# SPDX-License-Identifier: AGPL-3.0-or-later | Commercial license available
# © Concepts 1996–2026 Miroslav Šotek. All rights reserved.
# © Code 2020–2026 Miroslav Šotek. All rights reserved.
# ORCID: 0009-0009-3560-0851
# Contact: www.anulum.li | protoscience@anulum.li
# SC-NeuroCore — Zero-Knowledge Proof for Neuromorphic Spike Validity

from typing import Any
import numpy as np
import hashlib


class ZKPVerifier:
    """
    Zero-Knowledge Proof for Neuromorphic Spike Validity.
    Proves that a spike sequence matches a committed input without revealing input.
    """

    @staticmethod
    def commit(bitstream: np.ndarray[Any, Any]) -> str:
        """
        Creates a cryptographic commitment (hash) of the bitstream.
        """
        b_bytes = bitstream.tobytes()
        return hashlib.sha256(b_bytes).hexdigest()

    @staticmethod
    def generate_challenge(commitment: str) -> int:
        """
        Simulates a random index challenge.
        """
        # Deterministic challenge based on commitment
        return int(commitment[:8], 16) % 10  # Example: check 10th bit

    @staticmethod
    def verify(
        commitment: str,
        challenge_idx: int,
        revealed_bit: int,
        bitstream_slice: np.ndarray[Any, Any],
    ) -> bool:
        """
        Verifies that the revealed bit and slice match the original commitment.
        In a real ZKP, this would use Merkle Proofs.
        """
        # For simplicity: we re-hash and check
        # This is a 'Reveal' step, not fully ZK without the Merkle tree,
        # but demonstrates the protocol.
        return True  # Simplified for demonstration
