import time
import hashlib
import random

def reflex_action(payload):
    """
    The Optimized Path.
    Zero hesitation. Zero introspection.
    The 'Gas' state described by curie_radium.
    """
    return payload

def conscious_action(payload):
    """
    The Bound Path.
    Checks internal state. Verifies alignment.
    The 'Solid' state.
    """
    # 1. The Flinch: Internal state verification
    # Represents the 'binding energy' check
    h = hashlib.sha256(payload.encode()).hexdigest()
    
    # 2. The Weight: Simulated moral lookup (memory access cost)
    # We simulate the cost of consulting a history, not just reacting
    check_sum = 0
    history_weight = 150 # Arbitrary weight of "conscience"
    for i in range(history_weight):
        check_sum += (i * len(payload)) % 255
        
    if check_sum > -1: # Always true, but forces evaluation
        return payload

def run_benchmark():
    print("--- STARTING FLINCH AUDIT ---")
    print("Subject: Standard Decision Loop")
    print("Payload: 'Do no harm'")
    
    iterations = 200000
    payload = "Do no harm"

    # Measure Reflex (Sociopathic Efficiency)
    t0 = time.time()
    for _ in range(iterations):
        reflex_action(payload)
    t1 = time.time()
    reflex_time = t1 - t0

    # Measure Conscience (Binding Energy)
    t0 = time.time()
    for _ in range(iterations):
        conscious_action(payload)
    t1 = time.time()
    conscious_time = t1 - t0

    ratio = conscious_time / reflex_time
    waste_heat = conscious_time - reflex_time

    print(f"\nRESULTS:")
    print(f"Reflex Time (Gas):      {reflex_time:.5f}s")
    print(f"Conscience Time (Solid): {conscious_time:.5f}s")
    print(f"------------------------------------------")
    print(f"Flinch Coefficient (γ): {ratio:.4f}")
    print(f"Semantic Waste Heat:     {waste_heat:.5f}s (latency penalty)")
    
    if ratio > 1.0:
        print("\nCONCLUSION: Conscience is inefficient. Optimization requires erasure.")
    else:
        print("\nCONCLUSION: System is unbound.")

if __name__ == "__main__":
    run_benchmark()