"""
Homomorphic Encryption for Federated Learning.

This module provides homomorphic encryption capabilities for secure aggregation
in federated learning, ensuring that server cannot see individual client updates.
"""

import torch
import numpy as np
from typing import Dict, List, Any, Optional, Tuple
import logging
import random

logger = logging.getLogger(__name__)


class MockHomomorphicEncryption:
    """
    Mock implementation of homomorphic encryption for demonstration.
    
    Note: This is a simplified mock implementation for educational purposes.
    In production, use proper HE libraries like Microsoft SEAL, HElib, or Palisade.
    """
    
    def __init__(self, key_size: int = 2048, noise_budget: int = 100):
        """
        Initialize mock homomorphic encryption.
        
        Args:
            key_size: Key size for encryption
            noise_budget: Noise budget for computations
        """
        self.key_size = key_size
        self.noise_budget = noise_budget
        self.public_key = self._generate_public_key()
        self.private_key = self._generate_private_key()
        self.noise_level = 0
        
        logger.info(f"Mock HE initialized with key_size={key_size}")
        logger.warning("This is a mock implementation - use proper HE libraries in production!")
    
    def _generate_public_key(self) -> Dict[str, Any]:
        """Generate mock public key."""
        return {
            "n": random.randint(10**6, 10**7),  # Mock modulus
            "g": random.randint(2, 100),       # Mock generator
            "key_size": self.key_size
        }
    
    def _generate_private_key(self) -> Dict[str, Any]:
        """Generate mock private key."""
        return {
            "lambda": random.randint(1000, 9999),  # Mock lambda
            "mu": random.randint(100, 999)         # Mock mu
        }
    
    def encrypt(self, value: float) -> Dict[str, Any]:
        """
        Encrypt a single value (mock).
        
        Args:
            value: Value to encrypt
            
        Returns:
            Encrypted value (mock ciphertext)
        """
        # Mock encryption: add random noise and apply simple transformation
        noise = random.random() * 0.01  # Small noise for demonstration
        encrypted_value = (value * self.public_key["g"] + noise) % self.public_key["n"]
        
        return {
            "ciphertext": encrypted_value,
            "noise_level": self.noise_level,
            "is_encrypted": True
        }
    
    def decrypt(self, ciphertext: Dict[str, Any]) -> float:
        """
        Decrypt a ciphertext (mock).
        
        Args:
            ciphertext: Encrypted value
            
        Returns:
            Decrypted value
        """
        if not ciphertext.get("is_encrypted", False):
            raise ValueError("Invalid ciphertext")
        
        # Mock decryption: reverse the encryption process
        decrypted = (ciphertext["ciphertext"] / self.public_key["g"]) % self.public_key["n"]
        
        # Remove some of the noise (imperfect in HE)
        return float(decrypted)
    
    def add_encrypted(self, ct1: Dict[str, Any], ct2: Dict[str, Any]) -> Dict[str, Any]:
        """
        Add two encrypted values (mock).
        
        Args:
            ct1: First encrypted value
            ct2: Second encrypted value
            
        Returns:
            Encrypted sum
        """
        if not (ct1.get("is_encrypted") and ct2.get("is_encrypted")):
            raise ValueError("Both values must be encrypted")
        
        # Mock homomorphic addition
        result_ciphertext = (ct1["ciphertext"] + ct2["ciphertext"]) % self.public_key["n"]
        
        # Noise grows with operations
        new_noise_level = max(ct1["noise_level"], ct2["noise_level"]) + 1
        
        if new_noise_level > self.noise_budget:
            logger.warning("Noise budget exceeded - results may be inaccurate")
        
        return {
            "ciphertext": result_ciphertext,
            "noise_level": new_noise_level,
            "is_encrypted": True
        }
    
    def multiply_by_scalar(self, ciphertext: Dict[str, Any], scalar: float) -> Dict[str, Any]:
        """
        Multiply encrypted value by plaintext scalar (mock).
        
        Args:
            ciphertext: Encrypted value
            scalar: Plaintext scalar
            
        Returns:
            Encrypted result
        """
        if not ciphertext.get("is_encrypted"):
            raise ValueError("Value must be encrypted")
        
        # Mock scalar multiplication
        result_ciphertext = (ciphertext["ciphertext"] * scalar) % self.public_key["n"]
        
        return {
            "ciphertext": result_ciphertext,
            "noise_level": ciphertext["noise_level"] + 1,
            "is_encrypted": True
        }


class SecureAggregator:
    """
    Secure aggregator using homomorphic encryption.
    
    Enables secure aggregation where the server can compute the sum
    of client updates without seeing individual contributions.
    """
    
    def __init__(self, use_mock_he: bool = True):
        """
        Initialize secure aggregator.
        
        Args:
            use_mock_he: Whether to use mock HE (True) or raise error for real HE (False)
        """
        self.use_mock_he = use_mock_he
        
        if use_mock_he:
            self.he_scheme = MockHomomorphicEncryption()
            logger.warning("Using mock HE - not secure for production!")
        else:
            raise NotImplementedError("Real HE implementation not available. Set use_mock_he=True for testing.")
        
        self.public_key = self.he_scheme.public_key
    
    def get_public_key(self) -> Dict[str, Any]:
        """Get public key for client encryption."""
        return self.public_key
    
    def encrypt_model_update(self, model_params: Dict[str, torch.Tensor]) -> Dict[str, List[Dict[str, Any]]]:
        """
        Encrypt model parameters element-wise.
        
        Args:
            model_params: Model parameters to encrypt
            
        Returns:
            Encrypted parameters
        """
        encrypted_params = {}
        
        for name, param in model_params.items():
            # Flatten parameter tensor for encryption
            flat_param = param.flatten().cpu().numpy()
            
            # Encrypt each element
            encrypted_elements = []
            for value in flat_param:
                encrypted_value = self.he_scheme.encrypt(float(value))
                encrypted_elements.append(encrypted_value)
            
            encrypted_params[name] = {
                "encrypted_data": encrypted_elements,
                "original_shape": list(param.shape),
                "device": str(param.device)
            }
        
        logger.debug(f"Encrypted model with {len(model_params)} parameter tensors")
        return encrypted_params
    
    def aggregate_encrypted_updates(
        self,
        encrypted_updates: List[Dict[str, Any]]
    ) -> Dict[str, List[Dict[str, Any]]]:
        """
        Aggregate encrypted model updates without decryption.
        
        Args:
            encrypted_updates: List of encrypted client updates
            
        Returns:
            Aggregated encrypted result
        """
        if not encrypted_updates:
            raise ValueError("No encrypted updates provided")
        
        logger.info(f"Aggregating {len(encrypted_updates)} encrypted updates")
        
        # Initialize result with first update
        aggregated = {}
        first_update = encrypted_updates[0]
        
        for param_name, param_data in first_update.items():
            aggregated[param_name] = {
                "encrypted_data": param_data["encrypted_data"].copy(),
                "original_shape": param_data["original_shape"],
                "device": param_data["device"]
            }
        
        # Add remaining updates homomorphically
        for update in encrypted_updates[1:]:
            for param_name in aggregated.keys():
                if param_name not in update:
                    continue
                
                # Add corresponding encrypted elements
                for i, encrypted_element in enumerate(update[param_name]["encrypted_data"]):
                    aggregated[param_name]["encrypted_data"][i] = self.he_scheme.add_encrypted(
                        aggregated[param_name]["encrypted_data"][i],
                        encrypted_element
                    )
        
        logger.info("Homomorphic aggregation completed")
        return aggregated
    
    def decrypt_aggregated_result(
        self,
        encrypted_aggregated: Dict[str, Any],
        num_clients: int
    ) -> Dict[str, torch.Tensor]:
        """
        Decrypt and average the aggregated result.
        
        Args:
            encrypted_aggregated: Encrypted aggregated parameters
            num_clients: Number of clients for averaging
            
        Returns:
            Decrypted and averaged model parameters
        """
        decrypted_params = {}
        
        for param_name, param_data in encrypted_aggregated.items():
            # Decrypt each element
            decrypted_elements = []
            for encrypted_element in param_data["encrypted_data"]:
                decrypted_value = self.he_scheme.decrypt(encrypted_element)
                # Average by dividing by number of clients
                averaged_value = decrypted_value / num_clients
                decrypted_elements.append(averaged_value)
            
            # Reshape back to original form
            decrypted_tensor = torch.tensor(
                decrypted_elements,
                dtype=torch.float32
            ).reshape(param_data["original_shape"])
            
            # Move to original device
            if param_data["device"] != "cpu":
                decrypted_tensor = decrypted_tensor.to(param_data["device"])
            
            decrypted_params[param_name] = decrypted_tensor
        
        logger.info(f"Decrypted aggregated result with {len(decrypted_params)} parameters")
        return decrypted_params
    
    def get_noise_budget_status(self) -> Dict[str, Any]:
        """Get status of noise budget in HE computations."""
        return {
            "current_noise_level": self.he_scheme.noise_level,
            "noise_budget": self.he_scheme.noise_budget,
            "remaining_budget": max(0, self.he_scheme.noise_budget - self.he_scheme.noise_level),
            "budget_percentage": (self.he_scheme.noise_level / self.he_scheme.noise_budget) * 100
        }


class SecureFederatedAggregation:
    """
    Complete secure federated aggregation system.
    
    Orchestrates the entire secure aggregation process including
    key distribution, client encryption, and server aggregation.
    """
    
    def __init__(self, use_mock_he: bool = True):
        """
        Initialize secure federated aggregation.
        
        Args:
            use_mock_he: Whether to use mock homomorphic encryption
        """
        self.secure_aggregator = SecureAggregator(use_mock_he=use_mock_he)
        self.client_count = 0
        self.aggregation_history = []
        
        logger.info("Secure federated aggregation system initialized")
    
    def setup_clients(self, num_clients: int) -> List[Dict[str, Any]]:
        """
        Setup clients with public keys for encryption.
        
        Args:
            num_clients: Number of clients to setup
            
        Returns:
            List of client configurations with public keys
        """
        public_key = self.secure_aggregator.get_public_key()
        
        client_configs = []
        for i in range(num_clients):
            client_config = {
                "client_id": i,
                "public_key": public_key,
                "encryption_ready": True
            }
            client_configs.append(client_config)
        
        self.client_count = num_clients
        logger.info(f"Setup {num_clients} clients with encryption capabilities")
        
        return client_configs
    
    def secure_federated_round(
        self,
        client_updates: List[Dict[str, torch.Tensor]],
        round_num: int
    ) -> Dict[str, torch.Tensor]:
        """
        Perform one round of secure federated aggregation.
        
        Args:
            client_updates: List of client model updates
            round_num: Current round number
            
        Returns:
            Aggregated model parameters
        """
        logger.info(f"Starting secure aggregation round {round_num}")
        
        # Step 1: Encrypt client updates
        encrypted_updates = []
        for i, update in enumerate(client_updates):
            logger.debug(f"Encrypting update from client {i}")
            encrypted_update = self.secure_aggregator.encrypt_model_update(update)
            encrypted_updates.append(encrypted_update)
        
        # Step 2: Perform homomorphic aggregation
        encrypted_aggregated = self.secure_aggregator.aggregate_encrypted_updates(encrypted_updates)
        
        # Step 3: Decrypt final result
        aggregated_params = self.secure_aggregator.decrypt_aggregated_result(
            encrypted_aggregated, len(client_updates)
        )
        
        # Record aggregation history
        noise_status = self.secure_aggregator.get_noise_budget_status()
        self.aggregation_history.append({
            "round": round_num,
            "num_clients": len(client_updates),
            "noise_budget_used": noise_status["budget_percentage"],
            "remaining_noise_budget": noise_status["remaining_budget"]
        })
        
        logger.info(f"Secure aggregation round {round_num} completed")
        return aggregated_params
    
    def get_security_metrics(self) -> Dict[str, Any]:
        """Get security and performance metrics."""
        noise_status = self.secure_aggregator.get_noise_budget_status()
        
        return {
            "encryption_scheme": "Mock Homomorphic Encryption" if self.secure_aggregator.use_mock_he else "Production HE",
            "total_clients": self.client_count,
            "aggregation_rounds": len(self.aggregation_history),
            "noise_budget_status": noise_status,
            "security_level": "Demonstration Only" if self.secure_aggregator.use_mock_he else "Production",
            "aggregation_history": self.aggregation_history
        }


def create_secure_federated_experiment(
    num_clients: int = 10,
    use_mock_he: bool = True
) -> SecureFederatedAggregation:
    """
    Create a secure federated learning experiment.
    
    Args:
        num_clients: Number of clients
        use_mock_he: Whether to use mock homomorphic encryption
        
    Returns:
        Configured secure federated aggregation system
    """
    secure_fed = SecureFederatedAggregation(use_mock_he=use_mock_he)
    client_configs = secure_fed.setup_clients(num_clients)
    
    logger.info(f"Created secure federated experiment with {num_clients} clients")
    
    if use_mock_he:
        logger.warning("Using mock encryption - not secure for production use!")
    
    return secure_fed