
    sg                     Z    d Z ddlmZ ddlmZ  ej
                  e      Z G d de      Zy)zViViT model configuration   )PretrainedConfig)loggingc                   H     e Zd ZdZdZddg dddddd	d
dddddf fd	Z xZS )VivitConfiga  
    This is the configuration class to store the configuration of a [`VivitModel`]. It is used to instantiate a ViViT
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the ViViT
    [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        num_frames (`int`, *optional*, defaults to 32):
            The number of frames in each video.
        tubelet_size (`List[int]`, *optional*, defaults to `[2, 16, 16]`):
            The size (resolution) of each tubelet.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_fast"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"`, `"gelu_fast"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the layer normalization layers.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.

    Example:

    ```python
    >>> from transformers import VivitConfig, VivitModel

    >>> # Initializing a ViViT google/vivit-b-16x2-kinetics400 style configuration
    >>> configuration = VivitConfig()

    >>> # Initializing a model (with random weights) from the google/vivit-b-16x2-kinetics400 style configuration
    >>> model = VivitModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```vivit       )      r   r   i      i   	gelu_fastg        g{Gz?gư>Tc                     || _         || _        || _        || _        |	| _        |
| _        || _        || _        || _        || _	        || _
        || _        || _        || _        t        | <  di | y )N )hidden_sizenum_hidden_layersnum_attention_headsintermediate_size
hidden_acthidden_dropout_probattention_probs_dropout_probinitializer_rangelayer_norm_eps
image_size
num_framestubelet_sizenum_channelsqkv_biassuper__init__)selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   kwargs	__class__s                   `/var/www/html/venv/lib/python3.12/site-packages/transformers/models/vivit/configuration_vivit.pyr   zVivitConfig.__init__R   s    $ '!2#6 !2$#6 ,H)!2,$$(( "6"    )__name__
__module____qualname____doc__
model_typer   __classcell__)r"   s   @r#   r   r      sF    5n J  %("# "#r$   r   N)	r(   configuration_utilsr   utilsr   
get_loggerr%   loggerr   r   r$   r#   <module>r/      s3      3  
		H	%\#" \#r$   