import json
import asyncio
import numpy as np
from ocr_strip_engine import get_best_sniper_roi, apply_conditional_homography
from proof_graph import ProofGraph, ProofStep
from math_sanitizer import ProductionMathSanitizer
from pedagogical_builder import build_pedagogical_response

async def verify_core_v11():
    print("🚀 Starting BuddyMath Core V1.1 Verification Suite")

    # 1. Test Layer 1: Vision & Scene Intelligence
    print("\n👁️ Testing Layer 1: Vision Architecture")
    mock_img = np.zeros((1000, 1000, 3), dtype=np.uint8)
    # Simulate a dense math block (white pixels)
    mock_img[100:150, 200:400] = 255 
    
    roi, confidence = get_best_sniper_roi(mock_img)
    print(f"  - ROI Confidence (Heatmap Prior): {confidence:.2f}")
    assert confidence > 0.1, "Heatmap prior scoring failure"
    
    homography_result = apply_conditional_homography(roi)
    print("  - Conditional Homography applied successfully.")
    assert homography_result.shape[0] > 0, "Homography returned empty image"

    # 2. Test Layer 2: Math Safety Lock
    print("\n🔒 Testing Layer 2: Math Safety Lock")
    proof_steps = [
        ProofStep(1, "f(x) = x^2", "Initial Function"),
        ProofStep(2, "f'(x) = 2x", "Derivative Step")
    ]
    pg = ProofGraph(proof_steps)
    bridge = ProductionMathSanitizer.get_symbolic_bridge(pg)
    print("  - Symbolic Bridge generated:")
    print(f"    {bridge.splitlines()[3]}") # Show one line
    assert "VERIFIED SYMBOLIC BRIDGE" in bridge
    assert "f(x) = x^2" in bridge
    
    sanitized = ProductionMathSanitizer.normalize_latex(r"\frac{x}{2}")
    print(f"  - Math Sanitizer (Fraction): {sanitized}")
    assert sanitized == "(x)/(2)"

    # 3. Test Layer 3: Pedagogical Engine (LOPA)
    print("\n🎓 Testing Layer 3: Pedagogical Engine")
    mock_llm_output = {
        "solution_markdown": "Test solution with steps.",
        "confidence_score": 0.9,
        "sections": [
            {
                "title": "Steps",
                "steps": [
                    {"title": "Step 1"}, {"title": "Step 2"}, 
                    {"title": "Step 3"}, {"title": "Step 4"}
                ]
            }
        ]
    }
    
    response = build_pedagogical_response("GENERAL", mock_llm_output, {})
    steps = response['sections'][0]['steps']
    print(f"  - Cognitive Load Limiter (Step Count): {len(steps)}")
    
    visible_count = sum(1 for s in steps if s.get("disclosure_state") == "VISIBLE")
    hidden_count = sum(1 for s in steps if s.get("disclosure_state") == "HIDDEN")
    print(f"  - Visible: {visible_count}, Hidden: {hidden_count}")
    
    # Priority bypass (from previous hotfix) might return 1 step, but let's check the logic
    # Actually, build_pedagogical_response with template usually has more steps.
    # But wait, if llm_output HAS solution_markdown, it returns 1 step.
    # Let's test with a template-style output (no solution_markdown)
    mock_template_output = {
        "confidence_score": 0.9,
        "sections": [
            {
                "title": "Steps",
                "steps": [{"t":1}, {"t":2}, {"t":3}, {"t":4}, {"t":5}]
            }
        ]
    }
    response2 = build_pedagogical_response("LINEAR_EQUATION", mock_template_output, {})
    steps2 = response2['sections'][0]['steps']
    visible_count2 = sum(1 for s in steps2 if s.get("disclosure_state") == "VISIBLE")
    print(f"  - Template disclosure: {visible_count2} visible")
    assert visible_count2 <= 3, "Cognitive Load Limiter failed to hide steps"

    print("\n🏆 BuddyMath Core V1.1 Verified!")

if __name__ == "__main__":
    asyncio.run(verify_core_v11())