import spaces
import os
import cv2
import torch
import gradio as gr
import torchvision
import warnings
import numpy as np
from PIL import Image, ImageSequence
from moviepy.editor import VideoFileClip
import imageio
from diffusers import (
    TextToVideoSDPipeline,
    AutoencoderKL,
    DDPMScheduler,
    DDIMScheduler,
    UNet3DConditionModel,
)
from transformers import CLIPTokenizer, CLIPTextModel
from diffusers.utils import export_to_video
from typing import List
from text2vid_modded import TextToVideoSDPipelineModded
from invert_utils import ddim_inversion as dd_inversion
from gifs_filter import filter
import subprocess
import uuid

from huggingface_hub import snapshot_download

def load_frames(image: Image, mode='RGBA'):
    return np.array([np.array(frame.convert(mode)) for frame in ImageSequence.Iterator(image)])
 
os.makedirs("t2v_sketch-lora", exist_ok=True)
snapshot_download(
    repo_id="Hmrishav/t2v_sketch-lora",
    local_dir="./t2v_sketch-lora"   
)

def save_gif(frames, path):
    imageio.mimsave(
        path,
        [frame.astype(np.uint8) for frame in frames],
        format="GIF",
        duration=1 / 10,
        loop=0  # 0 means infinite loop
    )

def load_image(imgname, target_size=None):
    pil_img = Image.open(imgname).convert('RGB')
    if target_size:
        if isinstance(target_size, int):
            target_size = (target_size, target_size)
        pil_img = pil_img.resize(target_size, Image.Resampling.LANCZOS)
    return torchvision.transforms.ToTensor()(pil_img).unsqueeze(0)

def prepare_latents(pipe, x_aug):
    with torch.cuda.amp.autocast():
        batch_size, num_frames, channels, height, width = x_aug.shape
        x_aug = x_aug.reshape(batch_size * num_frames, channels, height, width)
        latents = pipe.vae.encode(x_aug).latent_dist.sample()
        latents = latents.view(batch_size, num_frames, -1, latents.shape[2], latents.shape[3])
        latents = latents.permute(0, 2, 1, 3, 4)
    return pipe.vae.config.scaling_factor * latents


@torch.no_grad()
def invert(pipe, inv, load_name, device="cuda", dtype=torch.bfloat16):
    input_img = [load_image(load_name, 256).to(device, dtype=dtype).unsqueeze(1)] * 5
    input_img = torch.cat(input_img, dim=1)
    latents = prepare_latents(pipe, input_img).to(torch.bfloat16)
    inv.set_timesteps(25)
    id_latents = dd_inversion(pipe, inv, video_latent=latents, num_inv_steps=25, prompt="")[-1].to(dtype)
    return torch.mean(id_latents, dim=2, keepdim=True)

def load_primary_models(pretrained_model_path):
    return (
        DDPMScheduler.from_config(pretrained_model_path, subfolder="scheduler"),
        CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer"),
        CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder"),
        AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae"),
        UNet3DConditionModel.from_pretrained(pretrained_model_path, subfolder="unet"),
    )

def initialize_pipeline(model: str, device: str = "cuda"):
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        scheduler, tokenizer, text_encoder, vae, unet = load_primary_models(model)
    pipe = TextToVideoSDPipeline.from_pretrained(
        pretrained_model_name_or_path="damo-vilab/text-to-video-ms-1.7b",
        scheduler=scheduler,
        tokenizer=tokenizer,
        text_encoder=text_encoder.to(device=device, dtype=torch.bfloat16),
        vae=vae.to(device=device, dtype=torch.bfloat16),
        unet=unet.to(device=device, dtype=torch.bfloat16),
    )
    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
    return pipe, pipe.scheduler

# Initialize the models
LORA_CHECKPOINT = "t2v_sketch-lora/checkpoint-2500"
os.environ["TORCH_CUDNN_V8_API_ENABLED"] = "1"
device = 'cuda' if torch.cuda.is_available() else 'cpu'
dtype = torch.bfloat16

pipe_inversion, inv = initialize_pipeline(LORA_CHECKPOINT, device)
pipe = TextToVideoSDPipelineModded.from_pretrained(
    pretrained_model_name_or_path="damo-vilab/text-to-video-ms-1.7b",
    scheduler=pipe_inversion.scheduler,
    tokenizer=pipe_inversion.tokenizer,
    text_encoder=pipe_inversion.text_encoder,
    vae=pipe_inversion.vae,
    unet=pipe_inversion.unet,
).to(device)

@torch.no_grad()
def process_video(num_frames, num_seeds, generator, exp_dir, load_name, caption, lambda_):
    pipe_inversion.to(device)
    id_latents = invert(pipe_inversion, inv, load_name).to(device, dtype=dtype)
    latents = id_latents.repeat(num_seeds, 1, 1, 1, 1)
    generator = [torch.Generator(device="cuda").manual_seed(i) for i in range(num_seeds)]
    video_frames = pipe(
        prompt=caption,
        negative_prompt="",
        num_frames=num_frames,
        num_inference_steps=25,
        inv_latents=latents,
        guidance_scale=9,
        generator=generator,
        lambda_=lambda_,
    ).frames

    gifs = []
    for seed in range(num_seeds):
        vid_name = f"{exp_dir}/mp4_logs/vid_{os.path.basename(load_name)[:-4]}-rand{seed}.mp4"
        gif_name = f"{exp_dir}/gif_logs/vid_{os.path.basename(load_name)[:-4]}-rand{seed}.gif"
        
        os.makedirs(os.path.dirname(vid_name), exist_ok=True)
        os.makedirs(os.path.dirname(gif_name), exist_ok=True)
        
        video_path = export_to_video(video_frames[seed], output_video_path=vid_name)
        VideoFileClip(vid_name).write_gif(gif_name)
        
        with Image.open(gif_name) as im:
            frames = load_frames(im)

        frames_collect = np.empty((0, 1024, 1024), int)
        for frame in frames:
            frame = cv2.resize(frame, (1024, 1024))[:, :, :3]
            frame = cv2.cvtColor(255 - frame, cv2.COLOR_RGB2GRAY)
            _, frame = cv2.threshold(255 - frame, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
            frames_collect = np.append(frames_collect, [frame], axis=0)

        save_gif(frames_collect, gif_name)
        gifs.append(gif_name)

    return gifs

def generate_output(image, apply_filter, prompt: str, num_seeds: int = 3, lambda_value: float = 0.5, progress=gr.Progress(track_tqdm=True)) -> List[str]:
    """Main function to generate output GIFs"""
    unique_id = str(uuid.uuid4())
    exp_dir = f"static/app_tmp_{unique_id}"
    os.makedirs(exp_dir, exist_ok=True)
    
    # Save the input image temporarily
    temp_image_path = os.path.join(exp_dir, "temp_input.png")

    # Get original dimensions
    width, height = image.size

    # Step 1: Make the image square if it's not already
    if width != height:
        # Use the smaller dimension to crop to a square
        min_dim = min(width, height)
        left = (width - min_dim) // 2
        top = (height - min_dim) // 2
        right = left + min_dim
        bottom = top + min_dim
        image = image.crop((left, top, right, bottom))

    # Step 2: Resize to 512x512 if the image is larger
    if image.size[0] > 512:  # Image is square at this point
        image = image.resize((512, 512), Image.LANCZOS)

    image.save(temp_image_path)
    
    # Generate the GIFs
    generated_gifs = process_video(
        num_frames=10,
        num_seeds=num_seeds,
        generator=None,
        exp_dir=exp_dir,
        load_name=temp_image_path,
        caption=prompt,
        lambda_=1 - lambda_value
    )

    if apply_filter == True:
        print("APPLYING FILTER")
        # Apply filtering (assuming filter function is imported)
        filtered_gifs = filter(generated_gifs, temp_image_path)   
        return filtered_gifs, filtered_gifs
    else:
        print("NOT APPLYING FILTER")
        return generated_gifs, generated_gifs

def generate_output_from_sketchpad(image, apply_filter, prompt: str, num_seeds: int = 3, lambda_value: float = 0.5, progress=gr.Progress(track_tqdm=True)):
    image = image['composite']
    results, results_to_download= generate_output(image, apply_filter, prompt, num_seeds, lambda_value)
    return results, results_to_download

css=""" """


with gr.Blocks(css=css) as demo:
    with gr.Column():
        gr.Markdown(
            """
                        
            <div align="center" id = "user-content-toc">
            <img align="left" width="70" height="70" src="https://github.com/user-attachments/assets/c61cec76-3c4b-42eb-8c65-f07e0166b7d8" alt="">
            
            # [FlipSketch: Flipping assets Drawings to Text-Guided Sketch Animations](https://hmrishavbandy.github.io/flipsketch-web/)
            ## [Hmrishav Bandyopadhyay](https://hmrishavbandy.github.io/) . [Yi-Zhe Song](https://personalpages.surrey.ac.uk/y.song/)
            </div>

            """
        )

        with gr.Tab("Upload your sketch"):
        
            with gr.Row():       
                with gr.Column():

                    input_sketch = gr.Image(
                        type="pil",
                        label="Selected Sketch",
                        scale=1,
                        interactive=True,
                        height=300  # Fixed height for consistency
                    )
                    motion_prompt = gr.Textbox(
                        label="Prompt",
                        placeholder="Describe the motion...",
                        lines=2
                    )
                
                
                    num_seeds = gr.Slider(
                        minimum=1, 
                        maximum=10, 
                        value=5, 
                        step=1, 
                        label="Seeds"
                    )
                    lambda_ = gr.Slider(
                        minimum=0, 
                        maximum=1, 
                        value=0.5, 
                        step=0.1, 
                        label="Motion Strength"
                    )          
                    apply_filter = gr.Checkbox(
                        label="Apply GIFs Filters",
                        value=True,
                        info="If Apply Filters is checked, non accurate results compared to input sketch will be filtered off",
                    )

                with gr.Column():
                    gr.Examples(
                        examples=[
                            ['./static/examples/sketch1.png', 'The camel walks slowly'],
                            ['./static/examples/sketch2.png', 'The wine in the wine glass sways from side to side'],
                            ['./static/examples/sketch3.png', 'The squirrel is eating a nut'],
                            ['./static/examples/sketch4.png', 'The surfer surfs on the waves'],
                            ['./static/examples/sketch5.png', 'A galloping horse'],
                            ['./static/examples/sketch6.png', 'The cat walks forward'],
                            ['./static/examples/sketch7.png', 'The eagle flies in the sky'],
                            ['./static/examples/sketch8.png', 'The flower is blooming slowly'],
                            ['./static/examples/sketch9.png', 'The reindeer looks around'],
                            ['./static/examples/sketch10.png', 'The cloud floats in the sky'],
                            ['./static/examples/sketch11.png', 'The jazz saxophonist performs on stage with a rhythmic sway, his upper body sways subtly to the rhythm of the music.'],
                            ['./static/examples/sketch12.png', 'The biker rides on the road']
                        ],
                        inputs=[input_sketch, motion_prompt],
                        examples_per_page=4,   
                    
                    )

                    generate_btn = gr.Button(
                        "Generate Animation",
                        variant="primary",
                        elem_classes="generate-btn",
                        interactive=True,
                    )
                    
                
        with gr.Tab("Draw your own"):
            with gr.Row():
                with gr.Column():
                    draw_sketchpad = gr.Sketchpad(
                        label="Draw your own Sketch",
                        value={
                            "background": "./static/examples/background.jpeg",
                            "layers": None,
                            "composite": None
                        },
                        type="pil",
                        image_mode="RGB",
                        layers=False,
                        
                    )
                with gr.Column():
                    draw_motion_prompt = gr.Textbox(
                        label="Prompt",
                        placeholder="Describe the motion...",
                        lines=2
                    )
                
                
                    draw_num_seeds = gr.Slider(
                        minimum=1, 
                        maximum=10, 
                        value=5, 
                        step=1, 
                        label="Seeds"
                    )
                    draw_lambda_ = gr.Slider(
                        minimum=0, 
                        maximum=1, 
                        value=0.5, 
                        step=0.1, 
                        label="Motion Strength"
                    )
                    draw_apply_filter = gr.Checkbox(
                        label="Apply GIFs Filters",
                        info="If Apply Filters is checked, non accurate results compared to input sketch will be filtered off",
                        value=False
                    )
                    
                    sketchpad_generate_btn = gr.Button(
                        "Generate Animation",
                        variant="primary",
                        elem_classes="generate-btn",
                        interactive=True,
                    )
              
                
        output_gallery = gr.Gallery(
                    label="Results",
                    elem_classes="output-gallery",
                    columns=3,
                    rows=2,
                    height="auto"                  
        )

        download_gifs = gr.Files(
            label="Download GIFs"
        )
           
        
    # Event handlers
    generate_btn.click(
            fn=generate_output,
            inputs=[
                input_sketch,
                apply_filter,
                motion_prompt,
                num_seeds,
                lambda_
            ],
            outputs=[output_gallery, download_gifs]
    )
    def reload_pad():
         blank_pad ={
           "background": "./static/examples/background.jpeg",
            "layers": None,
            "composite": None
        }
         return blank_pad
        
    draw_sketchpad.clear(
            fn=reload_pad,
            inputs = None,
            outputs = [draw_sketchpad],
            queue=False
    )
    sketchpad_generate_btn.click(
            fn=generate_output_from_sketchpad,
            inputs=[
                draw_sketchpad,
                draw_apply_filter,
                draw_motion_prompt,
                draw_num_seeds,
                draw_lambda_,
            ],
            outputs=[output_gallery, download_gifs]
    )
    

# Launch the app
if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_api=False,
        #ssr_mode=False
    )