
    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I-BERT configuration    )OrderedDict)Mapping   )PretrainedConfig)
OnnxConfig)loggingc                   L     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )IBertConfiga  
    This is the configuration class to store the configuration of a [`IBertModel`]. It is used to instantiate a I-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 IBERT
    [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) 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 I-BERT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`IBertModel`]
        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 [`IBertModel`]
        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).
        quant_mode (`bool`, *optional*, defaults to `False`):
            Whether to quantize the model or not.
        force_dequant (`str`, *optional*, defaults to `"none"`):
            Force dequantize specific nonlinear layer. Dequatized layers are then executed with full precision.
            `"none"`, `"gelu"`, `"softmax"`, `"layernorm"` and `"nonlinear"` are supported. As deafult, it is set as
            `"none"`, which does not dequantize any layers. Please specify `"gelu"`, `"softmax"`, or `"layernorm"` to
            dequantize GELU, Softmax, or LayerNorm, respectively. `"nonlinear"` will dequantize all nonlinear layers,
            i.e., GELU, Softmax, and LayerNorm.
    ibertc                     t        |   d|||d| || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
| _        || _        || _        || _        || _        || _        y )N)pad_token_idbos_token_ideos_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
quant_modeforce_dequant)selfr   r   r   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/ibert/configuration_ibert.pyr   zIBertConfig.__init__V   s    , 	sl\hslrs$&!2#6 $!2#6 ,H)'>$.!2,'>$$*    )i:w  i      r'   i   gelu皙?r)   i      g{Gz?g-q=   r   r*   absoluteFnone)__name__
__module____qualname____doc__
model_typer   __classcell__)r$   s   @r%   r
   r
      sR    3j J %( # *'&+ &+r&   r
   c                   6    e Zd Zedeeeeef   f   fd       Zy)IBertOnnxConfigreturnc                 Z    | j                   dk(  rdddd}nddd}t        d|fd|fg      S )	Nzmultiple-choicebatchchoicesequence)r   r+   r*   )r   r+   	input_idsattention_mask)taskr   )r"   dynamic_axiss     r%   inputszIBertOnnxConfig.inputs   sG    99))&8
CL&:6Ll+!<0
 	
r&   N)r.   r/   r0   propertyr   strintr?   r   r&   r%   r5   r5      s.    

WS#X%6 67 

 

r&   r5   N)r1   collectionsr   typingr   configuration_utilsr   onnxr   utilsr   
get_loggerr.   loggerr
   r5   r   r&   r%   <module>rJ      sI   "  #  3   
		H	%^+" ^+B
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