# 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 — SCPN L7 Geometrical-Symbolic Adapter (JAX Implementation)

"""
SCPN L7: Geometrical-Symbolic Adapter (JAX Implementation)
==========================================================

This module implements the JAX-accelerated uplift of Layer 7, focusing on
Metatron's Cube as a routing matrix and Symbolic Operators that phase-shift
stochastic bitstreams as described in Paper 7.

Key Equations:
- Symbolic Actuation: P = sum(G_k * exp(i * theta_k))
- Functorial Mapping: S: Geom -> Psi Field
- Routing: Out_m = sum(M_mn * In_n) where M is Metatron's Matrix.
"""

from __future__ import annotations
from dataclasses import dataclass
from typing import Any, Dict, Optional

from ._jax_compat import jnp, make_rng, maybe_jit, split_rng, uniform

from ..base import BaseStochasticAdapter


@dataclass
class L7_HolonomicParameters:
    """Parameters derived from Paper 7 and Metatron's Cube geometry."""

    n_nodes: int = 13  # Standard Metatron's Cube node count
    bitstream_length: int = 1024

    # Symbolic Constants
    g_geometric_gain: float = 1.2
    phi_golden_ratio: float = 1.61803398875

    # Routing Constants
    coupling_leak: float = 0.05


class L7_SymbolicAdapter(BaseStochasticAdapter):
    """
    JAX-traceable adapter for the SCPN Geometrical-Symbolic layer.
    """

    def __init__(self, params: Optional[L7_HolonomicParameters] = None, seed: int = 47) -> None:
        self.params = params or L7_HolonomicParameters()
        self.rng_key = make_rng(seed)

        # State: Node Phases (representing symbolic glyphs)
        self.node_phases = jnp.zeros((self.params.n_nodes,))
        # State: Metatron's Cube Adjacency Matrix (13x13)
        self.metatron_matrix = self._init_metatron_matrix()

    def _init_metatron_matrix(self) -> jnp.ndarray:
        """Initializes the standard Metatron's Cube connection topology."""
        # Simple placeholder for the complex 13-node geometry
        # In a full implementation, this is a specific sparse matrix.
        import numpy as _np

        n = self.params.n_nodes
        m = _np.eye(n) * 0.5
        m[0, :] = 0.1
        return jnp.array(m)

    def encode(self, domain_state: Any) -> jnp.ndarray:
        """
        Maps symbolic phases to stochastic bitstreams.
        """
        # Activation = (1 + cos(phase)) / 2
        activation = (1.0 + jnp.cos(self.node_phases)) / 2.0

        self.rng_key, subkey = split_rng(self.rng_key)
        rands = uniform(subkey, (self.params.n_nodes, self.params.bitstream_length))
        bitstreams = (rands < activation[:, None]).astype(jnp.uint8)
        return bitstreams

    @staticmethod
    @maybe_jit
    def _symbolic_kernel(
        phases: jnp.ndarray, metatron: jnp.ndarray, inputs: jnp.ndarray, dt: float
    ) -> jnp.ndarray:
        """
        Solves the Symbolic routing dynamics:
        dTheta/dt = Metatron * inputs - decay
        """
        # Phases rotate based on weighted inputs from the Metatron routing
        drive = jnp.dot(metatron, inputs)
        d_phase = drive - 0.1 * phases
        return phases + d_phase * dt

    def step_jax(self, dt: float, inputs: Optional[jnp.ndarray] = None) -> jnp.ndarray:
        """
        Advances the L7 holonomic dynamics using JAX.

        inputs: (n_nodes, bitstream_length) representing L6 or L8 signals.
        Returns: (n_nodes, bitstream_length) output bitstreams.
        """
        # 1. Extract Input Influence
        if inputs is not None:
            input_drive = jnp.mean(inputs.astype(jnp.float32), axis=1)
            if input_drive.shape[0] != self.params.n_nodes:
                input_drive = jnp.full((self.params.n_nodes,), jnp.mean(input_drive))
        else:
            input_drive = jnp.zeros((self.params.n_nodes,))

        # 2. Execute Symbolic Kernel
        self.node_phases = self._symbolic_kernel(
            self.node_phases, self.metatron_matrix, input_drive, dt
        )

        # 3. Return encoded bitstreams
        return self.encode(None)

    def decode(self, bitstreams: jnp.ndarray) -> Dict[str, float]:
        """
        Maps bitstreams back to Symbolic Coherence.
        """
        return {"symbolic_unity_r7": float(jnp.abs(jnp.mean(jnp.exp(1j * self.node_phases))))}

    def get_metrics(self) -> Dict[str, float]:
        """
        Returns L7-specific metrics like Routing Density.
        """
        return {
            "routing_coherence": float(jnp.abs(jnp.mean(jnp.exp(1j * self.node_phases)))),
            "metatron_stability": float(jnp.mean(jnp.cos(self.node_phases))),
        }
