import gradio as gr
import torch
import os
import random
import time
import math
import spaces
from glob import glob
from pathlib import Path
from typing import Optional

from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import export_to_video
from PIL import Image

fps25Pipe = StableVideoDiffusionPipeline.from_pretrained(
    "vdo/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16"
)
fps25Pipe.to("cuda")

fps14Pipe = StableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid", torch_dtype=torch.float16, variant="fp16"
)
fps14Pipe.to("cuda")

max_64_bit_int = 2**63 - 1

def animate(
    image: Image,
    seed: Optional[int] = 42,
    randomize_seed: bool = True,
    motion_bucket_id: int = 127,
    fps_id: int = 6,
    noise_aug_strength: float = 0.1,
    decoding_t: int = 3,
    video_format: str = "mp4",
    frame_format: str = "webp",
    version: str = "auto",
    output_folder: str = "outputs",
):
    start = time.time()
    if image.mode == "RGBA":
        image = image.convert("RGB")
        
    if randomize_seed:
        seed = random.randint(0, max_64_bit_int)

    frames = animate_on_gpu(
        image,
        seed,
        motion_bucket_id,
        fps_id,
        noise_aug_strength,
        decoding_t,
        version
    )
    
    os.makedirs(output_folder, exist_ok=True)
    base_count = len(glob(os.path.join(output_folder, "*." + video_format)))
    video_path = os.path.join(output_folder, f"{base_count:06d}." + video_format)

    export_to_video(frames, video_path, fps=fps_id)
    end = time.time()
    secondes = int(end - start)
    minutes = math.floor(secondes / 60)
    secondes = secondes - (minutes * 60)
    hours = math.floor(minutes / 60)
    minutes = minutes - (hours * 60)
    information = ("Start the process again if you want a different result. " if randomize_seed else "") + \
    "Wait 2 min before a new run to avoid quota penalty or use another computer. " + \
    "The video has been generated in " + \
    ((str(hours) + " h, ") if hours != 0 else "") + \
    ((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + \
    str(secondes) + " sec."
    
    return gr.update(value=video_path, format=video_format), gr.update(value=video_path, visible=True), gr.update(label="Generated frames in *." + frame_format + " format", format = frame_format, value = frames, visible=True), seed, gr.update(value = information, visible = True)

@spaces.GPU(duration=120)
def animate_on_gpu(
    image: Image,
    seed: Optional[int] = 42,
    motion_bucket_id: int = 127,
    fps_id: int = 6,
    noise_aug_strength: float = 0.1,
    decoding_t: int = 3,
    version: str = "auto"
):
    generator = torch.manual_seed(seed)
    
    if version == "svdxt" or (14 < fps_id and version != "svd"):
        return fps25Pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=noise_aug_strength, num_frames=25).frames[0]
    else:
        return fps14Pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=noise_aug_strength, num_frames=25).frames[0]


def resize_image(image, output_size=(1024, 576)):
    # Calculate aspect ratios
    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
    image_aspect = image.width / image.height  # Aspect ratio of the original image

    # Do not touch the image if the size is good
    if image.width == output_size[0] and image.height == output_size[1]:
        return image

    # Resize if the original image is larger
    if image_aspect > target_aspect:
        # Resize the image to match the target height, maintaining aspect ratio
        new_height = output_size[1]
        new_width = int(new_height * image_aspect)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        # Calculate coordinates for cropping
        left = (new_width - output_size[0]) / 2
        top = 0
        right = (new_width + output_size[0]) / 2
        bottom = output_size[1]
    else:
        # Resize the image to match the target width, maintaining aspect ratio
        new_width = output_size[0]
        new_height = int(new_width / image_aspect)
        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
        # Calculate coordinates for cropping
        left = 0
        top = (new_height - output_size[1]) / 2
        right = output_size[0]
        bottom = (new_height + output_size[1]) / 2

    # Crop the image
    cropped_image = resized_image.crop((left, top, right, bottom))
    return cropped_image

with gr.Blocks() as demo:
  gr.Markdown('''# Community demo for Stable Video Diffusion - Img2Vid - XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets), [stability's ui waitlist](https://stability.ai/contact))
#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). this demo uses [🧨 diffusers for low VRAM and fast generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/svd).
  ''')
  with gr.Row():
      with gr.Column():
          image = gr.Image(label="Upload your image", type="pil")
          with gr.Accordion("Advanced options", open=False):
              fps_id = gr.Slider(label="Frames per second", info="The length of your video in seconds will be 25/fps", value=6, minimum=5, maximum=30)
              motion_bucket_id = gr.Slider(label="Motion bucket id", info="Controls how much motion to add/remove from the image", value=127, minimum=1, maximum=255)
              noise_aug_strength = gr.Slider(label="Noise strength", info="The noise to add", value=0.1, minimum=0, maximum=1, step=0.1)
              decoding_t = gr.Slider(label="Decoding", info="Number of frames decoded at a time; this eats more VRAM; reduce if necessary", value=3, minimum=1, maximum=5, step=1)
              video_format = gr.Radio([["*.mp4", "mp4"], ["*.avi", "avi"]], label="Video format for result", info="File extention", value="mp4", interactive=True)
              frame_format = gr.Radio([["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif", "gif"], ["*.bmp", "bmp"]], label="Image format for frames", info="File extention", value="webp", interactive=True)
              version = gr.Radio([["Auto", "auto"], ["🏃🏻‍♀️ SVD (trained on 14 f/s)", "svd"], ["🏃🏻‍♀️💨 SVD-XT (trained on 25 f/s)", "svdxt"]], label="Model", info="Trained model", value="auto", interactive=True)
              seed = gr.Slider(label="Seed", value=42, randomize=True, minimum=0, maximum=max_64_bit_int, step=1)
              randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

          generate_btn = gr.Button(value="🚀 Animate", variant="primary")

      with gr.Column():
          video = gr.Video(label="Generated video", autoplay=True)
          download_button = gr.DownloadButton(label="💾 Download video", visible=False)
          information_msg = gr.HTML(visible = False)
          gallery = gr.Gallery(label="Generated frames", visible=False)
      
  image.upload(fn=resize_image, inputs=image, outputs=image, queue=False)
  generate_btn.click(fn=animate, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id, noise_aug_strength, decoding_t, video_format, frame_format, version], outputs=[video, download_button, gallery, seed, information_msg], api_name="video")
    
  gr.Examples(
    examples=[
        ["Examples/Fire.webp", 42, True, 127, 25, 0.1, 3, "mp4", "png", "auto"],
        ["Examples/Water.png", 42, True, 127, 25, 0.1, 3, "mp4", "png", "auto"],
        ["Examples/Town.jpeg", 42, True, 127, 25, 0.1, 3, "mp4", "png", "auto"]
    ],
    inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id, noise_aug_strength, decoding_t, video_format, frame_format, version],
    outputs=[video, download_button, gallery, seed, information_msg],
    fn=animate,
    run_on_click=True,
    cache_examples=False,
  )

if __name__ == "__main__":
    demo.launch(share=True, show_api=False)