"""Deep dialog forward function implementation."""
import jax #type: ignore
import jax.numpy as jnp #type: ignore
import haiku as hk #type: ignore
from typing import Tuple, Optional

class DeepDialogModel(hk.Module):
    """Deep Dialog Model implementation."""
    def __init__(self, 
                 hidden_size: int = 768,
                 num_layers: int = 12,
                 dropout_rate: float = 0.1,
                 name: Optional[str] = None):
        super().__init__(name=name)
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.dropout_rate = dropout_rate

    def __call__(self, inputs: jnp.ndarray, context: jnp.ndarray, is_training: bool = True) -> Tuple[jnp.ndarray, jnp.ndarray]:
        """Forward pass of the model."""
        # Input projection
        x = hk.Linear(self.hidden_size)(inputs)
        
        # Context processing
        context_proj = hk.Linear(self.hidden_size)(context)
        
        # Combine input and context
        x = jnp.concatenate([x, context_proj], axis=-1)
        
        # Multi-layer processing
        for _ in range(self.num_layers):
            # Self-attention layer
            attention = hk.MultiHeadAttention(
                num_heads=8,
                key_size=64,
                model_size=self.hidden_size
            )(x, x, x)
            
            # Residual connection and layer norm
            x = hk.LayerNorm(axis=-1)(x + attention)
            
            # Feed-forward network
            ff_output = hk.Sequential([
                hk.Linear(4 * self.hidden_size),
                jax.nn.gelu,
                hk.Dropout(self.dropout_rate if is_training else 0.0),
                hk.Linear(self.hidden_size),
            ])(x)
            
            # Residual connection and layer norm
            x = hk.LayerNorm(axis=-1)(x + ff_output)
        
        # Output projection
        output = hk.Linear(self.hidden_size)(x)
        
        return output, x

def forward_fn(inputs: jnp.ndarray, context: jnp.ndarray, is_training: bool = True) -> Tuple[jnp.ndarray, jnp.ndarray]:
    """Forward function for the deep dialog model."""
    model = DeepDialogModel()
    return model(inputs, context, is_training)
