import os
import gradio as gr
import spaces
import torch
import gc
from huggingface_hub import snapshot_download

# import argparse

snapshot_download(repo_id="fffiloni/svd_keyframe_interpolation", local_dir="checkpoints")
checkpoint_dir = "checkpoints/svd_reverse_motion_with_attnflip"

from diffusers.utils import load_image, export_to_video
from diffusers import UNetSpatioTemporalConditionModel
from custom_diffusers.pipelines.pipeline_frame_interpolation_with_noise_injection import FrameInterpolationWithNoiseInjectionPipeline
from custom_diffusers.schedulers.scheduling_euler_discrete import EulerDiscreteScheduler
from attn_ctrl.attention_control import (AttentionStore, 
                                         register_temporal_self_attention_control, 
                                         register_temporal_self_attention_flip_control,
)


pretrained_model_name_or_path = "stabilityai/stable-video-diffusion-img2vid-xt"
noise_scheduler = EulerDiscreteScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler")

pipe = FrameInterpolationWithNoiseInjectionPipeline.from_pretrained(
    pretrained_model_name_or_path, 
    scheduler=noise_scheduler,
    variant="fp16",
    torch_dtype=torch.float16, 
)
ref_unet = pipe.ori_unet

state_dict = pipe.unet.state_dict()
# computing delta w
finetuned_unet = UNetSpatioTemporalConditionModel.from_pretrained(
    checkpoint_dir,
    subfolder="unet",
    torch_dtype=torch.float16,
) 
assert finetuned_unet.config.num_frames==14
ori_unet = UNetSpatioTemporalConditionModel.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid",
    subfolder="unet",
    variant='fp16',
    torch_dtype=torch.float16,
)

finetuned_state_dict = finetuned_unet.state_dict()
ori_state_dict = ori_unet.state_dict()
for name, param in finetuned_state_dict.items():
    if 'temporal_transformer_blocks.0.attn1.to_v' in name or "temporal_transformer_blocks.0.attn1.to_out.0" in name:
        delta_w = param - ori_state_dict[name]
        state_dict[name] = state_dict[name] + delta_w
pipe.unet.load_state_dict(state_dict)

controller_ref= AttentionStore()
register_temporal_self_attention_control(ref_unet, controller_ref)

controller = AttentionStore()
register_temporal_self_attention_flip_control(pipe.unet, controller, controller_ref)

device = "cuda"
pipe = pipe.to(device)

def check_outputs_folder(folder_path):
    # Check if the folder exists
    if os.path.exists(folder_path) and os.path.isdir(folder_path):
        # Delete all contents inside the folder
        for filename in os.listdir(folder_path):
            file_path = os.path.join(folder_path, filename)
            try:
                if os.path.isfile(file_path) or os.path.islink(file_path):
                    os.unlink(file_path)  # Remove file or link
                elif os.path.isdir(file_path):
                    shutil.rmtree(file_path)  # Remove directory
            except Exception as e:
                print(f'Failed to delete {file_path}. Reason: {e}')
    else:
        print(f'The folder {folder_path} does not exist.')

# Custom CUDA memory management function
def cuda_memory_cleanup():
    torch.cuda.empty_cache()
    torch.cuda.ipc_collect()
    gc.collect()

@spaces.GPU(duration=90)
def infer(frame1_path, frame2_path, progress=gr.Progress(track_tqdm=True)):

    seed = 42
    num_inference_steps = 10
    noise_injection_steps = 0
    noise_injection_ratio = 0.5
    weighted_average = False

    generator = torch.Generator(device)
    if seed is not None:
        generator = generator.manual_seed(seed)
    

    frame1 = load_image(frame1_path)
    frame1 = frame1.resize((512, 288))

    frame2 = load_image(frame2_path)
    frame2 = frame2.resize((512, 288))

    cuda_memory_cleanup()

    frames = pipe(image1=frame1, image2=frame2, 
        num_inference_steps=num_inference_steps, # 50
        generator=generator,
        weighted_average=weighted_average, # True
        noise_injection_steps=noise_injection_steps, # 0
        noise_injection_ratio= noise_injection_ratio, # 0.5
        decode_chunk_size=18
    ).frames[0]

   # cuda_memory_cleanup()

    print(f"FRAMES: {frames}")
    
    out_dir = "result"

    check_outputs_folder(out_dir)
    os.makedirs(out_dir, exist_ok=True)
    out_path = "result/video_result.mp4"

    
    if out_path.endswith('.gif'):
        frames[0].save(out_path, save_all=True, append_images=frames[1:], duration=142, loop=0)
    else:
        export_to_video(frames, out_path, fps=7)
    
    return out_path

with gr.Blocks() as demo:

    with gr.Column():
        gr.Markdown("# Keyframe Interpolation with Stable Video Diffusion")
        gr.Markdown("## Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation")
        gr.HTML("""
        <div style="display:flex;column-gap:4px;">
            <a href='https://svd-keyframe-interpolation.github.io/'>
                <img src='https://img.shields.io/badge/Project-Page-Green'>
            </a> 
            <a href='https://arxiv.org/abs/2408.15239'>
                <img src='https://img.shields.io/badge/Paper-Arxiv-red'>
            </a>
        </div>
        """)
        with gr.Row():
            with gr.Column():
                image_input1 = gr.Image(label="FRAME 1", type="filepath")
                image_input2 = gr.Image(label="FRAME 2", type="filepath")
                submit_btn = gr.Button("Submit")
            with gr.Column():
                output = gr.Video(label="Interpolated result")
                gr.Examples(
                    examples = [
                        ["examples/example_001/frame1.png", "examples/example_001/frame2.png"],
                        ["examples/example_002/frame1.png", "examples/example_002/frame2.png"],
                        ["examples/example_003/frame1.png", "examples/example_003/frame2.png"],
                        ["examples/example_004/frame1.png", "examples/example_004/frame2.png"]
                    ],
                    inputs = [image_input1, image_input2]
                )
    
    submit_btn.click(
        fn = infer, 
        inputs = [image_input1, image_input2],
        outputs = [output],
        show_api = False
    )

demo.queue().launch(show_api=False, show_error=True)