import os
import json
import numpy as np
import torch
import soundfile as sf
import gradio as gr
from diffusers import DDPMScheduler
from pico_model import PicoDiffusion, build_pretrained_models
from audioldm.variational_autoencoder.autoencoder import AutoencoderKL

class dotdict(dict):
    """dot.notation access to dictionary attributes"""
    __getattr__ = dict.get
    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

class InferRunner:
    def __init__(self, device):
        vae_config = json.load(open("ckpts/ldm/vae_config.json"))
        self.vae = AutoencoderKL(**vae_config).to(device)
        vae_weights = torch.load("ckpts/ldm/pytorch_model_vae.bin", map_location=device)
        self.vae.load_state_dict(vae_weights)

        train_args = dotdict(json.loads(open("ckpts/pico_model/summary.jsonl").readlines()[0]))
        self.pico_model = PicoDiffusion(
            scheduler_name=train_args.scheduler_name, 
            unet_model_config_path=train_args.unet_model_config, 
            snr_gamma=train_args.snr_gamma,
            freeze_text_encoder_ckpt="ckpts/laion_clap/630k-audioset-best.pt",
            diffusion_pt="ckpts/pico_model/diffusion.pt",
        ).eval().to(device)
        self.scheduler = DDPMScheduler.from_pretrained(train_args.scheduler_name, subfolder="scheduler")

device = "cuda" if torch.cuda.is_available() else "cpu"
runner = InferRunner(device)

def infer(caption, num_steps=200, guidance_scale=3.0, audio_len=16000*10):
    with torch.no_grad():
        latents = runner.pico_model.demo_inference(caption, runner.scheduler, num_steps=num_steps, guidance_scale=guidance_scale, num_samples_per_prompt=1, disable_progress=True)
        mel = runner.vae.decode_first_stage(latents)
        wave = runner.vae.decode_to_waveform(mel)[0][:audio_len]
    outpath = f"synthesized/output.wav"
    sf.write(outpath, wave, samplerate=16000, subtype='PCM_16')
    return outpath
with gr.Blocks() as demo:
    with gr.Row():
        gr.Markdown("## PicoAudio")

    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt: Input your caption formatted as 'event1 at onset1-offset1_onset2-offset2 and event2 at onset1-offset1.",
                value="spraying at 0.38-1.176_3.06-3.856 and gunshot at 1.729-3.729_4.367-6.367_7.031-9.031.",)
            run_button = gr.Button()

            with gr.Accordion("Advanced options", open=False):
                num_steps = gr.Slider(label="num_steps", minimum=1,
                                       maximum=300, value=200, step=1)
                guidance_scale = gr.Slider(
                    label="guidance_scale Scale:(Large => more relevant to text but the quality may drop)", minimum=0.1, maximum=8.0, value=3.0, step=0.1
                )
            
        with gr.Column():
            outaudio = gr.Audio()
    
    run_button.click(fn=infer, 
                    inputs=[prompt, num_steps, guidance_scale], 
                    outputs=[outaudio])
    # with gr.Row():
    #     with gr.Column():
    #         gr.Examples(
    #                     examples = [['An amateur recording features a steel drum playing in a higher register',25,5,55],
    #                                 ['An instrumental song with a caribbean feel, happy mood, and featuring steel pan music, programmed percussion, and bass',25,5,55],
    #                                 ['This musical piece features a playful and emotionally melodic male vocal accompanied by piano',25,5,55],
    #                                 ['A eerie yet calming experimental electronic track featuring haunting synthesizer strings and pads',25,5,55],
    #                                 ['A slow tempo pop instrumental piece featuring only acoustic guitar with fingerstyle and percussive strumming techniques',25,5,55]],
    #                     inputs = [prompt, ddim_steps, scale, seed],
    #                     outputs = [outaudio],
    #                     )
    #                     cache_examples="lazy", # Turn on to cache.
    #     with gr.Column():
    #         pass
    
demo.launch()