src/transformers/models/x_clip/convert_x_clip_original_pytorch_to_hf.py/convert_xclip_checkpoint
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")