
    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
  e
j                  e      Z G d d	e      Z G d
 de      Zy)zELECTRA model configuration    )OrderedDict)Mapping   )PretrainedConfig)
OnnxConfig)loggingc                   R     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )ElectraConfiga  
    This is the configuration class to store the configuration of a [`ElectraModel`] or a [`TFElectraModel`]. It is
    used to instantiate a ELECTRA 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 ELECTRA
    [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) 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 ELECTRA model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`ElectraModel`] or [`TFElectraModel`].
        embedding_size (`int`, *optional*, defaults to 128):
            Dimensionality of the encoder layers and the pooler layer.
        hidden_size (`int`, *optional*, defaults to 256):
            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 4):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the "intermediate" (i.e., 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 [`ElectraModel`] or [`TFElectraModel`].
        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.
        summary_type (`str`, *optional*, defaults to `"first"`):
            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.

            Has to be one of the following options:

                - `"last"`: Take the last token hidden state (like XLNet).
                - `"first"`: Take the first token hidden state (like BERT).
                - `"mean"`: Take the mean of all tokens hidden states.
                - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
                - `"attn"`: Not implemented now, use multi-head attention.
        summary_use_proj (`bool`, *optional*, defaults to `True`):
            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.

            Whether or not to add a projection after the vector extraction.
        summary_activation (`str`, *optional*):
            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.

            Pass `"gelu"` for a gelu activation to the output, any other value will result in no activation.
        summary_last_dropout (`float`, *optional*, defaults to 0.0):
            Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.

            The dropout ratio to be used after the projection and activation.
        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).
        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 ElectraConfig, ElectraModel

    >>> # Initializing a ELECTRA electra-base-uncased style configuration
    >>> configuration = ElectraConfig()

    >>> # Initializing a model (with random weights) from the electra-base-uncased style configuration
    >>> model = ElectraModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```electrac                 @   t        |   dd|i| || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
| _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        y )Npad_token_id )super__init__
vocab_sizeembedding_sizehidden_sizenum_hidden_layersnum_attention_headsintermediate_size
hidden_acthidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_epssummary_typesummary_use_projsummary_activationsummary_last_dropoutposition_embedding_type	use_cacheclassifier_dropout)selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r    r!   r   r"   r#   r$   kwargs	__class__s                          d/var/www/html/venv/lib/python3.12/site-packages/transformers/models/electra/configuration_electra.pyr   zElectraConfig.__init__y   s    2 	=l=f=$,&!2#6 !2$#6 ,H)'>$.!2,( 0"4$8!'>$""4    )i:w              i   gelu皙?r/   i      g{Gz?g-q=firstTr.   r/   r   absoluteTN)__name__
__module____qualname____doc__
model_typer   __classcell__)r'   s   @r(   r
   r
      s\    Wr J %( #!  *-/5 /5r)   r
   c                   6    e Zd Zedeeeeef   f   fd       Zy)ElectraOnnxConfig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      r0   )r   r@   	input_idsattention_masktoken_type_ids)taskr   )r%   dynamic_axiss     r(   inputszElectraOnnxConfig.inputs   sO    99))&8
CL&:6Ll+!<0!<0
 	
r)   N)r3   r4   r5   propertyr   strintrF   r   r)   r(   r:   r:      s.    
WS#X%6 67 
 
r)   r:   N)r6   collectionsr   typingr   configuration_utilsr   onnxr   utilsr   
get_loggerr3   loggerr
   r:   r   r)   r(   <module>rQ      sI     " #  3   
		H	%K5$ K5\

 
r)   