"""
Module that implements an enhanced Synthetic Embedding layer for neural networks using JAX/Flax.

This module provides an implementation of the Synthetic Embedding layer,
which combines Mamba SSM, multi-head attention, BitNet, Liquid layers,
and sparse Mamba for efficient processing of inputs.

Classes:
    EnhancedSyntheticEmbeddingLayer: Implements an enhanced Synthetic Embedding layer.

Dependencies:
    - jax: For array operations and automatic differentiation.
    - flax: For neural network module definitions.
"""

import jax
import jax.numpy as jnp
from flax import linen as nn
import logging

from .bitnet import BitNetLayer
from .liquid import LiquidLayer
from .sparse_mamba import SparseMambaLayer
from .bitnet_quantizer import BitNetQuantizer
from .meta_bamdp import MetaBAMDPLayer
from .mamba_ssm import MambaSSMLayer

logger = logging.getLogger(__name__)


class EnhancedSyntheticEmbeddingLayer(nn.Module):
    """
    EnhancedSyntheticEmbeddingLayer: An optimized neural network layer for synthetic embeddings,
    designed for efficient execution on TPUs.

    This layer implements a sequence of operations including Mamba SSM, multi-head attention,
    BitNet, Liquid layers, sparse Mamba, and quantization.

    Attributes:
        dim (int): Input and output dimension.
        dropout_rate (float): Dropout rate for regularization.
        use_residual (bool): Whether to use a residual connection.
        num_heads (int): Number of attention heads.
        mamba_dim (int): Dimension of Mamba SSM.
        num_bitnet_layers (int): Number of BitNet layers.
        num_liquid_layers (int): Number of Liquid layers.
    """
    dim: int
    dropout_rate: float = 0.1
    use_residual: bool = True
    num_heads: int = 4
    mamba_dim: int = 16
    num_bitnet_layers: int = 3
    num_liquid_layers: int = 3

    def setup(self):
        self.mamba_byte = MambaSSMLayer(self.dim)
        self.bitnet_layers = [BitNetLayer()
                              for _ in range(self.num_bitnet_layers)]
        self.liquid_layers = [LiquidLayer()
                              for _ in range(self.num_liquid_layers)]
        self.sparse_mamba = SparseMambaLayer()
        self.mamba2 = MambaSSMLayer(self.dim)
        self.bitnet_quantizer = BitNetQuantizer()
        self.meta_bamdp = MetaBAMDPLayer()
        self.final_fc = nn.Dense(features=self.dim)

    @nn.compact
    def __call__(self, x: jnp.ndarray, mask: jnp.ndarray = None, training: bool = True) -> jnp.ndarray:
        """
        Forward pass of the EnhancedSyntheticEmbeddingLayer.

        Args:
            x (jnp.ndarray): Input array of shape (batch_size, seq_len, dim).
            mask (jnp.ndarray, optional): Mask array of shape (batch_size, seq_len).
            training (bool): Whether the model is in training mode.

        Returns:
            jnp.ndarray: Output array of shape (batch_size, seq_len, dim).
        """
        self._validate_input(x)
        logger.debug(f"Input shape: {x.shape}, dtype: {x.dtype}")

        if mask is not None:
            self._validate_mask(mask, x.shape)
            x = x * mask[:, :, jnp.newaxis]

        if self.use_residual:
            residual = x

        # Apply Mamba SSM
        x = self.mamba_byte(x)

        # Apply multi-head attention
        x = self._multi_head_attention(x, mask, training)

        # Apply BitNet and Liquid layers
        def apply_bitnet_liquid(bitnet, liquid, x):
            x = jnp.transpose(bitnet(jnp.transpose(x, (0, 2, 1))), (0, 2, 1))
            return liquid(x)

        x = jax.vmap(apply_bitnet_liquid, in_axes=(0, 0, None))(
            jnp.array(self.bitnet_layers),
            jnp.array(self.liquid_layers),
            x
        )

        # Apply sparse Mamba
        x = self.sparse_mamba(x)

        # Apply second Mamba SSM
        x = self.mamba2(x)

        # Apply BitNet quantizer
        x = self.bitnet_quantizer(x)

        # Apply Meta BAMDP
        x = self.meta_bamdp(x)

        # Final fully connected layer
        x = self.final_fc(x)

        if self.use_residual:
            x = x + residual

        x = nn.LayerNorm()(x)
        x = nn.Dropout(rate=self.dropout_rate)(x, deterministic=not training)

        logger.debug(f"Output shape: {x.shape}")
        return x

    def _multi_head_attention(self, x, mask, training):
        """Simplified implementation of multi-head attention."""
        batch_size, seq_len, _ = x.shape
        head_dim = self.dim // self.num_heads

        def attention_head(q, k, v):
            attn_weights = jnp.matmul(
                q, k.transpose(-1, -2)) / jnp.sqrt(head_dim)
            if mask is not None:
                attn_weights = jnp.where(
                    mask[:, None, None, :], attn_weights, -1e9)
            attn_weights = jax.nn.softmax(attn_weights, axis=-1)
            return jnp.matmul(attn_weights, v)

        q = nn.Dense(features=self.dim, use_bias=False)(x)
        k = nn.Dense(features=self.dim, use_bias=False)(x)
        v = nn.Dense(features=self.dim, use_bias=False)(x)

        q = q.reshape(batch_size, seq_len, self.num_heads,
                      head_dim).transpose(0, 2, 1, 3)
        k = k.reshape(batch_size, seq_len, self.num_heads,
                      head_dim).transpose(0, 2, 1, 3)
        v = v.reshape(batch_size, seq_len, self.num_heads,
                      head_dim).transpose(0, 2, 1, 3)

        heads = jax.vmap(attention_head)(q, k, v)
        heads = heads.transpose(0, 2, 1, 3).reshape(
            batch_size, seq_len, self.dim)

        return nn.Dense(features=self.dim)(heads)

    def _validate_input(self, x: jnp.ndarray):
        """Validate the input array dimensions."""
        if x.ndim != 3:
            error_msg = f"Expected input array with 3 dimensions (batch_size, seq_len, dim), but got {
                x.ndim} dimensions."
            logger.error(error_msg)
            raise ValueError(error_msg)
        if x.shape[-1] != self.dim:
            error_msg = f"Expected dim to be {
                self.dim}, but got {x.shape[-1]}."
            logger.error(error_msg)
            raise ValueError(error_msg)

    def _validate_mask(self, mask: jnp.ndarray, input_shape: tuple):
        """Validate the mask array dimensions."""
        if mask.ndim != 2:
            error_msg = f"Expected mask array with 2 dimensions (batch_size, seq_len), but got {
                mask.ndim} dimensions."
            logger.error(error_msg)
            raise ValueError(error_msg)
        if mask.shape != input_shape[:-1]:
            error_msg = f"Expected mask shape to be {
                input_shape[:-1]}, but got {mask.shape}."
            logger.error(error_msg)
            raise ValueError(error_msg)

    def get_config(self) -> dict:
        """
        Get the configuration of the EnhancedSyntheticEmbeddingLayer.

        Returns:
            dict: A dictionary containing the layer's configuration.
        """
        return {
            "dim": self.dim,
            "dropout_rate": self.dropout_rate,
            "use_residual": self.use_residual,
            "num_heads": self.num_heads,
            "mamba_dim": self.mamba_dim,
            "num_bitnet_layers": self.num_bitnet_layers,
            "num_liquid_layers": self.num_liquid_layers
        }


# Example usage
if __name__ == "__main__":
    try:
        # Set up logging
        logging.basicConfig(level=logging.DEBUG)

        # Create a sample input array
        batch_size, seq_len, dim = 32, 10, 256
        key = jax.random.PRNGKey(0)
        x = jax.random.normal(key, (batch_size, seq_len, dim))

        # Create a sample mask
        mask = jnp.ones((batch_size, seq_len))
        mask = mask.at[:, 5:].set(0)  # Mask out the last 5 tokens

        # Initialize the EnhancedSyntheticEmbeddingLayer
        layer = EnhancedSyntheticEmbeddingLayer(dim=256)

        # Initialize parameters
        params_key, dropout_key = jax.random.split(key)
        params = layer.init(params_key, x, mask, training=True)

        # Perform forward pass
        output = layer.apply(params, x, mask, training=True,
                             rngs={'dropout': dropout_key})

        print(f"Input shape: {x.shape}")
        print(f"Output shape: {output.shape}")
        print(f"Layer config: {layer.get_config()}")

        logger.info(
            "EnhancedSyntheticEmbeddingLayer example completed successfully")
    except Exception as e:
        logger.exception(
            f"An error occurred during the EnhancedSyntheticEmbeddingLayer example: {str(e)}")
