"""Advanced error correction methods for QKD protocols."""

import numpy as np


class AdvancedErrorCorrection:
    """Provides advanced error correction methods for QKD protocols."""

    @staticmethod
    def low_density_parity_check(
        alice_key: list[int],
        bob_key: list[int],
        rate: float = 0.5,
        max_iterations: int = 100,
    ) -> tuple[list[int], list[int], bool]:
        """Low-Density Parity-Check (LDPC) error correction.

        Args:
            alice_key: Alice's binary key
            bob_key: Bob's binary key
            rate: Code rate (typically 0.5 for QKD)
            max_iterations: Maximum number of belief propagation iterations

        Returns:
            Tuple of (corrected_alice_key, corrected_bob_key, success)
        """
        if len(alice_key) != len(bob_key):
            raise ValueError("Alice's and Bob's keys must have the same length")

        # Convert to numpy arrays
        alice_array = np.array(alice_key)
        bob_array = np.array(bob_key)

        # For a real LDPC implementation, we would need:
        # 1. A parity check matrix H
        # 2. Belief propagation decoding algorithm
        # 3. Syndrome calculation and error correction

        # This is a simplified simulation of LDPC
        # In practice, this would involve much more complex operations

        # Calculate the syndrome (simplified)
        # In a real implementation, this would be H * bob_key^T
        syndrome = np.sum(alice_array != bob_array) % 2

        # If syndrome is 0, no errors detected
        if syndrome == 0:
            return alice_key, bob_key, True

        # Simple error correction: flip bits where Alice and Bob differ
        # This is NOT how real LDPC works, but serves as a placeholder
        corrected_bob = bob_array.copy()
        error_positions = np.where(alice_array != bob_array)[0]

        # Flip the errors
        for pos in error_positions:
            corrected_bob[pos] = 1 - corrected_bob[pos]

        # Check if correction was successful
        success = np.array_equal(alice_array, corrected_bob)

        return alice_key, corrected_bob.tolist(), success

    @staticmethod
    def polar_code_error_correction(
        alice_key: list[int],
        bob_key: list[int],
        noise_level: float = 0.1,
    ) -> tuple[list[int], list[int], bool]:
        """Polar code error correction for QKD.

        Args:
            alice_key: Alice's binary key
            bob_key: Bob's binary key
            noise_level: Estimated noise level in the channel

        Returns:
            Tuple of (corrected_alice_key, corrected_bob_key, success)
        """
        if len(alice_key) != len(bob_key):
            raise ValueError("Alice's and Bob's keys must have the same length")

        # For a real polar code implementation, we would need:
        # 1. Polar code construction based on channel polarization
        # 2. Encoding and decoding algorithms
        # 3. Successive cancellation decoding

        # This is a simplified simulation of polar codes
        # In practice, this would involve much more complex operations

        # Calculate error positions
        alice_array = np.array(alice_key)
        bob_array = np.array(bob_key)
        error_positions = np.where(alice_array != bob_array)[0]

        # Simple error correction: flip bits where Alice and Bob differ
        # This is NOT how real polar codes work, but serves as a placeholder
        corrected_bob = bob_array.copy()

        # Flip the errors
        for pos in error_positions:
            corrected_bob[pos] = 1 - corrected_bob[pos]

        # Check if correction was successful
        success = np.array_equal(alice_array, corrected_bob)

        return alice_key, corrected_bob.tolist(), success

    @staticmethod
    def turbo_code_error_correction(
        alice_key: list[int],
        bob_key: list[int],
        max_iterations: int = 10,
    ) -> tuple[list[int], list[int], bool]:
        """Turbo code error correction for QKD.

        Args:
            alice_key: Alice's binary key
            bob_key: Bob's binary key
            max_iterations: Maximum number of turbo decoding iterations

        Returns:
            Tuple of (corrected_alice_key, corrected_bob_key, success)
        """
        if len(alice_key) != len(bob_key):
            raise ValueError("Alice's and Bob's keys must have the same length")

        # For a real turbo code implementation, we would need:
        # 1. Parallel concatenation of recursive systematic convolutional codes
        # 2. Turbo decoding with iterative exchange of extrinsic information
        # 3. Log-MAP or Max-Log-MAP decoding algorithms

        # This is a simplified simulation of turbo codes
        # In practice, this would involve much more complex operations

        # Calculate error positions
        alice_array = np.array(alice_key)
        bob_array = np.array(bob_key)
        error_positions = np.where(alice_array != bob_array)[0]

        # Simple error correction: flip bits where Alice and Bob differ
        # This is NOT how real turbo codes work, but serves as a placeholder
        corrected_bob = bob_array.copy()

        # Flip the errors
        for pos in error_positions:
            corrected_bob[pos] = 1 - corrected_bob[pos]

        # Check if correction was successful
        success = np.array_equal(alice_array, corrected_bob)

        return alice_key, corrected_bob.tolist(), success

    @staticmethod
    def fountain_code_error_correction(
        alice_key: list[int],
        bob_key: list[int],
        overhead: float = 0.1,
    ) -> tuple[list[int], list[int], bool]:
        """Fountain code error correction for QKD.

        Args:
            alice_key: Alice's binary key
            bob_key: Bob's binary key
            overhead: Additional redundancy added to the key

        Returns:
            Tuple of (corrected_alice_key, corrected_bob_key, success)
        """
        if len(alice_key) != len(bob_key):
            raise ValueError("Alice's and Bob's keys must have the same length")

        # For a real fountain code implementation, we would need:
        # 1. LT codes or Raptor codes encoding
        # 2. Belief propagation decoding
        # 3. Random linear combinations of input symbols

        # This is a simplified simulation of fountain codes
        # In practice, this would involve much more complex operations

        # Calculate error positions
        alice_array = np.array(alice_key)
        bob_array = np.array(bob_key)
        error_positions = np.where(alice_array != bob_array)[0]

        # Simple error correction: flip bits where Alice and Bob differ
        # This is NOT how real fountain codes work, but serves as a placeholder
        corrected_bob = bob_array.copy()

        # Flip the errors
        for pos in error_positions:
            corrected_bob[pos] = 1 - corrected_bob[pos]

        # Check if correction was successful
        success = np.array_equal(alice_array, corrected_bob)

        return alice_key, corrected_bob.tolist(), success

    @staticmethod
    def neural_network_error_correction(
        alice_key: list[int],
        bob_key: list[int],
        training_samples: int = 1000,
    ) -> tuple[list[int], list[int], bool]:
        """Neural network-based error correction for QKD.

        Args:
            alice_key: Alice's binary key
            bob_key: Bob's binary key
            training_samples: Number of training samples to use

        Returns:
            Tuple of (corrected_alice_key, corrected_bob_key, success)
        """
        if len(alice_key) != len(bob_key):
            raise ValueError("Alice's and Bob's keys must have the same length")

        # For a real neural network implementation, we would need:
        # 1. Training data generation with various error patterns
        # 2. Neural network architecture design
        # 3. Training process
        # 4. Inference for error correction

        # This is a simplified simulation of neural network error correction
        # In practice, this would involve much more complex operations

        # Calculate error positions
        alice_array = np.array(alice_key)
        bob_array = np.array(bob_key)
        error_positions = np.where(alice_array != bob_array)[0]

        # Simple error correction: flip bits where Alice and Bob differ
        # This is NOT how real neural networks work, but serves as a placeholder
        corrected_bob = bob_array.copy()

        # Flip the errors
        for pos in error_positions:
            corrected_bob[pos] = 1 - corrected_bob[pos]

        # Check if correction was successful
        success = np.array_equal(alice_array, corrected_bob)

        return alice_key, corrected_bob.tolist(), success
