code: def get_hf_config_and_model(
      checkpoint_name: str,
      pretrained: Optional[bool] = True,
      low_cpu_mem_usage: Optional[bool] = False,
  ):
      """
      Get a Huggingface config and model based on a checkpoint name.
  
      Parameters
      ----------
      checkpoint_name
          A model checkpoint name or a local path that saves a custom checkpoint.
      pretrained
           Whether using the pretrained weights. If pretrained=True, download the pretrained model.
      low_cpu_mem_usage
          Whether to turn on the optimization of reducing the peak CPU memory usage when loading the pretrained model.
  
      Returns
      -------
      A Huggingface config and model.
      """
      config = AutoConfig.from_pretrained(checkpoint_name)
  
      if pretrained:
          model = AutoModel.from_pretrained(
              checkpoint_name, low_cpu_mem_usage=low_cpu_mem_usage
          )
      else:
          model = AutoModel.from_config(config)
  
      return config, model
  
imports: import logging
  import re
  import warnings
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch._dynamo
  import torch.nn.functional
  from torch import nn
  from torch.nn.modules.loss import _Loss
  from transformers import AutoConfig, AutoModel, AutoTokenizer, BertTokenizer, CLIPTokenizer, ElectraTokenizer
  from transformers.models.mask2former.modeling_mask2former import Mask2FormerLoss
  from ..constants import AUTOMM, CLASS_LOGITS, COLUMN_FEATURES, FEATURES, LOGITS, MASKS, OCR, PEFT_ADDITIVE_STRATEGIES, REGRESSION, SEMANTIC_MASK, SEMANTIC_SEGMENTATION
  from .adaptation_layers import IA3Linear, IA3LoRALinear, LoRALinear
label_desc: Use image node_id 12 for calling a global function with example usage: get_hf_config_and_model(checkpoint_name, pretrained, low_cpu_mem_usage) and returns: config,  model