code: def copy_flax_attn_params(hf_backbone, flax_attn_params):
      for k, v in flax_attn_params.items():
          if k.startswith("transformer"):
              torch_key = k.replace(
                  "transformer.resblocks", "text_model.encoder.layers"
              )
          else:
              torch_key = k.replace(
                  "visual.transformer.resblocks",
                  "vision_model.encoder.layers",
              )
  
          torch_key = torch_key.replace("attn", "self_attn")
          torch_key = torch_key.replace("key", "k_proj")
          torch_key = torch_key.replace("value", "v_proj")
          torch_key = torch_key.replace("query", "q_proj")
          torch_key = torch_key.replace("out", "out_proj")
  
          if "bias" in torch_key and v.ndim == 2:
              shape = v.shape[0] * v.shape[1]
              v = v.reshape(shape)
  
          if "weight" in torch_key and "out" in torch_key:
              shape = (v.shape[0] * v.shape[1], v.shape[2])
              v = v.reshape(shape).T
  
          if "weight" in torch_key and "out" not in torch_key:
              shape = (v.shape[0], v.shape[1] * v.shape[2])
              v = v.reshape(shape).T
  
          # Copy flax CLIP attn params to HF PyTorch params
          v = torch.from_numpy(v)
          hf_backbone.state_dict()[torch_key].copy_(v)
  
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 13 for calling a global function with example usage: copy_flax_attn_params(hf_backbone, flax_attn_params) without return types