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from transformers.configuration_utils import PretrainedConfig

class StockLlamaConfig(PretrainedConfig):
    model_type = "stockllama"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(

        self,

        vocab_size=32000,

        hidden_size=4096,

        intermediate_size=11008,

        num_hidden_layers=32,

        num_attention_heads=32,

        num_key_value_heads=None,

        hidden_act="silu",

        max_position_embeddings=2048,

        term_number=4,

        initializer_range=0.02,

        rms_norm_eps=1e-6,

        use_cache=True,

        pad_token_id=None,

        bos_token_id=1,

        eos_token_id=2,

        pretraining_tp=1,

        tie_word_embeddings=False,

        rope_theta=10000.0,

        rope_scaling=None,

        attention_bias=False,

        attention_dropout=0.0,

        mlp_bias=False,

        head_dim=None,

        **kwargs,

    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.term_number = term_number
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.pretraining_tp = pretraining_tp
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.mlp_bias = mlp_bias
        self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads


        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )