code: def convert_xclip_checkpoint(
      model_name, pytorch_dump_folder_path=None, push_to_hub=False
  ):
      model_to_url = {
          # fully supervised kinetics-400 checkpoints
          "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
          "xclip-base-patch32-16-frames": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
          ),
          "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
          "xclip-base-patch16-16-frames": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
          ),
          "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
          "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
          # fully supervised kinetics-600 checkpoints
          "xclip-base-patch16-kinetics-600": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
          ),
          "xclip-base-patch16-kinetics-600-16-frames": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
          ),
          "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
          # few shot
          "xclip-base-patch16-hmdb-2-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
          ),
          "xclip-base-patch16-hmdb-4-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
          ),
          "xclip-base-patch16-hmdb-8-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
          ),
          "xclip-base-patch16-hmdb-16-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
          ),
          "xclip-base-patch16-ucf-2-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
          ),
          "xclip-base-patch16-ucf-4-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
          ),
          "xclip-base-patch16-ucf-8-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
          ),
          "xclip-base-patch16-ucf-16-shot": (
              "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
          ),
          # zero shot
          "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
      }
  
      checkpoint_url = model_to_url[model_name]
      num_frames = 8
      if "16-frames" in model_name:
          num_frames = 16
      elif "shot" in model_name:
          num_frames = 32
  
      config = get_xclip_config(model_name, num_frames)
      model = XCLIPModel(config)
      model.eval()
  
      if "drive" in checkpoint_url:
          output = "pytorch_model.bin"
          gdown.cached_download(checkpoint_url, output, quiet=False)
          state_dict = torch.load(output, map_location="cpu")["model"]
      else:
          state_dict = torch.hub.load_state_dict_from_url(
              checkpoint_url
          )["model"]
  
      state_dict = convert_state_dict(state_dict, config)
  
      model = XCLIPModel(config)
      missing_keys, unexpected_keys = model.load_state_dict(
          state_dict, strict=False
      )
      assert missing_keys == [
          "text_model.embeddings.position_ids",
          "vision_model.embeddings.position_ids",
      ]
      model.eval()
  
      size = (
          336 if model_name == "xclip-large-patch14-16-frames" else 224
      )
      feature_extractor = VideoMAEFeatureExtractor(size=size)
      slow_tokenizer = CLIPTokenizer.from_pretrained(
          "openai/clip-vit-base-patch32"
      )
      fast_tokenizer = CLIPTokenizerFast.from_pretrained(
          "openai/clip-vit-base-patch32"
      )
      processor = XCLIPProcessor(
          feature_extractor=feature_extractor, tokenizer=fast_tokenizer
      )
  
      video = prepare_video(num_frames)
      inputs = processor(
          text=["playing sports", "eating spaghetti", "go shopping"],
          videos=video,
          return_tensors="pt",
          padding=True,
      )
  
      print("Shape of pixel values:", inputs.pixel_values.shape)
  
      with torch.no_grad():
          outputs = model(**inputs)
  
      # Verify outputs
      logits_per_video = outputs.logits_per_video
      probs = logits_per_video.softmax(dim=1)
      print("Probs:", probs)
      # kinetics-400
      if model_name == "xclip-base-patch32":
          expected_probs = torch.tensor([[0.0019, 0.9951, 0.0030]])
      elif model_name == "xclip-base-patch32-16-frames":
          expected_probs = torch.tensor(
              [[7.0999e-04, 9.9883e-01, 4.5580e-04]]
          )
      elif model_name == "xclip-base-patch16":
          expected_probs = torch.tensor([[0.0083, 0.9681, 0.0236]])
      elif model_name == "xclip-base-patch16-16-frames":
          expected_probs = torch.tensor(
              [[7.6937e-04, 9.9728e-01, 1.9473e-03]]
          )
      elif model_name == "xclip-large-patch14":
          expected_probs = torch.tensor([[0.0062, 0.9864, 0.0075]])
      elif model_name == "xclip-large-patch14-16-frames":
          expected_probs = torch.tensor(
              [[3.3877e-04, 9.9937e-01, 2.8888e-04]]
          )
      # kinetics-600
      elif model_name == "xclip-base-patch16-kinetics-600":
          expected_probs = torch.tensor([[0.0555, 0.8914, 0.0531]])
      elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          expected_probs = torch.tensor(
              [[3.8554e-04, 9.9929e-01, 3.2754e-04]]
          )
      elif model_name == "xclip-large-patch14-kinetics-600":
          expected_probs = torch.tensor([[0.0036, 0.9920, 0.0045]])
      # few shot
      elif model_name == "xclip-base-patch16-hmdb-2-shot":
          expected_probs = torch.tensor(
              [[7.1890e-06, 9.9994e-01, 5.6559e-05]]
          )
      elif model_name == "xclip-base-patch16-hmdb-4-shot":
          expected_probs = torch.tensor(
              [[1.0320e-05, 9.9993e-01, 6.2435e-05]]
          )
      elif model_name == "xclip-base-patch16-hmdb-8-shot":
          expected_probs = torch.tensor(
              [[4.1377e-06, 9.9990e-01, 9.8386e-05]]
          )
      elif model_name == "xclip-base-patch16-hmdb-16-shot":
          expected_probs = torch.tensor(
              [[4.1347e-05, 9.9962e-01, 3.3411e-04]]
          )
      elif model_name == "xclip-base-patch16-ucf-2-shot":
          expected_probs = torch.tensor(
              [[8.5857e-05, 9.9928e-01, 6.3291e-04]]
          )
      elif model_name == "xclip-base-patch16-ucf-4-shot":
          expected_probs = torch.tensor(
              [[8.5857e-05, 9.9928e-01, 6.3291e-04]]
          )
      elif model_name == "xclip-base-patch16-ucf-8-shot":
          expected_probs = torch.tensor([[0.0027, 0.9904, 0.0070]])
      elif model_name == "xclip-base-patch16-ucf-16-shot":
          expected_probs = torch.tensor(
              [[9.8219e-04, 9.9593e-01, 3.0863e-03]]
          )
      # zero shot
      elif model_name == "xclip-base-patch16-zero-shot":
          expected_probs = torch.tensor(
              [[3.5082e-04, 9.9785e-01, 1.7966e-03]]
          )
      else:
          raise ValueError(f"Model name {model_name} not supported")
      assert torch.allclose(probs, expected_probs, atol=1e-3)
      print("Looks ok!")
  
      if pytorch_dump_folder_path is not None:
          print(
              f"Saving model {model_name} to {pytorch_dump_folder_path}"
          )
          model.save_pretrained(pytorch_dump_folder_path)
  
      if push_to_hub:
          print(
              "Pushing model, processor and slow tokenizer files to the hub..."
          )
          model.push_to_hub(model_name, organization="nielsr")
          processor.push_to_hub(model_name, organization="nielsr")
          slow_tokenizer.push_to_hub(model_name, organization="nielsr")
  
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 5 for calling a global function with example usage: convert_xclip_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub) without return types