"""Pretrained mel-conditioned vocoder helpers (NVIDIA BigVGAN)."""

import json
import os

import torch

from bigvgan.env import AttrDict
from bigvgan.bigvgan import BigVGAN

VOC_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "vocoder")

BUILTIN = {
    "bigvgan_base": ("bigvgan_base_24k_100band.json",
                     "bigvgan_base_24k_100band.pt"),
    "bigvgan_v2": ("bigvgan_v2_24k_100band.json",
                   "bigvgan_v2_24k_100band.pt"),
}

_cache = {}


def load_bigvgan(name="bigvgan_base", device="cpu"):
    """Load a pretrained BigVGAN generator (frozen, eval mode)."""
    if name in _cache:
        return _cache[name]
    if name not in BUILTIN:
        raise ValueError(f"Unknown vocoder '{name}', choose from {list(BUILTIN)}")
    cfg_file, ckpt_file = BUILTIN[name]
    h = AttrDict(json.load(open(os.path.join(VOC_DIR, cfg_file))))
    model = BigVGAN(h)
    ckpt = torch.load(os.path.join(VOC_DIR, ckpt_file), map_location="cpu",
                      weights_only=True)
    model.load_state_dict(ckpt["generator"])
    model.remove_weight_norm()
    model.eval().to(device)
    for p in model.parameters():
        p.requires_grad = False
    _cache[name] = model
    return model