#!/usr/bin/env python

from __future__ import annotations

import os
import random
import tempfile

import gradio as gr
import imageio
import numpy as np
import spaces
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler

DESCRIPTION = "# zeroscope v2"
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

MAX_NUM_FRAMES = int(os.getenv("MAX_NUM_FRAMES", "200"))
DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES, int(os.getenv("DEFAULT_NUM_FRAMES", "24")))
MAX_SEED = np.iinfo(np.int32).max

if torch.cuda.is_available():
    pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    pipe.enable_model_cpu_offload()
    pipe.enable_vae_slicing()


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


def to_video(frames: np.ndarray, fps: int) -> str:
    frames = np.clip((frames * 255), 0, 255).astype(np.uint8)
    out_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
    writer = imageio.get_writer(out_file.name, format="FFMPEG", fps=fps)
    for frame in frames:
        writer.append_data(frame)
    writer.close()
    return out_file.name


@spaces.GPU
def generate(
    prompt: str,
    seed: int,
    num_frames: int,
    num_inference_steps: int,
    progress=gr.Progress(track_tqdm=True),
) -> str:
    generator = torch.Generator().manual_seed(seed)
    frames = pipe(
        prompt,
        num_inference_steps=num_inference_steps,
        num_frames=num_frames,
        width=576,
        height=320,
        generator=generator,
    ).frames[0]
    return to_video(frames, 8)


examples = [
    ["An astronaut riding a horse", 0, 24, 25],
    ["A panda eating bamboo on a rock", 0, 24, 25],
    ["Spiderman is surfing", 0, 24, 25],
]

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    with gr.Group():
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            run_button = gr.Button("Generate video", scale=0)
        result = gr.Video(label="Result", show_label=False)
    with gr.Accordion("Advanced options", open=False):
        seed = gr.Slider(
            label="Seed",
            minimum=0,
            maximum=MAX_SEED,
            step=1,
            value=0,
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        num_frames = gr.Slider(
            label="Number of frames",
            minimum=24,
            maximum=MAX_NUM_FRAMES,
            step=1,
            value=24,
            info="Note that the content of the video also changes when you change the number of frames.",
        )
        num_inference_steps = gr.Slider(
            label="Number of inference steps",
            minimum=10,
            maximum=50,
            step=1,
            value=25,
        )

    inputs = [
        prompt,
        seed,
        num_frames,
        num_inference_steps,
    ]
    gr.Examples(
        examples=examples,
        inputs=inputs,
        outputs=result,
        fn=generate,
    )

    gr.on(
        triggers=[prompt.submit, run_button.click],
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=result,
        api_name="run",
    )

if __name__ == "__main__":
    demo.queue(max_size=10).launch()