Licenses of the Third-Party Components therein:
# The below Model in this distribution may have been modified by THL A29 Limited 
# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.

# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved. 
# The below software and/or models in this distribution may have been 
# modified by THL A29 Limited ("Tencent Modifications"). 
# All Tencent Modifications are Copyright (C) THL A29 Limited.

# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT 
# except for the third-party components listed below. 
# Hunyuan 3D does not impose any additional limitations beyond what is outlined 
# in the repsective licenses of these third-party components. 
# Users must comply with all terms and conditions of original licenses of these third-party 
# components and must ensure that the usage of the third party components adheres to 
# all relevant laws and regulations. 

# For avoidance of doubts, Hunyuan 3D means the large language models and 
# their software and algorithms, including trained model weights, parameters (including 
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code, 
# fine-tuning enabling code and other elements of the foregoing made publicly available 
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import spaces
import os
os.environ['CUDA_HOME'] = '/usr/local/cuda-11*'
import warnings
import argparse
import gradio as gr
from glob import glob
import shutil
import torch
import numpy as np
from PIL import Image
from einops import rearrange
from huggingface_hub import snapshot_download

from infer import seed_everything, save_gif
from infer import Text2Image, Removebg, Image2Views, Views2Mesh, GifRenderer

warnings.simplefilter('ignore', category=UserWarning)
warnings.simplefilter('ignore', category=FutureWarning)
warnings.simplefilter('ignore', category=DeprecationWarning)

parser = argparse.ArgumentParser()
parser.add_argument("--use_lite", default=False, action="store_true")
parser.add_argument("--mv23d_cfg_path", default="./svrm/configs/svrm.yaml", type=str)
parser.add_argument("--mv23d_ckt_path", default="weights/svrm/svrm.safetensors", type=str)
parser.add_argument("--text2image_path", default="weights/hunyuanDiT", type=str)
parser.add_argument("--save_memory", default=False) # , action="store_true")
parser.add_argument("--device", default="cuda:0", type=str)
args = parser.parse_args()

@spaces.GPU
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")



def download_models():
    # Create weights directory if it doesn't exist
    os.makedirs("weights", exist_ok=True)
    os.makedirs("weights/hunyuanDiT", exist_ok=True)

    # Download Hunyuan3D-1 model
    try:
        snapshot_download(
            repo_id="tencent/Hunyuan3D-1",
            local_dir="./weights",
            resume_download=True
        )
        print("Successfully downloaded Hunyuan3D-1 model")
    except Exception as e:
        print(f"Error downloading Hunyuan3D-1: {e}")

    # Download HunyuanDiT model
    try:
        snapshot_download(
            repo_id="Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled",
            local_dir="./weights/hunyuanDiT",
            resume_download=True
        )
        print("Successfully downloaded HunyuanDiT model")
    except Exception as e:
        print(f"Error downloading HunyuanDiT: {e}")

# Download models before starting the app
download_models()

################################################################

CONST_PORT = 8080
CONST_MAX_QUEUE = 1
CONST_SERVER = '0.0.0.0'

CONST_HEADER = '''
<h2><b>✦👁️ Genisis 3D: for Text-to-3D and Image-to-3D Generation</b></h2>

**📝 Note:**
By default, a .obj mesh with vertex colors or a .glb mesh is exported, if the result is unsatisfactory, please try a different seed value (Default: 0).
'''

################################################################

def get_example_img_list():
    print('Loading example img list ...')
    return sorted(glob('./demos/example_*.png'))

def get_example_txt_list():
    print('Loading example txt list ...')
    txt_list  = list()
    for line in open('./demos/example_list.txt'):
        txt_list.append(line.strip())
    return txt_list

example_is = get_example_img_list()
example_ts = get_example_txt_list()
################################################################

worker_xbg = Removebg()
print(f"loading {args.text2image_path}")
worker_t2i = Text2Image(
    pretrain = args.text2image_path, 
    device = args.device, 
    save_memory = args.save_memory
)
worker_i2v = Image2Views(
    use_lite = args.use_lite, 
    device = args.device,
    save_memory = args.save_memory
)
worker_v23 = Views2Mesh(
    args.mv23d_cfg_path, 
    args.mv23d_ckt_path, 
    use_lite = args.use_lite, 
    device = args.device,
    save_memory = args.save_memory
)
worker_gif = GifRenderer(args.device)

@spaces.GPU
def stage_0_t2i(text, image, seed, step):
    os.makedirs('./outputs/app_output', exist_ok=True)
    exists = set(int(_) for _ in os.listdir('./outputs/app_output') if not _.startswith("."))
    if len(exists) == 30: shutil.rmtree(f"./outputs/app_output/0");cur_id = 0
    else:                 cur_id = min(set(range(30)) - exists)
    if os.path.exists(f"./outputs/app_output/{(cur_id + 1) % 30}"):
        shutil.rmtree(f"./outputs/app_output/{(cur_id + 1) % 30}")
    save_folder = f'./outputs/app_output/{cur_id}'
    os.makedirs(save_folder, exist_ok=True)

    dst = os.path.join(save_folder, 'img.png')
    
    if not text:
        if image is None: 
            return dst, save_folder
            raise gr.Error("Upload image or provide text ...")
        image.save(dst)
        return dst, save_folder
        
    image = worker_t2i(text, seed, step)
    image.save(dst)
    dst = worker_xbg(image, save_folder)
    return dst, save_folder
    
@spaces.GPU
def stage_1_xbg(image, save_folder): 
    if isinstance(image, str):
        image = Image.open(image)
    dst =  save_folder + '/img_nobg.png'
    rgba = worker_xbg(image)
    rgba.save(dst)
    return dst
    
@spaces.GPU
def stage_2_i2v(image, seed, step, save_folder):
    if isinstance(image, str):
        image = Image.open(image)
    gif_dst = save_folder + '/views.gif'
    res_img, pils = worker_i2v(image, seed, step)
    save_gif(pils, gif_dst)
    views_img, cond_img = res_img[0], res_img[1]
    img_array = np.asarray(views_img, dtype=np.uint8)
    show_img = rearrange(img_array, '(n h) (m w) c -> (n m) h w c', n=3, m=2)
    show_img = show_img[worker_i2v.order, ...]
    show_img = rearrange(show_img, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
    show_img = Image.fromarray(show_img) 
    return views_img, cond_img, show_img

@spaces.GPU
def stage_3_v23(
    views_pil, 
    cond_pil, 
    seed, 
    save_folder,
    target_face_count = 30000,
    do_texture_mapping = True,
    do_render =True
): 
    do_texture_mapping = do_texture_mapping or do_render
    obj_dst = save_folder + '/mesh_with_colors.obj'
    glb_dst = save_folder + '/mesh.glb'
    worker_v23(
        views_pil, 
        cond_pil, 
        seed = seed, 
        save_folder = save_folder,
        target_face_count = target_face_count,
        do_texture_mapping = do_texture_mapping
    )
    return obj_dst, glb_dst

@spaces.GPU
def stage_4_gif(obj_dst, save_folder, do_render_gif=True):
    if not do_render_gif: return None
    gif_dst = save_folder + '/output.gif'
    worker_gif(
        save_folder + '/mesh.obj',
        gif_dst_path = gif_dst
    )
    return gif_dst

#===============================================================
with gr.Blocks() as demo:
    gr.Markdown(CONST_HEADER)
    with gr.Row(variant="panel"):
        with gr.Column(scale=2):
            with gr.Tab("Text to 3D"):
                with gr.Column():
                    text = gr.TextArea('Green Apple', lines=1, max_lines=10, label='Input text')
                    with gr.Row():
                        textgen_seed = gr.Number(value=0, label="T2I seed", precision=0)
                        textgen_step = gr.Number(value=25, label="T2I step", precision=0)
                        textgen_SEED = gr.Number(value=0, label="Gen seed", precision=0)
                        textgen_STEP = gr.Number(value=50, label="Gen step", precision=0)
                        textgen_max_faces = gr.Number(value=90000, label="max number of faces", precision=0)
                        
                    with gr.Row():
                        # textgen_do_texture_mapping = gr.Checkbox(label="texture mapping", value=False, interactive=True)
                        # textgen_do_render_gif = gr.Checkbox(label="Render gif", value=False, interactive=True)
                        textgen_submit = gr.Button("Generate", variant="primary")

                    with gr.Row():
                        gr.Examples(examples=example_ts, inputs=[text], label="Txt examples", examples_per_page=10)
                    
            with gr.Tab("Image to 3D"):
                with gr.Column():
                    input_image = gr.Image(label="Input image",
                                           width=256, height=256, type="pil",
                                           image_mode="RGBA", sources="upload",
                                           interactive=True)
                    with gr.Row(): 
                        imggen_SEED = gr.Number(value=0, label="Gen seed", precision=0)
                        imggen_STEP = gr.Number(value=50, label="Gen step", precision=0)
                        imggen_max_faces = gr.Number(value=90000, label="max number of faces", precision=0)

                    with gr.Row():
                        # imggen_do_texture_mapping = gr.Checkbox(label="texture mapping", value=False, interactive=True)
                        # imggen_do_render_gif = gr.Checkbox(label="Render gif", value=False, interactive=True)
                        imggen_submit = gr.Button("Generate", variant="primary")       
                    with gr.Row():
                        gr.Examples(examples=example_is, inputs=[input_image], label="Img examples", examples_per_page=10)
           
        with gr.Column(scale=3):
            with gr.Row():                
                result_3dobj = gr.Model3D(
                    clear_color=[0.0, 0.0, 0.0, 0.0],
                    label="Output Obj",
                    show_label=True,
                    visible=True,
                    camera_position=[90, 90, None],
                    interactive=False
                )

               
            with gr.Row():
                rem_bg_image = gr.Image(label="No backgraound image", type="pil",
                                           image_mode="RGBA", interactive=False)
                
                result_image = gr.Image(label="Multi views", type="pil", interactive=False)
                result_3dglb = gr.Model3D(
                    clear_color=[0.0, 0.0, 0.0, 0.0],
                    label="Output Glb",
                    show_label=True,
                    visible=True,
                    camera_position=[90, 90, None],
                    interactive=False
                )
                # result_gif = gr.Image(label="Rendered GIF", interactive=False)
                
            with gr.Row():    
                gr.Markdown("""
                We recommend download and open Glb using 3D software, such as Blender, MeshLab, etc.
                
                Limited by gradio, Obj file here only be shown as vertex shading, but Glb can be texture shading.
                """)

#===============================================================
    textgen_do_texture_mapping = gr.State(False)
    textgen_do_render_gif = gr.State(False)
    imggen_do_texture_mapping = gr.State(False)
    imggen_do_render_gif = gr.State(False)
    
    none = gr.State(None)
    save_folder = gr.State()
    cond_image = gr.State()
    views_image = gr.State()
    text_image = gr.State()
    
    textgen_submit.click(
        fn=stage_0_t2i, inputs=[text, none, textgen_seed, textgen_step], 
        outputs=[rem_bg_image, save_folder],
    ).success(
        fn=stage_2_i2v, inputs=[rem_bg_image, textgen_SEED, textgen_STEP, save_folder], 
        outputs=[views_image, cond_image, result_image],
    ).success(
        fn=stage_3_v23, inputs=[views_image, cond_image, textgen_SEED, save_folder, textgen_max_faces, textgen_do_texture_mapping, textgen_do_render_gif], 
        outputs=[result_3dobj, result_3dglb],
    ).success(lambda: print('Text_to_3D Done ...'))
    # .success(
    #     fn=stage_4_gif, inputs=[result_3dglb, save_folder, textgen_do_render_gif], 
    #     outputs=[result_gif],
    # ).success(lambda: print('Text_to_3D Done ...'))

    imggen_submit.click(
        fn=stage_0_t2i, inputs=[none, input_image, textgen_seed, textgen_step], 
        outputs=[text_image, save_folder],
    ).success(
        fn=stage_1_xbg, inputs=[text_image, save_folder], 
        outputs=[rem_bg_image],
    ).success(
        fn=stage_2_i2v, inputs=[rem_bg_image, imggen_SEED, imggen_STEP, save_folder], 
        outputs=[views_image, cond_image, result_image],
    ).success(
        fn=stage_3_v23, inputs=[views_image, cond_image, imggen_SEED, save_folder, imggen_max_faces, imggen_do_texture_mapping, imggen_do_render_gif], 
        outputs=[result_3dobj, result_3dglb],
    ).success(lambda: print('Image_to_3D Done ...'))
    # success(
    #     fn=stage_4_gif, inputs=[result_3dglb, save_folder, imggen_do_render_gif], 
    #     outputs=[result_gif],
    # ).success(lambda: print('Image_to_3D Done ...'))
    
#===============================================================

    demo.queue()
    demo.launch()