# configuration_mixture_of_recursions.py
# Create this file in your repository root

from transformers import PretrainedConfig


class MixtureOfRecursionsConfig(PretrainedConfig):
    """
    Configuration class for MixtureOfRecursions model.
    
    This class stores the configuration of a MixtureOfRecursions model with recursive transformers.
    """
    model_type = "mixture_of_recursions"
    
    def __init__(
        self,
        vocab_size=10000,
        hidden_size=256,
        num_hidden_layers=4,
        num_attention_heads=8,
        intermediate_size=1024,
        max_position_embeddings=512,
        max_recursion_depth=3,
        attention_dropout=0.1,
        hidden_dropout=0.1,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        use_cache=True,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        **kwargs
    ):
        """
        Args:
            vocab_size (int): Vocabulary size of the model
            hidden_size (int): Dimension of the hidden representations
            num_hidden_layers (int): Number of transformer layers
            num_attention_heads (int): Number of attention heads
            intermediate_size (int): Dimension of the feedforward network
            max_position_embeddings (int): Maximum sequence length
            max_recursion_depth (int): Maximum depth of recursive processing
            attention_dropout (float): Dropout probability for attention layers
            hidden_dropout (float): Dropout probability for hidden layers
            initializer_range (float): Standard deviation for weight initialization
            layer_norm_eps (float): Epsilon for layer normalization
            use_cache (bool): Whether to use past key values for faster generation
            pad_token_id (int): Token ID for padding
            bos_token_id (int): Token ID for beginning of sequence
            eos_token_id (int): Token ID for end of sequence
            tie_word_embeddings (bool): Whether to tie input and output embeddings
        """
        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
        )
        
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.max_position_embeddings = max_position_embeddings
        self.max_recursion_depth = max_recursion_depth
        self.attention_dropout = attention_dropout
        self.hidden_dropout = hidden_dropout
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.use_cache = use_cache