# 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 — Training loops for stochastic computing networks (RL and

from typing import Any

"""Training loops for stochastic computing networks (RL and multimodal fusion)."""

import logging
import numpy as np
from typing import Callable
from ..synapses.r_stdp import RewardModulatedSTDPSynapse
from ..layers.sc_learning_layer import SCLearningLayer

logger = logging.getLogger(__name__)


class SCTrainingLoop:
    """
    Standard and Reinforcement Learning loops for SC Networks.
    """

    @staticmethod
    def run_rl_epoch(  # type: ignore
        agent: SCLearningLayer,
        env_step_func: Callable[[np.ndarray[Any, Any]], float],
        input_data: np.ndarray[Any, Any],
        generations: int = 10,
    ):
        """
        Runs a reinforcement learning epoch.
        Uses RewardModulatedSTDPSynapse logic.
        """
        for gen in range(generations):
            # 1. Run forward pass
            spikes = agent.run_epoch(input_data)

            # 2. Get reward from environment
            reward = env_step_func(spikes)

            # 3. Apply reward to all synapses
            for i in range(agent.n_neurons):
                for j in range(agent.n_inputs):
                    syn = agent.synapses[i][j]
                    if isinstance(syn, RewardModulatedSTDPSynapse):
                        syn.apply_reward(reward)

            logger.info("RL Epoch %d: Reward = %.4f", gen, reward)

    @staticmethod
    def train_multimodal_fusion(fusion_layer, dataset, epochs: int = 5) -> None:
        """
        Stub for training weights in a fusion layer.
        """
        raise NotImplementedError("multimodal fusion training not implemented")
