code: def get_xclip_config(model_name, num_frames):
      text_config = XCLIPTextConfig()
  
      # derive patch size from model name
      start_idx = model_name.find("patch")
      patch_size = int(
          model_name[
              start_idx + len("patch") : start_idx + len("patch") + 2
          ]
      )
      vision_config = XCLIPVisionConfig(
          patch_size=patch_size, num_frames=num_frames
      )
  
      if "large" in model_name:
          text_config.hidden_size = 768
          text_config.intermediate_size = 3072
          text_config.num_attention_heads = 12
  
          vision_config.hidden_size = 1024
          vision_config.intermediate_size = 4096
          vision_config.num_attention_heads = 16
          vision_config.num_hidden_layers = 24
          vision_config.mit_hidden_size = 768
          vision_config.mit_intermediate_size = 3072
  
      if model_name == "xclip-large-patch14-16-frames":
          vision_config.image_size = 336
  
      config = XCLIPConfig.from_text_vision_configs(
          text_config, vision_config
      )
  
      if "large" in model_name:
          config.projection_dim = 768
  
      return config
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/x_clip/convert_x_clip_original_pytorch_to_hf.py
imports: import argparse
  import numpy
  import torch
  import gdown
  from huggingface_hub import hf_hub_download
  from transformers import CLIPTokenizer, CLIPTokenizerFast, VideoMAEFeatureExtractor, XCLIPConfig, XCLIPModel, XCLIPProcessor, XCLIPTextConfig, XCLIPVisionConfig
label_desc: Use image node_id 1 for calling a global function with example usage: get_xclip_config(model_name, num_frames) and returns: config