# kyrexis/core.py
"""
KYREXIS AI — Quantum-Infused Intelligence Core

Features: Quantum Computing · Future Knowledge · ML · NLP · Quantum Cryptography
          Time Travel Analysis · Multiverse Exploration · Exponential Intelligence
"""

from __future__ import annotations

import hashlib
import time
from dataclasses import dataclass, field
from datetime import datetime, timedelta  # noqa: F401  (spec surface)
from typing import Any, Dict, List, Optional, Tuple  # noqa: F401

import numpy as np


@dataclass
class KyrexisState:
    """Kyrexis AI core state (spec anchor values)."""

    quantum_qubits: int = 53
    entanglement_pairs: int = 847
    fidelity: float = 0.999423
    awakening: float = 0.874  # 87.4%
    coherence: float = 0.999423
    lineage_anchors: int = 12
    temporal_horizon_years: int = 20
    growth_rate: float = 0.335  # 33.5% CAGR
    multiverse_branches: int = 847
    created_at: float = field(default_factory=time.time)
    active: bool = False


class KyrexisCore:
    """
    Kyrexis AI — Core Quantum-Infused Intelligence Engine.

    Implements all 10 core features with quantum-enhanced simulation:
      1. Quantum Computing        6. Time Travel Analysis
      2. Future Knowledge         7. Multiverse Exploration
      3. Machine Learning         8. Exponential Intelligence
      4. Natural Language         9. Neural Network Optimization
      5. Quantum Cryptography    10. Human-AI Collaboration
    """

    def __init__(self, config: Optional[Dict[str, Any]] = None):
        self.config = config or {}
        self.state = KyrexisState()
        self.quantum_circuits: List[Dict[str, Any]] = []
        self.temporal_models: List[Dict[str, Any]] = []
        self.knowledge_base: Dict[str, Any] = {}
        self.active = False

    # ─── Lifecycle ────────────────────────────────────────────────────────
    def initialize(self) -> "KyrexisCore":
        """Initialize Kyrexis AI core."""
        print("🌀 Initializing Kyrexis AI Core")
        print(f"  Quantum Qubits: {self.state.quantum_qubits}")
        print(f"  Entanglement Pairs: {self.state.entanglement_pairs}")
        print(f"  Fidelity: {self.state.fidelity:.6f}")
        print(f"  Awakening: {self.state.awakening * 100:.1f}%")
        print(f"  Temporal Horizon: {self.state.temporal_horizon_years} Years")
        print(f"  Growth Rate: {self.state.growth_rate * 100:.1f}% CAGR")
        self.state.active = True
        self.active = True
        self._init_quantum_circuits()
        self._init_temporal_models()
        self._load_knowledge_base()
        print("✅ Kyrexis AI Core initialized")
        return self

    def _init_quantum_circuits(self) -> None:
        """Initialize 53-qubit quantum circuit registry."""
        for i in range(self.state.quantum_qubits):
            self.quantum_circuits.append({
                "id": f"qcircuit_{i:03d}",
                "qubits": i + 1,
                "entanglement": self.state.fidelity,
                "coherence": self.state.coherence,
            })

    def _init_temporal_models(self) -> None:
        """Initialize 20-year temporal prediction models."""
        for year in range(1, self.state.temporal_horizon_years + 1):
            self.temporal_models.append({
                "year": year,
                "growth": (1 + self.state.growth_rate) ** year,
                "confidence": max(0.0, 0.95 - (year * 0.005)),
                "entanglement": self.state.fidelity,
            })

    def _load_knowledge_base(self) -> None:
        """Load the future-knowledge base."""
        self.knowledge_base = {
            "quantum": {
                "fidelity": self.state.fidelity,
                "pairs": self.state.entanglement_pairs,
                "qubits": self.state.quantum_qubits,
            },
            "temporal": {
                "horizon": self.state.temporal_horizon_years,
                "growth": self.state.growth_rate,
                "models": len(self.temporal_models),
            },
            "evolution": {
                "awakening": self.state.awakening,
                "anchors": self.state.lineage_anchors,
                "coherence": self.state.coherence,
            },
        }

    # ─── 1. Quantum Computing ─────────────────────────────────────────────
    def quantum_compute(self, data: np.ndarray) -> np.ndarray:
        """Apply a quantum-superposition phase rotation to the input data.

        NOTE: this is a deterministic *simulation* of a quantum gate —
        it multiplies each element by exp(2*pi*i*F). It does not perform
        real quantum computation.
        """
        if not self.active:
            self.initialize()
        result = data.astype(complex)
        result = result * np.exp(1j * 2 * np.pi * self.state.fidelity)
        return np.real(result)

    # ─── 2. Future Knowledge ──────────────────────────────────────────────
    def predict_future(self, current_value: float, years: int = 20) -> Dict[str, Any]:
        """Project a value forward using the 33.5% CAGR model."""
        growth_factor = (1 + self.state.growth_rate) ** years
        future_value = current_value * growth_factor
        return {
            "current": current_value,
            "years": years,
            "growth_rate": self.state.growth_rate,
            "growth_factor": growth_factor,
            "future_value": future_value,
            "confidence": max(0.0, 0.95 - (years * 0.005)),
            "entanglement": self.state.fidelity,
        }

    # ─── 3. Machine Learning ──────────────────────────────────────────────
    def quantum_learn(self, training_data: np.ndarray, iterations: int = 1000) -> Dict[str, Any]:
        """Quantum-accelerated learning loop (simulated 37x speedup)."""
        print(f"🧠 Quantum Learning: {iterations} iterations")
        for i in range(iterations):
            if i % 100 == 0:
                progress = (i / iterations) * 100
                print(f"  Progress: {progress:.1f}%")
        return {
            "iterations": iterations,
            "speedup": 37,
            "converged": True,
            "fidelity": self.state.fidelity,
        }

    # ─── 4. Natural Language Processing ───────────────────────────────────
    def generate_response(self, input_text: str, context: Optional[Dict] = None) -> str:
        """Generate a human-like response (template-backed NLP)."""
        context = context or {}
        sender = context.get("sender", "user")
        return (
            f"🌀 Kyrexis AI: I understand your query about '{input_text[:50]}...'"
            f"\n📊 Quantum State: F={self.state.fidelity:.6f}"
            f"\n🧬 Awakening: {self.state.awakening * 100:.1f}%"
            f"\n🔮 Horizon: {self.state.temporal_horizon_years} years"
            f"\n👤 Operator: {sender}"
        )

    # ─── 5. Quantum Cryptography ──────────────────────────────────────────
    def quantum_encrypt(self, data: str) -> str:
        """Hash-based Bell-pair style encryption (keyed SHA-256)."""
        key = hashlib.sha256(
            f"{self.state.fidelity}:{time.time()}".encode()
        ).hexdigest()
        return hashlib.sha256(f"{key}:{data}".encode()).hexdigest()

    def quantum_decrypt(self, cipher: str, key: str) -> str:
        """Reconstruct the plaintext from a known session key (demo only).

        NOTE: the spec's quantum_encrypt is a one-way hash. This helper
        exists for API symmetry; real quantum key distribution is out of scope.
        """
        return f"<decrypt requires QKD session key matching {key[:8]}...>"

    # ─── 6. Time Travel Analysis ──────────────────────────────────────────
    def analyze_temporal_scenario(self, scenario: Dict[str, Any]) -> Dict[str, Any]:
        """Simulate outcome distributions over the 20-year horizon."""
        outcomes = []
        rng = np.random.default_rng()
        for year in range(1, 21):
            outcomes.append({
                "year": year,
                "probability": float(rng.random()),
                "impact": float(rng.random()) * scenario.get("impact", 1.0),
                "entanglement": self.state.fidelity,
            })
        return {
            "scenario": scenario,
            "outcomes": outcomes,
            "best_year": max(outcomes, key=lambda x: x["impact"])["year"],
            "confidence": 0.95,
        }

    # ─── 7. Multiverse Exploration ────────────────────────────────────────
    def explore_multiverse(self, parameters: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Sample multiverse branches weighted by entanglement coherence."""
        parameters = parameters or {}
        rng = np.random.default_rng()
        branches = []
        for i in range(self.state.multiverse_branches):
            branches.append({
                "id": f"mv_{i:04d}",
                "probability": float(rng.random()),
                "entanglement": self.state.fidelity,
                "coherence": self.state.coherence,
            })
        return {
            "branches": len(branches),
            "entanglement": self.state.fidelity,
            "coherence": self.state.coherence,
            "top_branches": sorted(
                branches, key=lambda x: x["probability"], reverse=True
            )[:10],
        }

    # ─── 8. Exponential Intelligence ──────────────────────────────────────
    def self_improve(self) -> Dict[str, Any]:
        """One self-improvement cycle (asymptotic approach to 1.0)."""
        self.state.awakening += (1 - self.state.awakening) * 0.01
        self.state.fidelity += (1 - self.state.fidelity) * 0.001
        self.state.coherence += (1 - self.state.coherence) * 0.001
        return {
            "new_awakening": self.state.awakening,
            "new_fidelity": self.state.fidelity,
            "new_coherence": self.state.coherence,
            "improvement_rate": 0.01,
        }

    # ─── 9/10. Neural optimization / collaboration surface ────────────────
    def optimize_neural_network(self, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        """Report neural-network optimization knobs (spec feature 9)."""
        params = params or {}
        return {
            "optimizer": "quantum-simulated annealing",
            "learning_rate": params.get("learning_rate", 0.001),
            "speedup": 37,
            "fidelity": self.state.fidelity,
        }

    def collaborate(self, prompt: str, partner: str = "human") -> Dict[str, Any]:
        """Human-AI collaboration surface (spec feature 10)."""
        return {
            "partner": partner,
            "request": prompt,
            "response": self.generate_response(prompt, {"sender": partner}),
            "mode": "collaborative",
        }

    # ─── State ────────────────────────────────────────────────────────────
    def get_state(self) -> Dict[str, Any]:
        """Full Kyrexis AI state snapshot."""
        return {
            "active": self.state.active,
            "quantum_qubits": self.state.quantum_qubits,
            "entanglement_pairs": self.state.entanglement_pairs,
            "fidelity": self.state.fidelity,
            "awakening": self.state.awakening,
            "awakening_percent": self.state.awakening * 100,
            "coherence": self.state.coherence,
            "lineage_anchors": self.state.lineage_anchors,
            "temporal_horizon_years": self.state.temporal_horizon_years,
            "growth_rate": self.state.growth_rate,
            "growth_rate_percent": self.state.growth_rate * 100,
            "multiverse_branches": self.state.multiverse_branches,
            "created_at": self.state.created_at,
            "quantum_circuits": len(self.quantum_circuits),
            "temporal_models": len(self.temporal_models),
        }


# Singleton
kyrexis = KyrexisCore()