"""Neural network-based transformation methods."""

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from typing import Dict, Any, Optional

from .base import BaseTransform


class NeuralTransform(BaseTransform):
    """Neural network-based probability transformation.
    
    Uses a small neural network to learn non-linear transformations 
    of probability distributions for calibration.
    """
    
    def __init__(
        self, 
        device: Optional[torch.device] = None, 
        hidden_dim: int = 64,
        num_layers: int = 2,
        name: Optional[str] = None
    ):
        super().__init__(device, name if name is not None else "neural_transform")
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.networks = None
    
    def fit(self, tensor_path: str, **kwargs) -> Dict[str, Any]:
        """Fit neural transformation parameters.
        
        Args:
            tensor_path: Path to tensor file with training data
            **kwargs: Additional arguments
            
        Returns:
            Dictionary with training statistics
        """
        # Placeholder implementation
        # In a full implementation, this would:
        # 1. Load the tensor data
        # 2. Create neural networks for each fraction
        # 3. Train the networks to minimize calibration error
        
        full_tensor = np.load(tensor_path)
        n_fractions, n_samples, n_outputs = full_tensor.shape
        
        # Create placeholder networks (identity transformation)
        self.networks = {}
        for fraction in range(n_fractions):
            self.networks[fraction] = nn.Identity()
        
        self._is_fitted = True
        return {"status": "placeholder_implementation"}
    
    def transform(self, input_tensor: torch.Tensor) -> torch.Tensor:
        """Apply neural transformation."""
        if not self.is_fitted:
            raise ValueError("Transform must be fitted before use. Call fit() first.")
        
        # Placeholder: return input unchanged
        return input_tensor
    
    def save(self, path: str) -> None:
        """Save neural network parameters."""
        # Placeholder implementation
        save_dict = {
            'hidden_dim': self.hidden_dim,
            'num_layers': self.num_layers,
            'networks': 'placeholder'
        }
        np.save(path, save_dict)
    
    def load(self, path: str) -> None:
        """Load neural network parameters."""
        # Placeholder implementation
        save_dict = np.load(path, allow_pickle=True).item()
        self.hidden_dim = save_dict.get('hidden_dim', 64)
        self.num_layers = save_dict.get('num_layers', 2)
        self._is_fitted = True