/* -*- Mode: C++; tab-width: 8; indent-tabs-mode: nil; c-basic-offset: 2 -*- */
/* vim: set ts=8 sts=2 et sw=2 tw=80: */
// Copyright (c) 2011 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.

// Histogram is an object that aggregates statistics, and can summarize them in
// various forms, including ASCII graphical, HTML, and numerically (as a
// vector of numbers corresponding to each of the aggregating buckets).
// See header file for details and examples.

#include "base/histogram.h"

#include <math.h>

#include <algorithm>
#include <string>

#include "base/logging.h"
#include "base/pickle.h"
#include "base/string_util.h"
#include "base/logging.h"

namespace base {

#define CHECK_GT DCHECK_GT
#define CHECK_LT DCHECK_LT

// Static table of checksums for all possible 8 bit bytes.
const uint32_t Histogram::kCrcTable[256] = {
    0x0,         0x77073096L, 0xee0e612cL, 0x990951baL, 0x76dc419L,
    0x706af48fL, 0xe963a535L, 0x9e6495a3L, 0xedb8832L,  0x79dcb8a4L,
    0xe0d5e91eL, 0x97d2d988L, 0x9b64c2bL,  0x7eb17cbdL, 0xe7b82d07L,
    0x90bf1d91L, 0x1db71064L, 0x6ab020f2L, 0xf3b97148L, 0x84be41deL,
    0x1adad47dL, 0x6ddde4ebL, 0xf4d4b551L, 0x83d385c7L, 0x136c9856L,
    0x646ba8c0L, 0xfd62f97aL, 0x8a65c9ecL, 0x14015c4fL, 0x63066cd9L,
    0xfa0f3d63L, 0x8d080df5L, 0x3b6e20c8L, 0x4c69105eL, 0xd56041e4L,
    0xa2677172L, 0x3c03e4d1L, 0x4b04d447L, 0xd20d85fdL, 0xa50ab56bL,
    0x35b5a8faL, 0x42b2986cL, 0xdbbbc9d6L, 0xacbcf940L, 0x32d86ce3L,
    0x45df5c75L, 0xdcd60dcfL, 0xabd13d59L, 0x26d930acL, 0x51de003aL,
    0xc8d75180L, 0xbfd06116L, 0x21b4f4b5L, 0x56b3c423L, 0xcfba9599L,
    0xb8bda50fL, 0x2802b89eL, 0x5f058808L, 0xc60cd9b2L, 0xb10be924L,
    0x2f6f7c87L, 0x58684c11L, 0xc1611dabL, 0xb6662d3dL, 0x76dc4190L,
    0x1db7106L,  0x98d220bcL, 0xefd5102aL, 0x71b18589L, 0x6b6b51fL,
    0x9fbfe4a5L, 0xe8b8d433L, 0x7807c9a2L, 0xf00f934L,  0x9609a88eL,
    0xe10e9818L, 0x7f6a0dbbL, 0x86d3d2dL,  0x91646c97L, 0xe6635c01L,
    0x6b6b51f4L, 0x1c6c6162L, 0x856530d8L, 0xf262004eL, 0x6c0695edL,
    0x1b01a57bL, 0x8208f4c1L, 0xf50fc457L, 0x65b0d9c6L, 0x12b7e950L,
    0x8bbeb8eaL, 0xfcb9887cL, 0x62dd1ddfL, 0x15da2d49L, 0x8cd37cf3L,
    0xfbd44c65L, 0x4db26158L, 0x3ab551ceL, 0xa3bc0074L, 0xd4bb30e2L,
    0x4adfa541L, 0x3dd895d7L, 0xa4d1c46dL, 0xd3d6f4fbL, 0x4369e96aL,
    0x346ed9fcL, 0xad678846L, 0xda60b8d0L, 0x44042d73L, 0x33031de5L,
    0xaa0a4c5fL, 0xdd0d7cc9L, 0x5005713cL, 0x270241aaL, 0xbe0b1010L,
    0xc90c2086L, 0x5768b525L, 0x206f85b3L, 0xb966d409L, 0xce61e49fL,
    0x5edef90eL, 0x29d9c998L, 0xb0d09822L, 0xc7d7a8b4L, 0x59b33d17L,
    0x2eb40d81L, 0xb7bd5c3bL, 0xc0ba6cadL, 0xedb88320L, 0x9abfb3b6L,
    0x3b6e20cL,  0x74b1d29aL, 0xead54739L, 0x9dd277afL, 0x4db2615L,
    0x73dc1683L, 0xe3630b12L, 0x94643b84L, 0xd6d6a3eL,  0x7a6a5aa8L,
    0xe40ecf0bL, 0x9309ff9dL, 0xa00ae27L,  0x7d079eb1L, 0xf00f9344L,
    0x8708a3d2L, 0x1e01f268L, 0x6906c2feL, 0xf762575dL, 0x806567cbL,
    0x196c3671L, 0x6e6b06e7L, 0xfed41b76L, 0x89d32be0L, 0x10da7a5aL,
    0x67dd4accL, 0xf9b9df6fL, 0x8ebeeff9L, 0x17b7be43L, 0x60b08ed5L,
    0xd6d6a3e8L, 0xa1d1937eL, 0x38d8c2c4L, 0x4fdff252L, 0xd1bb67f1L,
    0xa6bc5767L, 0x3fb506ddL, 0x48b2364bL, 0xd80d2bdaL, 0xaf0a1b4cL,
    0x36034af6L, 0x41047a60L, 0xdf60efc3L, 0xa867df55L, 0x316e8eefL,
    0x4669be79L, 0xcb61b38cL, 0xbc66831aL, 0x256fd2a0L, 0x5268e236L,
    0xcc0c7795L, 0xbb0b4703L, 0x220216b9L, 0x5505262fL, 0xc5ba3bbeL,
    0xb2bd0b28L, 0x2bb45a92L, 0x5cb36a04L, 0xc2d7ffa7L, 0xb5d0cf31L,
    0x2cd99e8bL, 0x5bdeae1dL, 0x9b64c2b0L, 0xec63f226L, 0x756aa39cL,
    0x26d930aL,  0x9c0906a9L, 0xeb0e363fL, 0x72076785L, 0x5005713L,
    0x95bf4a82L, 0xe2b87a14L, 0x7bb12baeL, 0xcb61b38L,  0x92d28e9bL,
    0xe5d5be0dL, 0x7cdcefb7L, 0xbdbdf21L,  0x86d3d2d4L, 0xf1d4e242L,
    0x68ddb3f8L, 0x1fda836eL, 0x81be16cdL, 0xf6b9265bL, 0x6fb077e1L,
    0x18b74777L, 0x88085ae6L, 0xff0f6a70L, 0x66063bcaL, 0x11010b5cL,
    0x8f659effL, 0xf862ae69L, 0x616bffd3L, 0x166ccf45L, 0xa00ae278L,
    0xd70dd2eeL, 0x4e048354L, 0x3903b3c2L, 0xa7672661L, 0xd06016f7L,
    0x4969474dL, 0x3e6e77dbL, 0xaed16a4aL, 0xd9d65adcL, 0x40df0b66L,
    0x37d83bf0L, 0xa9bcae53L, 0xdebb9ec5L, 0x47b2cf7fL, 0x30b5ffe9L,
    0xbdbdf21cL, 0xcabac28aL, 0x53b39330L, 0x24b4a3a6L, 0xbad03605L,
    0xcdd70693L, 0x54de5729L, 0x23d967bfL, 0xb3667a2eL, 0xc4614ab8L,
    0x5d681b02L, 0x2a6f2b94L, 0xb40bbe37L, 0xc30c8ea1L, 0x5a05df1bL,
    0x2d02ef8dL,
};

typedef Histogram::Count Count;

// static
const size_t Histogram::kBucketCount_MAX = 16384u;

Histogram* Histogram::FactoryGet(Sample minimum, Sample maximum,
                                 size_t bucket_count, Flags flags,
                                 const int* buckets) {
  DCHECK(buckets);
  Histogram* histogram(NULL);

  // Defensive code.
  if (minimum < 1) minimum = 1;
  if (maximum > kSampleType_MAX - 1) maximum = kSampleType_MAX - 1;

  histogram = new Histogram(minimum, maximum, bucket_count);
  histogram->InitializeBucketRangeFromData(buckets);
  histogram->SetFlags(flags);

  DCHECK_EQ(HISTOGRAM, histogram->histogram_type());
  DCHECK(histogram->HasConstructorArguments(minimum, maximum, bucket_count));
  return histogram;
}

void Histogram::Add(int value) {
  if (value > kSampleType_MAX - 1) value = kSampleType_MAX - 1;
  if (value < 0) value = 0;
  size_t index = BucketIndex(value);
  DCHECK_GE(value, ranges(index));
  DCHECK_LT(value, ranges(index + 1));
  Accumulate(value, 1, index);
}

void Histogram::Subtract(int value) {
  if (value > kSampleType_MAX - 1) value = kSampleType_MAX - 1;
  if (value < 0) value = 0;
  size_t index = BucketIndex(value);
  DCHECK_GE(value, ranges(index));
  DCHECK_LT(value, ranges(index + 1));
  Accumulate(value, -1, index);
}

void Histogram::AddBoolean(bool value) { DCHECK(false); }

void Histogram::AddSampleSet(const SampleSet& sample) { sample_.Add(sample); }

void Histogram::Clear() { sample_.Clear(); }

void Histogram::SetRangeDescriptions(const DescriptionPair descriptions[]) {
  DCHECK(false);
}

//------------------------------------------------------------------------------
// Methods for the validating a sample and a related histogram.
//------------------------------------------------------------------------------

Histogram::Inconsistencies Histogram::FindCorruption(
    const SampleSet& snapshot) const {
  int inconsistencies = NO_INCONSISTENCIES;
  Sample previous_range = -1;  // Bottom range is always 0.
  int64_t count = 0;
  for (size_t index = 0; index < bucket_count(); ++index) {
    count += snapshot.counts(index);
    int new_range = ranges(index);
    if (previous_range >= new_range) inconsistencies |= BUCKET_ORDER_ERROR;
    previous_range = new_range;
  }

  if (!HasValidRangeChecksum()) inconsistencies |= RANGE_CHECKSUM_ERROR;

  int64_t delta64 = snapshot.redundant_count() - count;
  if (delta64 != 0) {
    int delta = static_cast<int>(delta64);
    if (delta != delta64) delta = INT_MAX;  // Flag all giant errors as INT_MAX.
    // Since snapshots of histograms are taken asynchronously relative to
    // sampling (and snapped from different threads), it is pretty likely that
    // we'll catch a redundant count that doesn't match the sample count.  We
    // allow for a certain amount of slop before flagging this as an
    // inconsistency.  Even with an inconsistency, we'll snapshot it again (for
    // UMA in about a half hour, so we'll eventually get the data, if it was
    // not the result of a corruption.  If histograms show that 1 is "too tight"
    // then we may try to use 2 or 3 for this slop value.
    const int kCommonRaceBasedCountMismatch = 1;
    if (delta > 0) {
      if (delta > kCommonRaceBasedCountMismatch)
        inconsistencies |= COUNT_HIGH_ERROR;
    } else {
      DCHECK_GT(0, delta);
      if (-delta > kCommonRaceBasedCountMismatch)
        inconsistencies |= COUNT_LOW_ERROR;
    }
  }
  return static_cast<Inconsistencies>(inconsistencies);
}

Histogram::ClassType Histogram::histogram_type() const { return HISTOGRAM; }

Histogram::Sample Histogram::ranges(size_t i) const { return ranges_[i]; }

size_t Histogram::bucket_count() const { return bucket_count_; }

Histogram::SampleSet Histogram::SnapshotSample() const {
  return sample_.Clone();
}

bool Histogram::HasConstructorArguments(Sample minimum, Sample maximum,
                                        size_t bucket_count) {
  return ((minimum == declared_min_) && (maximum == declared_max_) &&
          (bucket_count == bucket_count_));
}

bool Histogram::HasConstructorTimeDeltaArguments(TimeDelta minimum,
                                                 TimeDelta maximum,
                                                 size_t bucket_count) {
  return ((minimum.InMilliseconds() == declared_min_) &&
          (maximum.InMilliseconds() == declared_max_) &&
          (bucket_count == bucket_count_));
}

bool Histogram::HasValidRangeChecksum() const {
  return CalculateRangeChecksum() == range_checksum_;
}

size_t Histogram::SizeOfIncludingThis(mozilla::MallocSizeOf aMallocSizeOf) {
  size_t n = 0;
  n += aMallocSizeOf(this);
  n += sample_.SizeOfExcludingThis(aMallocSizeOf);
  return n;
}

size_t Histogram::SampleSet::SizeOfExcludingThis(
    mozilla::MallocSizeOf aMallocSizeOf) {
  return counts_.ShallowSizeOfExcludingThis(aMallocSizeOf);
}

Histogram::Histogram(Sample minimum, Sample maximum, size_t bucket_count)
    : sample_(),
      declared_min_(minimum),
      declared_max_(maximum),
      bucket_count_(bucket_count),
      flags_(kNoFlags),
      range_checksum_(0) {
  Initialize();
}

Histogram::Histogram(TimeDelta minimum, TimeDelta maximum, size_t bucket_count)
    : sample_(),
      declared_min_(static_cast<int>(minimum.InMilliseconds())),
      declared_max_(static_cast<int>(maximum.InMilliseconds())),
      bucket_count_(bucket_count),
      flags_(kNoFlags),
      range_checksum_(0) {
  Initialize();
}

Histogram::~Histogram() {
  // Just to make sure most derived class did this properly...
  DCHECK(ValidateBucketRanges());
}

void Histogram::InitializeBucketRangeFromData(const int* buckets) {
  ranges_ = buckets;
  ResetRangeChecksum();
  DCHECK(ValidateBucketRanges());
}

bool Histogram::PrintEmptyBucket(size_t index) const { return true; }

size_t Histogram::BucketIndex(Sample value) const {
  // Use simple binary search.  This is very general, but there are better
  // approaches if we knew that the buckets were linearly distributed.
  DCHECK_LE(ranges(0), value);
  DCHECK_GT(ranges(bucket_count()), value);
  size_t under = 0;
  size_t over = bucket_count();
  size_t mid;

  do {
    DCHECK_GE(over, under);
    mid = under + (over - under) / 2;
    if (mid == under) break;
    if (ranges(mid) <= value)
      under = mid;
    else
      over = mid;
  } while (true);

  DCHECK_LE(ranges(mid), value);
  CHECK_GT(ranges(mid + 1), value);
  return mid;
}

// Use the actual bucket widths (like a linear histogram) until the widths get
// over some transition value, and then use that transition width.  Exponentials
// get so big so fast (and we don't expect to see a lot of entries in the large
// buckets), so we need this to make it possible to see what is going on and
// not have 0-graphical-height buckets.
double Histogram::GetBucketSize(Count current, size_t i) const {
  DCHECK_GT(ranges(i + 1), ranges(i));
  static const double kTransitionWidth = 5;
  double denominator = ranges(i + 1) - ranges(i);
  if (denominator > kTransitionWidth)
    denominator = kTransitionWidth;  // Stop trying to normalize.
  return current / denominator;
}

void Histogram::ResetRangeChecksum() {
  range_checksum_ = CalculateRangeChecksum();
}

const std::string Histogram::GetAsciiBucketRange(size_t i) const {
  std::string result;
  if (kHexRangePrintingFlag & flags_)
    StringAppendF(&result, "%#x", ranges(i));
  else
    StringAppendF(&result, "%d", ranges(i));
  return result;
}

// Update histogram data with new sample.
void Histogram::Accumulate(Sample value, Count count, size_t index) {
  sample_.Accumulate(value, count, index);
}

bool Histogram::ValidateBucketRanges() const {
  // Standard assertions that all bucket ranges should satisfy.
  DCHECK_EQ(0, ranges_[bucket_count_ + 1]);
  DCHECK_EQ(0, ranges_[0]);
  DCHECK_EQ(declared_min(), ranges_[1]);
  DCHECK_EQ(declared_max(), ranges_[bucket_count_ - 1]);
  DCHECK_EQ(kSampleType_MAX, ranges_[bucket_count_]);
  return true;
}

uint32_t Histogram::CalculateRangeChecksum() const {
  DCHECK_EQ(0, ranges_[bucket_count_ + 1]);
  uint32_t checksum =
      static_cast<uint32_t>(bucket_count_ + 1);  // Seed checksum.
  for (size_t index = 0; index < bucket_count(); ++index)
    checksum = Crc32(checksum, ranges(index));
  return checksum;
}

void Histogram::Initialize() {
  sample_.Resize(*this);
  if (declared_min_ < 1) declared_min_ = 1;
  if (declared_max_ > kSampleType_MAX - 1) declared_max_ = kSampleType_MAX - 1;
  DCHECK_LE(declared_min_, declared_max_);
  DCHECK_GT(bucket_count_, 1u);
  CHECK_LT(bucket_count_, kBucketCount_MAX);
  size_t maximal_bucket_count = declared_max_ - declared_min_ + 2;
  DCHECK_LE(bucket_count_, maximal_bucket_count);
}

// We generate the CRC-32 using the low order bits to select whether to XOR in
// the reversed polynomial 0xedb88320L.  This is nice and simple, and allows us
// to keep the quotient in a uint32_t.  Since we're not concerned about the
// nature of corruptions (i.e., we don't care about bit sequencing, since we are
// handling memory changes, which are more grotesque) so we don't bother to
// get the CRC correct for big-endian vs little-ending calculations.  All we
// need is a nice hash, that tends to depend on all the bits of the sample, with
// very little chance of changes in one place impacting changes in another
// place.
uint32_t Histogram::Crc32(uint32_t sum, Histogram::Sample range) {
  const bool kUseRealCrc = true;  // TODO(jar): Switch to false and watch stats.
  if (kUseRealCrc) {
    union {
      Histogram::Sample range;
      unsigned char bytes[sizeof(Histogram::Sample)];
    } converter;
    converter.range = range;
    for (size_t i = 0; i < sizeof(converter); ++i)
      sum = kCrcTable[(sum & 0xff) ^ converter.bytes[i]] ^ (sum >> 8);
  } else {
    // Use hash techniques provided in ReallyFastHash, except we don't care
    // about "avalanching" (which would worsten the hash, and add collisions),
    // and we don't care about edge cases since we have an even number of bytes.
    union {
      Histogram::Sample range;
      uint16_t ints[sizeof(Histogram::Sample) / 2];
    } converter;
    DCHECK_EQ(sizeof(Histogram::Sample), sizeof(converter));
    converter.range = range;
    sum += converter.ints[0];
    sum = (sum << 16) ^ sum ^ (static_cast<uint32_t>(converter.ints[1]) << 11);
    sum += sum >> 11;
  }
  return sum;
}

//------------------------------------------------------------------------------
// Private methods

double Histogram::GetPeakBucketSize(const SampleSet& snapshot) const {
  double max = 0;
  for (size_t i = 0; i < bucket_count(); ++i) {
    double current_size = GetBucketSize(snapshot.counts(i), i);
    if (current_size > max) max = current_size;
  }
  return max;
}

//------------------------------------------------------------------------------
// Methods for the Histogram::SampleSet class
//------------------------------------------------------------------------------

Histogram::SampleSet::SampleSet() : counts_(), sum_(0), redundant_count_(0) {}

Histogram::SampleSet::~SampleSet() {}

void Histogram::SampleSet::Resize(const Histogram& histogram) {
  size_t oldSize = counts_.Length();
  counts_.SetLength(histogram.bucket_count());

  for (size_t i = oldSize; i < histogram.bucket_count(); ++i) counts_[i] = 0;
}

void Histogram::SampleSet::Accumulate(Sample value, Count count, size_t index) {
  DCHECK(count == 1 || count == -1);
  counts_[index] += count;
  redundant_count_ += count;
  sum_ += static_cast<int64_t>(count) * value;
  DCHECK_GE(counts_[index], 0);
  DCHECK_GE(sum_, 0);
  DCHECK_GE(redundant_count_, 0);
}

Count Histogram::SampleSet::TotalCount() const {
  Count total = 0;
  for (Counts::const_iterator it = counts_.begin(); it != counts_.end(); ++it) {
    total += *it;
  }
  return total;
}

void Histogram::SampleSet::Add(const SampleSet& other) {
  DCHECK_EQ(counts_.Length(), other.counts_.Length());
  sum_ += other.sum_;
  redundant_count_ += other.redundant_count_;
  for (size_t index = 0; index < counts_.Length(); ++index)
    counts_[index] += other.counts_[index];
}

//------------------------------------------------------------------------------
// LinearHistogram: This histogram uses a traditional set of evenly spaced
// buckets.
//------------------------------------------------------------------------------

LinearHistogram::~LinearHistogram() {}

Histogram* LinearHistogram::FactoryGet(Sample minimum, Sample maximum,
                                       size_t bucket_count, Flags flags,
                                       const int* buckets) {
  Histogram* histogram(NULL);

  if (minimum < 1) minimum = 1;
  if (maximum > kSampleType_MAX - 1) maximum = kSampleType_MAX - 1;

  LinearHistogram* linear_histogram =
      new LinearHistogram(minimum, maximum, bucket_count);
  linear_histogram->InitializeBucketRangeFromData(buckets);
  linear_histogram->SetFlags(flags);
  histogram = linear_histogram;

  DCHECK_EQ(LINEAR_HISTOGRAM, histogram->histogram_type());
  DCHECK(histogram->HasConstructorArguments(minimum, maximum, bucket_count));
  return histogram;
}

Histogram::ClassType LinearHistogram::histogram_type() const {
  return LINEAR_HISTOGRAM;
}

void LinearHistogram::Accumulate(Sample value, Count count, size_t index) {
  sample_.Accumulate(value, count, index);
}

void LinearHistogram::SetRangeDescriptions(
    const DescriptionPair descriptions[]) {
  for (int i = 0; descriptions[i].description; ++i) {
    bucket_description_[descriptions[i].sample] = descriptions[i].description;
  }
}

LinearHistogram::LinearHistogram(Sample minimum, Sample maximum,
                                 size_t bucket_count)
    : Histogram(minimum >= 1 ? minimum : 1, maximum, bucket_count) {}

LinearHistogram::LinearHistogram(TimeDelta minimum, TimeDelta maximum,
                                 size_t bucket_count)
    : Histogram(minimum >= TimeDelta::FromMilliseconds(1)
                    ? minimum
                    : TimeDelta::FromMilliseconds(1),
                maximum, bucket_count) {}

double LinearHistogram::GetBucketSize(Count current, size_t i) const {
  DCHECK_GT(ranges(i + 1), ranges(i));
  // Adjacent buckets with different widths would have "surprisingly" many (few)
  // samples in a histogram if we didn't normalize this way.
  double denominator = ranges(i + 1) - ranges(i);
  return current / denominator;
}

const std::string LinearHistogram::GetAsciiBucketRange(size_t i) const {
  int range = ranges(i);
  BucketDescriptionMap::const_iterator it = bucket_description_.find(range);
  if (it == bucket_description_.end()) return Histogram::GetAsciiBucketRange(i);
  return it->second;
}

bool LinearHistogram::PrintEmptyBucket(size_t index) const {
  return bucket_description_.find(ranges(index)) == bucket_description_.end();
}

//------------------------------------------------------------------------------
// This section provides implementation for BooleanHistogram.
//------------------------------------------------------------------------------

Histogram* BooleanHistogram::FactoryGet(Flags flags, const int* buckets) {
  Histogram* histogram(NULL);

  BooleanHistogram* tentative_histogram = new BooleanHistogram();
  tentative_histogram->InitializeBucketRangeFromData(buckets);
  tentative_histogram->SetFlags(flags);
  histogram = tentative_histogram;

  DCHECK_EQ(BOOLEAN_HISTOGRAM, histogram->histogram_type());
  return histogram;
}

Histogram::ClassType BooleanHistogram::histogram_type() const {
  return BOOLEAN_HISTOGRAM;
}

void BooleanHistogram::AddBoolean(bool value) { Add(value ? 1 : 0); }

BooleanHistogram::BooleanHistogram() : LinearHistogram(1, 2, 3) {}

void BooleanHistogram::Accumulate(Sample value, Count count, size_t index) {
  // Callers will have computed index based on the non-booleanified value.
  // So we need to adjust the index manually.
  LinearHistogram::Accumulate(!!value, count, value ? 1 : 0);
}

//------------------------------------------------------------------------------
// FlagHistogram:
//------------------------------------------------------------------------------

Histogram* FlagHistogram::FactoryGet(Flags flags, const int* buckets) {
  Histogram* h(nullptr);

  FlagHistogram* fh = new FlagHistogram();
  fh->InitializeBucketRangeFromData(buckets);
  fh->SetFlags(flags);
  size_t zero_index = fh->BucketIndex(0);
  fh->LinearHistogram::Accumulate(0, 1, zero_index);
  h = fh;

  return h;
}

FlagHistogram::FlagHistogram() : BooleanHistogram(), mSwitched(false) {}

Histogram::ClassType FlagHistogram::histogram_type() const {
  return FLAG_HISTOGRAM;
}

void FlagHistogram::Accumulate(Sample value, Count count, size_t index) {
  if (mSwitched) {
    return;
  }

  mSwitched = true;
  DCHECK_EQ(value, 1);
  LinearHistogram::Accumulate(value, 1, index);
  size_t zero_index = BucketIndex(0);
  LinearHistogram::Accumulate(0, -1, zero_index);
}

void FlagHistogram::AddSampleSet(const SampleSet& sample) {
  DCHECK_EQ(bucket_count(), sample.size());
  // We can't be sure the SampleSet provided came from another FlagHistogram,
  // so we take the following steps:
  //  - If our flag has already been set do nothing.
  //  - Set our flag if the following hold:
  //      - The sum of the counts in the provided SampleSet is 1.
  //      - The bucket index for that single value is the same as the index
  //      where we
  //        would place our set flag.
  //  - Otherwise, take no action.

  if (mSwitched) {
    return;
  }

  if (sample.sum() != 1) {
    return;
  }

  size_t one_index = BucketIndex(1);
  if (sample.counts(one_index) == 1) {
    Accumulate(1, 1, one_index);
  }
}

void FlagHistogram::Clear() {
  Histogram::Clear();

  mSwitched = false;
  size_t zero_index = BucketIndex(0);
  LinearHistogram::Accumulate(0, 1, zero_index);
}

//------------------------------------------------------------------------------
// CountHistogram:
//------------------------------------------------------------------------------

Histogram* CountHistogram::FactoryGet(Flags flags, const int* buckets) {
  Histogram* h(nullptr);

  CountHistogram* fh = new CountHistogram();
  fh->InitializeBucketRangeFromData(buckets);
  fh->SetFlags(flags);
  h = fh;

  return h;
}

CountHistogram::CountHistogram() : LinearHistogram(1, 2, 3) {}

Histogram::ClassType CountHistogram::histogram_type() const {
  return COUNT_HISTOGRAM;
}

void CountHistogram::Accumulate(Sample value, Count count, size_t index) {
  size_t zero_index = BucketIndex(0);
  LinearHistogram::Accumulate(value, 1, zero_index);
}

void CountHistogram::AddSampleSet(const SampleSet& sample) {
  DCHECK_EQ(bucket_count(), sample.size());
  // We can't be sure the SampleSet provided came from another CountHistogram,
  // so we at least check that the unused buckets are empty.

  const size_t indices[] = {BucketIndex(0), BucketIndex(1), BucketIndex(2)};

  if (sample.counts(indices[1]) != 0 || sample.counts(indices[2]) != 0) {
    return;
  }

  if (sample.counts(indices[0]) != 0) {
    Histogram::AddSampleSet(sample);
  }
}

}  // namespace base
