from transformers import PreTrainedModel
from typing import Optional
import torch

class MinerUModel(PreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self._setup_models()
        
    def _setup_models(self):
        from model_loader import MinerUModelLoader
        self.models = MinerUModelLoader.load_models("./")
        
    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        config = kwargs.pop("config", None)
        model = cls(config)
        model._setup_models()
        return model
        
    def forward(self, input_data):
        # 实现前向传播逻辑
        return self.models["layout"](input_data)

def load_model():
    model = MinerUModel.from_pretrained("./")
    return model

def inference(pdf_content):
    model = load_model()
    return model(pdf_content)