modeling_autoencoder.py

"""
PyTorch Autoencoder model for Hugging Face Transformers.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Union, Dict, Any, List
from dataclasses import dataclass
import random

from transformers import PreTrainedModel
from transformers.modeling_outputs import BaseModelOutput
from transformers.utils import ModelOutput

try:
    from .configuration_autoencoder import AutoencoderConfig  # when loaded via HF dynamic module
except Exception:
    from configuration_autoencoder import AutoencoderConfig  # local usage


class NeuralScaler(nn.Module):
    """Learnable alternative to StandardScaler using neural networks."""

    def __init__(self, config: AutoencoderConfig):
        super().__init__()
        self.config = config
        input_dim = config.input_dim
        hidden_dim = config.preprocessing_hidden_dim

        # Networks to learn data-dependent statistics
        self.mean_estimator = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, input_dim)
        )

        self.std_estimator = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, input_dim),
            nn.Softplus()  # Ensure positive standard deviation
        )

        # Learnable affine transformation parameters
        self.weight = nn.Parameter(torch.ones(input_dim))
        self.bias = nn.Parameter(torch.zeros(input_dim))

        # Running statistics for inference (like BatchNorm)
        self.register_buffer('running_mean', torch.zeros(input_dim))
        self.register_buffer('running_std', torch.ones(input_dim))
        self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long))

        # Momentum for running statistics
        self.momentum = 0.1

    def forward(self, x: torch.Tensor, inverse: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Forward pass through neural scaler.

        Args:
            x: Input tensor (2D or 3D)
            inverse: Whether to apply inverse transformation

        Returns:
            Tuple of (transformed_tensor, regularization_loss)
        """
        if inverse:
            return self._inverse_transform(x)

        # Handle both 2D and 3D tensors
        original_shape = x.shape
        if x.dim() == 3:
            # Reshape (batch, seq, features) -> (batch*seq, features)
            x = x.view(-1, x.size(-1))

        if self.training:
            # Training mode: learn statistics from current batch
            batch_mean = x.mean(dim=0, keepdim=True)
            batch_std = x.std(dim=0, keepdim=True)

            # Learn data-dependent adjustments
            learned_mean_adj = self.mean_estimator(batch_mean)
            learned_std_adj = self.std_estimator(batch_std)

            # Combine batch statistics with learned adjustments
            effective_mean = batch_mean + learned_mean_adj
            effective_std = batch_std + learned_std_adj + 1e-8

            # Update running statistics
            with torch.no_grad():
                self.num_batches_tracked += 1
                if self.num_batches_tracked == 1:
                    self.running_mean.copy_(batch_mean.squeeze())
                    self.running_std.copy_(batch_std.squeeze())
                else:
                    self.running_mean.mul_(1 - self.momentum).add_(batch_mean.squeeze(), alpha=self.momentum)
                    self.running_std.mul_(1 - self.momentum).add_(batch_std.squeeze(), alpha=self.momentum)
        else:
            # Inference mode: use running statistics
            effective_mean = self.running_mean.unsqueeze(0)
            effective_std = self.running_std.unsqueeze(0) + 1e-8

        # Normalize
        normalized = (x - effective_mean) / effective_std

        # Apply learnable affine transformation
        transformed = normalized * self.weight + self.bias

        # Reshape back to original shape if needed
        if len(original_shape) == 3:
            transformed = transformed.view(original_shape)

        # Regularization loss to encourage meaningful learning
        reg_loss = 0.01 * (self.weight.var() + self.bias.var())

        return transformed, reg_loss

    def _inverse_transform(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        """Apply inverse transformation to get back original scale."""
        if not self.config.learn_inverse_preprocessing:
            return x, torch.tensor(0.0, device=x.device)

        # Handle both 2D and 3D tensors
        original_shape = x.shape
        if x.dim() == 3:
            # Reshape (batch, seq, features) -> (batch*seq, features)
            x = x.view(-1, x.size(-1))

        # Reverse affine transformation
        x = (x - self.bias) / (self.weight + 1e-8)

        # Reverse normalization using running statistics
        effective_mean = self.running_mean.unsqueeze(0)
        effective_std = self.running_std.unsqueeze(0) + 1e-8
        x = x * effective_std + effective_mean

        # Reshape back to original shape if needed
        if len(original_shape) == 3:
            x = x.view(original_shape)

        return x, torch.tensor(0.0, device=x.device)



class LearnableMinMaxScaler(nn.Module):
    """Learnable MinMax scaler that adapts bounds during training.

    Scales features to [0, 1] using batch min/range with learnable adjustments and
    a learnable affine transform. Supports 2D (B, F) and 3D (B, T, F) inputs.
    """

    def __init__(self, config: AutoencoderConfig):
        super().__init__()
        self.config = config
        input_dim = config.input_dim
        hidden_dim = config.preprocessing_hidden_dim

        # Networks to learn adjustments to batch min and range
        self.min_estimator = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, input_dim),
        )
        self.range_estimator = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, input_dim),
            nn.Softplus(),  # Ensure positive adjustment to range
        )

        # Learnable affine transformation parameters
        self.weight = nn.Parameter(torch.ones(input_dim))
        self.bias = nn.Parameter(torch.zeros(input_dim))

        # Running statistics for inference
        self.register_buffer("running_min", torch.zeros(input_dim))
        self.register_buffer("running_range", torch.ones(input_dim))
        self.register_buffer("num_batches_tracked", torch.tensor(0, dtype=torch.long))

        self.momentum = 0.1

    def forward(self, x: torch.Tensor, inverse: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
        if inverse:
            return self._inverse_transform(x)

        original_shape = x.shape
        if x.dim() == 3:
            x = x.view(-1, x.size(-1))

        eps = 1e-8
        if self.training:
            batch_min = x.min(dim=0, keepdim=True).values
            batch_max = x.max(dim=0, keepdim=True).values
            batch_range = (batch_max - batch_min).clamp_min(eps)

            # Learn adjustments
            learned_min_adj = self.min_estimator(batch_min)
            learned_range_adj = self.range_estimator(batch_range)

            effective_min = batch_min + learned_min_adj
            effective_range = batch_range + learned_range_adj + eps

            # Update running stats with raw batch min/range for stable inversion
            with torch.no_grad():
                self.num_batches_tracked += 1
                if self.num_batches_tracked == 1:
                    self.running_min.copy_(batch_min.squeeze())
                    self.running_range.copy_(batch_range.squeeze())
                else:
                    self.running_min.mul_(1 - self.momentum).add_(batch_min.squeeze(), alpha=self.momentum)
                    self.running_range.mul_(1 - self.momentum).add_(batch_range.squeeze(), alpha=self.momentum)
        else:
            effective_min = self.running_min.unsqueeze(0)
            effective_range = self.running_range.unsqueeze(0)

        # Scale to [0, 1]
        scaled = (x - effective_min) / effective_range

        # Learnable affine transform
        transformed = scaled * self.weight + self.bias

        if len(original_shape) == 3:
            transformed = transformed.view(original_shape)

        # Regularization: encourage non-degenerate range and modest affine params
        reg_loss = 0.01 * (self.weight.var() + self.bias.var())
        if self.training:
            reg_loss = reg_loss + 0.001 * (1.0 / effective_range.clamp_min(1e-3)).mean()

        return transformed, reg_loss

    def _inverse_transform(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        if not self.config.learn_inverse_preprocessing:
            return x, torch.tensor(0.0, device=x.device)

        original_shape = x.shape
        if x.dim() == 3:
            x = x.view(-1, x.size(-1))

        # Reverse affine
        x = (x - self.bias) / (self.weight + 1e-8)
        # Reverse MinMax using running stats
        x = x * self.running_range.unsqueeze(0) + self.running_min.unsqueeze(0)

        if len(original_shape) == 3:
            x = x.view(original_shape)

        return x, torch.tensor(0.0, device=x.device)


class LearnableRobustScaler(nn.Module):
    """Learnable Robust scaler using median and IQR with learnable adjustments.

    Normalizes as (x - median) / IQR with learnable adjustments and an affine head.
    Supports 2D (B, F) and 3D (B, T, F) inputs.
    """

    def __init__(self, config: AutoencoderConfig):
        super().__init__()
        self.config = config
        input_dim = config.input_dim
        hidden_dim = config.preprocessing_hidden_dim

        self.median_estimator = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, input_dim),
        )
        self.iqr_estimator = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, input_dim),
            nn.Softplus(),  # Ensure positive IQR adjustment
        )

        self.weight = nn.Parameter(torch.ones(input_dim))
        self.bias = nn.Parameter(torch.zeros(input_dim))

        self.register_buffer("running_median", torch.zeros(input_dim))
        self.register_buffer("running_iqr", torch.ones(input_dim))
        self.register_buffer("num_batches_tracked", torch.tensor(0, dtype=torch.long))

        self.momentum = 0.1

    def forward(self, x: torch.Tensor, inverse: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
        if inverse:
            return self._inverse_transform(x)

        original_shape = x.shape
        if x.dim() == 3:
            x = x.view(-1, x.size(-1))

        eps = 1e-8
        if self.training:
            qs = torch.quantile(x, torch.tensor([0.25, 0.5, 0.75], device=x.device), dim=0)
            q25, med, q75 = qs[0:1, :], qs[1:2, :], qs[2:3, :]
            iqr = (q75 - q25).clamp_min(eps)

            learned_med_adj = self.median_estimator(med)
            learned_iqr_adj = self.iqr_estimator(iqr)

            effective_median = med + learned_med_adj
            effective_iqr = iqr + learned_iqr_adj + eps

            with torch.no_grad():
                self.num_batches_tracked += 1
                if self.num_batches_tracked == 1:
                    self.running_median.copy_(med.squeeze())
                    self.running_iqr.copy_(iqr.squeeze())
                else:
                    self.running_median.mul_(1 - self.momentum).add_(med.squeeze(), alpha=self.momentum)
                    self.running_iqr.mul_(1 - self.momentum).add_(iqr.squeeze(), alpha=self.momentum)
        else:
            effective_median = self.running_median.unsqueeze(0)
            effective_iqr = self.running_iqr.unsqueeze(0)

        normalized = (x - effective_median) / effective_iqr
        transformed = normalized * self.weight + self.bias

        if len(original_shape) == 3:
            transformed = transformed.view(original_shape)

        reg_loss = 0.01 * (self.weight.var() + self.bias.var())
        if self.training:
            reg_loss = reg_loss + 0.001 * (1.0 / effective_iqr.clamp_min(1e-3)).mean()

        return transformed, reg_loss

    def _inverse_transform(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        if not self.config.learn_inverse_preprocessing:
            return x, torch.tensor(0.0, device=x.device)

        original_shape = x.shape
        if x.dim() == 3:
            x = x.view(-1, x.size(-1))

        x = (x - self.bias) / (self.weight + 1e-8)
        x = x * self.running_iqr.unsqueeze(0) + self.running_median.unsqueeze(0)

        if len(original_shape) == 3:
            x = x.view(original_shape)

        return x, torch.tensor(0.0, device=x.device)


class LearnableYeoJohnsonPreprocessor(nn.Module):
    """Learnable Yeo-Johnson power transform with per-feature λ and affine head.

    Applies Yeo-Johnson transform elementwise with learnable lambda per feature,
    followed by standardization and a learnable affine transform. Supports 2D and 3D inputs.
    """

    def __init__(self, config: AutoencoderConfig):
        super().__init__()
        self.config = config
        input_dim = config.input_dim

        # Learnable lambda per feature (unconstrained). Initialize around 1.0
        self.lmbda = nn.Parameter(torch.ones(input_dim))

        # Learnable affine parameters after standardization
        self.weight = nn.Parameter(torch.ones(input_dim))
        self.bias = nn.Parameter(torch.zeros(input_dim))

        # Running stats for transformed data
        self.register_buffer("running_mean", torch.zeros(input_dim))
        self.register_buffer("running_std", torch.ones(input_dim))
        self.register_buffer("num_batches_tracked", torch.tensor(0, dtype=torch.long))
        self.momentum = 0.1

    def _yeo_johnson(self, x: torch.Tensor, lmbda: torch.Tensor) -> torch.Tensor:
        eps = 1e-6
        lmbda = lmbda.unsqueeze(0)  # broadcast over batch
        pos = x >= 0
        # For x >= 0
        if_part = torch.where(
            torch.abs(lmbda) > eps,
            ((x + 1.0).clamp_min(eps) ** lmbda - 1.0) / lmbda,
            torch.log((x + 1.0).clamp_min(eps)),
        )
        # For x < 0
        two_minus_lambda = 2.0 - lmbda
        else_part = torch.where(
            torch.abs(two_minus_lambda) > eps,
            -(((1.0 - x).clamp_min(eps)) ** two_minus_lambda - 1.0) / two_minus_lambda,
            -torch.log((1.0 - x).clamp_min(eps)),
        )
        return torch.where(pos, if_part, else_part)

    def _yeo_johnson_inverse(self, y: torch.Tensor, lmbda: torch.Tensor) -> torch.Tensor:
        eps = 1e-6
        lmbda = lmbda.unsqueeze(0)
        pos = y >= 0
        # Inverse for y >= 0
        x_pos = torch.where(
            torch.abs(lmbda) > eps,
            (y * lmbda + 1.0).clamp_min(eps) ** (1.0 / lmbda) - 1.0,
            torch.exp(y) - 1.0,
        )
        # Inverse for y < 0
        two_minus_lambda = 2.0 - lmbda
        x_neg = torch.where(
            torch.abs(two_minus_lambda) > eps,
            1.0 - (1.0 - y * two_minus_lambda).clamp_min(eps) ** (1.0 / two_minus_lambda),
            1.0 - torch.exp(-y),
        )
        return torch.where(pos, x_pos, x_neg)

    def forward(self, x: torch.Tensor, inverse: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
        if inverse:
            return self._inverse_transform(x)

        orig_shape = x.shape
        if x.dim() == 3:
            x = x.view(-1, x.size(-1))

        # Apply Yeo-Johnson
        y = self._yeo_johnson(x, self.lmbda)

        # Batch stats and running stats on transformed data
        if self.training:
            batch_mean = y.mean(dim=0, keepdim=True)
            batch_std = y.std(dim=0, keepdim=True).clamp_min(1e-6)
            with torch.no_grad():
                self.num_batches_tracked += 1
                if self.num_batches_tracked == 1:
                    self.running_mean.copy_(batch_mean.squeeze())
                    self.running_std.copy_(batch_std.squeeze())
                else:
                    self.running_mean.mul_(1 - self.momentum).add_(batch_mean.squeeze(), alpha=self.momentum)
                    self.running_std.mul_(1 - self.momentum).add_(batch_std.squeeze(), alpha=self.momentum)
            mean = batch_mean
            std = batch_std
        else:
            mean = self.running_mean.unsqueeze(0)
            std = self.running_std.unsqueeze(0)

        y_norm = (y - mean) / std
        out = y_norm * self.weight + self.bias

        if len(orig_shape) == 3:
            out = out.view(orig_shape)

        # Regularize lambda to avoid extreme values; encourage identity around 1
        reg = 0.001 * (self.lmbda - 1.0).pow(2).mean() + 0.01 * (self.weight.var() + self.bias.var())
        return out, reg

    def _inverse_transform(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        if not self.config.learn_inverse_preprocessing:
            return x, torch.tensor(0.0, device=x.device)

        orig_shape = x.shape
        if x.dim() == 3:
            x = x.view(-1, x.size(-1))

        # Reverse affine and normalization with running stats
        y = (x - self.bias) / (self.weight + 1e-8)
        y = y * self.running_std.unsqueeze(0) + self.running_mean.unsqueeze(0)

        # Inverse Yeo-Johnson
        out = self._yeo_johnson_inverse(y, self.lmbda)

        if len(orig_shape) == 3:
            out = out.view(orig_shape)

        return out, torch.tensor(0.0, device=x.device)

class CouplingLayer(nn.Module):
    """Coupling layer for normalizing flows."""

    def __init__(self, input_dim: int, hidden_dim: int = 64, mask_type: str = "alternating"):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim

        # Create mask for coupling
        if mask_type == "alternating":
            self.register_buffer('mask', torch.arange(input_dim) % 2)
        elif mask_type == "half":
            mask = torch.zeros(input_dim)
            mask[:input_dim // 2] = 1
            self.register_buffer('mask', mask)
        else:
            raise ValueError(f"Unknown mask type: {mask_type}")

        # Scale and translation networks
        masked_dim = int(self.mask.sum().item())
        unmasked_dim = input_dim - masked_dim

        self.scale_net = nn.Sequential(
            nn.Linear(masked_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, unmasked_dim),
            nn.Tanh()  # Bounded output for stability
        )

        self.translate_net = nn.Sequential(
            nn.Linear(masked_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, unmasked_dim)
        )

    def forward(self, x: torch.Tensor, inverse: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Forward pass through coupling layer.

        Args:
            x: Input tensor
            inverse: Whether to apply inverse transformation

        Returns:
            Tuple of (transformed_tensor, log_determinant)
        """
        mask = self.mask.bool()
        x_masked = x[:, mask]
        x_unmasked = x[:, ~mask]

        # Compute scale and translation
        s = self.scale_net(x_masked)
        t = self.translate_net(x_masked)

        if not inverse:
            # Forward transformation
            y_unmasked = x_unmasked * torch.exp(s) + t
            log_det = s.sum(dim=1)
        else:
            # Inverse transformation
            y_unmasked = (x_unmasked - t) * torch.exp(-s)
            log_det = -s.sum(dim=1)

        # Reconstruct output
        y = torch.zeros_like(x)
        y[:, mask] = x_masked
        y[:, ~mask] = y_unmasked

        return y, log_det


class NormalizingFlowPreprocessor(nn.Module):
    """Normalizing flow for learnable data preprocessing."""

    def __init__(self, config: AutoencoderConfig):
        super().__init__()
        self.config = config
        input_dim = config.input_dim
        hidden_dim = config.preprocessing_hidden_dim
        num_layers = config.flow_coupling_layers

        # Create coupling layers with alternating masks
        self.layers = nn.ModuleList()
        for i in range(num_layers):
            mask_type = "alternating" if i % 2 == 0 else "half"
            self.layers.append(CouplingLayer(input_dim, hidden_dim, mask_type))

        # Optional: Add batch normalization between layers
        if config.use_batch_norm:
            self.batch_norms = nn.ModuleList([
                nn.BatchNorm1d(input_dim) for _ in range(num_layers - 1)
            ])
        else:
            self.batch_norms = None

    def forward(self, x: torch.Tensor, inverse: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Forward pass through normalizing flow.

        Args:
            x: Input tensor (2D or 3D)
            inverse: Whether to apply inverse transformation

        Returns:
            Tuple of (transformed_tensor, total_log_determinant)
        """
        # Handle both 2D and 3D tensors
        original_shape = x.shape
        if x.dim() == 3:
            # Reshape (batch, seq, features) -> (batch*seq, features)
            x = x.view(-1, x.size(-1))

        log_det_total = torch.zeros(x.size(0), device=x.device)

        if not inverse:
            # Forward pass
            for i, layer in enumerate(self.layers):
                x, log_det = layer(x, inverse=False)
                log_det_total += log_det

                # Apply batch normalization (except for last layer)
                if self.batch_norms and i < len(self.layers) - 1:
                    x = self.batch_norms[i](x)
        else:
            # Inverse pass
            for i, layer in enumerate(reversed(self.layers)):
                # Reverse batch normalization (except for first layer in reverse)
                if self.batch_norms and i > 0:
                    # Note: This is approximate inverse of batch norm
                    bn_idx = len(self.layers) - 1 - i
                    x = self.batch_norms[bn_idx](x)

                x, log_det = layer(x, inverse=True)
                log_det_total += log_det

        # Reshape back to original shape if needed
        if len(original_shape) == 3:
            x = x.view(original_shape)

        # Convert log determinant to regularization loss
        # Encourage the flow to preserve information (log_det close to 0)
        reg_loss = 0.01 * log_det_total.abs().mean()

        return x, reg_loss


class LearnablePreprocessor(nn.Module):
    """Unified interface for learnable preprocessing methods."""

    def __init__(self, config: AutoencoderConfig):
        super().__init__()
        self.config = config

        if not config.has_preprocessing:
            self.preprocessor = nn.Identity()
        elif config.is_neural_scaler:
            self.preprocessor = NeuralScaler(config)
        elif config.is_normalizing_flow:
            self.preprocessor = NormalizingFlowPreprocessor(config)
        elif getattr(config, "is_minmax_scaler", False):
            self.preprocessor = LearnableMinMaxScaler(config)
        elif getattr(config, "is_robust_scaler", False):
            self.preprocessor = LearnableRobustScaler(config)
        elif getattr(config, "is_yeo_johnson", False):
            self.preprocessor = LearnableYeoJohnsonPreprocessor(config)
        else:
            raise ValueError(f"Unknown preprocessing type: {config.preprocessing_type}")

    def forward(self, x: torch.Tensor, inverse: bool = False) -> Tuple[torch.Tensor, torch.Tensor]:
        """
        Apply preprocessing transformation.

        Args:
            x: Input tensor
            inverse: Whether to apply inverse transformation

        Returns:
            Tuple of (transformed_tensor, regularization_loss)
        """
        if isinstance(self.preprocessor, nn.Identity):
            return x, torch.tensor(0.0, device=x.device)

        return self.preprocessor(x, inverse=inverse)


@dataclass
class AutoencoderOutput(ModelOutput):
    """
    Output type of AutoencoderModel.

    Args:
        last_hidden_state (torch.FloatTensor): The latent representation of the input.
        reconstructed (torch.FloatTensor, optional): The reconstructed input.
        hidden_states (tuple(torch.FloatTensor), optional): Hidden states of the encoder layers.
        attentions (tuple(torch.FloatTensor), optional): Not used in basic autoencoder.
        preprocessing_loss (torch.FloatTensor, optional): Loss from learnable preprocessing.
    """

    last_hidden_state: torch.FloatTensor = None
    reconstructed: Optional[torch.FloatTensor] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    preprocessing_loss: Optional[torch.FloatTensor] = None


@dataclass
class AutoencoderForReconstructionOutput(ModelOutput):
    """
    Output type of AutoencoderForReconstruction.

    Args:
        loss (torch.FloatTensor, optional): The reconstruction loss.
        reconstructed (torch.FloatTensor): The reconstructed input.
        last_hidden_state (torch.FloatTensor): The latent representation.
        hidden_states (tuple(torch.FloatTensor), optional): Hidden states of the encoder layers.
        preprocessing_loss (torch.FloatTensor, optional): Loss from learnable preprocessing.
    """

    loss: Optional[torch.FloatTensor] = None
    reconstructed: torch.FloatTensor = None
    last_hidden_state: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    preprocessing_loss: Optional[torch.FloatTensor] = None


class AutoencoderEncoder(nn.Module):
    """Encoder part of the autoencoder."""

    def __init__(self, config: AutoencoderConfig):
        super().__init__()
        self.config = config

        # Build encoder layers
        layers = []
        input_dim = config.input_dim

        for hidden_dim in config.hidden_dims:
            layers.append(nn.Linear(input_dim, hidden_dim))

            if config.use_batch_norm:
                layers.append(nn.BatchNorm1d(hidden_dim))

            layers.append(self._get_activation(config.activation))

            if config.dropout_rate > 0:
                layers.append(nn.Dropout(config.dropout_rate))

            input_dim = hidden_dim

        self.encoder = nn.Sequential(*layers)

        # For variational autoencoders, we need separate layers for mean and log variance
        if config.is_variational:
            self.fc_mu = nn.Linear(input_dim, config.latent_dim)
            self.fc_logvar = nn.Linear(input_dim, config.latent_dim)
        else:
            # Standard encoder output
            self.fc_out = nn.Linear(input_dim, config.latent_dim)

    def _get_activation(self, activation: str) -> nn.Module:
        """Get activation function by name."""
        activations = {
            "relu": nn.ReLU(),
            "tanh": nn.Tanh(),
            "sigmoid": nn.Sigmoid(),
            "leaky_relu": nn.LeakyReLU(),
            "gelu": nn.GELU(),
            "swish": nn.SiLU(),
            "silu": nn.SiLU(),
            "elu": nn.ELU(),
            "prelu": nn.PReLU(),
            "relu6": nn.ReLU6(),
            "hardtanh": nn.Hardtanh(),
            "hardsigmoid": nn.Hardsigmoid(),
            "hardswish": nn.Hardswish(),
            "mish": nn.Mish(),
            "softplus": nn.Softplus(),
            "softsign": nn.Softsign(),
            "tanhshrink": nn.Tanhshrink(),
            "threshold": nn.Threshold(threshold=0.1, value=0),
        }
        return activations[activation]

    def forward(self, x: torch.Tensor) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
        """Forward pass through encoder."""
        # Add noise for denoising autoencoders
        if self.config.is_denoising and self.training:
            noise = torch.randn_like(x) * self.config.noise_factor
            x = x + noise

        encoded = self.encoder(x)

        if self.config.is_variational:
            # Variational autoencoder: return mean, log variance, and sampled latent
            mu = self.fc_mu(encoded)
            logvar = self.fc_logvar(encoded)

            # Reparameterization trick
            if self.training:
                std = torch.exp(0.5 * logvar)
                eps = torch.randn_like(std)
                z = mu + eps * std
            else:
                z = mu  # Use mean during inference

            return z, mu, logvar
        else:
            # Standard autoencoder
            latent = self.fc_out(encoded)

            # Add sparsity constraint for sparse autoencoders
            if self.config.is_sparse and self.training:
                # Apply L1 regularization to encourage sparsity
                latent = F.relu(latent)  # Ensure non-negative activations

            return latent


class AutoencoderDecoder(nn.Module):
    """Decoder part of the autoencoder."""

    def __init__(self, config: AutoencoderConfig):
        super().__init__()
        self.config = config

        # Build decoder layers (reverse of encoder)
        layers = []
        input_dim = config.latent_dim
        decoder_dims = config.decoder_dims + [config.input_dim]

        for i, hidden_dim in enumerate(decoder_dims):
            layers.append(nn.Linear(input_dim, hidden_dim))

            # Don't add batch norm, activation, or dropout to the final layer
            if i < len(decoder_dims) - 1:
                if config.use_batch_norm:
                    layers.append(nn.BatchNorm1d(hidden_dim))

                layers.append(self._get_activation(config.activation))

                if config.dropout_rate > 0:
                    layers.append(nn.Dropout(config.dropout_rate))
            else:
                # Final layer - add appropriate activation based on reconstruction loss
                if config.reconstruction_loss == "bce":
                    layers.append(nn.Sigmoid())

            input_dim = hidden_dim

        self.decoder = nn.Sequential(*layers)

    def _get_activation(self, activation: str) -> nn.Module:
        """Get activation function by name."""
        activations = {
            "relu": nn.ReLU(),
            "tanh": nn.Tanh(),
            "sigmoid": nn.Sigmoid(),
            "leaky_relu": nn.LeakyReLU(),
            "gelu": nn.GELU(),
            "swish": nn.SiLU(),
            "silu": nn.SiLU(),
            "elu": nn.ELU(),
            "prelu": nn.PReLU(),
            "relu6": nn.ReLU6(),
            "hardtanh": nn.Hardtanh(),
            "hardsigmoid": nn.Hardsigmoid(),
            "hardswish": nn.Hardswish(),
            "mish": nn.Mish(),
            "softplus": nn.Softplus(),
            "softsign": nn.Softsign(),
            "tanhshrink": nn.Tanhshrink(),
            "threshold": nn.Threshold(threshold=0.1, value=0),
        }
        return activations[activation]

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass through decoder."""
        return self.decoder(x)


class RecurrentEncoder(nn.Module):
    """Recurrent encoder for sequence data."""

    def __init__(self, config: AutoencoderConfig):
        super().__init__()
        self.config = config

        # Get RNN class
        if config.rnn_type == "lstm":
            rnn_class = nn.LSTM
        elif config.rnn_type == "gru":
            rnn_class = nn.GRU
        elif config.rnn_type == "rnn":
            rnn_class = nn.RNN
        else:
            raise ValueError(f"Unknown RNN type: {config.rnn_type}")

        # Create RNN layers
        self.rnn = rnn_class(
            input_size=config.input_dim,
            hidden_size=config.latent_dim,
            num_layers=config.num_layers,
            batch_first=True,
            dropout=config.dropout_rate if config.num_layers > 1 else 0,
            bidirectional=config.bidirectional
        )

        # Projection layer for bidirectional RNN
        if config.bidirectional:
            self.projection = nn.Linear(config.latent_dim * 2, config.latent_dim)
        else:
            self.projection = None

        # Batch normalization
        if config.use_batch_norm:
            self.batch_norm = nn.BatchNorm1d(config.latent_dim)
        else:
            self.batch_norm = None

        # Dropout
        if config.dropout_rate > 0:
            self.dropout = nn.Dropout(config.dropout_rate)
        else:
            self.dropout = None

    def forward(self, x: torch.Tensor, lengths: Optional[torch.Tensor] = None) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
        """
        Forward pass through recurrent encoder.

        Args:
            x: Input tensor of shape (batch_size, seq_len, input_dim)
            lengths: Sequence lengths for packed sequences (optional)

        Returns:
            Encoded representation or tuple for VAE
        """
        batch_size, seq_len, _ = x.shape

        # Add noise for denoising autoencoders
        if self.config.is_denoising and self.training:
            noise = torch.randn_like(x) * self.config.noise_factor
            x = x + noise

        # Pack sequences if lengths provided
        if lengths is not None:
            x = nn.utils.rnn.pack_padded_sequence(x, lengths, batch_first=True, enforce_sorted=False)

        # RNN forward pass
        if self.config.rnn_type == "lstm":
            output, (hidden, cell) = self.rnn(x)
        else:
            output, hidden = self.rnn(x)
            cell = None

        # Unpack if necessary
        if lengths is not None:
            output, _ = nn.utils.rnn.pad_packed_sequence(output, batch_first=True)

        # Use last hidden state as encoding
        if self.config.bidirectional:
            # Concatenate forward and backward hidden states
            hidden = hidden.view(self.config.num_layers, 2, batch_size, self.config.latent_dim)
            hidden = hidden[-1]  # Take last layer
            hidden = hidden.transpose(0, 1).contiguous().view(batch_size, -1)  # Concatenate directions

            # Project to latent dimension
            if self.projection:
                hidden = self.projection(hidden)
        else:
            hidden = hidden[-1]  # Take last layer

        # Apply batch normalization
        if self.batch_norm:
            hidden = self.batch_norm(hidden)

        # Apply dropout
        if self.dropout and self.training:
            hidden = self.dropout(hidden)

        # Handle variational encoding
        if self.config.is_variational:
            # Split hidden into mean and log variance
            mu = hidden[:, :self.config.latent_dim // 2]
            logvar = hidden[:, self.config.latent_dim // 2:]

            # Reparameterization trick
            if self.training:
                std = torch.exp(0.5 * logvar)
                eps = torch.randn_like(std)
                z = mu + eps * std
            else:
                z = mu

            return z, mu, logvar
        else:
            return hidden


class RecurrentDecoder(nn.Module):
    """Recurrent decoder for sequence data."""

    def __init__(self, config: AutoencoderConfig):
        super().__init__()
        self.config = config

        # Get RNN class
        if config.rnn_type == "lstm":
            rnn_class = nn.LSTM
        elif config.rnn_type == "gru":
            rnn_class = nn.GRU
        elif config.rnn_type == "rnn":
            rnn_class = nn.RNN
        else:
            raise ValueError(f"Unknown RNN type: {config.rnn_type}")

        # Create RNN layers
        self.rnn = rnn_class(
            input_size=config.latent_dim,
            hidden_size=config.latent_dim,
            num_layers=config.num_layers,
            batch_first=True,
            dropout=config.dropout_rate if config.num_layers > 1 else 0,
            bidirectional=False  # Decoder is always unidirectional
        )

        # Output projection
        self.output_projection = nn.Linear(config.latent_dim, config.input_dim)

        # Batch normalization
        if config.use_batch_norm:
            self.batch_norm = nn.BatchNorm1d(config.latent_dim)
        else:
            self.batch_norm = None

        # Dropout
        if config.dropout_rate > 0:
            self.dropout = nn.Dropout(config.dropout_rate)
        else:
            self.dropout = None

    def forward(self, z: torch.Tensor, target_length: int, target_sequence: Optional[torch.Tensor] = None) -> torch.Tensor:
        """
        Forward pass through recurrent decoder.

        Args:
            z: Latent representation of shape (batch_size, latent_dim)
            target_length: Length of sequence to generate
            target_sequence: Target sequence for teacher forcing (optional)

        Returns:
            Decoded sequence of shape (batch_size, seq_len, input_dim)
        """
        batch_size = z.size(0)
        device = z.device

        # Initialize hidden state with latent representation
        if self.config.rnn_type == "lstm":
            h_0 = z.unsqueeze(0).repeat(self.config.num_layers, 1, 1)
            c_0 = torch.zeros_like(h_0)
            hidden = (h_0, c_0)
        else:
            hidden = z.unsqueeze(0).repeat(self.config.num_layers, 1, 1)

        outputs = []

        # Initialize input (can be learned or zero)
        current_input = torch.zeros(batch_size, 1, self.config.latent_dim, device=device)

        for t in range(target_length):
            # Teacher forcing decision
            use_teacher_forcing = (target_sequence is not None and
                                 self.training and
                                 random.random() < self.config.teacher_forcing_ratio)

            if use_teacher_forcing and t > 0:
                # Use previous target as input
                current_input = target_sequence[:, t-1:t, :]
                # Project to latent dimension if needed
                if current_input.size(-1) != self.config.latent_dim:
                    current_input = torch.zeros(batch_size, 1, self.config.latent_dim, device=device)

            # RNN forward step
            if self.config.rnn_type == "lstm":
                output, hidden = self.rnn(current_input, hidden)
            else:
                output, hidden = self.rnn(current_input, hidden)

            # Apply batch normalization and dropout
            output_flat = output.squeeze(1)  # Remove sequence dimension

            if self.batch_norm:
                output_flat = self.batch_norm(output_flat)

            if self.dropout and self.training:
                output_flat = self.dropout(output_flat)

            # Project to output dimension
            step_output = self.output_projection(output_flat)
            outputs.append(step_output.unsqueeze(1))

            # Use output as next input (for non-teacher forcing)
            if not use_teacher_forcing:
                # Project output back to latent dimension for next step
                current_input = torch.zeros(batch_size, 1, self.config.latent_dim, device=device)

        # Concatenate all outputs
        return torch.cat(outputs, dim=1)


class AutoencoderModel(PreTrainedModel):
    """
    The bare Autoencoder Model transformer outputting raw hidden-states without any specific head on top.

    This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the
    PyTorch documentation for all matter related to general usage and behavior.
    """

    config_class = AutoencoderConfig
    base_model_prefix = "autoencoder"
    supports_gradient_checkpointing = False

    def __init__(self, config: AutoencoderConfig):
        super().__init__(config)
        self.config = config

        # Initialize learnable preprocessing
        if config.has_preprocessing:
            self.preprocessor = LearnablePreprocessor(config)
        else:
            self.preprocessor = None

        # Initialize encoder and decoder based on type
        if config.is_recurrent:
            self.encoder = RecurrentEncoder(config)
            self.decoder = RecurrentDecoder(config)
        else:
            self.encoder = AutoencoderEncoder(config)
            self.decoder = AutoencoderDecoder(config)

        # Tie weights if specified
        if config.tie_weights:
            self._tie_weights()

        # Initialize weights
        self.post_init()

    def _tie_weights(self):
        """Tie encoder and decoder weights (transpose relationship)."""
        # This is a simplified weight tying - in practice, you might want more sophisticated tying
        pass

    def get_input_embeddings(self):
        """Get input embeddings (not applicable for basic autoencoder)."""
        return None

    def set_input_embeddings(self, value):
        """Set input embeddings (not applicable for basic autoencoder)."""
        pass

    def forward(
        self,
        input_values: torch.Tensor,
        sequence_lengths: Optional[torch.Tensor] = None,
        target_length: Optional[int] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], AutoencoderOutput]:
        """
        Forward pass through the autoencoder.

        Args:
            input_values (torch.Tensor): Input tensor. Shape depends on autoencoder type:
                - Standard: (batch_size, input_dim)
                - Recurrent: (batch_size, seq_len, input_dim)
            sequence_lengths (torch.Tensor, optional): Sequence lengths for recurrent AE.
            target_length (int, optional): Target sequence length for recurrent decoder.
            output_hidden_states (bool, optional): Whether to return hidden states.
            return_dict (bool, optional): Whether to return a ModelOutput instead of a plain tuple.

        Returns:
            AutoencoderOutput or tuple: The model outputs.
        """
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # Apply learnable preprocessing
        preprocessing_loss = torch.tensor(0.0, device=input_values.device)
        if self.preprocessor is not None:
            input_values, preprocessing_loss = self.preprocessor(input_values, inverse=False)

        # Handle different autoencoder types
        if self.config.is_recurrent:
            # Recurrent autoencoder
            if sequence_lengths is not None:
                encoder_output = self.encoder(input_values, sequence_lengths)
            else:
                encoder_output = self.encoder(input_values)

            if self.config.is_variational:
                latent, mu, logvar = encoder_output
                self._mu = mu
                self._logvar = logvar
            else:
                latent = encoder_output
                self._mu = None
                self._logvar = None

            # Determine target length for decoder
            if target_length is None:
                if self.config.sequence_length is not None:
                    target_length = self.config.sequence_length
                else:
                    target_length = input_values.size(1)  # Use input sequence length

            # Decode latent back to sequence space
            reconstructed = self.decoder(latent, target_length, input_values if self.training else None)
        else:
            # Standard autoencoder
            encoder_output = self.encoder(input_values)

            if self.config.is_variational:
                latent, mu, logvar = encoder_output
                self._mu = mu
                self._logvar = logvar
            else:
                latent = encoder_output
                self._mu = None
                self._logvar = None

            # Decode latent back to input space
            reconstructed = self.decoder(latent)

        # Apply inverse preprocessing to reconstruction
        if self.preprocessor is not None and self.config.learn_inverse_preprocessing:
            reconstructed, inverse_loss = self.preprocessor(reconstructed, inverse=True)
            preprocessing_loss += inverse_loss

        hidden_states = None
        if output_hidden_states:
            if self.config.is_variational:
                hidden_states = (latent, mu, logvar)
            else:
                hidden_states = (latent,)

        if not return_dict:
            return tuple(v for v in [latent, reconstructed, hidden_states] if v is not None)

        return AutoencoderOutput(
            last_hidden_state=latent,
            reconstructed=reconstructed,
            hidden_states=hidden_states,
            preprocessing_loss=preprocessing_loss,
        )


class AutoencoderForReconstruction(PreTrainedModel):
    """
    Autoencoder Model with a reconstruction head on top for reconstruction tasks.

    This model inherits from PreTrainedModel and adds a reconstruction loss calculation.
    """

    config_class = AutoencoderConfig
    base_model_prefix = "autoencoder"

    def __init__(self, config: AutoencoderConfig):
        super().__init__(config)
        self.config = config

        # Initialize the base autoencoder model
        self.autoencoder = AutoencoderModel(config)

        # Initialize weights
        self.post_init()

    def get_input_embeddings(self):
        """Get input embeddings."""
        return self.autoencoder.get_input_embeddings()

    def set_input_embeddings(self, value):
        """Set input embeddings."""
        self.autoencoder.set_input_embeddings(value)

    def _compute_reconstruction_loss(
        self,
        reconstructed: torch.Tensor,
        target: torch.Tensor
    ) -> torch.Tensor:
        """Compute reconstruction loss based on the configured loss type."""
        if self.config.reconstruction_loss == "mse":
            return F.mse_loss(reconstructed, target, reduction="mean")
        elif self.config.reconstruction_loss == "bce":
            return F.binary_cross_entropy_with_logits(reconstructed, target, reduction="mean")
        elif self.config.reconstruction_loss == "l1":
            return F.l1_loss(reconstructed, target, reduction="mean")
        elif self.config.reconstruction_loss == "huber":
            return F.huber_loss(reconstructed, target, reduction="mean")
        elif self.config.reconstruction_loss == "smooth_l1":
            return F.smooth_l1_loss(reconstructed, target, reduction="mean")
        elif self.config.reconstruction_loss == "kl_div":
            return F.kl_div(F.log_softmax(reconstructed, dim=-1), F.softmax(target, dim=-1), reduction="mean")
        elif self.config.reconstruction_loss == "cosine":
            return 1 - F.cosine_similarity(reconstructed, target, dim=-1).mean()
        elif self.config.reconstruction_loss == "focal":
            return self._focal_loss(reconstructed, target)
        elif self.config.reconstruction_loss == "dice":
            return self._dice_loss(reconstructed, target)
        elif self.config.reconstruction_loss == "tversky":
            return self._tversky_loss(reconstructed, target)
        elif self.config.reconstruction_loss == "ssim":
            return self._ssim_loss(reconstructed, target)
        elif self.config.reconstruction_loss == "perceptual":
            return self._perceptual_loss(reconstructed, target)
        else:
            raise ValueError(f"Unknown reconstruction loss: {self.config.reconstruction_loss}")

    def _focal_loss(self, pred: torch.Tensor, target: torch.Tensor, alpha: float = 1.0, gamma: float = 2.0) -> torch.Tensor:
        """Compute focal loss for handling class imbalance."""
        ce_loss = F.mse_loss(pred, target, reduction="none")
        pt = torch.exp(-ce_loss)
        focal_loss = alpha * (1 - pt) ** gamma * ce_loss
        return focal_loss.mean()

    def _dice_loss(self, pred: torch.Tensor, target: torch.Tensor, smooth: float = 1e-6) -> torch.Tensor:
        """Compute Dice loss for segmentation-like tasks."""
        pred_flat = pred.view(-1)
        target_flat = target.view(-1)
        intersection = (pred_flat * target_flat).sum()
        dice = (2.0 * intersection + smooth) / (pred_flat.sum() + target_flat.sum() + smooth)
        return 1 - dice

    def _tversky_loss(self, pred: torch.Tensor, target: torch.Tensor, alpha: float = 0.7, beta: float = 0.3, smooth: float = 1e-6) -> torch.Tensor:
        """Compute Tversky loss, a generalization of Dice loss."""
        pred_flat = pred.view(-1)
        target_flat = target.view(-1)
        true_pos = (pred_flat * target_flat).sum()
        false_neg = (target_flat * (1 - pred_flat)).sum()
        false_pos = ((1 - target_flat) * pred_flat).sum()
        tversky = (true_pos + smooth) / (true_pos + alpha * false_neg + beta * false_pos + smooth)
        return 1 - tversky

    def _ssim_loss(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        """Compute SSIM-based loss (simplified version)."""
        # Simplified SSIM for 1D data
        mu1 = pred.mean(dim=-1, keepdim=True)
        mu2 = target.mean(dim=-1, keepdim=True)
        sigma1_sq = ((pred - mu1) ** 2).mean(dim=-1, keepdim=True)
        sigma2_sq = ((target - mu2) ** 2).mean(dim=-1, keepdim=True)
        sigma12 = ((pred - mu1) * (target - mu2)).mean(dim=-1, keepdim=True)

        c1, c2 = 0.01, 0.03
        ssim = ((2 * mu1 * mu2 + c1) * (2 * sigma12 + c2)) / ((mu1**2 + mu2**2 + c1) * (sigma1_sq + sigma2_sq + c2))
        return 1 - ssim.mean()

    def _perceptual_loss(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        """Compute perceptual loss (simplified version using feature differences)."""
        # For simplicity, use L2 loss on normalized features
        pred_norm = F.normalize(pred, p=2, dim=-1)
        target_norm = F.normalize(target, p=2, dim=-1)
        return F.mse_loss(pred_norm, target_norm)

    def forward(
        self,
        input_values: torch.Tensor,
        labels: Optional[torch.Tensor] = None,
        sequence_lengths: Optional[torch.Tensor] = None,
        target_length: Optional[int] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], AutoencoderForReconstructionOutput]:
        """
        Forward pass with reconstruction loss calculation.

        Args:
            input_values (torch.Tensor): Input tensor. Shape depends on autoencoder type:
                - Standard: (batch_size, input_dim)
                - Recurrent: (batch_size, seq_len, input_dim)
            labels (torch.Tensor, optional): Target tensor for reconstruction. If None, uses input_values.
            sequence_lengths (torch.Tensor, optional): Sequence lengths for recurrent AE.
            target_length (int, optional): Target sequence length for recurrent decoder.
            output_hidden_states (bool, optional): Whether to return hidden states.
            return_dict (bool, optional): Whether to return a ModelOutput instead of a plain tuple.

        Returns:
            AutoencoderForReconstructionOutput or tuple: The model outputs including loss.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # If no labels provided, use input as target (standard autoencoder)
        if labels is None:
            labels = input_values

        # Forward pass through autoencoder
        outputs = self.autoencoder(
            input_values=input_values,
            sequence_lengths=sequence_lengths,
            target_length=target_length,
            output_hidden_states=output_hidden_states,
            return_dict=True,
        )

        reconstructed = outputs.reconstructed
        latent = outputs.last_hidden_state
        hidden_states = outputs.hidden_states

        # Compute reconstruction loss
        recon_loss = self._compute_reconstruction_loss(reconstructed, labels)

        # Add regularization losses based on autoencoder type
        total_loss = recon_loss

        # Add preprocessing loss if available
        if hasattr(outputs, 'preprocessing_loss') and outputs.preprocessing_loss is not None:
            total_loss += outputs.preprocessing_loss

        if self.config.is_variational and hasattr(self.autoencoder, '_mu') and self.autoencoder._mu is not None:
            # KL divergence loss for variational autoencoders
            kl_loss = -0.5 * torch.sum(1 + self.autoencoder._logvar - self.autoencoder._mu.pow(2) - self.autoencoder._logvar.exp())
            kl_loss = kl_loss / (self.autoencoder._mu.size(0) * self.autoencoder._mu.size(1))  # Normalize by batch size and latent dim
            total_loss = recon_loss + self.config.beta * kl_loss

        elif self.config.is_sparse:
            # Sparsity loss for sparse autoencoders
            latent = outputs.last_hidden_state
            sparsity_loss = torch.mean(torch.abs(latent))  # L1 sparsity
            total_loss = recon_loss + 0.1 * sparsity_loss  # Sparsity weight

        elif self.config.is_contractive:
            # Contractive loss - penalize large gradients of hidden representation w.r.t. input
            latent = outputs.last_hidden_state
            latent.retain_grad()
            if latent.grad is not None:
                contractive_loss = torch.sum(latent.grad ** 2)
                total_loss = recon_loss + 0.1 * contractive_loss

        loss = total_loss

        if not return_dict:
            output = (reconstructed, latent)
            if hidden_states is not None:
                output = output + (hidden_states,)
            return ((loss,) + output) if loss is not None else output

        return AutoencoderForReconstructionOutput(
            loss=loss,
            reconstructed=reconstructed,
            last_hidden_state=latent,
            hidden_states=hidden_states,
            preprocessing_loss=outputs.preprocessing_loss if hasattr(outputs, 'preprocessing_loss') else None,
        )