qi`#8ddlmZddlmZGddeZdgZy))RopeParameters) LlamaConfigcpeZdZdZdZ ddeeeefzdzffd ZxZ S)EuroBertConfiga This is the configuration class to store the configuration of a [`EuroBertModel`]. It is used to instantiate an EuroBert model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of [EuroBERT/EuroBERT-210m](https://huggingface.co/EuroBERT/EuroBERT-210m). 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 128256): Vocabulary size of the EuroBert model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`EuroBertModel`] hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. 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. 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, check out [this paper](https://huggingface.co/papers/2305.13245). 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 encoder and pooler. max_position_embeddings (`int`, *optional*, defaults to 8192): The maximum sequence length that this model might ever be used with. EuroBert supports up to 8192 tokens, EuroBert-pretrained up to 2048. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. bos_token_id (`int`, *optional*, defaults to 128000): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 128001): End of stream token id. pad_token_id (`int`, *optional*, defaults to 128001): Padding token id. mask_token_id (`int`, *optional*, defaults to 128002): Mask token id. pretraining_tp (`int`, *optional*, defaults to 1): Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is necessary to ensure exact reproducibility of the pretraining results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232). tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. attention_bias (`bool`, *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. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_attention_heads classifier_pooling (`str`, *optional*, defaults to `"late"`): The pooling strategy to use for the classifier. Can be one of ['bos', 'mean', 'late']. ```python >>> from transformers import EuroBertModel, EuroBertConfig >>> # Initializing a EuroBert eurobert-base style configuration >>> configuration = EuroBertConfig() >>> # Initializing a model from the eurobert-base style configuration >>> model = EuroBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```eurobertNrope_parametersc ||}|jddt|did|d|d|d|d|d|d|d |d | d | dd d | d| d| d|d|d|d|d|d|d||||_||_d |_y)N use_cache vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range rms_norm_epsF bos_token_id eos_token_id pad_token_idpretraining_tptie_word_embeddingsr attention_biasattention_dropoutmlp_biashead_dim)popsuper__init__ mask_token_idclassifier_pooling is_causal)selfr r rrrrrrrrrrrr#rrr rrrrr$kwargs __class__s e/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/eurobert/configuration_eurobert.pyr"zEuroBertConfig.__init__ms4  &"5  ;%  ! # 0 0  !4  !4  " %< 0 &  & & & * !4! ",# $*% &0' () *- 0+"4)iii r+Nsilui g{Gz?gh㈵>ir-iFNFgFNlate) __name__ __module__ __qualname____doc__ model_typerdictstrr" __classcell__)r(s@r)rrs}N `J  $!MQ!/8$($sN/B*CCdJ%88r*rN)modeling_rope_utilsrllamarr__all__rr*r)r;s&,2K[K\  r*