o
    Zh                     @   s   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eZG d	d
 d
eZG dd de
Zd
dgZdS )zDeiT model configuration    OrderedDict)Mapping)version   )PretrainedConfig)
OnnxConfig)loggingc                       sF   e Zd ZdZdZ											
						d fdd	Z  ZS )
DeiTConfiga  
    This is the configuration class to store the configuration of a [`DeiTModel`]. It is used to instantiate an DeiT
    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 DeiT
    [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224)
    architecture.

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


    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.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether to add a bias to the queries, keys and values.
        encoder_stride (`int`, *optional*, defaults to 16):
            Factor to increase the spatial resolution by in the decoder head for masked image modeling.
        pooler_output_size (`int`, *optional*):
           Dimensionality of the pooler layer. If None, defaults to `hidden_size`.
        pooler_act (`str`, *optional*, defaults to `"tanh"`):
           The activation function to be used by the pooler. Keys of ACT2FN are supported for Flax and
           Pytorch, and elements of https://www.tensorflow.org/api_docs/python/tf/keras/activations are
           supported for Tensorflow.

    Example:

    ```python
    >>> from transformers import DeiTConfig, DeiTModel

    >>> # Initializing a DeiT deit-base-distilled-patch16-224 style configuration
    >>> configuration = DeiTConfig()

    >>> # Initializing a model (with random weights) from the deit-base-distilled-patch16-224 style configuration
    >>> model = DeiTModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Zdeit         gelu        {Gz?-q=      r   TNtanhc                    s~   t  jdi | || _|| _|| _|| _|| _|| _|| _|| _	|	| _
|
| _|| _|| _|| _|| _|r7|n|| _|| _d S )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qkv_biasencoder_stridepooler_output_size
pooler_act)selfr   r   r   r   r   r   r   r   r    r!   r"   r#   r$   r%   r&   r'   kwargs	__class__r   Z/var/www/auris/lib/python3.10/site-packages/transformers/models/deit/configuration_deit.pyr   `   s"   
zDeiTConfig.__init__)r   r   r   r   r   r   r   r   r   r   r   r   Tr   Nr   )__name__
__module____qualname____doc__Z
model_typer   __classcell__r   r   r*   r,   r
      s(    ?r
   c                   @   sJ   e Zd ZedZedeeee	ef f fddZ
edefddZdS )DeiTOnnxConfigz1.11returnc                 C   s   t ddddddfgS )NZpixel_valuesbatchr#   heightwidth)r         r   r   r(   r   r   r,   inputs   s   zDeiTOnnxConfig.inputsc                 C   s   dS )Ng-C6?r   r9   r   r   r,   atol_for_validation   s   z"DeiTOnnxConfig.atol_for_validationN)r-   r.   r/   r   parseZtorch_onnx_minimum_versionpropertyr   strintr:   floatr;   r   r   r   r,   r2      s    
 r2   N)r0   collectionsr   typingr   	packagingr   Zconfiguration_utilsr   Zonnxr   utilsr	   Z
get_loggerr-   loggerr
   r2   __all__r   r   r   r,   <module>   s   
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