"""Helion model configuration."""

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


class HelionConfig(PretrainedConfig):
    """
    Configuration class for Helion model.
    
    Args:
        vocab_size (int, optional): Vocabulary size. Defaults to 32768.
        hidden_size (int, optional): Dimensionality of hidden layers. Defaults to 4096.
        intermediate_size (int, optional): Dimensionality of MLP. Defaults to 14336.
        num_hidden_layers (int, optional): Number of decoder layers. Defaults to 32.
        num_attention_heads (int, optional): Number of attention heads. Defaults to 32.
        num_key_value_heads (int, optional): Number of key-value heads for GQA. Defaults to 8.
        hidden_act (str, optional): Activation function. Defaults to "silu".
        max_position_embeddings (int, optional): Maximum sequence length. Defaults to 8192.
        initializer_range (float, optional): Standard deviation for weight initialization. Defaults to 0.02.
        rms_norm_eps (float, optional): Epsilon for RMS normalization. Defaults to 1e-6.
        use_cache (bool, optional): Whether to use KV cache. Defaults to True.
        pad_token_id (int, optional): Padding token ID. Defaults to None.
        bos_token_id (int, optional): Beginning of sequence token ID. Defaults to 1.
        eos_token_id (int, optional): End of sequence token ID. Defaults to 2.
        tie_word_embeddings (bool, optional): Tie input/output embeddings. Defaults to False.
        rope_theta (float, optional): Base for RoPE. Defaults to 10000.0.
        rope_scaling (dict, optional): RoPE scaling config. Defaults to None.
        attention_bias (bool, optional): Add bias to attention projections. Defaults to False.
        attention_dropout (float, optional): Dropout for attention. Defaults to 0.0.
        mlp_bias (bool, optional): Add bias to MLP. Defaults to False.
    """
    
    model_type = "helion"
    keys_to_ignore_at_inference = ["past_key_values"]
    
    def __init__(
        self,
        vocab_size=32768,
        hidden_size=4096,
        intermediate_size=14336,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=8,
        hidden_act="silu",
        max_position_embeddings=8192,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        attention_bias=False,
        attention_dropout=0.0,
        mlp_bias=False,
        residual_dropout=0.0,
        embedding_dropout=0.0,
        use_sliding_window=False,
        sliding_window=None,
        use_flash_attention_2=True,
        pretraining_tp=1,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        
        # Grouped Query Attention
        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.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.residual_dropout = residual_dropout
        self.embedding_dropout = embedding_dropout
        self.use_sliding_window = use_sliding_window
        self.sliding_window = sliding_window
        self.use_flash_attention_2 = use_flash_attention_2
        self.pretraining_tp = pretraining_tp
        
        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,
        )