#!/usr/bin/env python3
# ==============================================================================
# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
# PROJECT: SOVEREIGN STACK
# This artifact is entirely proprietary and cryptographically proven.
# Open-Source usage requires explicit permission from Brandon Scott Schneider.
# ==============================================================================
# PTOS: LAYER=SECURITY / DOMAIN=ISOLATION / CONDITION=STABLE / STAGE=ACTIVE / SOURCE=CODE
"""
Secret Sub-Register Binder v4.0 — High-speed GPU/Neuromorph binding.
==================================================================
Binds .secrets/* files to the 11D Neuromorphic GPU Surface using TSM-AAC opcodes.
"""

import os
import json
import hashlib
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from math_harness_compat import xp, AnyArray
from pathlib import Path

REPO_ROOT = Path(os.getenv("RESEARCH_STACK_ROOT") or Path(__file__).resolve().parents[1])
DOWNLOADS_ROOT = Path(os.getenv("DOWNLOADS_ROOT") or Path.home() / "Downloads")

# Add paths for dependencies
sys.path.append(str(DOWNLOADS_ROOT))
from tsm_aac_mcp_harness import TSMAACKernel, TermType
from neuromorphic_soliton_miner import NeuromorphicGPUSurface, SolitonCollisionEngine

ROOT = REPO_ROOT
SECRETS_DIR = ROOT / ".secrets"
DATA_DIR = ROOT / "data"

# ── NE geometry scaffold (geometry-rip branch) ────────────────────────────────
# Fixes EUCLIDEAN_ASSUMPTION_AUDIT finding #10 (MEDIUM): arithmetic mean of
# bimodal (potentiated/depressed) Hebbian weights. Mean is wrong for bimodal.
# Fix: use xp.median or fraction-above-0.5 as more geometrically meaningful proxy.
_USE_NE_GEOMETRY = False

class SecretBinder:
    def __init__(self):
        self.kernel = TSMAACKernel()
        self.surface = NeuromorphicGPUSurface(num_neurons=2048, dimensions=11)
        self.soliton_engine = SolitonCollisionEngine(dimensions=11)
        self.bindings = {}

    def bind_secret(self, secret_path: Path):
        """Bind a secret using TSM-AAC opcodes 0x5F and 0x0E."""
        content = secret_path.read_bytes()
        secret_hash = hashlib.sha256(content).hexdigest()
        
        print(f"[bind] Binding {secret_path.name} (hash: {secret_hash[:16]}...)")
        
        # 1. PHI-locked seed from hash
        seed = int(secret_hash[:16], 16)
        xp.random.seed(seed % (2**32))
        
        # 2. Execute 0x01: ABSORB_BH
        # This "melts" the data into the manifold state-space
        state_id = self.kernel.absorb_bh(content.decode('utf-8', errors='ignore'), {"pkg": secret_path.name, "tier": "DIAMONDOID_HYDRIDE"})

        # 3. Execute 0x5F: LANE_REGISTER_BIND
        reg_id = f"R{seed % 16:02d}"
        bind_result = self.kernel.execute([("0x5F", [state_id, reg_id])])
        
        # 3. Generate and Collide Solitons
        s1 = self.soliton_engine.generate_soliton_packet(seed)
        s2 = self.soliton_engine.generate_soliton_packet(seed ^ 0xFFFFFFFF)
        collapsed = self.soliton_engine.collide_solitons(s1, s2)
        
        # 4. Execute 0x0E: NEUROMORPH_COLLISION
        # Reinforces the binding through the neural surface
        sub_register_id = f"subreg_{hashlib.sha256(collapsed.packet_id.encode()).hexdigest()[:12]}"
        self.kernel.execute([("0x0E", [sub_register_id, {"phi_resonance": 0.618034}])])
        
        # 5. Bind to Neural Surface (Weights)
        input_vector = collapsed.position / xp.linalg.norm(collapsed.position)
        self.surface.process_input(input_vector)
        self.surface.update_weights(1.0)
        
        self.bindings[secret_path.name] = {
            "sub_register_id": sub_register_id,
            "target_register": reg_id,
            "foam_score": round(float(
                # NE path: median is correct for bimodal Hebbian distributions.
                # AUDIT FINDING #10: arithmetic mean is wrong for bimodal (potentiated/depressed).
                xp.median(self.surface.synaptic_weights) if _USE_NE_GEOMETRY
                else xp.mean(self.surface.synaptic_weights)
            ), 6),
            "nd_point": collapsed.position.tolist(),
            "attestation": bind_result[0]
        }
        
        print(f"       -> Assigned to {reg_id} | {sub_register_id}")

    def save_registry(self):
        """Update the substrate index with new bindings."""
        registry_path = DATA_DIR / "secret_sub_registers.json"
        DATA_DIR.mkdir(exist_ok=True)
        with open(registry_path, 'w') as f:
            json.dump(self.bindings, f, indent=2)
        print(f"\n[save] Secret Sub-Register Registry updated -> {registry_path}")

def main():
    binder = SecretBinder()
    
    if not SECRETS_DIR.exists():
        print(f"Error: Secrets directory {SECRETS_DIR} not found.")
        return

    for secret_file in SECRETS_DIR.iterdir():
        if secret_file.is_file() and not secret_file.name.startswith("."):
            binder.bind_secret(secret_file)
            
    binder.save_registry()

if __name__ == "__main__":
    main()