code: def convert_owlvit_checkpoint(
      pt_backbone,
      flax_params,
      attn_params,
      pytorch_dump_folder_path,
      config_path=None,
  ):
      """
      Copy/paste/tweak model's weights to transformers design.
      """
      repo = Repository(
          pytorch_dump_folder_path,
          clone_from=f"google/{pytorch_dump_folder_path}",
      )
      repo.git_pull()
  
      if config_path is not None:
          config = OwlViTConfig.from_pretrained(config_path)
      else:
          config = OwlViTConfig()
  
      hf_backbone = OwlViTModel(config).eval()
      hf_model = OwlViTForObjectDetection(config).eval()
  
      copy_text_model_and_projection(hf_backbone, pt_backbone)
      copy_vision_model_and_projection(hf_backbone, pt_backbone)
      hf_backbone.logit_scale = pt_backbone.logit_scale
      copy_flax_attn_params(hf_backbone, attn_params)
  
      hf_model.owlvit = hf_backbone
      copy_class_merge_token(hf_model, flax_params)
      copy_class_box_heads(hf_model, flax_params)
  
      # Save HF model
      hf_model.save_pretrained(repo.local_dir)
  
      # Initialize image processor
      image_processor = OwlViTImageProcessor(
          size=config.vision_config.image_size,
          crop_size=config.vision_config.image_size,
      )
      # Initialize tokenizer
      tokenizer = CLIPTokenizer.from_pretrained(
          "openai/clip-vit-base-patch32",
          pad_token="!",
          model_max_length=16,
      )
  
      # Initialize processor
      processor = OwlViTProcessor(
          image_processor=image_processor, tokenizer=tokenizer
      )
      image_processor.save_pretrained(repo.local_dir)
      processor.save_pretrained(repo.local_dir)
  
      repo.git_add()
      repo.git_commit("Upload model and processor")
      repo.git_push()
  
imports: import argparse
  import collections
  import jax
  import jax.numpy
  import torch
  import torch.nn
  from clip.model import CLIP
  from flax.training import checkpoints
  from huggingface_hub import Repository
  from transformers import CLIPTokenizer, OwlViTConfig, OwlViTForObjectDetection, OwlViTImageProcessor, OwlViTModel, OwlViTProcessor
label_desc: Use image node_id 16 for calling a global function with example usage: convert_owlvit_checkpoint(pt_backbone, flax_params, attn_params, pytorch_dump_folder_path, config_path) without return types