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)zVideoMAE model configurationé    )Ústricté   )ÚPreTrainedConfig)Úauto_docstringzMCG-NJU/videomae-base)Ú
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    num_frames (`int`, *optional*, defaults to 16):
        The number of frames in each video.
    tubelet_size (`int`, *optional*, defaults to 2):
        The number of tubelets.
    use_mean_pooling (`bool`, *optional*, defaults to `True`):
        Whether to mean pool the final hidden states instead of using the final hidden state of the [CLS] token.
    decoder_num_attention_heads (`int`, *optional*, defaults to 6):
        Number of attention heads for each attention layer in the decoder.
    decoder_hidden_size (`int`, *optional*, defaults to 384):
        Dimensionality of the decoder.
    decoder_num_hidden_layers (`int`, *optional*, defaults to 4):
        Number of hidden layers in the decoder.
    decoder_intermediate_size (`int`, *optional*, defaults to 1536):
        Dimensionality of the "intermediate" (i.e., feed-forward) layer in the decoder.
    norm_pix_loss (`bool`, *optional*, defaults to `True`):
        Whether to normalize the target patch pixels.

    Example:

    ```python
    >>> from transformers import VideoMAEConfig, VideoMAEModel

    >>> # Initializing a VideoMAE videomae-base style configuration
    >>> configuration = VideoMAEConfig()

    >>> # Randomly initializing a model from the configuration
    >>> model = VideoMAEModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Úvideomaeéà   Ú
image_sizeé   Ú
patch_sizer   Únum_channelsÚ
num_framesé   Útubelet_sizei   Úhidden_sizeé   Únum_hidden_layersÚnum_attention_headsi   Úintermediate_sizeÚgeluÚ
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__module__Ú__qualname__Ú__doc__Ú
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