
    sgH                         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RemBERT model configuration    )OrderedDict)Mapping   )PretrainedConfig)
OnnxConfig)loggingc                   N     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )RemBertConfiga  
    This is the configuration class to store the configuration of a [`RemBertModel`]. It is used to instantiate an
    RemBERT 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 RemBERT
    [google/rembert](https://huggingface.co/google/rembert) 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 250300):
            Vocabulary size of the RemBERT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`RemBertModel`] or [`TFRemBertModel`]. Vocabulary size of the model.
            Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of
            [`RemBertModel`].
        hidden_size (`int`, *optional*, defaults to 1152):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 18):
            Number of attention heads for each attention layer in the Transformer encoder.
        input_embedding_size (`int`, *optional*, defaults to 256):
            Dimensionality of the input embeddings.
        output_embedding_size (`int`, *optional*, defaults to 1664):
            Dimensionality of the output embeddings.
        intermediate_size (`int`, *optional*, defaults to 4608):
            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):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0):
            The dropout ratio for the attention probabilities.
        classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the classifier layer when fine-tuning.
        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 [`RemBertModel`] or [`TFRemBertModel`].
        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.
        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`.

    Example:

    ```python
    >>> from transformers import RemBertModel, RemBertConfig

    >>> # Initializing a RemBERT rembert style configuration
    >>> configuration = RemBertConfig()

    >>> # Initializing a model from the rembert style configuration
    >>> model = RemBertModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```rembertc                    t        |   d|||d| || _        || _        || _        || _        || _        || _        || _        || _	        || _
        |	| _        |
| _        || _        || _        || _        || _        || _        d| _        y )N)pad_token_idbos_token_ideos_token_idF )super__init__
vocab_sizeinput_embedding_sizeoutput_embedding_sizemax_position_embeddingshidden_sizenum_hidden_layersnum_attention_headsintermediate_size
hidden_acthidden_dropout_probattention_probs_dropout_probclassifier_dropout_probinitializer_rangetype_vocab_sizelayer_norm_eps	use_cachetie_word_embeddings)selfr   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/rembert/configuration_rembert.pyr   zRemBertConfig.__init__b   s    . 	sl\hslrs$$8!%:"'>$&!2#6 !2$#6 ,H)'>$!2.,"#(     )i i            i  i   gelu        r-   g?i      g{Gz?g-q=Tr   i8  i9  )__name__
__module____qualname____doc__
model_typer   __classcell__)r&   s   @r'   r
   r
      sV    AF J  "%( # #))) ))r(   r
   c                   L    e Zd Zedeeeeef   f   fd       Zedefd       Z	y)RemBertOnnxConfig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   r<   	input_idsattention_masktoken_type_ids)taskr   )r$   dynamic_axiss     r'   inputszRemBertOnnxConfig.inputs   sO    99))&8
CL&:6Ll+!<0!<0
 	
r(   c                      y)Ng-C6?r   )r$   s    r'   atol_for_validationz%RemBertOnnxConfig.atol_for_validation   s    r(   N)
r/   r0   r1   propertyr   strintrB   floatrD   r   r(   r'   r6   r6      sI    
WS#X%6 67 
 
 U  r(   r6   N)r2   collectionsr   typingr   configuration_utilsr   onnxr   utilsr   
get_loggerr/   loggerr
   r6   r   r(   r'   <module>rP      sI    " #  3   
		H	%o)$ o)d
 r(   