i'<ddlmZmZddlmZGddeZdgZy))PreTrainedConfiglayer_type_validation)RopeParametersc*peZdZdZdZdgZddddddddZdgdgfd d gd gfd gd gfd Z d"d ed zded zded zded zded zded zde d zded zde d zded zde d zded zded zded zde d zde e e e fzd zde d zded zded zd ee d zf(fd! ZxZS)# Exaone4Configa This is the configuration class to store the configuration of a [`Exaone4Model`]. It is used to instantiate a EXAONE 4.0 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 EXAONE-4.0-32B [LGAI-EXAONE/EXAONE-4.0-32B](https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B) 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 102400): Vocabulary size of the EXAONE 4.0 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Exaone4Model`]. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to `hidden_size * 4`): Dimensionality of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. 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://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 decoder. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 32768 for EXAONE 3.5). 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 layer normalization layers. 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``. bos_token_id (`int`, *optional*, defaults to 0): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. pad_token_id (`int`, *optional*): The id of the padding token. 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_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. sliding_window (`int`, *optional*): The size of the sliding window for the sliding window attention. sliding_window_pattern (`str`, *optional*): The pattern to use for sliding window attention. Can be one of: - `None`: No sliding window attention is used - `int`: Every `sliding_window` layers, use global attention, else use local attention. - `str`: A sequence of "L" (local attention) and "G" (global attention) characters that defines the attention pattern. The pattern starts from layer 0 and repeats every `sliding_window` layers. The final layer always uses global attention regardless of the pattern. For instance, sliding_window_pattern="LLLG" same as sliding_window=4, which means: - Layer 0, 1, 2: local attention, - Layer 3: global attention, ...(repeated) layer_types (`list`, *optional*): Attention pattern for each layer. Prioritized over `sliding_window_pattern`. Example: ```python >>> from transformers import Exaone4Model, Exaone4Config >>> # Initializing a EXAONE configuration >>> configuration = Exaone4Config() >>> # Initializing a model from configuration >>> model = Exaone4Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```exaone4past_key_valuescolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.gate_projzlayers.*.mlp.up_projzlayers.*.mlp.down_proj input_ids inputs_embeds hidden_statesattention_mask) embed_tokenslayersnormN vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range rms_norm_eps use_cache bos_token_id eos_token_id pad_token_idtie_word_embeddingsrope_parametersattention_dropoutsliding_windowsliding_window_pattern layer_typesc B||_||_||_||_||_||_||_||_| |_| |_ | |_ ||_ ||_ ||_ | |_| |_||_||_||_|jd}|j$Dt'|jDcgc] }|dz|zdk7r||jkrdnd"c}|_t)|j$|j||_t-|\di|ycc}w)Nsliding_attentionfull_attention)rrrrrrrrrrrr#r$r%rrr r!r&rangerr"super__init__)selfrrrrrrrrrrrrrr r!r"r#r$r%r&kwargsi __class__s m/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/exaone4/configuration_exaone4.pyr/zExaone4Config.__init__s@0%&!2#6 #6 !2$'>$!2("!2,&<#(((#6 &    &%& "    # t556  U56!;DDZDZ@Z$%& D  d..0F0FG. "6" s8%D)ii@ r6r6siluig{Gz?gh㈵>Tr(NFNgr5N)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planintstrfloatboolrdictlistr/ __classcell__)r3s@r4rrsS jJ#4"5&/%.%.%."+ )"+&(9:#%568IJ!"_$56"("&(-(**,*,!'.2*.#'!%#$#$#'+0MQ*-%)-.(,+9#$J9#4Z9#: 9# : 9# !4Z 9#!4Z9#$J9#"%t9#!4<9#Dj9#$;9#Dj9#Dj9#Dj9# "D[!9#"($sN/B*CCdJ#9#$!4<%9#&d '9#(!$d )9#*#Y%+9#9#rN)configuration_utilsrrmodeling_rope_utilsrr__all__r,rIr4rMs(*K1a#$a#H  rI