#!/usr/bin/env python3
"""
CHRONO-TORQUE v2.0 - PAPR (Positive Actionable Productive Regression) Sieve
Module: papr_sieve.py

Demonstrates:
  1. The PAPR Lookahead Principle:
     When cultivating quantum magic states or multi-stage cognitive reasoning chains,
     soft syndrome failures usually trigger full abort and discard.
     PAPR introduces a strategic, reversible regression step (d=5 -> d=3 backstep),
     repairing soft errors rather than throwing away cultivated state.
  2. Side-by-side benchmark:
     - Standard Cultivation Protocol: binary pass/fail discard on soft syndrome.
     - PAPR Lookahead Protocol: reversible partial regression and re-cultivation.
  3. Measured metrics:
     - Keep-Rate (Yield): fraction of successful cultivation cycles.
     - Resource Overhead: total gate/cycle cost per verified output.
  4. Zero external dependencies: pure Python 3 standard library.
"""

import random
import time
import sys

def benchmark_papr(n_trials: int = 50_000, seed: int = 42):
    random.seed(seed)

    print("==================================================================")
    print("CHRONO-TORQUE PAPR Lookahead Sieve vs Standard Cultivation")
    print(f"Trials: {n_trials:,} state cultivation attempts")
    print("==================================================================")

    # Error model:
    # 70% clean pass, 20% soft syndrome (repairable), 10% hard syndrome (fatal)
    p_clean = 0.70
    p_soft  = 0.20
    p_hard  = 0.10

    # 1. Standard Protocol (Gidney-style fixed threshold discard)
    std_success = 0
    std_discard = 0
    std_cost = 0

    t0 = time.perf_counter()
    for _ in range(n_trials):
        r = random.random()
        std_cost += 1.0  # Base cultivation cost
        if r < p_clean:
            std_success += 1
        else:
            # Any syndrome (soft or hard) triggers full abort
            std_discard += 1
    t_std = time.perf_counter() - t0
    std_yield = (std_success / n_trials) * 100.0
    std_cost_per_yield = std_cost / std_success if std_success > 0 else float("inf")

    # 2. PAPR Lookahead Sieve
    # Reversible regression on soft syndrome: pays an extra 0.35 cost to re-purify,
    # recovering 85% of soft syndromes. Hard syndromes still abort.
    papr_success = 0
    papr_discard = 0
    papr_cost = 0

    t0 = time.perf_counter()
    for _ in range(n_trials):
        r = random.random()
        papr_cost += 1.0  # Base cultivation cost
        if r < p_clean:
            papr_success += 1
        elif r < (p_clean + p_soft):
            # Soft syndrome detected: execute PAPR lookahead regression
            papr_cost += 0.35  # Reversible regression tax
            # 85% recovered successfully through lookahead repair
            if random.random() < 0.85:
                papr_success += 1
            else:
                papr_discard += 1
        else:
            # Fatal hard error: abort cleanly
            papr_discard += 1
    t_papr = time.perf_counter() - t0
    papr_yield = (papr_success / n_trials) * 100.0
    papr_cost_per_yield = papr_cost / papr_success if papr_success > 0 else float("inf")

    yield_boost = papr_yield - std_yield
    efficiency_gain = (std_cost_per_yield / papr_cost_per_yield - 1.0) * 100.0

    print(f"[Standard Cultivation Protocol]:")
    print(f"  Cultivation Yield:     {std_yield:.2f}% ({std_success:,} / {n_trials:,})")
    print(f"  Discarded Cycles:      {std_discard:,}")
    print(f"  Cost per Valid Output: {std_cost_per_yield:.3f} units")
    print(f"  Wall-Clock Time:       {t_std * 1000:.2f} ms")

    print(f"\n[PAPR Lookahead Sieve Protocol]:")
    print(f"  Cultivation Yield:     {papr_yield:.2f}% ({papr_success:,} / {n_trials:,})")
    print(f"  Discarded Cycles:      {papr_discard:,}")
    print(f"  Cost per Valid Output: {papr_cost_per_yield:.3f} units")
    print(f"  Yield Improvement:     +{yield_boost:.2f}% absolute gain ({std_yield:.1f}% -> {papr_yield:.1f}%)")
    print(f"  Effective Efficiency:  +{efficiency_gain:.1f}% net yield per resource unit")
    print(f"  Wall-Clock Time:       {t_papr * 1000:.2f} ms")
    print("==================================================================")

    return {
        "n_trials": n_trials,
        "std_yield": std_yield,
        "papr_yield": papr_yield,
        "yield_boost": yield_boost,
        "efficiency_gain": efficiency_gain
    }

if __name__ == "__main__":
    trials = 50_000
    if len(sys.argv) > 1:
        trials = int(sys.argv[1])
    benchmark_papr(trials)