
    sgy                     Z    d Z ddlmZ ddlmZ  ej
                  e      Z G d de      Zy)z*Donut Swin Transformer model configuration   )PretrainedConfig)loggingc                   Z     e Zd ZdZdZdddZdddd	g d
g dddddddddddf fd	Z xZS )DonutSwinConfiga  
    This is the configuration class to store the configuration of a [`DonutSwinModel`]. It is used to instantiate a
    Donut 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 Donut
    [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) 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.
        patch_size (`int`, *optional*, defaults to 4):
            The size (resolution) of each patch.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        embed_dim (`int`, *optional*, defaults to 96):
            Dimensionality of patch embedding.
        depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`):
            Depth of each layer in the Transformer encoder.
        num_heads (`list(int)`, *optional*, defaults to `[3, 6, 12, 24]`):
            Number of attention heads in each layer of the Transformer encoder.
        window_size (`int`, *optional*, defaults to 7):
            Size of windows.
        mlp_ratio (`float`, *optional*, defaults to 4.0):
            Ratio of MLP hidden dimensionality to embedding dimensionality.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether or not a learnable bias should be added to the queries, keys and values.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings and encoder.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        drop_path_rate (`float`, *optional*, defaults to 0.1):
            Stochastic depth rate.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
            `"selu"` and `"gelu_new"` are supported.
        use_absolute_embeddings (`bool`, *optional*, defaults to `False`):
            Whether or not to add absolute position embeddings to the patch embeddings.
        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-05):
            The epsilon used by the layer normalization layers.

    Example:

    ```python
    >>> from transformers import DonutSwinConfig, DonutSwinModel

    >>> # Initializing a Donut naver-clova-ix/donut-base style configuration
    >>> configuration = DonutSwinConfig()

    >>> # Randomly initializing a model from the naver-clova-ix/donut-base style configuration
    >>> model = DonutSwinModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```z
donut-swin	num_heads
num_layers)num_attention_headsnum_hidden_layers      r   `   )   r      r   )r   r            g      @Tg        g?geluFg{Gz?gh㈵>c                 h   t        |   di | || _        || _        || _        || _        || _        t        |      | _        || _	        || _
        || _        |	| _        |
| _        || _        || _        || _        || _        || _        || _        t)        |dt        |      dz
  z  z        | _        y )Nr       )super__init__
image_size
patch_sizenum_channels	embed_dimdepthslenr   r   window_size	mlp_ratioqkv_biashidden_dropout_probattention_probs_dropout_probdrop_path_rate
hidden_actuse_absolute_embeddingslayer_norm_epsinitializer_rangeinthidden_size)selfr   r   r   r   r   r   r   r    r!   r"   r#   r$   r%   r&   r(   r'   kwargs	__class__s                     e/var/www/html/venv/lib/python3.12/site-packages/transformers/models/donut/configuration_donut_swin.pyr   zDonutSwinConfig.__init__[   s    ( 	"6"$$("f+"&" #6 ,H),$'>$,!2 y1Vq+AAB    )__name__
__module____qualname____doc__
model_typeattribute_mapr   __classcell__)r-   s   @r.   r   r      s]    9v J  +)M  %( %#)C )Cr/   r   N)	r3   configuration_utilsr   utilsr   
get_loggerr0   loggerr   r   r/   r.   <module>r;      s5    1 3  
		H	%lC& lCr/   