import gradio as gr
import os
import sys
import argparse
import random
import time
from omegaconf import OmegaConf
import torch
import torchvision
from pytorch_lightning import seed_everything
from huggingface_hub import hf_hub_download
from einops import repeat
import torchvision.transforms as transforms
from utils.utils import instantiate_from_config
sys.path.insert(0, "scripts/evaluation")
from funcs import (
    batch_ddim_sampling,
    load_model_checkpoint,
    get_latent_z,
    save_videos
)

SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')

def download_model():
    REPO_ID = 'Doubiiu/DynamiCrafter_1024'
    filename_list = ['model.ckpt']
    if not os.path.exists('./checkpoints/dynamicrafter_1024_v1/'):
        os.makedirs('./checkpoints/dynamicrafter_1024_v1/')
    for filename in filename_list:
        local_file = os.path.join('./checkpoints/dynamicrafter_1024_v1/', filename)
        if not os.path.exists(local_file):
            hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_1024_v1/', force_download=True)
    

def infer(secret_token, image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123):
    if secret_token != SECRET_TOKEN:
        raise gr.Error(
            f'Invalid secret token. Please fork the original space if you want to use it for yourself.')


    resolution = (576, 1024)
    download_model()
    ckpt_path='checkpoints/dynamicrafter_1024_v1/model.ckpt'
    config_file='configs/inference_1024_v1.0.yaml'
    config = OmegaConf.load(config_file)
    model_config = config.pop("model", OmegaConf.create())
    model_config['params']['unet_config']['params']['use_checkpoint']=False   
    model = instantiate_from_config(model_config)
    assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!"
    model = load_model_checkpoint(model, ckpt_path)
    model.eval()
    model = model.cuda()
    save_fps = 8

    seed_everything(seed)
    transform = transforms.Compose([
        transforms.Resize(min(resolution)),
        transforms.CenterCrop(resolution),
        ])
    torch.cuda.empty_cache()
    print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))
    start = time.time()
    if steps > 60:
        steps = 60 

    batch_size=1
    channels = model.model.diffusion_model.out_channels
    frames = model.temporal_length
    h, w = resolution[0] // 8, resolution[1] // 8
    noise_shape = [batch_size, channels, frames, h, w]

    # text cond
    text_emb = model.get_learned_conditioning([prompt])

    # img cond
    img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device)
    img_tensor = (img_tensor / 255. - 0.5) * 2

    image_tensor_resized = transform(img_tensor) #3,256,256
    videos = image_tensor_resized.unsqueeze(0) # bchw
    
    z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw
    
    img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)

    cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc
    img_emb = model.image_proj_model(cond_images)

    imtext_cond = torch.cat([text_emb, img_emb], dim=1)

    fs = torch.tensor([fs], dtype=torch.long, device=model.device)
    cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]}
    
    ## inference
    batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale)
    ## b,samples,c,t,h,w

    video_path = './output.mp4'
    save_videos(batch_samples, './', filenames=['output'], fps=save_fps)
    model = model.cpu()

        # Read the content of the video file and encode it to base64
    with open(video_path, "rb") as video_file:
        video_base64 = base64.b64encode(video_file.read()).decode('utf-8')

    # Prepend the appropriate data URI header with MIME type
    video_data_uri = 'data:video/mp4;base64,' + video_base64
    
    # clean-up (otherwise there is a risk of "ghosting", eg. someone seeing the previous generated video",
    # of one of the steps go wrong)
    os.remove(video_path)

    return video_data_uri



with gr.Blocks() as app:
    gr.HTML("""
        <div style="z-index: 100; position: fixed; top: 0px; right: 0px; left: 0px; bottom: 0px; width: 100%; height: 100%; background: white; display: flex; align-items: center; justify-content: center; color: black;">
        <div style="text-align: center; color: black;">
        <p style="color: black;">This space is a REST API to programmatically generate MP4 videos.</p>
        <p style="color: black;">Interested in using it? Look no further than the <a href="https://huggingface.co/spaces/Doubiiu/DynamiCrafter" target="_blank">original space</a>!</p>
        </div>
        </div>""")
    

    secret_token = gr.Text(label='Secret Token', max_lines=1)
    i2v_input_image = gr.Image(label="Input Image",elem_id="input_img")
    i2v_input_text = gr.Text(label='Prompts')
    i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123)
    i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta")
    i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale")
    i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
    i2v_motion = gr.Slider(minimum=5, maximum=20, step=1, elem_id="i2v_motion", label="FPS", value=10)
    i2v_end_btn = gr.Button("Generate")

    i2v_output_video_base64 = gr.Text()

    i2v_end_btn.click(inputs=[secret_token, i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed],
                        outputs=[i2v_output_video_base64],
                        fn = infer
    )

app.queue(max_size=4).launch(show_api=True)