import os
import warnings
from huggingface_hub import hf_hub_download
import gradio as gr
from glob import glob
import shutil
import torch
import numpy as np
from PIL import Image
from einops import rearrange
import argparse

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

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:
        hf_hub_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:
        hf_hub_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()

# Parse arguments
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()

# Constants
CONST_PORT = 8080
CONST_MAX_QUEUE = 1
CONST_SERVER = '0.0.0.0'

CONST_HEADER = '''
<h2><b>Official 🤗 Gradio Demo</b></h2>
<h2><a href='https://github.com/tencent/Hunyuan3D-1' target='_blank'>
<b>Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation</b></a></h2>
'''

# Helper functions
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 = []
    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()

# Import required workers
from infer import seed_everything, save_gif
from infer import Text2Image, Removebg, Image2Views, Views2Mesh, GifRenderer

# Initialize workers
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
)
worker_v23 = Views2Mesh(
    args.mv23d_cfg_path,
    args.mv23d_ckt_path,
    use_lite=args.use_lite,
    device=args.device
)
worker_gif = GifRenderer(args.device)

# Pipeline stages
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("."))
    cur_id = min(set(range(30)) - exists) if len(exists) < 30 else 0
    
    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 = save_folder + '/img.png'
    
    if not text:
        if image is None:
            return dst, save_folder
        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

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

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

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

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

# Gradio Interface
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('一只黑白相间的熊猫在白色背景上居中坐着，呈现出卡通风格和可爱氛围。', 
                                     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)
                        textgen_do_render_gif = gr.Checkbox(label="Render gif", value=False)
                        textgen_submit = gr.Button("Generate", variant="primary")

                    gr.Examples(examples=example_ts, inputs=[text], label="Txt examples")

            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")
                    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)
                        imggen_do_render_gif = gr.Checkbox(label="Render gif", value=False)
                        imggen_submit = gr.Button("Generate", variant="primary")
                    
                    gr.Examples(examples=example_is, inputs=[input_image], label="Img examples")

        with gr.Column(scale=3):
            with gr.Tab("rembg image"):
                rem_bg_image = gr.Image(label="No background image", width=256, height=256, 
                                      type="pil", image_mode="RGBA")
            
            with gr.Tab("Multi views"):
                result_image = gr.Image(label="Multi views", type="pil")
            with gr.Tab("Obj"):
                result_3dobj = gr.Model3D(label="Output obj")
            with gr.Tab("Glb"):
                result_3dglb = gr.Model3D(label="Output glb")
            with gr.Tab("GIF"):
                result_gif = gr.Image(label="Rendered GIF")

    # States
    none = gr.State(None)
    save_folder = gr.State()
    cond_image = gr.State()
    views_image = gr.State()
    text_image = gr.State()
    
    # Event handlers
    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(
        fn=stage_4_gif,
        inputs=[result_3dglb, save_folder, textgen_do_render_gif],
        outputs=[result_gif],
    )

    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(
        fn=stage_4_gif,
        inputs=[result_3dglb, save_folder, imggen_do_render_gif],
        outputs=[result_gif],
    )

    demo.queue(max_size=CONST_MAX_QUEUE)
    demo.launch(server_name=CONST_SERVER, server_port=CONST_PORT)