import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
import sys

tokenizer = AutoTokenizer.from_pretrained("./bert/bert-base-japanese-v3")

def get_bert_feature(text, word2ph, device=None):
    if sys.platform == "darwin" and torch.backends.mps.is_available() and device == "cpu":
        device = "mps"
    if not device:
        device = "cuda"
    model = AutoModelForMaskedLM.from_pretrained("./bert/bert-base-japanese-v3").to(device)
    with torch.no_grad():
        inputs = tokenizer(text, return_tensors='pt')
        for i in inputs:
            inputs[i] = inputs[i].to(device)
        res = model(**inputs, output_hidden_states=True)
        res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu()
    assert inputs['input_ids'].shape[-1] == len(word2ph)
    word2phone = word2ph
    phone_level_feature = []
    for i in range(len(word2phone)):
        repeat_feature = res[i].repeat(word2phone[i], 1)
        phone_level_feature.append(repeat_feature)

    phone_level_feature = torch.cat(phone_level_feature, dim=0)

    return phone_level_feature.T