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 de      Zy)zBERT model configuration    )OrderedDict)Mapping   )PretrainedConfig)
OnnxConfig)loggingc                   H     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )
BertConfiga  
    This is the configuration class to store the configuration of a [`BertModel`] or a [`TFBertModel`]. It is used to
    instantiate a BERT 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 BERT
    [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) architecture.

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


    Args:
        vocab_size (`int`, *optional*, defaults to 30522):
            Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`].
        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" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`BertModel`] or [`TFBertModel`].
        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.
        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        is_decoder (`bool`, *optional*, defaults to `False`):
            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        classifier_dropout (`float`, *optional*):
            The dropout ratio for the classification head.

    Examples:

    ```python
    >>> from transformers import BertConfig, BertModel

    >>> # Initializing a BERT google-bert/bert-base-uncased style configuration
    >>> configuration = BertConfig()

    >>> # Initializing a model (with random weights) from the google-bert/bert-base-uncased style configuration
    >>> model = BertModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```bertc                     t        |   dd|i| || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
| _        || _        || _        || _        || _        || _        y )Npad_token_id )super__init__
vocab_sizehidden_sizenum_hidden_layersnum_attention_heads
hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_epsposition_embedding_type	use_cacheclassifier_dropout)selfr   r   r   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/bert/configuration_bert.pyr   zBertConfig.__init__c   s    ( 	=l=f=$&!2#6 $!2#6 ,H)'>$.!2,'>$""4    )i:w  i      r%   i   gelu皙?r'   i      g{Gz?g-q=r   absoluteTN)__name__
__module____qualname____doc__
model_typer   __classcell__)r"   s   @r#   r
   r
      sM    AF J %( # *#$5 $5r$   r
   c                   6    e Zd Zedeeeeef   f   fd       Zy)BertOnnxConfigreturnc                 `    | j                   dk(  rdddd}nddd}t        d|fd|fd	|fg      S )
Nzmultiple-choicebatchchoicesequence)r      r(   )r   r7   	input_idsattention_masktoken_type_ids)taskr   )r    dynamic_axiss     r#   inputszBertOnnxConfig.inputs   sO    99))&8
CL&:6Ll+!<0!<0
 	
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WS#X%6 67 
 
r$   r1   N)r-   collectionsr   typingr   configuration_utilsr   onnxr   utilsr   
get_loggerr*   loggerr
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