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diff --git a/third_party/libwebrtc/rtc_base/rolling_accumulator.h b/third_party/libwebrtc/rtc_base/rolling_accumulator.h
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+++ b/third_party/libwebrtc/rtc_base/rolling_accumulator.h
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+/*
+ *  Copyright 2011 The WebRTC Project Authors. All rights reserved.
+ *
+ *  Use of this source code is governed by a BSD-style license
+ *  that can be found in the LICENSE file in the root of the source
+ *  tree. An additional intellectual property rights grant can be found
+ *  in the file PATENTS.  All contributing project authors may
+ *  be found in the AUTHORS file in the root of the source tree.
+ */
+
+#ifndef RTC_BASE_ROLLING_ACCUMULATOR_H_
+#define RTC_BASE_ROLLING_ACCUMULATOR_H_
+
+#include <stddef.h>
+
+#include <algorithm>
+#include <vector>
+
+#include "rtc_base/checks.h"
+#include "rtc_base/numerics/running_statistics.h"
+
+namespace rtc {
+
+// RollingAccumulator stores and reports statistics
+// over N most recent samples.
+//
+// T is assumed to be an int, long, double or float.
+template <typename T>
+class RollingAccumulator {
+ public:
+  explicit RollingAccumulator(size_t max_count) : samples_(max_count) {
+    RTC_DCHECK(max_count > 0);
+    Reset();
+  }
+  ~RollingAccumulator() {}
+
+  RollingAccumulator(const RollingAccumulator&) = delete;
+  RollingAccumulator& operator=(const RollingAccumulator&) = delete;
+
+  size_t max_count() const { return samples_.size(); }
+
+  size_t count() const { return static_cast<size_t>(stats_.Size()); }
+
+  void Reset() {
+    stats_ = webrtc::webrtc_impl::RunningStatistics<T>();
+    next_index_ = 0U;
+    max_ = T();
+    max_stale_ = false;
+    min_ = T();
+    min_stale_ = false;
+  }
+
+  void AddSample(T sample) {
+    if (count() == max_count()) {
+      // Remove oldest sample.
+      T sample_to_remove = samples_[next_index_];
+      stats_.RemoveSample(sample_to_remove);
+      if (sample_to_remove >= max_) {
+        max_stale_ = true;
+      }
+      if (sample_to_remove <= min_) {
+        min_stale_ = true;
+      }
+    }
+    // Add new sample.
+    samples_[next_index_] = sample;
+    if (count() == 0 || sample >= max_) {
+      max_ = sample;
+      max_stale_ = false;
+    }
+    if (count() == 0 || sample <= min_) {
+      min_ = sample;
+      min_stale_ = false;
+    }
+    stats_.AddSample(sample);
+    // Update next_index_.
+    next_index_ = (next_index_ + 1) % max_count();
+  }
+
+  double ComputeMean() const { return stats_.GetMean().value_or(0); }
+
+  T ComputeMax() const {
+    if (max_stale_) {
+      RTC_DCHECK(count() > 0)
+          << "It shouldn't be possible for max_stale_ && count() == 0";
+      max_ = samples_[next_index_];
+      for (size_t i = 1u; i < count(); i++) {
+        max_ = std::max(max_, samples_[(next_index_ + i) % max_count()]);
+      }
+      max_stale_ = false;
+    }
+    return max_;
+  }
+
+  T ComputeMin() const {
+    if (min_stale_) {
+      RTC_DCHECK(count() > 0)
+          << "It shouldn't be possible for min_stale_ && count() == 0";
+      min_ = samples_[next_index_];
+      for (size_t i = 1u; i < count(); i++) {
+        min_ = std::min(min_, samples_[(next_index_ + i) % max_count()]);
+      }
+      min_stale_ = false;
+    }
+    return min_;
+  }
+
+  // O(n) time complexity.
+  // Weights nth sample with weight (learning_rate)^n. Learning_rate should be
+  // between (0.0, 1.0], otherwise the non-weighted mean is returned.
+  double ComputeWeightedMean(double learning_rate) const {
+    if (count() < 1 || learning_rate <= 0.0 || learning_rate >= 1.0) {
+      return ComputeMean();
+    }
+    double weighted_mean = 0.0;
+    double current_weight = 1.0;
+    double weight_sum = 0.0;
+    const size_t max_size = max_count();
+    for (size_t i = 0; i < count(); ++i) {
+      current_weight *= learning_rate;
+      weight_sum += current_weight;
+      // Add max_size to prevent underflow.
+      size_t index = (next_index_ + max_size - i - 1) % max_size;
+      weighted_mean += current_weight * samples_[index];
+    }
+    return weighted_mean / weight_sum;
+  }
+
+  // Compute estimated variance.  Estimation is more accurate
+  // as the number of samples grows.
+  double ComputeVariance() const { return stats_.GetVariance().value_or(0); }
+
+ private:
+  webrtc::webrtc_impl::RunningStatistics<T> stats_;
+  size_t next_index_;
+  mutable T max_;
+  mutable bool max_stale_;
+  mutable T min_;
+  mutable bool min_stale_;
+  std::vector<T> samples_;
+};
+
+}  // namespace rtc
+
+#endif  // RTC_BASE_ROLLING_ACCUMULATOR_H_