import torch
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from pathlib import Path
from vocoder.bigvgan.models import VocoderBigVGAN
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config
from wav_evaluation.models.CLAPWrapper import CLAPWrapper

SAMPLE_RATE = 16000

torch.set_grad_enabled(False)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

def dur_to_size(duration):
    latent_width = int(duration * 7.8)
    if latent_width % 4 != 0:
        latent_width = (latent_width // 4 + 1) * 4
    return latent_width

def initialize_model(config, ckpt):
    config = OmegaConf.load(config)
    model = instantiate_from_config(config.model)
    model.load_state_dict(torch.load(ckpt,map_location='cpu')["state_dict"], strict=False)

    model = model.to(device)
    model.cond_stage_model.to(model.device)
    model.cond_stage_model.device = model.device
    print(model.device,device,model.cond_stage_model.device)
    sampler = DDIMSampler(model)

    return sampler

sampler = initialize_model('configs/text_to_audio/txt2audio_args.yaml', 'useful_ckpts/maa1_full.ckpt')
vocoder = VocoderBigVGAN('vocoder/logs/bigvnat',device=device)
clap_model = CLAPWrapper('useful_ckpts/CLAP/CLAP_weights_2022.pth','useful_ckpts/CLAP/config.yml',use_cuda=torch.cuda.is_available())

def select_best_audio(prompt,wav_list):
    text_embeddings = clap_model.get_text_embeddings([prompt])
    score_list = []
    for data in wav_list:
        sr,wav = data
        audio_embeddings = clap_model.get_audio_embeddings([(torch.FloatTensor(wav),sr)], resample=True)
        score = clap_model.compute_similarity(audio_embeddings, text_embeddings,use_logit_scale=False).squeeze().cpu().numpy()
        score_list.append(score)
    max_index = np.array(score_list).argmax()
    print(score_list,max_index)
    return wav_list[max_index]

def txt2audio(sampler,vocoder,prompt, seed, scale, ddim_steps, n_samples=1, W=624, H=80):
    prng = np.random.RandomState(seed)
    start_code = prng.randn(n_samples, sampler.model.first_stage_model.embed_dim, H // 8, W // 8)
    start_code = torch.from_numpy(start_code).to(device=device, dtype=torch.float32)
    
    uc = None
    if scale != 1.0:
        uc = sampler.model.get_learned_conditioning(n_samples * [""])
    c = sampler.model.get_learned_conditioning(n_samples * [prompt])# shape:[1,77,1280],即还没有变成句子embedding，仍是每个单词的embedding
    shape = [sampler.model.first_stage_model.embed_dim, H//8, W//8]  # (z_dim, 80//2^x, 848//2^x)
    samples_ddim, _ = sampler.sample(S=ddim_steps,
                                        conditioning=c,
                                        batch_size=n_samples,
                                        shape=shape,
                                        verbose=False,
                                        unconditional_guidance_scale=scale,
                                        unconditional_conditioning=uc,
                                        x_T=start_code)

    x_samples_ddim = sampler.model.decode_first_stage(samples_ddim)

    wav_list = []
    for idx,spec in enumerate(x_samples_ddim):
        wav = vocoder.vocode(spec)
        wav_list.append((SAMPLE_RATE,wav))
    best_wav = select_best_audio(prompt,wav_list)
    return best_wav


def predict(prompt, ddim_steps, num_samples, scale, seed):
    melbins,mel_len = 80,624
    with torch.no_grad():
        result = txt2audio(
            sampler=sampler,
            vocoder=vocoder,
            prompt=prompt,
            seed=seed,
            scale=scale,
            ddim_steps=ddim_steps,
            n_samples=num_samples,
            H=melbins, W=mel_len
        )

    return result


with gr.Blocks() as demo:
    with gr.Row():
        gr.Markdown("## Make-An-Audio: Text-to-Audio Generation")

    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt: Input your text here.        ")
            run_button = gr.Button(label="Run")

            
            with gr.Accordion("Advanced options", open=False):
                num_samples = gr.Slider(
                    label="Select from audios num.This number control the number of candidates \
                        (e.g., generate three audios and choose the best to show you). A Larger value usually lead to \
                        better quality with heavier computation", minimum=1, maximum=10, value=3, step=1)
                # num_samples = 1
                ddim_steps = gr.Slider(label="Steps", minimum=1,
                                       maximum=150, value=100, step=1)
                scale = gr.Slider(
                    label="Guidance Scale:(Large => more relevant to text but the quality may drop)", minimum=0.1, maximum=8.0, value=3.0, step=0.1
                )
                seed = gr.Slider(
                    label="Seed:Change this value (any integer number) will lead to a different generation result.",
                    minimum=0,
                    maximum=2147483647,
                    step=1,
                    value=44,
                )

        with gr.Column():
            # audio_list = []
            # for i in range(int(num_samples)):
            #     audio_list.append(gr.outputs.Audio())
            outaudio = gr.Audio()


    run_button.click(fn=predict, inputs=[
                    prompt,ddim_steps, num_samples, scale, seed], outputs=[outaudio])# inputs的参数只能传gr.xxx
    with gr.Row():
        with gr.Column():
            gr.Examples(
                        examples = [['a dog barking and a bird chirping',100,3,3,55],['Pigeons peck, coo, and flap their wings before a man speaks',100,3,3,55],
                                        ['music of violin and piano',100,3,2,88],['wind thunder and rain falling',100,3,3,55],['music made by drum kit',100,3,3,55]],
                        inputs = [prompt,ddim_steps, num_samples, scale, seed],
                        outputs = [outaudio]
                        )
        with gr.Column():
            pass

demo.launch()