
    sg$                         d Z ddlmZ ddlmZ ddlmZ ddlmZ ddl	m
Z
 ddlmZ  ej                  e      Z G d	 d
e      Z G d de
      Zy)z"Data2VecVision model configuration    OrderedDict)Mapping)version   )PretrainedConfig)
OnnxConfig)loggingc                   f     e Zd ZdZdZddddddddd	d
dddddddddg dg dddddddf fd	Z xZS )Data2VecVisionConfigaJ  
    This is the configuration class to store the configuration of a [`Data2VecVisionModel`]. It is used to instantiate
    an Data2VecVision 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 Data2VecVision
    [facebook/data2vec-vision-base](https://huggingface.co/facebook/data2vec-vision-base) architecture.

    Args:
        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"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` 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-12):
            The epsilon used by the layer normalization layers.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 16):
            The size (resolution) of each patch.
        num_channels (`int`, *optional*, defaults to 3):
            The number of input channels.
        use_mask_token (`bool`, *optional*, defaults to `False`):
            Whether to use a mask token for masked image modeling.
        use_absolute_position_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to use BERT-style absolute position embeddings.
        use_relative_position_bias (`bool`, *optional*, defaults to `False`):
            Whether to use T5-style relative position embeddings in the self-attention layers.
        use_shared_relative_position_bias (`bool`, *optional*, defaults to `False`):
            Whether to use the same relative position embeddings across all self-attention layers of the Transformer.
        layer_scale_init_value (`float`, *optional*, defaults to 0.1):
            Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale.
        drop_path_rate (`float`, *optional*, defaults to 0.1):
            Stochastic depth rate per sample (when applied in the main path of residual layers).
        use_mean_pooling (`bool`, *optional*, defaults to `True`):
            Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
            CLS token, before applying the classification head.
        out_indices (`List[int]`, *optional*, defaults to `[3, 5, 7, 11]`):
            Indices of the feature maps to use for semantic segmentation.
        pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
            Pooling scales used in Pooling Pyramid Module applied on the last feature map.
        use_auxiliary_head (`bool`, *optional*, defaults to `True`):
            Whether to use an auxiliary head during training.
        auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
            Weight of the cross-entropy loss of the auxiliary head.
        auxiliary_channels (`int`, *optional*, defaults to 256):
            Number of channels to use in the auxiliary head.
        auxiliary_num_convs (`int`, *optional*, defaults to 1):
            Number of convolutional layers to use in the auxiliary head.
        auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
            Whether to concatenate the output of the auxiliary head with the input before the classification layer.
        semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
            The index that is ignored by the loss function of the semantic segmentation model.

    Example:

    ```python
    >>> from transformers import Data2VecVisionConfig, Data2VecVisionModel

    >>> # Initializing a Data2VecVision data2vec_vision-base-patch16-224-in22k style configuration
    >>> configuration = Data2VecVisionConfig()

    >>> # Initializing a model (with random weights) from the data2vec_vision-base-patch16-224-in22k style configuration
    >>> model = Data2VecVisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```zdata2vec-visioni      i   gelug        g{Gz?g-q=      r   Fg?T)r            )      r      g?   r      c                    t        |   di | || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
| _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        y )N )super__init__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
patch_sizenum_channelsuse_mask_token use_absolute_position_embeddingsuse_relative_position_bias!use_shared_relative_position_biaslayer_scale_init_valuedrop_path_rateuse_mean_poolingout_indicespool_scalesuse_auxiliary_headauxiliary_loss_weightauxiliary_channelsauxiliary_num_convsauxiliary_concat_inputsemantic_loss_ignore_index)selfr   r   r   r    r!   r"   r#   r$   r%   r&   r'   r(   r)   r*   r+   r,   r-   r.   r/   r0   r1   r2   r3   r4   r5   r6   r7   kwargs	__class__s                                m/var/www/html/venv/lib/python3.12/site-packages/transformers/models/data2vec/configuration_data2vec_vision.pyr   zData2VecVisionConfig.__init__p   s    > 	"6"&!2#6 !2$#6 ,H)!2,$$(,0P-*D'1R.&<#, 0&&"4%:""4#6 &<#*D'    )__name__
__module____qualname____doc__
model_typer   __classcell__)r:   s   @r;   r   r      sp    M^ #J %().#(*/"! !$#&9>E >Er<   r   c                   p    e Zd Z ej                  d      Zedeeee	ef   f   fd       Z
edefd       Zy)Data2VecVisionOnnxConfigz1.11returnc                 (    t        ddddddfg      S )Npixel_valuesbatchr(   heightwidth)r   r   r   r   r   r8   s    r;   inputszData2VecVisionOnnxConfig.inputs   s&    WHQX!YZ
 	
r<   c                      y)Ng-C6?r   rK   s    r;   atol_for_validationz,Data2VecVisionOnnxConfig.atol_for_validation   s    r<   N)r=   r>   r?   r   parsetorch_onnx_minimum_versionpropertyr   strintrL   floatrN   r   r<   r;   rD   rD      sZ    !.v!6
WS#X%6 67 
 
 U  r<   rD   N)r@   collectionsr   typingr   	packagingr   configuration_utilsr   onnxr	   utilsr
   
get_loggerr=   loggerr   rD   r   r<   r;   <module>r]      sN    ) #   3   
		H	%PE+ PEhz r<   