You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


/*
 * Copyright (c) 2015-2017, Intel Corporation
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 *  * Redistributions of source code must retain the above copyright notice,
 *    this list of conditions and the following disclaimer.
 *  * Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
 *    documentation and/or other materials provided with the distribution.
 *  * Neither the name of Intel Corporation nor the names of its contributors
 *    may be used to endorse or promote products derived from this software
 *    without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
 * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
 * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
 * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
 * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
 * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 * POSSIBILITY OF SUCH DAMAGE.
 */

/*
 * Hyperscan pattern benchmarker.
 *
 * This program allows users to detect which signatures may be the most
 * expensive in a set of patterns. It is designed for use with small to medium
 * pattern set sizes (e.g. 5-500). If used with very large pattern sets it may
 * take a very long time - the number of recompiles done is g * O(lg2(n)) where
 * g is the number of generations and n is the number of patterns (assuming
 * that n >> g).
 *
 * This utility will return a cumulative series of removed patterns. The first
 * generation will find and remove a single pattern. The second generation will
 * begin with the first pattern removed and find another pattern to remove,
 * etc. So if we have 100 patterns and 15 generations, the final generation's
 * score will be a run over 85 patterns.
 *
 * This utility is probabilistic. It is possible that the pattern removed in a
 * generation is not a particularly expensive pattern. To reduce noise in the
 * results use 'taskset' and set the number of repeats to a level that still
 * completes in reasonable time (this will reduce the effect of random
 * measurement noise).
 *
 * The criterion for performance can be altered by use of the -C<x> flag where
 * <x> can be t,r,s,c,b, selecting pattern matching throughput, scratch size,
 * stream state size (only available in streaming mode), compile time and
 * bytecode size respectively.
 *
 * This utility will also not produce good results if all the patterns are
 * roughly equally expensive.
 *
 * Factor Group Size:
 *
 * If there are multiple expensive patterns that are very similar on the
 * left-hand-side or identical, this utility will typically not find these
 * groups unless the -F flag is used to search for a group size that is equal
 * to or larger than the size of the group of similar patterns.
 *
 * Otherwise, removing a portion of the similar patterns will have no or almost
 * no effect, and the search procedure used relies on the ability to remove all
 * of the similar patterns in at least one search case, something which will
 * only happen if the factor_group_size is large enough.
 *
 * This alters the operation of our tool so that instead of trying to find the
 * single pattern whose removal has the most effect by binary search (the
 * default with factor_group_size == 1), we attempt to find the N patterns
 * whose removal has the most effect by searching over N+1 evenly sized groups,
 * removing only 1/(N+1) of the search signatures per iteration.
 *
 * Note that the number of recompiles done greatly increases with increased
 * factor group size.  For example, with factor_group_size = 1, we do g * 2 *
 * lg2(n) recompiles, while with factor_group_size = 4, we do g * 4 *
 * log(5/4)(n). Informally the number of generations we require goes up as we
 * eliminate a smaller number of signatures and the we have to do more work per
 * generation.
 *
 *
 * Build instructions:
 *
 *     g++ -o patbench patbench.cc $(pkg-config --cflags --libs libhs) -lpcap
 *
 * Usage:
 *
 *     ./patbench [ -n repeats] [ -G generations] [ -C criterion ]
 *             [ -F factor_group_size ] [ -N | -S ] <pattern file> <pcap file>
 *
 *     -n repeats sets the number of times the PCAP is repeatedly scanned
 *        with the pattern
 *     -G generations sets the number of generations that the algorithm is
 *        run for
 *     -N sets non-streaming mode, -S sets streaming mode (default)
 *     -F sets the factor group size (must be >0); this allows the detection
 *        of multiple interacting factors
 *
 *     -C sets the "criterion", which can be either:
 *          t  throughput (the default) - this requires a pcap file
 *          r  scratch size
 *          s  stream state size
 *          c  compile time
 *          b  bytecode size
 *
 * We recommend the use of a utility like 'taskset' on multiprocessor hosts to
 * lock execution to a single processor: this will remove processor migration
 * by the scheduler as a source of noise in the results.
 *
 */

#include <algorithm>
#include <cstring>
#include <chrono>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <set>
#include <string>
#include <vector>
#include <unordered_map>

#include <unistd.h>

// We use the BSD primitives throughout as they exist on both BSD and Linux.
#define __FAVOR_BSD
#include <netinet/in.h>
#include <netinet/in_systm.h>
#include <netinet/ip.h>
#include <netinet/tcp.h>
#include <netinet/udp.h>
#include <netinet/ip_icmp.h>
#include <net/ethernet.h>
#include <arpa/inet.h>

#include <pcap.h>

#include <hs.h>

using std::cerr;
using std::cout;
using std::endl;
using std::ifstream;
using std::string;
using std::unordered_map;
using std::vector;
using std::set;
using std::min;
using std::max;
using std::copy;

enum Criterion {
    CRITERION_THROUGHPUT,
    CRITERION_BYTECODE_SIZE,
    CRITERION_COMPILE_TIME,
    CRITERION_STREAM_STATE,
    CRITERION_SCRATCH_SIZE
};

static bool higher_is_better(Criterion c) {
    return c == CRITERION_THROUGHPUT;
}

static void print_criterion(Criterion c, double val) {
    std::ios::fmtflags f(cout.flags());
    switch (c) {
    case CRITERION_THROUGHPUT:
        cout << std::fixed << std::setprecision(3) << val << " Megabits/s";
        break;
    case CRITERION_COMPILE_TIME:
        cout << std::fixed << std::setprecision(3) << val << " seconds";
        break;
    case CRITERION_BYTECODE_SIZE:
    case CRITERION_STREAM_STATE:
    case CRITERION_SCRATCH_SIZE:
    default:
        cout << static_cast<size_t>(val) << " bytes";
        break;
    }
    cout.flags(f);
}

// Key for identifying a stream in our pcap input data, using data from its IP
// headers.
struct FiveTuple {
    unsigned int protocol;
    unsigned int srcAddr;
    unsigned int srcPort;
    unsigned int dstAddr;
    unsigned int dstPort;

    // Construct a FiveTuple from a TCP or UDP packet.
    FiveTuple(const struct ip *iphdr) {
        // IP fields
        protocol = iphdr->ip_p;
        srcAddr = iphdr->ip_src.s_addr;
        dstAddr = iphdr->ip_dst.s_addr;

        // UDP/TCP ports
        const struct udphdr *uh = (const struct udphdr *)
                (((const char *)iphdr) + (iphdr->ip_hl * 4));
        srcPort = uh->uh_sport;
        dstPort = uh->uh_dport;
    }

    bool operator==(const FiveTuple &a) const {
        return protocol == a.protocol && srcAddr == a.srcAddr &&
               srcPort == a.srcPort && dstAddr == a.dstAddr &&
               dstPort == a.dstPort;
    }
};

// A *very* simple hash function, used when we create an unordered_map of
// FiveTuple objects.
struct FiveTupleHash {
    size_t operator()(const FiveTuple &x) const {
        return x.srcAddr ^ x.dstAddr ^ x.protocol ^ x.srcPort ^ x.dstPort;
    }
};

// Helper function. See end of file.
static bool payloadOffset(const unsigned char *pkt_data, unsigned int *offset,
                          unsigned int *length);

// Match event handler: called every time Hyperscan finds a match.
static
int onMatch(unsigned int id, unsigned long long from, unsigned long long to,
            unsigned int flags, void *ctx) {
    // Our context points to a size_t storing the match count
    size_t *matches = (size_t *)ctx;
    (*matches)++;
    return 0; // continue matching
}

// Simple timing class
class Clock {
public:
    void start() {
        time_start = std::chrono::system_clock::now();
    }

    void stop() {
        time_end = std::chrono::system_clock::now();
    }

    double seconds() const {
        std::chrono::duration<double> delta = time_end - time_start;
        return delta.count();
    }
private:
    std::chrono::time_point<std::chrono::system_clock> time_start, time_end;
};

// Class wrapping all state associated with the benchmark
class Benchmark {
private:
    // Packet data to be scanned
    vector<string> packets;

    // Stream ID for each packet
    vector<size_t> stream_ids;

    // Map used to construct stream_ids
    unordered_map<FiveTuple, size_t, FiveTupleHash> stream_map;

    // Hyperscan compiled database
    hs_database_t *db = nullptr;

    // Hyperscan temporary scratch space
    hs_scratch_t *scratch = nullptr;

    // Vector of Hyperscan stream state
    vector<hs_stream_t *> streams;

    // Count of matches found while scanning
    size_t matchCount = 0;
public:
    ~Benchmark() {
        hs_free_scratch(scratch);
        hs_free_database(db);
    }

    // Initialisation; after this call, Benchmark owns the database and will
    // ensure it is freed.
    void setDatabase(hs_database_t *hs_db) {
        hs_free_database(db); // Free previous database.
        db = hs_db;
        // (Re)allocate scratch to ensure that it is large enough to handle the
        // database.
        hs_error_t err = hs_alloc_scratch(db, &scratch);
        if (err != HS_SUCCESS) {
            cerr << "ERROR: could not allocate scratch space. Exiting." << endl;
            exit(-1);
        }
    }
    const hs_database_t *getDatabase() const {
        return db;
    }

    size_t getScratchSize() const {
        size_t scratch_size;
        hs_error_t err = hs_scratch_size(scratch, &scratch_size);
        if (err != HS_SUCCESS) {
            cerr << "ERROR: could not query scratch space size. Exiting."
                 << endl;
            exit(-1);
        }
        return scratch_size;
    }

    // Read a set of streams from a pcap file
    bool readStreams(const char *pcapFile) {
        // Open PCAP file for input
        char errbuf[PCAP_ERRBUF_SIZE];
        pcap_t *pcapHandle = pcap_open_offline(pcapFile, errbuf);
        if (pcapHandle == nullptr) {
            cerr << "ERROR: Unable to open pcap file \"" << pcapFile
                 << "\": " << errbuf << endl;
            return false;
        }

        struct pcap_pkthdr pktHeader;
        const unsigned char *pktData;
        while ((pktData = pcap_next(pcapHandle, &pktHeader)) != nullptr) {
            unsigned int offset = 0, length = 0;
            if (!payloadOffset(pktData, &offset, &length)) {
                continue;
            }

            // Valid TCP or UDP packet
            const struct ip *iphdr = (const struct ip *)(pktData
                    + sizeof(struct ether_header));
            const char *payload = (const char *)pktData + offset;

            size_t id = stream_map.insert(std::make_pair(FiveTuple(iphdr),
                                          stream_map.size())).first->second;

            packets.push_back(string(payload, length));
            stream_ids.push_back(id);
        }
        pcap_close(pcapHandle);

        return !packets.empty();
    }

    // Return the number of bytes scanned
    size_t bytes() const {
        size_t sum = 0;
        for (const auto &packet : packets) {
            sum += packet.size();
        }
        return sum;
    }

    // Return the number of matches found.
    size_t matches() const {
        return matchCount;
    }

    // Clear the number of matches found.
    void clearMatches() {
        matchCount = 0;
    }

    // Open a Hyperscan stream for each stream in stream_ids
    void openStreams() {
        streams.resize(stream_map.size());
        for (auto &stream : streams) {
            hs_error_t err = hs_open_stream(db, 0, &stream);
            if (err != HS_SUCCESS) {
                cerr << "ERROR: Unable to open stream. Exiting." << endl;
                exit(-1);
            }
        }
    }

    // Close all open Hyperscan streams (potentially generating any
    // end-anchored matches)
    void closeStreams() {
        for (auto &stream : streams) {
            hs_error_t err =
                hs_close_stream(stream, scratch, onMatch, &matchCount);
            if (err != HS_SUCCESS) {
                cerr << "ERROR: Unable to close stream. Exiting." << endl;
                exit(-1);
            }
        }
    }

    // Scan each packet (in the ordering given in the PCAP file) through
    // Hyperscan using the streaming interface.
    void scanStreams() {
        for (size_t i = 0; i != packets.size(); ++i) {
            const std::string &pkt = packets[i];
            hs_error_t err = hs_scan_stream(streams[stream_ids[i]],
                                            pkt.c_str(), pkt.length(), 0,
                                            scratch, onMatch, &matchCount);
            if (err != HS_SUCCESS) {
                cerr << "ERROR: Unable to scan packet. Exiting." << endl;
                exit(-1);
            }
        }
    }

    // Scan each packet (in the ordering given in the PCAP file) through
    // Hyperscan using the block-mode interface.
    void scanBlock() {
        for (size_t i = 0; i != packets.size(); ++i) {
            const std::string &pkt = packets[i];
            hs_error_t err = hs_scan(db, pkt.c_str(), pkt.length(), 0,
                                     scratch, onMatch, &matchCount);
            if (err != HS_SUCCESS) {
                cerr << "ERROR: Unable to scan packet. Exiting." << endl;
                exit(-1);
            }
        }
    }
};

// helper function - see end of file
static void parseFile(const char *filename, vector<string> &patterns,
                      vector<unsigned> &flags, vector<unsigned> &ids,
                      vector<string> &originals);

class Sigdata {
    vector<unsigned> flags;
    vector<unsigned> ids;
    vector<string> patterns;
    vector<string> originals;

public:
    Sigdata() {}
    Sigdata(const char *filename) {
        parseFile(filename, patterns, flags, ids, originals);

    }

    const string &get_original(unsigned index) const {
        return originals[index];
    }

    hs_database_t *compileDatabase(unsigned mode, double *compileTime) const {
        hs_database_t *db = nullptr;
        hs_compile_error_t *compileErr;

        // Turn our vector of strings into a vector of char*'s to pass in to
        // hs_compile_multi. (This is just using the vector of strings as
        // dynamic storage.)
        vector<const char *> cstrPatterns;
        cstrPatterns.reserve(patterns.size());
        for (const auto &pattern : patterns) {
            cstrPatterns.push_back(pattern.c_str());
        }

        Clock clock;
        clock.start();
        hs_error_t err = hs_compile_multi(cstrPatterns.data(), flags.data(),
                                          ids.data(), cstrPatterns.size(), mode,
                                          nullptr, &db, &compileErr);
        clock.stop();
        if (err != HS_SUCCESS) {
            if (compileErr->expression < 0) {
                // The error does not refer to a particular expression.
                cerr << "ERROR: " << compileErr->message << endl;
            } else {
                cerr << "ERROR: Pattern '"
                     << patterns[compileErr->expression]
                     << "' failed with error '" << compileErr->message << "'"
                     << endl;
            }
            // As the compileErr pointer points to dynamically allocated memory,
            // if we get an error, we must be sure to release it. This is not
            // necessary when no error is detected.
            hs_free_compile_error(compileErr);
            exit(-1);
        }

        *compileTime = clock.seconds();
        return db;
    }

    unsigned size() const {
        return patterns.size();
    }

    Sigdata cloneExclude(const set<unsigned> &excludeIndexSet) const {
        Sigdata c;
        for (unsigned i = 0, e = size(); i != e; ++i) {
            if (excludeIndexSet.find(i) == excludeIndexSet.end()) {
                c.flags.push_back(flags[i]);
                c.ids.push_back(ids[i]);
                c.patterns.push_back(patterns[i]);
                c.originals.push_back(originals[i]);
            }
        }
        return c;
    }
};

static
void usage(const char *) {
    cerr << "Usage:" << endl << endl;
    cerr << "  patbench [-n repeats] [ -G generations] [ -C criterion ]" << endl
         << "           [ -F factor_group_size ] [ -N | -S ] "
         << "<pattern file> <pcap file>" << endl << endl
         << "    -n repeats sets the number of times the PCAP is repeatedly "
            "scanned" << endl << "       with the pattern." << endl
         << "    -G generations sets the number of generations that the "
            "algorithm is" << endl << "       run for." << endl
         << "    -N sets non-streaming mode, -S sets streaming mode (default)."
         << endl << "    -F sets the factor group size (must be >0); this "
                    "allows the detection" << endl
         << "       of multiple interacting factors." << endl << "" << endl
         << "    -C sets the 'criterion', which can be either:" << endl
         << "         t  throughput (the default) - this requires a pcap file"
         << endl << "         r  scratch size" << endl
         << "         s  stream state size" << endl
         << "         c  compile time" << endl << "         b  bytecode size"
         << endl << endl
         << "We recommend the use of a utility like 'taskset' on "
            "multiprocessor hosts to" << endl
         << "lock execution to a single processor: this will remove processor "
            "migration" << endl
         << "by the scheduler as a source of noise in the results." << endl;
}

static
double measure_stream_time(Benchmark &bench, unsigned int repeatCount) {
    Clock clock;
    bench.clearMatches();
    clock.start();
    for (unsigned int i = 0; i < repeatCount; i++) {
        bench.openStreams();
        bench.scanStreams();
        bench.closeStreams();
    }
    clock.stop();
    double secsScan = clock.seconds();
    return secsScan;
}

static
double measure_block_time(Benchmark &bench, unsigned int repeatCount) {
    Clock clock;
    bench.clearMatches();
    clock.start();
    for (unsigned int i = 0; i < repeatCount; i++) {
        bench.scanBlock();
    }
    clock.stop();
    double secsScan = clock.seconds();
    return secsScan;
}

static
double eval_set(Benchmark &bench, Sigdata &sigs, unsigned int mode,
                unsigned repeatCount, Criterion criterion,
                bool diagnose = true) {
    double compileTime = 0;
    bench.setDatabase(sigs.compileDatabase(mode, &compileTime));

    switch (criterion) {
    case CRITERION_BYTECODE_SIZE: {
        size_t dbSize;
        hs_error_t err = hs_database_size(bench.getDatabase(), &dbSize);
        if (err != HS_SUCCESS) {
            cerr << "ERROR: could not retrieve bytecode size" << endl;
            exit(1);
        }
        return dbSize;
    }
    case CRITERION_COMPILE_TIME:
        return compileTime;
    case CRITERION_STREAM_STATE: {
        size_t streamStateSize;
        hs_error_t err = hs_stream_size(bench.getDatabase(), &streamStateSize);
        if (err != HS_SUCCESS) {
            cerr << "ERROR: could not retrieve stream state size" << endl;
            exit(1);
        }
        return streamStateSize;
    }
    case CRITERION_SCRATCH_SIZE:
        return bench.getScratchSize();
    case CRITERION_THROUGHPUT:
    default:
        break; // do nothing - we are THROUGHPUT
    }
    double scan_time;
    if (mode == HS_MODE_NOSTREAM) {
        scan_time = measure_block_time(bench, repeatCount);
    } else {
        scan_time = measure_stream_time(bench, repeatCount);
    }
    size_t bytes = bench.bytes();
    size_t matches = bench.matches();
    if (diagnose) {
        std::ios::fmtflags f(cout.flags());
        cout << "Scan time " << std::fixed << std::setprecision(3) << scan_time
             << " sec, Scanned " << bytes * repeatCount << " bytes, Throughput "
             << std::fixed << std::setprecision(3)
             << (bytes * 8 * repeatCount) / (scan_time * 1000000)
             << " Mbps, Matches " << matches << endl;
        cout.flags(f);
    }
    return (bytes * 8 * repeatCount) / (scan_time * 1000000);
}

// Main entry point.
int main(int argc, char **argv) {
    unsigned int repeatCount = 1;
    unsigned int mode = HS_MODE_STREAM;
    Criterion criterion = CRITERION_THROUGHPUT;
    unsigned int gen_max = 10;
    unsigned int factor_max = 1;
    // Process command line arguments.
    int opt;
    while ((opt = getopt(argc, argv, "SNn:G:F:C:")) != -1) {
        switch (opt) {
        case 'F':
            factor_max = atoi(optarg);
            break;
        case 'G':
            gen_max = atoi(optarg);
            break;
        case 'S':
            mode = HS_MODE_STREAM;
            break;
        case 'N':
            mode = HS_MODE_NOSTREAM;
            break;
        case 'C':
            switch (optarg[0]) {
            case 't':
                criterion = CRITERION_THROUGHPUT;
                break;
            case 'b':
                criterion = CRITERION_BYTECODE_SIZE;
                break;
            case 'c':
                criterion = CRITERION_COMPILE_TIME;
                break;
            case 's':
                criterion = CRITERION_STREAM_STATE;
                break;
            case 'r':
                criterion = CRITERION_SCRATCH_SIZE;
                break;
            default:
                cerr << "Unrecognised criterion: " << optarg[0] << endl;
                usage(argv[0]);
                exit(-1);
            }
            break;
        case 'n':
            repeatCount = atoi(optarg);
            break;
        default:
            usage(argv[0]);
            exit(-1);
        }
    }

    if (argc - optind != ((criterion == CRITERION_THROUGHPUT) ? 2 : 1)) {
        usage(argv[0]);
        exit(-1);
    }

    const char *patternFile = argv[optind];
    const char *pcapFile = argv[optind + 1];

    // Read our input PCAP file in
    Benchmark bench;
    if (criterion == CRITERION_THROUGHPUT) {
        if (!bench.readStreams(pcapFile)) {
            cerr << "Unable to read packets from PCAP file. Exiting." << endl;
            exit(-1);
        }
    }

    if ((criterion == CRITERION_STREAM_STATE) && (mode != HS_MODE_STREAM)) {
        cerr << "Cannot evaluate stream state for block mode compile. Exiting."
             << endl;
        exit(-1);
    }

    cout << "Base signatures: " << patternFile;
    if (pcapFile) {
        cout << "\tPCAP input file: " << pcapFile
             << "\tRepeat count: " << repeatCount;
    }
    if (mode == HS_MODE_STREAM) {
        cout << "\tMode: streaming";
    } else {
        cout << "\tMode: block";
    }
    cout << endl;

    Sigdata sigs(patternFile);

    // calculate and show a baseline
    eval_set(bench, sigs, mode, repeatCount, criterion);

    set<unsigned> work_sigs, exclude;

    for (unsigned i = 0; i < sigs.size(); ++i) {
        work_sigs.insert(i);
    }

    double score_base =
        eval_set(bench, sigs, mode, repeatCount, criterion, false);
    bool maximize = higher_is_better(criterion);
    cout << "Number of signatures: " << sigs.size() << endl;
    cout << "Base performance: ";
    print_criterion(criterion, score_base);
    cout << endl;

    unsigned generations = min(gen_max, (sigs.size() - 1) / factor_max);

    cout << "Cutting signatures cumulatively for " << generations
         << " generations" << endl;
    for (unsigned gen = 0; gen < generations; ++gen) {
        cout << "Generation " << gen << " ";
        set<unsigned> s(work_sigs.begin(), work_sigs.end());
        double best = maximize ? 0 : 1000000000000.0;
        unsigned count = 0;
        while (s.size() > factor_max) {
            count++;
            cout << "." << std::flush;
            vector<unsigned> sv(s.begin(), s.end());
            random_shuffle(sv.begin(), sv.end());
            unsigned groups = factor_max + 1;
            for (unsigned current_group = 0; current_group < groups;
                 current_group++) {
                unsigned sz = sv.size();
                unsigned lo = (current_group * sz) / groups;
                unsigned hi = ((current_group + 1) * sz) / groups;

                set<unsigned> s_part1(sv.begin(), sv.begin() + lo);
                set<unsigned> s_part2(sv.begin() + hi, sv.end());
                set<unsigned> s_tmp = s_part1;
                s_tmp.insert(s_part2.begin(), s_part2.end());
                set<unsigned> tmp = s_tmp;
                tmp.insert(exclude.begin(), exclude.end());
                Sigdata sigs_tmp = sigs.cloneExclude(tmp);
                double score = eval_set(bench, sigs_tmp, mode, repeatCount,
                                        criterion, false);

                if ((current_group == 0) ||
                    (!maximize ? (score < best) : (score > best))) {
                    s = s_tmp;
                    best = score;
                }
            }
        }
        for (unsigned i = count; i < 16; i++) {
            cout << " ";
        }
        std::ios::fmtflags out_f(cout.flags());
        cout << "Performance: ";
        print_criterion(criterion, best);
        cout << " (" << std::fixed << std::setprecision(3) << (best / score_base)
             << "x) after cutting:" << endl;
        cout.flags(out_f);

        // s now has factor_max signatures
        for (const auto &found : s) {
            exclude.insert(found);
            work_sigs.erase(found);
            cout << sigs.get_original(found) << endl;
        }

        cout << endl;
    }
    return 0;
}

/**
 * Helper function to locate the offset of the first byte of the payload in the
 * given ethernet frame. Offset into the packet, and the length of the payload
 * are returned in the arguments @a offset and @a length.
 */
static
bool payloadOffset(const unsigned char *pkt_data, unsigned int *offset,
                   unsigned int *length) {
    const ip *iph = (const ip *)(pkt_data + sizeof(ether_header));
    const tcphdr *th = nullptr;

    // Ignore packets that aren't IPv4
    if (iph->ip_v != 4) {
        return false;
    }

    // Ignore fragmented packets.
    if (iph->ip_off & htons(IP_MF | IP_OFFMASK)) {
        return false;
    }

    // IP header length, and transport header length.
    unsigned int ihlen = iph->ip_hl * 4;
    unsigned int thlen = 0;

    switch (iph->ip_p) {
    case IPPROTO_TCP:
        th = (const tcphdr *)((const char *)iph + ihlen);
        thlen = th->th_off * 4;
        break;
    case IPPROTO_UDP:
        thlen = sizeof(udphdr);
        break;
    default:
        return false;
    }

    *offset = sizeof(ether_header) + ihlen + thlen;
    *length = sizeof(ether_header) + ntohs(iph->ip_len) - *offset;

    return *length != 0;
}

static unsigned parseFlags(const string &flagsStr) {
    unsigned flags = 0;
    for (const auto &c : flagsStr) {
        switch (c) {
        case 'i':
            flags |= HS_FLAG_CASELESS; break;
        case 'm':
            flags |= HS_FLAG_MULTILINE; break;
        case 's':
            flags |= HS_FLAG_DOTALL; break;
        case 'H':
            flags |= HS_FLAG_SINGLEMATCH; break;
        case 'V':
            flags |= HS_FLAG_ALLOWEMPTY; break;
        case '8':
            flags |= HS_FLAG_UTF8; break;
        case 'W':
            flags |= HS_FLAG_UCP; break;
        case '\r': // stray carriage-return
            break;
        default:
            cerr << "Unsupported flag \'" << c << "\'" << endl;
            exit(-1);
        }
    }
    return flags;
}

static void parseFile(const char *filename, vector<string> &patterns,
                      vector<unsigned> &flags, vector<unsigned> &ids,
                      vector<string> &originals) {
    ifstream inFile(filename);
    if (!inFile.good()) {
        cerr << "ERROR: Can't open pattern file \"" << filename << "\"" << endl;
        exit(-1);
    }

    for (unsigned i = 1; !inFile.eof(); ++i) {
        string line;
        getline(inFile, line);

        // if line is empty, or a comment, we can skip it
        if (line.empty() || line[0] == '#') {
            continue;
        }

        // otherwise, it should be ID:PCRE, e.g.
        //  10001:/foobar/is

        size_t colonIdx = line.find_first_of(':');
        if (colonIdx == string::npos) {
            cerr << "ERROR: Could not parse line " << i << endl;
            exit(-1);
        }

        // we should have an unsigned int as an ID, before the colon
        unsigned id = std::stoi(line.substr(0, colonIdx).c_str());

        // rest of the expression is the PCRE
        const string expr(line.substr(colonIdx + 1));

        size_t flagsStart = expr.find_last_of('/');
        if (flagsStart == string::npos) {
            cerr << "ERROR: no trailing '/' char" << endl;
            exit(-1);
        }

        string pcre(expr.substr(1, flagsStart - 1));
        string flagsStr(expr.substr(flagsStart + 1, expr.size() - flagsStart));
        unsigned flag = parseFlags(flagsStr);

        originals.push_back(line);
        patterns.push_back(pcre);
        flags.push_back(flag);
        ids.push_back(id);
    }
}