"""
Block-level AoT-Inductor loading for ZeroGPU.

`HunyuanVideo15Transformer3DModel._repeated_blocks` lists
["HunyuanVideo15TransformerBlock", "HunyuanVideo15PatchEmbed", "HunyuanVideo15TokenRefiner"],
so this generic helper works unchanged from the Wan reference space -- but it needs a Hub repo
containing a `package.pt2` per block name, compiled for your exact GPU/CUDA/torch combination.
No public repo of prebuilt packages exists for HunyuanVideo 1.5 yet, so set AOTI_REPO only after
you build and upload your own. Without it the model still runs, just without AoTI.
"""

from typing import cast

import torch
from huggingface_hub import hf_hub_download
from spaces.zero.torch.aoti import ZeroGPUCompiledModel
from spaces.zero.torch.aoti import ZeroGPUWeights
from torch._functorch._aot_autograd.subclass_parametrization import unwrap_tensor_subclass_parameters


def _shallow_clone_module(module: torch.nn.Module) -> torch.nn.Module:
    clone = object.__new__(module.__class__)
    clone.__dict__ = module.__dict__.copy()
    clone._parameters = module._parameters.copy()
    clone._buffers = module._buffers.copy()
    clone._modules = {k: _shallow_clone_module(v) for k, v in module._modules.items() if v is not None}
    return clone


def aoti_blocks_load(module: torch.nn.Module, repo_id: str, variant: str | None = None):
    repeated_blocks = cast(list[str], module._repeated_blocks)
    aoti_files = {name: hf_hub_download(
        repo_id=repo_id,
        filename='package.pt2',
        subfolder=name if variant is None else f'{name}.{variant}',
    ) for name in repeated_blocks}
    for block_name, aoti_file in aoti_files.items():
        for block in module.modules():
            if block.__class__.__name__ == block_name:
                block_ = _shallow_clone_module(block)
                unwrap_tensor_subclass_parameters(block_)
                weights = ZeroGPUWeights(block_.state_dict())
                block.forward = ZeroGPUCompiledModel(aoti_file, weights)