# 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 L14 Transdimensional Resonance Adapter (JAX

"""
SCPN L14: Transdimensional Resonance Adapter (JAX Implementation)
==================================================================

This module implements the JAX-accelerated uplift of Layer 14, focusing on
Inter-brane Resonance, Keystone-Frequency Tuning, and the broadcast of
intentional signals across higher-dimensional bulk geometries (Paper 14).

Key Equations:
- Bridge Lagrangian: L_Bridge = integral(Psi_A * Psi_B * dV)
- Keystone Resonance: det(M_resonance) = 0
- Global Tuning: f_keystone = f_local * sqrt(G_bulk / G_local)
"""

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

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

from ..base import BaseStochasticAdapter


@dataclass
class L14_HolonomicParameters:
    """Parameters derived from Paper 14 and Keystone Tuning specs."""

    n_bulk_dimensions: int = 11
    bitstream_length: int = 1024

    # Resonance Constants
    keystone_frequency: float = 144.0  # Hz (Symbolic Anchor)
    resonance_width: float = 0.01
    bulk_coupling: float = 0.25


class L14_TransdimensionalAdapter(BaseStochasticAdapter):
    """
    JAX-traceable adapter for the SCPN Transdimensional layer.
    """

    def __init__(self, params: Optional[L14_HolonomicParameters] = None, seed: int = 414) -> None:
        self.params = params or L14_HolonomicParameters()
        self.rng_key = make_rng(seed)

        # State: Brane Alignment (0.0 to 1.0)
        self.brane_alignment = jnp.zeros((self.params.n_bulk_dimensions,))
        # State: Resonance Intensity
        self.resonance_intensity = jnp.zeros((self.params.n_bulk_dimensions,))

    def encode(self, domain_state: Any) -> jnp.ndarray:
        """
        Maps resonance alignment to stochastic bitstreams.
        """
        self.rng_key, subkey = split_rng(self.rng_key)
        rands = uniform(subkey, (self.params.n_bulk_dimensions, self.params.bitstream_length))
        bitstreams = (rands < self.brane_alignment[:, None]).astype(jnp.uint8)
        return bitstreams

    @staticmethod
    @maybe_jit
    def _resonance_kernel(
        alignment: jnp.ndarray, pta_input: jnp.ndarray, keystone_f: float, dt: float
    ) -> Tuple[jnp.ndarray, jnp.ndarray]:
        """
        Solves the Inter-brane Resonance dynamics:
        dAlignment/dt = overlap(PTA, Keystone) * coupling - dissipation
        """
        # Alignment increases when inputs match the keystone frequency proxy
        # Here we use input coherence as a proxy for frequency alignment
        d_align = 0.1 * pta_input - 0.02 * alignment
        alignment_next = jnp.clip(alignment + d_align * dt, 0.0, 1.0)

        # Intensity maps to the sharpness of the peak
        intensity = jnp.exp(-jnp.abs(alignment_next - 1.0) / 0.1)

        return alignment_next, intensity

    def step_jax(self, dt: float, inputs: Optional[jnp.ndarray] = None) -> jnp.ndarray:
        """
        Advances the L14 holonomic dynamics using JAX.

        inputs: (N, bitstream_length) representing L8 Cosmic PTA signals.
        Returns: (n_bulk_dimensions, bitstream_length) output bitstreams.
        """
        # 1. Extract Cosmic Clock Reference (L8 -> L14)
        if inputs is not None:
            clock_ref = jnp.mean(inputs.astype(jnp.float32), axis=1)
            if clock_ref.shape[0] != self.params.n_bulk_dimensions:
                clock_ref = jnp.full((self.params.n_bulk_dimensions,), jnp.mean(clock_ref))
        else:
            clock_ref = jnp.zeros((self.params.n_bulk_dimensions,))

        # 2. Execute Resonance Kernel
        self.brane_alignment, self.resonance_intensity = self._resonance_kernel(
            self.brane_alignment, clock_ref, self.params.keystone_frequency, dt
        )

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

    def decode(self, bitstreams: jnp.ndarray) -> Dict[str, float]:
        """
        Maps bitstreams back to Brane Alignment index.
        """
        return {"brane_resonance_r14": float(jnp.mean(bitstreams.astype(jnp.float32)))}

    def get_metrics(self) -> Dict[str, float]:
        """
        Returns L14-specific metrics.
        """
        return {
            "avg_brane_alignment": float(jnp.mean(self.brane_alignment)),
            "resonance_sharpness": float(jnp.mean(self.resonance_intensity)),
        }
