"""Split the fused img_mlp.gate_up LoRA from an ai-toolkit (ComfyUI-style)
Qwen-Image-2.1 checkpoint into diffusers' separate gate_layer + proj modules.

Ground truth (diffusers @ 0121a91f9d, transformer_qwenimage21.py):
  QwenImage21SwiGLUFeedForward has proj, out, gate_layer, all nn.Linear(bias=False),
  gate_layer: Linear(4096, 12288), proj: Linear(4096, 12288), out: Linear(12288, 4096).
  ai-toolkit exports gate_up.lora_B as cat([gate_B, proj_B], dim=0) -> (24576, rank).

Everything else in the checkpoint is kept verbatim (attn + img_mlp.out keys are
proven to load via QwenImage21Pipeline.load_lora_weights).
"""
import json

import torch
from huggingface_hub import hf_hub_download, HfApi
from safetensors.torch import load_file, save_file

SRC_REPO = "ysharma/orbit-alpha-lora"
SRC_FILE = "checkpoints/steps2000res768/orbit_alpha_lora/orbit_alpha_lora.safetensors"
DST_NAME = "orbit_alpha_lora_gate_up_split.safetensors"
DST_REPO = "ysharma/orbit-alpha-lora"
RANK = 32
GATE_OUT = 12288  # mlp_ratio 3 * hidden 4096, from Qwen/Qwen-Image-2.1 transformer config

path = hf_hub_download(repo_id=SRC_REPO, filename=SRC_FILE)
sd = load_file(path)
print(f"loaded {len(sd)} tensors from {SRC_FILE}")

out = {}
n_split = 0
for key, tensor in sd.items():
    if ".img_mlp.gate_up.lora_A.weight" in key:
        base = key.replace(".gate_up.lora_A.weight", "")
        assert tensor.shape == (RANK, 4096), f"unexpected lora_A shape {tensor.shape} for {key}"
        out[f"{base}.gate_layer.lora_A.weight"] = tensor.clone()
        out[f"{base}.proj.lora_A.weight"] = tensor.clone()
    elif ".img_mlp.gate_up.lora_B.weight" in key:
        base = key.replace(".gate_up.lora_B.weight", "")
        assert tensor.shape == (2 * GATE_OUT, RANK), f"unexpected fused lora_B shape {tensor.shape} for {key}"
        gate_b, proj_b = tensor.chunk(2, dim=0)
        assert gate_b.shape == (GATE_OUT, RANK) and proj_b.shape == (GATE_OUT, RANK)
        out[f"{base}.gate_layer.lora_B.weight"] = gate_b.contiguous()
        out[f"{base}.proj.lora_B.weight"] = proj_b.contiguous()
        n_split += 1
    else:
        out[key] = tensor

assert n_split == 32, f"expected 32 blocks with gate_up, found {n_split}"
assert not any(".gate_up." in k for k in out), "fused gate_up keys still present"
assert sum(1 for k in out if ".gate_layer.lora_B" in k) == 32
assert sum(1 for k in out if ".proj.lora_B" in k) == 32
assert len(out) == len(sd) + 2 * 32, f"tensor count changed unexpectedly: {len(sd)} -> {len(out)}"

nonzero = all(torch.any(t != 0) for k, t in out.items() if "lora_B" in k)
assert nonzero, "a lora_B tensor is all-zero"

save_file(out, "/tmp/" + DST_NAME, metadata={"format": "pt"})
api = HfApi()
api.upload_file(
    path_or_fileobj="/tmp/" + DST_NAME,
    path_in_repo="checkpoints/steps2000res768/" + DST_NAME,
    repo_id=DST_REPO,
    repo_type="model",
    commit_message="Split fused img_mlp.gate_up into diffusers gate_layer + proj",
)
print("DONE: uploaded", DST_NAME, "tensors:", len(out), "splits:", n_split)