import timm
import json
import torch
from torchaudio.functional import resample
import numpy as np
from torchaudio.compliance import kaldi
import torch.nn.functional as F
import requests 


TAG = "gaunernst/vit_base_patch16_1024_128.audiomae_as2m_ft_as20k"
MODEL = timm.create_model(f"hf_hub:{TAG}", pretrained=True).eval()

LABEL_URL = "https://huggingface.co/datasets/huggingface/label-files/raw/main/audioset-id2label.json"
AUDIOSET_LABELS = list(json.loads(requests.get(LABEL_URL).content).values())

SAMPLING_RATE = 16_000
MEAN = -4.2677393
STD = 4.5689974

def preprocess(x: torch.Tensor):
    x = x - x.mean()
    melspec = kaldi.fbank(x.unsqueeze(0), htk_compat=True, window_type="hanning", num_mel_bins=128)
    if melspec.shape[0] < 1024:
        melspec = F.pad(melspec, (0, 0, 0, 1024 - melspec.shape[0]))
    else:
        melspec = melspec[:1024]
    melspec = (melspec - MEAN) / (STD * 2)
    return melspec

def predict_class(x: np.ndarray):
    
    x = torch.from_numpy(x) 
    if x.ndim > 1:
        x = x.mean(-1)
    assert x.ndim == 1

    x = preprocess(x)

    with torch.inference_mode():
        logits = MODEL(x.view(1, 1, 1024, 128)).squeeze(0)

    topk_probs, topk_classes = logits.sigmoid().topk(10)
    preds = [[AUDIOSET_LABELS[cls], prob.item()*100] for cls, prob in zip(topk_classes, topk_probs)]

    return preds