
    sg                     l    d Z ddlmZ ddlmZ ddlmZ ddlmZ  G d de      Z	 G d	 d
e      Z
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gZy)zALBERT model configuration    )OrderedDict)Mapping   )PretrainedConfig)
OnnxConfigc                   P     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )AlbertConfiga  
    This is the configuration class to store the configuration of a [`AlbertModel`] or a [`TFAlbertModel`]. It is used
    to instantiate an ALBERT 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 ALBERT
    [albert/albert-xxlarge-v2](https://huggingface.co/albert/albert-xxlarge-v2) 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 30000):
            Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`AlbertModel`] or [`TFAlbertModel`].
        embedding_size (`int`, *optional*, defaults to 128):
            Dimensionality of vocabulary embeddings.
        hidden_size (`int`, *optional*, defaults to 4096):
            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_hidden_groups (`int`, *optional*, defaults to 1):
            Number of groups for the hidden layers, parameters in the same group are shared.
        num_attention_heads (`int`, *optional*, defaults to 64):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 16384):
            The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        inner_group_num (`int`, *optional*, defaults to 1):
            The number of inner repetition of attention and ffn.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu_new"`):
            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):
            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.
        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
            (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 [`AlbertModel`] or [`TFAlbertModel`].
        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.
        classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for attached classifiers.
        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).
        pad_token_id (`int`, *optional*, defaults to 0):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 2):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 3):
            End of stream token id.

    Examples:

    ```python
    >>> from transformers import AlbertConfig, AlbertModel

    >>> # Initializing an ALBERT-xxlarge style configuration
    >>> albert_xxlarge_configuration = AlbertConfig()

    >>> # Initializing an ALBERT-base style configuration
    >>> albert_base_configuration = AlbertConfig(
    ...     hidden_size=768,
    ...     num_attention_heads=12,
    ...     intermediate_size=3072,
    ... )

    >>> # Initializing a model (with random weights) from the ALBERT-base style configuration
    >>> model = AlbertModel(albert_xxlarge_configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```albertc                    t        |   d|||d| || _        || _        || _        || _        || _        || _        || _        |	| _	        || _
        |
| _        || _        || _        || _        || _        || _        || _        || _        y )N)pad_token_idbos_token_ideos_token_id )super__init__
vocab_sizeembedding_sizehidden_sizenum_hidden_layersnum_hidden_groupsnum_attention_headsinner_group_num
hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_rangelayer_norm_epsclassifier_dropout_probposition_embedding_type)selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   r    r!   r"   r   r   r   kwargs	__class__s                         b/var/www/html/venv/lib/python3.12/site-packages/transformers/models/albert/configuration_albert.pyr   zAlbertConfig.__init__l   s    0 	sl\hslrs$,&!2!2#6 .$!2#6 ,H)'>$.!2,'>$'>$    )i0u     i         @   i @  r*   gelu_newr   r   i      g{Gz?g-q=g?absoluter   r-   r   )__name__
__module____qualname____doc__
model_typer   __classcell__)r%   s   @r&   r	   r	      sY    N` J %& # # *+*? *?r'   r	   c                   6    e Zd Zedeeeeef   f   fd       Zy)AlbertOnnxConfig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   r*   	input_idsattention_masktoken_type_ids)taskr   )r#   dynamic_axiss     r&   inputszAlbertOnnxConfig.inputs   sO    99))&8
CL&:6Ll+!<0!<0
 	
r'   N)r/   r0   r1   propertyr   strintrA   r   r'   r&   r6   r6      s.    
WS#X%6 67 
 
r'   r6   N)r2   collectionsr   typingr   configuration_utilsr   onnxr   r	   r6   __all__r   r'   r&   <module>rJ      s?     ! #  3 }?# }?B
z 
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