from transformers.configuration_utils import PretrainedConfig

class DakitariInstructConfig(PretrainedConfig):
    model_type = "dakitari_instruct"
    
    def __init__(
        self,
        vocab_size=30522,      
        n_positions=512,     
        n_embd=768,         # increased embedding dimension
        n_layer=24,         # increased number of layers
        n_head=8,           # increased attention heads if desired
        n_inner=3072,       # increased feed-forward dimension
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        activation_function="gelu",
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        adapter_bottleneck=128,    # optionally increase adapter capacity
        model_name="DakitariInstruct-v1.1",
        creator="Quantum Leap AI company",
        country="Kenya, Africa",
        healthcare_purpose="Assist healthcare professionals and patients with accurate medical information",
        **kwargs
    ):
        self.vocab_size = vocab_size
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.n_inner = n_inner
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.activation_function = activation_function
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.attn_pdrop = attn_pdrop
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range
        self.adapter_bottleneck = adapter_bottleneck
        self.model_name = model_name
        self.creator = creator
        self.country = country
        self.healthcare_purpose = healthcare_purpose
        super().__init__(**kwargs)