
    sg!                     ,    d dl mZ  G d de      ZdgZy)   )PretrainedConfigc                   Z     e Zd ZdZdZdgZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Zd Z xZS )Olmo2Configa  
    This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2
    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 [allenai/Olmo2-7B-1124-hf](https://huggingface.co/allenai/Olmo2-7B-1124-hf).

    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 50304):
            Vocabulary size of the Olmo2 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Olmo2Model`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
            `num_attention_heads`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        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`.
        pad_token_id (`int`, *optional*, defaults to 1):
            Padding token id.
        bos_token_id (`int`, *optional*):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 50279):
            End of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
            these scaling strategies behave:
            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
            experimental feature, subject to breaking API changes in future versions.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the rms normalization layers.

    ```python
    >>> from transformers import Olmo2Model, Olmo2Config

    >>> # Initializing a Olmo2 7B style configuration
    >>> configuration = Olmo2Config()

    >>> # Initializing a model from the Olmo2 7B style configuration
    >>> model = Olmo2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    olmo2past_key_valuesc                 (   t        |   d||||d| || _        || _        || _        || _        || _        || _        ||}|| _        || _	        |	| _
        |
| _        || _        || _        | j                          || _        || _        || _        y )N)pad_token_idbos_token_ideos_token_idtie_word_embeddings )super__init__
vocab_sizemax_position_embeddingshidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads
hidden_actinitializer_range	use_cache
rope_thetarope_scaling_rope_scaling_validationattention_biasattention_dropoutrms_norm_eps)selfr   r   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/olmo2/configuration_olmo2.pyr   zOlmo2Config.__init__\   s    . 	 	
%%% 3		

 	
 %'>$&!2!2#6  &"5#6 $!2"$(%%',!2(    c                    | j                   yt        | j                   t              rt        | j                         dk7  rt	        d| j                          | j                   j                  dd      }| j                   j                  dd      }||dvrt	        d|       |t        |t              r|dk  rt	        d	|       y)
z<
        Validate the `rope_scaling` configuration.
        N   zN`rope_scaling` must be a dictionary with two fields, `type` and `factor`, got typefactor)lineardynamiczF`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got g      ?z7`rope_scaling`'s factor field must be a float > 1, got )r   
isinstancedictlen
ValueErrorgetfloat)r    rope_scaling_typerope_scaling_factors      r#   r   z$Olmo2Config._rope_scaling_validation   s     $$++T2c$:K:K6LPQ6Qcdhduducvw  !--11&$?"//33HdC$(9AV(VXYjXkl  &j9Le.TXkorXrVWjVklmm Ysr$   )i  i   i +      r3   Nsilui   g{Gz?T   Nig  Fg     @NFg        gh㈵>)	__name__
__module____qualname____doc__
model_typekeys_to_ignore_at_inferencer   r   __classcell__)r"   s   @r#   r   r      sa    KZ J#4"5   $!)3)jnr$   r   N)configuration_utilsr   r   __all__r   r$   r#   <module>r?      s%    4Xn" Xnv /r$   