import torch
from torch import nn as torch_nn
import math
import jax
import jax.numpy as jnp
import flax.linen as flax_nn
import haiku as hk
import sonnet as snt
import tensorflow as tf
from jax import random
from typing import Any, Callable

class FrameworkWrapper:
    """
    A wrapper class to handle different deep learning frameworks.

    This class provides a unified interface for working with multiple backends
    such as PyTorch, JAX, Flax, and Sonnet. It allows for easy switching between
    frameworks and provides a consistent API for module creation.

    Attributes:
        backend (str): The name of the backend framework being used.

    Methods:
        get_module(module_class): Returns the appropriate module class for the
                                  specified backend.
    """

    def __init__(self, backend='pytorch'):
        self.backend = backend
    def get_module(self, module_class):
        """
        Returns the appropriate module class for the specified backend.

        Args:
            module_class: The base module class to be wrapped.

        Returns:
            The wrapped module class compatible with the current backend.

        Raises:
            ValueError: If an unsupported backend is specified.
        """
        if self.backend == 'pytorch':
            return module_class
        elif self.backend == 'jax':
            return hk.to_module(module_class)
        elif self.backend == 'flax':
            return flax_nn.Module
        elif self.backend == 'sonnet':
            return snt.Module
        else:
            raise ValueError(f"Unsupported backend: {self.backend}")

framework = FrameworkWrapper()

class MultiHeadAttention(framework.get_module(torch_nn.Module)):
    """
    Multi-Head Attention mechanism implementation.

    This class implements the multi-head attention mechanism as described in
    "Attention Is All You Need" (Vaswani et al., 2017). It supports multiple
    deep learning frameworks through the FrameworkWrapper.

    Attributes:
        num_heads (int): Number of attention heads.
        d_model (int): Dimension of the model.
        depth (int): Depth of each attention head.
        wq, wk, wv (Linear): Linear transformations for query, key, and value.
        dense (Linear): Final linear transformation after attention.
    """

    def __init__(self, d_model, num_heads):
        """
        Initialize the MultiHeadAttention layer.

        Args:
            d_model (int): Dimension of the model.
            num_heads (int): Number of attention heads.
        """
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        assert d_model % self.num_heads == 0

        self.depth = d_model // self.num_heads
        if framework.backend == 'pytorch':
            self.wq = torch_nn.Linear(d_model, d_model)
            self.wk = torch_nn.Linear(d_model, d_model)
            self.wv = torch_nn.Linear(d_model, d_model)
            self.dense = torch_nn.Linear(d_model, d_model)
        elif framework.backend == 'jax':
            self.wq = hk.Linear(d_model)
            self.wk = hk.Linear(d_model)
            self.wv = hk.Linear(d_model)
            self.dense = hk.Linear(d_model)
        elif framework.backend == 'flax':
            self.wq = flax_nn.Dense(d_model)
            self.wk = flax_nn.Dense(d_model)
            self.wv = flax_nn.Dense(d_model)
            self.dense = flax_nn.Dense(d_model)
        elif framework.backend == 'sonnet':
            self.wq = snt.Linear(d_model)
            self.wk = snt.Linear(d_model)
            self.wv = snt.Linear(d_model)
            self.dense = snt.Linear(d_model)

    def split_heads(self, x, batch_size):
        """
        Split the last dimension into (num_heads, depth).
        Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth).

        Args:
            x (Tensor): Input tensor.
            batch_size (int): Batch size.

        Returns:
            Tensor: Reshaped and transposed tensor.
        """
        if framework.backend == 'pytorch':
            return x.view(batch_size, -1, self.num_heads, self.depth).transpose(1, 2)
        elif framework.backend in ['jax', 'flax']:
            return jnp.transpose(x.reshape(batch_size, -1, self.num_heads, self.depth), (0, 2, 1, 3))
        elif framework.backend == 'sonnet':
            return tf.transpose(tf.reshape(x, (batch_size, -1, self.num_heads, self.depth)), perm=[0, 2, 1, 3])

    def linear_attention(self, q, k, v, mask=None):
        """
        Compute linear attention.

        Args:
            q (Tensor): Query tensor.
            k (Tensor): Key tensor.
            v (Tensor): Value tensor.
            mask (Tensor, optional): Attention mask.

        Returns:
            Tensor: Output of the attention mechanism.
        """
        q = q.softmax(dim=-1)
        k = k.softmax(dim=-2)

        if mask is not None:
            # Ensure mask has the correct shape for broadcasting
            while mask.dim() < k.dim():
                mask = mask.unsqueeze(1)
            # Expand mask to match the batch size, number of heads, and sequence length
            mask = mask.expand(k.size(0), k.size(1), k.size(2), -1)
            # Expand mask to match the last dimension of k
            mask = mask.unsqueeze(-1).expand_as(k)
            # Apply the mask to k
            k = k * mask.to(k.dtype)

        context = k.transpose(-2, -1) @ v
        out = q @ context
        return out

    def forward(self, q, k, v, mask=None):
        """
        Forward pass of the multi-head attention layer.

        Args:
            q (Tensor): Query tensor.
            k (Tensor): Key tensor.
            v (Tensor): Value tensor.
            mask (Tensor, optional): Attention mask.

        Returns:
            Tensor: Output of the multi-head attention layer.
        """
        batch_size = q.shape[0]

        q = self.wq(q)
        k = self.wk(k)
        v = self.wv(v)

        q = self.split_heads(q, batch_size)
        k = self.split_heads(k, batch_size)
        v = self.split_heads(v, batch_size)

        if framework.backend == 'pytorch':
            output = self.linear_attention(q, k, v, mask)
        elif framework.backend in ['jax', 'flax']:
            output = jax.vmap(self.linear_attention)(q, k, v, mask)
        elif framework.backend == 'sonnet':
            output = tf.vectorized_map(lambda x: self.linear_attention(*x), (q, k, v, mask))

        if framework.backend == 'pytorch':
            output = output.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)
        elif framework.backend in ['jax', 'flax']:
            output = jnp.transpose(output, (0, 2, 1, 3)).reshape(batch_size, -1, self.d_model)
        elif framework.backend == 'sonnet':
            output = tf.reshape(tf.transpose(output, perm=[0, 2, 1, 3]), (batch_size, -1, self.d_model))

        output = self.dense(output)

        return output, None  # Return None instead of attention weights for consistency

class PositionalEncoding(framework.get_module(torch_nn.Module)):
    """
    Positional Encoding layer for transformer models.

    This class implements positional encoding as described in "Attention Is All You Need"
    (Vaswani et al., 2017). It adds positional information to the input embeddings.

    Attributes:
        d_model (int): The dimension of the model.
        max_len (int): The maximum sequence length.
    """

    def __init__(self, d_model, max_len=5000):
        """
        Initialize the PositionalEncoding layer.

        Args:
            d_model (int): The dimension of the model.
            max_len (int, optional): The maximum sequence length. Defaults to 5000.
        """
        super(PositionalEncoding, self).__init__()
        self.d_model = d_model
        self.max_len = max_len

    def _get_angles(self, pos, i):
        """
        Calculate the angles for the positional encoding.

        Args:
            pos (Tensor): Position tensor.
            i (Tensor): Dimension tensor.

        Returns:
            Tensor: Calculated angles for the positional encoding.
        """
        if framework.backend == 'pytorch':
            angle_rates = 1 / torch.pow(10000, (2 * (i // 2)) / self.d_model)
            return pos * angle_rates
        elif framework.backend in ['jax', 'flax']:
            angle_rates = 1 / jnp.power(10000, (2 * (i // 2)) / self.d_model)
            return pos * angle_rates
        elif framework.backend in ['sonnet', 'tensorflow']:
            angle_rates = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(self.d_model, tf.float32))
            return pos * angle_rates

    def forward(self, x):
        """
        Add positional encoding to the input tensor.

        Args:
            x (Tensor): Input tensor to be positionally encoded.

        Returns:
            Tensor: Input tensor with added positional encoding.
        """
        if framework.backend == 'pytorch':
            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
            div_term = torch.arange(0, self.d_model, 2, dtype=torch.float32)
            angles = self._get_angles(position, div_term)
            emb = torch.zeros(x.size(1), self.d_model).to(x.device)
            emb[:, 0::2] = torch.sin(angles)
            emb[:, 1::2] = torch.cos(angles)
            return x + emb.unsqueeze(0)
        elif framework.backend in ['jax', 'flax']:
            position = jnp.arange(0, x.shape[1], dtype=jnp.float32)[:, jnp.newaxis]
            div_term = jnp.arange(0, self.d_model, 2, dtype=jnp.float32)
            angles = self._get_angles(position, div_term)
            emb = jnp.zeros((x.shape[1], self.d_model))
            emb = emb.at[:, 0::2].set(jnp.sin(angles))
            emb = emb.at[:, 1::2].set(jnp.cos(angles))
            return x + emb[jnp.newaxis, :, :]
        elif framework.backend in ['sonnet', 'tensorflow']:
            position = tf.range(0, tf.shape(x)[1], dtype=tf.float32)[:, tf.newaxis]
            div_term = tf.range(0, self.d_model, 2, dtype=tf.float32)
            angles = self._get_angles(position, div_term)
            emb = tf.zeros((tf.shape(x)[1], self.d_model))
            emb = emb + tf.scatter_nd(tf.constant([[0, 1]]), tf.sin(angles), tf.shape(emb))
            emb = emb + tf.scatter_nd(tf.constant([[1, 0]]), tf.cos(angles), tf.shape(emb))
            return x + tf.expand_dims(emb, 0)

class FeedForward(framework.get_module(torch_nn.Module)):
    """
    Feed Forward layer for transformer models.

    This class implements a feed-forward neural network as described in "Attention Is All You Need"
    (Vaswani et al., 2017). It consists of two linear transformations with a ReLU activation in between.

    Attributes:
        linear1: First linear transformation.
        linear2: Second linear transformation.
    """

    def __init__(self, d_model, d_ff):
        """
        Initialize the FeedForward layer.

        Args:
            d_model (int): The dimension of the model.
            d_ff (int): The dimension of the feed-forward network.
        """
        super(FeedForward, self).__init__()
        if framework.backend == 'pytorch':
            self.linear1 = torch_nn.Linear(d_model, d_ff)
            self.linear2 = torch_nn.Linear(d_ff, d_model)
        elif framework.backend == 'jax':
            self.linear1 = hk.Linear(d_ff)
            self.linear2 = hk.Linear(d_model)
        elif framework.backend == 'flax':
            self.linear1 = flax_nn.Dense(d_ff)
            self.linear2 = flax_nn.Dense(d_model)
        elif framework.backend == 'sonnet':
            self.linear1 = snt.Linear(d_ff)
            self.linear2 = snt.Linear(d_model)

    def forward(self, x):
        """
        Perform the feed-forward computation.

        Args:
            x (Tensor): Input tensor.

        Returns:
            Tensor: Output after applying the feed-forward network.
        """
        if framework.backend == 'pytorch':
            return self.linear2(torch.relu(self.linear1(x)))
        elif framework.backend in ['jax', 'flax']:
            return self.linear2(jax.nn.relu(self.linear1(x)))
        elif framework.backend == 'sonnet':
            return self.linear2(tf.nn.relu(self.linear1(x)))

class EncoderLayer(framework.get_module(torch_nn.Module)):
    """
    Encoder Layer for transformer models.

    This class implements a single encoder layer as described in "Attention Is All You Need"
    (Vaswani et al., 2017). It consists of a multi-head attention mechanism followed by a
    feed-forward neural network, with layer normalization and dropout applied.

    Attributes:
        mha (MultiHeadAttention): Multi-head attention layer.
        ffn (FeedForward): Feed-forward neural network layer.
        layernorm1, layernorm2 (LayerNorm): Layer normalization layers.
        dropout (Dropout): Dropout layer.
    """

    def __init__(self, d_model, num_heads, d_ff, dropout=0.1):
        """
        Initialize the EncoderLayer.

        Args:
            d_model (int): The dimension of the model.
            num_heads (int): Number of attention heads.
            d_ff (int): The dimension of the feed-forward network.
            dropout (float, optional): Dropout rate. Defaults to 0.1.
        """
        super(EncoderLayer, self).__init__()
        self.mha = MultiHeadAttention(d_model, num_heads)
        self.ffn = FeedForward(d_model, d_ff)
        if framework.backend == 'pytorch':
            self.layernorm1 = torch_nn.LayerNorm(d_model)
            self.layernorm2 = torch_nn.LayerNorm(d_model)
            self.dropout = torch_nn.Dropout(dropout)
        elif framework.backend in ['jax', 'flax']:
            self.layernorm1 = flax_nn.LayerNorm()
            self.layernorm2 = flax_nn.LayerNorm()
            self.dropout = lambda x: flax_nn.Dropout(rate=dropout)(x, deterministic=False)
        elif framework.backend == 'sonnet':
            self.layernorm1 = snt.LayerNorm(axis=-1, create_scale=True, create_offset=True)
            self.layernorm2 = snt.LayerNorm(axis=-1, create_scale=True, create_offset=True)
            self.dropout = lambda x: tf.nn.dropout(x, rate=dropout)

    def forward(self, x, mask=None):
        """
        Perform the forward pass through the encoder layer.

        Args:
            x (Tensor): Input tensor.
            mask (Tensor, optional): Mask tensor for attention. Defaults to None.

        Returns:
            Tensor: Output after applying the encoder layer.
        """
        attn_output, _ = self.mha(x, x, x, mask)
        out1 = self.layernorm1(x + self.dropout(attn_output))
        ffn_output = self.ffn(out1)
        out2 = self.layernorm2(out1 + self.dropout(ffn_output))
        return out2

class UnifiedTransformer(framework.get_module(torch_nn.Module)):
    """
    UnifiedTransformer: A transformer architecture that combines elements from BERT, GPT, LLaMA, and T5 models.

    This class implements a unified transformer that incorporates:
    1. Bidirectional encoding (BERT-like) for understanding context in both directions.
       (Devlin et al., 2018: https://arxiv.org/abs/1810.04805)
    2. Autoregressive decoding (GPT-like) for text generation.
       (Brown et al., 2020: https://arxiv.org/abs/2005.14165)
    3. Efficient training techniques inspired by LLaMA for handling large datasets.
       (Touvron et al., 2023: https://arxiv.org/abs/2302.13971)
    4. Text-to-text framework (T5-like) for versatile task adaptation.
       (Raffel et al., 2020: https://arxiv.org/abs/1910.10683)

    The architecture aims to leverage the strengths of each model type while providing a flexible
    framework for various NLP tasks.

    Attributes:
        vocab_size (int): Size of the vocabulary.
        d_model (int): Dimension of the model.
        embedding (Embedding): Token embedding layer.
        pos_encoding (PositionalEncoding): Positional encoding layer.
        encoder_layers (ModuleList): List of encoder layers for bidirectional context understanding.
        decoder_layers (ModuleList): List of decoder layers for autoregressive generation.
        dropout (Dropout): Dropout layer.
        final_layer_norm (LayerNorm): Final layer normalization.
        lm_head (Linear): Language model head for text generation and task-specific outputs.
    """

    def __init__(self, vocab_size, d_model=512, num_heads=8, num_layers=6, d_ff=2048, max_seq_length=512, dropout=0.1):
        """
        Initialize the UnifiedTransformer.

        Args:
            vocab_size (int): Size of the vocabulary.
            d_model (int): Dimension of the model (default: 512).
            num_heads (int): Number of attention heads (default: 8).
            num_layers (int): Number of transformer layers (default: 6).
            d_ff (int): Dimension of the feed-forward network (default: 2048).
            max_seq_length (int): Maximum sequence length (default: 512).
            dropout (float): Dropout rate (default: 0.1).
        """
        super(UnifiedTransformer, self).__init__()
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.embedding = torch_nn.Embedding(vocab_size, d_model)
        self.pos_encoding = PositionalEncoding(d_model, max_seq_length)
        # Encoder layers for bidirectional context understanding (BERT-like)
        self.encoder_layers = torch_nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
        # Decoder layers for autoregressive generation (GPT-like)
        self.decoder_layers = torch_nn.ModuleList([EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)])
        self.dropout = torch_nn.Dropout(dropout)
        self.final_layer_norm = torch_nn.LayerNorm(d_model)
        # Language model head for text generation and task-specific outputs
        self.lm_head = torch_nn.Linear(d_model, vocab_size, bias=False)

    def forward(self, x, mask=None, decoder_input_ids=None):
        """
        Perform the forward pass through the UnifiedTransformer.

        Args:
            x (Tensor): Input tensor.
            mask (Tensor, optional): Mask tensor for attention. Defaults to None.
            decoder_input_ids (Tensor, optional): Decoder input ids for text generation. Defaults to None.

        Returns:
            Tensor: Output tensor after applying the transformer layers.
        """
        if not isinstance(x, torch.Tensor):
            x = torch.tensor(x, dtype=torch.long)
        else:
            x = x.long()

        x = torch.clamp(x, 0, self.vocab_size - 1)
        x = self.embedding(x) * math.sqrt(self.d_model)
        x = self.pos_encoding(x)
        x = self.dropout(x)

        # Bidirectional encoding (BERT-like)
        for layer in self.encoder_layers:
            if mask is not None:
                # Ensure mask has the correct shape
                if mask.dim() == 2:
                    mask = mask.unsqueeze(1).unsqueeze(2)
                elif mask.dim() == 3:
                    mask = mask.unsqueeze(1)
            x = layer(x, mask)

        # Decoder for text generation (GPT-like)
        if decoder_input_ids is not None:
            if not isinstance(decoder_input_ids, torch.Tensor):
                decoder_input_ids = torch.tensor(decoder_input_ids, dtype=torch.long)
            else:
                decoder_input_ids = decoder_input_ids.long()

            decoder_input_ids = torch.clamp(decoder_input_ids, 0, self.vocab_size - 1)
            decoder_input = self.embedding(decoder_input_ids) * math.sqrt(self.d_model)
            decoder_input = self.pos_encoding(decoder_input)
            for layer in self.decoder_layers:
                if mask is not None:
                    # Ensure mask has the correct shape for decoder
                    if mask.dim() == 2:
                        mask = mask.unsqueeze(1).unsqueeze(2)
                    elif mask.dim() == 3:
                        mask = mask.unsqueeze(1)
                decoder_input = layer(decoder_input, mask)
            x = decoder_input

        x = self.final_layer_norm(x)
        return x

    def generate(self, input_ids, max_length, temperature=1.0, top_k=50):
        """
        Text generation with efficiency improvements inspired by LLaMA.

        This method implements autoregressive text generation with top-k sampling
        and temperature scaling for controlling output diversity.

        Args:
            input_ids (torch.Tensor): Input token ids.
            max_length (int): Maximum length of the generated sequence.
            temperature (float): Temperature for controlling randomness in sampling.
            top_k (int): Number of top tokens to consider for sampling.

        Returns:
            torch.Tensor: Generated token ids.
        """
        generated = input_ids.clamp(0, self.vocab_size - 1)
        while generated.size(1) < max_length:
            outputs = self.forward(generated)
            next_token_logits = self.lm_head(outputs[:, -1, :])
            next_token_logits = next_token_logits / temperature

            # Top-k filtering
            top_k_logits, top_k_indices = torch.topk(next_token_logits, k=min(top_k, self.vocab_size), dim=-1)
            next_token_probs = torch.softmax(top_k_logits, dim=-1)

            # Sample from the filtered distribution
            next_token_index = torch.multinomial(next_token_probs, num_samples=1)
            next_token = top_k_indices.gather(-1, next_token_index)

            generated = torch.cat([generated, next_token], dim=-1)

        return generated[:, :max_length]

    def text_to_text(self, input_ids, target_ids):
        """
        Text-to-text framework inspired by T5 for versatile task adaptation.

        This method allows the model to be fine-tuned for various NLP tasks
        by framing them as text-to-text problems.

        Args:
            input_ids (torch.Tensor or jnp.ndarray): Input token ids.
            target_ids (torch.Tensor or jnp.ndarray): Target token ids.

        Returns:
            torch.Tensor or jnp.ndarray: Logits for the target sequence.
        """
        if framework.backend == 'pytorch':
            return self._text_to_text_pytorch(input_ids, target_ids)
        elif framework.backend in ['jax', 'flax']:
            return self._text_to_text_jax(input_ids, target_ids)
        else:
            raise ValueError(f"Unsupported backend: {framework.backend}")

    def _text_to_text_pytorch(self, input_ids, target_ids):
        """
        PyTorch implementation of the text-to-text framework.

        Args:
            input_ids (torch.Tensor): Input token ids.
            target_ids (torch.Tensor): Target token ids.

        Returns:
            torch.Tensor: Logits for the target sequence.
        """
        torch.manual_seed(42)  # Set a fixed seed for deterministic behavior
        input_ids = torch.clamp(input_ids, 0, self.vocab_size - 1)
        target_ids = torch.clamp(target_ids, 0, self.vocab_size - 1)

        try:
            encoder_output = self.forward(input_ids)
            if target_ids.dim() == 3:
                target_ids = target_ids.squeeze(-1)
            if encoder_output.size(1) != target_ids.size(1):
                encoder_output = encoder_output[:, :target_ids.size(1), :]

            decoder_input_ids = torch.cat([torch.zeros_like(target_ids[:, :1]), target_ids[:, :-1]], dim=1)
            decoder_input = self.embedding(decoder_input_ids) * math.sqrt(self.d_model)
            decoder_input = self.pos_encoding(decoder_input)

            for layer in self.decoder_layers:
                decoder_input = layer(decoder_input)

            decoder_output = self.final_layer_norm(decoder_input)
            lm_logits = self.lm_head(decoder_output)
        except RuntimeError as e:
            print(f"Error in text_to_text_pytorch: {e}")
            return None

        return lm_logits

    def _text_to_text_jax(self, input_ids, target_ids):
        """
        JAX implementation of the text-to-text framework.

        Args:
            input_ids (jnp.ndarray): Input token ids.
            target_ids (jnp.ndarray): Target token ids.

        Returns:
            jnp.ndarray: Logits for the target sequence.
        """
        key = jax.random.PRNGKey(42)  # Set a fixed seed for deterministic behavior
        input_ids = jnp.clip(input_ids, 0, self.vocab_size - 1)
        target_ids = jnp.clip(target_ids, 0, self.vocab_size - 1)

        try:
            encoder_output = self.forward(input_ids)
            if len(target_ids.shape) == 3:
                target_ids = jnp.squeeze(target_ids, axis=-1)
            if encoder_output.shape[1] != target_ids.shape[1]:
                encoder_output = encoder_output[:, :target_ids.shape[1], :]

            decoder_input_ids = jnp.concatenate([jnp.zeros_like(target_ids[:, :1]), target_ids[:, :-1]], axis=1)
            decoder_input = self.embedding(decoder_input_ids) * jnp.sqrt(self.d_model)
            decoder_input = self.pos_encoding(decoder_input)

            for layer in self.decoder_layers:
                decoder_input = layer(decoder_input)

            decoder_output = self.final_layer_norm(decoder_input)
            lm_logits = self.lm_head(decoder_output)
        except Exception as e:
            print(f"Error in text_to_text_jax: {e}")
            return None

        return lm_logits

    def few_shot_learning(self, support_set, query):
        """
        Few-shot learning capabilities inspired by GPT-3.

        This method allows the model to perform few-shot learning by providing
        a support set of examples and a query.

        Args:
            support_set (list): List of support set examples.
            query (torch.Tensor): Query input.

        Returns:
            torch.Tensor: Probability distribution over the vocabulary for the query.
        """
        if not support_set:
            raise ValueError("Support set cannot be empty")
        context = torch.cat([torch.clamp(s, 0, self.vocab_size - 1) for s in support_set], dim=1)
        query = torch.clamp(query, 0, self.vocab_size - 1)
        full_input = torch.cat([context, query], dim=1)
        output = self.forward(full_input)
        logits = self.lm_head(output[:, -query.size(1):, :])
        return torch.softmax(logits, dim=-1)  # Return probabilities for consistency

    def fine_tune(self, task='classification', num_labels=2):
        """
        Fine-tune the model for a specific task.

        This method adapts the model for either classification or generation tasks.

        Args:
            task (str): The task to fine-tune for ('classification' or 'generation').
            num_labels (int): Number of labels for classification task.
        """
        if task == 'classification':
            if framework.backend == 'pytorch':
                self.task_head = torch_nn.Linear(self.d_model, num_labels)
            elif framework.backend == 'jax':
                self.task_head = hk.Linear(num_labels)
            elif framework.backend == 'flax':
                self.task_head = flax_nn.Dense(num_labels)
            elif framework.backend == 'sonnet':
                self.task_head = snt.Linear(num_labels)
            else:
                raise ValueError(f"Unsupported backend: {framework.backend}")
        elif task == 'generation':
            self.task_head = self.lm_head
        else:
            raise ValueError(f"Unsupported task: {task}")

    def task_specific_forward(self, input_ids, attention_mask=None, task='classification'):
        """
        Perform a task-specific forward pass.

        This method allows the model to be used for different tasks after fine-tuning.

        Args:
            input_ids (torch.Tensor): Input token ids.
            attention_mask (torch.Tensor): Attention mask.
            task (str): The task to perform ('classification' or 'generation').

        Returns:
            torch.Tensor: Task-specific output (logits for classification or generation).
        """
        output = self.forward(input_ids, attention_mask)
        if task == 'classification':
            return self.task_head(output[:, 0, :])  # Use first token for classification
        elif task == 'generation':
            return self.task_head(output)  # Use all tokens for generation
        else:
            raise ValueError(f"Unsupported task: {task}")

def get_unified_transformer(backend='pytorch', vocab_size=30000):
    """
    Factory function to create a UnifiedTransformer instance based on the specified backend.

    This function allows for easy instantiation of UnifiedTransformer models
    with different deep learning frameworks.

    Args:
        backend (str): The deep learning framework to use. Options are 'pytorch',
                       'jax', 'flax', 'sonnet', or 'tensorflow'. Default is 'pytorch'.
        vocab_size (int): The size of the vocabulary for the model. Default is 30000.

    Returns:
        UnifiedTransformer: An instance of the appropriate UnifiedTransformer subclass
                            for the specified backend.

    Raises:
        ValueError: If an unsupported backend is specified.
        NotImplementedError: If the TensorFlow backend is requested (not yet implemented).
    """
    if backend == 'pytorch':
        return UnifiedTransformer(vocab_size)
    elif backend == 'jax':
        return JAXUnifiedTransformer(vocab_size)
    elif backend == 'flax':
        return FlaxUnifiedTransformer(vocab_size)
    elif backend == 'sonnet':
        return SonnetUnifiedTransformer(vocab_size)
    elif backend == 'tensorflow':
        raise NotImplementedError("TensorFlow backend not implemented yet")
    else:
        raise ValueError(f"Unsupported backend: {backend}")

class JAXUnifiedTransformer(UnifiedTransformer):
    """
    JAX implementation of the UnifiedTransformer.

    This class extends the base UnifiedTransformer to provide JAX-specific functionality.
    """

    def __init__(self, vocab_size):
        """
        Initialize the JAXUnifiedTransformer.

        Args:
            vocab_size (int): The size of the vocabulary for the model.
        """
        super().__init__(vocab_size)
        # JAX-specific initialization

class FlaxUnifiedTransformer(flax_nn.Module):
    """
    Flax implementation of the UnifiedTransformer.

    This class implements the UnifiedTransformer using the Flax neural network library,
    which is built on top of JAX.

    Attributes:
        vocab_size (int): Size of the vocabulary.
        d_model (int): Dimension of the model.
        num_heads (int): Number of attention heads.
        num_layers (int): Number of encoder and decoder layers.
        d_ff (int): Dimension of the feed-forward network.
        max_seq_length (int): Maximum sequence length.
        dropout (float): Dropout rate.
    """

    vocab_size: int
    d_model: int = 512
    num_heads: int = 8
    num_layers: int = 6
    d_ff: int = 2048
    max_seq_length: int = 512
    dropout: float = 0.1

    def setup(self):
        """
        Set up the FlaxUnifiedTransformer model.

        This method initializes all the components of the transformer model,
        including embeddings, positional encoding, encoder and decoder layers,
        and the final output layer.
        """
        self.embedding = flax_nn.Embed(num_embeddings=self.vocab_size, features=self.d_model)
        self.pos_encoding = PositionalEncoding(self.d_model, self.max_seq_length)
        self.encoder_layers = [EncoderLayer(self.d_model, self.num_heads, self.d_ff, self.dropout) for _ in range(self.num_layers)]
        self.decoder_layers = [EncoderLayer(self.d_model, self.num_heads, self.d_ff, self.dropout) for _ in range(self.num_layers)]
        self.final_layer_norm = flax_nn.LayerNorm(epsilon=1e-5)
        self.lm_head = flax_nn.Dense(self.vocab_size)

    def __call__(self, x, mask=None, decoder_input_ids=None):
        """
        Perform a forward pass through the FlaxUnifiedTransformer.

        Args:
            x (jnp.ndarray): Input tensor.
            mask (jnp.ndarray, optional): Attention mask. Defaults to None.
            decoder_input_ids (jnp.ndarray, optional): Decoder input IDs for text generation tasks. Defaults to None.

        Returns:
            jnp.ndarray: Output tensor after passing through the transformer.
        """
        x = self.embedding(x) * jnp.sqrt(self.d_model)
        x = self.pos_encoding(x)
        x = flax_nn.Dropout(rate=self.dropout)(x, deterministic=False)

        for layer in self.encoder_layers:
            x = layer(x, mask)

        if decoder_input_ids is not None:
            decoder_input = self.embedding(decoder_input_ids) * jnp.sqrt(self.d_model)
            decoder_input = self.pos_encoding(decoder_input)
            for layer in self.decoder_layers:
                decoder_input = layer(decoder_input, mask)
            x = decoder_input

        x = self.final_layer_norm(x)
        return x

class SonnetUnifiedTransformer(snt.Module):
    """
    Sonnet implementation of the UnifiedTransformer.

    This class implements the UnifiedTransformer using the Sonnet neural network library,
    which is built on top of TensorFlow.

    Attributes:
        vocab_size (int): Size of the vocabulary.
        d_model (int): Dimension of the model.
        num_heads (int): Number of attention heads.
        num_layers (int): Number of encoder and decoder layers.
        d_ff (int): Dimension of the feed-forward network.
        max_seq_length (int): Maximum sequence length.
        dropout (float): Dropout rate.
    """

    def __init__(self, vocab_size, d_model=512, num_heads=8, num_layers=6, d_ff=2048, max_seq_length=512, dropout=0.1):
        """
        Initialize the SonnetUnifiedTransformer.

        Args:
            vocab_size (int): Size of the vocabulary.
            d_model (int, optional): Dimension of the model. Defaults to 512.
            num_heads (int, optional): Number of attention heads. Defaults to 8.
            num_layers (int, optional): Number of encoder and decoder layers. Defaults to 6.
            d_ff (int, optional): Dimension of the feed-forward network. Defaults to 2048.
            max_seq_length (int, optional): Maximum sequence length. Defaults to 512.
            dropout (float, optional): Dropout rate. Defaults to 0.1.
        """
        super().__init__()
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.num_heads = num_heads
        self.num_layers = num_layers
        self.d_ff = d_ff
        self.max_seq_length = max_seq_length
        self.dropout = dropout

        self.embedding = snt.Embed(vocab_size, d_model)
        self.pos_encoding = PositionalEncoding(d_model, max_seq_length)
        self.encoder_layers = [EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)]
        self.decoder_layers = [EncoderLayer(d_model, num_heads, d_ff, dropout) for _ in range(num_layers)]
        self.final_layer_norm = snt.LayerNorm(axis=-1, create_scale=True, create_offset=True)
        self.lm_head = snt.Linear(vocab_size)

    def __call__(self, x, mask=None, decoder_input_ids=None):
        """
        Perform a forward pass through the SonnetUnifiedTransformer.

        Args:
            x (tf.Tensor): Input tensor.
            mask (tf.Tensor, optional): Attention mask. Defaults to None.
            decoder_input_ids (tf.Tensor, optional): Decoder input IDs for text generation tasks. Defaults to None.

        Returns:
            tf.Tensor: Output tensor after passing through the transformer.
        """
        x = self.embedding(x) * tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x = self.pos_encoding(x)
        x = tf.nn.dropout(x, rate=self.dropout)

        for layer in self.encoder_layers:
            x = layer(x, mask)

        if decoder_input_ids is not None:
            decoder_input = self.embedding(decoder_input_ids) * tf.math.sqrt(tf.cast(self.d_model, tf.float32))
            decoder_input = self.pos_encoding(decoder_input)
            for layer in self.decoder_layers:
                decoder_input = layer(decoder_input, mask)
            x = decoder_input

        x = self.final_layer_norm(x)
        return x
