qi&'<ddlmZmZddlmZGddeZdgZy))PreTrainedConfiglayer_type_validation)RopeParametersc,|eZdZdZdZdgZddddddddZdgdgfd d gd gfd gd gfd Z d#d ed zded zded zde d zded zded zded zde d zded zde d zded zded zded zded zded zde d zde e e e fzd zde d zde d zd ed zd!ee d zf*fd" ZxZS)$ Cohere2Configac This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere model according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Instantiating a configuration with the defaults will yield a similar configuration to that of the [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) model. Args: vocab_size (`int`, *optional*, defaults to 256000): Vocabulary size of the Cohere model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CohereModel`] hidden_size (`int`, *optional*, defaults to 8192): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 22528): Dimension of the MLP representations. logit_scale (`float`, *optional*, defaults to 0.0625): The scaling factor for the output logits. num_hidden_layers (`int`, *optional*, defaults to 40): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 64): 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, 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 decoder. max_position_embeddings (`int`, *optional*, defaults to 8192): 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. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization. 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 0): Padding token id. bos_token_id (`int`, *optional*, defaults to 5): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 255001): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): 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`, 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. sliding_window (`int`, *optional*, defaults to 4096): Size of the sliding window attention context. layer_types (`list`, *optional*): Attention pattern for each layer. ```python >>> from transformers import Cohere2Model, Cohere2Config >>> # Initializing a Cohere Nextmodel configuration >>> configuration = Cohere2Config() >>> # Initializing a model from the Cohere2 configuration >>> model = Cohere2Model(configuration) # doctest: +SKIP >>> # Accessing the model configuration >>> configuration = model.config # doctest: +SKIP ``` cohere2past_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_size logit_scalenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_rangelayer_norm_eps use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddingsrope_parametersattention_biasattention_dropoutsliding_window layer_typesc ||_| |_||_||_||_||_||_||}||_||_| |_ | |_ | |_ ||_ ||_ ||_||_||z|_| |_||_||_||_|j+dd|_|jWt/|dd|_t1|j Dcgc]!}t3|dz|j,zrdnd#c}|_t5|j|j ||_t9|tdi|ycc}w)Nsliding_window_patternsliding_attentionfull_attention)rrrrrrrrrrrrr$r%r&r'head_dimrr r!r"get_sliding_window_patterngetattrrangeboolrr#super__init__)selfrrrrrrrrrrrrrr r!r"r#r$r%r&r'kwargsi __class__s c/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/cohere2/configuration_cohere2.pyr6zCohere2Config.__init__xsn2%'>$&&!2!2#6   &"5 #6 $!2,",!2,&$':: (((#6 (.zz2JA'N$    #+249QST+UD (t556 (,QUd6R6R,R'S#Yii D  d..0F0FG. "6" s(&E )i iXg?(@Nsilur<g{Gz?gh㈵>TiTNFgiN)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferencebase_model_tp_planbase_model_pp_planintfloatstrr4rdictlistr6 __classcell__)r:s@r;rrsKZJ#4"5%.%.%.%."+ )"+&(9:#%568IJ!"_$56"("&(-$*(**,*.!'.2*.%) $#$#$#)+/MQ&+*-%)(,-D#$JD#4ZD#: D# T\ D# : D#!4ZD#!4ZD#$JD#"%tD#!4<D#d D#:D#DjD#DjD# Dj!D#""D[#D#$($sN/B*CCdJ%D#&t 'D#(!4<)D#*d +D#,#Y%-D#D#rN)configuration_utilsrrmodeling_rope_utilsrr__all__r.rPr;rTs(*K1c#$c#L  rP