# This file is modified from TRELLIS:
# https://github.com/microsoft/TRELLIS
# Original license: MIT
# Copyright (c) the TRELLIS authors
# Modifications Copyright (c) 2026 Ze-Xin Yin, Robot labs of Horizon Robotics, and D-Robotics.

import importlib

__attributes = {
    'SparseStructureEncoder': 'sparse_structure_vae',
    'SparseStructureDecoder': 'sparse_structure_vae',
    
    'SparseStructureFlowModel': 'sparse_structure_flow',
    
    'SLatEncoder': 'structured_latent_vae',
    'SLatGaussianDecoder': 'structured_latent_vae',
    'SLatRadianceFieldDecoder': 'structured_latent_vae',
    'SLatMeshDecoder': 'structured_latent_vae',
    'ElasticSLatEncoder': 'structured_latent_vae',
    'ElasticSLatGaussianDecoder': 'structured_latent_vae',
    'ElasticSLatRadianceFieldDecoder': 'structured_latent_vae',
    'ElasticSLatMeshDecoder': 'structured_latent_vae',

    'SLatFlowModel': 'structured_latent_flow',
    'ElasticSLatFlowModel': 'structured_latent_flow',
    
    'SceneSLatFlowModel': 'scene_structured_latent_flow',
    'ElasticSceneSLatFlowModel': 'scene_structured_latent_flow',
    'SceneSparseStructureFlowModule': 'scene_sparse_structure_flow',
}

__submodules = []

__all__ = list(__attributes.keys()) + __submodules

def __getattr__(name):
    if name not in globals():
        if name in __attributes:
            module_name = __attributes[name]
            module = importlib.import_module(f".{module_name}", __name__)
            globals()[name] = getattr(module, name)
        elif name in __submodules:
            module = importlib.import_module(f".{name}", __name__)
            globals()[name] = module
        else:
            raise AttributeError(f"module {__name__} has no attribute {name}")
    return globals()[name]


def from_pretrained(path: str, **kwargs):
    """
    Load a model from a pretrained checkpoint.

    Args:
        path: The path to the checkpoint. Can be either local path or a Hugging Face model name.
              NOTE: config file and model file should take the name f'{path}.json' and f'{path}.safetensors' respectively.
        **kwargs: Additional arguments for the model constructor.
    """
    import os
    import json
    import torch
    from safetensors.torch import load_file
    from ..utils.dist_utils import read_file_dist
    is_local = os.path.exists(f"{path}.json") and (os.path.exists(f"{path}.safetensors") or os.path.exists(f"{path}.pt"))
    
    if is_local:
        config_file = f"{path}.json"
        model_file = f"{path}.safetensors" if os.path.exists(f"{path}.safetensors") else f"{path}.pt"
    else:
        from huggingface_hub import hf_hub_download
        path_parts = path.split('/')
        repo_id = f'{path_parts[0]}/{path_parts[1]}'
        model_name = '/'.join(path_parts[2:])
        config_file = hf_hub_download(repo_id, f"{model_name}.json")
        model_file = hf_hub_download(repo_id, f"{model_name}.safetensors")

    with open(config_file, 'r') as f:
        config = json.load(f)
    model = __getattr__(config['name'])(**config['args'], **kwargs)
    if model_file.endswith(".safetensors"):
        model.load_state_dict(load_file(model_file))
    else:
        model_ckpt = torch.load(read_file_dist(model_file), map_location='cpu', weights_only=True)
        model.load_state_dict(model_ckpt)
        if model.dtype == torch.float16:
            model.convert_to_fp16()

    return model


# For Pylance
if __name__ == '__main__':
    from .sparse_structure_vae import (
        SparseStructureEncoder, 
        SparseStructureDecoder,
    )
    
    from .sparse_structure_flow import SparseStructureFlowModel
    
    from .structured_latent_vae import (
        SLatEncoder,
        SLatGaussianDecoder,
        SLatRadianceFieldDecoder,
        SLatMeshDecoder,
        ElasticSLatEncoder,
        ElasticSLatGaussianDecoder,
        ElasticSLatRadianceFieldDecoder,
        ElasticSLatMeshDecoder,
    )
    
    from .structured_latent_flow import (
        SLatFlowModel,
        ElasticSLatFlowModel,
    )

    from .scene_sparse_structure_flow import (
        SceneSparseStructureFlowModule
    )

    from .scene_structured_latent_flow import (
        SceneSLatFlowModel,
        ElasticSceneSLatFlowModel
    )