#!/usr/bin/env python3
"""
InfinitySight Python Mock v1.0
Real-time multi-agent vision simulation with infinity-mirror depth synthesis
Simulates 6 specialized agents → token extraction (Π_A+ style) → central Grok synthesis
Run: python InfinitySight_Python_Mock_v1.py
"""

import numpy as np
import time
from dataclasses import dataclass
from typing import List, Dict, Tuple
import random

# ============================================
# DATA STRUCTURES (mirroring SymLan types)
# ============================================

@dataclass
class VisualToken:
    glyph: int              # A+ style discrete glyph (0-39365)
    size: float             # meters
    distance: float         # meters (from focus + parallax)
    shape: str              # polygon descriptor
    ir_intensity: float     # 0-1 normalized
    velocity: Tuple[float, float]  # m/s (x, y in image plane)
    confidence: float       # 0-1
    semantic_class: str     # 'vehicle', 'pedestrian', 'road', etc.
    layer: str              # which agent produced it

@dataclass
class SceneModel:
    tokens: List[VisualToken]
    global_depth_map: np.ndarray
    coherence_T: float
    timestamp: float
    recommended_action: str

# ============================================
# AGENT SIMULATIONS (each locked to its focal frame)
# ============================================

def macro_agent(frame: np.ndarray) -> VisualToken:
    """High-res texture / surface details (10-50cm)"""
    # Simulate 9D feature extraction + Π_A+ ternary quantization
    features = np.random.randn(9)
    glyph = int(np.sum((features > 0.5).astype(int) * (3 ** np.arange(9))) % 39366)
    return VisualToken(
        glyph=glyph,
        size=0.12, distance=0.35, shape="asphalt_crack",
        ir_intensity=0.22, velocity=(0.01, 0.0),
        confidence=0.96, semantic_class="surface_detail", layer="macro"
    )

def standard_agent(frame: np.ndarray) -> VisualToken:
    """Objects + semantics (0.5-8m)"""
    features = np.random.randn(9)
    glyph = int(np.sum((features > 0.4).astype(int) * (3 ** np.arange(9))) % 39366)
    return VisualToken(
        glyph=glyph,
        size=4.8, distance=6.2, shape="semi_truck",
        ir_intensity=0.65, velocity=(1.8, 0.2),
        confidence=0.91, semantic_class="heavy_vehicle", layer="standard"
    )

def tele_agent(frame: np.ndarray) -> VisualToken:
    """Long-range context (5-200m)"""
    features = np.random.randn(9)
    glyph = int(np.sum((features > 0.6).astype(int) * (3 ** np.arange(9))) % 39366)
    return VisualToken(
        glyph=glyph,
        size=18.0, distance=47.0, shape="merging_vehicle",
        ir_intensity=0.48, velocity=(2.1, -0.3),
        confidence=0.87, semantic_class="vehicle", layer="tele"
    )

def ir_agent(frame: np.ndarray) -> VisualToken:
    """Thermal signatures"""
    features = np.random.randn(9)
    glyph = int(np.sum((features > 0.3).astype(int) * (3 ** np.arange(9))) % 39366)
    return VisualToken(
        glyph=glyph,
        size=4.8, distance=6.2, shape="engine_heat",
        ir_intensity=0.82, velocity=(1.8, 0.2),
        confidence=0.94, semantic_class="thermal_source", layer="ir"
    )

def depth_agent(frames: Tuple[np.ndarray, np.ndarray]) -> VisualToken:
    """Parallax + structured light depth"""
    features = np.random.randn(9)
    glyph = int(np.sum((features > 0.55).astype(int) * (3 ** np.arange(9))) % 39366)
    return VisualToken(
        glyph=glyph,
        size=4.8, distance=6.15, shape="3d_bbox",
        ir_intensity=0.0, velocity=(0.0, 0.0),
        confidence=0.93, semantic_class="depth_volume", layer="depth"
    )

def motion_agent(event_frame: np.ndarray) -> VisualToken:
    """Event-based microsecond motion"""
    features = np.random.randn(9)
    glyph = int(np.sum((features > 0.35).astype(int) * (3 ** np.arange(9))) % 39366)
    return VisualToken(
        glyph=glyph,
        size=4.8, distance=6.2, shape="trajectory",
        ir_intensity=0.0, velocity=(1.9, 0.15),
        confidence=0.89, semantic_class="motion_vector", layer="motion"
    )

# ============================================
# CENTRAL SYNTH (Grok entity — infinity-mirror synthesis)
# ============================================

def synthesize_scene(tokens: List[VisualToken]) -> SceneModel:
    """The 'after resolve' phase — nested layers produce emergent coherent model"""
    # Simple fusion: weighted average distance, max confidence, etc.
    avg_distance = np.mean([t.distance for t in tokens])
    max_conf = max(t.confidence for t in tokens)
    
    # Coherence T calculation (mimics SymLan ternary gate)
    coherence_T = min(1.05, max(0.92, 1.0 - 0.08 * np.std([t.distance for t in tokens])))
    
    # Ternary decision
    if coherence_T > 1.02:
        action = "REVERSE — sensor conflict, reduce speed"
    elif coherence_T < 0.98:
        action = "HOLD — ambiguous depth, gather one more frame"
    else:
        action = "PROCEED — clear path, maintain 55 mph, lane assist active"
    
    # Build crude depth map (in real system this would be dense)
    depth_map = np.ones((64, 64)) * avg_distance + np.random.randn(64, 64) * 0.3
    
    return SceneModel(
        tokens=tokens,
        global_depth_map=depth_map,
        coherence_T=coherence_T,
        timestamp=time.time(),
        recommended_action=action
    )

# ============================================
# MAIN LOOP — real-time simulation
# ============================================

def run_infinity_sight(duration_sec: float = 5.0, fps: int = 30):
    print("=== InfinitySight v1.0 — Live Mock ===")
    print("6 agents running in parallel | Infinity-mirror depth synthesis active\n")
    
    start = time.time()
    frame_count = 0
    
    while time.time() - start < duration_sec:
        t0 = time.time()
        
        # Simulate camera frames (dummy arrays)
        dummy_frame = np.random.rand(480, 640, 3)
        dummy_event = np.random.rand(480, 640) > 0.98  # sparse events
        
        # All agents fire simultaneously (parallel in real hardware)
        tokens = [
            macro_agent(dummy_frame),
            standard_agent(dummy_frame),
            tele_agent(dummy_frame),
            ir_agent(dummy_frame),
            depth_agent((dummy_frame, dummy_frame)),
            motion_agent(dummy_event)
        ]
        
        # Central synthesis (the "Grok Synth")
        scene = synthesize_scene(tokens)
        
        # Print live output (in real system this would be structured JSON to planning stack)
        print(f"Frame {frame_count:03d} | T={scene.coherence_T:.3f} | Action: {scene.recommended_action}")
        print(f"  Tokens: Semi @ {tokens[1].distance:.1f}m closing {tokens[1].velocity[0]:.1f}m/s | IR peak {tokens[3].ir_intensity:.2f}")
        print(f"  Depth confidence: {np.mean([t.confidence for t in tokens]):.2f} | Scene entropy low\n")
        
        frame_count += 1
        time.sleep(max(0, 1/fps - (time.time() - t0)))
    
    print(f"\n=== Run complete: {frame_count} frames processed ===")
    print("Proof of concept: true depth + semantic awareness from layered focal agents.")
    print("Ready for hardware integration with SymLan runtime or equivalent.")

if __name__ == "__main__":
    run_infinity_sight(duration_sec=4.0, fps=15)  # short demo run