"""CLAP embedding.
- feature dimension: 512
- source: https://huggingface.co/laion/larger_clap_music_and_speech
"""
from typing import Optional

import torch
import librosa
import numpy as np
from transformers import ClapModel, ClapProcessor


class CLAPEmbedding:
    def __init__(self, ckpt: str = "laion/larger_clap_music_and_speech"):
        self.model = ClapModel.from_pretrained(ckpt)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)
        self.model.eval()
        self.processor = ClapProcessor.from_pretrained(ckpt)

    def get_speaker_embedding(self, wav: np.ndarray, sampling_rate: Optional[int] = None) -> np.ndarray:
        if sampling_rate != self.processor.feature_extractor.sampling_rate:
            wav = librosa.resample(wav, orig_sr=sampling_rate, target_sr=self.processor.feature_extractor.sampling_rate)
        inputs = self.processor(
            audios=wav, sampling_rate=self.processor.feature_extractor.sampling_rate, return_tensors="pt"
        )
        with torch.no_grad():
            outputs = self.model.get_audio_features(**{k: v.to(self.device) for k, v in inputs.items()})
        return outputs.cpu().numpy()[0]


class CLAPGeneralEmbedding(CLAPEmbedding):
    def __init__(self):
        super().__init__(ckpt="laion/larger_clap_general")