"""Initialize the decoder student by channel-slicing a frozen pretrained vocoder.

Training a decoder this small from scratch does not converge to intelligible
audio; slicing the top-left corner of a pretrained neural vocoder's decoder
weights does. Pass any ConvNeXt-style vocoder state dict with --vocoder-ckpt;
this takes the first `--dim` channels, the first `--pw` hidden units and the
first `--blocks` blocks (dim 130 / pw 337 / blocks 4 for the shipped model).

The sliced checkpoint still needs decoder training (train_decoder.py) before
it is usable: a fresh slice renders noise, and only fine-tuning brings it back.

    python init_decoder.py --dim 130 --pw 337 --blocks 4 \
        --vocoder-ckpt <vocos-mel-24khz state dict> --out decoder_init.pt
"""
import sys
from pathlib import Path

import torch

sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from microtts.models import DecoderStudent


def main():
    import argparse
    ap = argparse.ArgumentParser()
    ap.add_argument("--dim", type=int, default=130)
    ap.add_argument("--pw", type=int, default=337)
    ap.add_argument("--blocks", type=int, default=4)
    ap.add_argument("--vocoder-ckpt", default=None,
                    help="state dict / checkpoint of a ConvNeXt-style neural vocoder")
    ap.add_argument("--out", default="decoder_init.pt")
    args = ap.parse_args()

    if args.vocoder_ckpt:
        obj = torch.load(args.vocoder_ckpt, map_location="cpu", weights_only=False)
        sd = obj.state_dict() if hasattr(obj, "state_dict") else obj
    else:
        raise ImportError("pass --vocoder-ckpt pointing at a ConvNeXt-style vocoder state dict")

    student = DecoderStudent(dim=args.dim, pw_hidden=args.pw, blocks=args.blocks)
    ssd = student.state_dict()

    # embedding conv: [512, 100, 7] -> [dim, 100, 7]
    ssd["embed.weight"] = sd["backbone.embed.weight"][:args.dim].contiguous()
    ssd["embed.bias"] = sd["backbone.embed.bias"][:args.dim].contiguous()
    ssd["norm.weight"] = sd["backbone.norm.weight"][:args.dim].contiguous()
    ssd["norm.bias"] = sd["backbone.norm.bias"][:args.dim].contiguous()
    for i in range(args.blocks):
        src = f"backbone.convnext.{i}"
        dst = f"blocks.{i}"
        ssd[f"{dst}.dw.weight"] = sd[f"{src}.dwconv.weight"][:args.dim].contiguous()
        ssd[f"{dst}.dw.bias"] = sd[f"{src}.dwconv.bias"][:args.dim].contiguous()
        ssd[f"{dst}.norm.weight"] = sd[f"{src}.norm.weight"][:args.dim].contiguous()
        ssd[f"{dst}.norm.bias"] = sd[f"{src}.norm.bias"][:args.dim].contiguous()
        # pointwise slices: pw0 [pw, dim], pw1 [dim, pw]
        ssd[f"{dst}.pw0.weight"] = sd[f"{src}.pwconv1.weight"][:args.pw, :args.dim].contiguous()
        ssd[f"{dst}.pw0.bias"] = sd[f"{src}.pwconv1.bias"][:args.pw].contiguous()
        ssd[f"{dst}.pw1.weight"] = sd[f"{src}.pwconv2.weight"][:args.dim, :args.pw].contiguous()
        ssd[f"{dst}.pw1.bias"] = sd[f"{src}.pwconv2.bias"][:args.dim].contiguous()
        ssd[f"{dst}.gamma"] = sd[f"{src}.gamma"][:args.dim].contiguous()
    # final norm + head
    ssd["fnorm.weight"] = sd["backbone.final_layer_norm.weight"][:args.dim].contiguous()
    ssd["fnorm.bias"] = sd["backbone.final_layer_norm.bias"][:args.dim].contiguous()
    ssd["head.weight"] = sd["head.out.weight"][:, :args.dim].contiguous()
    ssd["head.bias"] = sd["head.out.bias"].contiguous()

    student.load_state_dict(ssd)
    torch.save(student.state_dict(), args.out)
    n = sum(p.numel() for p in student.parameters())
    print(f"saved {args.out} | student params: {n:,}")


if __name__ == "__main__":
    main()