#!/usr/bin/env python3
"""
SRE Regression Tests — Verify all 5 critical vulnerability patches work.

These test the EXACT failure scenarios from the SRE audit:
  S1: Dict iteration during modification (MemoryStore)
  S2: UNKNOWN action propagation (Actor)
  S3: Context overflow from heuristic bloat (Actor prompt)
  S4: Race condition in parallel swarm (ExperienceReplay)
  S5: None score crash in trajectory math (Trajectory)
"""
import sys, os, json, threading
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))

PASS = FAIL = 0
def check(name, cond, detail=""):
    global PASS, FAIL
    PASS += int(cond); FAIL += int(not cond)
    print(f"  {'✓' if cond else '✗'} {name}" + (f": {detail}" if detail and not cond else ""))

# Import triggers sre_patches.apply_all()
import purpose_agent as pa
from purpose_agent.types import State, Action, Trajectory, TrajectoryStep, PurposeScore, Heuristic, MemoryTier
from purpose_agent.memory import MemoryStore, MemoryCard, MemoryKind, MemoryStatus
from purpose_agent.v2_types import MemoryScope

print("═══ SRE Scenario 1: Dict Snapshot During Iteration ═══")
store = MemoryStore()
for i in range(100):
    store.add(MemoryCard(kind=MemoryKind.SKILL_CARD, status=MemoryStatus.PROMOTED,
        pattern=f"p{i}", strategy=f"s{i}"))

# Simulate concurrent modification during retrieval
def modify_during_retrieve():
    """Add cards while retrieve is iterating — should NOT crash."""
    for i in range(100, 200):
        store.add(MemoryCard(kind=MemoryKind.SKILL_CARD, status=MemoryStatus.PROMOTED,
            pattern=f"p{i}", strategy=f"s{i}"))

t = threading.Thread(target=modify_during_retrieve)
t.start()
try:
    results = store.retrieve("test query", top_k=10)
    check("S1 No RuntimeError during concurrent modify", True)
except RuntimeError as e:
    check("S1 No RuntimeError during concurrent modify", False, str(e))
t.join()

print("\n═══ SRE Scenario 2: UNKNOWN Action Rejection ═══")
mock = pa.MockLLMBackend()
# Return garbage that parser can't handle
mock.register_handler("goal-directed agent", "totally unparseable garbage !@#$%")
mock.set_structured_default({"phi_before":0,"phi_after":0,"reasoning":"x","evidence":"x","confidence":0.5})

from purpose_agent.orchestrator import SimpleEnvironment
env = SimpleEnvironment(execute_fn=lambda a,s: State(data={"x":1}))
orch = pa.Orchestrator(llm=mock, environment=env, available_actions={"test":"test","DONE":"done"}, critic_mode="standard")

try:
    r = orch.run_task(purpose="test", max_steps=2)
    # Should get DONE (from UNKNOWN rejection) not crash
    check("S2 No crash on garbage LLM output", True)
    # The action should have been converted to DONE
    if r.trajectory.steps:
        last_action = r.trajectory.steps[-1].action.name
        check("S2 UNKNOWN → DONE fallback", last_action == "DONE", f"got {last_action}")
    else:
        check("S2 Has steps", False, "no steps recorded")
except Exception as e:
    check("S2 No crash on garbage LLM output", False, f"{type(e).__name__}: {e}")

print("\n═══ SRE Scenario 3: Heuristic Cap (Context Overflow Prevention) ═══")
mock2 = pa.MockLLMBackend()
mock2.register_handler("goal-directed agent", json.dumps({"thought":"t","action":{"name":"DONE","params":{}},"expected_delta":"d"}))
mock2.set_structured_default({"phi_before":0,"phi_after":5,"reasoning":"r","evidence":"e","confidence":0.7})

env2 = SimpleEnvironment(execute_fn=lambda a,s: State(data={}))
orch2 = pa.Orchestrator(llm=mock2, environment=env2, available_actions={"DONE":"done"}, critic_mode="standard")

# Inject 200 heuristics (would overflow SLM context without cap)
for i in range(200):
    orch2.optimizer.heuristic_library.append(Heuristic(
        pattern=f"Pattern {i} " * 10, strategy=f"Strategy {i} " * 10,
        steps=[], tier=MemoryTier.STRATEGIC, q_value=0.5 + i*0.001,
    ))
orch2.sync_memory()

# Build the prompt — should be capped, not 200 entries
prompt = orch2.actor._build_system_prompt()
# Count heuristic entries in prompt
heuristic_lines = [l for l in prompt.split("\n") if l.strip().startswith("- When:") or l.strip().startswith("- Pattern")]
check("S3 Heuristics capped", len(heuristic_lines) <= 10, f"got {len(heuristic_lines)} (should be ≤10)")
check("S3 Prompt not massive", len(prompt) < 5000, f"prompt is {len(prompt)} chars")

print("\n═══ SRE Scenario 4: Thread-Safe ExperienceReplay ═══")
er = pa.ExperienceReplay(capacity=100)
errors = []

def add_many(start):
    for i in range(50):
        try:
            t = Trajectory(task_description=f"task_{start}_{i}", purpose=f"p_{start}_{i}")
            t.steps.append(TrajectoryStep(
                state_before=State(data={}), action=Action(name="x"),
                state_after=State(data={"i": i}),
                score=PurposeScore(phi_before=0, phi_after=5, delta=5, reasoning="r", evidence="e", confidence=0.8),
            ))
            er.add(t)
        except Exception as e:
            errors.append(str(e))

# 4 threads adding concurrently
threads = [threading.Thread(target=add_many, args=(j*100,)) for j in range(4)]
for t in threads: t.start()
for t in threads: t.join()

check("S4 No errors in concurrent add", len(errors) == 0, f"{len(errors)} errors")
check("S4 All items added", er.size > 0, f"size={er.size}")

print("\n═══ SRE Scenario 5: None Score Guard ═══")
t = Trajectory(task_description="test", purpose="test")
# Add steps with None scores (simulates HITL interrupt mid-eval)
t.steps.append(TrajectoryStep(
    state_before=State(data={}), action=Action(name="x"),
    state_after=State(data={}), score=None,  # None score!
))
t.steps.append(TrajectoryStep(
    state_before=State(data={}), action=Action(name="y"),
    state_after=State(data={}),
    score=PurposeScore(phi_before=0, phi_after=7, delta=7, reasoning="r", evidence="e", confidence=0.9),
))

try:
    cr = t.cumulative_reward
    check("S5 cumulative_reward with None score", isinstance(cr, float), f"got {cr}")
except TypeError as e:
    check("S5 cumulative_reward with None score", False, str(e))

try:
    td = t.total_delta
    check("S5 total_delta with None score", isinstance(td, float), f"got {td}")
except TypeError as e:
    check("S5 total_delta with None score", False, str(e))

try:
    sr = t.success_rate
    check("S5 success_rate with None score", isinstance(sr, float))
except TypeError as e:
    check("S5 success_rate with None score", False, str(e))

try:
    fp = t.final_phi
    check("S5 final_phi with None score", fp == 7.0 or fp is not None, f"got {fp}")
except (TypeError, AttributeError) as e:
    check("S5 final_phi with None score", False, str(e))

# ═══ REPORT ═══
print(f"\n{'='*50}")
print(f"  SRE Regression: {PASS} pass, {FAIL} fail")
print(f"  {'ALL PASS ✓' if FAIL == 0 else f'{FAIL} FAILURES — CRITICAL'}")
print(f"{'='*50}")
sys.exit(0 if FAIL == 0 else 1)