o
    ei4                     @   s@   d Z ddlmZ ddlmZ eeZG dd deZdgZ	dS )zRoBERTa configuration   )PreTrainedConfig)loggingc                       sN   e Zd ZdZdZ											
					
					d fdd	Z  ZS )RobertaConfiga  
    This is the configuration class to store the configuration of a [`RobertaModel`]. It is
    used to instantiate a RoBERTa 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 RoBERTa
    [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/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 50265):
            Vocabulary size of the RoBERTa model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`RobertaModel`].
        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 [`RobertaModel`].
        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`.
        classifier_dropout (`float`, *optional*):
            The dropout ratio for the classification head.

    Examples:

    ```python
    >>> from transformers import RobertaConfig, RobertaModel

    >>> # Initializing a RoBERTa configuration
    >>> configuration = RobertaConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = RobertaModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```robertaY           gelu皙?      {Gz?-q=       TNFc                    s   t  jdi | || _|| _|| _|| _|| _|| _|| _|| _	|| _
|| _|| _|| _|| _|| _|	| _|
| _|| _|| _|| _|| _d S )N )super__init__pad_token_idbos_token_ideos_token_idtie_word_embeddings
is_decoderadd_cross_attention
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	use_cacheclassifier_dropout)selfr   r   r   r   r    r   r!   r"   r#   r$   r%   r&   r   r   r   r'   r(   r   r   r   kwargs	__class__r   o/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/transformers/models/roberta/configuration_roberta.pyr   X   s*   
zRobertaConfig.__init__)r   r   r   r   r	   r
   r   r   r   r   r   r   r   r   r   TNFFT)__name__
__module____qualname____doc__
model_typer   __classcell__r   r   r+   r-   r      s0    =r   N)
r1   configuration_utilsr   utilsr   
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