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

Training a decoder this small from scratch does not converge to intelligible
audio; slicing the top-left corner of a pretrained Vocos vocoder's weights
does. The Vocos backbone is 512-dim with 4 ConvNeXt blocks and pw 1536; this
takes the first `--dim` channels, the first `--pw` hidden units and the first
3 blocks (dim=40 / pw=120 for the shipped model).

The sliced checkpoint still needs the recovery + z-mix stages (train_decoder.py)
before it is usable -- a fresh slice renders noise, and only the fine-tuning
brings it back.
"""
import torch

import sys
from pathlib import Path

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


def slice_linear(src, out_rows, in_cols):
    """Slice a [R, C] weight to [out_rows, in_cols] (top-left corner)."""
    return src[:out_rows, :in_cols].contiguous()


def main():
    import argparse
    ap = argparse.ArgumentParser()
    ap.add_argument("--dim", type=int, default=40)
    ap.add_argument("--pw", type=int, default=120)
    ap.add_argument("--out", default="decoder_init.pt")
    args = ap.parse_args()

    from vocos import Vocos
    v = Vocos.from_pretrained("charactr/vocos-mel-24khz")
    sd = v.state_dict()

    student = DecoderStudent(dim=args.dim, pw_hidden=args.pw)  # 3 blocks
    ssd = student.state_dict()

    # embed: [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()
    # norm
    ssd["norm.weight"] = sd["backbone.norm.weight"][:args.dim].contiguous()
    ssd["norm.bias"] = sd["backbone.norm.bias"][:args.dim].contiguous()
    # blocks 0..2 (student has 3, teacher 4)
    for i in range(3):
        t = f"backbone.convnext.{i}"
        s = f"blocks.{i}"
        ssd[f"{s}.gamma"] = sd[f"{t}.gamma"][:args.dim].contiguous()
        ssd[f"{s}.dw.weight"] = sd[f"{t}.dwconv.weight"][:args.dim].contiguous()
        ssd[f"{s}.dw.bias"] = sd[f"{t}.dwconv.bias"][:args.dim].contiguous()
        ssd[f"{s}.norm.weight"] = sd[f"{t}.norm.weight"][:args.dim].contiguous()
        ssd[f"{s}.norm.bias"] = sd[f"{t}.norm.bias"][:args.dim].contiguous()
        ssd[f"{s}.pw0.weight"] = slice_linear(sd[f"{t}.pwconv1.weight"], args.pw, args.dim)
        ssd[f"{s}.pw0.bias"] = sd[f"{t}.pwconv1.bias"][:args.pw].contiguous()
        ssd[f"{s}.pw1.weight"] = slice_linear(sd[f"{t}.pwconv2.weight"], args.dim, args.pw)
        ssd[f"{s}.pw1.bias"] = sd[f"{t}.pwconv2.bias"][:args.dim].contiguous()
    # fnorm
    ssd["fnorm.weight"] = sd["backbone.norm.weight"][:args.dim].contiguous()
    ssd["fnorm.bias"] = sd["backbone.norm.bias"][:args.dim].contiguous()
    # head: [1026, 512] -> [1026, dim]
    ssd["head.weight"] = slice_linear(sd["head.out.weight"], 1026, args.dim)
    ssd["head.bias"] = sd["head.out.bias"][:1026].contiguous()

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


if __name__ == "__main__":
    main()